Image_Classfication_Models_after_denoising.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "801aa_xvWTnG"
      },
      "outputs": [],
      "source": [
        "#script to download data from the google drive\n",
        "!rm -r /content/Test\n",
        "!rm -r /content/Train\n",
        "!rm -r /content/Validation\n",
        "!rm -r /content/DataSet1\n",
        "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/121.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: en-US,en;q=0.9,fa;q=0.8\" --header=\"Cookie: SID=g.a000gAih6wfkjbcbADSz62FJPjIAu0M9JFQvPctI__Kec1ykJV_0UKFwxW_kYqkWSVnwLNbCdgACgYKAWwSAQASFQHGX2MiPA83fXJNYO5R44lOJHFU7hoVAUF8yKrYo9Jjp_WQrUAO_eP-00570076; __Secure-1PSID=g.a000gAih6wfkjbcbADSz62FJPjIAu0M9JFQvPctI__Kec1ykJV_0EaA3rzo3JlioS0jL98KYPQACgYKAccSAQASFQHGX2Miy8vlaewbNEPEU8dFD-DPNBoVAUF8yKo1GeLaYOqiGsvuYVQmR0Gj0076; __Secure-3PSID=g.a000gAih6wfkjbcbADSz62FJPjIAu0M9JFQvPctI__Kec1ykJV_0daBOqj8Sl6aWULeHb8tTFAACgYKASoSAQASFQHGX2MiU_Z8GO_YZyp2xk9ipkG_qRoVAUF8yKqwUJ6U4fkIpUEvqydw10-C0076; HSID=Abda-Gu80HucPyRdw; SSID=AYc-aPVVRZHjx9zPm; APISID=woK740gzd8Mq8kes/Aad25S2XAz6880nUJ; SAPISID=ULq-o5cD2WNC6_GS/AqprLuiDBmBUYwwbH; __Secure-1PAPISID=ULq-o5cD2WNC6_GS/AqprLuiDBmBUYwwbH; __Secure-3PAPISID=ULq-o5cD2WNC6_GS/AqprLuiDBmBUYwwbH; AEC=Ae3NU9MGppRzqPBz29a_V94PJiE37Upgo3fiOY7BQf6fUQy0fifpTrmNUA; __Secure-ENID=17.SE=mSlGvO_Zx1CoU0XyDrk5A9MsubxTHPSq69XI7GooDN15PPT5s7kXg9Fnnp9MhlE7jiJXQgKeD95KnKc7TDO7DIdGebg4F9fT-IO4KVAcsPmWo0ASzhd4e_tgvfR8PungZt9jURyXcjUDpoHJNdF-Ola5RdaKaCAm0pfoZIxIX-VQ2lJ3WOXmhoxKoyF6OcAaDUQGqTrXCG4Ul2uLZe1PVf9Duui7gpnLfKBwsgkaSdEYTK8kBeKc06y9K6HLvvyUhKEiD9GLLrjRIP6e51hgg9aeLUGcD8Hk-A4o961nJi8rHfPALwgkkhvSewckxgzBXjmkzOnIFeW4vzwfNR6TpZp-cC0tZ0oeOIWJjF7TtIH1sm_siprks0spbE76QiKtMJrwo61OGR2PJok; AMP_MKTG_630874745a=JTdCJTdE; AMP_630874745a=JTdCJTIyZGV2aWNlSWQlMjIlM0ElMjI0ODU1MGQ2NC1kMmM2LTQwNjMtYmQyMy1mNjM2YTg1NDkxOGIlMjIlMkMlMjJ1c2VySWQlMjIlM0ElMjJobTIxMDBtaCU0MGdtYWlsLmNvbSUyMiUyQyUyMnNlc3Npb25JZCUyMiUzQTE3MDc2NDk3MjE3MjElMkMlMjJvcHRPdXQlMjIlM0FmYWxzZSUyQyUyMmxhc3RFdmVudFRpbWUlMjIlM0ExNzA3NjQ5NzIxOTMyJTJDJTIybGFzdEV2ZW50SWQlMjIlM0EzJTdE; NID=511=YT1iftDi-fBjdwnvc3CoI6x5mPzJbtEWlZf1jrTWPISJQztb-YbJcuRp3X7f6ZolaO_l29b_unlLbZ8vNFZKGM2Q2aAnYrLVDzwM5ftxqX7ThxCVQ6cQrWqIT-CwxkETn4ivK1_rKBlPL_oLTX96W72LLqjFVUnJNf_XXREZ-oEzT8-XHjPgdTaCP1BqeH36N8RS2wqsXvIgHD60tSJ0OXtdygH_2AgKi9eVZxTmJQSw2MRNoTSVDYl2LmRb_liylBHzblVdLP4kJ0Kjj3_v8QOIKZRg8Dd79yu3KHbWU0Dd2lbCsuq79t0tdmCVUiVr2odUxoJ1WMya82QNJEJ_zWLKrA2EbaeVtqVcPJUbKsWJA5OVJKcNIUXnP0CCIA7y05uMTDiQojryVmQJz58kgr7XgxyknnXpOiTJRlc_rqurtUOf9BOX4IJVhT_9PkWD4If9cl4n8ZMCY5JA4tz15bEzEuhY0RlammGJR-7d0nbRnm9fCofANemqIj-m6dDC2jP2_Hs7jWmnSphH4He80GoCFHwpifjXvLDEe9NN_haWutR3rtjua0aAFryK09cEAROYSFgfmfawe2E10jV6iXKlwF1gzLma9jUTiOrgsC49VLGTGFcSh-8C0nO7QWICsup4DRug89wSoNzbxe-VkViVLXptK7DCU8REv4SCujqhoexC50WHD197hV73vlBa3vq_NXqc; __Secure-1PSIDTS=sidts-CjEBPVxjSiPBKn40DYo4nGcMQp1z758lZH6I_Hc-ZgXqILnbgFHs3tqG8vn_bv9zUhrLEAA; __Secure-3PSIDTS=sidts-CjEBPVxjSiPBKn40DYo4nGcMQp1z758lZH6I_Hc-ZgXqILnbgFHs3tqG8vn_bv9zUhrLEAA; ph_phc_cjuWI21zkiw3B30S2vdrLd2HtfmqRWuzZMldJrXEpPw_posthog=%7B%22distinct_id%22%3A%2218d987a092756c-0cf9e32b2aae19-1e525637-fa000-18d987a0928f50%22%2C%22%24device_id%22%3A%2218d987a092756c-0cf9e32b2aae19-1e525637-fa000-18d987a0928f50%22%2C%22%24user_state%22%3A%22anonymous%22%2C%22%24referrer%22%3A%22%24direct%22%2C%22%24referring_domain%22%3A%22%24direct%22%2C%22%24sesid%22%3A%5B1707660162905%2C%2218d987a092f2e7-0cd80a666118e5-1e525637-fa000-18d987a093036f%22%2C1707660151087%5D%2C%22%24session_recording_enabled_server_side%22%3Afalse%2C%22%24active_feature_flags%22%3A%5B%5D%2C%22%24enabled_feature_flags%22%3A%7B%7D%2C%22%24feature_flag_payloads%22%3A%7B%7D%7D; 1P_JAR=2024-02-11-14; mp_851392464b60e8cc1948a193642f793b_mixpanel=%7B%22distinct_id%22%3A%20%22%24device%3A18d9774c207714-093e713ddbba6f-1e525637-fa000-18d9774c207714%22%2C%22%24device_id%22%3A%20%2218d9774c207714-093e713ddbba6f-1e525637-fa000-18d9774c207714%22%2C%22%24initial_referrer%22%3A%20%22%24direct%22%2C%22%24initial_referring_domain%22%3A%20%22%24direct%22%7D; SIDCC=ABTWhQGUo_c7QMzJlJ7B3MDwdlxCSzVEGzdnCZ2vTNYf7jPYvq5f87wepPlk8GZSZi-XzOJ_HRKQ;__Secure-1PSIDCC=ABTWhQHvLXW1lnu41w0U9AXYAOP4JPcogay1dxjKeHVVrg6S4wHEoj-nuMfcIOQBc6beobZei1I; __Secure-3PSIDCC=ABTWhQFw_jZxYIo4ZvYRabvxKWJg3MBGwNQ0i5FSiV-tskZHup5Y9QKUAScdVHFFZNrYcPYFZI0\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1P4E6cTn4X6rgK9SyOsMA344kLNyISPHQ&export=download&authuser=0&confirm=t&uuid=cd6bed3e-4a7c-47ba-a27e-b581a55f3623&at=APZUnTVMN7FsFLBDI4_D7CAJCR7d%3A1707660525023\" -c -O 'Phase1.zip'\n",
        "!unzip Phase1.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4uMifr__aDc"
      },
      "source": [
        "#### **1.1. Import Required Libraries**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "def rename_images(main_folders):\n",
        "  for folder in main_folders:\n",
        "    for filename in os.listdir(folder):\n",
        "      # Extract the file extension and last part of the name\n",
        "      file_extension = os.path.splitext(filename)[1]\n",
        "      new_name = os.path.splitext(filename.split(\"_\")[-1])[0] + file_extension\n",
        "\n",
        "      # Construct the full paths for old and new filenames\n",
        "      old_path = os.path.join(folder, filename)\n",
        "      new_path = os.path.join(folder, new_name)\n",
        "\n",
        "      # Rename the image\n",
        "      os.rename(old_path, new_path)\n",
        "\n",
        "# Replace with your actual folder paths\n",
        "main_folders = [\"/content/DataSet1/Defected\", \"/content/DataSet1/OK\"]\n",
        "\n",
        "# Call the function to rename images\n",
        "rename_images(main_folders)\n",
        "\n",
        "print(\"Images renamed successfully!\")\n"
      ],
      "metadata": {
        "id": "i6GgkurtVSqO",
        "outputId": "612f1be7-df13-40aa-dc4c-4eac94e70180",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images renamed successfully!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: en-US,en;q=0.9,fa;q=0.8\" --header=\"Cookie: HSID=AVZaa1f5ErIEtLvcQ; SSID=Awf3XhMFJkemeGrY_; APISID=oRIlfG55p19VZpcr/A7Mj475R1IXF1DkbI; SAPISID=wKEwIU5TeyVYC2ac/AvFuZHnZAzf78Ftge; __Secure-1PAPISID=wKEwIU5TeyVYC2ac/AvFuZHnZAzf78Ftge; __Secure-3PAPISID=wKEwIU5TeyVYC2ac/AvFuZHnZAzf78Ftge; AMP_MKTG_630874745a=JTdCJTdE; AEC=Ae3NU9PERSlemibeYmMyedaimM4SxvIUXlqa-lfszTAF2dtB57vXFvbB9pw; __Secure-ENID=18.SE=vqxdXcv88tHZZOPjdmrlHZQDP3eK9m1PD2rWQ0wthQIU6HdnO8f84wy8LZo_4i9PHf1026gjy2U11wX06XdYucjjJUjLrUfO8l3tz-47MmsNgN_lQ53vs9CnM3k6UKJojiNsYv99Bl8HD2oDe31vrv2ZZxpM3oI_9gfQq5CjbHNXCvFY2mi8oemSD-BJ_hiXAbU2fKI6E5Puu9xhvZFIIKcih2HyyKwZWDiqc8QNaXAs5l9zpOPdEw-BqVbmM2pIdAF0X2dTmr4t1cO7YDKYwcKNYPp0Mizqc4wjm_XIzksmYPZMVU7JO52sfbobiwSA0acSjQmqBMo; SID=g.a000hQih628xwViRKIbEKZmqaYlo2LFBLAKN5vtxgUnRRzvXkyzQ-euIvoj6T3TRK4B6I6YmDQACgYKAd8SAQASFQHGX2MigK_tHJSKciWktZCRFj7N0hoVAUF8yKq4nAUmx4mL85b88-Iuj9xk0076; __Secure-1PSID=g.a000hQih628xwViRKIbEKZmqaYlo2LFBLAKN5vtxgUnRRzvXkyzQ6r_iUCUNfRfYdSqlFEFObwACgYKAfUSAQASFQHGX2MiH962ggWZj6mViGbOX2fiIBoVAUF8yKrfqnSczdF9XuTA_9rgJvhw0076; __Secure-3PSID=g.a000hQih628xwViRKIbEKZmqaYlo2LFBLAKN5vtxgUnRRzvXkyzQ8biOiKuASX8x_REdL6HeswACgYKAWQSAQASFQHGX2MiH94JHPSiSnTdInWKoUXsEBoVAUF8yKrNfvGhwqFcMb36VKLW2k2H0076; 1P_JAR=2024-03-12-12; AMP_630874745a=JTdCJTIyZGV2aWNlSWQlMjIlM0ElMjJjOTAzMjUxNy0xNzc5LTRjNGMtODY1ZC04ODczNmQ5MGI2NzYlMjIlMkMlMjJ1c2VySWQlMjIlM0ElMjJobTIxMDBtaCU0MGdtYWlsLmNvbSUyMiUyQyUyMnNlc3Npb25JZCUyMiUzQTE3MTAyNDY1MjU0NjElMkMlMjJvcHRPdXQlMjIlM0FmYWxzZSUyQyUyMmxhc3RFdmVudFRpbWUlMjIlM0ExNzEwMjQ3NzgxOTA1JTJDJTIybGFzdEV2ZW50SWQlMjIlM0E1OCU3RA==; NID=512=WtnnqelcehamY-yvQ9uzcdprhXlMDLs2ne_U3VG5YRK9m-sv8YGMQBw3x0d-Nn_DbxYuefusMpDjukhEzDmONknBO9EfJM_2rizXevS0nj4ed295XXnV9XDWcDyOjFW6V6-IRaprfplnjBT0_p0PpjGi_TgYJ0mqC9ZIdQYul2584bRY3bKrJCBShHhGIVg0oJoctr4OHsrGI6doSzaIk-UkWc8qNBiDIT1MpGSMWdgWKj2TWg1qsBNgw2SXffa4VFHTQbNRwfY9wdpTh_iYne4CjmHyiH6Iq4X52X_E9jj9W85ppEhN0egbOeNegElUZp-W9lrEDIFVLKjfrwHOdhNFLvPrwbM8HsNusa0pt4y3YEWhWy0XJeXwQTNdKyzK7uBNjakUscmg8jse6iy5Z8c--fR_Tr7LBFj7Gkb-68In895wP6RaOac-SUpIdri4k1gjfN6QLpPMI0LhIIJ9DsJfsHOP82maiATMcXzJs7g3yP7FfcsnAj0xw1BLxeVAcS96RBOxmi_qC0FDZP0Lu7YnWtrTzyzkXd7hiz-GfKF6RFZZQdum_WYJ5Plt_3h-Siq0PAFpDB1fByz7Tv2LyKlMlI8ebFyMYp7f0hvJX0nWHOprGaIVSZOGy7GWxZFYJScrBCV9lpKbvCOOfVgGSIzXwSo1EFFaNSlbJnG1dYbnF6eo9pPnv6BLa_58Cl0teBfhMm4cU0Ds7A_e_R2Ohhz2yRS-c3htFEOcGAWemKxnT13XvgIlHK-pPBSmrt9pONvFaZa_ELMv23H6JzA-xL_VvqRR70wF2wq3-xtx7NNjVWrtmQY9HQg; mp_851392464b60e8cc1948a193642f793b_mixpanel=%7B%22distinct_id%22%3A%20%22%24device%3A18e2809b62e22a7-02d0447f591d22-26001b51-1fa400-18e2809b62e22a7%22%2C%22%24device_id%22%3A%20%2218e2809b62e22a7-02d0447f591d22-26001b51-1fa400-18e2809b62e22a7%22%2C%22%24initial_referrer%22%3A%20%22%24direct%22%2C%22%24initial_referring_domain%22%3A%20%22%24direct%22%2C%22%24search_engine%22%3A%20%22google%22%7D; __Secure-1PSIDTS=sidts-CjEBYfD7Z4Tpd7defH8M_RlAEQltipkI55WiLm1Oaw1jXMoLNaszI2umHzG93eVmtAU-EAA; __Secure-3PSIDTS=sidts-CjEBYfD7Z4Tpd7defH8M_RlAEQltipkI55WiLm1Oaw1jXMoLNaszI2umHzG93eVmtAU-EAA; ph_phc_cjuWI21zkiw3B30S2vdrLd2HtfmqRWuzZMldJrXEpPw_posthog=%7B%22distinct_id%22%3A%2218e283efb04a61-02d2a699f5b2f-26001b51-1fa400-18e283efb051bfa%22%2C%22%24device_id%22%3A%2218e283efb04a61-02d2a699f5b2f-26001b51-1fa400-18e283efb051bfa%22%2C%22%24user_state%22%3A%22anonymous%22%2C%22%24referrer%22%3A%22https%3A%2F%2Fcolab.research.google.com%2F%22%2C%22%24referring_domain%22%3A%22colab.research.google.com%22%2C%22%24sesid%22%3A%5B1710250070638%2C%2218e3230738ce2c-0436790055fa18-26001b51-1fa400-18e3230738d2a0f%22%2C1710239019916%5D%2C%22%24session_recording_enabled_server_side%22%3Afalse%2C%22%24active_feature_flags%22%3A%5B%5D%2C%22%24enabled_feature_flags%22%3A%7B%7D%2C%22%24feature_flag_payloads%22%3A%7B%7D%2C%22%24search_engine%22%3A%22google%22%7D; SIDCC=AKEyXzWTifFpDIsM6gEi7eiCAv07JNDFxZpNTmqINtm9O46FnkE7tvq1S8Ikax6Ku3aBVMiJ58Qa; __Secure-1PSIDCC=AKEyXzXwHXXarIKRGzv8n86YJ-kuryhLoC3y_o-PSRRsRqb1jdWv3AfcplkdOqX7LtXkBdf7vNyK; __Secure-3PSIDCC=AKEyXzWxf-2rr1Uu2TkBdKy9TB9f1Ykkyi_02q7hTTJ53fd9BMzZjWcg2xTICFKqakER1GgPRZdS\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=1-8p5Dhg7cFNwaeVq_8hBXLdrSmhMcq27&export=download&authuser=3&confirm=t&uuid=9ae2b19b-1adf-4205-ae6b-3730c1ae757c&at=APZUnTVzdDFWTwF3HUKRnkSayETA:1710250079075\" -c -O 'vgg16_Freq_Classification.h5'\n",
        "noise_classify_model = load_model(\"/content/vgg16_Freq_Classification.h5\")"
      ],
      "metadata": {
        "id": "-mvaMBZCQUee",
        "outputId": "4278b7f6-79e5-4953-872f-f09bb530e3e8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-12 13:28:20--  https://drive.usercontent.google.com/download?id=1-8p5Dhg7cFNwaeVq_8hBXLdrSmhMcq27&export=download&authuser=3&confirm=t&uuid=9ae2b19b-1adf-4205-ae6b-3730c1ae757c&at=APZUnTVzdDFWTwF3HUKRnkSayETA:1710250079075\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.197.132, 2607:f8b0:400e:c00::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.197.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 213118632 (203M) [application/octet-stream]\n",
            "Saving to: ‘vgg16_Freq_Classification.h5’\n",
            "\n",
            "vgg16_Freq_Classifi 100%[===================>] 203.25M  82.9MB/s    in 2.5s    \n",
            "\n",
            "2024-03-12 13:28:23 (82.9 MB/s) - ‘vgg16_Freq_Classification.h5’ saved [213118632/213118632]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --header=\"Host: drive.usercontent.google.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\" --header=\"Accept-Language: en-US,en;q=0.9,fa;q=0.8\" --header=\"Cookie: HSID=AVZaa1f5ErIEtLvcQ; SSID=Awf3XhMFJkemeGrY_; APISID=oRIlfG55p19VZpcr/A7Mj475R1IXF1DkbI; SAPISID=wKEwIU5TeyVYC2ac/AvFuZHnZAzf78Ftge; __Secure-1PAPISID=wKEwIU5TeyVYC2ac/AvFuZHnZAzf78Ftge; __Secure-3PAPISID=wKEwIU5TeyVYC2ac/AvFuZHnZAzf78Ftge; AMP_MKTG_630874745a=JTdCJTdE; AEC=Ae3NU9PERSlemibeYmMyedaimM4SxvIUXlqa-lfszTAF2dtB57vXFvbB9pw; __Secure-ENID=18.SE=vqxdXcv88tHZZOPjdmrlHZQDP3eK9m1PD2rWQ0wthQIU6HdnO8f84wy8LZo_4i9PHf1026gjy2U11wX06XdYucjjJUjLrUfO8l3tz-47MmsNgN_lQ53vs9CnM3k6UKJojiNsYv99Bl8HD2oDe31vrv2ZZxpM3oI_9gfQq5CjbHNXCvFY2mi8oemSD-BJ_hiXAbU2fKI6E5Puu9xhvZFIIKcih2HyyKwZWDiqc8QNaXAs5l9zpOPdEw-BqVbmM2pIdAF0X2dTmr4t1cO7YDKYwcKNYPp0Mizqc4wjm_XIzksmYPZMVU7JO52sfbobiwSA0acSjQmqBMo; SID=g.a000hQih628xwViRKIbEKZmqaYlo2LFBLAKN5vtxgUnRRzvXkyzQ-euIvoj6T3TRK4B6I6YmDQACgYKAd8SAQASFQHGX2MigK_tHJSKciWktZCRFj7N0hoVAUF8yKq4nAUmx4mL85b88-Iuj9xk0076; __Secure-1PSID=g.a000hQih628xwViRKIbEKZmqaYlo2LFBLAKN5vtxgUnRRzvXkyzQ6r_iUCUNfRfYdSqlFEFObwACgYKAfUSAQASFQHGX2MiH962ggWZj6mViGbOX2fiIBoVAUF8yKrfqnSczdF9XuTA_9rgJvhw0076; __Secure-3PSID=g.a000hQih628xwViRKIbEKZmqaYlo2LFBLAKN5vtxgUnRRzvXkyzQ8biOiKuASX8x_REdL6HeswACgYKAWQSAQASFQHGX2MiH94JHPSiSnTdInWKoUXsEBoVAUF8yKrNfvGhwqFcMb36VKLW2k2H0076; 1P_JAR=2024-03-12-12; AMP_630874745a=JTdCJTIyZGV2aWNlSWQlMjIlM0ElMjJjOTAzMjUxNy0xNzc5LTRjNGMtODY1ZC04ODczNmQ5MGI2NzYlMjIlMkMlMjJ1c2VySWQlMjIlM0ElMjJobTIxMDBtaCU0MGdtYWlsLmNvbSUyMiUyQyUyMnNlc3Npb25JZCUyMiUzQTE3MTAyNDY1MjU0NjElMkMlMjJvcHRPdXQlMjIlM0FmYWxzZSUyQyUyMmxhc3RFdmVudFRpbWUlMjIlM0ExNzEwMjQ3NzgxOTA1JTJDJTIybGFzdEV2ZW50SWQlMjIlM0E1OCU3RA==; NID=512=WtnnqelcehamY-yvQ9uzcdprhXlMDLs2ne_U3VG5YRK9m-sv8YGMQBw3x0d-Nn_DbxYuefusMpDjukhEzDmONknBO9EfJM_2rizXevS0nj4ed295XXnV9XDWcDyOjFW6V6-IRaprfplnjBT0_p0PpjGi_TgYJ0mqC9ZIdQYul2584bRY3bKrJCBShHhGIVg0oJoctr4OHsrGI6doSzaIk-UkWc8qNBiDIT1MpGSMWdgWKj2TWg1qsBNgw2SXffa4VFHTQbNRwfY9wdpTh_iYne4CjmHyiH6Iq4X52X_E9jj9W85ppEhN0egbOeNegElUZp-W9lrEDIFVLKjfrwHOdhNFLvPrwbM8HsNusa0pt4y3YEWhWy0XJeXwQTNdKyzK7uBNjakUscmg8jse6iy5Z8c--fR_Tr7LBFj7Gkb-68In895wP6RaOac-SUpIdri4k1gjfN6QLpPMI0LhIIJ9DsJfsHOP82maiATMcXzJs7g3yP7FfcsnAj0xw1BLxeVAcS96RBOxmi_qC0FDZP0Lu7YnWtrTzyzkXd7hiz-GfKF6RFZZQdum_WYJ5Plt_3h-Siq0PAFpDB1fByz7Tv2LyKlMlI8ebFyMYp7f0hvJX0nWHOprGaIVSZOGy7GWxZFYJScrBCV9lpKbvCOOfVgGSIzXwSo1EFFaNSlbJnG1dYbnF6eo9pPnv6BLa_58Cl0teBfhMm4cU0Ds7A_e_R2Ohhz2yRS-c3htFEOcGAWemKxnT13XvgIlHK-pPBSmrt9pONvFaZa_ELMv23H6JzA-xL_VvqRR70wF2wq3-xtx7NNjVWrtmQY9HQg; mp_851392464b60e8cc1948a193642f793b_mixpanel=%7B%22distinct_id%22%3A%20%22%24device%3A18e2809b62e22a7-02d0447f591d22-26001b51-1fa400-18e2809b62e22a7%22%2C%22%24device_id%22%3A%20%2218e2809b62e22a7-02d0447f591d22-26001b51-1fa400-18e2809b62e22a7%22%2C%22%24initial_referrer%22%3A%20%22%24direct%22%2C%22%24initial_referring_domain%22%3A%20%22%24direct%22%2C%22%24search_engine%22%3A%20%22google%22%7D; __Secure-1PSIDTS=sidts-CjEBYfD7Z4Tpd7defH8M_RlAEQltipkI55WiLm1Oaw1jXMoLNaszI2umHzG93eVmtAU-EAA; __Secure-3PSIDTS=sidts-CjEBYfD7Z4Tpd7defH8M_RlAEQltipkI55WiLm1Oaw1jXMoLNaszI2umHzG93eVmtAU-EAA; ph_phc_cjuWI21zkiw3B30S2vdrLd2HtfmqRWuzZMldJrXEpPw_posthog=%7B%22distinct_id%22%3A%2218e283efb04a61-02d2a699f5b2f-26001b51-1fa400-18e283efb051bfa%22%2C%22%24device_id%22%3A%2218e283efb04a61-02d2a699f5b2f-26001b51-1fa400-18e283efb051bfa%22%2C%22%24user_state%22%3A%22anonymous%22%2C%22%24referrer%22%3A%22https%3A%2F%2Fcolab.research.google.com%2F%22%2C%22%24referring_domain%22%3A%22colab.research.google.com%22%2C%22%24sesid%22%3A%5B1710250020368%2C%2218e3230738ce2c-0436790055fa18-26001b51-1fa400-18e3230738d2a0f%22%2C1710239019916%5D%2C%22%24session_recording_enabled_server_side%22%3Afalse%2C%22%24active_feature_flags%22%3A%5B%5D%2C%22%24enabled_feature_flags%22%3A%7B%7D%2C%22%24feature_flag_payloads%22%3A%7B%7D%2C%22%24search_engine%22%3A%22google%22%7D; SIDCC=AKEyXzUhWryxQncrEtSBHZs_DoxJHh_mUwE1XN2TxyPRi-Q-POqfs5UfGldOPWBvtGaTzeY0mvWz; __Secure-1PSIDCC=AKEyXzWIu7qSqt3wPc1-w2aj5CBxiZsO9jJ_QDGJwxSIupTY3RhdGoTSFxEwQPbL9IaIFb0gPRq6; __Secure-3PSIDCC=AKEyXzVK3MybK5aSv-LiiRkgRkTZbekmHJ_GPwKSzpnRHZQqVz8SAQboLK6gZWiWvKwPbIUatqdy\" --header=\"Connection: keep-alive\" \"https://drive.usercontent.google.com/download?id=160pz3QPwtqvwenGwEH7sGLCJnHq2l_mi&export=download&authuser=3&confirm=t&uuid=153949fe-88c0-4871-885c-45fe28c3c98e&at=APZUnTXVpsxVD1cO2-j2Vnko7Yn3:1710250049208\" -c -O 'denoiser_model.h5'\n",
        "denoiser_model = load_model(\"/content/denoiser_model.h5\")"
      ],
      "metadata": {
        "id": "GhvU2shXRVMp",
        "outputId": "0ac3787e-e077-492e-d34b-d82b29f4bb97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-12 13:28:25--  https://drive.usercontent.google.com/download?id=160pz3QPwtqvwenGwEH7sGLCJnHq2l_mi&export=download&authuser=3&confirm=t&uuid=153949fe-88c0-4871-885c-45fe28c3c98e&at=APZUnTXVpsxVD1cO2-j2Vnko7Yn3:1710250049208\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 74.125.197.132, 2607:f8b0:400e:c00::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|74.125.197.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 161646072 (154M) [application/octet-stream]\n",
            "Saving to: ‘denoiser_model.h5’\n",
            "\n",
            "denoiser_model.h5   100%[===================>] 154.16M  53.1MB/s    in 2.9s    \n",
            "\n",
            "2024-03-12 13:28:29 (53.1 MB/s) - ‘denoiser_model.h5’ saved [161646072/161646072]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import keras.utils as image\n",
        "\n",
        "def resize_images(folder_path, target_size=(224, 224)):\n",
        "    for filename in os.listdir(folder_path):\n",
        "        file_path = os.path.join(folder_path, filename)\n",
        "        if os.path.isfile(file_path):\n",
        "            img = image.load_img(file_path, target_size=target_size)\n",
        "            img.save(file_path)\n",
        "\n",
        "resize_images(\"/content/DataSet1/Defected\")\n",
        "resize_images(\"/content/DataSet1/OK\")\n",
        "print (\"resized status: OK!\")"
      ],
      "metadata": {
        "id": "VJkB-YzJWmEV",
        "outputId": "276f98cd-3b96-44d0-c756-e4e9f161a64e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resized status: OK!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "selected_folder = \"Defected/\"\n",
        "\n",
        "# !pip install lpips"
      ],
      "metadata": {
        "id": "dOkvwhAdnhe3"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "import lpips\n",
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "from skimage.metrics import structural_similarity as ssim\n",
        "\n",
        "def prepare(img_path):\n",
        "    img = image.load_img(img_path, target_size=(224,224))\n",
        "    x = image.img_to_array(img)\n",
        "    x = x/255\n",
        "    return np.expand_dims(x, axis=0)\n",
        "\n",
        "def cal_periodic_gaussian(img_name):\n",
        "  test_image_path = \"/content/DataSet1/\"+ selected_folder + img_name\n",
        "  print(test_image_path)\n",
        "  test_image = cv2.imread(test_image_path)\n",
        "  test_image = cv2.cvtColor(test_image, cv2.COLOR_BGR2RGB)\n",
        "\n",
        "  # Preprocess the image (resize and normalize)\n",
        "  test_image = cv2.resize(test_image, (224, 224))  # Resize to match the input shape of your model\n",
        "  test_image = test_image / 255.0  # Normalize pixel values\n",
        "\n",
        "  # Expand dimensions to create a batch of size 1\n",
        "  test_image = np.expand_dims(test_image, axis=0)\n",
        "\n",
        "  # Use the model to predict the denoised image\n",
        "  denoised_image = denoiser_model.predict(test_image)\n",
        "  cv2.imwrite(\"/content/DataSet1/\"+ selected_folder+ img_name ,(denoised_image[0] * 255))\n",
        "\n",
        "\n",
        "  return '/content/DataSet1/'+ selected_folder +img_name + \"  denoised ! \"\n",
        "\n",
        "\n",
        "def cal_salt(img_name):\n",
        "  print('/content/DataSet1/'+selected_folder+img_name)\n",
        "  noisyimage = cv2.imread('/content/DataSet1/'+selected_folder+img_name , cv2.IMREAD_COLOR)\n",
        "\n",
        "  # Apply median filter several times\n",
        "  filtered_image = cv2.medianBlur(noisyimage, 3)  # Kernel size of 3\n",
        "  filtered_image = cv2.medianBlur(filtered_image,3)\n",
        "  filtered_image = cv2.medianBlur(filtered_image,3)\n",
        "\n",
        "  # Convert BGR to RGB for displaying with Matplotlib\n",
        "  filtered_image_rgb = cv2.cvtColor(filtered_image, cv2.COLOR_BGR2RGB)\n",
