material/deep_learning/unet_segementation.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "unet_segementation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYzKnHXzN0lB"
      },
      "source": [
        "# 폐 영역분할"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ROQ8VnQXGeU4"
      },
      "source": [
        "다음의 2019 대한의료인공지능학회 Summer School에서 진행한 AI hands-on의 자료에서 코드르 가져와서 수정함.\n",
        "\n",
        "https://github.com/mi2rl/ai_handson/blob/main/KoSAIM_Summer_School_2019_Handson_03_Segmentation.ipynb\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bMHwQjK9OhLf"
      },
      "source": [
        "## 폐 데이터 다운로드\n",
        "\n",
        "다음에서 다운받은 데이터\n",
        "\n",
        "https://www.kaggle.com/nikhilpandey360/lung-segmentation-from-chest-x-ray-dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAKPT_j5GITc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "938ad8db-4359-4bac-d9a4-273a9ba143c8"
      },
      "source": [
        "!rm -rf Lung_Segmentation.zip\n",
        "!wget https://github.com/medicieducation/trainingcourse/raw/main/bigdata/deeplearning/material/deep_learning/Lung_Segmentation.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-07-12 05:31:39--  https://github.com/medicieducation/trainingcourse/raw/main/bigdata/deeplearning/material/deep_learning/Lung_Segmentation.zip\n",
            "Resolving github.com (github.com)... 140.82.121.4\n",
            "Connecting to github.com (github.com)|140.82.121.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/medicieducation/trainingcourse/main/bigdata/deeplearning/material/deep_learning/Lung_Segmentation.zip [following]\n",
            "--2022-07-12 05:31:39--  https://raw.githubusercontent.com/medicieducation/trainingcourse/main/bigdata/deeplearning/material/deep_learning/Lung_Segmentation.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 37648324 (36M) [application/zip]\n",
            "Saving to: ‘Lung_Segmentation.zip’\n",
            "\n",
            "\rLung_Segmentation.z   0%[                    ]       0  --.-KB/s               \rLung_Segmentation.z 100%[===================>]  35.90M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2022-07-12 05:31:39 (316 MB/s) - ‘Lung_Segmentation.zip’ saved [37648324/37648324]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8TLRtBfGsKj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1794a689-c731-4de2-eede-8f8d3a007143"
      },
      "source": [
        "!rm -rf Lung_Segmentation\n",
        "!unzip Lung_Segmentation.zip"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  Lung_Segmentation.zip\n",
            "   creating: Lung_Segmentation/\n",
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            "  inflating: Lung_Segmentation/image/CHNCXR_0277_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0278_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0279_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0280_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0281_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0282_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0283_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0284_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0285_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0286_0.png  \n",
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            "  inflating: Lung_Segmentation/image/CHNCXR_0289_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0290_0.png  \n",
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            "  inflating: Lung_Segmentation/image/CHNCXR_0292_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0293_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0294_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0295_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0296_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0297_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0298_0.png  \n",
            " extracting: Lung_Segmentation/image/CHNCXR_0299_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0300_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0301_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0302_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0303_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0304_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0305_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0306_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0307_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0308_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0309_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0310_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0311_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0312_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0313_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0314_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0315_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0316_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0317_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0318_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0319_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0320_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0321_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0322_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0323_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0324_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0325_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0326_0.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0327_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0328_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0329_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0330_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0331_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0332_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0333_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0334_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0335_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0337_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0338_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0339_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0340_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0361_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0362_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0363_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0364_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0365_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0366_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0367_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0368_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0369_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0370_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0371_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0372_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0373_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0374_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0375_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0376_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0377_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0378_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0379_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0380_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0381_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0382_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0383_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0384_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0385_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0386_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0387_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0388_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0389_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0390_1.png  \n",
            " extracting: Lung_Segmentation/image/CHNCXR_0391_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0392_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0393_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0394_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0395_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0396_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0397_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0398_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0399_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0400_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0401_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0402_1.png  \n",
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            "  inflating: Lung_Segmentation/image/CHNCXR_0404_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0405_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0406_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0407_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0408_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0409_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0410_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0411_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0412_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0413_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0414_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0415_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0416_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0417_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0418_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0419_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0420_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0421_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0422_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0423_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0424_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0425_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0426_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0427_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0428_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0429_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0430_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0431_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0432_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0433_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0434_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0435_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0436_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0437_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0438_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0439_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0440_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0441_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0442_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0443_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0444_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0445_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0446_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0447_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0448_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0449_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0450_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0451_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0452_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0453_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0454_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0455_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0456_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0457_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0458_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0459_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0460_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0461_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0462_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0463_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0464_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0465_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0466_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0467_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0468_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0469_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0470_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0471_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0472_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0473_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0474_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0475_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0476_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0477_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0478_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0479_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0480_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0501_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0503_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0504_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0506_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0507_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0508_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0509_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0510_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0511_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0512_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0513_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0514_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0515_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0516_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0517_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0518_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0519_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0520_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0521_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0522_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0523_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0524_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0525_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0526_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0527_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0528_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0529_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0530_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0531_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0532_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0533_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0534_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0535_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0536_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0537_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0538_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0539_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0540_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0541_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0542_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0543_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0544_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0545_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0546_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0547_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0548_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0549_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0550_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0551_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0552_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0553_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0554_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0555_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0556_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0557_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0558_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0559_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0566_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0567_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0568_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0569_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0570_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0571_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0572_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0573_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0574_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0575_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0576_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0577_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0578_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0579_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0580_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0581_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0582_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0583_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0584_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0585_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0586_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0587_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0588_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0589_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0590_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0591_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0592_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0593_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0594_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0595_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0596_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0597_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0598_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0599_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0600_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0601_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0602_1.png  \n",
            "  inflating: Lung_Segmentation/image/CHNCXR_0603_1.png  \n",
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            "  inflating: Lung_Segmentation/label/CHNCXR_0272_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0273_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0274_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0275_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0276_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0277_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0278_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0279_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0280_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0281_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0282_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0283_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0284_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0285_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0286_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0287_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0288_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0289_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0290_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0291_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0292_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0293_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0294_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0295_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0296_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0297_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0298_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0299_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0300_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0301_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0302_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0303_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0304_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0305_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0306_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0307_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0308_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0309_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0310_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0311_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0312_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0313_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0314_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0315_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0316_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0317_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0318_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0319_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0320_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0321_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0322_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0323_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0324_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0325_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0326_0.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0327_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0328_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0329_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0330_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0331_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0332_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0333_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0334_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0335_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0337_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0338_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0339_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0340_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0361_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0362_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0363_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0364_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0365_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0366_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0367_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0368_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0369_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0370_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0371_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0372_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0373_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0374_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0375_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0376_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0377_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0378_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0379_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0380_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0381_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0382_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0383_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0384_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0385_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0386_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0387_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0388_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0389_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0390_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0391_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0392_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0393_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0394_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0395_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0396_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0397_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0398_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0399_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0400_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0401_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0402_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0403_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0404_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0405_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0406_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0407_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0408_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0409_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0410_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0411_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0412_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0413_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0414_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0415_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0416_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0417_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0418_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0419_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0420_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0421_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0422_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0423_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0424_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0425_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0426_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0427_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0428_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0429_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0430_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0431_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0432_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0433_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0434_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0435_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0436_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0437_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0438_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0439_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0440_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0441_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0442_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0443_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0444_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0445_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0446_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0447_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0448_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0449_