        "  filtered_image_rgb = cv2.normalize(filtered_image_rgb, None, 0, 255, cv2.NORM_MINMAX).reshape(224,224, 3)\n",
        "\n",
        "  cv2.imwrite(\"/content/DataSet1/\" + selected_folder + img_name , filtered_image_rgb)\n",
        "\n",
        "  return '/content/DataSet1/'+ selected_folder + img_name + \" denoised ! \""
      ],
      "metadata": {
        "id": "0Cmb77TlP6Dy"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6JWomVLOQj5m"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.preprocessing import image\n",
        "\n",
        "classes= ['Gaussian', 'Periodic', 'Salt']\n",
        "\n",
        "# Load all image files from the specified directory\n",
        "img_folder = '/content/DataSet1/'\n",
        "\n",
        "periodic_psnr = []\n",
        "gaussian_psnr = []\n",
        "salt_psnr = []\n",
        "periodic_lpips = []\n",
        "gaussian_lpips = []\n",
        "salt_lpips = []\n",
        "periodic_ssim = []\n",
        "gaussian_ssim = []\n",
        "salt_ssim = []\n",
        "\n",
        "for img_name in os.listdir(img_folder + selected_folder):\n",
        "    img_path = os.path.join(img_folder+ selected_folder, img_name)\n",
        "\n",
        "    result = noise_classify_model.predict([prepare(img_path)])\n",
        "    classresult=np.argmax(result,axis=1)\n",
        "    # Perform noise detection on the image\n",
        "    if (classes[classresult[0]] == 'Periodic'):\n",
        "      print(cal_periodic_gaussian(img_name) + \"  -> type : Periodic \" )\n",
        "\n",
        "\n",
        "    elif (classes[classresult[0]] == 'Gaussian'):\n",
        "      print(cal_periodic_gaussian(img_name) + \"  -> type : Gaussian \" )\n",
        "\n",
        "\n",
        "    elif (classes[classresult[0]] == 'Salt'):\n",
        "      print(cal_salt(img_name) + \"  -> type : Salt \")\n",
        "# !zip options dataset1_cleaned.zip /content/DataSet1\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!cp /content/options.zip  /content/drive/MyDrive/\n"
      ],
      "metadata": {
        "id": "a6Gnc9LmgZUx",
        "outputId": "793cf1af-730e-4caa-f3d7-dba23fc3b878",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "58da9001"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import cv2\n",
        "import glob\n",
        "import random\n",
        "import shutil\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import Dense\n",
        "from keras.models import Sequential\n",
        "from keras.preprocessing import image\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.models import Model,load_model\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.applications.vgg19 import VGG19\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
        "from keras.layers import Convolution2D,Dense,MaxPooling2D,Activation,Dropout,Flatten\n",
        "from keras.layers import Input, Add, ZeroPadding2D, BatchNormalization, Conv2D, AveragePooling2D, GlobalMaxPooling2D\n",
        "\n",
        "!mkdir Train\n",
        "!mkdir Test\n",
        "!mkdir Validation\n",
        "!mv /content/DataSet1/Defected /content/Train\n",
        "!mv /content/DataSet1/OK /content/Train\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6tmATL4_ecG"
      },
      "source": [
        "#### **1.2. Test-Train Data**\n",
        "**Split the dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "907a8041",
        "outputId": "5010e8c8-0936-4e31-ba75-d5dd1c85655e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images split successfully!\n"
          ]
        }
      ],
      "source": [
        "\n",
        "Train_dir = '/content/Train'\n",
        "Test_dir = '/content/Test'\n",
        "Val_dir = '/content/Validation'\n",
        "\n",
        "os.makedirs(os.path.join(Test_dir, 'OK'), exist_ok=True)\n",
        "os.makedirs(os.path.join(Test_dir, 'Defected'), exist_ok=True)\n",
        "os.makedirs(os.path.join(Val_dir, 'OK'), exist_ok=True)\n",
        "os.makedirs(os.path.join(Val_dir, 'Defected'), exist_ok=True)\n",
        "\n",
        "for category in ['OK', 'Defected']:\n",
        "    images = os.listdir(os.path.join(Train_dir, category))\n",
        "    num_images_to_move = int(0.15 * len(images))  # Randomly select 15% of images to move\n",
        "    images_to_move = random.sample(images, num_images_to_move)\n",
        "\n",
        "    # Move the selected images to Test and Validation directories\n",
        "    for image in images_to_move:\n",
        "        source_path = os.path.join(Train_dir, category, image)\n",
        "        if random.random() < 0.5:  # 50% chance to move to Test directory\n",
        "            dest_path = os.path.join(Test_dir, category, image)\n",
        "        else:\n",
        "            dest_path = os.path.join(Val_dir, category, image)\n",
        "        shutil.move(source_path, dest_path)\n",
        "\n",
        "print(\"Images split successfully!\")\n",
        "\n",
        "def get_files(directory):\n",
        "    if not os.path.exists(directory):\n",
        "        return 0\n",
        "    count = 0\n",
        "    # crawls inside folders\n",
        "    for current_path, dirs, files in os.walk(directory):\n",
        "        for dr in dirs:\n",
        "            count += len(glob.glob(os.path.join(current_path, dr + \"/*\")))\n",
        "    return count\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a07bcaa8",
        "outputId": "7ec3cd12-e823-44e6-c268-90318001e485"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 Classes\n",
            "1063 Train images\n",
            "101 Test images\n",
            "101 Val images\n"
          ]
        }
      ],
      "source": [
        "\n",
        "train_samples =get_files(Train_dir)     #train file image count\n",
        "test_samples=get_files(Test_dir)  #test file image count\n",
        "val_samples = get_files(Val_dir)\n",
        "num_classes=len(glob.glob(Train_dir+\"/*\"))\n",
        "\n",
        "print(num_classes,\"Classes\")\n",
        "print(train_samples,\"Train images\")\n",
        "print(test_samples,\"Test images\")\n",
        "print(test_samples,\"Val images\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NuGK0vnQ_jRe"
      },
      "source": [
        "#### **1.3. ImageDataGenerator**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aa4DsLKmxl6w"
      },
      "source": [
        "**ImageDataGenerator**,Data augmentation is used to increase the size of training set. Through Data augmentation we want to prevent overfitting ,this refers to randomly changing the images in ways that shouldn’t impact their interpretation, such as horizontal flipping, zooming, and rotating\n",
        "* **Rescale:** One of the many magnification parameters adjusts the pixel values of our image.\n",
        "* **Shear_range:** counterclockwise shear angle in degrees\n",
        "* **Zoom_range:** zoom\n",
        "* **Horizontal_flip:** flip image horizontally"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "d3d73be1"
      },
      "outputs": [],
      "source": [
        "train_datagen=ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    )\n",
        "\n",
        "test_datagen=ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-U7ZGKPT8ChD"
      },
      "source": [
        "**flow_from_directory() -->** Another method to read images into TensorFlow environment is to use the .\n",
        "\n",
        "**Parameters:**\n",
        "* **directory:** The path of the target directory. It must contain one subdirectory per class. Any PNG, JPG, BMP or TIF formatted images found in each of the subdirectories will be included in the generator.\n",
        "* **target_size:** A tuple of integers, (height, width), by default (256,256). All found images will be resized.\n",
        "* **batch_size:** The size of the data chunks (default: 32).\n",
        "* **shuffle:** Decides whether to shuffle data (default: True). If set to false, it sorts the data in alphanumeric order.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwD6Vmh4mrDl",
        "outputId": "95ea7b3f-e796-4cc2-fb7e-a3c9ff190066"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-ca8a9f717433>:9: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use LANCZOS or Resampling.LANCZOS instead.\n",
            "  img_resized = img.resize(target_size, Image.ANTIALIAS)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Images resized successfully!\n"
          ]
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import os\n",
        "\n",
        "def resize_images(folder_path, target_size=(224, 224)):  #resizing fusnction\n",
        "    for filename in os.listdir(folder_path):\n",
        "        file_path = os.path.join(folder_path, filename)\n",
        "        if os.path.isfile(file_path):\n",
        "            img = Image.open(file_path)\n",
        "            img_resized = img.resize(target_size, Image.ANTIALIAS)\n",
        "            img_resized.save(file_path)\n",
        "\n",
        "#Defected Casting paths for the Train and Test directories\n",
        "resize_images(Train_dir+\"/Defected\")\n",
        "resize_images(Test_dir+\"/Defected\")\n",
        "resize_images(Val_dir+\"/Defected\")\n",
        "#Non defected Casting paths for the Train and Test directories\n",
        "resize_images(Train_dir+\"/OK\")\n",
        "resize_images(Test_dir+\"/OK\")\n",
        "resize_images(Val_dir+\"/OK\")\n",
        "\n",
        "\n",
        "print(\"Images resized successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WpHBoue9d3M",
        "outputId": "d138d4b5-272f-45b5-ebac-5159bd8ee775"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".ipynb_checkpoints folder does not exist.\n",
            "Found 1063 images belonging to 2 classes.\n",
            "Found 101 images belonging to 2 classes.\n",
            "Found 86 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "checkpoint_folder = os.path.join('/content/Train', \".ipynb_checkpoints\")\n",
        "\n",
        "if os.path.exists(checkpoint_folder):\n",
        "    os.rmdir(checkpoint_folder)\n",
        "    print(f\"Deleted .ipynb_checkpoints folder successfully.\")\n",
        "else:\n",
        "    print(\".ipynb_checkpoints folder does not exist.\")\n",
        "\n",
        "input_shape=(224,224,3)\n",
        "Train_generator =train_datagen.flow_from_directory(Train_dir,target_size=(224,224),batch_size=32)\n",
        "Test_generator=test_datagen.flow_from_directory(Test_dir,shuffle=True,target_size=(224,224),batch_size=32)\n",
        "Val_generator=val_datagen.flow_from_directory(Val_dir,shuffle=True,target_size=(224,224),batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cal_sensitivity(conf_matrix):\n",
        "    TP = conf_matrix[1, 1]\n",
        "    FN = conf_matrix[1, 0]\n",
        "    sensitivity = TP / (TP + FN)\n",
        "    return sensitivity\n",
        "\n",
        "def cal_specificity(conf_matrix):\n",
        "    TN = conf_matrix[0, 0]\n",
        "    FP = conf_matrix[0, 1]\n",
        "    specificity = TN / (TN + FP)\n",
        "    return specificity"
      ],
      "metadata": {
        "id": "NyHSzU3hvoU0"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EnyAcVHQCmUG"
      },
      "source": [
        "#### **1.5. VGG16 Model**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_CAE0q1c4Tg"
      },
      "source": [
        "**VGG16** Architecture consists of 16 layers.\n",
        "\n",
        "* **include_top :** Whether to include 3 layers fully connected to the top of the network\n",
        "* **weight:** checkpoint from which model is initialized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GND3gR2Cr9K",
        "outputId": "b1135224-3389-4d91-9b91-d45c2b62bc15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58889256/58889256 [==============================] - 0s 0us/step\n",
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14714688 (56.13 MB)\n",
            "Trainable params: 0 (0.00 Byte)\n",
            "Non-trainable params: 14714688 (56.13 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def create_Base_model_using_VGG16():\n",
        "    model = VGG16(\n",
        "        weights = \"imagenet\",\n",
        "        include_top=False,\n",
        "        input_shape = (224,224, 3) # goruntu boyutu\n",
        "        )\n",
        "    for layer in model.layers:\n",
        "      layer.trainable = False\n",
        "    return model\n",
        "create_Base_model_using_VGG16().summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wy5A0gDCC-2j",
        "outputId": "7c4442ec-a73f-4f70-d455-2c264a53a703"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 25088)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               3211392   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               33024     \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 2)                 130       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 18000386 (68.67 MB)\n",
            "Trainable params: 3285698 (12.53 MB)\n",
            "Non-trainable params: 14714688 (56.13 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "def add_new_layers():\n",
        "    model = create_Base_model_using_VGG16()\n",
        "    x = model.output\n",
        "    x = tf.keras.layers.Flatten()(x)\n",
        "    x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(256, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(128, activation=\"relu\")(x)\n",
        "    x = tf.keras.layers.Dense(64, activation=\"relu\")(x)\n",
        "    predictions = tf.keras.layers.Dense(num_classes, activation=\"softmax\")(x)\n",
        "\n",
        "    final_model = tf.keras.models.Model(\n",
        "        inputs = model.input,\n",
        "        outputs = predictions)\n",
        "\n",
        "    final_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "    return final_model\n",
        "\n",
        "add_new_layers().summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z9u0Ny_CC-_H",
        "outputId": "e107e019-5f91-43ec-d531-54841b593f6c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.6355 - accuracy: 0.6927"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 4 batches). You may need to use the repeat() function when building your dataset.\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/32 [==============================] - 30s 801ms/step - loss: 0.6355 - accuracy: 0.6927 - val_loss: 0.7960 - val_accuracy: 0.5465 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 13s 406ms/step - loss: 0.5561 - accuracy: 0.7257 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 13s 398ms/step - loss: 0.4142 - accuracy: 0.8168 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 13s 401ms/step - loss: 0.2700 - accuracy: 0.8839 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 13s 400ms/step - loss: 0.2687 - accuracy: 0.8959 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 13s 408ms/step - loss: 0.2263 - accuracy: 0.9109 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 14s 444ms/step - loss: 0.1595 - accuracy: 0.9309 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 12s 383ms/step - loss: 0.2913 - accuracy: 0.8749 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 13s 407ms/step - loss: 0.1286 - accuracy: 0.9443 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 13s 410ms/step - loss: 0.1162 - accuracy: 0.9540 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 12s 385ms/step - loss: 0.1418 - accuracy: 0.9389 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 12s 386ms/step - loss: 0.1440 - accuracy: 0.9449 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 13s 405ms/step - loss: 0.0810 - accuracy: 0.9710 - lr: 0.0010\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 13s 387ms/step - loss: 0.0925 - accuracy: 0.9690 - lr: 0.0010\n",
            "Epoch 15/15\n",
            "32/32 [==============================] - 13s 388ms/step - loss: 0.1468 - accuracy: 0.9439 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "# model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# model_checkpoint = ModelCheckpoint('best_CNN_model.h5', monitor='loss', save_best_only=True)\n",
        "# early_stopping = EarlyStopping(monitor='loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "model_from_vgg16 = add_new_layers()\n",
        "\n",
        "model_checkpoint = ModelCheckpoint('best_VGG16_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=3, restore_best_weights=True)\n",
        "\n",
        "history2 = model_from_vgg16.fit(\n",
        "    Train_generator,\n",
        "    steps_per_epoch=32,\n",
        "    epochs=15,\n",
        "    validation_data=Val_generator,\n",
        "    validation_steps=4,\n",
        "    verbose=1,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3,patience=3, min_lr=0.000001),early_stopping, model_checkpoint],\n",
        "    use_multiprocessing=False,\n",
        "    shuffle=True\n",
        "    )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "93fUErbvC_C6",
        "outputId": "10e08b1e-a78f-48c0-9383-8dd3a2cfd144",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 3s 724ms/step - loss: 0.0969 - accuracy: 0.9703\n",
            "Test Accuracy: 97.02970385551453\n",
            "4/4 [==============================] - 1s 126ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[40 23]\n",
            " [26 12]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.61      0.63      0.62        63\n",
            "          OK       0.34      0.32      0.33        38\n",
            "\n",
            "    accuracy                           0.51       101\n",
            "   macro avg       0.47      0.48      0.47       101\n",
            "weighted avg       0.51      0.51      0.51       101\n",
            "\n",
            "Sensitivity: 0.3157894736842105\n",