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0450_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0451_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0452_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0453_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0454_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0455_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0456_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0457_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0458_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0459_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0460_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0461_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0462_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0463_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0464_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0465_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0466_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0467_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0468_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0469_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0470_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0471_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0472_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0473_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0474_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0475_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0476_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0477_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0478_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0479_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0480_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0501_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0503_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0504_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0506_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0507_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0508_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0509_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0510_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0511_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0512_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0513_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0514_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0515_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0516_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0517_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0518_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0519_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0520_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0521_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0522_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0523_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0524_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0525_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0526_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0527_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0528_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0529_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0530_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0531_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0532_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0533_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0534_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0535_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0536_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0537_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0538_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0539_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0540_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0541_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0542_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0543_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0544_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0545_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0546_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0547_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0548_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0549_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0550_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0551_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0552_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0553_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0554_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0555_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0556_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0557_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0558_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0559_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0566_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0567_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0568_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0569_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0570_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0571_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0572_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0573_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0574_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0575_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0576_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0577_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0578_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0579_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0580_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0581_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0582_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0583_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0584_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0585_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0586_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0587_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0588_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0589_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0590_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0591_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0592_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0593_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0594_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0595_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0596_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0597_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0598_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0599_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0600_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0601_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0602_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0603_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0604_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0605_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0606_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0607_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0608_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0609_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0610_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0611_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0612_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0613_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0614_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0615_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0616_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0617_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0618_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0619_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0620_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0621_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0622_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0623_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0624_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0625_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0626_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0627_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0628_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0629_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0630_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0631_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0632_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0633_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0634_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0635_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0636_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0637_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0638_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0639_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0640_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0641_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0642_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0643_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0644_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0645_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0646_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0647_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0648_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0649_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0650_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0651_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0652_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0653_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0654_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0655_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0656_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0657_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0658_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0659_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0660_1.png  \n",
            "  inflating: Lung_Segmentation/label/CHNCXR_0661_1.png  \n",
            " extracting: Lung_Segmentation/label/CHNCXR_0662_1.png  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AoxCIJscPmAC"
      },
      "source": [
        "## 풀린 데이터 파일 구조\n",
        "\n",
        "```\n",
        "Lung_segmentaiton/\n",
        "  image/\n",
        "    CHNCXR_0001_0.png\n",
        "    CHNCXR_0002_0.png\n",
        "    ...\n",
        "  label/\n",
        "    CHNCXR_0001_0.png\n",
        "    CHNCXR_0002_0.png\n",
        "    ...\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZN8aC7EMQpDL"
      },
      "source": [
        "## 파일 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IaPaC-FnQKU6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "outputId": "826bbc31-cadd-4467-9a3e-57045c337407"
      },
      "source": [
        "from IPython.display import Image, display\n",
        "\n",
        "display(Image('./Lung_Segmentation/image/CHNCXR_0001_0.png'))\n",
        "display(Image('./Lung_Segmentation/label/CHNCXR_0001_0.png'))\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVdJ49RtQtry"
      },
      "source": [
        "## 데이터 로딩"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ME_l5d-GuRE"
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "\n",
        "from skimage.io import imread\n",
        "from skimage.transform import resize\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "IMG_WIDTH = 256\n",
        "IMG_HEIGHT = 256\n",
        "IMG_CHANNELS = 3\n",
        "\n",
        "data_path = \"Lung_Segmentation\"\n",
        "\n",
        "files = os.listdir(os.path.join(data_path, 'image'))\n",
        "# files = ['CHNCXR_0402_1.png', 'CHNCXR_0461_1.png', 'CHNCXR_0422_1.png', ... ]    \n",
        "\n",
        "\n",
        "raw_x = []\n",
        "raw_y = []\n",
        "for file_name in files:\n",
        "    # file_name = \"CHNCXR_0402_1\"\n",
        "\n",
        "    # input_file = \"./Lung_Segmentation/image/CHNCXR_0402_1.png\"\n",
        "    input_file = os.path.join(data_path, 'image', file_name)\n",
        "    # output_file = \"./Lung_Segmentation/label/CHNCXR_0402_1.png\"\n",
        "    output_file = os.path.join(data_path, 'label', file_name)\n",
        "    \n",
        "    image = imread(input_file)[:,:,:IMG_CHANNELS]\n",
        "    # image.shape = (256, 256, 3)\n",
        "\n",
        "    label = imread(output_file)\n",
        "    # label.shape = (256, 256)\n",
        "\n",
        "    label = np.expand_dims(label, axis=-1)\n",
        "    # label.shape = (256, 256, 1)\n",
        "\n",
        "    raw_x.append(image)\n",
        "    raw_y.append(label)\n",
        "\n",
        "raw_x = np.array(raw_x)\n",
        "raw_y = np.array(raw_y)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhp-Lt0OWcxT"
      },
      "source": [
        "## 원시 데이터 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXnw8NwqTJd8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9065a990-b03d-418d-c404-6892aa6250e1"
      },
      "source": [
        "print(raw_x[0])\n",
        "print(raw_y[0])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 70  70  70]\n",
            "  [ 44  44  44]\n",
            "  [ 41  41  41]\n",
            "  ...\n",
            "  [ 46  46  46]\n",
            "  [ 46  46  46]\n",
            "  [ 45  45  45]]\n",
            "\n",
            " [[ 65  65  65]\n",
            "  [ 40  40  40]\n",
            "  [ 32  32  32]\n",
            "  ...\n",
            "  [ 31  31  31]\n",
            "  [ 34  34  34]\n",
            "  [ 36  36  36]]\n",
            "\n",
            " [[ 58  58  58]\n",
            "  [ 35  35  35]\n",
            "  [ 27  27  27]\n",
            "  ...\n",
            "  [ 24  24  24]\n",
            "  [ 23  23  23]\n",
            "  [ 28  28  28]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 56  56  56]\n",
            "  [ 37  37  37]\n",
            "  [ 32  32  32]\n",
            "  ...\n",
            "  [ 23  23  23]\n",
            "  [ 24  24  24]\n",
            "  [ 26  26  26]]\n",
            "\n",
            " [[ 48  48  48]\n",
            "  [ 39  39  39]\n",
            "  [ 31  31  31]\n",
            "  ...\n",
            "  [ 23  23  23]\n",
            "  [255 255 255]\n",
            "  [247 247 247]]\n",
            "\n",
            " [[ 56  56  56]\n",
            "  [ 42  42  42]\n",
            "  [ 30  30  30]\n",
            "  ...\n",
            "  [ 24  24  24]\n",
            "  [181 181 181]\n",
            "  [249 249 249]]]\n",
            "[[[0]\n",
            "  [0]\n",
            "  [0]\n",
            "  ...\n",
            "  [0]\n",
            "  [0]\n",
            "  [0]]\n",
            "\n",
            " [[0]\n",
            "  [0]\n",
            "  [0]\n",
            "  ...\n",
            "  [0]\n",
            "  [0]\n",
            "  [0]]\n",
            "\n",
            " [[0]\n",
            "  [0]\n",
            "  [0]\n",
            "  ...\n",
            "  [0]\n",
            "  [0]\n",
            "  [0]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0]\n",
            "  [0]\n",
            "  [0]\n",
            "  ...\n",
            "  [0]\n",
            "  [0]\n",
            "  [0]]\n",
            "\n",
            " [[0]\n",
            "  [0]\n",
            "  [0]\n",
            "  ...\n",
            "  [0]\n",
            "  [0]\n",
            "  [0]]\n",
            "\n",
            " [[0]\n",
            "  [0]\n",
            "  [0]\n",
            "  ...\n",
            "  [0]\n",
            "  [0]\n",
            "  [0]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uf0F31eBQ2S0"
      },
      "source": [
        "## 데이터 정규화"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S75v2lDp90rl",
        "outputId": "e4e7c427-d44f-4687-9820-1c78d7303514",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "x = (raw_x / 255).astype(np.float)\n",
        "y = (raw_y / 255).astype(np.int16)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.min(x), np.max(x))\n",
        "print(np.min(y), np.max(y))"
      ],
      "metadata": {
        "id": "sTAbzC6PdGPH",
        "outputId": "6143e91c-0ccd-454c-f9da-c0c3db70d514",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0 1.0\n",
            "0 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3qyO591H90rx"
      },
      "source": [
        "## 데이터 나누기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQihsIMJ90rx"
      },
      "source": [
        "split_index = int(x.shape[0]*0.8)\n",
        "train_x, test_x = x[:split_index], x[split_index:]\n",
        "train_y, test_y = y[:split_index], y[split_index:]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YAlIG-SDHGCk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6d453dd-351f-4bca-e3e7-8fbccacd1444"
      },
      "source": [
        "print(train_x.shape)\n",
        "print(train_y.shape)\n",
        "print(test_x.shape)\n",
        "print(test_y.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(452, 256, 256, 3)\n",
            "(452, 256, 256, 1)\n",
            "(114, 256, 256, 3)\n",
            "(114, 256, 256, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcqjqeYpHL-7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8880af36-135f-435e-953b-6df2378b0f2f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plotTrainData(x, y, set_type):\n",
        "    print(set_type)  \n",
        "    for i in range(3):\n",
        "        ix = np.random.randint(0, len(x))\n",
        "        plt.subplot(1,2,1)\n",
        "        plt.imshow(x[ix])\n",
        "        \n",
        "        plt.subplot(1,2,2)\n",
        "        plt.imshow(np.squeeze(y[ix]), 'gray') # np.squeeze()는 (256,26,1)을 (256,256)으로 변환\n",
        "        plt.show()\n",
        "        \n",
        "plotTrainData(train_x, train_y, 'train')\n",
        "plotTrainData(test_x, test_y, 'test')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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Xw9/JO3Q6HZ04cULHjh0Ln0POuLy8HFYSXuQE0LKqmZiYUKFQCG0D+AxtHSqVimZmZjQ1NZVwmKxoisWiJiYm1Ol0dPz48QRt4tSSa/WZL354fXl5+ZSfxcVFPfLII/rRj34U6gdw5PDCf/EXfzEG9yewAwcO6C1veYve+MY3bghAzs/P69ixY7r11lt1//3367777gur2Y20l770pfrQhz6kZzzjGefkjJ7MXvnKV+rOO+/c8ONutI00uM/MzMSvfe1rE5I/olzAjmixWq0m6BiW45iDPEt7okB4Wo/ms9ms9u3bF5Q5nU5Hy8vL2rNnT6J3SbPZDElIjoXOnFUCkj0+A89dKBQCb93r9RLnR+VTKBTUaDS0d+9e1Wo15fP5wM8TuTL2VqulVqsV5Ji7du1SpVJJ9NYZDAY6cuSIjhw5EnT1c3Nzobq1VqsFB4PKhqSvr3qmpqYSyVZAP5fLBfqlVCppenpae/bsSdyHdrsdVDuNRkOLi4uan59PSEN5r0tgmXena3g/LRaWlpYS0fzs7Kz+3//7fyHfgZoniiLde++9Y3B/HHvpS1+q//bf/pt27dq1aef4yle+olarpTvvvFO33XbbhhzzOc95ju666y7t3r17Q453Orv33nt1zTXXbDnNdLb2eOA+EglVp1KoVpyamlKhUAiVpgAxhUNotl35Au8O2KcVONJ6aX6v11M+n9e+ffsUx7Ha7bZ6vV5oH+D67BMnTujEiROBZoFagR7BmUBNwAPn8/kQFQPklUpFKysrWlxcDICJFLHdbodEMauRdI6g2+2GgiKiXhLQAC7KmG63G6pgifzh9T2J6lW/zBnn8yInAJYIv1wuB7Cdm5tTsVhUo9EI88+KAxprenpaURRpfn4+XBsATsTowA+Fxhz4fPAZrmPPnj2amJjQo48+qoMHD2p1dTU41rFa5vT2sz/7s7rtttvUaDQ2/TyS9PDDD28IuOdyOf3cz/3cpgK7JF199dV6+ctfrttvv31Tz7NZNhLgns1mNTU1penp6UAxoH5xTta10kgAAVAiecwjQyyO46BI2bNnj+r1ekiCSgqySpchHjt2THNzc4GHxjmgv2elQMQ8HA5VLBYDX4zDIVnMimTv3r0hAQowLy8vq91uB1oI1Y4nh/mMtK4QQXsex3EA/YWFhUCjSGvc5PT0dMJBMW5X51CZmy6Q8oKqwWCQyClwLACeefU8xWAwUKfT0d69e1UoFEJ3SwdeP/fp2ku4asdXeVzj5ZdfrksuuUQ//uM/roMHD+rgwYNaWloaFzGdxqrVqm688cZNB3a3N73pTfr4xz+uH/7wh+d8jEqlog984AN6wxvesIEjO71FUaR3vvOduvPOO4O6bCfZSIB7pVLRT/7kTwYgQ43Ckl5a77QIWKIG8WjdgcL5ZBp0AVKlUknlcjkUCNH6l2OgdZ+fn1er1UqAXppHB7gBZddYr6ysBCrJ8wOrq6tB4dJqtQL4MaaFhYVAK+AkXLrY7XaDo4D3ltbbFlB56tLQarUaVgdUnTqXDYXixWTkOHAoOJlsNhu4fa8elqR+v69KpZKQfxaLxTC+5eXlUEDSbDYTuRDvVIlz8a6W3vuG1QnJ6927d+tv/s2/qU6no2azqeuuu06/93u/p29/e7wDXtrK5bJuueUWveIVr9jS805PT2v37t3nBe7/6T/9J73lLW/ZwFE9sT3zmc/UW97yFn30ox9NSG93go0EuAMY/iVHKUP054oK1B5QBK5x53ieVKvVaoG+4FjQMIAL/LakUKDUbrcTOnB04yhQfFUhrakFoHuQBi4tLanb7QaQ37VrlyYnJyUpnJvkpDukZrMZ5JiSEk274O0pIiFqJ8lIERZjAphXV1cD5dRsNhOOi897FE+CmRYFSCtJ8pbL5UR03+/31W63wwqIGgH65/h9phePr0TS7Z4Bes+deCdN2hf3+31NTEwol8vp9ttv14EDB/Tggw/qoosuUqVS0T333LNFT/LOsH/2z/6Zbrrppi0/bxRFuvnmm/Wyl73snD7/t//2395yh1QsFvWBD3xAkkauWOvJbCTA3cFnZWVFlUolAD1fZKK+dO8Zl745hQC4wWFXKpWQdESvXiqVEglW+oxns1m1Wi2Vy+XQ4qDVaoWEJJG269hxEM5Xe7vi4XAYJILLy8vatWtXUNBQhERyMp/Pq1KphP4yOCt07gBftVoN1bnMCUqWYrEYCptwfsPhUO12W3Nzc4Fa4v2AKOOFPpIUEsJ+P7x3PK0JmEccKo6Je8mcEXFXq9VwPKfduI/eI8eL0HwFgfOfmJhQr9fTFVdcEc5Jj3ic9tikK664Qu95z3u2LQ+xd+9e7d2795x6ulxxxRV6+tOfvgmjemLLZrN6//vfr1wupw996EM7prhpJMDdS/QlJb7sAIrzrQA30a506nZzAI2ksAo4duyYer1eSNR6tSsADXXR7XbDMWkzsHv37tCbhUjawQh9OfSMfx4Azufz6vV6OnLkSEgeo/7xjSxYIdTr9dDuYPfu3UFiCLijCJGUSMYC7A7qnU4nQR2RGE2DeXrueY338ds3KsnlcqrX6yEix1EBsuQDuN9QTcg//QuDE8ARe092n2t4/+FwGFYh7OhVr9f12GOPjWzfk+2wK664Qnfeeaee8YxnbNsYDhw4oH/yT/6Jfv/3f3/bxnAuViqV9N73vleDwWDHUDQjAe6AoDe5gj6BpoF/hSLxviJeuk5yMo7j0AcGPTTOg6U94AIQDYfDEJXTywWqgERstVpVvV4PoBNFUUKWR9LRAcm37fOdioh+q9VqcDach/d5MjMNsFAqjMcdiq8eFhcXE1WpHtXy/2KxGFYNOCFWQACkF43hGCkoQ1OOTBFFD9dOboP7Da1ED3tJQcbImHDmcPZ+v3kfqweeD5w9G6YsLCyMWxBobc4//OEPbyuwY7fccoseeeQR3X333ds9lLOyYrGoD37wg5Kkj3zkIyOfqB8JcF9aWtKxY8dOoSKQR3q/cy/SAfiJunEOJC7hu6EyXNLn5fto0omcFxYWQiToRTTuhDgXQAmlhLYdCSDSRN+VSFpPfgKCXA9UC1QS88DmFn7dAL+kUGiEI+x2u5qfnw+bbHD9zKknTaX17fHQ15MEdqrFG3Z5jkRKboNHMpfWBSRz0aS7Vp0VRLlcTuRTmCdPuDL3vsryxC3XhZPhOhjHU9UymYze/va367nPfe52D0WSNDk5qRe+8IU7DtyltbmEovngBz840gA/EuAOQMCpx3Ec+rmw/Ia6AeBc+02XxcnJyUCxLCwsBMD293M+bwOQy+XUbDZ14sQJLS4uJiSE6Omdlyc5SyKUoiBfUZBcBdCk9ZL7paWlEGUCtk4JUZkKfcR1kvQF/Hy3pCiKAoivrKyElr5xHAeQdmmmtOZooHV4D7kJab24yDtaYq6B55ieAF5dXVW3202sxrypmn8pWCmRa+A+ew2DSza5DlYF3hffAwGvc3iq2t69e3XDDTfo3e9+d1gBjYJdf/31+uhHP6ojR46c8WfuuecefeMb39BP/MRPbOLIntygaObm5ka6/8zIgDt9QVhyE/1BrRCJ0xrXFTFeqg+wEtFns9kEOBHd0U8G0Dl8+LBOnDgREn7IML2gCOojLQuUlEg48rqPnfFwfNockANIK35oQLZ79+4A9CRfOZc3ywKI2bqOqlNXw5DAnZiYSETqGPQGm3JwXLh1HCvySTh9XuPfcbzeNnh5eTn0n+EaWq1WcArMC6sOz0OgZvLCJgdteHrO6UlbnChU3VPRnvWsZ+muu+7S05/+9JFzcM94xjNCEHGmdvjwYR0/fnyTRnR2ViwW9YY3vEFf/vKXA26Mmo0MuKOpltYrDwEBkooANct0IlsailEo5Nwz/LGULIKB/lhZWdHRo0dDu1qAhsQpToEdl7yxF+CGeZToYyA5y+vQLBwD4KILpauBXP3D+HCCrhjxtsCLi4uJZDMAjToFh+T8NglSp4k4P9SYj9mPDdBCRXHPAFaUEbt37w4UDBt8cI8YjzvGcrms+fn5BHj7KsGdCfeWe4rz4fWnmo1C8vRCt+c973n6+te/rg9+8IMjSdGMDLgTCUvrpehEXwATQOZ0Axw9ESqfAxC9RB1qh8RoJpPRkSNHND8/H7hxSSECJqqnZQAdF6FCXGKH8+Ez/BvwAoDgppELci3+Pk/i4jCIal1BQvtdqmb5G7w/2wCm2+1yPt/EgMib+fJ74JQMc+xFTEtLS1pcXAyrLfTnROWSdOzYsaCMQQ3kHD7jg2qS1orbPMntORgvOiNiZ56oEnb66alk+/fvv2CB/WMf+5he9KIXjcxK5KKLLtJ73vMezc7O6lOf+tR2Dydh5wXuURQ9IqklaVXSII7jq6Iompb0WUmXSXpE0vVxHM8/4SByudC4yKM4gBTAgx5w5Ynr46X16JwEI2CECiSO49D/pNvtBm6+1+sFLl1SolEZOntJoUhpcXExUXXqtIR3riSyTffEAQQ9USkpjAWummtzWSPFWACogxj5AXTnTlOwCsD5OcWRdkw4RMDSG5IRFXONOAN3PtBNOKo4jvXoo48GnfLExERo++Dy1rQslZbIJLiZX6fZ0jp6CrlI3J6LbG2jnu2ttiiK9OpXv3pb9OBbYQ8++OB2D+EUK5VKesMb3qAvfvGLmp8fncdhIyL3a+I4PmH//2VJd8dx/P4oin755P/f8WQH8aW2J/2IyJaWlkL0Doh4hAyV4kAJmErrGzLDs8dxrMOHDwcgABTiOA6tAfr9fuCZoYQkhUiSzoyuEyfiB6QYRza7tvUcfDJRNMeFlvEkMjQE1APl+ukeNg7G7kC4dsYCpUN0XygUQhdMSYHW8V1sHo/awIH5+ODrGTfzxXUuLS1pdnZWMzMzGg6HqtVqgbJy2sX/n8vlAsBTKeu1DDh+2i4A5l7Veh62Ic/2Vtq1116r3/iN3xiZyPapYs973vP0ghe8QHfdddd2DyXYZtAy10l64cl/f1rSn+gMvgC+/HcpJJWWRMh8kT052mw2Ez1OPOnpRuI1k1nbHajT6UhSiHZRjLjawhOSJEJdpgfwoUTwvICkUDQkrfW5pjQf3n1iYiJE984pF4tF1Wo11et11ev1UDDFWEkk0yNeWpN1xvHafqcOrKw8AHLv00Kfbe+yieOQ1vX4Pg8uB/Ue8K6R97YJzEsUrbUrnpycDNePU+U93DPOwzPhqxyMxDvJW6eccC7pZ+A87Zye7a2ybDart7/97SOlink8m5+fT+wGdqZ26NAhfeELX9CrXvWqTRjVuVsURfrFX/xFffWrX92U3vXnYucL7rGkr0ZrPat/J47jT0jaG8fx4ZN/PyJp75kcyIuPiD75IlOJyTKbL3w+n1ez2Qw9YDyCxDkAVihvkNl1Oh1FURSiQa+SdI650+mEDosciwiY6J6oFKkiGm7AzrXgFBQxzvn5+bBVHg6B6BeQZDz1ej18IZAtEr0THZNcpmiLjT2QZXrFaboYyht3+crJk5MuR+x0OkHdQiLYlT+Mx3v4IIecmZkJSVp3bIzNeX/vNcN5GJs3ckvLSn1lcw62Yc/2Vtk/+kf/SH//7//97R7GGdnv/M7v6Ec/+tFZf67b7eqRRx7Z+AFtgL3gBS/Qi170In3pS1/a7qFIOn9wf14cx49GUbRH0v+Koug7/sc4juPocTYriKLoRkk3SmtaXIAd9YlXRUIruDwP4GbrOI/kMS90AXCkNZkhkR7ySRwAwAGoA9SSTokEiaIBn2azGY6HqoQxocWW1ikiQBRO2FUiXqHJfHjC15OjGCsGVgI0R3P+2dU2rjRxbt774Pi5sDQtgnOt1+uhyIg8A1p6VjlevQsY04XTaRkcDnNB10q4fZLeODmnwDyZ6zULZ2kb8mxvlVUqFb3pTW9SvV7fytOes52PPPXBBx8MsuhRsyuvvFJf/vKXRyKJf14asTiOHz35+5ikOyX9jKSjURTtl6STv489zmc/EcfxVXEcX8VuP15lyJeY5X16mU7/FACOH+drnXun9B1QIKpMJxxbrZZmZ2cDn+7Ro4MpUTFAhPoD6oYGYw7I3kKAYxFV0/o33BijZ3xF4YVIXKPLHnEqnU7nFK7cgZMxeQWwSyqproWf903+FE4AACAASURBVN7r7ow4DnkRViZcL+OGRiKBS9sB6Bx3GPweDochz+CqGECdVUKlUgm5DK6RFc+5yiA36tk+p5Ofg+3Zs2fLOyaeqw0GA83NzZ3z52+77TYtLCxs4Ig2zt70pjdtNBV4znbO4B5FUTWKojr/lvRiSd+S9IeS/tXJt/0rSV88k+Oli4I8qpSUADjADa7WAT0NXiRp+btv8iGtq2uQOpKUA8RJcnLeNK/vY6AHO7x6tVpNdKH0RLBLILnOZrOp2dnZ0JQM8PaNAji3tB6losTxY7FlIKsDj4oxl4hK6+2B+e1KHU8aO23iFBkrH1RIXpPA+Oj1vrS0FO4NEbwnoRkPfLpz8dAxFEW58iftwM4F3Df62d4Ke8UrXrFj9PzHjh3TRz/60fM6xrnw9U81Ox9aZq+kO0+CRU7SZ+I4/koURfdLuiOKojdK+qGk65/sQHz5oS1cYw2Y8H8ScV78wt8AKwAHEPKkH6DkMsJWqxUiAQDC+8EwPu8xw45D0jpf7yA+HA5DwtA38V5cXFSr1QorFDh7pJ6tVku5XE4TExMhIvWxuyqGeSJKpnVwr9dLNDdzR/Z4q5x0HxnkmqwMoLEmJyfDF4tjsypCqYSDWFhYCMlZompoGqJwnAJzx/X4KqnX6wXNO4Y0FLB3tQ3OmtfOwTbs2d4qu/baa3eMQuZ8VUxLS0u6+eab9YlPfGIDR3Xh2TmDexzHD0s6pclDHMezkl50tsdzjtbBB/WFNwiDSkhTLzaGxLHdQRBJQ3HMz8+HBKekRJTqShF6tcMBS+sdCb1MnwQkQOT0wNLSkiYnJ1UulxXHcWhq5v3q8/l8oEO4FqdcOK5vYOLUEMvdQqGger2uY8eOhflcXV0NnD5jhvahDw/njKL1fjnw4dBYmDch41oZI8cnoTs9PR040iiKwhykHS5/93Pwf9fUp521O3bX3Lu65kxto5/tzbZnP/vZuvrqq7d7GGdsv/Vbv3XeLXMPHTqkbrd71i0MNtv27t2r17/+9SPRc2YkKlSJvrw4h0iZL65XU/J+p0r4YntS1pUqrluP41idTidsWgGYe2QM0NF+mDYHc3NzYTzeu4Vka1qlE8dxItHJj8s7AU+uzXl1nJurRKAknCKRlKiypStjo9EIBVrQGJ689NUGOQCvqI1OyhChTTKZjGq1WpCkMtdE4j6H7lTm5+cVx3Fi1cX9JP/R6XQS99jvv0fmTrvh2Px1nKzTRheyzczMaHp6eruHcUb20EMP6Qtf+MJ5Jxz/+I//WPfdd5+uueaaDRrZxlixWNRll1223cOQNCLgLq23wPUEJrQMX1DvuQ6N48DuX2K+6F4URbTQbrc1Ozub2D4Oh0HUSak8oMbmF7VaLewkVCgUAh+O80GLj8OAooFOItqnpQGfpSEa4IyToMiK83IsnArXDJgio6Q9QS6XC5uUOKfttJRX19JbxmkNqBPPQzA/6P6J/plPqJF0MzGS5FyDS169bw1qGl73++vX7HkEf3ZY3Z1L5L6TLIoiXXXVluVtz9seeeQRHTx4cEOO9bWvfW3kwH2UbCTAHdAAWOF6AWaiZ0kJjXhaYeHUDCDIZ4na2TsUIHfuWVJCt+0yOuR6ACSghrKE1r70XWePT4BqYmJC09PTofqVzb5dP16r1YI+nuiffi1w4KfjK1294pWtgBu9XPicOxJ4eW/NC5DinKR1Gag7CPIKADWOzStu/R6hjc9ms9qzZ084jjtEngdXA3F/cHjpDT1Q+bikk/dvQJXqSFs2m9XP/dzPbfcwzsjiONaHP/zhDTverbfeqv/wH/7Dhh3vQrORAHePxODDkS/Sg8UBFePLzhfYaRiXxEkK7QIWFxcT1Ie0XjQzPT2dkBcSdSO/k5QoCOp2u0HWB5h2Op1Ey1yic1QflUpF9Xo9UdRDlMtOTJVKRRMTE8GhMC84OOegvdyeilHvY48EEf05MkUqO6FWSqVSiKwZN9JF3zwcigkQZa7g0X28vqLyhDebmTQajZBwTUs7XcYI9eLSS0mB0iIfg8PmPRd61C6tJVJ3CiWzsrKihx56aMOOd/ToUf3Jn/yJXvjCF27YMS8kGwlwl9apEVdbSDrli85rRHQAnMsDpfUoloTm4uKi5ufnA2hzHNQbExMTASC8YyG7MwHq3tnRk5LI9VgJoCpptVph5VCv10NRER0dvaKSVQqA7FQUCUJWLN7wy1cfyBcBQrjxEydOhKia5DURs9NOcRyrVqsFCab31nfH46sDb+42HK5tmM15iegdvHGICwsL2rt3bzhGmtZxBRXHJ/eAg/E8BMf3azmfYpmdYFdfffWOKVz67d/+bf3gBz/YsOPNz8/r//7f/zty4H7FFVeoWq2G9ibbZSMD7p5sBLCkda1zpVJJROkAGtGpL+sxgAANO393JQotg9OtDZBIojnn/a5r92QvYO9ctkfenmTletPFRDTtAkhJnHI8JJjpKl4i2zTN1G63NT8/r4WFhXANRLsu6XQJZK1WCxQWQMwxc7lc6E8D0KPlB8T9h3vAyuJ0/fobjUZQ0eTz+URy2fl0ngeS7swlVBW0j8s9z1eRMeqWz+e1Z8+e7R7GGdlgMNAPfvCDC97ZStIrX/lKLS4u6hd+4RfUarW2bRwjUfXgfCqRN2oQkopEka7G8IRaOrFGZJrL5UK7AS9AQqfuUkp48F6vp8XFRS0uLqrdbie4YfrA1Gq1AMCM17Xw3W43dI8EZDw6ZycmHAjA5Pp46Bq6NTrX7bpwd3ZYt9sNtJG/XigUVK1WQ98VFDDs2drr9dRsNoNuHufFfeH9/X5fzWZTR44cUbPZTMy/yz85n99bT47Oz8+Hz7ryhutyJwqnTgSPo8NxUiHs1cQXMphcdNFFevOb37zdwzgjO3HihD72sY9t+HG/9KUvqd1ub/hxz9d+/ud/Xr/1W7+1rS0SRiJydxUEIJ/uw0IkKa3L5NKNuVw3DcBQiu9LeDbaAHyhF1ZWVtRutwMXDUg4gPv7+RtjSHc6RA0ChQDIe38VSQHAAV2i+EqlEpK63hsHB0JOgSTjYLC2FykOAyrJ9fidTifR3KtWqymXy2l2djbMEz1gXB4KteKKIt7HPBaLxcD/4sRwIPV6PdEbCIBnzIzJ9051pQ2OAQeKZNM3IXeFD3N7Icsgr7766h1TlXrfffdtiqPleRlFe93rXqdvfetb+s3f/M1tOf9IgLu0Xn3KBhgkMh1IiNS8xNy