            "Specificity: 0.6349206349206349\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+y4BBiIiIiMrH1B4Yd7dcq+gD2NYvFYMG7cbq1f3QpUu98u0zPR2YbFm+dcqAQYiIiIgq3G+/3YKxsSE6dXJU1xo0qIVz596v1HmByqtG3WKDiIiIqre8PCWmTz+BXr22YNiwvcjI0JwRujqFIIBBiIiIiCpIdHQy3N034OuvT0MIIC4uBWvWnJO6refioTEiIiJ6KUIIBAdH4ZNPjuHJk8L7hRka6mH+/NcwZUpXibt7PgYhIiIiemEPH2bh/fd/xoED0epas2Z1sGPHYLRvX74ry6TAIERERESFSpswMUvz8vWQkBvw9z+AxMRMdW38eDcsW+YFExPDyuy0wjAIERERUaGyTpgoN0dSUiYGDNiFnJzCQ2HW1ibYuPFteHs3q+QmKxZPliYiIqJC/zthoplj8Y/azYFu82Bra4avv34dAODl1QiXLk2ocSEI4IgQERER/VsxEyaqVAJKpQqGhvrq2ocfdka9ehYYOLAF9PSq12XxZcURISIiInquhIQMvPHGdsya9atGXU9PhsGDW9bYEAQwCBEREdFzHDhwHa1br8Hx47FYsiQcv/56U+qWKhQPjREREVERWVl5mDLlOIKCItU1W1szCTuqHAxCREREpCHytjWGt1+HmJhH6lr//s2wfv3bsLY2kbCziscgRERERAAApUqGpb92w6xjr6NAVRiCTEwMsWKFF8aObV/t7hNWERiEiIiICMnJ2Riyoh/CYhzUNTc3e+zYMRhNm9aRsLPKxZOliYiICJaWCmTmFs4GLZMJTJ/eHeHhY7Q6BAEMQkRERATA0FAf2987iRY2D3Fy8iEsWPA65HL90les4XhojIiISAdFRMTDxMQQbdrYqWtNbdNw+dPvoGfh8Jw1tQtHhIiIiHRIQYEKc+eGoUePTRg2bC+ys/M1XtfTExJ1Jg0GISIiIh0RF5eCV1/dhDlzfoNSKXDtWjK+++4vqduSFA+NERERaTkhBLZtu4hJk44gIyMPAKCvL8Ps2R745JMuEncnLQYhIiIiLZaS8gTjxx/G7t1X1LVGjazw/feD0KVLPQk7qx4YhIiIiLRUWNgt+Pntx9276era6NFtsXJlX5ibKyTsrPpgECIiItJCCQkZ8PL6Hnl5SgCAlZURgoLewpAhr0jcWfXCk6WJiIi0kL29OWbP9gAA9OrljIsXJzAEFYMjQkRERFpACAGVSkBf/9kYx9Sp3eDkZIERI1yhp6d99wmrCBwRIiIiquEePszCwIG78NVXv2vU9fX14OfXhiHoOTgiREREVIOFhNyAv/8BJCZm4tChGPTp0wju7k5St1VjMAgRERHVQDk5BZg+/QRWrDijrllZGavnCaKyYRAiIiKqYS5dSsKIEftw6dIDdc3LqxE2bx4AOzszCTureRiEiIiIagiVSmDVqjOYOvUEcnMLL4tXKPSxeHFvTJrUiecCvQAGISIiohrg0aNsjBixDyEhsepa69Y22LFjMFq1spGws5qNV40RERHVAKamcty7l6F+PnlyF5w9+z5D0EtiECIiIqoBjIwMsGPHIDRsWAshIb5YvtwLRkY8sPOy+BMkIiKqhiIj78PUVI7mza3VtdatbRET8yEMDDiOUVH4kyQiIqpGlEoVFi06hS5dNmDYsL3IzS3QeJ0hqGLxp0lERFRNxMen4fXXt2LatFAUFKhw/nwivvvuL6nb0mo8NEZERFQN7N59BePGHUJqag4AQCYDpk3rjokTO0ncmXZjECIiIpJQenouPvroKLZsuaCuOTlZYNu2gfDwcJauMR3BIERERCSRiIh4+PruR1xcirrm4/MK1qx5E1ZWxhJ2pjsYhIiIiCRw7146evbcgry8whmizc3lWL26H3x9XSGTcYboqsKTpYmIiCTg6GiBTz91BwB07eqECxfGw8+vDUNQFeOIEBERURUQQgCARtCZM6cn6te3xJgx7avmsvjoPUB4IJCXUfzrWQmV30M1wyBERERUyVJSnmD8+MPo2NEBn37aVV03NNTHuHEdqq6R8EDg8fXSl5ObV34v1QSDEBERUSUKC7sFP7/9uHs3Hfv3X8PrrzdEu3b20jTzdCRIpgeYltCD3BzoNq/qepIYgxAREVElyMtTIjDwJBYvPo3/PyoGMzM5EhMzpW0MKAxB4+5K3UW1wCBERERUwaKjkzF8+D5ERT0756ZXL2ds3ToQ9epZSNgZ/RuDEBERUQURQmDdukhMnhyCJ08K7xFmaKiH+fNfw5QpXaGnxyvCqhsGISIiogrw+PETjB59AAcPRqtrzZrVwY4dg9G+vUTnBFGpGISIiIgqgEKhj+vXk9XPJ0zogKVL+8DExFDCrqg0nFCRiIioApiayrF9+yA4OJjj4MF38d13bzIE1QAcESIiInoBly4lwdRUDhcXK3WtQwcHxMV9BIWCX681BUeEiIiIykGlEli58k907BiMESP2oaBApfE6Q1DNwiBERERURgkJGXjjje345JMQ5OYq8eefd7FmzV9St0UvQfIgtHr1ajg7O8PIyAidO3fG2bNnn7v8ihUr0KxZMxgbG8PJyQmTJ09GTk5OFXVLRES66sCB62jdeg2OH49V1yZP7oL333eTsCt6WZKO3+3atQsBAQFYu3YtOnfujBUrVsDLywvR0dGwsbEpsvyOHTswbdo0bNy4EV27dkVMTAz8/f0hk8mwfPlyCd4BERFpu6ysPEyZchxBQZHqmr29GTZvHoA+fRpJ2BlVBElHhJYvX473338fo0ePRsuWLbF27VqYmJhg48aNxS4fHh6Obt26Yfjw4XB2dkafPn0wbNiwUkeRiIiIXkRk5H20b79OIwQNGNAcFy9OYAjSEpIFoby8PERGRsLT0/NZM3p68PT0RERERLHrdO3aFZGRkergExcXhyNHjqBfv34l7ic3Nxfp6ekaDyIiotLEx6eha9eNiIl5BAAwMTFEcLA39u0bCmtrE4m7o4oiWRBKTk6GUqmEra2tRt3W1haJiYnFrjN8+HB8+eWX6N69OwwNDdGoUSP07NkTM2bMKHE/CxcuhKWlpfrh5ORUoe+DiIi0k5OTJT74oAMAwM3NHn//PQ5jx7aHTMbbZGgTyU+WLo+wsDAsWLAA3333HaKiorBv3z4cPnwY8+bNK3Gd6dOnIy0tTf2Ij4+vwo6JiKgmEU9vE///Fi70xPLlfRAePgZNm9aRqCuqTJKdLG1tbQ19fX0kJSVp1JOSkmBnZ1fsOl988QX8/PwwduxYAEDr1q2RlZWF//znP5g5cyb09IrmOoVCAYVCUfFvgIiItEZ6ei4++ugoOnVyxAcfdFTXjYwMMHmyu4SdUWWTbERILpfDzc0NoaGh6ppKpUJoaCjc3Yv/Q5ednV0k7Ojr6wMomuKJiIjKIiIiHm3brsWWLRcwZcpxXLv2UOqWqApJevl8QEAARo0ahQ4dOqBTp05YsWIFsrKyMHr0aADAyJEj4ejoiIULFwIAvL29sXz5crRr1w6dO3fGjRs38MUXX8Db21sdiIiIiMqioECFr776HV999TuUysJfpg0N9RAbm4IWLepK3B1VFUmDkI+PDx4+fIjAwEAkJiaibdu2OHbsmPoE6jt37miMAM2aNQsymQyzZs3CvXv3ULduXXh7e2P+/PlSvQUiIqqB4uJS4Ou7DxERd9W1rl2d8P33A9GwodVz1iRtIxM6dkwpPT0dlpaWSEtLg4WFhdTtEBFRFRJCYOvWC5g06SgyM/MAAPr6MgQGemDGjB4wMKhR1xCVX1A9IPMeYOYIjLtb+vLVSGV9f/POcEREpBNSU3Mwbtwh7N59RV1zcbHC9u2D0KVLPQk7IykxCBERkU6QyYAzZ56Ngvj7t8W33/aFuTmvLNZlWj4GSEREVMjS0gjbtg2EtbUJdu9+B5s29WcIIo4IERGRdoqOToapqRz16j07n6RHjwa4detjmJrKJeyMqhOOCBERkVYRQiAo6BzatQvCyJH7oVJpXhPEEET/i0GIiIi0xsOHWRgwYBfGjz+MJ08KcPLkLaxbF1n6iqSzeGiMiIi0QkjIDfj7H0BiYqa6Nn68G0aObCNhV1TdMQgREVGNlpNTgOnTT2DFijPqmrW1CTZufBve3s0k7IxqAgYhIiKqsS5dSsKIEftw6dIDdc3LqxE2bx4AOzszCTujmoJBiIiIaqTbt1PRsWMwcnOVAACFQh+LF/fGpEmdoKcnk7g7qil4sjQREdVIDRrUUp//07q1Dc6d+w8++qgzQxCVC0eEiIioxvrmGy80aGCJKVO6wsiIX2lUfhwRIiKiai8rKw/jxx/C5s3nNeqmpnLMnPkqQxC9MP7JISKiai0y8j5GjNiH6OhH2L79Enr0qI9GjWpL3RZpCQYhIiKqlpRKFZYuDcesWSdRUKACAKhUApcvP6jZQSh6DxAeCORlVP2+sxKqfp/VHIMQERFVO/HxafDz24/ffrutrrm52WPHjsFo2rSOhJ1VgPBA4PF1aXuQm0u7/2qEQYiIiKqV3buvYNy4Q0hNzQEAyGTAtGndMWdOT8jl+hJ3VwGejgTJ9ABT+6rfv9wc6Dav6vdbTTEIERFRtZCRkYsPPzyKLVsuqGtOThbYtm0gPDycpWusspjaA+PuSt2FzmMQIiKiaiE3V4njx2PVz318XsGaNW/CyspYwq5I2/HyeSIiqhasrU2wZcsAWFgosHXrAPzww2CGIKp0HBEiIiJJxMWlwNTUELa2z+4J1rt3I9y+/Qlq1TKSsDPSJRwRIiKiKiWEwJYt59GmzVq8995BCCE0XmcIoqrEIERERFUmJeUJ3n13L/z9DyAzMw9HjvyDTZvOS90W6TAeGiMioioRFnYLfn77cfduurrm798WQ4a0lLAr0nUMQkREVKny8pQIDDyJxYtP4+lRMCsrIwQFvYUhQ16RtjnSeQxCRERUaa5fT8aIEfsQFfXs1g69ejlj69aBqFfPQsLOiAoxCBERUaWIi0tB+/ZBePKkAABgaKiH+fNfw5QpXaGnJ5O4O6JCPFmaiIgqhYuLFQYNagEAaNasDv78cyw++6wbQxBVKxwRIiKiSrN6dT80aGCJmTNfhYmJodTtEBXBESEiInppOTkFmDz5GPbsuaJRt7Q0wvz5rzMEUbXFESEiInoply4lYcSIfbh06QE2b76ALl3qwcnJUuq2iMqEI0JERPRCVCqBlSv/RMeOwbh06QEA4MmTfJw7d1/izojKjiNCRERUbgkJGRg9+gBCQp7dLb51axvs2DEYrVrZSNgZUfkwCBERUbkcOHAdY8f+jOTkbHVt8uQuWLDgdRgZ8WuFahb+iSUiojLJysrDlCnHERQUqa7Z25th8+YB6NOnkYSdEb04BiEiIiqT9PRc7N17Tf18wIDmCA72hrW1iYRdEb0cnixNRERlYm9vjvXrvWFiYojgYG/s2zeUIYhqPI4IERFRseLj02BqKkft2sbqWv/+zXHz5sewsTGVsDOiisMRISIiKmL37itwdV2LceMOQTy9Zfz/YwgibcIgREREaunpufD3/wk+Pj8iNTUHP/54FTt2XJK6LaJKw0NjREQEAIiIiMeIEftw82aquubj8wr69WsiXVNElYxBiIhIxxUUqDB//u+YN+93KJWFh8HMzeVYvboffH1dIZPxbvGkvRiEiIh0WFxcCnx99yEi4q661rWrE77/fiAaNrSSsDOiqsEgRESko27ceIz27YOQkZEHANDXlyEw0AMzZvSAgQFPISXdwD/pREQ6qlEjK7z+ugsAwMXFCqdOvYfAQA+GINIpHBEiItJRMpkMwcHeaNDAEvPm9YK5uULqloiqHGM/EZEOyMtTYtq0Ezh8OEajbm1tghUr+jIEkc5iECIi0nLR0clwd9+ARYtO4733DiIpKVPqloiqDQYhIiItJYRAUNA5tGsXhKioBABASsoTnD4dL3FnRNUHzxEiItJCDx9mYezYn3HwYLS61qxZHezYMRjt29tL2BlR9cIgRESkZUJCbsDf/wASE58dApswoQOWLu0DExNDCTsjqn4YhIiItEROTgGmTz+BFSvOqGvW1ibYuPFteHs3k7AzHRO9BwgPBPIyin89K6Fq+6HnYhAiItISDx5kYdOm8+rnffs2xqZN/WFnZyZdU7ooPBB4fL305eTmld8LlYonSxMRaYn69S2xZs2bUCj08e23fXHkyHCGICk8HQmS6QFmjsU/ajcHus2Ttk8CwBEhIqIaKyEhA6amclhYPJsDaNiw1ujevT6cnCwl7IwAAKb2wLi7pS9HkuKIEBFRDXTgwHW4uq7FRx8dLfIaQxBR2TEIERHVIFlZeRg//hAGDNiF5ORsbNlyAXv3XpW6LaIai4fGiIhqiMjI+xg+fB9iYh6pawMGNIeHh7N0TRHVcAxCRETVnFKpwtKl4Zg16yQKClQAABMTQ6xc2RdjxrSDTCaTuEOimkvyQ2OrV6+Gs7MzjIyM0LlzZ5w9e/a5y6empmLixImwt7eHQqFA06ZNceTIkSrqloioasXHp+H117di2rRQdQhyc7PH33+Pw9ix7RmCiF6SpCNCu3btQkBAANauXYvOnTtjxYoV8PLyQnR0NGxsbIosn5eXh969e8PGxgY//vgjHB0dcfv2bdSqVavqmyciqmQxMY/QufN6pKbmAABkMmDatO6YM6cn5HJ9ibsj0g6SBqHly5fj/fffx+jRowEAa9euxeHDh7Fx40ZMmzatyPIbN27E48ePER4eDkPDwmninZ2dq7JlIqIq07hxbXTu7IiQkFg4OVlg27aB2nM+UGmzL9dknDm6RpEsCOXl5SEyMhLTp09X1/T09ODp6YmIiIhi1zl48CDc3d0xceJEHDhwAHXr1sXw4cMxdepU6OvztyMi0i56ejJs2tQfc+aE4euvPWFlZSx1SxWnrLMv12ScObpGkCwIJScnQ6lUwtbWVqNua2uL69eL/8sRFxeHX3/9FSNGjMCRI0dw48YNfPDBB8jPz8fs2bOLXSc3Nxe5ubnq5+np6RX3JoiIKkhBgQrz5/+OHj0a4LXXGqrr9vbmCArylrCzSvK/sy+b2kvbS2WQm3Pm6BqiRl01plKpYGNjg3Xr1kFfXx9ubm64d+8elixZUmIQWrhwIebOnVvFnRIRlV1cXAp8ffchIuIuHB3NcfHiBNSurUWjP8/D2ZdJYi901VhqairWr1+P6dOn4/HjxwCAqKgo3Lt3r8zbsLa2hr6+PpKSkjTqSUlJsLOzK3Yde3t7NG3aVOMwWIsWLZCYmIi8vLxi15k+fTrS0tLUj/j4+DL3SERUmYQQ2Lr1Atq2XYuIiMIwkJiYiZMnb0rcGZHuKHcQunjxIpo2bYpFixZh6dKlSE1NBQDs27dP43yf0sjlcri5uSE0NFRdU6lUCA0Nhbu7e7HrdOvWDTdu3IBKpVLXYmJiYG9vD7lcXuw6CoUCFhYWGg8iIqmlpDzBu+/uxahRPyEjo/AXORcXK5w69R4GD24pcXdEuqPcQSggIAD+/v74559/YGRkpK7369cPv//+e7m3FRwcjC1btuDatWuYMGECsrKy1FeRjRw5UiNcTZgwAY8fP8bHH3+MmJgYHD58GAsWLMDEiRPL+zaIiCQTFnYLrq5rsXv3FXXN378tzp8fhy5d6knYGZHuKfc5Qn/99ReCgoKK1B0dHZGYmFiubfn4+ODhw4cIDAxEYmIi2rZti2PHjqlPoL5z5w709J5lNScnJ4SEhGDy5MlwdXWFo6MjPv74Y0ydOrW8b4OIqMrl5Skxe/ZJLFp0GkIU1mrVMsK6dW9hyJBXpG2OSEeVOwgpFIpir7yKiYlB3bp1y93ApEmTMGnSpGJfCwsLK1Jzd3fHn3/+We79EBFJ7e7ddKxadVYdgnr2dMbWrQN4t3giCZU7CL399tv48ssvsXv3bgCATCbDnTt3MHXqVAwePLjCGyQi0hYuLlZYubIvJkw4jPnzX8OUKV2hp6elt8gobcJETjpI1YRMiKe/m5RNWloa3nnnHZw7dw4ZGRlwcHBAYmIi3N3dceTIEZiamlZWrxUiPT0dlpaWSEtL44nTRFSpkpOzYWJiCBMTQ3VNCIHY2BQ0blxbws6qwKYWZZswsXZzYPS1yu+HarzK+v4u94iQpaUlfvnlF5w+fRoXLlxAZmYm2rdvD09PzwprioiopgsJuQF//wMYNKg5Vq9+U12XyWTaH4KAsk2YyEkHqRoo94jQ1q1b4ePjA4VCoVHPy8vDzp07MXLkyAptsKJxRIiIKlNOTgGmTz+BFSvOqGuHDg3Dm282lbArCQTVAzLvAWaOnDCRKkRlfX+X+/L50aNHIy0trUg9IyNDfdk7EZEuunQpCZ06BWuEoL59G8PNzUHCrojoecp9aEwIAZms6Ml9d+/ehaUlr3wgIt2jUgmsWnUGU6eeQG6uEgCgUOhjyZLemDSpU7H/ZhJR9VDmINSuXTvIZDLIZDK8/vrrMDB4tqpSqcTNmzfRt2/fSmmSiKi6SkjIwOjRBxASEquutW5tgx07BqNVKxsJOyOisihzEBowYAAA4Pz58/Dy8oKZmZn6NblcDmdnZ14+T0Q6JTo6Gd27b0Jycra6NnlyFyxY8DqMjGrUPa2JdFaZ/6Y+vbu7s7MzfHx8NG6vQUSkixo3ro2WLevi999vw97eDJs3D0CfPo2kbouIyqHcJ0uPGjWKIYiICIC+vh62bRsIPz9XXLw4gSGIqAYqdxBSKpVYunQpOnXqBDs7O9SuXVvjQUSkjZRKFRYtOoXw8HiNev36lti6dSCsrU0k6oyIXka5g9DcuXOxfPly+Pj4IC0tDQEBARg0aBD09PQwZ86cSmiRiEha8fFpeP31rZg2LRS+vvuQnp4rdUtEVEHKHYS2b9+O4OBgTJkyBQYGBhg2bBjWr1+PwMBA3gyViLTO7t1X4Oq6Fr/9dhsAcOtWKo4fjy1lLSKqKcodhBITE9G6dWsAgJmZmXpyxbfeeguHDx+u2O6IiCSSnp4Lf/+f4OPzI1JTcwAATk4WOHlyFN55p6XE3RFRRSl3EKpXrx4SEgrvGtyoUSMcP34cAPDXX38Vue0GEVFNFBERj3btgrBlywV1zcfnFVy4MB4eHs7SNUZEFa7cQWjgwIEIDQ0FAHz44Yf44osv0KRJE4wcORLvvfdehTdIRFRVCgpUmDs3DD16bEJcXAoAwNxcjq1bB+CHHwbDyspY4g6JqKKVe8avr7/+Wv3/Pj4+aNCgAcLDw9GkSRN4e3tXaHNERFUpNvYxFi48BaWy8F7UXbs64fvvB6JhQyuJOyOiylLuEaF/69KlCwICAuDt7Y1z585VRE9ERJJo1swaixf3hr6+DHPn9sRvv/kzBBFpuXKPCGVmZkJfXx/Gxs+GiM+fP48vvvgCR44cgVKprNAGiYgqS0rKE5iYGEKhePZP4YcfdsJrrzXkfcJKE70HCA8E8jKKfz0roWr7IXpBZR4Rio+Ph7u7OywtLWFpaYmAgABkZ2dj5MiR6Ny5M0xNTREeHl6ZvRIRVZiwsFtwdV2LWbN+1ajLZDKGoLIIDwQeXwcy7xX/EKrC5eTm0vZJVIoyjwh99tlnyMnJwcqVK7Fv3z6sXLkSf/zxBzp37ozY2FjUq1evMvskIqoQeXlKzJ59EosWnYYQwNKlEejbtzFef91F6tZqlqcjQTI9wNS++GXk5kC3eVXXE9ELKHMQ+v3337Fv3z506dIFQ4cOhZ2dHUaMGIFPPvmkEtsjIqo40dHJGD58H6Kinh226dXLGc2aWUvYVQ1nag+Muyt1F0QvrMxBKCkpCQ0bNgQA2NjYwMTEBG+88UalNUZEVFGEEFi3LhKTJ4fgyZMCAIChoR7mz38NU6Z0hZ6eTOIOiUgq5TpZWk9PT+P/5XJ5hTdERFSRHj7MwtixP+PgwWh1rVmzOtixYzDaty/hkA4R6YwyByEhBJo2bQqZrPA3p8zMTLRr104jHAHA48ePK7ZDIqIXFB2djJ49tyAxMVNdmzChA5Yu7QMTE0MJOyOi6qLMQWjTpk2V2QcRUYVzcbGCk5MFEhMzYW1tgo0b34a3dzOp2yKiaqTMQWjUqFGV2QcRUYUzNNTH9u2DMG1aKFav7gc7OzOpWyKiaualZ5YmIqoOVCqBb789g7//1pzIr0mTOti7dyhDEBEVq9wzSxMRVTcJCRkYPfoAQkJi0by5NSIj/8NzgEpT2szQpeHM0aQlGISIqEY7cOA6xo79GcnJ2QCA69eTcfToPxg8uKXEnVVzT2eGflmcOZpqOAYhIqqRsrLyMGXKcQQFRapr9vZm2Lx5APr0aSRhZzVEWWaGLg1njiYtUK4glJ6ejjNnziAvLw+dOnVC3bp1K6svIqISRUbex/Dh+xAT80hdGzCgOYKDvWFtbSJhZzUQZ4YmHVfmIHT+/Hn069cPSUlJEELA3Nwcu3fvhpeXV2X2R0SkplSqsGRJOL744iQKCgpv6mliYogVK7wwdmx79TxnRERlVearxqZOnYqGDRvi1KlTiIyMxOuvv45JkyZVZm9ERBquX0/WCEFubvb4++9xeP99N4YgInohZQ5CkZGRWLVqFdzd3dGuXTts3LgRsbGxSE9Pr8z+iIjUXnnFBvPm9YJMBkyf3h3h4WPQtGkdqdsiohqszIfGHj9+jHr16qmf16pVC6ampnj06BEsLCwqpTki0m0ZGbkwNjaEgcGz39k++6wrPD1d0KGDg4SdEZG2KNfJ0levXkViYqL6uRAC165dQ0bGs3koXF1dK647ItJZERHx8PXdDz8/V8yZ01Nd19fXYwgiogojE0KIsiyop6cHmUyG4hZ/WpfJZFAqlRXeZEVKT0+HpaUl0tLSOJJFVA0VFKgwf/7vmDfvdyiVAnp6Mvzxx2h07eokdWtV62UnPCxNVgIgVICZI68aoxqhsr6/yzwidPPmzQrbKRFRceLiUuDruw8REc++mLt0qQd7ex28PUZFTXhYGk6ISDquzEFoy5Yt+PTTT2Fiwjk6iKhiCSGwbdtFTJp0BBkZeQAAfX0ZAgM9MGNGD41zhHRGRUx4WBpOiEhU9kNj+vr6SEhIgI2NTWX3VKl4aIyoeklJeYIJEw5j164r6pqLixW2bx+ELl3qPWdNLRdUD8i8x0NXRP9P8kNjZcxLRERlFh2djN69tyE+/tk0HP7+bfHtt31hbq6QsDMi0hXlGm/mhGVEVJEaNKiFWrWMAABWVkbYvfsdbNrUnyGIiKpMuS6fb9q0aalh6PHjxy/VEBHpDiMjA+zYMRhTp55AUNBbqFePh6uJqGqVKwjNnTsXlpaWldULEWkxIQSCg6PQvXt9tGz57IbNrVrZ4PDh4RJ2RkS6rFxB6N13363xJ0sTUdV7+DALY8f+jIMHo9GmjS3OnBkLhaJc//wQEVWKMv9LxPODiOhFhITcgL//ASQmZgIALlxIwqFDMRg8uKXEnVWBl5kUMSuh4vshoiJ41RgRVYqcnAJMm3YCK1eeUdesrU2wcePb8PZuJmFnVagiJkXkhIdElarMQUilUlVmH0SkRS5dSsLw4ftw+fIDdc3LqxE2bx4AOzsdmiX6ZSdF5ISHRJWOB+mJqMKoVAKrVp3B1KknkJtbeN9BhUIfixf3xqRJnaCnp6OH2E3tOSkiUTXFIEREFebSpSQEBByHSlV4KL11axvs2DEYrVrxIgsiqp508AY+RFRZ2rSxw4wZ3QEAkyd3wdmz7zMEEVG1xhEhInph2dn5MDIy0DjkFRjogT59GqFHjwYSdkZEVDYcESKiFxIZeR/t2gVh2bJwjbqhoT5DEBHVGAxCRFQuSqUKixadQpcuGxAT8wgzZ/6KqCjOeUNENRMPjRFRmcXHp8HPbz9+++22uubqagszM7mEXRERvTgGISIqk927r2DcuENITc0BAMhkwLRp3TFnTk/I5fovvuGXmX25uuPs0ETVHoMQET1XenouPvroKLZsuaCuOTlZYNu2gfDwcH75HVTE7MvVHWeHJqq2GISIqETR0cno128H4uJS1DUfn1ewdu1bqFXLqGJ28rKzL1d3nB2aqFpjECKiEtWrZwEDg8JrKszN5Vi9uh98fV0r5ybMnH2ZiCRQLa4aW716NZydnWFkZITOnTvj7NmzZVpv586dkMlkGDBgQOU2SKSjTE3l2LFjEHr2dMaFC+Ph59emckIQEZFEJA9Cu3btQkBAAGbPno2oqCi0adMGXl5eePDgwXPXu3XrFj799FP06NGjijol0m5CCGzdegGxsY816m5uDvj115Fo2NBKos6IiCqP5EFo+fLleP/99zF69Gi0bNkSa9euhYmJCTZu3FjiOkqlEiNGjMDcuXPh4uJShd0SaaeUlCd49929GDXqJ4wYsQ/5+UqN1zkKRETaStIglJeXh8jISHh6eqprenp68PT0RERERInrffnll7CxscGYMWNK3Udubi7S09M1HkT0TFjYLbi6rsXu3VcAAGfO3MOhQzESd0VEVDUkDULJyclQKpWwtbXVqNva2iIxMbHYdU6dOoUNGzYgODi4TPtYuHAhLC0t1Q8nJ6eX7ptIG+TlKTFt2gm89toW3L1b+AuClZUR9uwZgoEDW0jcHRFR1ahRV41lZGTAz88PwcHBsLa2LtM606dPR0BAgPp5eno6wxBplxeYkDA60RLDN/ZC1J266lqvZvex1T8M9R6tAoIqo9EScNJBIpKQpEHI2toa+vr6SEpK0qgnJSXBzs6uyPKxsbG4desWvL291TWVSgUAMDAwQHR0NBo1aqSxjkKhgEKhqITuiaqJckxIKASw7k83TD7YF0/yDQEAhvpKzO8biikeEdDTE0BmZTb7HJx0kIgkIGkQksvlcHNzQ2hoqPoSeJVKhdDQUEyaNKnI8s2bN8elS5c0arNmzUJGRgZWrlzJkR7STeWYkPDvO3Uwfu+zXySa2aZix5hf0b7+IwAOldhkKTjpIBFJRPJDYwEBARg1ahQ6dOiATp06YcWKFcjKysLo0aMBACNHjoSjoyMWLlwIIyMjtGrVSmP9WrVqAUCROpHOKcOEhO0BBOSEYPnyPzFhQgcsXdoHJiaGVdMfEVE1JHkQ8vHxwcOHDxEYGIjExES0bdsWx44dU59AfefOHejpSX6VP1GNlJtbALlcX+Py9wULXkffvo3Ru3ej56xJRKQbZEIIIXUTVSk9PR2WlpZIS0uDhYWF1O0QvbygekDmPcDMUWNE6NKlJAwfvg8TJnTABx90lLBBIqKXV1nf3xxqIdIyKpXAypV/omPHYFy+/ABTphzH1asPpW6LiKhakvzQGBFVnISEDIwefQAhIbHqWpMmtSXsiIioeuOIEJGWOHC+AVxd12qEoMmTu+Ds2ffRsmXd56xJRKS7OCJEVMNl5Rpgyo9vIejPDgCyAQD29mbYvHkA+vThCdFERM/DIERUg8XEPIL3goGISaqlrg0Y0BzBwd6wtjaRrjEiohqCQYioBrO1NUVegT4AwESej5WrB2HMmHa8WzwRURnxHCGiGszS0gjfjz6JzvXv4u+Z+zF2bHuGICKicmAQIqpB9uy5gvj4NI1at8ZJiPhwPZrappWwFhERlYRBiKgGSE/Phb//Txg69EeMHPkTlEqVxuscBCIiejEMQkTVXEREPNq1C8KWLRcAAGFht3DoUIzEXRERaQcGIaJqqqBAhblzw9CjxybExaUAAMzN5di6dQDefruZxN0REWkHXjVGVA3FxaXA13cfIiKe3Tusa1cnfP/9QDRsaCVhZ0RE2oVBiKgaEUJg27aLmDTpCDIy8gAA+voyBAZ6YMaMHjAw4CAuEVFFYhAiqkbOnbuPUaN+Uj93cbHC9u2D0KVLPemaIiLSYvz1kqga6djREePGuQEA/P3b4vz5cQxBRESViCNCRBLKz1fCwEBPYxLEZcv6oF+/JjwhmoioCnBEiEgi0dHJ6NJlg/qy+KdMTeUMQUREVYRBiKiKCSEQFHQO7doFISoqAR9+eBQ3bjyWui0iIp3EQ2NEVejhwyyMHfszDh6MVtccHc3x5Em+hF0REekuBiGiKhIScgP+/geQmJipro0f74Zly7xgYmIoYWdERLqLQYiokuXkFGD69BNYseKMumZtbYKNG9+GtzfPBSIikhKDEFElunHjMQYN2oVLlx6oa337NsamTf1hZ2cmYWdERAQwCBFVKisrIzx69AQAoDBQYsngM5jUMxiyA1MrbidZCRW3LSIiHcMgRFSJ6tQxwebN/fHZmFX4fugPaGX/AMiqpJ3JzStpw0RE2otBiKgC/fxzNDp2dNQ47NW7dyNETt8L/ewHgEwPMLWv+B3LzYFu8yp+u0REWo5BiKgCZGXlYcqU4wgKisQbbzTG4cPDNWaL1tcThf9jag+Mu1vCVoiIqKpxQkWilxQZeR/t269DUFAkAODo0Rs4dChG4q6IiKgsGISIXpBSqcKiRafQpcsGxMQ8AgCYmBgiONgbb73VVOLuiIioLHhojOgFxMenwc9vP3777ba65uZmjx07BqNp0zoSdkZEROXBIERUTrt2Xcb48YeRmpoDAJDJgGnTumPOnJ6Qy/Ul7o6IiMqDQYioHP788y7efXev+rmTkwW2bRsIDw9n6ZoiIqIXxnOEiMqhS5d68PNzBQD4+LyCCxfGMwQREdVgHBEieg6VSkBPT6ZR++9/++HNN5tg6NBXNC6RJyKimocjQkQliItLQffuG7F79xWNuoWFAj4+rRiCiIi0AEeEiP5FCIFt2y5i0qQjyMjIw7Vrh+DuXg9OTpZSt0ZERBWMI0JE/yMl5QnefXcvRo36CRkZeQCA2rWN1TdOJSIi7cIRIaL/FxZ2C35++3H3brq65u/fFt9+2xfm5goJOyMiosrCIEQ6Ly9PicDAk1i8+DTE/98SrFYtI6xb9xaGDHlF2uaIiKhSMQiRTouLS8GQIXsQFZWgrvXs6YytWwfwnCAiIh3Ac4RIpxkbG+DOnTQAgKGhHhYv9kRo6EiGICIiHcEgRDrN3t4cGza8jebNrfHnn2Px2WfdiswbRERE2ouHxkinnDgRh3bt7FCnjom69vbbzfDGG41haMj7hBER6RqOCJFOyMkpwOTJx9C79zaMG3cI4ulZ0f+PIYiISDcxCJHWu3QpCZ06BWPFijMAgL17r+HYsRsSd0VERNUBgxBpLZVKYOXKP9GxYzAuXXoAAFAo9PHtt33Rt29jibsjIqLqgOcIkVZKSMjA6NEHEBISq661bm2DHTsGo1UrGwk7IyKi6oRBiLTOwYPRGDPmIJKTs9W1yZO7YMGC12FkxD/yRET0DL8VSKucPn0H/fvvVD+3szPDli0D0KdPIwm7IiKi6ornCJFW6drVCQMHNgcA9O/fDJcuTWAIIiKiEnFEiGo0IQRksmcTIMpkMgQHe+Ptt5th1Kg2Gq8RERH9G0eEqMaKj0/Da69txaFDMRr1OnVM4O/fliGIiIhKxREhqpF2776CceMOITU1B1euPMDFixNgZ2cmdVtERFTDcESIapT09Fz4+/8EH58fkZqaAwAwMjLA/fsZEndGREQ1EUeEqMaIiIjHiBH7cPNmqrrm4/MK1qx5E1ZWxtI1RkRENRaDEFV7BQUqfPXV7/jqq9+hVBbeI8zcXI7Vq/vB19eV5wIREdELYxCiau3WrVQMH74XERF31bWuXZ3w/fcD0bChlYSdERGRNuA5QlSt6enJcPXqQwCAvr4Mc+f2xG+/+TMEERFRhWAQomqtfn1LrF37FlxcrHDq1HsIDPSAgQH/2BIRUcXgNwpVK3/8cRvp6bkatXffbYUrVz5Aly71JOqKiIi0VbUIQqtXr4azszOMjIzQuXNnnD17tsRlg4OD0aNHD1hZWcHKygqenp7PXZ5qhrw8JaZNOwEPj8348MOjRV6v9jdLjd4DbGoBBNUr/pGVIHWHRERUDMmD0K5duxAQEIDZs2cjKioKbdq0gZeXFx48eFDs8mFhYRg2bBhOnjyJiIgIODk5oU+fPrh3714Vd04VJTo6Ge7uG7Bo0WkIAWzdegHHj8dK3Vb5hAcCj68DmfeKfwhV4XJyc2n7JCIiDTIhhJCygc6dO6Njx47473//CwBQqVRwcnLChx9+iGnTppW6vlKphJWVFf773/9i5MiRpS6fnp4OS0tLpKWlwcLC4qX7pxcnhMC6dZGYPDkET54UAAAMDfUwf/5rmDKlK/T0atBl8UH1CgOPTA8wtS9+Gbk50G0e0PSdqu2NiEgLVNb3t6THG/Ly8hAZGYnp06era3p6evD09ERERESZtpGdnY38/HzUrl272Ndzc3ORm/vsnJP09PSXa5oqxMOHWRg79mccPBitrjVrVgc7dgxG+/YlBImawNQeGHe39OWIiKhakPTQWHJyMpRKJWxtbTXqtra2SExMLNM2pk6dCgcHB3h6ehb7+sKFC2Fpaal+ODk5vXTf9HJCQm7A1XWtRgiaMKEDoqLG1ewQRERENY7k5wi9jK+//ho7d+7E/v37YWRkVOwy06dPR1pamvoRHx9fxV3S//rjj9vo23c7EhMzAQDW1iY4ePBdfPfdmzAxMZS4OyIi0jWSHhqztraGvr4+kpKSNOpJSUmws7N77rpLly7F119/jRMnTsDV1bXE5RQKBRQKRYX0Sy+ve/f66Nu3MY4du4G+fRtj06b+vGs8ERFJRtIRIblcDjc3N4SGhqprKpUKoaGhcHd3L3G9xYsXY968eTh27Bg6dOhQFa1SBZHJZNi0qT+++64fjhwZzhBERESSkvzQWEBAAIKDg7FlyxZcu3YNEyZMQFZWFkaPHg0AGDlypMbJ1IsWLcIXX3yBjRs3wtnZGYmJiUhMTERmZqZUb4FKkJiYiTff3IHQ0DiNup2dGSZM6MibpRIRkeQkn6XOx8cHDx8+RGBgIBITE9G2bVscO3ZMfQL1nTt3oKf3LK+tWbMGeXl5eOcdzUuQZ8+ejTlz5lRl6/QcBw9GY8yYg0hOzsaFC4m4cGE86tQxka6h6D2Fc/3kZVTO9jlhIhFRjST5PEJVjfMIVa6srDxMmXIcQUGR6pq9vRl+/nkY3NwcpGtsU4vCCQ8rW+3mwOhrlb8fIiIdo5XzCJF2iYy8jxEj9iE6+pG6NmBAcwQHe8PaWsLRIODZSNDzJjx8WU8nTCQiohqDQYhemlKpwtKl4Zg16yQKCgpvJWFiYoiVK/tizJh21etcIE54SERE/4NBiF7K3bvp8PPbj7CwW+qam5s9duwYjKZN60jXGBERURlIftUY1WxPnuTjr78Kb3grkwHTp3dHePgYhiAiIqoRGITopTRpUgfffvsGnJwscPLkKCxY8Drkcn2p2yIiIioTBiEql7Nn7yE7O1+jNnp0W1y9OhEeHs7SNEVERPSCGISoTAoKVJg7Nwxdu27Ap58e13hNJpPBzEwuUWdEREQvjidLU6ni4lLg67sPERGFV1utWXMOQ4a0RK9eDau2kZeZFJETHhIRUTEYhKhEQghs23YRkyYdQUZGHgBAX1+GwEAP9OjRoOobCg98+UkR5eYV0wsREWkFBiEqVkrKE0yYcBi7dl1R11xcrLB9+yB06VJPmqZedlJETnhIRET/wiBERfz22y34+e1HfHy6uubv3xbfftsX5uYKCTv7f5wUkYiIKgiDEGn47bdb6NVrC57egc7KyghBQW9hyJBXpG2MiIioEvCqMdLQvXt9vPpq4fk/vXo54+LFCQxBRESktTgiRBr09fWwbdtA7NlzFZ980gV6etXoPmFEREQVjCNCOuzhwywMHrwbp0/f0ag7OVkiIMCdIYiIiLQeR4R0VEjIDfj7H0BiYiaiohJw4cJ4WFhUgxOhiYiIqhCDkC6J3oOc3+Zg2q4WWPlra3U5MzkBMV93RocGyRI2VwacFJGIiCoYg5AOubR7GYZ/54HLibbqWt9m/2CTzwHYWWQCmRI2Vx6cFJGIiCoIg5AOUKkEVq06g6lf9kZuQeFHrjAowJLBZzGp5xXIZJYALKVtsqw4KSIREVUgBiEtl5CQgdGjDyAkJBZPP+7Wjo+w49gstGplI21zREREEuNVY1ru8eMnCAu7pX4++dUInJ12gCGIiIgIDEJa75VXbLBkSW/Y2Zkh5KMjWP52CIwMlVK3RUREVC0wCGmZCxcSkZtboFGbNKkTrl79AH1a3pOoKyIiouqJQUhLKJUqLFp0Ch06BGPmzF81XpPJZLCyMpaoMyIiouqLQUgLxMen4fXXt2LatFAUFKiwbFkETp26U/qKREREOo5XjdVwu3dfwbhxh5CamgMAkMmAadO6o1MnR4k7IyIiqv4YhGqo9PRcfPTRUWzZckFdc7I3wLaRIfBoEAxsKmYlzsxMRESkgUGoBoqIiIev737ExaWoaz4+r2BN9y9hlXup9BmiOTMzERERAAahGics7BY8PbdCqRQAAHNzOVav7gdfX1fI1n0C5AKQ6QGm9sVvgDMzExERqTEI1TDdujnBzc0BZ8/eQ9euTvj++4Fo2NBKcyFTe2DcXWkaJCIiqkEYhGoYQ0N9bN8+CLt2XcbUqd1hYMAL/4iIiF4Ug1A1lpLyBJMmHUVAQBe4uTmo640b18bMma9K2BmRbhFCoKCgAEolZ2UnqkyGhobQ19ev0n0yCFVTYWG34Oe3H3fvpiMy8j6iosbBxMRQ6raIdE5eXh4SEhKQnZ0tdStEWk8mk6FevXowMzOrsn0yCFUzeXlKBAaexOLFpyEKz4fGgwdZuHLlATp25NxARFVJpVLh5s2b0NfXh4ODA+RyOWQymdRtEWklIQQePnyIu3fvokmTJlU2MsQgVI1ERydj+PB9iIp6Nt9Pr17O2Lp1IOrVs5CwMyLdlJeXB5VKBScnJ5iYmEjdDpHWq1u3Lm7duoX8/HwGIV0ihMC6dZGYPDkET54U3jDV0FAP8+e/hilTukJP739+A43eA4QHAnkZRTfECROJKoWeHi9KIKoKUoy4MghJ7OHDLIwd+zMOHoxW15o1q4MdOwajffti5gIKDwQeX3/+RjlhIhERUZkwCEksPj4dR478o34+YUIHLF3ap+QTo5+OBJU0aSInTCQiIiozBiGJtW9vj6++6oWlSyOwcePb8PZuVrYVOWkiEVGliI6OhoeHB/755x+Ym3OEvaIkJyejZcuWiIqKQr169aRuR40HvqvY9evJyM/XnIvk00+74sqVD8oegoiISuHv7w+ZTAaZTAZDQ0M0bNgQn3/+OXJycoose+jQIXh4eMDc3BwmJibo2LEjNm/eXOx29+7di549e8LS0hJmZmZwdXXFl19+icePH1fyO6o606dPx4cffqjVIWj16tVwdnaGkZEROnfujLNnz5Z53Z07d0Imk2HAgAHqWn5+PqZOnYrWrVvD1NQUDg4OGDlyJO7fv69extraGiNHjsTs2bMr8q28NAahKqJSCaxc+Sfatl2Lr776XeM1fX092NiYStQZEWmrvn37IiEhAXFxcfjmm28QFBRU5Eto1apV6N+/P7p164YzZ87g4sWLePfddzF+/Hh8+umnGsvOnDkTPj4+6NixI44ePYrLly9j2bJluHDhArZt21Zl7ysvL6/Stn3nzh0cOnQI/v7+L7WdyuzxZe3atQsBAQGYPXs2oqKi0KZNG3h5eeHBgwelrnvr1i18+umn6NGjh0Y9OzsbUVFR+OKLLxAVFYV9+/YhOjoab7/9tsZyo0ePxvbt26tXcBY6Ji0tTQAQaWlpVbbP+/fThZfXNgHMEcAcoac3V5w5c/fFNrbWUYilKPwvEVWqJ0+eiKtXr4onT55I3Uq5jRo1SvTv31+jNmjQINGuXTv18zt37ghDQ0MREBBQZP1vv/1WABB//vmnEEKIM2fOCABixYoVxe4vJSWlxF7i4+PFu+++K6ysrISJiYlwc3NTb7e4Pj/++GPh4eGhfu7h4SEmTpwoPv74Y1GnTh3Rs2dPMWzYMDF06FCN9fLy8kSdOnXEli1bhBBCKJVKsWDBAuHs7CyMjIyEq6ur2LNnT4l9CiHEkiVLRIcOHTRqycnJ4t133xUODg7C2NhYtGrVSuzYsUNjmeJ6FEKIS5cuib59+wpTU1NhY2MjfH19xcOHD9XrHT16VHTr1k1YWlqK2rVrizfffFPcuHHjuT2+rE6dOomJEyeqnyuVSuHg4CAWLlz43PUKCgpE165dxfr164v93P7t7NmzAoC4ffu2Rr1hw4Zi/fr1xa7zvL9zlfX9zXOEKtmBA9cxduzPSE5+NivtRx91gqurrYRdEdFL+b4DkJVY9fs1tQN8z73QqpcvX0Z4eDgaNGigrv3444/Iz88vMvIDAOPGjcOMGTPwww8/oHPnzti+fTvMzMzwwQcfFLv9WrVqFVvPzMyEh4cHHB0dcfDgQdjZ2SEqKgoqlapc/W/ZsgUTJkzA6dOnAQA3btzAkCFDkJmZqZ6FOCQkBNnZ2Rg4cCAAYOHChfj++++xdu1aNGnSBL///jt8fX1Rt25deHh4FLufP/74Ax06dNCo5eTkwM3NDVOnToWFhQUOHz4MPz8/NGrUCJ06dSqxx9TUVLz22msYO3YsvvnmGzx58gRTp07F0KFD8euvvwIAsrKyEBAQAFdXV2RmZiIwMBADBw7E+fPnS5y2YcGCBViwYMFzf15Xr15F/fr1i9Tz8vIQGRmJ6dOnq2t6enrw9PRERETEc7f55ZdfwsbGBmPGjMEff/zx3GUBIC0tDTKZrMifjU6dOuGPP/7AmDFjSt1GVWAQqiRZWXmYMuU4goIi1TU7OzNs2TIAffo0krAzInppWYlA5j2puyjVoUOHYGZmhoKCAuTm5kJPTw///e9/1a/HxMTA0tIS9vZFr0CVy+VwcXFBTEwMAOCff/6Bi4sLDA3Ld6ufHTt24OHDh/jrr79Qu3ZtAEDjxo3L/V6aNGmCxYsXq583atQIpqam2L9/P/z8/NT7evvtt2Fubo7c3FwsWLAAJ06cgLu7OwDAxcUFp06dQlBQUIlB6Pbt20WCkKOjo0ZY/PDDDxESEoLdu3drBKF/9/jVV1+hXbt2GqFl48aNcHJyQkxMDJo2bYrBgwdr7Gvjxo2oW7curl69ilatWhXb4/jx4zF06NDn/rwcHByKrScnJ0OpVMLWVvOXcVtbW1y/XvLULKdOncKGDRtw/vz55+73qZycHEydOhXDhg2DhYXmhMAODg74+++/y7SdqsAgVNGi9yByxzcYHtQZMUm11OX+bW5hvd/vsL75DRD0EtvnpIlE0jO1qxH77dWrF9asWYOsrCx88803MDAwKPLFW1bi6T1/yun8+fNo166dOgS9KDc3N43nBgYGGDp0KLZv3w4/Pz9kZWXhwIED2LlzJ4DCEaPs7Gz07t1bY728vDy0a9euxP08efIERkZGGjWlUokFCxZg9+7duHfvHvLy8pCbm1tktvF/93jhwgWcPHmy2PtmxcbGomnTpvjnn38QGBiIM2fOIDk5WT1SdufOnRKDUO3atV/651keGRkZ8PPzQ3BwMKytrUtdPj8/H0OHDoUQAmvWrCnyurGxcbW6dx+DUAX7dcN/4bXMEwWqwqnBTQzzsKL/MYztHAUZAGRW0I44aSKRdF7w8FRVMzU1VY++bNy4EW3atMGGDRvUhySaNm2KtLQ03L9/v8gIQl5eHmJjY9GrVy/1sqdOnUJ+fn65RoWMjY2f+7qenl6RkJWfn1/se/m3ESNGwMPDAw8ePMAvv/wCY2Nj9O3bF0DhITkAOHz4MBwdNe/TqFAoSuzH2toaKSkpGrUlS5Zg5cqVWLFihfqqqE8++aTICdH/7jEzMxPe3t5YtGhRkf08HYXz9vZGgwYNEBwcDAcHB6hUKrRq1eq5J1u/zKExa2tr6OvrIykpSaOelJQEO7vig3ZsbCxu3boFb29vde1pYDMwMEB0dDQaNSo80vE0BN2+fRu//vprkdEgAHj8+DHq1q373P6rEoNQBetW/yZa2rbExQQ7uNV/iB1jTqKpbRqACrxhKidNJKJy0tPTw4wZMxAQEIDhw4fD2NgYgwcPxtSpU7Fs2TIsW7ZMY/m1a9ciKysLw4YNAwAMHz4c3377Lb777jt8/PHHRbafmppa7HlCrq6uWL9+PR4/flzsKEbdunVx+fJljdr58+fLFLa6du0KJycn7Nq1C0ePHsWQIUPU67Vs2RIKhQJ37twp8TBYcdq1a4erV69q1E6fPo3+/fvD19cXQGEIiImJQcuWLZ+7rfbt22Pv3r1wdnaGgUHRr9tHjx4hOjoawcHB6quwTp06VWqPL3NoTC6Xw83NDaGhoerL31UqFUJDQzFp0qRi12nevDkuXbqkUZs1axYyMjKwcuVKODk5AXgWgv755x+cPHkSderUKXZ7ly9fRs+ePZ/bf5Wq0FOva4BKv2psraO4/GldMfON/iI3t6By9kFEVULbrhrLz88Xjo6OYsmSJeraN998I/T09MSMGTPEtWvXxI0bN8SyZcuEQqEQU6ZM0Vj/888/F/r6+uKzzz4T4eHh4tatW+LEiRPinXfeKfFqstzcXNG0aVPRo0cPcerUKREbGyt+/PFHER4eLoQQ4tixY0Imk4ktW7aImJgYERgYKCwsLIpcNfbxxx8Xu/2ZM2eKli1bCgMDA/HHH38Uea1OnTpi8+bN4saNGyIyMlJ8++23YvPmzSX+3A4ePChsbGxEQcGzf78nT54snJycxOnTp8XVq1fF2LFjhYWFhcbPt7ge7927J+rWrSveeecdcfbsWXHjxg1x7Ngx4e/vLwoKCoRSqRR16tQRvr6+4p9//hGhoaGiY8eOAoDYv39/iT2+rJ07dwqFQiE2b94srl69Kv7zn/+IWrVqicTERPUyfn5+Ytq0aSVu499/vvLy8sTbb78t6tWrJ86fPy8SEhLUj9zcXPVyWVlZwtjYWPz+++/FbleKq8YYhF5qWzli7NgD4vLlpGdFXt5OpDW0LQgJIcTChQtF3bp1RWZmprp24MAB0aNHD2FqaiqMjIyEm5ub2LhxY7Hb3bVrl3j11VeFubm5MDU1Fa6uruLLL7987uXzt27dEoMHDxYWFhbCxMREdOjQQZw5c0b9emBgoLC1tRWWlpZi8uTJYtKkSWUOQlevXhUARIMGDYRKpdJ4TaVSiRUrVohmzZoJQ0NDUbduXeHl5SV+++23EnvNz88XDg4O4tixY+rao0ePRP/+/YWZmZmwsbERs2bNEiNHjiw1CAkhRExMjBg4cKCoVauWMDY2Fs2bNxeffPKJutdffvlFtGjRQigUCuHq6irCwsIqPQgJIcSqVatE/fr1hVwuF506dVJPZ/C/72fUqFElrv/vP183b94UAIp9nDx5Ur3cjh07RLNmzUrcrhRBSCbEC54BV0Olp6fD0tISaWlpxR67LKuIiHj4+u5HXFwKXF1tcfbsWCgUBkBQvcKrScwceQsMohouJycHN2/eRMOGDYucQEvaa/Xq1Th48CBCQkKkbkXrdOnSBR999BGGDx9e7OvP+ztXUd/f/8aZpcupoECFuXPD0KPHJsTFFZ5Qd/NmCi5eTCplTSIiqgnGjRuHV199FRkZGVK3olWSk5MxaNAg9Xln1QVPli6HuLgU+PruQ0TEs5Gerl2d8P33A9GwoZWEnRERUUUxMDDAzJkzpW5D61hbW+Pzzz+Xuo0iGITKQAiBbdsuYtKkI8jIKLykUV9fhsBAD8yY0QMGBhxYIyIiqol0NwhtbA4Ylx5gUrLkmPBDd+w692w2aBfrdGx/7yS62K4DNvxrBU54SEREVGPobhDKSgCUpS927ZYT9kQ2VD/37/A3vh1wFOZGec+fHJETHhJpDR27poRIMlL8XdPdICSTAWbFTzj1v7q2UmHmG+exKuwVrBvxB4a43QRQyoyYnPCQSCs8nZwvOzu71BmSiejlPZ1RW19fv8r2qbtByMSu2Mvbb95MQf36ltDXf3bY7Iv3lBj3IAuOjs+f0pyItIu+vj5q1aqFBw8eAABMTEwgk8kk7opIO6lUKjx8+BAmJibFzsRdWXQ3CP2LEALr1kVi8uQQzJ7tgalTu6tfMzTUh6Njxc1ZQEQ1x9P7Lz0NQ0RUefT09FC/fv0q/YWDQQjAw4dZGDv2Zxw8GA0AmDXrJPr0aYR27ewl7oyIpCaTyWBvbw8bG5tibwZKRBVHLpdDT69qr8SuFkFo9erVWLJkCRITE9GmTRusWrUKnTp1KnH5PXv24IsvvsCtW7fQpEkTLFq0CP369XuhfYeE3IC//wEkJj4783ns2HZo1sz6hbZHRNpJX1+/Ss9bIKKqIfkEOLt27UJAQABmz56NqKgotGnTBl5eXiUOQ4eHh2PYsGEYM2YM/v77bwwYMAADBgwocvfi0uTk6+OTT46hb9/t6hBkbW2CgwffxZo1b8HEpPQ7HxMREVHNJvm9xjp37oyOHTviv//9L4DCk6WcnJzw4YcfYtq0aUWW9/HxQVZWFg4dOqSudenSBW3btsXatWtL3d/Te5W0sBuHa4nPDn317dsYmzb1h52dWQW8KyIiIqpIWnmvsby8PERGRsLT01Nd09PTg6enJyIiIopdJyIiQmN5APDy8ipx+ZJcSyy8JYZCoY9vv+2LI0eGMwQRERHpGEnPEUpOToZSqYStra1G3dbWFtevXy92ncTExGKXT0xMLHb53Nxc5Obmqp+npaU9fQUtW9bFhg390bJlXd5cj4iIqBpLT08HUPGTLlaLk6Ur08KFCzF37txiXvkGV68C7u5TqrwnIiIiejGPHj2CpaVlhW1P0iBkbW0NfX19JCUladSTkpLUc3f8m52dXbmWnz59OgICAtTPU1NT0aBBA9y5c6dCf5BUfunp6XByckJ8fHyFHu+lF8PPo/rgZ1F98LOoPtLS0lC/fn3Url27QrcraRCSy+Vwc3NDaGgoBgwYAKDwZOnQ0FBMmjSp2HXc3d0RGhqKTz75RF375Zdf4O7uXuzyCoUCCoWiSN3S0pJ/qKsJCwsLfhbVCD+P6oOfRfXBz6L6qOh5hiQ/NBYQEIBRo0ahQ4cO6NSpE1asWIGsrCyMHj0aADBy5Eg4Ojpi4cKFAICPP/4YHh4eWLZsGd58803s3LkT586dw7p166R8G0RERFQDSR6EfHx88PDhQwQGBiIxMRFt27bFsWPH1CdE37lzRyP9de3aFTt27MCsWbMwY8YMNGnSBD/99BNatWol1VsgIiKiGkryIAQAkyZNKvFQWFhYWJHakCFDMGTIkBfal0KhwOzZs4s9XEZVi59F9cLPo/rgZ1F98LOoPirrs5B8QkUiIiIiqUh+iw0iIiIiqTAIERERkc5iECIiIiKdxSBEREREOksrg9Dq1avh7OwMIyMjdO7cGWfPnn3u8nv27EHz5s1hZGSE1q1b48iRI1XUqfYrz2cRHByMHj16wMrKClZWVvD09Cz1s6PyKe/fjad27twJmUymnviUXl55P4vU1FRMnDgR9vb2UCgUaNq0Kf+tqiDl/SxWrFiBZs2awdjYGE5OTpg8eTJycnKqqFvt9fvvv8Pb2xsODg6QyWT46aefSl0nLCwM7du3h0KhQOPGjbF58+by71homZ07dwq5XC42btworly5It5//31Rq1YtkZSUVOzyp0+fFvr6+mLx4sXi6tWrYtasWcLQ0FBcunSpijvXPuX9LIYPHy5Wr14t/v77b3Ht2jXh7+8vLC0txd27d6u4c+1U3s/jqZs3bwpHR0fRo0cP0b9//6ppVsuV97PIzc0VHTp0EP369ROnTp0SN2/eFGFhYeL8+fNV3Ln2Ke9nsX37dqFQKMT27dvFzZs3RUhIiLC3txeTJ0+u4s61z5EjR8TMmTPFvn37BACxf//+5y4fFxcnTExMREBAgLh69apYtWqV0NfXF8eOHSvXfrUuCHXq1ElMnDhR/VypVAoHBwexcOHCYpcfOnSoePPNNzVqnTt3FuPGjavUPnVBeT+LfysoKBDm5uZiy5YtldWiTnmRz6OgoEB07dpVrF+/XowaNYpBqIKU97NYs2aNcHFxEXl5eVXVos4o72cxceJE8dprr2nUAgICRLdu3Sq1T11TliD0+eefi1deeUWj5uPjI7y8vMq1L606NJaXl4fIyEh4enqqa3p6evD09ERERESx60RERGgsDwBeXl4lLk9l8yKfxb9lZ2cjPz+/wm+wp4te9PP48ssvYWNjgzFjxlRFmzrhRT6LgwcPwt3dHRMnToStrS1atWqFBQsWQKlUVlXbWulFPouuXbsiMjJSffgsLi4OR44cQb9+/aqkZ3qmor6/q8XM0hUlOTkZSqVSfXuOp2xtbXH9+vVi10lMTCx2+cTExErrUxe8yGfxb1OnToWDg0ORP+hUfi/yeZw6dQobNmzA+fPnq6BD3fEin0VcXBx+/fVXjBgxAkeOHMGNGzfwwQcfID8/H7Nnz66KtrXSi3wWw4cPR3JyMrp37w4hBAoKCjB+/HjMmDGjKlqm/1HS93d6ejqePHkCY2PjMm1Hq0aESHt8/fXX2LlzJ/bv3w8jIyOp29E5GRkZ8PPzQ3BwMKytraVuR+epVCrY2Nhg3bp1cHNzg4+PD2bOnIm1a9dK3ZrOCQsLw4IFC/Ddd98hKioK+/btw+HDhzFv3jypW6MXpFUjQtbW1tDX10dSUpJGPSkpCXZ2dsWuY2dnV67lqWxe5LN4aunSpfj6669x4sQJuLq6VmabOqO8n0dsbCxu3boFb29vdU2lUgEADAwMEB0djUaNGlVu01rqRf5u2Nvbw9DQEPr6+upaixYtkJiYiLy8PMjl8krtWVu9yGfxxRdfwM/PD2PHjgUAtG7dGllZWfjPf/6DmTNnatwknCpXSd/fFhYWZR4NArRsREgul8PNzQ2hoaHqmkqlQmhoKNzd3Ytdx93dXWN5APjll19KXJ7K5kU+CwBYvHgx5s2bh2PHjqFDhw5V0apOKO/n0bx5c1y6dAnnz59XP95++2306tUL58+fh5OTU1W2r1Ve5O9Gt27dcOPGDXUYBYCYmBjY29szBL2EF/kssrOzi4SdpwFV8NadVarCvr/Ldx539bdz506hUCjE5s2bxdWrV8V//vMfUatWLZGYmCiEEMLPz09MmzZNvfzp06eFgYGBWLp0qbh27ZqYPXs2L5+vIOX9LL7++mshl8vFjz/+KBISEtSPjIwMqd6CVinv5/FvvGqs4pT3s7hz544wNzcXkyZNEtHR0eLQoUPCxsZGfPXVV1K9Ba1R3s9i9uzZwtzcXPzwww8iLi5OHD9+XDRq1EgMHTpUqregNTIyMsTff/8t/v77bwFALF++XPz999/i9u3bQgghpk2bJvz8/NTLP718/rPPPhPXrl0Tq1ev5uXzT61atUrUr19fyOVy0alTJ/Hnn3+qX/Pw8BCjRo3SWH737t2iadOmQi6Xi1deeUUcPny4ijvWXuX5LBo0aCAAFHnMnj276hvXUuX9u/G/GIQqVnk/i/DwcNG5c2ehUCiEi4uLmD9/vigoKKjirrVTeT6L/Px8MWfOHNGoUSNhZGQknJycxAcffCBSUlKqvnEtc/LkyWK/A57+/EeNGiU8PDyKrNO2bVshl8uFi4uL2LRpU7n3KxOCY3lERESkm7TqHCEiIiKi8mAQIiIiIp3FIEREREQ6i0GIiIiIdBaDEBEREeksBiEiIiLSWQxCREREpLMYhIiIiEhnMQgRUZXx9/eHTCYr8rhx40aR1+VyORo3bowvv/wSBQUFJW7T2dlZvY6JiQlat26N9evXl7s3mUyGn3766UXfGhHVUAxCRFSl+vbti4SEBI1Hw4YNi7z+zz//YMqUKZgzZw6WLFny3G1++eWXSEhIwOXLl+Hr64v3338fR48erey3QkRagEGIiKqUQqGAnZ2dxuPp3bv/9/UGDRpgwoQJ8PT0xMGDB5+7TXNzc9jZ2cHFxQVTp05F7dq18csvv6hf/+uvv9C7d29YW1vD0tISHh4eiIqKUr/u7OwMABg4cCBkMpn6OQAcOHAA7du3h5GREVxcXDB37tznjlARUc3CIERE1ZqxsTHy8vLKtKxKpcLevXuRkpICuVyurmdkZGDUqFE4deoU/vzzTzRp0gT9+vVDRkYGgMKgBACbNm1CQkKC+vkff/yBkSNH4uOPP8bVq1cRFBSEzZs3Y/78+RX8LolIKgxCRFSlDh06BDMzM/VjyJAhxS4nhMCJEycQEhKC11577bnbnDp1KszMzKBQKPDOO+/AysoKY8eOVb/+2muvwdfXF82bN0eLFi2wbt06ZGdn47fffgMA1K1bFwBQq1Yt2NnZqZ/PnTsX06ZNw6hRo+Di4oLevXtj3rx5CAoKqogfBRFVAwZSN0BEuqVXr15Ys2aN+rmpqanG60+DUn5+PlQqFYYPH445c+Y8d5ufffYZ/P39kZCQgM8++wwffPABGjdurH49KSkJs2bNQlhYGB48eAClUons7GzcuXPnudu9cOECTp8+rTECpFQqkZOTg+zsbJiYmJTjnRNRdcQgRERVytTUVCOk/NvToCSXy+Hg4AADg9L/mbK2tkbjxo3RuHFj7NmzB61bt0aHDh3QsmVLAMCoUaPw6NEjrFy5Eg0aNIBCoYC7u3uph9wyMzMxd+5cDBo0qMhrRkZGpfZFRNUfgxARVSulBaXSODk5wcfHB9OnT8eBAwcAAKdPn8Z3332Hfv36AQDi4+ORnJyssZ6hoSGUSqVGrX379oiOjn6pfoioemMQIiKt8/HHH6NVq1Y4d+4cOnTogCZNmmDbtm3o0KED0tPT8dlnn8HY2FhjHWdnZ4SGhqJbt25QKBSwsrJCYGAg3nrrLdSvXx/vvPMO9PT0cOHCBVy+fBlfffWVRO+OiCoST5YmIq3TsmVL9OnTB4GBgQCADRs2ICUlBe3bt4efnx8++ugj2NjYaKyzbNky/PLLL3ByckK7du0AAF5eXjh06BCOHz+Ojh07okuXLvjmm2/QoEGDKn9PRFQ5ZEIIIXUTRERERFLgiBARERHpLAYhIiIi0lkMQkRERKSzGISIiIhIZzEIERERkc5iECIiIiKdxSBEREREOotBiIiIiHQWgxARERHpLAYhIiIi0lkMQkRERKSzGISIiIhIZ/0ffik/DTe383gAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load the saved model\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
        "\n",
        "model = load_model('/content/best_VGG16_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history2.history['accuracy'])\n",
        "plt.plot(history2.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9uxSFjxMY9w"
      },
      "source": [
        "#### **1.7. InceptionV3 Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MISclavSt04"
      },
      "source": [
        " Assigning the weight parameter to the imagenet will enable the weights of the imagenet model to be used. If we want to train something using the Inception mesh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "76v_E9vjMeTp",
        "outputId": "887cc911-b535-4d0c-a68e-c354774c8874",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87910968/87910968 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "IMAGE_SIZE = [224, 224]\n",
        "inception = InceptionV3(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
        "\n",
        "for layer in inception.layers:\n",
        "    layer.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZfyn8-HMeXE",
        "outputId": "0889f3c5-169d-4c51-ff0c-1748944a6a92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_4 (InputLayer)        [(None, 224, 224, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)             (None, 111, 111, 32)         864       ['input_4[0][0]']             \n",
            "                                                                                                  \n",
            " batch_normalization (Batch  (None, 111, 111, 32)         96        ['conv2d[0][0]']              \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation (Activation)     (None, 111, 111, 32)         0         ['batch_normalization[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 32)         9216      ['activation[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_1 (Bat  (None, 109, 109, 32)         96        ['conv2d_1[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)   (None, 109, 109, 32)         0         ['batch_normalization_1[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)           (None, 109, 109, 64)         18432     ['activation_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_2 (Bat  (None, 109, 109, 64)         192       ['conv2d_2[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_2 (Activation)   (None, 109, 109, 64)         0         ['batch_normalization_2[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2  (None, 54, 54, 64)           0         ['activation_2[0][0]']        \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)           (None, 54, 54, 80)           5120      ['max_pooling2d[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_3 (Bat  (None, 54, 54, 80)           240       ['conv2d_3[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_3 (Activation)   (None, 54, 54, 80)           0         ['batch_normalization_3[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)           (None, 52, 52, 192)          138240    ['activation_3[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_4 (Bat  (None, 52, 52, 192)          576       ['conv2d_4[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_4 (Activation)   (None, 52, 52, 192)          0         ['batch_normalization_4[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPoolin  (None, 25, 25, 192)          0         ['activation_4[0][0]']        \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " batch_normalization_8 (Bat  (None, 25, 25, 64)           192       ['conv2d_8[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_8 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_8[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)           (None, 25, 25, 48)           9216      ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)           (None, 25, 25, 96)           55296     ['activation_8[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_6 (Bat  (None, 25, 25, 48)           144       ['conv2d_6[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_9 (Bat  (None, 25, 25, 96)           288       ['conv2d_9[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " activation_6 (Activation)   (None, 25, 25, 48)           0         ['batch_normalization_6[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_9 (Activation)   (None, 25, 25, 96)           0         ['batch_normalization_9[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " average_pooling2d (Average  (None, 25, 25, 192)          0         ['max_pooling2d_1[0][0]']     \n",
            " Pooling2D)                                                                                       \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)           (None, 25, 25, 64)           12288     ['max_pooling2d_1[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)           (None, 25, 25, 64)           76800     ['activation_6[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_9[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)          (None, 25, 25, 32)           6144      ['average_pooling2d[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_5 (Bat  (None, 25, 25, 64)           192       ['conv2d_5[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_7 (Bat  (None, 25, 25, 64)           192       ['conv2d_7[0][0]']            \n",
            " chNormalization)                                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_10 (Ba  (None, 25, 25, 96)           288       ['conv2d_10[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_11 (Ba  (None, 25, 25, 32)           96        ['conv2d_11[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_5 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_5[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_7 (Activation)   (None, 25, 25, 64)           0         ['batch_normalization_7[0][0]'\n",
            "                                                                    ]                             \n",
            "                                                                                                  \n",
            " activation_10 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_10[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_11 (Activation)  (None, 25, 25, 32)           0         ['batch_normalization_11[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)        (None, 25, 25, 256)          0         ['activation_5[0][0]',        \n",
            "                                                                     'activation_7[0][0]',        \n",
            "                                                                     'activation_10[0][0]',       \n",
            "                                                                     'activation_11[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_15 (Ba  (None, 25, 25, 64)           192       ['conv2d_15[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_15 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_15[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)          (None, 25, 25, 48)           12288     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_15[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_13 (Ba  (None, 25, 25, 48)           144       ['conv2d_13[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_16 (Ba  (None, 25, 25, 96)           288       ['conv2d_16[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_13 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_13[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_16 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_16[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (Avera  (None, 25, 25, 256)          0         ['mixed0[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)          (None, 25, 25, 64)           16384     ['mixed0[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_13[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_16[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)          (None, 25, 25, 64)           16384     ['average_pooling2d_1[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_12 (Ba  (None, 25, 25, 64)           192       ['conv2d_12[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_14 (Ba  (None, 25, 25, 64)           192       ['conv2d_14[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_17 (Ba  (None, 25, 25, 96)           288       ['conv2d_17[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_18 (Ba  (None, 25, 25, 64)           192       ['conv2d_18[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_12 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_12[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_14 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_14[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_17 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_17[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_18 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_18[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)        (None, 25, 25, 288)          0         ['activation_12[0][0]',       \n",
            "                                                                     'activation_14[0][0]',       \n",
            "                                                                     'activation_17[0][0]',       \n",
            "                                                                     'activation_18[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_22 (Ba  (None, 25, 25, 64)           192       ['conv2d_22[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_22 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_22[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)          (None, 25, 25, 48)           13824     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_22[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_20 (Ba  (None, 25, 25, 48)           144       ['conv2d_20[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_23 (Ba  (None, 25, 25, 96)           288       ['conv2d_23[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_20 (Activation)  (None, 25, 25, 48)           0         ['batch_normalization_20[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_23 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_23[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (Avera  (None, 25, 25, 288)          0         ['mixed1[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed1[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)          (None, 25, 25, 64)           76800     ['activation_20[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)          (None, 25, 25, 96)           82944     ['activation_23[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)          (None, 25, 25, 64)           18432     ['average_pooling2d_2[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_19 (Ba  (None, 25, 25, 64)           192       ['conv2d_19[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_21 (Ba  (None, 25, 25, 64)           192       ['conv2d_21[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_24 (Ba  (None, 25, 25, 96)           288       ['conv2d_24[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_25 (Ba  (None, 25, 25, 64)           192       ['conv2d_25[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_19 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_19[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_21 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_21[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_24 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_24[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_25 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_25[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)        (None, 25, 25, 288)          0         ['activation_19[0][0]',       \n",
            "                                                                     'activation_21[0][0]',       \n",
            "                                                                     'activation_24[0][0]',       \n",
            "                                                                     'activation_25[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)          (None, 25, 25, 64)           18432     ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_27 (Ba  (None, 25, 25, 64)           192       ['conv2d_27[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_27 (Activation)  (None, 25, 25, 64)           0         ['batch_normalization_27[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)          (None, 25, 25, 96)           55296     ['activation_27[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_28 (Ba  (None, 25, 25, 96)           288       ['conv2d_28[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_28 (Activation)  (None, 25, 25, 96)           0         ['batch_normalization_28[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)          (None, 12, 12, 384)          995328    ['mixed2[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)          (None, 12, 12, 96)           82944     ['activation_28[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_26 (Ba  (None, 12, 12, 384)          1152      ['conv2d_26[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_29 (Ba  (None, 12, 12, 96)           288       ['conv2d_29[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_26 (Activation)  (None, 12, 12, 384)          0         ['batch_normalization_26[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_29 (Activation)  (None, 12, 12, 96)           0         ['batch_normalization_29[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPoolin  (None, 12, 12, 288)          0         ['mixed2[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)        (None, 12, 12, 768)          0         ['activation_26[0][0]',       \n",
            "                                                                     'activation_29[0][0]',       \n",
            "                                                                     'max_pooling2d_2[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_34 (Ba  (None, 12, 12, 128)          384       ['conv2d_34[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_34 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_34[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_34[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_35 (Ba  (None, 12, 12, 128)          384       ['conv2d_35[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_35 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_35[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)          (None, 12, 12, 128)          98304     ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_35[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_31 (Ba  (None, 12, 12, 128)          384       ['conv2d_31[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_36 (Ba  (None, 12, 12, 128)          384       ['conv2d_36[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_31 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_31[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_36 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_36[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_31[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)          (None, 12, 12, 128)          114688    ['activation_36[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_32 (Ba  (None, 12, 12, 128)          384       ['conv2d_32[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_37 (Ba  (None, 12, 12, 128)          384       ['conv2d_37[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_32 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_32[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_37 (Activation)  (None, 12, 12, 128)          0         ['batch_normalization_37[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (Avera  (None, 12, 12, 768)          0         ['mixed3[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed3[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_32[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)          (None, 12, 12, 192)          172032    ['activation_37[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_3[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_30 (Ba  (None, 12, 12, 192)          576       ['conv2d_30[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_33 (Ba  (None, 12, 12, 192)          576       ['conv2d_33[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_38 (Ba  (None, 12, 12, 192)          576       ['conv2d_38[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_39 (Ba  (None, 12, 12, 192)          576       ['conv2d_39[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_30 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_30[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_33 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_33[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_38 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_38[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_39 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_39[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)        (None, 12, 12, 768)          0         ['activation_30[0][0]',       \n",
            "                                                                     'activation_33[0][0]',       \n",
            "                                                                     'activation_38[0][0]',       \n",
            "                                                                     'activation_39[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_44 (Ba  (None, 12, 12, 160)          480       ['conv2d_44[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_44 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_44[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_44[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_45 (Ba  (None, 12, 12, 160)          480       ['conv2d_45[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_45 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_45[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_45[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_41 (Ba  (None, 12, 12, 160)          480       ['conv2d_41[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_46 (Ba  (None, 12, 12, 160)          480       ['conv2d_46[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_41 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_41[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_46 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_46[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_41[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_46[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_42 (Ba  (None, 12, 12, 160)          480       ['conv2d_42[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_47 (Ba  (None, 12, 12, 160)          480       ['conv2d_47[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_42 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_42[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_47 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_47[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (Avera  (None, 12, 12, 768)          0         ['mixed4[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed4[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_42[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_47[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_4[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_40 (Ba  (None, 12, 12, 192)          576       ['conv2d_40[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_43 (Ba  (None, 12, 12, 192)          576       ['conv2d_43[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_48 (Ba  (None, 12, 12, 192)          576       ['conv2d_48[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_49 (Ba  (None, 12, 12, 192)          576       ['conv2d_49[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_40 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_40[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_43 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_43[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_48 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_48[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_49 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_49[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)        (None, 12, 12, 768)          0         ['activation_40[0][0]',       \n",
            "                                                                     'activation_43[0][0]',       \n",
            "                                                                     'activation_48[0][0]',       \n",
            "                                                                     'activation_49[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_54 (Ba  (None, 12, 12, 160)          480       ['conv2d_54[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_54 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_54[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_54[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_55 (Ba  (None, 12, 12, 160)          480       ['conv2d_55[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_55 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_55[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)          (None, 12, 12, 160)          122880    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_55[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_51 (Ba  (None, 12, 12, 160)          480       ['conv2d_51[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_56 (Ba  (None, 12, 12, 160)          480       ['conv2d_56[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_51 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_51[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_56 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_56[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_51[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)          (None, 12, 12, 160)          179200    ['activation_56[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_52 (Ba  (None, 12, 12, 160)          480       ['conv2d_52[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_57 (Ba  (None, 12, 12, 160)          480       ['conv2d_57[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_52 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_52[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_57 (Activation)  (None, 12, 12, 160)          0         ['batch_normalization_57[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (Avera  (None, 12, 12, 768)          0         ['mixed5[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed5[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_52[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)          (None, 12, 12, 192)          215040    ['activation_57[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_5[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_50 (Ba  (None, 12, 12, 192)          576       ['conv2d_50[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_53 (Ba  (None, 12, 12, 192)          576       ['conv2d_53[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_58 (Ba  (None, 12, 12, 192)          576       ['conv2d_58[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_59 (Ba  (None, 12, 12, 192)          576       ['conv2d_59[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_50 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_50[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_53 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_53[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_58 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_58[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_59 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_59[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)        (None, 12, 12, 768)          0         ['activation_50[0][0]',       \n",
            "                                                                     'activation_53[0][0]',       \n",
            "                                                                     'activation_58[0][0]',       \n",
            "                                                                     'activation_59[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_64 (Ba  (None, 12, 12, 192)          576       ['conv2d_64[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_64 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_64[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_64[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_65 (Ba  (None, 12, 12, 192)          576       ['conv2d_65[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_65 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_65[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_65[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_61 (Ba  (None, 12, 12, 192)          576       ['conv2d_61[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_66 (Ba  (None, 12, 12, 192)          576       ['conv2d_66[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_61 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_61[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_66 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_66[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_61[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_66[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_62 (Ba  (None, 12, 12, 192)          576       ['conv2d_62[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_67 (Ba  (None, 12, 12, 192)          576       ['conv2d_67[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_62 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_62[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_67 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_67[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (Avera  (None, 12, 12, 768)          0         ['mixed6[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed6[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_62[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_67[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)          (None, 12, 12, 192)          147456    ['average_pooling2d_6[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_60 (Ba  (None, 12, 12, 192)          576       ['conv2d_60[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_63 (Ba  (None, 12, 12, 192)          576       ['conv2d_63[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_68 (Ba  (None, 12, 12, 192)          576       ['conv2d_68[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_69 (Ba  (None, 12, 12, 192)          576       ['conv2d_69[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_60 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_60[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_63 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_63[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_68 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_68[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_69 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_69[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)        (None, 12, 12, 768)          0         ['activation_60[0][0]',       \n",
            "                                                                     'activation_63[0][0]',       \n",
            "                                                                     'activation_68[0][0]',       \n",
            "                                                                     'activation_69[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_72 (Ba  (None, 12, 12, 192)          576       ['conv2d_72[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_72 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_72[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_72[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_73 (Ba  (None, 12, 12, 192)          576       ['conv2d_73[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_73 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_73[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)          (None, 12, 12, 192)          147456    ['mixed7[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)          (None, 12, 12, 192)          258048    ['activation_73[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_70 (Ba  (None, 12, 12, 192)          576       ['conv2d_70[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_74 (Ba  (None, 12, 12, 192)          576       ['conv2d_74[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_70 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_70[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_74 (Activation)  (None, 12, 12, 192)          0         ['batch_normalization_74[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)          (None, 5, 5, 320)            552960    ['activation_70[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)          (None, 5, 5, 192)            331776    ['activation_74[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_71 (Ba  (None, 5, 5, 320)            960       ['conv2d_71[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_75 (Ba  (None, 5, 5, 192)            576       ['conv2d_75[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_71 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_71[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_75 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_75[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPoolin  (None, 5, 5, 768)            0         ['mixed7[0][0]']              \n",
            " g2D)                                                                                             \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)        (None, 5, 5, 1280)           0         ['activation_71[0][0]',       \n",
            "                                                                     'activation_75[0][0]',       \n",
            "                                                                     'max_pooling2d_3[0][0]']     \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)          (None, 5, 5, 448)            573440    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_80 (Ba  (None, 5, 5, 448)            1344      ['conv2d_80[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_80 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_80[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)          (None, 5, 5, 384)            491520    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_80[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_77 (Ba  (None, 5, 5, 384)            1152      ['conv2d_77[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_81 (Ba  (None, 5, 5, 384)            1152      ['conv2d_81[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_77 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_77[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_81 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_81[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_77[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_81[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (Avera  (None, 5, 5, 1280)           0         ['mixed8[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)          (None, 5, 5, 320)            409600    ['mixed8[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_78 (Ba  (None, 5, 5, 384)            1152      ['conv2d_78[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_79 (Ba  (None, 5, 5, 384)            1152      ['conv2d_79[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_82 (Ba  (None, 5, 5, 384)            1152      ['conv2d_82[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_83 (Ba  (None, 5, 5, 384)            1152      ['conv2d_83[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)          (None, 5, 5, 192)            245760    ['average_pooling2d_7[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_76 (Ba  (None, 5, 5, 320)            960       ['conv2d_76[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_78 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_78[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_79 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_79[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_82 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_82[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_83 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_83[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_84 (Ba  (None, 5, 5, 192)            576       ['conv2d_84[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_76 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_76[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)      (None, 5, 5, 768)            0         ['activation_78[0][0]',       \n",