5dt7rmux2u51oO+vcLsC7tLSkbrcbWgf7akBadzBEzwAUYNJqtYICxncAole5b2zh6hKXNHKsfD4fVhlcAyDpkawreaIoCtE9Mk1WBQC7zxnHh/cHGHmf9/fBcTinThIWgCZBCyXCdoQAuzs+37AcsO/1eqGwilxCOpnKMSQFqsxXcaxQnJNn7i9Ue/3rX79jwP1//I//sSn34lWvetXI5hwymYz+9b/+1/r0pz+tY8dO24ZoU20kwJ0IlFJ7ImdJiSgPwEvzsM5fcyyA0PXgyBTRbBO9dzqdBO2B+kVSUHMAJOjFneOFNuj3+6G/DMoXL/bh89VqNaxG+Czte6GFPL/gW/gxJq7bAY4VgCecGSf8OXPlVAlKHECSJC7HpbdO+p6ko2Rf/dACodFoJHbBYiMQVDeuj8eZ+HPhihtpXa3kiVYcoKt4/HgXYvTuz8Oom7fyeKrZAw88sG393Ufi6UDjjTzPlRLOxZ5Os5wGeKJ/gA9qJIqioKIAOJEnepTu/D/HA7QAG/5NNCqtt0+gwIf2tzgXIlVala6srIT2A7S9dQAmUei7Ijmo+vVJyS/76aJYrhsqhjlho/BsNhu6XLpu3RPWvvE0wO6rEs5N9M1GJTMzM4maBUnhPVwP+n5PkKbrF1AtuZTVk+rMB7ROOt9wIdk//If/UC960ch1Qjit3XPPPfryl7+83cPYFvvLv/zLbcsJjAS4S9LCwkIACk/OEXk6DeOv8R6nEbwp1+rqqqrVqhYXFwNoUMxE0ZGkwHl70s7BB0rHd4SCC/fo2M8PuHtTLvrPT0xMqNFohBUDvVfS3Duf9Upbb0/AfKSTnsyXd4xk7KhW0K7Dl6cbeXnREisiZJ04La/CZcyeGF1ZWdHs7KwmJyc1NTUlab1VMPPkSh9oKZy2vweeXVp36tBFLhHldf42qpzs+djll18e7uOoW7lcVrVaHdk2vZtly8vLOnz48JO/cZNsJMB9dXVV3W43cOoAof9Ip0bu/gV3bTSRHdE0elP+jSMB6AErAIsoFa6+VCqFRlcANqsAHxfvQR+PWgVtOcADYAK+ACqUEWDlzsHVROmkJHOYbtlQq9XCaoFqWa7ZQZkWBzgQT0jjJJ3+4fgTExOJlUz6Hkjr9Qv9fl/tdluNRiOMOV1Ny1w63eQ/cPqY9xfiPPD6HMtpngvFKpWKfuVXfmW7h3HG9tM//dO69tpr9bnPfW67h7Kl9thjj+nWW2/dtvOPBLhLSgCL/9uX1a4SkdYjdk9yQikApgB7qVRSq9UK+4uiBGFTCwAYHjybzapWq6lcLqtYLIZ+NvDnKG4AEgpzAHn4dsDPq1eRHZJE9WImaBFP2FKpKyU33HAViTcA846IKysrajQamp2dTdBP7NFKNEU+AufAvx24087We93Tj4YIm/Ey1ziVdrutarUa9PusJjCuKb0yS/e0576jTOIY1ArU63V1u91wHReSve1tb9PTnva07R7G2EbcRgLcATSXvwG+rmGW1rltAM6PIa23oYXHldYcR7vdDtQMn2dDDHTTNBLLZDKq1+sBgAaDQYKyAXx9NyEibvIGAE82m1W1Wg1Rrr/uUTTXTqKXOfDkrF+jFzDxb8CWKlv46Xw+r0ajkYh8SRJLCrsd0QKBtsZelMXYXIlEMZG03vwMcIcSSTcdW1lZUbPZDMVLUE+sdlDvcB/SzttXBRjOAKDnOOl5u1CMwrOdZL/0S7+ku+66a0M32bjssst0ww03bNjxLjQbCXCXlKAB0pysR6iYUzWulHGFBRTCwsJCKL8nIqRhlksSKWzCAXi5PgoN+kcj/aPFABEiwEOzLkCr2WwqjmPt2rUrSP/SLRZKpVLQe9fr9dBPnWt3dQ58azabVbfbDbQL78Vh4UwqlYparZaGw7Wt68rlcigQI3/ASmdxcVGSgvMjyvdVBVp85/CZN5qupatJXULJpiResAVw+712CaZfG9fuwO/3EgksrRTGtr126aWXht5DG2WFQkGNRmPDjneh2ciIZPlSpykXokeXNjqgu1LDk6rwsQCrJyGhECQFRUq9Xk9UrLoqhKgaYDx06JAee+wxPfbYY5qfnw+ac5ceAv5Uf8Jtszcrla8AOzQM/L+U7NueTuy6sgTlCYDKvLAymJub09GjR3X8+PFQDk10zUqBeQIgHTTR8pOvYDtCry1wGSPHZPXjToFk6XA4VKvVCqseX6Wl77/3ifFVj6/ceD58FTQYDMJuUGPbXtuzZ4/e/va3b+gxDx48qN/7vd/b0GNeSDYy4C4ld/ZxThVgSEd07gi88MYrKR3YPTEHPwyn7gVDRNAs99nA+ujRo5qbmwuJUhQm0AEeXQLaJIvL5bL27NmjYrEYImgoE6JTb5frkkyuyxuhcS4cH+0ZGA/AC2/vLQ5YTbTb7TA/XqBE58g0RYLzoEUBIO+RtEtEi8ViaDTm8krGwbmceuF9jJ975u9hPE7RkKfxlYXnX8a2vcYKcCON53tU7aKLLtIb3/jGbTv/yIB7+ssrrUfz/mX197t5QhKOuN1uJ1Qqrp+mEpaoMd2ECyAj8m2324lcQFqq59G1OxtkhrVaLTR5ajabWlxcDJtxLywsaG5uLiRtAUH4616vF1YC3oMGMHQgXF1dDX1xvJLTWwpMT08rl8uFVQLnAsxZBWQyGVWr1fB3B07qBAD6drsdaCjfuERSOA6bY6ej63a7naDi3Il7PyFXxrhj99f4tz83Y7tw7Qtf+MK2Nud6IisUCrrxxhu3rSXzSIA7X1T/Anu0RkTrW8D53/ks3C+RNFyyq1EymYwmJiYCZcD5WSHQPwYunWpVpwO8Xa5ryz3qdIoJ9Qrb+lEw1O/3A8gjyYzjOFFp6rx/OoJlReJ9bjqdTmg9gHPL5/OJHvNYr9cL2np6zBCFM2YcgDsQrhvnxcoAdQpOGfpIWk+Ek/Dl85yHZLZTQb6COV3bCXempwN/78cztgvT/uqv/ipIlkfRfvInf1IveclLtuXcIwHubg4iaKM7nU6IEjudTgACj6AlJSJ0OhsStRKtVyqVcA6ifH4vLi7q6NGjAXAB1kKhoImJiZAs9KIkX/ITGUvrESi0C/2ds9ls2I8V/pzWAxwPisidns8Lx/d/O4hK63uaOrcOuM/OzkqSarVakGZWq9VEkZADLPJQ/5EU5oNIPJPJhAIxNlZhrvhMLre2t6s7QKgertcTyNxPj9adYjtdNM998Oh+bBem9ft9/ef//J+3exiPa5lMRm9961s1OTm59ed+sjdEUXRrFEXHoij6lr02HUXR/4qi6Hsnf0+dfD2KougjURQdjKLogSiKDpzpQFzCyFIf2oLOf1AlvtG1c8KAkXdCBNBKpVIAaHTq8MLNZlMnTpzQwsJCUFl48o5olfOhRKlUKkGlAo+PM0g7jkqlossvvzxss0c0yji5BqLbNH/vVIx0auGWV6ICbu5sfLs8ovPJycnQLgFHWCgUND09rXK5nEhC+65U/B+tv6SwauDeQDV1Op3EtoEkVJFcAty0NgCk07QL/3b1lIO2O0L/SaustuPZ3kibnp7Wy1/+8u049XnZ4uKiPv/5z2/4ceM41g9+8IOR7l3zD/7BP9DVV1+95ec9k8j9U5J+NvXaL0u6O47jZ0q6++T/Jemlkp558udGSR8/k0HwpQUU2u12AHM6GHrC0bsoAl6ATCaTSST5+BugWiqVAth0u10dO3ZMc3NzkpSgNxxEOLakoLSh42Iulwsa70qlokajoXq9HhKJu3fv1r59+5TNZlWv1zUxMaFSqaTJyclQnITzkBQcDmB1upJ6AN658NMlhtF603ueLf6mpqa0e/duzczMqFarqdFoaGpqSpdccon279+vqampcD0O7Dhg7gf3BwoMY5zIKhcWFk7bnoHKXxwjDZbSeQsHehycV+Smde/u9J4I3LUFz/ZGW7Va1Y//+I9vx6nPy7rdrh544IFNOfZdd92le++9d1OOvZPtSQnJOI7/NIqiy1IvXyfphSf//WlJfyLpHSdf/4N47dt2bxRFk1EU7Y/j+AkbLBDRodJIt7flPXDafJkBMzeAAhAkiqZYCLAgqTkYDFSpVIICxqkCInKiVFfouOSS1UCpVAq9aryAisZcDpjpDo9eHITD8GgUcHV+WVp3jOQLyuVyGD/b+eG0oLeWl5fVaDRUrVY1OTmZkDLOzc1pfn4+bHTiNJQXdeFYAHLG7vcHhQ3J4EajEeoEnL5CvZPuKUN+AiCnW6jLLzOZTCi6Yj58fp7kudv0Z3uj7Vd/9Vd3ZB7h+9///ilOeKMsnUsa25qd61Oy1x7qI5L2nvz3j0n6kb3v0MnXTvkCRFF0o9YiIO3duzdQMC7fS0duRH98eQFslCqSQqGSlOzh4rz13Nxc2Cgbh8FvjlkulxNl9IyjWq2qWq0mukQCUO5ocEzw3o1GI9AenBe6BpUJUjE0+o+nCvFzMAbAGefhG2pDfQCo9K+nQVmpVFKn0wlFSNBNcPB8ntUAChtoFE/wcj5vOIYzwIE2Go2EI0AeiUrJ+XKcBwlbjs81R9FaQRoUEc+CU1hnyblv6LO9kVYoFHTZZZdt9GG3xD7ykY+MtGzxQrTzDgHiOI6jKDprlxzH8SckfUKSnvnMZ8YLCwuhkMcB2RuJpRNlXrHJ+307PWk9kisUCmo2m5qfnw/AzGel9ZJuKAhoIS/Th9Ihouc8UEbeF73f74ce9VNTU5qamgoRqs1BIlp3YHLKgU6JJC1dpeOJReanUCgEAMfZMQecHyeDIsa3CWQFQLTvjcNojra6uhpyGDhknJKX/HNM/uZzKa0DtevfceA4I1dKcSw+66od+ulzbN5zrrYRz/a5fP7x7PnPf75e/OIXb9ThxnaB27mC+1GWpFEU7ZfENiOPSrrE3nfxydee0IbDoRzcPXHnhSnejtb3L/UKVZQdAAXA12q1QgsC11LzfzhgoscoikKrUi8mAiwcyDD4ebh4wKjZbAbHgG6c4h2ifnh7QJRr8pWIR7WAqSdN6arJ+PkcbQQajYaOHTsWJI+0GYC+8URkt9sNVaYoaRygUco4RVYulxPNuth8hJwC17uysqK5uTlNT08n7iPRPveSc7mDQhaaVinxzEDbpGWuZ2Eb+mxvpKVrAHaK3XvvvfrKV76yqed46KGH9PznP39Tz7HT7FzB/Q8l/StJ7z/5+4v2+tuiKLpd0tWSFs+Ek0QDDij6xg7+QAPUbCM3MTFxCki7Vh1AaLVaobTe+3sTZXtHSEmB2gDoaCObza41Aev1eqGvOf1VqtVqosITcIRump+fTyh1oigKbY4nJycTpfpp5Yu3NyAS9mpQxkr0K603F4N7pxiqVqsFRwnQsjUfBUX01YGGmpqaOmU1hRNk9ePtjL1PEI7K97Tl+M1mM1SzOmA7wPN8SEpE7vybcTAmKCnyH+cAhhv6bI9NOnz48KYXGt1yyy264YYbQk5nbGcA7lEU3aa1BNNMFEWHJP261h78O6IoeqOkH0q6/uTb/0jSyyQdlNSV9PNnMojV1VUtLCwktpMD3NKSREmhCMgTcAAxnwVMjh8/ntggm6g+l8sFvTyfh/cul8uJaNDPD0B647DFxcWw8xJgJSkoYVDIRNH6Zh2sMpaWlgIN4tWpgLlTDB4J4+icT2ac7KVKJIu6CCcEf812f7Q0hoYimgb4Wa14bgDundWKR9+APc6H++SOi4h8YWEhbMWXNk/OppUyXBcOwx0JiiZ682zns/1UtziO9Wu/9mubfp6HHnpIb3jDG/TJT34ysXvZU9nORC3zmsf50yl7fJ1UEvybsx0EwMsX2bsHevl4Gthcj+6GsoOyeLayg8d2TpeomISnc8aAKQoTVgOtVitRvUkidHFxMTgGVh21Wk2VSiWhLqE6leKlfr+vVqulSqWSAENPUnqEfnKuE8qQtF6cJGS6DoBxci3IEUulko4cOaLBYKBSqaSpqSmdOHEi0FyAZTabDdr1SqWicrkcOmb6BuYun/TxcX9IYC8vL2txcTG0RMCRcK98xSIpIUP1SJ6VGw6+Uqmo3++HFdXpbCue7Y202dlZnThxQjMzM9s5jLOyv/7rvw7032ZaHMe64447lMlk9PGPf3zcLVIj0vLXl/yni7a8wtSX/J1OJyzBPYoFkOnfDj2TllcSMbu0zAGGsmbkfFAJUA/D4TAA8vz8fPh7JpMJWnd071EUaXZ2NuzgBIiSLCWPkFbhpHurMB9pqWccrzUP881DOA8cOw6zXC4HgCSfwDxBJxWLRVWrVR0/fjwAJmocKlBxcGjsGRfOLp0vkZLUiifHl5eXEzQbiWN/BnBYrH7g4DkeNFEcr7U43rVrVyJnsdPta1/7mu655x5dd9112z2UM7bPfe5zOnTo0JacK45j3XbbbXrd616nl73sZVtyzlG2kQB3r8BkowUoDSJsByCPYunG6KAsrUU5rAigcbw3ST6f1+TkZIhKfacfj5qpiGUccO6cn02u08k8FCscQ1KgOKhiZVUBoKFRd7Aisey6cJcnuroEsHM6htdd/w/4As7QMByz2+0Gioy+7z53vl8rPD2AipqGa1xZWVGlUkm0VHAQ9jleWloKm6JwP52HX15eDqswVw15XQTJ3Pn5+UDZXCjgPrYzs3e+85166UtferYS2AvORiL1DhhAG/Dl52/OuzoFw5fZy/yz2WyISj1K94pFNsPwxCXHhn7wnjYAMoVWjAn+Hv02AOO9YuC/oXBIHNInh0SvS/wAa5dD4nC4bn7jZNIaeD+m5yIARipoc7mcFhcXA98ure9py/F9Uw7+zUoKyklSqEjtdDrhfnAMdxDejZPrgKLBQfqzwX32fAXX6DSVc+/D4VoPHa5tbE8d+/a3v61f+7Vf07e+9a0nf/MW2d/5O39ny885EuCe5s3TUjfpVGAHMIg4XUnTbrdDxAZdw//paeLVrr4nKnppdNVeiQpoQZ/gWPhsfFJLTmIV7fnKyor++q//WrOzswktN0AFbw/dAy0FIDpv7XkD5oHz4Ki8J4xXwvomH06XAIb06/HVSqPRUKPRCK/55wFjetNA/9DrHb6e43O9mcxaC2CSyK4C8kZnvkJjZYVD5pngHp6OV+/1evrhD394wYD7lVdeqZ/5mZ/Z7mGMvC0vL+u9732vbr75Zh07duzJP7AF9ra3vW3LZawjAe5ErC5xg1YACCWdEgVirpRgQ2xAAzCtVCqqVCoBzImMiUKpJO10OqE3OZptinwmJydVqVTCuKBkABzO4eNCBjk3N6d+v5+Q6U1OTmpyclJxHIf+L4VCIfSc8WjcI1Yvt3bVjMtH0aIDoKVSSbVaLbRCGA7XGqzxHqJ/T8xSxZvP54POnb1ZuQdOlcDrO9UF958eM3RZtVpNOCvPOfj1IkXl/uMAOBbPi1ur1dLDDz+8sQ/rNtq+ffu0f//+7R7GWdn5FJGdr33hC1/QP/7H//hs6xw2xaampvSc5zxnS885EuA+HA6DDtb5YqcdTkc9EKU6dUOJuvPI5XI5bBQhrSfnstls2IwDZQ2UiXPwrjNvt9sBBDOZTJAEpnXgtNFlpTAxMRH2aKXHTLValbTWepf3Evl74tg1405leATrTbk8QsfpkODFobB5dDab1dTUVKI/C+CPpNA7anougevxVYTPG7s94QB8NeXjxuG4KsevE8D31/zHV3448+XlZc3OziZ4+51smUxGv/7rv77dwzgrm52d1Yc//OFtO/9wONQD2AuVogAAIABJREFUDzygd73rXdveNXJ6eloveMELtvScIwHumUxGc3NziUSmUzNpUJeSNA2f89YDfAb+G2ByZ9Hr9cIeqHDTRLlQIa7SoNUv1AwJUN6XyWS0a9cu1ev1AKLVajUAKJE/US7JSVrsErEDzq75d/B2qWW6wMsB3itf6YhZrVZDFE/16cTEhGZmZjQzMxP6vuDAWJ14p0qSm4PBICSJ+eG8fo/YSQqnwH2DEiIRy7FJijKn0Cpp+afr5T1HMxwONT8/r3a7neD2d7rt3r17u4dwVjYYDELH1e2y5eVlfeADH9gyxc4T2atf/eot3ZVpJMBdUiK5KK3zqdKp+6UCYhTQeEsAABqqwzfWoMthHMean58PAMDGEjQFo00ulALRI6sCjgOQso3e1NRUaC9MVI5GHumg0yeDwfoWfDgfj9C90yLRKBSFUx1OzeB4WD14vQCrEmgN3kubgenp6VBwxRx4q4RarRaAFi6fAinG4S2VvYKUCtnFxUV1u90E55/JZIKjlJSItnGyvnLzBLg7NGx5eVlHjx5N5Bx2uu3EtgPbScm4dTodve9979vuYehv/a2/taUFViPx5BOlzc/Pa2JiIvDhrkBJKyPgy4kyJSWW9Ejq6HYIcCwsLKjVaiUqVp2z9YQpWnZJoSmYV186wBAN04sGmaFvAQa3z3Ec0Pm7y0Cdp15dXQ3j8TkB3Lge6CjnplH7cDxv5+srIKJ4nF2lUklQW9wnwJxzM34cSDq56/JHksWAuiej6U7pLSL4nDdd8yQz4/dCuFarFeScp1v17US76aabdOmll273MM7K3v3udydouu20UXA0mUxGz3rWs/Too1vTkmgkwF1au/ATJ05o3759pxSwpOWP/oUlQpTWVTeoVXAQ0AHew91pF0DQt5Mj8oUWoFq1WCwmAIfiHN9kA8oCrp4xtlqthGYdLp7rYnXBagAngwMi2nXn4hQIc0Y7ADhsSYkeMFEUJZwQ801RFvucMpfIVKMo0tTUVJCakqCmjQPnxxFCu/gcoE2n2Msbn+HcnGbx/Ig7K3fq7sBWVlZ0/PjxMDcXCiWDbHUn2fz8/HYPYaQsm83qpptu0le/+tUtOd/IPC1RFKnX64VmWmlte1rb7NGrV2w64EkKy6DDhw+r2WxKWqcU/Liu3aa0Hs6ZHixo3unRgvadgpulpaVE0hEqgupWL8QaDAaq1+tBaQKl45E03DOAmL5uHFga6ODC0dazEvDtBwFsisBwmKxAJIWE83C41rWzXq+rVCqpUqmEathisRhaN3jnSMYvrbf3JdnMNXU6HdVqtTBGFDFQP2n5ZJqyS9M49MJBMSWtO4exba098sgj+uY3v7ndw3hK28iAO3K7Y8eOBUUJYOY0w+m4eAc2okneNxgMND8/H77wUAB0J/QkLpE37Qy8Ta3TNnDXRJfw73Rw7Pf7gV6Ioij0RneqaXp6OkgT8/m8ut1uaDGcVsg4Ty8pALvTOQ5w3hOflQT/ZjWQy+WC8wBM2aP2dIoVwJtkr2vRGUe9Xg9j4JjQRK5PJ5HsTc7oVskxMcbtUk3G5PPDGGZnZ8PK4UKL3neS/eEf/qEefPDB7R7GU9pGKkuTyWRCtacnDfnyEqF6QtAjd4CbKBRgJyKUHp/iAQx9Nx+4aY96oS+IaAFckoXsF4oskAidzTokJToromaBQgFg0/JG5sednFs6ucq1skpwkKSnTqlUCtfrVbGsUk6cOKFOpxPmgeumcAlVkSdXkV6SVMah8BtqifwAHLnv6uQ7a7mWnev2++u5idXV1SCp9cT0hcC5f+5zn9Phwzunw/DHP74tW8w+rjWbzZHh/7fKRgLcPWFKMswrG9PFKs7Hus7dQWE4HIYbCrB433CUNkSTbF4Nh07VKjQBlI0bSpHFxUWdOHEiyCqJhgFiaJyJiYlQHYsihQjWdfleTMRv/g2weQTLNXgugnkiigeAcSjpjbO9jUA+nw9NvHq9nmZnZ0NiGpBnheOJTE9g1ut1NRoNzczMBI295wpcWjkcDkOTNynZSI7/MyfOOzuAs2JCeodD9OPsZHv44YeDWmvU7c///M91/Pjx7R5Gwj7/+c9v2gbdo2ojQ8sAUgB8o9EIyTW+wA7yfGGJ2DwaJGJHqeJROGBNlJzJZDQ1NRUqTwESj5ChcCjoQcrHhh3z8/MaDodqNBoJakRSkCOSYJXW+7zDKTN2f81VMmllCNEodIfTJ3wGx8PnnLeX1pRFaPCpLKVqFVULTdKcnyeSd6WMKxG8+CrtdMgB0FjNaSa6UU5MTITVijcE85WHgz4OX1JI0jpd4+8f2+bb8vKyPvWpT2l2dna7h5Kwp+JzMBLg7hOfza61z92/f3+IPgEfgBfzQieSi8ViUQsLC+p0OglKxykJkqBszOH0B87CI/ZsNqt2u61CoRDAju3yeJ2t7AApKlO9qAdOHjrEnYnz45OTkwG8oR0Ym+cX0soRd3zpijxv5cBc9/v9QK0Q3XtzNBqoMX/o59H8sypB0eNdJ4nofTckaBpX7XAfpTU9MvPqIJ1+Tvw1wJ2krwP/U+3LPAr2/e9/X//9v//37R7GSNpgMNDHPvaxLTvfSIC7lOx02Gq1gqKDKNa/2OmmW4AbIIRUz8HOKYvJyckQqaJn59zQL/SBabVagcctFAqBPnC1irRG66Adr9VqIfJsNpsJAPcWvqejl3ylAOB6YtkdlQOkAzx/I9dABC8pMYesMKhaZZy+0QaJbV6jDz3zDM3DLk0+Trb94xqc6iJp69QM1ArqJK6Ha8b5OIfOqqHX6yVaQzDmC4Vz3yn2vve9b2S57bvuumtbG6/FcazvfOc7W3a+keDcJSVAWFLgeHkdIACwpfXkqINIp9MJx0lH7lRCQvkAsgAGwIB8EQmj/4YCokgJbThROjtAxXEc9k9l5yaXCQKwnpSE6nFVyOnUIU7LeITqETEg7bSOJyQZg3P6uVwu0CKe/ERe6Elu1EP9fj/UDjBnOEYar0FH4Tjh9HEoTkOhmfeo29tROB2XLlxi9eS94H0rxrFtvo3yXH/2s5/VD37wg+0expbZyETuzgX3+33Nzs5qcnIyUVnpETxRHdw2UTy6cWgEgJBui5wLvpdokx4p/AYMSbwWi0W12+0QlQO0UCysACi1d2qEnYqq1aq63W5wBhRKMT5AF2D1RKq0zl27ZNCdEu/BaXFtXhTmCVHmhn1cvT0CrRGq1WqIyp1DJ9Kenp4OLRZIDvN+34Ach8GKgOuMoihQP5ICGKdbB6STyFIyaexFUYzRn6uxbb5985vf1N13373dw3hce/jhh/XKV75Sd955py677LLtHs6m28iAu5QsKup2uwlpIMAAOPAb0CGSJlJFx726uqp6vR5kkkR0kgIQLS4uhmiUPuaAA5EgHSNJ2FLEA6fs4yI6JXGIyiOXywUJoYMyGm+cmFfQplcv6WSv5yDc8Xn0zm93GMypR7nMPxE/bQigmFAL9fv90HuH5CugDLC7LJNzQ0n5frKZzNqWhL1eLzhUql8ZK/fBqS13TLRVdo7eC7zGtjW2sLCgI0eObPcwntC+/vWv6+Uvf7nuuuuuCx7gn/TJj6Lo1iiKjkVR9C177V1RFD0aRdHXT/68zP72K1EUHYyi6LtRFL3kTAcCiBCVsmGGJxU9cscASi/VhyculUqhyyHqkXQjMN/mjiZiUAlQD0TjVFUuLi6GsXkrAkmqVCrat2+fqtVqSKwi+/N8ATQMY+E1p2+cgvBr98j18YqOAEMcAUDuTgauHc09KwnG671sGFuaconjOOja0xuVOG3EOHGMnhzOZrOhDz6A7eCfvi7mBFqGHI3r/z0Q2O5ne2yjZd/4xjf0ile84oKnaM4krPmUpJ89zesfiuP4ypM/fyRJURT9XUmvlvSsk5/5WBRFj//tMuNLyZdxdnY2fMn5EhNxO11BxCutb/SQyazt9MPenSzVOQcA5RuC4Aycs/ZzQuv4ZhJ0U6QfCxWcRM4AFhEtY0Bzn8vlQpsDT4xCV3ifGaSD/FApyvW5osXzDP43aV2Z4uN0qgY54sTERCi8chBOJ3nh2b2NAc5YWs+lAO7uFHHGjBuqyttJMCcuEfUq2OXlZS0sLIRjMVbftvEJ7FPagmd7I+yGG27QRRddtFWnOydj68adYF//+td1++23J2S8m225XE7/9t/+2y0735OCexzHfyrpTJsyXyfp9jiOl+I4/oGkg5KeND3tKhFUDtFJXTRfYAdalwCm9c+AD5tIELXzHiJx+tj45ta+zR3n5e+u/qAXDD/QGN43hf1IHejm5uY0Nzen+fl5LS4uhtJ7r1YF0HFqRPVQIETpp0um4nSYQ+9LLynQVcyvlOT3mVMi+1qtpn379iU203BtujtBOPjV1VXVajU1Go3QWsE37WZF5uAMHYPu3q/N8wauGvJ5YV9XxiUlefntfLY3yi655JItbRd7LvZTP/VT+qf/9J9u9zDO2G6++WbdfvvtW3a+KIr0zGc+c8vOdz6E5NuiKHrg5NJ26uRrPybpR/aeQydfe0JzXtXVGy475Md7h6M2ccljFEVBuUJECo/LJsutVisRcQKi3jkRwPMeKcPh2l6nk5OTYfMNZJPw8RRSQU90u10dOXJEc3NzQXUD7TMcrpXzo6ZxnX26kClN63gU69w7GvGlpSV1u111Op3EZtztdjux8Qjz7h0XPUdQLBY1MTERdnNiY3EchSc9uT9LS0vK5XIh+ezRO3vcstm15xpIXnOtgLxLInkdJ03OhOv3OcOpbuezPbbRtX6/rz/7sz/b7mFsmp0ruH9c0tMlXSnpsKQPnO0Boii6MYqir0VR9DWScd7/ZTgcJjaUdtC3YyR4ek/eec8TSQm+2DlqQMcjYPh1L24imm00GpqYmAhJWt5bLBYTPXG63a7m5uaC9to3DYH+8DwCES1jdZ4Zc5UOxr+hl9gH1lUnXCfAhyIJwKfClpUHnLqkkBzGUbLlXqPRSNQbRFGUWK1wPi90YuUEMCNf9Lln9eSrAkmJ36xqWAk4reWrmXR+5gxtQ5/ts/3s6SyXy2nv3r0bcaixbbO94AUv0FVXXbUl5zontUwcx0f5dxRFvyvpyyf/+6ikS+ytF5987XTH+ISkT0jS5ORknJb7eQk9y32Pzvnisuz2KlaPZuM4DolS70sO1cK5aOFLVIusEXWL96OhxzkgiBKEzTlWVlZCK1vvhogzYry+TZ93WkzTH8wLrzlXjQF4zJXTGj4/jIN5gv+GdvIGXmyEXSqVwgYnOFM21SYJi7bfqSFWFqwAoN2cBmKDcZK4vgpJJ5A9sc49nJ2dDasVab2/v7d1PhuA3+hnO4qi89Zi7t27V29961vP9zBjGwHbtWuXpqamnvyNG2DnFLlHUeRbsL9CEmqDP5T06iiKilEU/Q1Jz5T0F2c0EJP3wRl3u91QCu+JD19uO42RTqjxmnP20B9OQ0CtpEv0iVL5zRZ8URSFYh0+1+l0EpE3sj+cAht+SErsCXo6uSKRsq9k0PO7LNKdBXPowE6+wSWB5A38vN7V0Xl9xupg65QSji+TyajRaISo29VJvvLivnIuKKrFxcWw4nEHcfJZC+fGkTvIU1iWVtT4qs6d4JPZZjzbG2Fncw3baVddddUTKpTGtnX2pJF7FEW3SXqhpJkoig5J+nVJL4yi6EpJsaRHJN0kSXEcPxhF0R2Svi1pIOnfxHF8RsQnkSfAAz1A86+0goLojWU6D7/r26FgisViiOYcBFwP71G9pABUSATpgZ7P59VutxPNx/L5vPr9fugXw2YWfjwcBvy/X6v3UUk31OJzvhJJR7PpYxBRu3wS5+n0S5q2wrxRGNdBYjStWPJjueNMAzG0FNeQ7i/j+6oCDp4sTz8nrLJIpuKAGAPg743VtuvZfirZa1/7Wv3qr/5qqDQeW9IWFxdDO5PNticF9ziOX3Oal3//Cd7/XknvPZtBONUiJTfBdk25R4DS+obYAH42u75zkEfiHuGS9HQwJFp1TTh0RKVSCdQBETgPLvI+dxSAj7S+GoGCocsiDciikxp3QI9ImCg1HcE6sPt1e7QqrecXOK4nqj3qZYVwus6bJEZJOsfxmp6dTU+Qp9J8TVrvgMmYWC3hAJl7wBhn5x0p/RisAJzmcekqvD3n57lgTrm2J3juNv3Z3ghjtTO2nW9333237r333i0518hUqLrMUVoHRlqHuiLGAT4dycdxHNQhvpQnUerRLD/wwi6FrFQqKhQKQV2TyWRUKpVCpWq6OpZqVEDWC6q8eMcleh6Be4TvMk+unf97stD5eI9cnbbhfZ7UJBLnvewdiwPzhGi/3w85i127dqnRaIQksp+Xc3jLBsbBnHkOwJ0RoJ3JrPWxYXNzX5mdTjXV6XQSLRHSdBXzttPt3e9+944B+FKppAMHDuiee+7Z7qE8qRUKhS1Lbm6HjcyT719koj3AJa2oABg8YQe4E7US9TllQRTty/put6soikK/l0qlEjaxIHL11rw4AefLSQZ6yb60vlG1R/2APhEonSQ5tleV+krCo3OfA0AMMPZVTDpXwd9xKkTwRMq+MvJWyIAmeQMHThzX6cAVkK/VamEuoIzcoXLdvuJySsr7C/EMSEpIIP3vkhKOe6cbxXA7wer1uv75P//n2z2MM7JKpaLrr79+u4exaTYy4O7UQFo6iBZdUiKaBTj5TByvbQ8HcPJ+ANHBBL4X8AHckTd2u131er3w/lqtFgqf2u12oIq8ZB8DVD1xKq1r0AFsmo4BoowTMD8dDeWvpVcPXK+DONfPHPI+V52kjyGttWJwBxafVB2RSJUUHLC3+6V+wHl+tPK+ExZjhwJz58QKx8fKs+HOeW5uLiGZdGrPk+xno5YZ2/mbK8TGtm6rq6v64he/uGXnG5k7AOA6YNGkq9lshmhOSlISROPD4dpuPoAa0a33k2FLORQvjUZDe/bsCVvAebQvrXPXkgLYLy0tBbkjETAAQ2GQa7Wl9Va31Wo1seUcvLc3H+P4OA+4b37g+bkuV5m45tyTwcwTY2HFk6akfLXgvLfTRL5HKWMFcL2njLdR5nUcKXp5p5oYX7oK1+WdjHtlZSWsqCQllFIuO3XQH9vW2Zve9CY94xnP2O5hjJxls1lde+21W3a+keHcPZkKBws4o7E+nTyOaA2ahegPQCkWi4GmcSpjeno6IR2EgybSY/9VujoCuK7mIFEqKUgVXX5HSb2kQL8QQXNtrAo8WUrlKBQHY2RuoDY8Kexz5nPj/eEBc3d8rJDSuydxvcwtc0qUTgtkp4JokgbYu6P0sbClIddG4tMbqVFj4Goovwacrmvm/br53E7hqh/PrrrqKl1zzTXbPYyzMt99a5TtF3/xF8P3c6vsx37sx1Sv17dEMTMyd8A5Uk8aAsBEbQ5gHplRzSopQenAKXukTzdEokOX5UVRpGazmWhBKylUf0I5eLTI3qLSurKhXq9reno6AB4rDNecOw3jK5Y0154ugnIdPIDpqpg0f8/nAHHGRPdMz3P4DlZOFZHEHAwG4cH0L3Cn0wm5ESgql3FyT/L5fGhj4HUNjB3HxibpXnfgyWg2D/HnRVLC2eCod3LkTquLnWQEBaNul1xyyZY7oWuuuUYvfOELt+RcIwPu0jqY84Vk4lFneAk7SVC+yO12O2zejJ7dQdGBEQAgms5ms6E/DK1seS+0AFFgFEWJqlinQ7LZbJAz8nlX9RSLxaDCQWbpfDLHgIoBdD2CdzqGFYVz5Tgpzs98esIZUKWlAg6CcyN9hPtmsw0UN1AigA4UjPfvQc0ChcWqinnDqbnTxhk5v+7g7vPBHrnMLU7CV2OSTgH/sW2u9ft93XTTTfre97633UMZWXvPe96zJQHHSNEyaR03IEXUmJYxAr5RFAXpIwVLACKA5htBu7cG6Obn50NFbBRFIbKHx3caxCNrSSEy952e4KQHg0E499LSUijTn5iYCIlOdOCufPFkorQud8QxcQ0emabbFXMcL5byFgPSeqEUQI/j4Hod7OHmpbVIndbA0DnezwYHyKYgLH8Zj6SQb4DuwgB4HJkXn/EscA2uqkGFwxzsdFCPokjveMc7tnsYZ2ytVkv//t//e/3BH/zBjp/7zTRyfps9RyMD7pJOUYgQtRGdpkvUnW8fDAZBIw3AA2C0npXWi28ymbWe7zT54vfy8nJCE061o4MSUSrb0MEvk4SE42ccNMjiemgyRr+bxcVFTU1NhetJa8H9WnB2RLKAqecjqCyVFNo3QPug7HFHIimsTCi0ogiKylt3vJy32+2qWCwGRwgH77w/97HX6yVaIkvrSXTuqfeDofLUk71QRzhNjsG4yHt4gZSrjXaiPe1pT9vuITypdbtd3X333fr4xz+uP/7jP97u4ZyRQWleyDYy4O50i0ejcN2+4xAAjRQSCoPt1vbs2RPK5YkO03psQLDX66nVagUQdJ6XCNBXA64ooQ1uu90OzbEAN+gJovJqtapqtRqifyoxs9msWq1WOC4Vqj4Wp0xc504SlXnygqO0mgiqiQibSN0B13Me0CasmLhH0jrX7t0wSaR6ghqOn+N2u92EkojrKZfLgQLivYuLi0Fm6lQaeQGcj0fuODnuE31xdjLnPup2xx136GMf+5juvffeEFDsBLvyyiv1L//lv9yWczcaDV155ZX6q7/6q009z8iAu5QED0+qpmVw0roqBafAl7/T6ajT6QS+GaqELziUAyDhDa7Q1/N+7xwZx3FICvL3YrGY0OAT/aPuAcQbjUYojHIVCdeABNKrdD1vgBNgleLywjQf7RE950GJ4u8hlwD/T2LYI3kcCZE50b1X2noXSapxfUXhY8tkMmq1WkGOSnSNA+WYztW7igdntry8rGazmSha8nO5LHIrlr9PNXvggQd0/PhxvfOd79Q3vvGNoFzaSea06lbbnj17dO211z41wN2/lA4q0jooA35pPj0doQ0GA83Pz2vv3r3h5vlnMpnM48r8kEOWy+VQgCStqz6osgR4eY9TINAJExMTITomQiYSBkydZ/akIE7Em289ngKE/8M3E4HzN58rzsO8Ej17tO7SQkCc6Jw57vf7wVmQmG232yFZ7LSRpKBWIgGMbp02yxwjm13b3KTZbIYVhx/Hk+rIOFm9uVyU54gxju3cLY5jLSwsKI5jffazn9V9992nL33pS5qbO9MNrEbTXv/612/3EDbdRgLcpfXGWN6r3NUT8/Pz2r9//2k3hOY1IuNWq6VqtaqZmZlEKTvLeqLNdrstKanScVmil/1Xq9WEVA/gy2Qymp6eDklJ6A8+l97oQkpWh0JbuJIHLTljc0UJ700noBkbY8dpPZ50FGBnjr3iFhUSFaDMS6lUCo3PXNrp1AnXi+PzVQrXQ2EaqwIH6k6no263q3q9nli9ubPzds5SskOoU1YXQuuBrbbPf/7zOnLkSPh/v9/X+9///hAQPVEjtp1iz3rWs3Tddddt9zA23UYC3D0Bly5Fh/tlyz0AzdvtOpVAYrPVaqleryekfL6c9+MT3SEJhEP3IhhXbBCZ1+v1AM5E8kTlTueQoCWJg2NCQUOzsnRhkoOXd4nEERFNA2IkJ13C6MVBfhyuBYfhSWnAk/lFskmE7UleciI4vX6/r1qtFnh0wDxdAYzCCWcKj86xfNXiuQDO4W1+PZnqoO/3YWynt+PHj+t//+//rXe/+92SpIcffjjswnUhWjab1Wtf+1pdcsklT/7mTbJ+v69HHz3tPi8baiMB7k4jAD5OLwBaTpWkpYCoatCuD4dDHT9+XNPT0xoMBqFJGIDobQkACqceoHqgSCj8gWZxuSFAx3lZGTgIMkaqVIksOZZH+84Xp6/TVzNOOzAOzx94hO5qHpwQ6hPPZwCy/IbyIEp3aSXROwogErmoaFi5kOzF8bFiGA6HajabqtVqkhRkoqwCpHUaya8RjtfVMZgHAMz/TrU4jvWpT31K73vf+zb82IuLi7rjjjv06U9/ekd0cNwoe/Ob36xf+qVf2tYx/OhHP9JnPvOZTT/PSIC7tB6hARgOasPhUO12O9EUzKspHdhYzq+srKjdbgdKpdPphC8+SVUiS5wHvz1KrdfrmpqaUqVSSeyk5Nw1x4DqAfiI/AEa16FLSvRvIXpn9eCUhHPs6d84FBQr6e3l/PMkTEk2U4yUbkmMFJHaAVos4AD8epwPz+VyoVhpdXW9DTJgDv3C6gzunQ1Q+Iwni6X1nIkk9Xo9NZvNRETuCdO0cmanJ1Pvv//+DT3eYDDQJz/5SX34wx/WQw89dEHQLGdjBw4c2PEtKc7URgrcHaxd9UH0SYk/CpL0ZzwyBKBnZ2fV6XQCGDkwA7bw5Cz/8/m8Go1G+CmXywn5HoDl4MwYaBwmKUGBeM8Zb3kLQNFvxTssOqgzR+lEswMZ2n0SnnD7TlnA7dOIDHrKqS0cBgngTCYTmp0xXzhgl1L6qoJoHWD2rfxwNMz1oUOHQvWuJ4hxOh59s80f44Ua8rnxHIpH9U91++pXv6p/9+/+nb773e8+ZfMRDz30kP70T/9UBw4cCCvGC9VGBtylpLrDC4ZQmrRarYTUzbl5AATKAIBBmjgzMxMAhfdBN8B9ZzIZ7dq1K3RvhIpJAzvdHz2p6kk95/4dfDzq5pochHivt0wFoJkfHAXndX6e374yACj5DX3lmn9XGsVxHPrn4GS8XQFOCodB5EdOg+PgMInGocwYNzkT2j3gPKCpuC6Xb0LbuSNJK6ycjrkQpJDf/OY3df/99+unf/qnz+nzq6urOnr0qG6++WbdeeedOnbs2AaPcGfZLbfcoltuuUUveclLND09fdr3vO51r9Pzn//8HQ/+IwfuSOO8OIWf48ePq9frJaIxj7YdsAqFgubm5kJUeuLEiSBJxIEMh+v7iVYqFe3fv1/VajXRa52I8/9v7/pjI6uu83ewvfZ67F2vvcuuu0TQIipE/yGbiEZHiNOXAAAfFklEQVTqqqpaUOkqSnb/iLYNalFolEpNlB8kSmmJFP5IpLYpQYmyJAHS/FDSIrIgBVUkEDaLKEqTlhJYEsjaW1qJJWSNd/Gv8Xi89tz+MfPd+d7x89oGr2c8c49kzfOb996997033znnO+eeSwDxBcDU0tSgpc/gIMAwq4agR+AmrcJSBQruGoTkPl9SmPvVS2CgkoqSQV1OpmItGPaPVRjpDek6sDqvoK+vL9IitM753Ly3oFlDaoUzBhJCiLns9CZ6enoyKZi8bxoX0DFrmicVgC7+vZkDqmfOnMHLL7/8hsA9hIB7770Xt956a1rT1Mmjjz667HdHjx7F/v378c53vhMHDhzA1Vdfva5tHzt2bEMMjqYAd82GIO+tYK1BULUoeS6BjEqA9AYXXe7q6sL8/DwmJyexffv2eAw5YdZ1LxQK0aJmFoxatARLjQ9oNg6tRIKh8sKaJgjUKzzyWj6IqrQUj/cKTyke5aVVKRFwOWuT98yv/kSPhp4HKSwtzQvUq15yfIVCIRPoPHv2bCZrh/QM6apSqRSzkZRiCqFazkAnkdEb0LhAsVjMlDJQBUpRvr3dOGXKyMgIbr75Zpw4cSIB+xrl/PnzOH78OI4fP44777wThw4dwvvf/35ce+2163L9hx56qH3AHajPCNXJPNyvFthrr72WWQhAZ2ySduFEJs15ZoBzcnIyZsBs2bIFg4OD2LlzZyxBSxqGFqBawgAyAMv+KSD5uvBq9eoKS7TgAWSyTzRdkNav9kEpFHLiSq0ww4cKgNYvj2Fb5PYJ0gRfWvo9PT3o6+uL8wL0OanXoh5GT08PCoUCZmZmMDExgXPnzmW8AJ1MRoqHionbmgrJtqgotKgZA9U8T7OHqJy819QOUi6XMTo6isOHD+OFF15odHc2vfzqV7/CkSNH8N3vfhff+MY3sH//fvT39ze6W6uSFaNNZvYWMztuZi+Y2S/M7CO1/YNm9kMzG6197qjtNzP7opmdMrMTZrZvFW3ET+WsgbpVT0DjxBogO9uSln4I1eXgtO4JAVYLdm3btg27d+/G4OBgrO+uOeq8llroBGuCU15GClAvSsS0zN7eXvT29kbLnZN+eF1/DzhmBSuNRfhYA/vEdn06IBVJZ2cnCoUCtmzZEjNr+B3HRFAnRcSSwPRm+D+ADDAzy4We0N69ezEwMBAtddYHIt3CGjPMEgIQM3iYUqngrJY7LXM+A0035XX0/Wjku72Rsri4iE9+8pN429veloB9nWVsbAzvete7cOjQITzwwANvymC4/vrr17Fny8tqUgkWAHw8hHANgHcA+KCZXQPgNgDHQghXAThW+x8A/gTAVbW/DwD48mo6ojeL28o3U2hZchq7TjTi9wQA5kMTWFgwa+fOnejr64ugrgW0lC8GsmuSeppEF83QYCcVBC1ZnZxEAPIpl5oCyWN0m4FEeic8j0Jg7e7uzuTNs08UjrO7uzszcYrKhn3h9bu7u9Hf3x8VEsdNL0H5bFVYXV1dGB4exvDwcMyOUfpEq3vyOlRm09PTUTnx2XIs/I4Ar8Fmn9/v73mObMi7vRHy7LPP4tZbb8Xdd9+d8bSSrJ8sLCzg2LFjeN/73ofrrrsOv/zlL9/QdQ4dOrQhWVwr0jIhhFcBvFrbnjazFwHsBfBuAH9QO+ybAJ4A8De1/d8KVRT8iZkNmNlw7TrLtbGkLgp/7PyeFMC5c+cifUNOlQDW0dGB8fHxOLOSs0E5cej8+fMYHh7Grl27YuBSQSaPFmK7nLVJsNBaNeyvBl01DqDnaRGsSqW+2LQCFa+rpQNU6WjKn94/nUylRcg8tUTAZBukttg2j2Gfuc0+qMdBJUUrmhOQGKDds2cPurq6MDY2hmKxGMfEZ0tuP4R63rveR5b41f36fPjJNjUt1SufRrzbGyE/+tGPcNNNN2XKBiS5eDI7O4unn34aBw8exHve8x7cfvvtsbRIM8maOHczuwLAWwH8FMBueal/DWB3bXsvgJfltNO1fSv+AJajKAh6ymeTXmFmBX/kMzMzEegJwAzKDQ0NYWhoCDt27IhWZm9vbybrw6cOkp/WdEYCnXLWCi605glGzCtndopeh99r+iZQD1j6NEteH8jmvQN1BaFWLbl1zb9Xi54cvdaA8YFb3nPeD15feXEFW77opNZ2796Nnp4enD17FhMTEzGYXalUi5OpB8J0U11gm2PTev4ch7fgea/YF+3bCu/eFbiI7/bFkieffBKHDx/G+Ph4o7rQtnLy5El89rOfxeuvv44vfOELGQ+5GWTV4G5mfQAeBPDREMKUWkQhhGBmayKhzOwDqLq2cao6r8nsDgIHwbzWVgQEBT2tDMgfOy1zM8OePXuwY8cODAwMxIAI+WOCDEsOECA1ldBb1dpX9oWZOQrIIVTzxlmvw3sGXMSDdIpeWzl8DeLynmigWMGci45oKWLeU+aT816yTebtq8JQC58piLwvSjX5GbtUbuwrFQspn4mJicwz5vXzspL4CdSXW+TcBPVKtNa9KrjVuL8X891+szI4OIhLL70097u5uTncd999CdgbKCEEfPWrXwUAfO5zn4ueeDPIqsDdzLpQffm/E0J4qLb7DF1SMxsGwNkRrwDQqjyX1fZlJIRwD4B7AKCvry9o3RKg7q4DWTCrVCqYmJiIS8L5olUKUqRSBgcHsWvXLmzbtg2FQiFyzrQw1SLWqe+0xtknpS7U+mVwz1u2IYRYDMuNfYnVzTY1g0aDtUrx8Bo8T2u/63eaIaNKjMFmpb0IvswlV4XBxUyomHp7e7F9+/bMSjaqtNgu4wjqnfAejY+PRw+GeftacdIrLU/D8HnobFWl0nT7QtTMxX6316oYvOzbtw/79+/P/e4zn/kMvv3tb7+ZyydZB1lYWMCRI0cwOjqKT3ziE7jhhhsa3SUAqwB3q/4yvgbgxRDC5+WrhwHcDODva5/fk/0fMrP7AfwugMnVcJLezVc3mz9+bjMbhqlzfX190ZVnfjq/LxQKGBoaQn9/P7Zv3x4tT051p0Lxa5iybQAZENRPBUitGknhTE+dEUtRoNaStwQjH2xUheLvDQHTKwaeS6+AbbFPTCf0bfI4zu5lOqR6GwsLC7GImj5DPjvO0OV94bOlp7WwsIDx8fFM3ENTRpUOY9+otH0OO9ugYtIYgT5PLxv1bl8MGR0dxdGjR99U1kaS9ZXHHnsMIyMjGBkZaYr6NasJ2f4egD8H8Idm9mzt7wCqL/4NZjYK4Pra/wDwCICXAJwCcC+Av151Z2pgq7QI9/NHWqlUKwmSw9acbVrMWqNELXZmfBQKhWgZas65Zr5obj1Qp2P0O24rP64VLBWUfQYHx6Pb5JW53xcaY1uakaPKcLk/Ah2QXYFG+WiOk4FnLtdHS18nQrFA2NTUVCbGQWud4KwxCVIyXFR7cHAQAwMDMYtJ151lqiSVFZUQawvpfWKfCeRKeWmModHv9npKpVLBe9/7Xpw8ebIRzSe5gJw+fRof+9jHmmLi2GqyZZ4CsNyv449yjg8APrjWjnh3O88dJ8jMzs5idnY21iFhSV8ue8drsKJjoVCIgVPlxHk9pWJ8OiJQBwflxBVgNZDCrB89D6inIOrKT/q9Xpufnp/Xmah5WSHsi/ZXi5uRRqHy5IQg9Vi0iiXPp6VvtUwc3mOex3INqrD0nnByFM8pFArRyqYXFkKIlAw9DfXgaLVzLBwHvSKt5aMB1RXeuQ15t9dTFhcXcd9996U89iaVhYUF3H333di2bdtFKdW8FmmaknkaBPXZELROgfpC0FNTU5kMDwIPaZmuri5cfvnl2Lp1a8yo4TUZBCQIq0Wuk2uUB+ef7uM5AKInQOBTGkW5dR80VYpBi3Gxzgr/GNDURcIZBKa1zTRF3kNtlyDNe6rUk4IgJzLRy9FSx1Qs+ryobBnkpBLR9vhszOrr0nZ3d6NQKGBwcDDGBJh7TwpOc/uLxWJ8ZnpdrSOjsRH1sFpJXnnlFXz4wx9u6QU1NruEEPDggw9idHQ09/uVJtetlzQNuBMIlZcGsjwuLcNyuYzZ2dlYj1wt2K1bt6KjowN79uzB0NBQ/F/pgcXFxcghq8WrQUsNZKo1SODXvHFa2TobVikbtfB5LkWBmBSFKh4NrLL/Ctq6yDcBln3iPdU6M6qo1IMBkKGl2K7OUqXnoTz4+fPnMT09HVe40klUHBufH9vlotycDaszVHt6ejIKr1KpZOghHRvvj1rtbE+prlaSO+64I01S2gQyMjKybA2ZT3/60xvyXjYNuCv9onyx/6GGWmphuVyORaQ0BRIACoUCLr300ggiSjswY0SvDWTX4WQ/lPMmoKjFrny4Wq4ENm/t81jNylEKSJUA21HKiMLj2Q6v7a1lnu89IVU8WpaYNJUuWn3JJdVa7ixboCUDeC7vp/do2LYWINM/XmvHjh3Ra6LC4PmVSgXFYjEqPn0fCPY+xsB9+tkK8swzz+CRRx5pdDeSrFK+9KUvLVlIfHZ2dkOW2AOaBNxVu5GTViueoEw3fGJiAnNzc3E5OKb20SoeGhqK/zMdj9fUxR58DrQuGE3QpGj9FM+X+4kz3mLkNRVk1brn8aQmdJFu/dMaMGoFe9qIohauWuu8hvaByo4zQrUcAxVGb29vZuwEbypRUmJas0ZjDbw3vA+dnZ3o6+vDwMBAvLYKPRJm50xPT+fGMzxnr89js4sqs69//es4c+ZMo7uUZJVy+vRpHD58GFNTU3Hfo48+iqeeempD2m8KcNegobdKfaYIs2OAapbEzMxMnMTEWuzMlmHGBbMmaBUSsAhsup4q21ZKg1wwwUeDvaQOfFDW57DrdZVvZ3AzL86gYOg9DVV4FFUqyjVr9ooGXdUjUCqKbeukLHoGpLl4DtMhOzo6Mtk06k1QAdEzYJ+6urri+Tt37sz0q7e3NypiKnJmxWj/eL/UaqdS4XPZzPLjH/8YP/jBDzAyMoKjR482ujtJ1ihPPPFEBPMQAp577rkNa7spwN1buwqaSlsQgDW4qPnuANDf358J4KmlyuPVWtVAq35q1ofy5gCWgC/B2R+jn7SCPfXC67Av6jGQKtGgJykUTX9UQPVlErynoVSS9wqUr1ePgpSTrxpJBaqLf7Bvms3DttQTAxCX1uP1OFOZwFwulyOfzwwpvgeaOeQVkub/a3ubUcrlMn72s5/h4MGDqXbMJpTFxUXcddddKJVKGBsbw1e+8pUNa7tpUgkIRJrySIDSWauLi4txxiQrQJJu2bZtW8yXVh5erWgFHLZFQFDQVhBWS5V9VUDW6ynoK9/NgKJSTgpkau0SsDWIqAFbKijeD/UGPHirpQ5klY7u8wqWGSusrKn3jxY9qRCCvvL56pHw/ulzYc35jo6OuKh5Z2dnVNIE9/n5+Rhjef311zOBYj4nDYRroNh7g5tVPvWpT21qBdXu8vjjj+OWW27B6dOnN5RWawpwV9pBl2cjwPFHrICt4F4qlWKtceV5FfD8bEYP+mq1amB0OStfrWOK/9+PUYFJhVaxZpbwHIK3Tm5SZaJ9Y4xBwZTA5wPBeQoIqMYduNIR22C7Sl2ZWZxnACADrPxfr6tKjVa8eh+aj0/qrVKpxEJjmraqz5HXpiKZn5+Pika9qs0sm73/SYD7779/w9tsClpGs1RIA2hA1XPynZ2dGB8fjymRU1NTS0ry0sXnddRS1nREtq0WMK1ipWQ0/VEteP/p6Zm8eIKOm58dHfXiVz79UfnkvHb13jHO4MsL6FgVLNT6ZY66t/zVQ6E3oVY5laVOudb7pP3W9Eul39hfncCkAd5isZiJC/h6P/R2tF0f2E6SpJ2kaSx3AEvqqXurmWAVQoiWOycO0Y1ncE/5dl9JUKkKXlspBa2GyP6xPzqJBshy9HmxAx6joKPWJBUJr628tOf/ddt7AFRQLKdLa105aR0Tz/GBX/aHfdVcdf7P9gnIZoa+vr7YHq+typbHqzfG8VCpzM3NxXEtLCxgcnIyZsrMzMzE/vO56RwB79Go57HZaZkkSd6INIXlDmQXlVBr21th/KEuLi7GwlycxcnUSE8lKNWjIM5jNIXQu/MKGN7q1b7z+vxfg5J6Df1OM1gIVuoV6PeqTNTD4B/pDk4kooLS2bYAligeKgVKR0dHnBtAL0CVoNbgoVLyk7o0qMp7xn6wDR0nyxewj5pvT4VVKpUyHhH7rn+6Ri3b9+muSZK0izTNm69WpIKEB1dul0qlWILAzCLQa0EpXld/6OoRKEes+xVMNe1RwVn76YOTSlWol8C0QO7TyUw+k4aKzqeEKsD68fAcfx8JwJrnrry7Uj+8hq4Bq1QNsDTLRguxsY/es1Drnf3VOjvqebDkAJV1uVzGzMxMbjBVn58WnaNSz4txJEnSDtIUtAyQrVXiOWIga2kCVYDhpBadvUiwJ12jFrAGSMm5EyjUStV2PIfLfimYKwetfVYwUuuc16Ly0pmweh0FePUmGFxUIdACiMsM8jjN9VYwpPhArQaV2S+WJOa4mfNOq10nF2nMQPf5e6MxFYqmtrLs8MzMTExtVFpK9/nAqRoDiZZJ0o7SFOCex1XT2iQPC9RnghJ0XnvttTi1nYCg1QkBxEWxeb5ar7QYvRXIPnhQB7LBTA08qvLR/mrbBCS1kqmEyHP71Eb2Ua9D8bEF7YPmgOux6gkp6JHW4fmqBHVNVk8JeQ/Fl4LQ+5mXtqnPVAOstOAXFxcxNjaWCYDz+nkprBqXWU4RJknSDtIU4A4sTSMkQHnemv8TJBloK5fLKJVK6O3tXfLjJgioBeyBwYOH58q1b2rpeuuc1+CnegAebD3doxk9QLbEL617XdBa6SBSUpVKJRNjALAkCKz3UfuaF6z11JMCtD4fr1w4Pg2ssi8au6BnpMsZcunBubk5zMzMxOUP+fw8Nab7fB98IbokSdpFmgbc81x53Q/U+Wzuu+SSS3DmzBlcffXV0dpjESugXqdGZ6Vq8JEAxSn2wNLVlrQColcAHrz1PCBr0SulodQFRRUWVxPysy81hZDHKb+sfeM2A5baDj95LO+H56a1z/q/ejtqoTOOoZO1NH6i3o4qCII3x8/JS+VyGefOncuU9QXqM5UZONVJWpqh4+9HkiTtJE0B7gQrutW09ghY3pIG6pbw1NRUBAK1XgFkFtJWgKdozRlKHs+v/fTWeR5YevGKS8dLUbDW4KPSFmo1K49OCkMDo546YZ/9bFtPrXjA1jFqmqpSKnnWvipFva4qKg2MMl7CtFZmQ3HxZ+XZFbypnDSFlXEMVS5JkrSbNN2bz5oxeUCpXC4/x8bGMDk5GS3AEKo58AQLzZ5RmoDf+UwUBXYf8POg5JWAWt9qses229cx8dMDn2Z/eAWhnL32I09R+fZ4TZ3J6nn7vP6pd8L++ZRVWu/aRxXNitEYA6+n9enPnTsXFT7Ho+mheXEDHYe/H0mStJM0heVO0UCZ59/5Q2YBMLVIy+UyzOoFxUIImdrfWgtFLT0FUwUEzeTwNJH20+/zYLKcB5AHomqpK79Mi9RXROR3HJ9WZGTVTI0pAHXw47YGMoGlk7aUOlkO9LVImB6rz5DPhAFScup6L0Kol2NmkPzs2bMZpaLvgSo5jQuoJ5DnhSRJ0i7SNOCu4MgfLsvEAnXrUkv28od79uxZXHbZZXG6OikYdeW5j8FJz816sFEgZPuej8+zEpejabzVrefxWouLi5naMN6r4D3R83SMy7XvlYHy03q8ZhLp/c2zklW58do+c4UWOK17Kl3NFmI7LOlL671YLGJ8fHxJdc884NZUV+2rNxKSJGknaTpaRvlZuvvK62qdcv6ox8fHM3nuCiJ+pqRue2pFqRDllIELUwwKVhSCm1dESrl4oQJj7rie56kT9o0lADg70wct2RcF+jwqR7/3wV5v/Wob7JdXlv5cDXZruiTBmkqZtWsmJiZiW37M/tmot+Vz+5PlnqRdZUVwN7O3mNlxM3vBzH5hZh+p7b/DzF4xs2drfwfknL81s1NmdtLM/ng1HVEQVB6XAO6Dg2qZT0xMxEU7GFwkn06ABbDkup7nJW3gAV6tYm+JK2B6BaHb+r1awQpaBGmtrc5r0BL1/eF96e7uXpJTvxwvn5dJospH/3R8vhyEHq+0i95DLSNAENdnTG+McRIu9n3q1KklQWGNPSwXH9GUTaV0GvluJ0nSCFkNLbMA4OMhhGfMrB/Af5vZD2vf3RVC+Cc92MyuAfCnAH4HwG8AeNzMfjuEcMGEY087qMWWx4ErBcDaI8ViEZ2dnZibm0N3d3esV8KsG7bj+Xd198kXUzEQVNWK1qn2vKb3CBRAfRDYewAUXXxbPQEtKKbZIrw/Ojt1amoqHpsXu6AQjBU4vfLyfaXS0GfhvSH1rPR49QZ431X5zs3NxVmp8/PzmJ2dzUzSUlGe3VNO+kz9mHNkQ97tJEkaISuCewjhVQCv1ranzexFAHsvcMq7AdwfQigD+F8zOwXgOgD/sUI7EXAIEjqFXgFNz+GPWldnmpqaQm9vL7Zs2QIAcQEICgOQyr2zVIF39YFsvXCgnmdNwPe1ZNQi1r7qOH1AVqkUrZtOj0IpDLVo9TitN+Nnm6rSUUpIv1eOnX314KgKwFM7/tg8+qtSqcSAL58DJyrxjwpKAVypGB27lofW9Eels5aTjXq3kyRphKyJczezKwC8FcBPa7s+ZGYnzOyfzWxHbd9eAC/Laadx4R+MXj8DHFoCWGkYBRGd2EKXnnnvBHUCGikBoG658jxalkqpaLqepyr8tp633P95nD2QrZuu1/YUDK9JhUegJ+1BEFTPQpUS2/Tehe9fHo2kfVMKy/eR3LnSUryXVL4aUGZNfk2BnJiYwPz8/BLPw2f+eOWS9y7lfbfMu3cFLuK7nSTJRsuqwd3M+gA8COCjIYQpAF8GcCWAa1G1fu5cS8Nm9gEze9rMnvapiQp0Cuj8kSqVwO8IDqwkWCwWM1Yv0/Hy+HCfc60Aqny9BzidUZp3HM/ntvL9HJfOugWydIny7r4Ojo5fLXJasDpLdDkPxNM0npbxik3pFW5rAFsV7Nzc3BLFxnvGUgqs/FgsFjE7Oxtrt09PT0ePiH3x3oXOaKWlzmfMNtfwLl60d3st5yVJsp6yqlRIM+tC9eX/TgjhIQAIIZyR7+8F8G+1f18B8BY5/bLavoyEEO4BcA8A9PT0BB80JFjlFaPywMmAHEGFgLF169ZYm1xBRikYgiPBlG3rwh78vjbWDAgqhaGf7P9yNI+nPghQ/I7UDEFMLXOep5QD2/WxAlUk3uvJ63Oexa79V8Wo90OfkYIugBhUpQImaLOUL/n2mZmZWOJX+64Bbn/vVYGp8uV5K1ntF/vdNrM0iypJQ2RFcLcqmnwNwIshhM/L/uEaZwkAhwD8vLb9MIB/MbPPoxp0ugrAf16ojfPnz2N8fDxDyygQaXASyIIRgeX555/HSy+9FBdr7urqwpVXXon+/v5MfXSu1OTTBdXl92mHuj+EgBtvvBG7du3K8MA8zvPEHpA8L8x2uru7USqVIh2hgCn3PP5x1SPO5gUQZ+hqobAQQow95KUHsm95AVUPlh7seV6e56IekC6+ocqjVCrFMZdKpaiYuRC23nO1xLWPVISkcGgQeAWaJxvxbidJ0iixVVg2+wH8O4DnARAV/g7An6HqtgYA/wfgr/iDMLPbAdyCajbCR0MI31+hjWkAJ9/wKDaf7AQw3uhObJA0w1gvDyHs8jvTu31RpBme90ZJM4w1990GVgHuGyFm9nQI4e2N7sdGSTuNt53GmiftNv52Gm+zj7XpZqgmSZIkSZI3LwnckyRJkqQFpVnA/Z5Gd2CDpZ3G205jzZN2G387jbepx9oUnHuSJEmSJFlfaRbLPUmSJEmSrKM0HNzN7EarVtg7ZWa3Nbo/6yG1KetjZvZz2TdoZj80s9Ha547afjOzL9bGf8LM9jWu52sXW76yYkuOdy3Sau92eq832Xh1NuJG/wHoAPA/AH4LwBYAzwG4ppF9Wqdx/T6AfQB+Lvv+EcBtte3bAPxDbfsAgO8DMADvAPDTRvd/jWMdBrCvtt0PYATANa063jXcl5Z7t9N7vbne60Zb7tcBOBVCeCmEMA/gflQr721qCSE8CeCc2/1uAN+sbX8TwEHZ/61QlZ8AGDCz4Y3p6ZuXEMKrIYRnatvTAFhZsSXHuwZpuXc7vdeb671uNLi3U5W93aE+pf3XAHbXtlvmHli2smLLj3cFaZdxtvxz3qzvdaPBvS0lVP24lkpTsqWVFaO04niTLJVWfM6b+b1uNLivqspei8gZumm1z7Ha/k1/DyynsiJaeLyrlHYZZ8s+583+Xjca3P8LwFVm9ptmtgXVJcwebnCfLpY8DODm2vbNAL4n+/+iFm1/B4BJcfuaXparrIgWHe8apF3e7ZZ8zi3xXjc6ootqlHkE1cyC2xvdn3Ua07+iusjDeVS5t78EMATgGIBRAI8DGKwdawCO1Mb/PIC3N7r/axzrflRd0xMAnq39HWjV8a7x3rTUu53e6831XqcZqkmSJEnSgtJoWiZJkiRJklwESeCeJEmSJC0oCdyTJEmSpAUlgXuSJEmStKAkcE+SJEmSFpQE7kmSJEnSgpLAPUmSJElaUBK4J0mSJEkLyv8D47d7Ct4ubCQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmbvBqVeWh18"