            "                                                                     'activation_79[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)   (None, 5, 5, 768)            0         ['activation_82[0][0]',       \n",
            "                                                                     'activation_83[0][0]']       \n",
            "                                                                                                  \n",
            " activation_84 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_84[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)        (None, 5, 5, 2048)           0         ['activation_76[0][0]',       \n",
            "                                                                     'mixed9_0[0][0]',            \n",
            "                                                                     'concatenate[0][0]',         \n",
            "                                                                     'activation_84[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)          (None, 5, 5, 448)            917504    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_89 (Ba  (None, 5, 5, 448)            1344      ['conv2d_89[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_89 (Activation)  (None, 5, 5, 448)            0         ['batch_normalization_89[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)          (None, 5, 5, 384)            786432    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)          (None, 5, 5, 384)            1548288   ['activation_89[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_86 (Ba  (None, 5, 5, 384)            1152      ['conv2d_86[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_90 (Ba  (None, 5, 5, 384)            1152      ['conv2d_90[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_86 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_86[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_90 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_90[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_86[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)          (None, 5, 5, 384)            442368    ['activation_90[0][0]']       \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (Avera  (None, 5, 5, 2048)           0         ['mixed9[0][0]']              \n",
            " gePooling2D)                                                                                     \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)          (None, 5, 5, 320)            655360    ['mixed9[0][0]']              \n",
            "                                                                                                  \n",
            " batch_normalization_87 (Ba  (None, 5, 5, 384)            1152      ['conv2d_87[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_88 (Ba  (None, 5, 5, 384)            1152      ['conv2d_88[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_91 (Ba  (None, 5, 5, 384)            1152      ['conv2d_91[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " batch_normalization_92 (Ba  (None, 5, 5, 384)            1152      ['conv2d_92[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)          (None, 5, 5, 192)            393216    ['average_pooling2d_8[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_85 (Ba  (None, 5, 5, 320)            960       ['conv2d_85[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_87 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_87[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_88 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_88[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_91 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_91[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " activation_92 (Activation)  (None, 5, 5, 384)            0         ['batch_normalization_92[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " batch_normalization_93 (Ba  (None, 5, 5, 192)            576       ['conv2d_93[0][0]']           \n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " activation_85 (Activation)  (None, 5, 5, 320)            0         ['batch_normalization_85[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)      (None, 5, 5, 768)            0         ['activation_87[0][0]',       \n",
            "                                                                     'activation_88[0][0]']       \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate  (None, 5, 5, 768)            0         ['activation_91[0][0]',       \n",
            " )                                                                   'activation_92[0][0]']       \n",
            "                                                                                                  \n",
            " activation_93 (Activation)  (None, 5, 5, 192)            0         ['batch_normalization_93[0][0]\n",
            "                                                                    ']                            \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)       (None, 5, 5, 2048)           0         ['activation_85[0][0]',       \n",
            "                                                                     'mixed9_1[0][0]',            \n",
            "                                                                     'concatenate_1[0][0]',       \n",
            "                                                                     'activation_93[0][0]']       \n",
            "                                                                                                  \n",
            " dense_10 (Dense)            (None, 5, 5, 128)            262272    ['mixed10[0][0]']             \n",
            "                                                                                                  \n",
            " dense_11 (Dense)            (None, 5, 5, 512)            66048     ['dense_10[0][0]']            \n",
            "                                                                                                  \n",
            " dense_12 (Dense)            (None, 5, 5, 256)            131328    ['dense_11[0][0]']            \n",
            "                                                                                                  \n",
            " dense_13 (Dense)            (None, 5, 5, 128)            32896     ['dense_12[0][0]']            \n",
            "                                                                                                  \n",
            " dense_14 (Dense)            (None, 5, 5, 64)             8256      ['dense_13[0][0]']            \n",
            "                                                                                                  \n",
            " flatten_2 (Flatten)         (None, 1600)                 0         ['dense_14[0][0]']            \n",
            "                                                                                                  \n",
            " dense_15 (Dense)            (None, 2)                    3202      ['flatten_2[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 22306786 (85.09 MB)\n",
            "Trainable params: 504002 (1.92 MB)\n",
            "Non-trainable params: 21802784 (83.17 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "x = (inception.output)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dense(256, activation='relu')(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Flatten()(Dense(64, activation='relu')(x))\n",
        "prediction = Dense(num_classes, activation='softmax')(x)\n",
        "model2 = Model(inputs=inception.input, outputs=prediction)# Create a model object\n",
        "\n",
        "model2.summary()# View the structure of the model\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "I_7g_QOKMyFH",
        "outputId": "79d00a23-8bef-4184-dbdc-c2513c8d3887",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "32/32 [==============================] - ETA: 0s - loss: 0.5612 - accuracy: 0.6967"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 4 batches). You may need to use the repeat() function when building your dataset.\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r32/32 [==============================] - 35s 688ms/step - loss: 0.5612 - accuracy: 0.6967 - val_loss: 0.3520 - val_accuracy: 0.8140 - lr: 0.0010\n",
            "Epoch 2/15\n",
            "32/32 [==============================] - 13s 406ms/step - loss: 0.4105 - accuracy: 0.8178 - lr: 0.0010\n",
            "Epoch 3/15\n",
            "32/32 [==============================] - 14s 447ms/step - loss: 0.3343 - accuracy: 0.8619 - lr: 0.0010\n",
            "Epoch 4/15\n",
            "32/32 [==============================] - 13s 404ms/step - loss: 0.2993 - accuracy: 0.8739 - lr: 0.0010\n",
            "Epoch 5/15\n",
            "32/32 [==============================] - 14s 424ms/step - loss: 0.2367 - accuracy: 0.9149 - lr: 0.0010\n",
            "Epoch 6/15\n",
            "32/32 [==============================] - 13s 417ms/step - loss: 0.2163 - accuracy: 0.8994 - lr: 0.0010\n",
            "Epoch 7/15\n",
            "32/32 [==============================] - 12s 377ms/step - loss: 0.2187 - accuracy: 0.9099 - lr: 0.0010\n",
            "Epoch 8/15\n",
            "32/32 [==============================] - 13s 405ms/step - loss: 0.2098 - accuracy: 0.9139 - lr: 0.0010\n",
            "Epoch 9/15\n",
            "32/32 [==============================] - 15s 460ms/step - loss: 0.1567 - accuracy: 0.9329 - lr: 0.0010\n",
            "Epoch 10/15\n",
            "32/32 [==============================] - 12s 377ms/step - loss: 0.1802 - accuracy: 0.9229 - lr: 0.0010\n",
            "Epoch 11/15\n",
            "32/32 [==============================] - 13s 418ms/step - loss: 0.1409 - accuracy: 0.9399 - lr: 0.0010\n",
            "Epoch 12/15\n",
            "32/32 [==============================] - 12s 375ms/step - loss: 0.1463 - accuracy: 0.9339 - lr: 0.0010\n",
            "Epoch 13/15\n",
            "32/32 [==============================] - 12s 374ms/step - loss: 0.1530 - accuracy: 0.9319 - lr: 0.0010\n",
            "Epoch 14/15\n",
            "32/32 [==============================] - 13s 400ms/step - loss: 0.1533 - accuracy: 0.9379 - lr: 0.0010\n"
          ]
        }
      ],
      "source": [
        "# Defining the cost and model optimization method to use\n",
        "model2.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "model_checkpoint = ModelCheckpoint('best_InceptionV3_model.h5', monitor='loss', save_best_only=True)\n",
        "early_stopping = EarlyStopping(monitor='loss', patience=3, restore_best_weights=True)\n",
        "\n",
        "history3 = model2.fit(\n",
        "    Train_generator,\n",
        "    steps_per_epoch=32,\n",
        "    epochs=15,\n",
        "    validation_data=Val_generator,\n",
        "    validation_steps=4,\n",
        "    verbose=1,\n",
        "    callbacks=[ReduceLROnPlateau(monitor='loss', factor=0.3,patience=3, min_lr=0.000001),early_stopping, model_checkpoint],\n",
        "    shuffle=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "mwdq-24FOFvT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f06f2142-6bba-454a-db2c-12b8ea0834f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 4s 652ms/step - loss: 0.2251 - accuracy: 0.9010\n",
            "Test Accuracy: 90.09901285171509\n",
            "4/4 [==============================] - 3s 67ms/step\n",
            "\n",
            "[INFO] Confusion Matrix:\n",
            "[[36 27]\n",
            " [19 19]]\n",
            "\n",
            "[INFO] classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Defected       0.65      0.57      0.61        63\n",
            "          OK       0.41      0.50      0.45        38\n",
            "\n",
            "    accuracy                           0.54       101\n",
            "   macro avg       0.53      0.54      0.53       101\n",
            "weighted avg       0.56      0.54      0.55       101\n",
            "\n",
            "Sensitivity: 0.5\n",
            "Specificity: 0.5714285714285714\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load the saved model\n",
        "model2 = load_model('/content/best_InceptionV3_model.h5')\n",
        "\n",
        "# Evaluate the model on the test set\n",
        "scores = model2.evaluate(Test_generator)\n",
        "print(f\"Test Accuracy: {scores[1]*100}\")\n",
        "\n",
        "# Generate predictions for the test set\n",
        "y_pred = model2.predict(Test_generator)\n",
        "y_true = Test_generator.classes\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "conf_matrix = confusion_matrix(y_true, y_pred.argmax(axis=1))\n",
        "print(\"\\n[INFO] Confusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "# Calculate metrics\n",
        "print(\"\\n[INFO] classification report:\")\n",
        "print(classification_report(y_true, y_pred.argmax(axis=1), target_names=Test_generator.class_indices))\n",
        "\n",
        "print(f\"Sensitivity: \"+ str(cal_sensitivity(conf_matrix)))\n",
        "print(f\"Specificity: \"+ str(cal_specificity(conf_matrix)))\n",
        "\n",
        "\n",
        "# Plot the confusion matrix\n",
        "plt.figure(figsize=(4, 4))\n",
        "plt.imshow(conf_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
        "plt.title('Confusion Matrix')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(Test_generator.class_indices))\n",
        "plt.xticks(tick_marks, Test_generator.class_indices, rotation=45)\n",
        "plt.yticks(tick_marks, Test_generator.class_indices)\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()\n",
        "\n",
        "# Calculate ROC curve and AUC\n",
        "fpr, tpr, thresholds = roc_curve(y_true, y_pred[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.plot(history3.history['accuracy'])\n",
        "plt.plot(history3.history['loss'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train_Acc', 'Train_Loss'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('FP Rate')\n",
        "plt.ylabel('TP Rate')\n",
        "plt.title('(ROC) Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "models = ['CNN', 'VGG16', 'InceptionV3', 'AlexNet', 'ResNet50']\n",
        "total_params = [33963586, 18000386, 22306786, 106621698, 24228482]\n",
        "sizes = [129.56, 68.67, 85.09, 406.73, 92.42]\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax1.set_xlabel('Models')\n",
        "ax1.set_ylabel('Total Params', color=color)\n",
        "ax1.bar(models, total_params, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:green'\n",
        "ax2.set_ylabel('Size (MB)', color=color)\n",
        "ax2.plot(models, sizes, color=color, marker='o')\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "plt.title('Total Params and Size of Each Model')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZRnQlVGeYe5-",
        "outputId": "54bb4201-c404-45e5-a50c-935f86ae82af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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M79y+xv1GixVv/HUUvyeeRnG5BdEB7nhqVBcMjqj8K/VAZhFsNoHHrusMhaLyF8o98Z3wn692wWy1Qa1kS3R92TubGwwGp1q3I0eOOPbb1fTL/KeffsKUKVPw5ptvOrZVVFTUeqRiffz888/Q6XT466+/oNVqHdsXL17cpOeoC3uSM2fOHGzZsgVxcXH4+OOPMX/+/GrLHzhwAMnJyfjyyy9xxx13OLavWrWq3jGEhYXhn3/+QUlJiVOt29GjR+t9TgCOBDsrK+uS5V555RUsXboUv/zyC7p06eK0z8/PD+7u7rBarZdN/qoTFhYGm82GY8eOOc1LmJ2djcLCwmpHwjakn376CcOHD8eiRYucthcWFjpNxREREYHt27fDbDbXaoBBQxg6dCg6dOiAdevW4dVXX62xXG3vof3fY8eOoVOnTo5yubm5VWoDIyIiUFJSUq/3lEhOzCwu4fnfDmHPyUK8d1sfrHwkHmN6BGDK4h1IyysFAPQI9oRCkvDj7lOw2gSKK8z4dW8mhkb6Mmm7QqNHj4bVasX777/vtP3tt9+GJEm4/vrrHdv0en21yZhSqaxSA/Dee+9VmYaiISmVSkiS5PQa6enpdVrZoSHOURvFxcWwWCxO23r06AGFQlFlioWL4wOca1eEEHj33XfrHcvo0aNhsVjw0UcfObZZrVa89957tTp+48aNMJvNVbbb+0Zdqsl19erVmDNnDp555plqJ3FWKpW46aab8PPPP+PgwYNV9ufm5l4yttGjRwNAlVGKb731FgBUGSXc0Kr7Ofjxxx+RmZnptO2mm25CXl5elZ85oO41abUlSRL++9//4vnnn8ftt99eY7na3sNrr70WarUa7733nlPM1Y0QveWWW7B161b89ddfVfYVFhZW+dkgai7adI3bpWQWluPH3RnY8tTV8PeonBD1nqsisD45Fz/uOoUnRnVBaDtXLLlrAGZ8uwdP/3oQVptAnw5eWHznAJmjb/nGjh2L4cOH45lnnkF6ejpiY2Px999/47fffsMjjzziNDVB3759sXr1arz11lsICgpCeHg4Bg4ciBtuuAFfffUVPD09ERMTg61bt2L16tVO/Xoa2pgxY/DWW29h1KhR+Pe//42cnBx88MEHiIyMrHaKg8Y6R22sWbMGM2bMwMSJExEdHQ2LxYKvvvrKkajUpEuXLoiIiMBjjz2GzMxMeHh44Oeff76iCVPHjh2LuLg4PPXUU0hPT0dMTAx++eWXWvdFfPXVV7F7927ceOONjmbLPXv2YMmSJWjXrt0lp+u47bbb4Ofnh6ioqCrLMI0YMQL+/v545ZVXsHbtWgwcOBD/+c9/EBMTg4KCAuzZswerV69GQUFBjeePjY3FlClTsHDhQhQWFiIhIQE7duzAl19+iQkTJmD48OG1usbqWCyWGpeO+te//gW9Xo8bbrgBL774Iu68804MGTIEBw4cwDfffONUIwUAd9xxB5YsWYJZs2Zhx44diI+PR2lpKVavXo0HHngA48ePr3eclzJ+/PjLnru299A+R6B9SpzRo0dj7969+PPPP51qFwHg8ccfx++//44bbrgBU6dORd++fVFaWooDBw7gp59+Qnp6epVjiJoFOYayNkdhTy4XKw9mOZ7/c/iMCHtyuej67J9Oj4jZf4gHvtkthBAiu7hcDHt9rXj5jyRxIKNQbDueJ275eIv496dbnYai0+VdPB2IEEIYDAYxc+ZMERQUJNRqtYiKihKvv/56lXt75MgRcdVVVzmmg7AP+T979qy48847ha+vr3BzcxMjR44UR44cEWFhYU7TAtR1OpCdO3destyiRYtEVFSU0Gq1okuXLmLx4sXi+eefr3J9AMT06dMb/Bz2KRtef/11p+0XTsUghBCpqali2rRpIiIiQuh0OtGuXTsxfPhwsXr16ktenxBCJCUliWuvvVa4ubkJX19f8Z///EckJiZWmbqjpuktqruW/Px8cfvttwsPDw/h6ekpbr/9dseUJZebDmTz5s1i+vTponv37sLT01Oo1WrRoUMHMXXqVHH8+HGnshdPB4IaptS4+DORnZ0tpk+fLkJDQ4VarRYBAQHimmuuEQsXLrzs/TKbzeKFF14Q4eHhQq1Wi9DQUDF79mxRUVHhVK6hpgPBBdO+VFRUiEcffVQEBgYKFxcXERcXJ7Zu3VrttChlZWXimWeeccQZEBAgbr75Zsc9rOmzZb+Pl5sy4+LPYE0ung5EiNrfQ6vVKl544QXH9Q4bNkwcPHiwys+9EJW/Y2bPni0iIyOFRqMRvr6+YsiQIeKNN94QJpOpTtdG1FQkIRqpDryF6fjUH06jSpclnsYj3+/D3zOvgvKiPlSuWiXau+vw5t9HsT45F7/PGOrYl1VUjsEL1uCXB4agTwfnUUxEREREV4JNpTXoFuQBq00gv8SEAeHtqi1TbrJW6RhvT/KYDxMREVFDa9M96EuNFhw6XYRDpyv70ZwqKMOh00XILCxHJz83TOgVhFk/7MPKg1k4VVCGfacK8cHaFKw5kg0AuLpLe+zPKMS7q48hLa8UBzOL8NhP+xHs5YJuQZeeB4uIiIiortp0U+nW4/m47dNtVbbf1CcEb94SC7PVhvfWpOCXPRnILq6At6sGvTt4YeaIaHQJqJzn6ffE0/hk/XGk5ZXCRa1E7w7eeOr6Lohs71blvERERERXok0nbkREREQtSZtuKiUiIiJqSZi4EREREbUQbW5UqcViwd69e+Hv7++0MDgRERE1XzabDdnZ2ejduzdUqjaXvji0uSvfu3cvBgzgygZEREQt0Y4dO9C/f3+5w5BNm0vc/P39AVS+8YGBgTJHQ0RERLWRlZWFAQMGOL7H26o2l7jZm0cDAwMREhIiczRERERUF229m1PbvnoiIiKiFoSJGxEREVELwcSNiIiIqIVg4kZERETUQjBxIyIiImohmLgRERERtRBM3IiIiIhaCCZuRERERC0EEzciIiKiFqLNrZxAREQNx2qzYk/OHuSW5cLP1Q992veBUqGUOyyiVouJGxER1cvqE6vxyo5XkF2W7djm7+qPpwY8hWvDrpUxMqLWi02lRERUZ6tPrMasdbOckjYAyCnLwax1s7D6xGqZIiNq3Zi4ERFRnVhtVryy4xUIiCr77Nte3fEqrDZrU4dG1OoxcSMiojrZk7OnSk3bhQQEzpSdwZ6cPU0YFVHbwMSNiIjqJLcst0HLEVHtcXACEVEL0/GpP2R9faXrcbiGXb7cg1+n4IEyeWNNf2WMrK9P1NBY40ZERHViLQuHzewJUbWLGwBACMBm9oS1LLxpAyO6hM8OfIYeX/bAqztedWwzWo2Yv20+hn43FAO+GYCZa2cirzzP6biskiw8sPoB9P+6PxK+T8Cbu96ExWZp6vAdmLgREVEdKWDMHgsAVZI3+/PK/fyKoebhYN5B/JT8E6K9o522v7bjNazPWI83E97E4lGLkVOeg5lrZzr2W21WPPDPAzDbzPhq9FeYHzcfv6X8hg/2fdDUl+DAnyoiIqozi6E7jDkjIUkX7RAqVGROhsXQXZa4iC5WZi7DUxufwvODn4eHxsOx3WAy4JeUX/B4v8cxMHAguvl0w7y4ediXuw+JuYkAgC2ntyC1KBUL4hegS7suiA+Jx4zeM/Ddke9gtppluR4mbkREVC+SsvKLy1IajoqckY7tlpLOcoVEVMVL219CfHA8BgcNdtqelJ8Ei82CQUGDHNs6eXZCoD4QiTmViVtibiKivKLg6+LrKDMkaAhKzCVIKUxpmgu4CBM3IiKqF5XbEQCAuagfzPnDYDN7QFJYoHRNlTkyas0MBgOKi4sdD6PRWGPZP9P+RFJ+Eh7p+0iVfXnleVAr1E61cADgo/NBXkWeo4yPi4/z/nPPL+4L11SYuBERUZ1JqmIodachhARrSWcAEiwlXQEAKvfD8gZHrVpMTAw8PT0djwULFlRb7kzpGbyy4xW8Ev8KtEptE0fZeDgdCBER1Zm9ts1WEQJhdQMAWEq6QuO9HSq3wzBiPICLO8ARXbmkpCQEBwc7nmu11Sdlh/IPoaCiALcuv9WxzSqs2J29G/878j98POJjmG1mFJuKnWrd8ivy4aurbBr1dfHFwbyDTufNL8937JMDEzciIqoz5bnEzVLSxbHNWhoBYVNDoS6CQpsFmzFIrvCoFXN3d4eHh8dlyw0KHIRfxv3itO3Zzc8i3DMc07pPQ4A+ACqFCtuztmNE2AgAQFpRGrJKsxDbPhYAEOsXi08PfIr88nxHE+nWrK1wU7shwiuiga+sdpi4ERFR3UgWqPSVHbMvTNwg1LCURkLtfhgqt8MwMXEjGenVekR5Rzltc1G5wEvr5dh+Y+SNeH3n6/DUeEKv0WPB9gWI9YtFrF9l4jYkaAg6eXbC05uexqy+s5BXnof3976PSV0mQaPUNPk1AUzciIiojpSuqZAUJtjM7rBVOCdn1pKulYmb+xGY8q+RKUKi2nliwBOQdkqYuW4mzDYzhgQNwZxBcxz7lQolPrjmA8zbNg+TV0yGi8oF4yLGYXqv6bLFzMSNiIjqxN6/zVJaOSjhQvYaOKXLKUhKA4TVvanDI6rR4lGLnZ5rlVrMGTTHKVm7WJBbED669qPGDq3WOKqUiIjqQDgSN6uha9W9Fg9Yy0MAnE/wiKjhMHEjIqJaU2hyodAUQNiUsJRGVlvGPi2IktOCEDU4Jm5ERFRr9tGk1rJOgKh+GgbLuZo4lf4YIMmzLBBRa8XEjYiIas3Rv+0Sy1rZjIGwmT0hKcxQuh5vqtCI2gQmbkREVDuKCihd0wFcNA1IFVxFgaixyDqqdHtqPhZuSMWBzCLkGIz45Pa+GNkt4JLHbD2ej/l/JOFYdgkCvXSYMTwSE/uFNlHERERtl0qfDEmywWr0gzBfetZ4i6ErNN7boHI7AiMEuIoCUcOQtcatzGxF10APvDi+e63Knyoow7QvdmJwJx+seHgopsWF46lfDmB9cm4jR0pERI7RpJdoJrWzlnWCsGnOraJwurFDI2ozZK1xG965PYZ3bl/r8l9vP4HQdi6Yc0MMACCyvTt2phdg0aY0JET7NVaYREQEG5RuyQAu10x6jmMVhSSo3A/DZAy+/DFEdFktqo/b3hOFiIt0rp6/KtoPe0+clSkiIqK2QaHLhEJVAmHVwlrWsVbHOEaXcj43ogbTolZOyC0xwtfNefi5n5sWBqMFFWYrdGpllWOMRiOMRqPjucFgaPQ4iYham/OrJUSjtl8d1pIuEEKC0iUDkqoYwnL5hcGJ6NJaVI1bfSxYsACenp6OR0xMjNwhERG1OLWZBuRiwuoOWwVXUSBqSC0qcfNz0yKvxOi0LbfECHetqtraNgCYPXs2ioqKHI+kpKSmCJWIqNWQVMVQumQCqN3AhAudby7ltCBEDaFFJW69w7ywJSXfadumY3noHeZd4zFarRYeHh6Oh7s7FzwmIqoLlf4oAMBaHlLnReMdy1/pU7iKAlEDkDVxKzVacOh0EQ6dLgJQOd3HodNFyCwsBwC8uvIIZn2/z1F+8sAwnCwow4IVh5GSU4KvtqbjjwNZuGtouBzhExG1CUpHM2ktRpNexGYMgM3sVbmKgj6loUMjanNkHZywP6MIt326zfF8/h+VVek39QnBm7fEIqfY6EjiACC0nSs+n9of85YnYfHmdAR46vDKjT04FQgRUWORLFC5HQNQv8QNkCon4223FSq3w7Ceq4EjovqRNXEbHOGD9FfG1Lj/zVtiqz1mxcPxjRkWERGdo3RNg6QwwWZxh60iqF7nsJTYEzeuokB0pVpUHzciImpazqNJ6/eVcX4VhWIodJkNGB1R28PEjYiIanR+mav6NJOeI1SwlESdOx9HlxJdCSZuRERULUmTC4UmH0IoYSmNuqJz2UeXcj43oivDxI2IiKrlqG0rDQds2suUvrTzqyhkQlIVNUR4RG0SEzciIqqW6gqmAbmYsLrBVh7qdF4iqjsmbkREVJWiAkrXNAANk7hVnoerKBBdKSZuRERUhUp/DJJkg83oC2H2bZBzOq+iYGqQcxK1NUzciIioioZsJrWzGf1hM3lBUli4igJRPTFxIyKii9igdKtcn7QhEzdAYnMp0RVi4kZERE4UukwoVCUQVi2sZR0b9NyOxM39CABbg56bqC1g4kZERE4czaSlkWjolRGtZZ0grBooVAauokBUD0zciIjIyfn+bY2wILxQwVIafe512FxKVFdM3IiIyEFSFUPpUlkTZi3p3Civcb65lIkbUV0xcSMiIgelvnJQgrU8BMLq3iivYS3pXLmKgi4LkqqwUV6DqLVi4kZERA7nm0kbp7YNsK+i0MHp9Yiodpi4ERHRORao9Mcq/9cY/dsufCU2lxLVCxM3IiICAChd0yApTbBZ3GCrCGrU17IYzq2i4HqcqygQ1QETNyIiAnC+2bJyUELjfj3YTO1hM7WDpDhfy0dEl8fEjYiIAACqRlktoSaS43WUbC4lqjUmbkREBEmTC4U2D0IoYSmNapLXtDeXVtb0cRUFotpg4kZEROebScvCAZuuSV7TWhYOYdVCoSqBQpfRJK9J1NIxcSMioguaSRtvGpBqXvX8KgpsLiWqFSZuRERtncIIpWsagKbq33be+eZSJm5EtdGwqwcTEVGLo9IfgyRZYTP5QJj8mvS1LaX2VRTOQFKdhbB4N+nrU+v1/ZHv8X3y9zhdchoAEOEVgft63of4kHgAwJ0r78Su7F1Ox0yMnojnBj/neJ5VkoV52+Zh55mdcFW7YlzEODzc52GoFPKlT0zciIjaOKV9tQRD09a2AQCseljLw6ByTYfK/QjMZwc3fQzUKvnr/fFIn0cQ5hEGIQR+P/47Hlr7EH684UdEekcCAG6Kugkzes9wHKNTnu/fabVZ8cA/D8DXxRdfjf4KuWW5eGbTM1ApVHi4z8NNfj12bColImrTbBcscyVD4gbAyuZSagTDQofhqpCrEOYRho6eHfFQn4fgqnLF/rz9jjIuKhf4uvg6Hm4aN8e+Lae3ILUoFQviF6BLuy6ID4nHjN4z8N2R72C2muW4JABM3IiI2jSF7jQUqhIIq6ZyRKkM7MtfKV2PAwqjLDFQy2EwGFBcXOx4GI2X/8xYbVb8mfYnyi3liPWLdWz/I/UPxH8Xj3/99i+8s/sdlFvKHfsScxMR5RUFXxdfx7YhQUNQYi5BSmFKw15UHbCplIioDXPUtpVGQa6vBJvJDzaTDxSafKj0x2AxdJclDmoZYmJinJ4///zzmDt3brVlk88mY/KKyTBZTXBVueKd4e8gwisCADC602gE6YPg5+qH5LPJeHv320gvTsc7w98BAOSV58HHxcfpfPbneeV5DXtRdcDEjYioDZO7mbSSBIuhCzQ+m6FyO8zEjS4pKSkJwcHBjudarbbGsuEe4fhp7E8wmA1Ylb4KczbNweJRixHhFYGJ0RMd5aK9o+Hn4oe7/74bp4pPIdQjtFGv4UqwqZSIqI2SlAYoXSonvrXKmrhd0FzqdhRcRYEuxd3dHR4eHo7HpRI3tVKNDh4d0M2nGx7p+wii20Xj68NfV1u2h28PAMBJw0kAgK+LL/LL853K2J9f2Hza1Ji4ERG1Ucpzk+5ay4MhrO6yxlK5ioKOqyhQoxJCwGQ1Vbvv6NnKnwd7UhbrF4tjhceckretWVvhpnZzNLfKgYkbEVEb1TyaSe2UsJRwFQVqOO/sfge7zuxCZkkmks8m453d72DnmZ0Y02kMThWfwseJH+NQ/iFklmRi7cm1eHrj0+jr3xed21WuHjIkaAg6eXbC05uextGCo9icuRnv730fk7pMgkapke262MeNiKhNskClP1b5v2aRuFU2l6o990Pldhim3JFyh0MtXEFFAZ7Z9Axyy3PhrnFHlHcUPh7xMYYEDcGZ0jPYlrUNXx/+GuXmcgToAzAibATu6XmP43ilQokPrvkA87bNw+QVk+GicsG4iHGY3mu6jFfFxI2IqE1SuqZDUhphs7jBVhF8+QOagKWkM4RQcBUFahAvxr1Y474AfQC+GPXFZc8R5BaEj679qAGjunJsKiUiaoPszaTWks5oNl8FNldYy8IAsLmUqCbN5KeViIiaUvPq33aefXQpV1Egqh4TNyKiNkZS50GhzYMQinMT7zYf51dRSOUqCkTVYOJGRNTGOJpJy8IBm+4ypZuWMPnBZvSFpLBCpU+WOxyiZoeJGxFRG9Ncm0nt7HGxuZSoKiZuRERticIIpT4NQHNO3LiKAlFNmLgREbUhKv0xSJIVNpMPhEm+ZXsuxVrW8dwqCqVQuJySOxyiZoWJGxFRG6J0aiaV5A2mRkpYSipnr2dzKZEzJm5ERG2GDapz65M212ZSO04LQlQ9Jm5ERG2EQncaCpUBwqapHFHajFlKos+topANSV0gdzhEzQYTNyKiNsIxmrQ0EhDNfMVDmyusZR0BsNaN6EJM3IiI2gh7M6m1mTeT2rG5lKgqJm5ERG2ApDRAocsA0Pz7t9lZDOemBdGnAYoKmaMhah6YuBERtQFKt6OQJAFreTCExUPucGpFmH1hNfpBkriKApEdEzciojbg/GjSzjJHUjdWNpcSOWHiRkTU6p2vsWopzaR2FkNlvFxFgagSEzciolZO6ZoOSWmEzaKHrSJE7nDqxFoeBmF1gUJVBqXLSbnDIZIdEzciolbO3sxoLemMlvdr//wqCko2lxK1uJ9gIiKqI2ULWS2hJvbRpSp3Jm5ETNyIiFoxSZ0PpTYXQihgKY2WO5x6sZR2rlxFQZsDSZ0vdzhEsmLiRkTUitlXS7CWdQRsOnmDqS+bzrFEF0eXUlsn+5onS7am45P1qcgtMaJroAdeGNcNvUK9aiy/aFMavtl2ApmF5Win1+D67oF4YlRn6NTKpguaiKiFcCxz1UKbSe0shq5Q6Y9D5X4Y5rND5Q6HSDay1rgtSzyN+csP4+Fro/DHg0MRE+iOOxZtR16Jsdryv+3LxKsrj+Dha6OwelYCXr2pJ5bvP43X/zraxJETEbUAkhFK11QALWeZq5rYE0+lK1dRoLZN1sTts01pmDQgFLf0C0WUvztemtADLholfth1qtryu0+cRb8wb4zvFYzQdq64KtoP42KDkHiqsGkDJyJqAVT6FEgKK2ymdrCZ/OQO54qcX0XBBpWef6xT2yVb4may2HAwswhxkb7ng1FIiIv0xZ4ThdUe0zfMGwcyi7DvXKJ2Mr8Ma4/mYHiX9k0QMRFRy6J0aiaV5A2mAThWUeDoUmrDZOvjdrbMBKtNwNdN67Tdz02L47ml1R4zvlcwCkpNmPjxFggBWGwC/zewA6YPj6zxdYxGI4zG802vBoOhYS6AiKhZE62mf5udxdAVGp8N55bvsgJg32Zqe1rUqNKtx/PxwdrjmDe+O5Y/NBQfT+6LtUdy8N9/jtV4zIIFC+Dp6el4xMTENGHERETyUOhOQ6E2QNg0sJZ1kjucBmEt7wBhcYWkLIfS5YTc4RDJQrbEzdtVA6VCqjIQIbfECL+LauHs3lp1FDf2CcakAR3QJcADo7oH4PFRnfHhuhTYbKLaY2bPno2ioiLHIykpqcGvhYiouXHUtpVGAkL2CQQaiBKW0spVFFTuR2SOhUgesiVuGpUC3YM9sSUlz7HNZhPYkpKPPmFe1R5TbrZCuqibhuLchurTNkCr1cLDw8PxcHd3b4DoiYiaN8f8ba2kmdTOvooCl7+itkrWP8PuHhqOR39MRI8QL/QK9cSiTekoM1kwsW8oAGDW9/vg76nDk6Mqf/Fc08UfizaloVuQJ3qHeiE9vxRvrUrGNV39oVS0/I63REQNQVKWQKHLAADHOp+thaU0+twqCrmQ1HkQZt/LH0TUisiauI2NDUJBqQlvr0pGrsGIrkEe+HLaAPi5VzaVZhaWQ7qgiu3BqyMhScCbfx/FmaIK+Og1uKarPx4b2bp+MRERXQml21FIkoC1IgjC4il3OA3LpoO1rBNU+pTKyXgL4uWOiKhJyd7xYcqQjpgypGO1+76/d7DTc5VSgUeujcYj17bM9faIiJpCaxtNejGLoUtl4ubGxI3anhY1qpSIiC7HCpU+GUBlgtMaWc7N56Z0TQcUZfIGQ9TEmLgREbUiStd0SEojbBY9bBUhcofTKITZB1Zj+8pVFNyS5Q6HqEkxcSMiakUqJ6cFrCWd0Zp/xdtHl6o4upTamNb7U01E1AbZp8lorf3b7BzLXzlWUSBqG5i4ERG1EpI6H0ptLoRQwFIaJXc4jcpa3gE2ix6SsqKyrxtRG8HEjYiolXBMulsWBthcZI6msSnONQezuZTaFiZuRESthL1/m33UZWtncTSXcvkrajtkn8eNiIgagGSE0vU4gNa3zFVNLKVREEIJhTYPkiYXwuQnd0jUjHx/5Ht8n/w9TpecBgBEeEXgvp73IT6kcu4/o9WI13e+jpXpK2GymhAXFIdnBj0DX5fzq3FklWRh3rZ52HlmJ1zVrhgXMQ4P93kYKoV86RNr3IiIWgGVPgWSwgqbqR1sbSWBselgLe0EgM2lVJW/3h+P9HkE39/wPb4b8x0GBgzEQ2sfQsrZFADAaztew/qM9Xgz4U0sHrUYOeU5mLl2puN4q82KB/55AGabGV+N/grz4+bjt5Tf8MG+D+S6JABM3IiIWgWlo5m0M4C2s3azffQsEze62LDQYbgq5CqEeYSho2dHPNTnIbiqXLE/bz8MJgN+SfkFj/d7HAMDB6KbTzfMi5uHfbn7kJibCADYcnoLUotSsSB+Abq064L4kHjM6D0D3x35DmarWbbrYuJGRNTiiQuWuWob/dvszq+icIKrKLQRBoMBxcXFjofRaLzsMVabFX+m/YlySzli/WKRlJ8Ei82CQUGDHGU6eXZCoD4QiTmViVtibiKivKKcmk6HBA1BibkEKYUpDX9htcTEjYiohVNoT0OhLoawqWEtC5c7nCYlzO1grfDnKgptSExMDDw9PR2PBQsW1Fg2+WwyBnwzAH2/7ot5W+fhneHvIMIrAnnleVAr1PDQeDiV99H5IK8iDwCQV54HHxcf5/3nnueV5zXwVdUeBycQEbVwjtq20khAqGWOpulZSrpCqcuGyu0wLMW95A6HGllSUhKCg4Mdz7VabY1lwz3C8dPYn2AwG7AqfRXmbJqDxaMWN0WYjYaJGxFRC3d+mau2MZr0YpaSrtD6rrtgFQWl3CFRI3J3d4eHh8flCwJQK9Xo4NEBANDNpxsO5h/E14e/xqiOo2C2mVFsKnaqdcuvyIevrrJp1NfFFwfzDjqdL78837FPLmwqJSJqwSRlCRQupwC0/mWuamIrD+UqClQrQgiYrCbE+MRApVBhe9Z2x760ojRklWYhtn0sACDWLxbHCo85kjUA2Jq1FW5qN0R4RTR57HascSMiasGU+mRIkoC1IhDC4il3ODJRwFrSBQqv3VC5HYa1TL4vVWo+3tn9DoYGD0WgWyBKzaVYkboCO8/sxMcjPoa7xh03Rt6I13e+Dk+NJ/QaPRZsX4BYv1jE+lUmbkOChqCTZyc8velpzOo7C3nleXh/7/uY1GUSNEqNbNfFxI2IqAVTudtHk7bN2jY7S0lXqL12Q+V+GMacMWhLU6JQ9QoqCvDMpmeQW54Ld407oryj8PGIjzEkaAgA4IkBT0DaKWHmupkw28wYEjQEcwbNcRyvVCjxwTUfYN62eZi8YjJcVC4YFzEO03tNl+uSADBxIyJqwaxQ6StHUjJxi4KwKaHQ5EOhyYXN1F7ukEhmL8a9eMn9WqUWcwbNcUrWLhbkFoSPrv2ooUO7IuzjRkTUQildT0BSVsBm0cNWHip3OPISWkcTqZKT8VIrxsSNiKiFsk8DYi2JBn+dX7DovDsTN2q9+JNORNRCKd3Yv+1CFkPlfVC6nACUpTJHQ9Q4mLgREbVAkroASm0OhFDAUhotdzjNgrB4w1oRAEkSjr5/RK0NEzciohbI0UxaFgbYXGSOpvlgcym1dkzciIhaIBWbSatlMZxL3PRHAVjkDYaoETBxIyJqaSQTlK6pANruMlc1sVWEwGZxg6Q0chUFapWYuBERtTBKfQokhQU2kzfnK6tC4aiFZHMptUZM3IiIWhjnZlKuEHAxq7251O0whBAyR0PUsJi4ERG1IEIIqNyOAmD/tppYSqMgbCooNAVILUqVOxyiBsXEjYioBUk+mwyFugjCpoa1rJPc4TRPQuNYRWHdqXWyhkLU0Ji4ERG1IOsz1gMArKWRgFDLHE3zZR9dar9fRK1FnRO3g5lFOHKm2PH870Nn8J8lu/DayiMwWWwNGhwRETnbkLEBAJtJL8d+fxJzE3G24qzM0RA1nDonbk//egBpuZVLiZzML8OD/9sLF7USKw5kYcGfHMFDRNRYzlacxf7c/QAAS0lnmaNp3oTFC9aKQNiEDRszN8odDlGDqXPilpZbipggDwDAHweyMCC8Hf57W2+8MTEWKw+eafAAiYio0qbMTRAQsFYEQli85A6n2bM3l7KfG7UmdU7cBADbudHVm1PyMLxz5RxCgV4uKCg1NWRsRER0ATaT1o19+astp7fAbDXLHA1Rw6hz4tYj2BPvrTmGX/ZkYHtaPq7uUpm4nSoog6+btsEDJCIiwGKzYPPpzZX/Z+JWK7aKYPi6+KLUXIqd2TvlDoeoQdQ5cXtubAwOZRbj+d8OYfrwSHT01QMA/jyQhb5h3g0eIBERAfty9sFgMsBL6wVbeajc4bQQCiSEJAAA1p/i6FJqHVR1PaBroAf+mnlVle2zR3eFUsEZvImIGsOGzMpm0rjgOHy3jzM51VZCSAJ+PvYz1mesx1MDnoIk8XuKWrYr+ukvNVpgqDDDUGGG2WpDhdnaUHEREdEFNmZUjoy01yBR7QwKGgStUovMkkykFKbIHQ7RFatzjdupgjI899tBbEstgNFyPlETqFwxL3XBmAYMj4iI7EmHUlJiSNAQAJvkDqnFcFG5YGDgQGzI2ID1GesR5R0ld0hEV6TOidsj3++DEAKv3dwTvm5asNaZiKhx2UeTxvrFwlPrKXM0LU9CSAI2ZGzAulPrcHePu+UOh+iK1DlxO5xVjGUPDkWEn1tjxENERBexJ25XhVTtX0yXlxCSgHmYh/25+5Ffng8fFx+5QyKqtzr3cesZ4omsworGiIWIiC5SZi7DjqwdANi/rb789f7o2q4rBARXUaAWr841bq/e1BPP/HoQZ4or0NnfHSqlc1tp10CPBguOiKit23FmB0w2E4L0QYjwipA7nBZrWOgwHC44jPWn1mNC5AS5w6E2xGwzI788H+WWcrTTtbvi7g51TtzyS004UVCKx39KdGyTwMEJRESNwd5MGh8Sz6ksrkBCaAI+SvwIW05vgclqgkapkTskasVKzaVYfnw5/kz/EwfzDsJsM0MIAUmS4O/qjyFBQ3Bz9M3o7tu9zueuc+L2xE/70S3QE/+d1JuDE4iIGpEQgv3bGkjXdl3h5+KH3PJc7DyzE3HBcXKHRK3Ul4e+xKcHPkWoWygSQhPwnx7/gZ+rH3RKHYqMRUgpTMHu7N24Z9U96OnbE7MHzkaYR1itz1/nxC3zbDk+u6OfY8UEIiJqHMlnk5Fdlg2dUocBAQPkDqdFU0gKXBVyFX4+9jPWnVrHxI0azaG8Q/hi5BeI9I6sdn8Pvx74V9S/YLKasDRlKfZk76lT4lbnwQlDInxwOKu4rocREVEd2WvbBgYOhE6lkzmalm9Y6DAAwPqM9RBCyBsMtVqvJbxWY9J2IY1Sg1s634J/Rf2rTuevc43bNV39MW95Eo6cMaBLgDtUSufcb0SMf11PSURE1WAzacMaGDgQWqUWWaVZSD6bjM7tOssdElGd1Tlxe2bpAQDAf9ccq7KPgxOIiBpGYUUh9uftBwDEB8fLHE3r4KJywaDAQVifsR7rM9YzcaNGtSNrBw4XHEZPv57o3b43fjj6Az498CmMFiOu7nA1nhrwVL1q0uucuKUxMSMianSbTm+CTdgQ5R2FQLdAucNpNRJCEyoTt1PrcU/Pe+QOh1qpn5J/wvxt8xHsFoz/7vkv7u91PxbuX4ixncZCkiT8kfoHPLWemNl3Zp3PXefEjYiIGp+9mZST7jYs+/08kHcAeeV58HXxlTkiao2+OfwNHu//OP6v6/9hU+YmPPjPg5g7ZC7GR44HAPQP6I9397zbdIlbmcmC7akFyCwsh9lqc9p3Z1x4fU5JRETnWGwWbMqsXEie/dsaVnvX9ojxiUFSfhI2Zmysc8dwotrIMGRgeOhwAMDQ4KGABPTw7eHY39O3J86UnqnXueucuB3MLMKdX+xEhcmKMrMVXi5qFJSZ4KJWwsdNw8SNiOgKJeYmwmAywFPriZ6+PeUOp9UZFjIMSflJWHdqHRM3ahRGqxFapdbxXKPQQK1UO56rlWpYhbVe567zdCDzlifh2q7tkfj8ddCpFPj1gThsfvJqdA/2xDOju9YrCCIiOs/eTBoXFAelQilzNK1PQmhlc+nWrK0wWo0yR0OtkSRJKDOXocRUAoPJ4PS8xFSCUnNpvc9d5xq3pKxivHxjDygUEhQKCSarFR183DH7+i549MdEjOrOTrRERFeC/dsaV9d2XdHetT1yynKwI2sH4kM4apcalhACNyy9wen5LctvcXpe3yXs6py4qZUKKM69mK+bFpmFFYhs7w53nRpZhRV1DmDJ1nR8sj4VuSVGdA30wAvjuqFXqFeN5YvKzXjjr6NYeegMisrMCPZ2wXM3xGB4l/Z1fm0ioubmdMlppBSmQCEpOLt/I5EkCQkhCfgx+Uesz1jPxI0a3KKRixrt3HVO3LoFeWB/RiHCffUYGN4Ob61KxtlSE37Zm4noAPc6nWtZ4mnMX34Y8//VHb1DvfD55jTcsWg71jw2DL5u2irlTRYbbl+0HT56DT76vz7w99Ahs7AcHjp1NWcnImp57LVtvfx6wVPrKXM0rdew0GGOxO0Z8Uy9az+o+frswGdYfWI10orSoFPpEOsXi5l9ZyLc83xf/DtX3old2bucjpsYPRHPDX7O8TyrJAvzts3DzjM74ap2xbiIcXi4z8NQKWpOofoH9G/4Czqnzonb4yM7o8RoAQA8NrIzZv2QiDlLD6Kjryteuym2Tuf6bFMaJg0IxS39QgEAL03ogTVHcvDDrlN4YFjV5SJ+2HUKhWVm/Hz/EKjPrdgQ2s61rpdARNRs2RM31gI1rgEBA6BT6nCm9AyOnj2KLu26yB0SNbBdZ3ZhUpdJ6O7THVZhxbt73sW9q+7F0vFL4ao+nzvcFHUTZvSe4XiuU56fFNdqs+KBfx6Ar4svvhr9FXLLcvHMpmegUqjwcJ+Hm/R67OqUuAkh4OOmRbR/Zc2ar5sWS6bVb+Fjk8WGg5lFeGBYhGObQiEhLtIXe04UVnvM6sPZ6NPBC8/9dhCrkrLRTq/B+F7BuC8hAkoF/1oiopat3FKOHWd2AGD/tsamU+kwKGgQ1p1ah3Wn1jFxa4U+HvGx0/P5Q+cj4fsEJOUnoV9AP8d2F5VLjfP5bTm9BalFqfj0uk/h6+KLLu26YEbvGXh799t4IPYBp5GiF4pdUruKrMQ7Emt5NefVMXEDhr2+Fn/PTEC4r77OL3ahs2UmWG2iSpOon5sWx3OrH21xsqAMW86WY0KvICyeOgDp+aV49reDMFtteOTa6GqPMRqNMBrPjxoyGAxXFDcRUWPZeWYnjFYjAvWBiPS6/CLVdGWGhQzDulPrsP7UetwXe5/c4VAjKzGVAECVLgh/pP6B5anL4evii4SQBNwbey9cVC4AKqfmifKKckrshgQNwbxt85BSmIKuPtXPpiGEQJBbEMZFjGvwPwrqlLgpFBI6+uhxtsyEcFxZ4lYfQgC+eg0W3NgTSoWEHiGeyC6uwCcbUmtM3BYsWIAXXnihiSMlIqq79afWA6icdJd9rhqffXLjg/kHuYpCC2IwGFBcXOx4rtVqodVW7Rd/IZuw4dWdr6J3+96I8o5ybB/daTSC9EHwc/VD8tlkvL37baQXp+Od4e8AAPLK8+Dj4uN0LvvzvPK8Gl/vf2P+h1+O/YKvD3+NELcQTIicgDGdxjRIv9U6z+P25KguWLDiMI6eubKaK29XDZQKCXklznPo5JYY4VfNwAQA8HPXItxP79QsGtHeDbkGI0wWW7XHzJ49G0VFRY5HUlLSFcVNRNQYhBDYkFnZv42rJTQNP1c/dPfpDuB830Jq/mJiYuDp6el4LFiw4LLHvLTtJaScTcFrV73mtH1i9ETEBcch2jsaN3S6AS8PfRn/nPwHp4pPXVGM3Xy74dnBz2LtLWtxe8ztWHNyDUb8NAKPr38cW05vuaJz13lwwqwf9qHCbMP1726AWqmATu08OWTi89fV6jwalQLdgz2xJSUPI7sFAABsNoEtKfm4Y0hYtcf0C/PGb/tOw2YTUJxL3tJyS9HeXQuNqvoc9OJM/MIsnYiouThWeAxnSs9Aq9Q26og0cpYQmoCD+Qex7tQ63Bh1o9zhUC0kJSUhODjY8fxytW0vbXsJ6zPW44tRXyBAH3DJsvZlqU4aTiLUIxS+Lr44mHfQqUx+eT4A1KqGVqvUYmzEWIyNGIsMQwae3/I87l99PzbcuqHetW91TtyeG9utXi9UnbuHhuPRHxPRI8QLvUI9sWhTOspMFkzsWznKdNb3++DvqcOToyrbhycPCsOSrSfwwrJDmDKkI9LzS/HhuhRMHdKxwWIiIpKDvcZnYOBAR/8aanzDQofhg30fYFvWtirLFFHz5O7uDg8Pj8uWE0Lg5e0vY83JNfh81OcIcQ+57DFHzx4FcD4pi/WLxacHPkV+eb6jiXRr1la4qd0Q4RVR43kudKb0DH5L+Q2/Hf8NFZYKTO02FXp1/bub1Tlxu7nv5S+8tsbGBqGg1IS3VyUj12BE1yAPfDltAPzcK39wMgvLnfp5BHm54MtpAzBveRJGvbsRAR463BkXjvsSanfziIiaK3vidlUwm0mbUmfvzgjQB+BM6Rlsz9rOZupW5KXtL2FF6gq8e/W70Kv1jj5pbmo36FQ6nCo+hT/S/kB8SDy8tF5ILkjGaztfQ1//vujcrjOAyoEInTw74elNT2NW31nIK8/D+3vfx6Quk6BRamp8bbPVjH9O/oNfjv2CPTl7MDR4KJ7s/ySGBg+94mXs6py4XajCbIXZ6ty3zL2Ok+FOGdIRU2qoMfv+3sFVtvUN88bS6ZxNnIhaj8KKQiTmVk4LwMShadlXUfj+6PdYf2o9738r8v3R7wEA0/6a5rR9Xtw8TIicALVSjW1Z2/D14a9Rbi5HgD4AI8JG4J6e9zjKKhVKfHDNB5i3bR4mr5gMF5ULxkWMw/Re0y/52sN/HA69So9xkeMwZ9ActNO1A1A55c+F3DRudb6uOiduZSYLXvnzCP7Yn4WzZaYq+1MXjKlzEEREbdnm05thEzZEekUi0I3rPTc1R+KWsR5zxByO6G0lDkw5cMn9AfoAfDHqi8ueJ8gtCB9d+1GdXrvYWIxiYzE+SfwEC/cvrLLfvlZpo8/jBgALVhzB1tR8zJ/QHTN/2IcXx3dHdlEFvt1x0tEXjYiIao+LystrQOAAuKhckF2WjSMFR2qcm4uotprVWqX/HM7Gm7f0wuAIHzz+034M6NgOHX31CPZ2wdJ9mZjQO/jyJyEiIgCAxWbBpsxNANhMKhetUovBgYOx5tQarMtYx8SNrlhjjgyv8zxuheVmdPCpXOPLTatCYbkZANC/YzvsSCto2OiIiFq5/bn7UWwqhofGAz39esodTps1LHQYgPOTIBPVV5m5rFHL1zlx69DOFacKKl8kor0ef+w/DaByHVGPOg5MICJq6+zNpHHBcVAprmi8GF2B+JB4SJBwKP8Qcspy5A6HWrAxv47BZwc+Q25Zbo1lhBDYcnoL7lt9H7498m2dzl+v6UAOZxVjUCcf3J8Qibu+3Ikvt56AxWrDnDExdT0dEVGbxtUSmgdfF1/08O2B/Xn7sSFjA26OvlnukKiF+nzk5/jvnv/io30foXO7zojxiUF71/bQKrUoMhYhtSgVibmJUEpK3N3jbkyMnlin89c5cbs7vpPj/0OjfPHPowk4mFmEMB89ugZefkI8IiKqlFWShWNnj0EhKTA0aKjc4bR5CaEJ2J+3H+tPrWfiRvUW7hmOt4e/jaySLPx94m/szt6NxNxEVFgq4K3zRpd2XTB38Nx6z+lW68TNZhP4ZEMqVh/Ohtlqw5AIXzxybRRCvF0R4u1a5xcmImrr7M2ksX6x8NJ5yRsMISEkAe/tfQ/bsrahwlIBnUond0jUggW6BWJKtymY0m1Kg5631n3c3l+bgtf/OgJXjRL+Hjos3pyGZ5cevPyBRERULTaTNi/R3tEI1AeiwlqB7Vnb5Q6HqFq1Ttx+2ZOBeRO646u7BuLTO/ph0ZT+jgXfiYiobiosFdiRtQMAEB8cL3M0BJxfRQEA1mWskzcYohrUOnE7XViB4Z3bO54PjfIFJCDbUNEogRERtWY7zuxAhbUCAfoARHtHyx0OnWOfFmTDqQ0QghUT1PzUOnGz2GzQqpyLqxUSLFZ+sImI6urCReW5xFLz0T+gP1xVrsgpz0FSQZLc4RBVUevBCQLAYz8mQnNB8ma02PD0rwfgqjk/KuKT2/s1aIBERK2NEOJ84sb+bc2KRqnBkKAhWH1yNdafWo9uPt3kDonISa1r3G7qEwIfNy3cdWrHY0LvYPh76Jy2ERHRpaUUpiCrNAtapRYDAgfIHQ5dJCH0XD+3U+tkjYNah93Zu/HUxqfwfyv+D9ml2QCAZceXYU/2nnqdr9Y1bm9MjK3XCxARkTN7bduAgMrFzal5iQ+uXEXhcMFhZJdmw1/vL3dI1EKtOrEKT298GmM6jcGR/CMw2UwAAIPJgE8PfIqP/D+q8znrvOQVERFdGTaTNm8+Lj6OdWPXZ3DtUqq/hfsX4tnBz2LukLlOS9r1bt8bh/MP1+ucTNyIiJpQkbEI+3L3AWDi1pw5Fp1n4kZXIL0oHX39+1bZ7qZxg8FkqNc5mbgRETWhzZmbYRM2RHpFIsgtSO5wqAb2+dy2Z21HuaVc5miopfJx8cHJ4pNVtu/N2YsQ95B6nZOJGxFRE7KvlhAfwkl3m7NIr0gEuwXDaDVi2+ltcodDLdTN0Tfj1R2vYn/ufkiShNyyXCxPXY43d72JWzrfUq9z1nmReSIiqh+rzYpNmZsAnK/RoebJvorCt0e+xfqM9RjeYbjcIVELdFf3u2ATNtz9992osFRg6sqp0Cg1mNJtCv6v6//V65y1StxWJWXX+oQjYjj6hoioOvvz9qPIWAQPjQdi/ThSv7lLCD2fuNmEDQqJjVRUN5Ik4Z6e9+DObnfipOEkysxliPCKgKvatd7nrFXids9Xu2oXIIDUBWPqHQwRUWtmH00aFxTnNMKMmqf+/v2hV+uRV56HpPwkdPftLndI1ML8fvx3dPfpjk5enRDhFeHYbrQa8Vf6XxgXMa7O56zVb440JmNERFfMnrixf1vLoFaqMSRoCFadWIV1p9YxcaM6m7NpDlxULpg/dD5GhI1wbDeYDHh287P1StxY70tE1ATOlJ5B8tlkKCQFhgYPlTscqiVOC0JX6oFeD+DpjU/jw30fNsj56lVXX2ayYHtqATILy2G22pz23RkX3iCBERG1Jvbatp6+PeGt85Y5Gqqt+OB4KCQFjhQcwZnSMwjQB8gdErUwN3S6Ab3a98Ijax9BSmEKXh768hWdr86J28HMItz5xU5UmKwoM1vh5aJGQZkJLmolfNw0TNyIiKrB1RJaJm+dN2L9YrE3Zy/Wn1qPW7vcKndI1IJIkgQAiPWLxbejv8WDax7E7X/ejmcHPVvvc9a5qXTe8iRc27U9Ep+/DjqVAr8+EIfNT16N7sGeeGZ013oHQkTUWlVYKrA9azsAJm4tkX3qlnUZ6+QNhFocIYTj/4Fugfhq9FcI0gfhnlX31PucdU7ckrKKcXd8JygUEhQKCSarFUFeLph9fRe89tfRegdCRNRa7TyzExXWCvi7+iPaO1rucKiO7P3cdmTtQJm5TN5gqEW5P/Z+p6k/XFQuePfqd3F7zO3VLoVVG3VO3NRKBRTnqv583bTILKwAALjr1Mg6938iIjrvwmZSe9MJtRydPDshxC0EJpsJW7O2yh0OtSD397ofLiqXKtun95qOz0d+Xq9z1rmPW7cgD+zPKES4rx4Dw9vhrVXJOFtqwi97MxEd4F6vIIiIWishBPu3tXCSJGFY6DB8ffhrrD+1Htd0uEbukKgZW3tyLYaGDIVaocbak2trLGf/XNVVnRO3x0d2RonRAgB4bGRnzPohEXOWHkRHX1e8elPPOgdARNSaHS88jtOlp6FRaDAgYIDc4VA9JYQm4OvDX2NDxgauokCX9PDah7H2lrXwcfHBw2sfrrGcJElIvCOxzuevc+LWM8TL8X9fNy2WTOMvIiKimtgXle8f2P+KlrkhefVt3xduajfkV+TjYN5B9PRjRQVVb/+U/dX+v6HU+U+G2xZuQ1G5ucp2Q4UZty3c1iBBERG1FvZmUi4q37LZV1EAgHWn1skaC7VtdU7ctqXlV5l0FwCMFht2phc0SFBERK1BkbEI+3L2AWD/ttaAqyhQbe3L2Yf1p5w/J78f/x2jfh6FhO8TMHfLXJispnqdu9ZNpYezih3/P5ZdglyD0fHcahNYn5wLfw9dvYIgImqNtpzeAquwIsIzAsFuwXKHQ1fIvopC8tlknC45jSC3ILlDombq4/0fo79/fySEVta0J59NxvObn8f4yPEI9wzHF4e+QHvX9nig1wN1PnetE7fR/90ICYAE4N+fVW0S1amUeGFctzoHQETUWnE0aevipfNCL79e2JOzB+sz1uO2LrfJHRI1U0cLjuLBXg86nq9MW4kefj0wd8hcAECAPgAf7vuwcRO3jU8MhxDAVa+vxW/T49BOr3Hs0ygV8HHTQqng/ERERABgtVmxKXMTACZurUlCaEJl4naKiRvVrNhYDB8XH8fzXdm7MDR4qON5d9/uOFN6pl7nrnUftxBvV4S2c0XagjHoGeKFEG9Xx6O9h45JGxHRBQ7kHUChsRDuGnf0at9L7nCogQwLGQYA2HFmB0rNpfIGQ82Wj4sPMgwZAACz1YzD+YedRiKXmkuhUtR5Yg8A9ZgOBABO5Jfi801pSMktAQBEtXfHnXEdEeajr1cQREStjb2ZNC4ort6/oKn5CfcMR6h7KE4ZTmHr6a24NuxauUOiZig+OB7v7HkHM/vOxJqTa6BT6dC3/fklrpLPJiPUPbRe567zqNL1ybkY8dYG7MsoQpcAD3QJ8MDeU4UY8fYGbDyWW68giIhaG/Zva50kSTq/6PypdbLGQs3XjN4zoJSUuHPlnfj52M+YO3gu1Eq1Y//SY0sd08vUVZ3/DHz1zyOYNjQcT13fxWn7K38ewSt/HkF8lF+9AiEiai3OlJ7B0bNHIUFCXHCc3OFQA7Mvf7UxcyOsNiuUCqXcIVE1PjvwGVafWI20ojToVDrE+sViZt+ZCPcMd5QxWo14fefrWJm+EiarCXFBcXhm0DPwdfF1lMkqycK8bfOw88xOuKpdMS5iHB7u8/Ala9K9dd748vovYTAZ4KpyrfIZeXPYm3BV1W9C7jrXuKXkluDW/lWr927pF4JjOSX1CoKIqDXZmLkRANDTryfa6drJHA01tD7+feCudkdBRQEO5B2QOxyqwa4zuzCpyyR8M/obLByxEBabBfeuuhdl5jJHmdd2vIb1GevxZsKbWDxqMXLKczBz7UzHfqvNigf+eQBmmxlfjf4K8+Pm47eU3/DBvg9qFYO7xr3axN5T6+lUA1cXdU7cfPQaJJ0urrI9KasYvheMNCUiaqs2nGIzaWumVqgdNamcjLf5+njEx5gQOQGR3pHo3K4z5g+dj6zSLCTlJwEADCYDfkn5BY/3exwDAweim083zIubh325+5CYW7mG6JbTW5BalIoF8QvQpV0XxIfEY0bvGfjuyHcwW6uuItUUap24vbv6GMpNVkzq3wGzf9mPj9Ydx460AuxIK8CH61Lw9C8HMGlAh8aMlYio2TNajdh+ZjsAJm6tmX1iVSZuTc9gMKC4uNjxMBqNlz8IQImpslXQU+sJAEjKT4LFZsGgoEGOMp08OyFQH4jEnMrELTE3EVFeUU5Np0OChqDEXIKUwpSGuqQ6qXUft3f/Scb/DeqAh66JhF6rxGcb0/DaX0cAAP7uOjxybTTujOvYWHESEbUIO8/sRLmlHO1d26Ozd2e5w6FGEh8cD6WkxLGzx7iKQhOLiYlxev78889j7ty5lzzGJmx4deer6N2+N6K8owAAeeV5UCvU8NB4OJX10fkgryLPUebC+dgAOJ7nleddyWXUW60TN3HuX0mScHd8J9wd3wklRgsAwE3Loe5ERIDzaFJJ4vyWrZWn1hO92vfC7uzdWHdqHf7d9d9yh9RmJCUlITj4/BJyWq32sse8tO0lpJxNwZfXf9mYoTWJOvVxu/hXkJtWxaSNiOgcIcT5xC2YzaStnX0yXjaXNi13d3d4eHg4HpdL3F7a9hLWZ6zHopGLEKAPcGz3dfGF2WZGscm5335+RT58db6OMvnl+c77zz2/sPm0KdUp6xr+xrrL/gWZ+Px1VxQQEVFLlVqUisySTGgUGgwMHCh3ONTIEkIT8ObuN7HzzE6UmkuhV3MS+uZECIGXt7+MNSfX4PNRnyPEPcRpf4xPDFQKFbZnbceIsBEAgLSiNGSVZiG2fSwAINYvFp8e+BT55fmOJtKtWVvhpnZDhFdE017QOXVK3GaOiIa7rn7DV4mIWjt7bVv/gP5wVddvjiZqOcI9wxHmEYYTxSew5fQWx5c/NQ8vbX8JK1JX4N2r34VerXf0SXNTu0Gn0sFd444bI2/E6ztfh6fGE3qNHgu2L0CsXyxi/SoTtyFBQ9DJsxOe3vQ0ZvWdhbzyPLy/931M6jIJGqU8M2nUKXEbGxsEX7fLtyUTEbVFXC2h7UkIScCSpCVYd2odE7dm5vuj3wMApv01zWn7vLh5mBA5AQDwxIAnIO2UMHPdTJhtZgwJGoI5g+Y4yioVSnxwzQeYt20eJq+YDBeVC8ZFjMP0XtOb7DouVuvEjV1siYhqVmwqxt6cvQCYuLUlw0KHYUnSEmzM4CoKzc2BKZefHFmr1GLOoDlOydrFgtyC8NG1HzVkaFek1oMTxOWLEBG1WVsyt8AqrOjk2alKXxpqvXq17wV3jTvOGs9yFQVqErVO3NIWjGEzKRFRDdhM2japFWoMDR4KgIvOU9Oo85JXRETkzGqzYlPmJgBM3NoiTgtCTYmJGxHRFTqYfxBnjWfhrnZHr/a95A6HmlhccByUkhIphSnIMGTIHQ61cs0icVuyNR1xr6xB9Jw/Mf6Dzdh3qrBWx/2eeBodn/oD/1myq3EDJCK6hPWnKmtahgQPgVrBKZPaGk+tJ/r49wHAWjdqfLInbssST2P+8sN4+Noo/PHgUMQEuuOORduRV3LpRWNPFZTh5T8OY0DHdk0UKRFR9TZmbgTAZtK2LCGkctF59nOjxiZ74vbZpjRMGhCKW/qFIsrfHS9N6AEXjRI/7DpV4zFWm8Aj3+/DzBFRCG3HSS6JSD7Zpdk4UnAEEiRHJ3Vqe4aFDgMA7MrehRJTibzBUKsma+JmsthwMLMIcZHn1/tSKCTERfpiz4nCGo97959j8NFrcGv/Dk0QJRFRzey1bT38eqCdji0AbVWYRxg6enSExWbB5tOb5Q6HWjFZE7ezZSZYbaLKNCN+blrk1tBUujO9AD/sPIVXbupZq9cwGo0oLi52PAwGwxXHTURkx0Xlyc5e62bv80jUGGRvKq2LEqMFM7/fhwU39UA7fe3WCFuwYAE8PT0dj5iYmEaOkojaCqPViG1Z2wCwfxud7+e2MbNyFQWixlCntUobmrerBkqFVGUgQm6JEX7VTPZ7Ir8UGWfLcfeX50eR2kTlmg4RT6/AmkcTEOajdzpm9uzZmDVrluN5ZmYmkzciahC7zuxCuaUc7V3ao0u7LnKHQzLr1b4XPDQeKDQWIjE30THSlKghyZq4aVQKdA/2xJaUPIzsFgAAsNkEtqTk444hYVXKR/i54a9HnP+qfePvoyg1WvD82G4I9HSpcoxWq4VWez4JLC4ubuCrIKK2yt5MGh8SD0niis5tnUqhQnxIPP5I/QPrMtYxcaNGIXtT6d1Dw/G/nafw0+4MpOQY8MzSgygzWTCxbygAYNb3+/DqyiMAAJ1aic4B7k4PD50aeq0KnQPcoVHJfjlE1EYIIRxzdrGZlOwcqyiwnxs1Ellr3ABgbGwQCkpNeHtVMnINRnQN8sCX0wbAz72yliyzsJx/yRJRs5NWlIbMkkyoFWoMChwkdzjUTMQFx0ElqZBalIpTxacQ6hEqd0jUysieuAHAlCEdMWVIx2r3fX/v4Ese++YtsY0QERHRpdmbSfsH9IermvNJUiV3jTv6+vfF9jPbsS5jHW6PuV3ukKiVYdsiEVE9bMg8Nw0Im0npIgmhlaNL2VxKjYGJGxFRHRWbirE3ey8Azt9GVdn7ue3O3g2DiXOHUsNi4kZEVEdbTm+BRVgQ7hnOPkxURahHKDp5doJFWLA5k6soUMNi4kZEVEcbM84tKs/aNqqBvbl0XcY6eQOhVoeJGxFRHdiEDZsyNwFg/zaqmb25dGPGRlhsFnmDoVaFiRsRUR0czDuIgooCuKnd0Nu/t9zhUDMV6xcLL60Xik3F2JezT+5wqBVh4kZEVAf2aUCGBA2BWqGWORpqrpQKJeKD4wHAMVEzUUNg4kZEVAf2xI3NpHQ5jn5up9bJGge1LkzciIhqKacsB4cLDkOChKHBQ+UOh5q5uKA4qBQqpBen40TxCbnDoVaCiRsRUS3ZR5N29+0OHxcfmaOh5s5N44Z+/v0AsNaNGg4TNyKiWmIzKdXVsNBhANjPjRoOEzciolowWU3YmrUVABM3qr2EkMp+bnuy96DIWCRzNNQaMHEjIqqFXWd2odxSDj8XP3Rt11XucKiFCHEPQaRXJKzCylUUqEEwcSMiqgX7ovLxIfGQJEnmaKglsde6cRUFaggquQNobTo+9YfcIbQY6a+MkTsEoloRQrB/G9XbsNBhWHRwETZlboLZZub8f3RFWONGRHQZ6cXpOGU4BbVCjcGBg+UOh1qYHr494K31hsFk4CoKdMWYuBERXYa9tq2ffz+4ql1ljoZaGqVCifiQylUUOC0IXSkmbkREl8FmUrpSnBaEGgoTNyKiSzCYDNiTvQfA+U7mRHVlX9v2RPEJpBWlyR0OtWBM3IiILmHr6a2wCAs6enREqEeo3OFQC6VX69E/oD8AYP0p1rpR/TFxIyK6BHvTFptJ6UpxWhBqCEzciIhqYBM2bMrcBICJG125hNDKxG1fzj6uokD1xsSNiKgGh/IOoaCiAHq1Hn3a95E7HGrhgt2CHasobMzcKHc41EIxcSMiqoF9tYQhQUOgVnLSVLpyjtGl7OdG9cSVE4iIamD/cmUzKTWUhJAEfHbgM2zO3MxVFJrArjO78MWhL5CUn4Tc8ly8M/wdXNPhGsf+ZzY9g9+P/+50TFxQHD4e8bHjeZGxCC9vfxnrM9ZDAQWuDbsWTw14SrY5HZm4ERFVI7csF4cLDgMAhgYPlTkaai16+PZAO107FFQUYE/2HgwMHCh3SK1auaUc0d7R+Ffkv/DIukeqLRMXHIf5cfMdzy9Opp/c+CTyyvKwcMRCWGwWPLv5WczdOhevXfVaY4ZeIzaVEhFVw94HqbtPd/i6+MocDbUWSoUS8cGVqyhwMt7GFx8Sj4f6PIRrwq6psYxGoYGvi6/j4an1dOxLLUzF5szNeGHIC+jp1xN9/Ptg9sDZWJm2EjllOU1xCVUwcSMiqoZjtYRQNpNSw7qwn5sQQt5gWiCDwYDi4mLHw2g0XtH5dp3ZhYTvEzD217GYt3UeCisKHfsScxPhrnFHN99ujm2DAgdBISlwIPfAFb1ufTFxIyK6iMlqwtbTWwGwfxs1PPsqCicNJ5FWzFUU6iomJgaenp6Ox4IFC+p9rqHBQ/HS0Jfw6XWf4pG+j2BX9i7cv/p+WG1WAEBeeR58dD5Ox6gUKnhqPZFXnndF11Ff7ONGRHSRXdm7UGYpg6+LL7q26yp3ONTKuKpdMSBgADaf3oz1p9ajk2cnuUNqUZKSkhAcHOx4rtVq632u68Ovd/w/2jsa0d7RGP3LaOzM3olBgYOuKM7Gwho3IqKLbMyo7N8WHxwPhcRfk9Tw7JPxrju1TtY4WiJ3d3d4eHg4HleSuF0s1D0U3lpvnCw+CQDwdfFFfkW+UxmLzYIiY5FsfV/5G4mI6CKO/m1sJqVGYl/+al/uPqc+VSSvM6VnUGgshJ+LHwAg1i8WBpMBh/IPOcrsyNoBm7Chh18PWWJk4kZEdIH0onScNJyESqHC4KDBcodDrVSQWxCivaNhEzauotCIysxlOFJwBEcKjgAAMg2ZOFJwBFklWSgzl+HNXW8iMTcRmSWZ2Ja1DQ+teQgdPDogLjgOANDJqxPiguPwwpYXcCD3APbm7MXLO17GqPBRaO/aXpZrYh83IqIL2Kdo6OffD3q1XuZoqDVLCElA8tlkrM9Yj7ERY+UOp1U6lH8I0/6a5nj++q7XAQDjIsbh2UHPIvlsMn4//juKTcVo79Ieg4MGY0bvGdAoNY5jXo1/FS9tfwl3/303FFLlBLyzB8xu8muxY+JGRHQBe/82NpNSYxsWOgyfHvi0chUFq5nLqjWC/gH9cWBKzdN2fDLik8uew1PrKdtku9VhUykR0TklphLszt4NgIkbNb7uvt3ho/NBibkEu3N2yx0OtRBM3IiIztmatRUWYUFHj44I8wiTOxxq5RSSwvEHAhedp9pi4kZEdI59NGl8SLzMkVBbceG0IFxFgWqDiRsREQCbsHEaEGpygwMHQ6PQIKMkA6lFqXKHQy0AEzciIgBJ+UkoqCiAXq1H3/Z95Q6H2ghXtSsGBA4AwMl4qXaYuBER4Xwz6ZCgIRzdR01qWMgwAOenoiG6FCZuRES4oH9bMPu3UdOy93NLzE3E2YqzMkdDzR0TNyJq83LLch1L2nBgAjW1AH0AurTrwlUUqFaYuLVqNihdj0PlsQ9K1+MAbHIHRNQsbcrcBADo5tNNtoWjqW2zr13Kfm50OVw5oZVSuR+E1n8ZFOoixzab2RPG7LGwGLrLGBlR88PRpCS3YaHD8Mn+T7Dl9BauokCXxBq3VkjlfhC64K8hqYqctkuqIuiCv4bK/aBMkRE1P2arGVtObwFwvtaDqKnF+MTA18UXpeZS7MzeKXc41Iyxxq3VsUHrvwwAIEnOeyQJEALQ+i+DxRAD5u1EwK7sXSizlMFH54OuPl3lDoeasY5P/dGo59cGhEPjnYc7v/8SxuyWPUgh/ZUxcofQavGbu5VRuqZBoS6qkrTZSRKgUBdB6ZrWtIERNVMXrpagkPgrkeRjKan8w0HldhgAV1Gg6vG3VCsjqQwNWo6otbOP4mP/NpKbtTQSwqaCQnMWCm223OFQM8XErZURFvcGLUfUmp0oPoETxSegUqgwOHCw3OFQWyc0sJZGArDXuhFVxcStlbGWhcNm9sSl1ioWAlC5JQGSqekCI2qG7M2kff37wk3jJnM0RBc3lxJVxcSt1VHAmD0WAKokb0JUPiQJ0Phshr7TO1C6psgQI1HzYF9i6KpgNpNS82Ap6QIAULicgqQskTkaao44qrQVshi6oyJzMrT+yyBdMI+bsFTO4yZsaugCf4FCUwDXsM9gLuyLiuwxgM1VxqipJWrsUXaNSmGEW/QuSBLw/Pc2PGdq3GvhKDuqDWHxhLU8GEqXTCjdjsBS1E/ukKiZYeLWSlkM3WExxEDpmgZJZYCwuMNaFg57JWtp6ixo/f6C2nsr1F67oXQ7CuOZ8ecm561hSCpRK6LSH4MkWWEz+kKY/OQOh8jBUtIFSpdMqNwOM3GjKthU2qopYC2LgKW4F6xlEXB6u21aGLPHoezEfbAa20OhKoFLyDfQhXwFSVUsW8RETUXpdgQAYCnpLHMkRM4c/dz0xwDJInM01Nw0ixq3JVvT8cn6VOSWGNE10AMvjOuGXqFe1Zb9346T+GVPBo6eqZzOokeIJx4f2aXG8nRptvIwlKU9BI3PGmh810HtngSV63EYc0bDXNgfzO2pdbJB5UjcusgcC5EzW0UwbGYPKNTFULqmwloaLXdI1IzI/q28LPE05i8/jIevjcIfDw5FTKA77li0HXklxmrLb0vNx7jYIPzvnkH45YE4BHq64PZF23GmqKKJI29FhAqmvOtQlvYQrOWhkJRG6AJ/hUuHzyCp8+SOjqjBKXSnoVCVQFg157oQEDUnkuMPCo4upYvJnrh9tikNkwaE4pZ+oYjyd8dLE3rARaPED7tOVVv+3Um9cfvgjugW5InI9m549aaeEALYnMIE40rZjAEoS78fFWdugLCpodKnQt/pHWh81gGwyhwdUcNx1LaVRqGZNDwQOXE0l7pzFQVyJmviZrLYcDCzCHGRvo5tCoWEuEhf7DlRWKtzlJutMFtt8HJVN1KUbY0C5rNDUZo6E5aSKEgKC7TtV8I1/AMotJlyB0fUINhMSs1d5SoKaijUhVBoz8gdDjUjsiZuZ8tMsNoEfN20Ttv93LTIraGp9GKv/HkY/h46p+TvQkajEcXFxY6HwcClnmpDmNuh/NQ0lJ+eCGF1gVJ3Gq7hH0Dj9ycgmeUOj6jeJKUBSpcMAICViRs1V0LNVRSoWrI3lV6JD9elYFliFj65vS90amW1ZRYsWABPT0/HIyYmpomjbMkkWIr6ovT4LJiLe0KSbND6roc+/F0oXVPlDo6oXpRuRwEA1vJgCCuXfqPmy7m5lKiSrImbt6sGSoVUZSBCbokRfhfVwl1s4Ybj+GjdcXx11wB0DfSosdzs2bNRVFTkeCQlJTVI7G2JsLqjIvPfKDt1R+VIJ20eXMMWQhvwC6DgoBBqWdhMSi2FYxUFXQYkJVuLqJKsiZtGpUD3YE9suWBggc0msCUlH33CvGo87uP1x/HePyn4ctoA9AypuRwAaLVaeHh4OB7u7vwLu76sJTEoTZ0F09mBAACN9w7oO70FldshmSMjqi1L5dxYYOJGzZ+weMBaHgJJEo55B4lkbyq9e2g4/rfzFH7anYGUHAOeWXoQZSYLJvYNBQDM+n4fXl15/gP70brjeOvvZLx2c0+EeLsgx1CBHEMFSo2cpLBJ2HQwnvkXyk7cA5vRFwp1MVxCv4Iu+Bv+RUjNntI1HZLSCJvFDbaKYLnDIbosx7QgbC6lc2QfBz82NggFpSa8vSoZuQYjugZ54MtpA+DnXtlUmllYDkk6vwTT19tOwGS14f5v9jid5+FrojBzBCcpbCrWsk4oTXsYGt9/oPHZALXHAaj0KajIHgNLUV9w2SxqjuzNpNaSzmgGf7cSXZbF0BVav9XnVlEwA4IzKLR1siduADBlSEdMGdKx2n3f3zvY6fnmp65ugoioVoQaptxRsBT3hC7wZyhdMuES9BMsHvtQceZfEGYfuSMkcsL+bdTS2IxBsJk9oVAXnVtFgUu0tXXNInGjls1mDEJZ+gNQt9sErd8qqNxSoO/0Doy518FcEIemqNno+NQfjf4arUX6K2PkDkEWkjoPCm0ehFCcm3iXqCWoXEVB470dKvfDTNyIbQXUUJQwFySgNPURWEo7QVKYofP/A64dP4RCmyV3cETnm0nLwgGbTuZoiGrPYjg3LYgbV1EgJm7UwITZF+Un/4OKrBshrDooXTLgGv4eNH5/AxIHkJB82ExKLZW1LOLcKgpF/EOYmLhRY5BgLhyA0tRZMBd3Ozdx7xq4hr8LpUu63MFRW6QwQqlPA8DEjVogoXY073N0KTFxo0YjLB6oyLwd5Rn/B5vFHUptLlzCPoHW/zdAUbslzYgagkp/DJJkhc3kA2Gqfnk8oubM6tRcSm0ZEzdqdBZDD5QenwlTYT9IkoCm3VboO72FDRkb5A6N2gilUzMpp6qhlsdeU6x0yYCkKpY5GpITEzdqGjZXGLNuRtmJu2EztYNCXYTp/0zHkxueREFFgdzRUatmg0pfuT4pm0mppRJWd1jLKyemV3EVhTaNiRs1KWtZJEpTH4Ep/yooJAVWpK3A+KXjsez4MgjB0VLU8BS601CoDRA2TeWIUqIWymI4V+vG5tJa23VmF2b8MwNX/3A1enzZA/+c/MdpvxAC7+99H8N/GI5+X/fD3X/fjRPFJ5zKFBmL8OSGJzHo20EY8u0QPLf5OZSZy5ryMpwwcaOmJzQw5ozGt6O/RbR3NAqNhXh609N44J8HcLrktNzRUSvjGE1aGgkITl1JLZel5Fw/N31K5SoKdFnllnJEe0fjmYHPVLv/84Of49vD3+LZQc/im9HfwEXlgntX3Quj9Xw/7Cc3PonjhcexcMRCvH/N+9idvRtzt85toiuoiokbyaabbzd8d8N3eKj3Q9AoNNiUuQkTfpuAbw5/A6vNKnd41Eqo3CqbSe2du4laKpsxEDazJySFGUr9cbnDaRHiQ+LxUJ+HcE3YNVX2CSHw9eGvcU/Pe3B1h6vRuV1nvDz0ZeSW5WLNyTUAgNTCVGzO3IwXhryAnn490ce/D2YPnI2VaSuRU5bT1JcDgIkbyUytUOM/Pf+Dn8b9hD7t+6DcUo5XdryCKSun4HghfzHRlZGUBih0GQAAC2ecpxZPOl/rxubSK5ZRkoG88jwMChrk2OaucUcPvx5IzE0EACTmJsJd445uvt0cZQYFDoJCUuBA7oEmjxlg4kbNRLhnOBaPWow5A+dAr9YjMTcRE5dNxEeJH8FsZZMA1Y/S7SgkScBaHgxh8ZA7HKIrdn4VhSNoq6soGAwGFBcXOx5GY/2ml8ovzwcA+Oic19X20fkgrzwPAJBXnldlv0qhgqfW01GmqTFxo2ZDISlwa5dbsXT8UiSEJMBsM+PDfR/iluW3YH/ufrnDoxbI3kxqKWFtG7UO1rJOEDbNuVUU2maf4JiYGHh6ejoeCxYskDukJsXEjZqdAH0A3rv6Pbx21Wtop2uHlMIUTF4xGa/ueFXWkTzU0lih0icD4DQg1IoIdeVAG7TdVRSSkpJQVFTkeMyePbte5/FxqaxJy6/Id9qeX5EPX5fKibp9XXyr7LfYLCgyFjnKNDUmbtQsSZKE68Ovx2/jf8O4iHEQqOxEeuPvN2JL5ha5w6MWQOmaDklphM2ih60iRO5wiBqMo7nUYx9UHvugdD0OwCZvUE3I3d0dHh4ejodWq63XeULcQuDr4ovtWdsd20pMJTiQewCxfrEAgFi/WBhMBhzKP+QosyNrB2zChh5+Pa7sQuqJY+OpWfPSeeGloS9hdPhovLj1RWSWZOLe1fdiXMQ4PN7vcXjpvOQOkZope+dta0ln8G9Uam2EAJTaPLgEfwcAsJk9YcweC4uhu8yRNS9l5jKcNJx0PM80ZOJIwRF4ajwR6BaIyV0n45P9n6CDewcEuwfj/b3vw8/VD1d3uBoA0MmrE+KC4/DClhfw7KBnYREWvLzjZYwKH4X2ru1luSb+NqMWIS44Dr+O/xWTu06GBAm/H/8d438bj5VpKzlxL1VL6cbVEqj1UbkfhC7w5yrbJVURdMFfQ+V+UIaomq9D+YcwcdlETFw2EQDw+q7XMXHZRLy/730AwLTu0/DvLv/GC1tfwG3Lb0OZpQwfX/sxtMrztXivxr+Kjp4dcfffd+OB1Q+gd/vemDt4rhyXA4A1btSCuKpd8eSAJzEqfBTmbpmLlMIUPL7hcfyR+gck1WAIi6fcIVIzIanzodTmQggFLKXRcodD1EBs0PovAwBIFy25K0mVtXBa/2WwGGLAeplK/QP648CUmqftkCQJM3rPwIzeM2os46n1xGtXvdYY4dUL31lqcWL9YvHDDT/ggdgHoFKosC5jHfSd3oLaaxvaUj8Pqpl9tQRrWUfAppM3GKIGonRNg0JdVCVps5MkQKEugtI1rWkDoybFxI1aJLVSjft73Y8fb/gRPf16QlIaoQtcCpcOn0LS5ModHsnMscwVm0mpFZFUhgYtRy0TEzdq0SK9I7Fk1BJUnBkLYdNApU+DPvxdaHzWAuCyWW2SZITSNRUAYGXiRq2IsLg3aDlqmZi4UYunVChhPhuH0uMzYSmJhqSwQNv+L7iGv+9Y7ojaDpU+BZLCCpupHWwmP7nDIWow1rJw2MyeqGk8lhCVo0utZeFNGxg1KSZu1GoIizfKT92J8sxbYbO4QqnLgmvHD6BtvwKQTHKHR43OBqXrcai9NwOwr5ZQQ2cgohZJAWP2WACokrzZn1fu51d7a8ZRpdTKSLAU94a1NApa/2VQeyZC47MBKvdDqMj6F6xlkXIHSI1A5X4QWv9lUKiLzm/zSIS1LILzWlGrYjF0R0XmZGj9l0G64PMuLJzHra1g4katkrC6oeL0bTAX94IuYCkUmny4hn0GU2E/GLPHADYXuUOkBqJyPwhd8NdVtkvKMuiCv0ZF5mR+mVGrYjF0h8UQA6VrGiSVAcLifq55lDVtbQETN2rVrCVdUZoaDq3fSmjabYPGaxdUbkdhPDOeX+atAue1orZKAWtZhNxBkAyYuFHrZ9PBmD0BluJe0Ab+DKU2Fy4hX8Nc3A3G7PEQFg+5I6RaE5BUxVBoz0ChzYZKf8ypefRikgRI5+a14pccEbUGTNyozbCWd0RZ2kPQ+K6Bxmc91B6HoNIfhzF7DMxF/cCO7M2MogxKbTYU2mwodGeg0J6BUpsNSVle51NxXisiai2YuFHbItQw5Y6EpbgHdIG/QOmSAV3Qz1B57kNF1o0QZh+5I2x7JDMU2hxHYlZZm3YGCnVxtcWFUMBm8oXN6A9h00DjtfuyL8F5rYiotWDiRm2SzRiEsvT7oW63GVq/VVDpj0Pf6R0Yc0fAXBAHQCl3iK2QFZKmAEp7YqbNhlJ7BpImH5JU/cRUNrMXbBUBsBr9YTMGwGb0r5ybTajtJSrnbVNVvwyQEJWj7TivFRG1FkzcqA1TwlxwFSyGbtAF/gKV/jh0/iug9khERdZNsBmD5A6whbqwH9qZykRNlw2FJgeSwlLtETaL6/nEzBgA67n/X36d0cp5rXTBX0MI5wEKnNeKiFojJm7U5gmzD8pP3g2V5y7o/P+A0iUTruHvw5R/FUx511xQu0NVKMrOJ2YX1qIpK6otLmzqc8mZ/7nkLAC2igAIqxvq28eQ81oRUVvCxI0IACDBUtQfpSVdoA34HWqPA9D6roPa/SAqsm6CtbxtN7VVWCpwvOg4Us6m4NjZY0gprPzXvXNOteXP90MLgK3ifC2aMHujMWq/OK8VEbUVTNyILiCs7qjI/D9Yig9C6/8bFNo8uHb8BKazA2HMub4WTXctm8VmwUnDSUdylnI2BccKj+Fk8UkI1NAPzeR1QfPmhf3QmvrXC+e1IqLWj4kbUTUshu6wlEZA2/5PaLx3QOO9HSq3w6g4MwHWkhi5w7tiQghkl2Uj+Wyyo/YspTAFqYWpMNmqX9fVW+uNKO8oRHpFOv6d8M7xVp/MEhE1J0zciGpic4HxzI2wFMdCF/hL5bJZoUtgLuoJY/a4c/2yWgBFGZS6yv5nCu0Z3PHn90g5mwKDufq5zVxULoj0inRK0KK8o+Cj84F08dBNW2YTXAAREdkxcSO6DGtZBEpTH4HGdzU0Phuh9twPlT4FFTljYCnqg2Yzca9kumA+tPOJmkLtnKDtPdctTSWp0NGzI6K8ohDpHen4N9gtGAqJfcOIiJojJm5EtSHUMOVeD4uhJ3SBP0Gpy4JL0I+weOxDxZl/QZjbNWEwVig0+ecnqtWdW1FAXVDzfGgm73P90Pzxzr+uR6R3JMI9wqFWcsQsEVFLwsSNqA5sFcEoS5sBjc9GaHxXQ+V2rHLi3pzrYD47BJWjGG0NNLpRQFIVnUvMzk9aWzkfmrX6+Cx65/nQKgJgM/kDNq2jzOhOo+tz6URE1AwwcSOqMyVM+cNgNnSDLuAXqPRp0AUsh9ozEeaiWGh8NjotfG4z12I+MWWpU/OmfeknSWmstriwaSrnQqs4n6TZjAEtp98dERHVCxM3onoSJj+Un/wP1F47oW2/AkqXU1DoTlUpJ6mKoAv+GhWZk2EpiT6XnGWfT9R0Z6CoYRF0IRSwGf0ciZl96Sdh9gLnKCMianuYuBFdEQXMhQNhKYmGPuLNapd0kqTK5Zd0wd8CsFW7piYA2EztLliT016T5gv+mBIRkR2/EYgagEJTUOM6nIB9DU0bAMBmcTufmDkWUPcHhLbG44mIiAAmbkQNQqqhqfNiFVkTYC4c1MjREBFRa8VOMkQNQFjca1XOZvJr5EiIiKg1Y+JG1ACsZeGwmT0hqp9GDUJUji6tnBqEiIiofpi4ETUIBYzZYwGgSvJmf165nz9yRERUf/wWIWogFkN3VGROhrB4Om0XFs/KqUAuNY8bERFRLXBwAlEDshi6w2KIaaCVE4iIiJwxcSNqcApYyyLkDoKIiFohVgMQERERtRBM3IiIiIhaCCZuRERERC0EEzciIiKiFoKJGxEREVEL0SxGlS7Zmo5P1qcit8SIroEeeGFcN/QK9aqx/B/7s/DmqqPIOFuOcB89nrq+C4Z3ad90ARMRERHJQPYat2WJpzF/+WE8fG0U/nhwKGIC3XHHou3IKzFWW373iQI89N1e3NovFCseGorruvnjnq924eiZ2i3yTURERNRSyZ64fbYpDZMGhOKWfqGI8nfHSxN6wEWjxA+7TlVb/vPN6UiI9sO9CRGIbO+OR6/rjG5Bnvhya3rTBk5ERETUxGRN3EwWGw5mFiEu0texTaGQEBfpiz0nCqs9Zu+Js07lAeCqaD/sOXG2MUMlIiIikp2sfdzOlplgtQn4ummdtvu5aXE8t7TaY3JLjPB101xUXlNj06rRaITReH5fUVERACArK+tKQq+RpTivUc7bGmVkZDTYuXjfa4/3XR687/LgfZdHQ953O/v3ts1ma/BztyTNYnBCY1qwYAFeeOGFKtsHDBggQzR0odCP5I6gbeJ9lwfvuzx43+XRmPc9OzsbHTp0aLwXaOZkTdy8XTVQKqQqtWW5JUb4XVQLZ+fnpkVeiemi8qYqtXZ2s2fPxqxZsxzPLRYLDh8+jNDQUCgUsnfxa3QGgwExMTFISkqCu7u73OG0Gbzv8uB9lwfvuzza2n232WzIzs5G79695Q5FVrImbhqVAt2DPbElJQ8juwUAAGw2gS0p+bhjSFi1x/QO88aWlDzcNTTcsW3TsVz0CfOutrxWq4VW65zUxcXFNdAVNH/FxcUAgODgYHh4eMgcTdvB+y4P3nd58L7Loy3e97Zc02Yne5XT3UPD8b+dp/DT7gyk5BjwzNKDKDNZMLFvKABg1vf78OrKI47y0+I6Yn1yLj7dkIqUnBK8vSoZBzKLMGVwR5mugIiIiKhpyN7HbWxsEApKTXh7VTJyDUZ0DfLAl9MGwM+9spYss7AckiQ5yvcNa4d3J/XGm38fxet/HUVHX1csvL0fOge0/mpiIiIiattkT9wAYMqQjpgypGO1+76/d3CVbWN6BmJMz8BGjqp10Gq1eP7556s0F1Pj4n2XB++7PHjf5cH73jZJQgghdxBEREREdHmy93EjIiIiotph4kZERETUQjBxIyIiImohmLgRUas0d+5c9OrVS+4wmr1169ZBkiQUFhbKHQoR1QITtxbozJkzePDBB9GpUydotVqEhoZi7Nix+OeffwAAHTt2hCRJ2LZtm9NxjzzyCIYNG+Z4PnfuXEiShPvuu8+p3L59+yBJEtLT0xv7UmQ1duxYjBo1qtp9GzduhCRJ2L9/PwDg559/xtVXXw1vb2+4uLigc+fOmDZtGvbu3et0nMlkwuuvv44+ffpAr9fD09MTsbGxmDNnDk6fPu0ot2HDBowdOxZBQUGQJAlLly6tNo7Dhw9j3Lhx8PT0hF6vR//+/XHy5MmGuQH1NHXqVEyYMEHWGC5W3T187LHHHD8TDeHnn3+GUqlEZmZmtfujoqIcq7TMnTsXXbp0gV6vh7e3N6699lps3769wWKpj61bt0KpVGLMmDGN/lrDhg2DJEn47rvvnLa/88476NixY53Odamfj+Zg6tSpkCQJkiRBrVYjPDwcTzzxBCoqKhrk/JIkQafT4cSJE07bJ0yYgKlTp9b6PDUl6PbvgQsfXbp0cSpTUVGB6dOnw8fHB25ubrjpppuQnZ1d30uiK8TErYVJT09H3759sWbNGrz++us4cOAAVq5cieHDh2P69OmOcjqdDk8++eRlz6fT6bBo0SIcO3asMcNulu666y6sWrWq2sWQFy9ejH79+qFnz5548sknceutt6JXr174/fffcfToUXz77bfo1KkTZs+e7TjGaDRixIgRePnllzF16lRs2LABBw4cwH//+1/k5eXhvffec5QtLS1FbGwsPvjggxrjO378OIYOHYouXbpg3bp12L9/P5599lnodLqGvRGtlJubG3x8fBrsfOPGjYOPjw++/PLLKvs2bNiAlJQU3HXXXQCA6OhovP/++zhw4AA2bdqEjh074rrrrkNubm6DxVNXixYtwoMPPogNGzY4/RHRWHQ6HebMmQOz2dzoryW3UaNGISsrC6mpqXj77bfxySef4Pnnn2+w80uShOeee67Bznexbt26ISsry/HYtGmT0/6ZM2di2bJl+PHHH7F+/XqcPn0aN954Y6PFQ5chqEW5/vrrRXBwsCgpKamy7+zZs0IIIcLCwsRDDz0kNBqN+OOPPxz7H374YZGQkOB4/vzzz4vY2FgxYsQIMXHiRMf2vXv3CgAiLS2tsS6jWTCbzcLf31/MmzfPabvBYBBubm7io48+Elu3bhUAxLvvvlvtOWw2m+P/CxYsEAqFQuzZs+eyZS8EQPz6669Vtt96661i8uTJtbyapjNlyhQxfvx4IYQQCQkJ4sEHHxSPP/648Pb2Fv7+/uL55593Kn/27Flxzz33iPbt2wutViu6desmli1b5ti/ceNGMXToUKHT6URISIh48MEHnT7fYWFh4sUXXxSTJk0Srq6uIigoSLz//vtO+wE4HmFhYUKI859vO6vVKl544QURHBwsNBqNiI2NFX/++adjf1pamgAgfv75ZzFs2DDh4uIievbsKbZs2eIoM2vWLBEVFVXtPRk4cGCN96yoqEgAEKtXr77kvW0s9s/0kSNHxK233ipeeuklx761a9cKAI7fH0Jc+j358ssvhV6vF8nJyY7y999/v+jcubMoLS0VQlR+Lu68807h4+MjPvjgA0e5t99+2/H+2C1dulT07t1baLVaER4eLubOnSvMZrMQoub3tjm58OfB7sYbbxS9e/cWQlR+7l5++WXRsWNHodPpRM+ePcWPP/7oKFtQUCD+/e9/C19fX6HT6URkZKT4/PPPHfsBiMcee0woFApx4MABx/bx48eLKVOmOJ5f6nXsn+0LH/ZjL/45uVhhYaFQq9VOMR8+fFgAEFu3bq3r7aIGwMStBcnPzxeSJImXX375kuXCwsLE22+/LR566CHRs2dPYbVahRA1J267d+8WCoVC7Ny5UwjRdhI3IYR4/PHHRUREhFNS9fnnnwsXFxdRWFgoHnroIeHm5ub4IrmUnj17ipEjR9Y5huoSN6vVKtzc3MSLL74orrvuOuHn5ycGDBhQbYLX1C5O3Dw8PMTcuXNFcnKy+PLLL4UkSeLvv/8WQlRex6BBg0S3bt3E33//LY4fPy6WLVsmVqxYIYQQIiUlRej1evH222+L5ORksXnzZtG7d28xdepUx+uFhYUJd3d3sWDBAnH06FHx3//+VyiVSsdr5OTkCABi8eLFIisrS+Tk5Aghqn4hvfXWW8LDw0P873//E0eOHBFPPPGEUKvVjgTE/uXWpUsXsXz5cnH06FFx8803i7CwMMf7f+jQIQFArF+/3nFeg8Eg9Hq9WLhwYbX3y2g0itdff114enqK3NzcBngH6m7RokWiX79+Qgghli1b5vSZvzhxq817MnHiRNG/f39hNpvF8uXLhVqtFrt27XLsT0hIEA8//LB46623hL+/vyPpuzhx27Bhg/Dw8BBffPGFOH78uPj7779Fx44dxdy5c4UQNb+3zcnFiduBAwdEQECAI5GfP3++6NKli1i5cqU4fvy4WLx4sdBqtWLdunVCCCGmT58uevXqJXbu3CnS0tLEqlWrxO+//+44n/33w7hx48SYMWMc2y9O3C71OhaLRfz8888CgDh69KjIysoShYWFQojKnxNXV1cRGBgowsPDxb///W9x4sQJx3n/+eefKom9EEJ06NBBvPXWWw11G6kOmLi1INu3bxcAxC+//HLJcvbELScnR7i7u4slS5YIIWpO3IQQYtKkSeLqq68WQrStxM3+l+PatWsd2+Lj4x01XaNGjRI9e/Z0OubNN98Uer3e8bD/AtTpdOKhhx5yKjthwgRHucGDB1cbQ3WJW1ZWlgAgXF1dxVtvvSX27t0rFixYICRJcvzCl8vFidvQoUOd9vfv3188+eSTQggh/vrrL6FQKMTRo0erPdddd90l7rnnHqdtGzduFAqFQpSXlwshKj/Po0aNcipz6623iuuvv97xvLp7eHHiFhQU5FTTZI/1gQceEEKcT9w+++wzx357onb48GHHtkGDBjl9YS5atEi4urqK4uJip3MvW7ZM6PV6IUmSCAoKEjt27Kj2HjSFIUOGiHfeeUcIUVnT7Ovr6/jMX5y41eY9KSgoECEhIeL+++8X/v7+Ve6rPXGrqKhw1JgKUTVxu+aaa6r8IfrVV1+JwMBAx/OaaqSbiylTpgilUin0er3QarUCgFAoFOKnn34SFRUVwtXV1anWVojKe3zbbbcJIYQYO3asuPPOO2s8v/36Dx06JJRKpdiwYYMQwjlxq83rVFezKoQQK1asED/88INITEwUK1euFIMHDxYdOnRwfJ6/+eYbodFoqsTVv39/8cQTT9T+RlGDYR+3FkTUcZELPz8/PPbYY3juuedgMpkuWXb+/PnYuHEj/v777ysJscXp0qULhgwZgs8//xwAkJKSgo0bNzr6KlVn2rRp2LdvHz755BOUlpZe8n358MMPsW/fPkybNg1lZWW1jstmswEAxo8fj5kzZ6JXr1546qmncMMNN+Djjz+u9XmaQs+ePZ2eBwYGIicnB0DlQJeQkBBER0dXe2xiYiK++OILuLm5OR4jR46EzWZDWlqao9zgwc5L3w0ePBiHDx+udYzFxcU4ffo04uLinLbHxcVVOc+F1xMYWLm0nv16gMr3/6effoLBYAAAfP7555g4cSLc3Z3XSx4+fDj27duHLVu2YNSoUbjlllucztNUjh49ih07duC2224DAKhUKtx6661YtGhRteVr8554e3tj0aJF+OijjxAREYGnnnqq2nNptVq8+OKLeOONN5CXl1fta7344otOr/Wf//wHWVlZdfp5kZv9vd6+fTumTJmCO++8EzfddBNSUlJQVlaGESNGOF3jkiVLcPz4cQDA/fffj++++w69evXCE088gS1btlT7GjExMbjjjjuqvde1eZ2aXH/99Zg4cSJ69uyJkSNHYsWKFSgsLMQPP/xw5TeGGkWzWKuUaicqKgqSJOHIkSO1PmbWrFn48MMP8eGHH16yXEREBP7zn//gqaeeqvEXemt111134cEHH8QHH3yAxYsXIyIiAgkJCQAq7/mmTZtgNpuhVqsBAF5eXvDy8qoyqCEqKgpHjx512mb/4m/Xrl2dYvL19YVKpUJMTIzT9q5du1bpOCw3+32xkyTJkXi6uLhc8tiSkhLce++9eOihh6rs69ChQ8MFWQcXXo8kSQDOJ9IAMGnSJMycORM//PADrrrqKmzevBkLFiyoch69Xo/IyEhERkZi0KBBiIqKwqJFi5wGtDSFRYsWwWKxICgoyLFNCAGtVov333+/SvnavicbNmyAUqlEVlYWSktLqySudpMnT8Ybb7yB+fPnVxlRWlJSghdeeKHaju4taRCO/b0GKhP52NhYLFq0CN27dwcA/PHHHwgODnY6xr6+6PXXX48TJ05gxYoVWLVqFa655hpMnz4db7zxRpXXeeGFFxAdHV1llG1JScllX6e2vLy8EB0djZSUFABAQEAATCYTCgsL4eXl5SiXnZ2NgICAOp2bGgZr3FqQdu3aYeTIkfjggw9QWlpaZX918zC5ubnh2WefxUsvveSoIajJc889h+Tk5CpD+Fu7W265BQqFAt9++y2WLFmCadOmOb6wb7vtNpSUlFw28bWXXbVqVZUpQupDo9Ggf//+VRLB5ORkhIWFXfH5m0rPnj2RkZGB5OTkavf36dMHSUlJjgTnwodGo3GUu3hqm23btqFr166O52q1GlartcY4PDw8EBQUhM2bNztt37x5c5Xk+HLc3d0xceJEfP7551i8eDGio6MRHx9/2eNsNhuMRmOdXutKWSwWLFmyBG+++Sb27dvneCQmJiIoKAj/+9//qhxTm/dky5YtePXVV7Fs2TK4ublhxowZNcagUCiwYMECfPTRR1WmGOrTpw+OHj1a7WspFJVfT5d7b5sbhUKBp59+GnPmzEFMTAy0Wi1OnjxZ5fpCQ0Mdx/j5+WHKlCn4+uuv8c4772DhwoXVnjs0NBQzZszA008/7XRPavM69vfucveypKQEx48fd/zR2bdvX6jVaqepdY4ePYqTJ09WqQmnJiJzUy3V0fHjx0VAQICIiYkRP/30k0hOThZJSUni3XffFV26dBFCnO/jZmcymURERITQ6XQ19nGze/bZZ4VOp2szfdzs7rrrLuHt7S2USqXIzMx02vfoo48KpVIpZs6cKTZu3CjS09PF1q1bxeTJk4UkSaKoqEgIIUR5ebmIi4sT3t7e4p133hG7d+8WqampYuXKlWLAgAGiT58+jnMaDAaxd+9eR39Cez+2CzsF//LLL0KtVouFCxeKY8eOiffee08olUqxcePGprkpNbi4j9vDDz/stP/iTtPDhg0T3bt3F3///bdITU0VK1ascIzmTExMFC4uLmL69Oli7969Ijk5WSxdulRMnz7dcXxYWJjw8PAQr776qjh69Kh4//33hVKpFCtXrnSUiYqKEvfff7/IysoSBQUFQoiqn++3335beHh4iO+++04cOXJEPPnkk9UOTti7d6/jmLNnz1bpAylEZZ8vAMLb21u88sorTvtKSkrE7NmzxdatW0V6errYtWuXuPPOO4VWqxUHDx6sy62+Yr/++qvQaDSOfpgXeuKJJ0S/fv2q9H263HtSXFwsOnXqJGbNmiWEEGL//v1Cq9U6jTqs7nMRHx8vdDqdUx+3lStXCpVKJebOnSsOHjwokpKSxP/+9z/xzDPPOMpU9942J9WNKjWbzSI4OFi8/vrr4plnnhE+Pj7iiy++ECkpKWL37t3iv//9r/jiiy+EEJW/c5cuXSqOHTsmDh48KG644QYxYMAAx7lwUR+//Px84enpKXQ6ndPP2eVeJyMjQ0iSJL744guRk5MjDAaDEKLy99u6detEWlqa2Lx5s7j22muFr6+v00CQ++67T3To0EGsWbNG7Nq1SwwePLjGPrvU+Ji4tUCnT58W06dPF2FhYUKj0Yjg4GAxbtw4x5fLxYmbEEJ8++23AsBlE7f/b+/+QqJo2zCAX4utOiqWpqHWrpJbpqSLHmhpQkGxllmYiJAGaiuZidiB1VFYgUkimRQWUm4RkQeaVEShBwpaohT9AaXSSos6Kgi03bK8v4No+Lbd+rJX2+Z7rx/MwcwzO3M/s+vuxcwz4/v37yUkJORfF9xu374tAGTTpk1u21tbW2Xt2rUyf/580ev1smTJEtm+fbv09/c7redwOKS2tlbMZrMoiiI+Pj6yYsUK2bt3r4yPj6vrffux/H767y9ika8D300mk/j6+orZbJaOjo5Z7/tMzTS4vX37Vn00hK+vr6xcuVKuX7+utg8MDMiGDRskICBA/P39JSEhwWmwe2RkpBw6dEhyc3PFz89PwsLCXB7PcvXqVTGZTDJv3ryfPg6kurpaFi9eLHq9/oePA/mV4CYiEhMTI15eXvL69Wun5Xa7XbKzsyUiIkK8vb0lPDxctmzZ4pGbEzZv3vzDz/S3m51OnDjhMmj9Z+9JUVGRxMfHi8PhUNevr6+X4OBgefXqlYi4/1x8+xv7/pEeN2/elNTUVFEURQIDAyU5OdnpDl137+3fxF1wE/n6eKDQ0FCZmJiQhoYGiYmJEb1eL6GhoWKxWNQ7k48cOSKxsbGiKIoEBwfL1q1b5dmzZ+p2vg9uIiI1NTUu3xfT09M/3Y+IyOHDhyUsLEx0Op362ry8PAkPD1d/S/Ly8mRkZMRpf3a7XcrKyiQoKEj8/PwkOztb3rx5888OHP02ncgMR7wTEf1BUVFRqKysRGVlpadLISLyOI5xIyIiItIIBjciIiIijeClUiIiIiKN4Bk3IiIiIo1gcCMiIiLSCAY3IiIiIo1gcCMiIiLSCAY3Ivq/093dDZ1O5/bfwP1IVFQUGhoa5qwmIqLZwOBGRH9cYWEhdDodSktLXdr27NkDnU6HwsLCP18YEdFfjsGNiDzCYDDg8uXLsNvt6jKHw4FLly7BaDR6sDIior8XgxsReURSUhIMBgPa29vVZe3t7TAajUhMTFSXffz4ERUVFVi0aBF8fX2xZs0aDA4OOm3rxo0bWL58ORRFwbp16/DixQuX/fX29iI9PR2KosBgMKCiogKTk5NuaxMRVFdXw2g0wsfHBxEREaioqJidjhMR/QMMbkTkMcXFxWhpaVHnz507h6KiIqd19u3bh7a2Npw/fx737t2DyWSCxWLBu3fvAAAvX77Etm3bkJWVhfv378NqteLAgQNO2xgdHUVGRgZycnLw8OFDtLa2ore3F+Xl5W7ramtrw/Hjx3HmzBk8ffoUHR0diI+Pn+XeExHNHIMbEXlMQUEBent7MTY2hrGxMfT19aGgoEBtn5ycRFNTE+rq6rBx40bExcWhubkZiqLg7NmzAICmpiZER0ejvr4eMTExyM/Pdxkfd/ToUeTn56OyshLLli1DamoqGhsbceHCBTgcDpe6xsfHERYWhvXr18NoNCI5ORklJSVzeiyIiH4FgxsReUxoaCgyMzNhs9nQ0tKCzMxMhISEqO2jo6OYmppCWlqaukyv1yM5ORnDw8MAgOHhYaSkpDhtd/Xq1U7zDx48gM1mQ0BAgDpZLBZMT0/j+fPnLnXl5ubCbrdj6dKlKCkpwZUrV/D58+fZ7DoR0W+Z5+kCiOjfrbi4WL1keerUqTnZx8TEBHbt2uV2nJq7GyEMBgMeP36Mrq4udHZ2oqysDHV1dejp6YFer5+TGomIfgXPuBGRR2VkZODTp0+YmpqCxWJxaouOjoa3tzf6+vrUZVNTUxgcHERcXBwAIDY2FgMDA06v6+/vd5pPSkrC0NAQTCaTy+Tt7e22LkVRkJWVhcbGRnR3d+POnTt49OjRbHSZiOi38YwbEXmUl5eXetnTy8vLqc3f3x+7d+9GVVUVgoODYTQacezYMXz48AE7d+4EAJSWlqK+vh5VVVWwWq24e/cubDab03b279+PVatWoby8HFarFf7+/hgaGkJnZydOnjzpUpPNZsOXL1+QkpICPz8/XLx4EYqiIDIycm4OAhHRL+IZNyLyuMDAQAQGBrptq62tRU5ODnbs2IGkpCSMjIzg1q1bCAoKAvD1UmdbWxs6OjpgNptx+vRp1NTUOG0jISEBPT09ePLkCdLT05GYmIiDBw8iIiLC7T4XLFiA5uZmpKWlISEhAV1dXbh27RoWLlw4ux0nIpohnYiIp4sgIiIiov+NZ9yIiIiINILBjYiIiEgjGNyIiIiINILBjYiIiEgjGNyIiIiINILBjYiIiEgjGNyIiIiINILBjYiIiEgjGNyIiIiINILBjYiIiEgjGNyIiIiINILBjYiIiEgj/gM+dgmrhJJTYwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "models = ['VGG16', 'ResNet50', 'InceptionV3']\n",
        "total_params = [16000000, 23000000, 25000000]\n",
        "sizes = [68.67, 406.73, 85.09]\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "\n",
        "color = 'tab:blue'\n",
        "ax1.set_xlabel('Models')\n",
        "ax1.set_ylabel('Total Params (Millions)', color=color)\n",
        "ax1.bar(models, total_params, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "color = 'tab:pink'\n",
        "ax2.set_ylabel('Size (MB)', color=color)\n",
        "ax2.plot(models, sizes, color=color, marker='o')\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "plt.title('Total Params and Size of Each Model')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZrvPan-ydaVh",
        "outputId": "a20d29b3-1653-4871-e563-23ce576840fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "euXbHlTReseg",
        "XluDawQiSBbc",
        "aq8erty27Ip2",
        "LS9_tKl-e1He"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}