      },
      "source": [
        "## U-Net 모델 정의"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLVmgCbuI2Vy"
      },
      "source": [
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, MaxPooling2D, Conv2DTranspose, concatenate\n",
        "from tensorflow.keras import backend as keras\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler\n",
        "\n",
        "def unet(input_size=(256,256,3)):\n",
        "    inputs = Input(input_size)\n",
        "    \n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)\n",
        "    conv1 = BatchNormalization()(conv1)\n",
        "    conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)\n",
        "    conv1 = BatchNormalization()(conv1)\n",
        "    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
        "\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)\n",
        "    conv2 = BatchNormalization()(conv2)\n",
        "    conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)\n",
        "    conv2 = BatchNormalization()(conv2)\n",
        "    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
        "\n",
        "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)\n",
        "    conv3 = BatchNormalization()(conv3)\n",
        "    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)\n",
        "    conv3 = BatchNormalization()(conv3)\n",
        "    pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)\n",
        "\n",
        "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)\n",
        "    conv4 = BatchNormalization()(conv4)\n",
        "    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)\n",
        "    conv4 = BatchNormalization()(conv4)\n",
        "    pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)\n",
        "\n",
        "    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)\n",
        "    conv5 = BatchNormalization()(conv5)\n",
        "    conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)\n",
        "    conv5 = BatchNormalization()(conv5)\n",
        "    \n",
        "    up = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conv5)\n",
        "    up6 = concatenate([up, conv4], axis=3)\n",
        "    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)\n",
        "    conv6 = BatchNormalization()(conv6)\n",
        "    conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)\n",
        "    conv6 = BatchNormalization()(conv6)\n",
        "    \n",
        "    up = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conv6)\n",
        "    up7 = concatenate([up, conv3], axis=3)\n",
        "    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)\n",
        "    conv7 = BatchNormalization()(conv7)\n",
        "    conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)\n",
        "    conv7 = BatchNormalization()(conv7)\n",
        "    \n",
        "    up = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conv7)\n",
        "    up8 = concatenate([up, conv2], axis=3)\n",
        "    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)\n",
        "    conv8 = BatchNormalization()(conv8)\n",
        "    conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)\n",
        "    conv8 = BatchNormalization()(conv8)\n",
        "    \n",
        "    up = Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conv8)\n",
        "    up9 = concatenate([up, conv1], axis=3)\n",
        "    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)\n",
        "    conv9 = BatchNormalization()(conv9)\n",
        "    conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)\n",
        "    conv9 = BatchNormalization()(conv9)\n",
        "\n",
        "    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)\n",
        "\n",
        "    return Model(inputs=[inputs], outputs=[conv10])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "raJk70zBWm7M"
      },
      "source": [
        "## 모델 컴파일과 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBpw9ae6Uiot",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54f9d70d-b42e-4bb6-ea1f-cd5447bc7d68"
      },
      "source": [
        "# 모델 생성\n",
        "model = unet()\n",
        "\n",
        "# 모델 컴파일\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"Adam\", metrics=['accuracy'])\n",
        "\n",
        "# 학습\n",
        "hist = model.fit(train_x, train_y, validation_split=0.2, epochs=100, batch_size=32, verbose=1)\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "12/12 [==============================] - 23s 668ms/step - loss: 0.3727 - accuracy: 0.8595 - val_loss: 0.6088 - val_accuracy: 0.6713\n",
            "Epoch 2/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.1678 - accuracy: 0.9639 - val_loss: 0.7235 - val_accuracy: 0.6872\n",
            "Epoch 3/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.1245 - accuracy: 0.9736 - val_loss: 0.7307 - val_accuracy: 0.7119\n",
            "Epoch 4/100\n",
            "12/12 [==============================] - 3s 216ms/step - loss: 0.1049 - accuracy: 0.9760 - val_loss: 0.6924 - val_accuracy: 0.7562\n",
            "Epoch 5/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0898 - accuracy: 0.9774 - val_loss: 0.8368 - val_accuracy: 0.7576\n",
            "Epoch 6/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0905 - accuracy: 0.9746 - val_loss: 0.8298 - val_accuracy: 0.7576\n",
            "Epoch 7/100\n",
            "12/12 [==============================] - 3s 217ms/step - loss: 0.0780 - accuracy: 0.9776 - val_loss: 0.8411 - val_accuracy: 0.7576\n",
            "Epoch 8/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0724 - accuracy: 0.9782 - val_loss: 0.9923 - val_accuracy: 0.7576\n",
            "Epoch 9/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0666 - accuracy: 0.9792 - val_loss: 0.8714 - val_accuracy: 0.7576\n",
            "Epoch 10/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0639 - accuracy: 0.9793 - val_loss: 1.0888 - val_accuracy: 0.7516\n",
            "Epoch 11/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0601 - accuracy: 0.9798 - val_loss: 0.9819 - val_accuracy: 0.7362\n",
            "Epoch 12/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0615 - accuracy: 0.9790 - val_loss: 1.2712 - val_accuracy: 0.7575\n",
            "Epoch 13/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0607 - accuracy: 0.9790 - val_loss: 1.3147 - val_accuracy: 0.7575\n",
            "Epoch 14/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0607 - accuracy: 0.9782 - val_loss: 1.2687 - val_accuracy: 0.7534\n",
            "Epoch 15/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0621 - accuracy: 0.9784 - val_loss: 1.4329 - val_accuracy: 0.7575\n",
            "Epoch 16/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0581 - accuracy: 0.9793 - val_loss: 1.5466 - val_accuracy: 0.7575\n",
            "Epoch 17/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0574 - accuracy: 0.9794 - val_loss: 1.5305 - val_accuracy: 0.7575\n",
            "Epoch 18/100\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0547 - accuracy: 0.9798 - val_loss: 1.4439 - val_accuracy: 0.7575\n",
            "Epoch 19/100\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0550 - accuracy: 0.9801 - val_loss: 1.7587 - val_accuracy: 0.7575\n",
            "Epoch 20/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0504 - accuracy: 0.9811 - val_loss: 1.3155 - val_accuracy: 0.7566\n",
            "Epoch 21/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0489 - accuracy: 0.9815 - val_loss: 1.3809 - val_accuracy: 0.7434\n",
            "Epoch 22/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0468 - accuracy: 0.9822 - val_loss: 1.2159 - val_accuracy: 0.7709\n",
            "Epoch 23/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0468 - accuracy: 0.9821 - val_loss: 1.3745 - val_accuracy: 0.7754\n",
            "Epoch 24/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0475 - accuracy: 0.9815 - val_loss: 1.1827 - val_accuracy: 0.7824\n",
            "Epoch 25/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0456 - accuracy: 0.9824 - val_loss: 1.4792 - val_accuracy: 0.7595\n",
            "Epoch 26/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0444 - accuracy: 0.9828 - val_loss: 1.4362 - val_accuracy: 0.7819\n",
            "Epoch 27/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0462 - accuracy: 0.9821 - val_loss: 1.2713 - val_accuracy: 0.7854\n",
            "Epoch 28/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0449 - accuracy: 0.9827 - val_loss: 0.5484 - val_accuracy: 0.8831\n",
            "Epoch 29/100\n",
            "12/12 [==============================] - 3s 217ms/step - loss: 0.0467 - accuracy: 0.9817 - val_loss: 0.3599 - val_accuracy: 0.9013\n",
            "Epoch 30/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0424 - accuracy: 0.9836 - val_loss: 0.6820 - val_accuracy: 0.8708\n",
            "Epoch 31/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0421 - accuracy: 0.9833 - val_loss: 0.6447 - val_accuracy: 0.8737\n",
            "Epoch 32/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0410 - accuracy: 0.9839 - val_loss: 0.4847 - val_accuracy: 0.8995\n",
            "Epoch 33/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0403 - accuracy: 0.9841 - val_loss: 0.6496 - val_accuracy: 0.8826\n",
            "Epoch 34/100\n",
            "12/12 [==============================] - 3s 215ms/step - loss: 0.0399 - accuracy: 0.9841 - val_loss: 0.4286 - val_accuracy: 0.9017\n",
            "Epoch 35/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0376 - accuracy: 0.9850 - val_loss: 0.4133 - val_accuracy: 0.9191\n",
            "Epoch 36/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0373 - accuracy: 0.9853 - val_loss: 0.1997 - val_accuracy: 0.9492\n",
            "Epoch 37/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0356 - accuracy: 0.9857 - val_loss: 0.1593 - val_accuracy: 0.9600\n",
            "Epoch 38/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0354 - accuracy: 0.9859 - val_loss: 0.2765 - val_accuracy: 0.9370\n",
            "Epoch 39/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0348 - accuracy: 0.9859 - val_loss: 0.1563 - val_accuracy: 0.9586\n",
            "Epoch 40/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0327 - accuracy: 0.9868 - val_loss: 0.1135 - val_accuracy: 0.9676\n",
            "Epoch 41/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0328 - accuracy: 0.9868 - val_loss: 0.1337 - val_accuracy: 0.9666\n",
            "Epoch 42/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0334 - accuracy: 0.9864 - val_loss: 0.1290 - val_accuracy: 0.9651\n",
            "Epoch 43/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0312 - accuracy: 0.9874 - val_loss: 0.1963 - val_accuracy: 0.9553\n",
            "Epoch 44/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0365 - accuracy: 0.9853 - val_loss: 0.1448 - val_accuracy: 0.9625\n",
            "Epoch 45/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0397 - accuracy: 0.9847 - val_loss: 0.1295 - val_accuracy: 0.9640\n",
            "Epoch 46/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0393 - accuracy: 0.9845 - val_loss: 0.1403 - val_accuracy: 0.9624\n",
            "Epoch 47/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0362 - accuracy: 0.9854 - val_loss: 0.0776 - val_accuracy: 0.9770\n",
            "Epoch 48/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0338 - accuracy: 0.9863 - val_loss: 0.1070 - val_accuracy: 0.9669\n",
            "Epoch 49/100\n",
            "12/12 [==============================] - 2s 203ms/step - loss: 0.0310 - accuracy: 0.9874 - val_loss: 0.0839 - val_accuracy: 0.9751\n",
            "Epoch 50/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0326 - accuracy: 0.9865 - val_loss: 0.0724 - val_accuracy: 0.9769\n",
            "Epoch 51/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0311 - accuracy: 0.9872 - val_loss: 0.0640 - val_accuracy: 0.9807\n",
            "Epoch 52/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0292 - accuracy: 0.9880 - val_loss: 0.0734 - val_accuracy: 0.9766\n",
            "Epoch 53/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0284 - accuracy: 0.9883 - val_loss: 0.0688 - val_accuracy: 0.9795\n",
            "Epoch 54/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0274 - accuracy: 0.9886 - val_loss: 0.0789 - val_accuracy: 0.9768\n",
            "Epoch 55/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0266 - accuracy: 0.9890 - val_loss: 0.0778 - val_accuracy: 0.9773\n",
            "Epoch 56/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0260 - accuracy: 0.9892 - val_loss: 0.0777 - val_accuracy: 0.9778\n",
            "Epoch 57/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0258 - accuracy: 0.9892 - val_loss: 0.0783 - val_accuracy: 0.9779\n",
            "Epoch 58/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0246 - accuracy: 0.9897 - val_loss: 0.0750 - val_accuracy: 0.9790\n",
            "Epoch 59/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0237 - accuracy: 0.9901 - val_loss: 0.0738 - val_accuracy: 0.9790\n",
            "Epoch 60/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0232 - accuracy: 0.9903 - val_loss: 0.0791 - val_accuracy: 0.9788\n",
            "Epoch 61/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0226 - accuracy: 0.9906 - val_loss: 0.0733 - val_accuracy: 0.9794\n",
            "Epoch 62/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0213 - accuracy: 0.9911 - val_loss: 0.0800 - val_accuracy: 0.9785\n",
            "Epoch 63/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0203 - accuracy: 0.9915 - val_loss: 0.0763 - val_accuracy: 0.9799\n",
            "Epoch 64/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0200 - accuracy: 0.9916 - val_loss: 0.0767 - val_accuracy: 0.9795\n",
            "Epoch 65/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0203 - accuracy: 0.9915 - val_loss: 0.0803 - val_accuracy: 0.9793\n",
            "Epoch 66/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0207 - accuracy: 0.9914 - val_loss: 0.0797 - val_accuracy: 0.9787\n",
            "Epoch 67/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0189 - accuracy: 0.9921 - val_loss: 0.0753 - val_accuracy: 0.9800\n",
            "Epoch 68/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0181 - accuracy: 0.9924 - val_loss: 0.0774 - val_accuracy: 0.9793\n",
            "Epoch 69/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0184 - accuracy: 0.9924 - val_loss: 0.0827 - val_accuracy: 0.9776\n",
            "Epoch 70/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0191 - accuracy: 0.9919 - val_loss: 0.0739 - val_accuracy: 0.9800\n",
            "Epoch 71/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0172 - accuracy: 0.9928 - val_loss: 0.0783 - val_accuracy: 0.9800\n",
            "Epoch 72/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0162 - accuracy: 0.9932 - val_loss: 0.0881 - val_accuracy: 0.9784\n",
            "Epoch 73/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0171 - accuracy: 0.9930 - val_loss: 0.0817 - val_accuracy: 0.9795\n",
            "Epoch 74/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0167 - accuracy: 0.9930 - val_loss: 0.0798 - val_accuracy: 0.9803\n",
            "Epoch 75/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0148 - accuracy: 0.9939 - val_loss: 0.0840 - val_accuracy: 0.9799\n",
            "Epoch 76/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0140 - accuracy: 0.9942 - val_loss: 0.0884 - val_accuracy: 0.9798\n",
            "Epoch 77/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0131 - accuracy: 0.9946 - val_loss: 0.0872 - val_accuracy: 0.9800\n",
            "Epoch 78/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0141 - accuracy: 0.9942 - val_loss: 0.0945 - val_accuracy: 0.9788\n",
            "Epoch 79/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0137 - accuracy: 0.9943 - val_loss: 0.0944 - val_accuracy: 0.9792\n",
            "Epoch 80/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0130 - accuracy: 0.9946 - val_loss: 0.0963 - val_accuracy: 0.9792\n",
            "Epoch 81/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0143 - accuracy: 0.9941 - val_loss: 0.0905 - val_accuracy: 0.9800\n",
            "Epoch 82/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0124 - accuracy: 0.9949 - val_loss: 0.0937 - val_accuracy: 0.9796\n",
            "Epoch 83/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0134 - accuracy: 0.9944 - val_loss: 0.0960 - val_accuracy: 0.9791\n",
            "Epoch 84/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0137 - accuracy: 0.9943 - val_loss: 0.0946 - val_accuracy: 0.9796\n",
            "Epoch 85/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0124 - accuracy: 0.9948 - val_loss: 0.1004 - val_accuracy: 0.9797\n",
            "Epoch 86/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0125 - accuracy: 0.9947 - val_loss: 0.0985 - val_accuracy: 0.9794\n",
            "Epoch 87/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0124 - accuracy: 0.9948 - val_loss: 0.0988 - val_accuracy: 0.9796\n",
            "Epoch 88/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0110 - accuracy: 0.9955 - val_loss: 0.1087 - val_accuracy: 0.9792\n",
            "Epoch 89/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0130 - accuracy: 0.9945 - val_loss: 0.1035 - val_accuracy: 0.9792\n",
            "Epoch 90/100\n",
            "12/12 [==============================] - 2s 208ms/step - loss: 0.0117 - accuracy: 0.9952 - val_loss: 0.1025 - val_accuracy: 0.9797\n",
            "Epoch 91/100\n",
            "12/12 [==============================] - 2s 207ms/step - loss: 0.0113 - accuracy: 0.9953 - val_loss: 0.1141 - val_accuracy: 0.9786\n",
            "Epoch 92/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0117 - accuracy: 0.9951 - val_loss: 0.0987 - val_accuracy: 0.9797\n",
            "Epoch 93/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0105 - accuracy: 0.9957 - val_loss: 0.1075 - val_accuracy: 0.9793\n",
            "Epoch 94/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0102 - accuracy: 0.9958 - val_loss: 0.1034 - val_accuracy: 0.9796\n",
            "Epoch 95/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0095 - accuracy: 0.9961 - val_loss: 0.1085 - val_accuracy: 0.9800\n",
            "Epoch 96/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0092 - accuracy: 0.9963 - val_loss: 0.1090 - val_accuracy: 0.9802\n",
            "Epoch 97/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0090 - accuracy: 0.9963 - val_loss: 0.1086 - val_accuracy: 0.9800\n",
            "Epoch 98/100\n",
            "12/12 [==============================] - 2s 205ms/step - loss: 0.0094 - accuracy: 0.9961 - val_loss: 0.1176 - val_accuracy: 0.9792\n",
            "Epoch 99/100\n",
            "12/12 [==============================] - 2s 204ms/step - loss: 0.0093 - accuracy: 0.9962 - val_loss: 0.1132 - val_accuracy: 0.9793\n",
            "Epoch 100/100\n",
            "12/12 [==============================] - 2s 206ms/step - loss: 0.0094 - accuracy: 0.9961 - val_loss: 0.1083 - val_accuracy: 0.9795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZtJeFKDyX5jx"
      },
      "source": [
        "## 학습시 로스와 정확도 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEUzxWz8T7Eh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "0bbc9b9a-6ceb-4012-8b2b-a68551719ce4"
      },
      "source": [
        "fig, loss_ax = plt.subplots()\n",
        "\n",
        "acc_ax = loss_ax.twinx()\n",
        "\n",
        "loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n",
        "loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')\n",
        "loss_ax.set_ylim([0.0, 1.0])\n",
        "\n",
        "acc_ax.plot(hist.history['accuracy'], 'b', label='train acc')\n",
        "acc_ax.plot(hist.history['val_accuracy'], 'g', label='val acc')\n",
        "acc_ax.set_ylim([0.0, 1.0])\n",
        "\n",
        "loss_ax.set_xlabel('epoch')\n",
        "loss_ax.set_ylabel('loss')\n",
        "acc_ax.set_ylabel('accuray')\n",
        "\n",
        "loss_ax.legend(loc='upper left')\n",
        "acc_ax.legend(loc='lower left')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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GjRvHqFGjmDu3IT3VkCFDKC4uZsuWLYwYMYLLL7+cUaNGcdJJJ1Fb23pOrBUrVjBx4kTGjBnD9OnTKQ0M7n3kkUcYOXIkY8aM4ZxzzgFg4cKF5OXlkZeXx6GHHkplZWVrRTdPFwnT3G/nUuoq5cXpLzKyl3l/V467kvwD8rn+4+u55N1L6J3cm/tPvj+idgiC0D3EnMe0fv11VFWtaPH4nj051NW9id3+AN99915IZaak5DFs2ENNd/r9xjuy27n7pptYtXYtK1asgO+/Z8Hq1SxfvpxVq1bVh18/88wzZGVlUVtbS35+PjNmzCB7n1DA9evX88orr/DUU09x9tln89Zbb3FBK9EZF110EX//+9855phjuO222/jTn/7EQw89xN13383mzZtJSEiobya87777ePTRR5k0aRJVVVU4nc2P0m+VLhCmOm8dDyx9gBNyTyC/f379fqvFyhM/f4L8p/LZXb2bf5/zbzKcGRGzQxCE7iPuPKbt2w8AYMCAnW2c2QbBLJTBXDDBTIuBprwJEyY0GRP0yCOPMHbsWCZOnMj27dtZv379fkXm5uaSl5cHwLhx49iyZUuLty8vL6esrIxjjjkGgIsvvphFixYBMGbMGM4//3z+9a9/1TcjTpo0ieuvv55HHnmEsrKyVpsXW6QLhOlfP/yLnZU7mX3U7P2OHdbvMB446QFunXwrZww/I2I2CILQvcScx7SfZ7MPKwLO1Mkn39e55rxg5RwUJjB5hLQGi4Xk5IZxbAsWLOCTTz5hyZIlJCUlceyxxzabpSIhIaH+Z6vV2mZTXkt8+OGHLFq0iPfff58///nPrFy5ktmzZ3Paaacxb948Jk2axMcff8zB7e1oi7Aw+fw+7ll8D+P6jeOE3BOaPed3E38XkXsLghA9xJ3HtHGjiU0YNKiTBQUrZ5uN1KwsKoMJ12C/uYrKy8vJzMwkKSmJNWvWsHTp0k7eHNLT08nMzOTzzz8H4MUXX+SYY47B7/ezfft2jjvuOO655x7Ky8upqqpi48aNHHLIIdx4443k5+ezZs2aNu7QDBEWpnfWvMP6veuZfdTsaE9mKQhCBIk5j6ktNm0yomTvbDRwI48pu18/Jo0dy+gjj+TUCRM4bebMJqeecsopPPHEE4wYMYLhw4czceLETt7c8Pzzz/PrX/+ampoahg4dyrPPPovP5+OCCy6gvLwcrTW//e1vycjI4NZbb+Wzzz7DYrEwatQoTj311PbfMILC5Pa5uWvRXRyUfRDTD54e9vIFQeg5xESuvNWrVzNixIiQrj/8cJPnbv78ThpSWGjSkB92mNlevtwUXFEBw4aZdNc9jDbfYyDYgzvugFtvDeu9f/3Br3ny2yd56+y3OGvEWWEtWxCEnpUrLy6b8oaGI01ZMOtDcHIju71hDohYzfwQfK4we0yPf/M4T377JDcddZOIkiAI8SVM5eUmIXg4xzDV43A06XeKSZQyzxxGYVq4ZSG//ei3nDbsNO487s6wlSsIQs8lroRp0yazjpgwBYlVjwn2E6b31r7Hkf88kj3Ve1q8xOv38vTypymqbpq9fGflTma+MZMDMw/kpbNeCnnWUkEQYpu4EqaNG806bE15cS5Ma4rXcMHbF7CkYAl//+rvLV4y99u5XP7+5Zz+yum4vCZM3uf3cf7b51Ptqebdc94l3dnz+uQEQYgMcSVMXeIxBfucYpWAMFW5qzjrtbNw2pwcM/gYHv3mUarcVfudXllXyZwFcxiaOZSvdnzFpf++FK01dy26iwVbFvDY1Mc4OCcciQsFQYgVYrQzpHk2boScnMDss53B5zNZHhr3JQWFKVb7l4LY7WiPm8veu4y1JWuZf+F8kuxJHPHPI3h6+dNcN/G6Jqf/7cu/UVRTxAfnfcCnmz7l5v/djEbz+o+vc+GYC7k47+JuehBBEKKVGK9FmxLWiDxo3mMKsRkvJSWFqqr9PYyW9kcNdjtvODbw2o8L+esJf+X43OMBmDx4Mg8seYCr86+unzJiZ+VO7l9yP7NGzWJC/wnkH5DPT8U/8a8f/sWwrGE8dtpj3fkkgiBEKTHc5rQ/Ycsq3jhPXpB2ClOPxW5nsb2QZHsyf5j0h/rdN066ke0V23ll1Sv1+27/7HY8Pg9/OeEvgJl64qnTn+LWybfy3rnvkeJI6XLzBUGIfuJGmDwe2LYtjP1LUC9Ms2fP5tEnnzTh1DZb/WR+VVVVnHDCCRx22GEccsgh/Pvf/w75FlprbrjhBkaPHs0hhxzCa6+9BkBhYSGTJ08mLy+P0aNH8/nnn+Pz+bjkkkvqz33wwQfD8JAtYLez3lbBsOxhWFTDn8+pPzuV0b1H85fP/8JNn9zEiS+cyDMrnuHq/KsZmtngpjptTu447g7pVxIEoUVirynvuusaMrU2Ymttf3y+lxj6xt3w+UftKzMvDx5qlBx2H2GaNWsW1113HVdPmQKJibz++ut8/PHHOJ1O3nnnHdLS0iguLmbixImcccYZIeWBe/vtt1mxYgXff/89xcXF5OfnM3nyZF5++WVOPvlkbrnlFnw+HzU1NaxYsYIdO3awatUqgHbNiNtu7HbWOyo5NOvoJruVUtx01E2c//b53LfkPsb0GcM1+dcw59g5kbNFEISYJPaEqQU21fYD4EBnJ6e7gP0G0h566KHs2bOHnSkpFBUXk5mZycCBA/F4PNx8880sWrQIi8XCjh072L17N3379m3zFl988QXnnnsuVquVPn36cMwxx/DNN9+Qn5/PpZdeisfj4cwzzyQvL4+hQ4eyadMmrr32Wk477TROOim0WV079OgOG5sdNZydNWy/Y+eOPpdx/cYxOGMwTlsH5nsSBEEgFoXpoeanvdj4OHAVDP3gERjQyXsEQ8UbeT4zZ87kzbfeYteuXcyaNQuAl156iaKiIr799lvsdjtDhgxpdrqL9jB58mQWLVrEhx9+yCWXXML111/PRRddxPfff8/HH3/ME088weuvv84zzzzTqfu0xJY0Pz6lGZa9vzAppRieMzwi9xUEIX6Imz6mAw6AadPMutPsO4YJ05z36quv8uabbzIzkF28vLyc3r17Y7fb+eyzz9i6dWvItzj66KN57bXX8Pl8FBUVsWjRIiZMmMDWrVvp06cPl19+OZdddhnLly+nuLgYv9/PjBkzuOuuu1i+fHkYHrJ51qcZb3FYMx6TIAhCOIg9j6kFpk0zS1jwevcbrzRq1CgqKyvp378//fqZZsPzzz+f008/nUMOOYTx48e3a2K+6dOns2TJEsaOHYtSinvvvZe+ffvy/PPP87e//Q273U5KSgovvPACO3bs4Je//CV+vx+Av/71r2F60P1Zn+oGaNZjEgRBCAdxN+1FWPjhB0hNhUZTp8cCobzHa349iBdzdlJ2p0cm8xOEHoRMexHL1NU125QXL6xPqmVYdYKIkiAIEUOEqSW8Xli9GrZvb4jCKy+Hn34yufCysrrXvm5ivbOGYZXxKcqCIHQNMdPHpLUO71e8ywXV1WbZs8fMSFtWBomJZpSuM7bCoUNp0nX73Gx11HBBeXYXWCQIQrwSEx6T0+mkpKQkpMo1ZNymk58DDzTeUVkZZGbCwQfHpCiVlJTgbOO5NpVuwq9gWFmMp10SBKFbiQmPacCAARQUFFBUVNT2yaFSUQGlpUaErFbjKbndsG5d+O4RRTidTgYMaH2A1/qS9QAMK5X+JUEQIkdMCJPdbic33BFyN90E991ngh1ieX6ldrB+b0CYSrrZEEEQYhqpcVuisBD69hVRasT6kvVk+RPIrvJ1tymCIMQwEa11lVKnKKXWKqU2KKVmN3N8kFLqM6XUd0qpH5RSUyNpT7soLITAQFnBsH7veob5MxqiFAVBiBmiqb6OmDAppazAo8CpwEjgXKXUyH1O+yPwutb6UOAcIHpmjhNh2o/1e9czTGeJMAlCjBFt9XUkPaYJwAat9SattRt4Fdg3KZAGghOdpwNhSP0dJkSYmuDyuthevp1hZIswCULsEVX1dSSDH/oD2xttFwCH73POHOC/SqlrgWTgxOYKUkpdAVwB4AjOFBtJ3G4oLhZhasTGvRvRaIZZexlh0rpJdnVBEKIem1JqWaPtuVrruYGfw1Zfh4Pu7tk/F3hOaz0AmAq8qJTazyat9Vyt9Xit9XibrQsCCXfvNmsRpnrqI/Ksvc0OnwRACEIPwxusRwPL3LYvaUJI9XU4iGQtvwMY2Gh7QGBfY34FnAKgtV6ilHICOcCeCNrVNoWFZh0nwqS1RqObTJUeZE/1HhZtXcRTy58CYJg9MMmhx7NfhnVBEHosUVVfR9Jj+gYYppTKVUo5MJ1l7+1zzjbgBACl1AjACYRxlGwHiRNhcnldPPr1owx+aDBTXpyCX/ubHL/9s9vpc18fZr4xk8XbFnPx2ItJd6Sag9LPJAixRFTV1xETJq21F7gG+BhYjYnm+FEpdYdS6ozAab8HLldKfQ+8Alyio2EejqAwhWVWwehj496N3P3F3Qx9eCjX/OcakuxJ/G/z/3jmu4ZZb5ftXMZdn9/FjBEzWPKrJZTeWMpzZz7XkFVdhEkQYoZoq69jYj6msHP77XDnnSYIIoaaq5757hke/uphftj9AwDHDjmW2ybfxrFDjuXY549l5e6VrL1mLZmJmeQ/lc/uqt2svno16c70hkIefxyuuqphALIgCD2CnjQfU+zUuuGksBB6944pUfpxz49c9t5l5PXN48GTH2T6wdMZnDG4/vjjpz1O3hN5/L/5/48xvcewYtcK3jr7raaiBOIxCYIQcWKn5g0nMTiGac7COaQ4Uph/4Xyyk/aftmJkr5HccOQN/OWLv5BgTeCM4Wcw/eDp+xckwiQIQoQRYWqEX/t5bdVrbE5eTuFEO3venIXP376w6MP6HcavDv0VfVL6NHv8ow0f8c/v/lm/nZ2YzckHnsyJQ08kxZHCT0U/MW/9PJYVLtsvGGFfFKrJHFSpjlQynBlkJ2Zz0diL6J/WH4AVu1bw5k9vctvk25oVpSC3TL6FV1a9QlFNEf849R/Nz28lwiQIQoSRPqZGvLvmXaa/ZryEdL+DPr2GYLeEPlur1+9lbcla7BY7M0fN5OajbmZU71H1x7eWbWX046NJsieRk5QDQEFFARV1Fdgtdnon92ZHpYnQHJo5FKet5fmRgiHeQfzaT5W7ijJXGTWeGgamDWT+hfMZnjOcM145g8+3fc7m320mw5nR6jNsK99GmauMMX3GNH/CG2/A2WfDypUwenSor0YQhG5G+ph6KG+vfptMZybb5pST8vsb4Jq72l3G2uK1PPbNYzz3/XPMWz+PRZcs4pA+h6C15rL3LwPg68u+ru/f8fg8LN6+mA/XfcjW8q2ckHsCU4dNZWD6wNZu0yordq3g5H+dzNHPHs1dx9/F++ve567j7mpTlAAGpQ9iUPqglk8Qj0kQhAgjHlMAt89Nn/v6MG3QSTx33uvwj3/A1Vd3uLwtZVuY9MwktNYsvnQxn2z6hCs+uIInTnuCK8dfGUbLm2ddyTpOfOFEtldsJycph02/3URqQmrnC/7wQ/j5z+Grr2DChM6XJwhCl9CTPKbuTkkUNSzYsoAyVxlnZRxhdnQy+GFIxhA+vuBjXF4XU16cwu//+3uOzz2eK8ZdEQZr2+ag7INYfOliJg+ezH1T7guPKIF4TIIgRBxpygvwzup3SLYnM8U/xOwIQ1Te6N6j+fC8DznxxRNRKP55xj+bDyiIEAPTB7LwkoXhLVSESRCECBPfwlRTA489hu+j//DOSSs5ddipJO7ea46FKVz8iIFHsPjSxdR56xiSMSQsZXYrIkyCIESY+BQmvx8efRT+8hfYtYulA2H30XDWwWfBvzeac8KY1SCvb17Yyup2RJgEQYgw8dnH9Npr8NvfwvDhsGgR7xzfD4dPcdqwqWZwbWYmOFsO1Y5rRJgEQYgw8ekx/fgjWK3wySdoq5W3F7o5YY0m7ccNMZn1IayIMAmCEGHi02PauhUGDACbjeWFy9nsK+GsdVZ44QURprYQYRIEIcLEpzBt2QJDhgBw24LbSEtIY/rQqfDyy7B9e8xOdxEWRJgEQYgw8SlMW7fCkCF8tOEj5q2fx62TbyX7giuguBh27BCPqTVEmARBiN9u7L0AACAASURBVDDx18fk8cCOHXgGD+D/Pv4/fpb1M357+G/Br8xUF3v2iDC1hgiTIAgRJv48poIC8Pt5PHsTa4rXcP9J9+OwOkyFe9555hwRppYRYRIEIcLEnzBt2UJJIsyp/IATh57I6Qed3nDsyith8GA47LDusy/aEWESBCHCxF1TnnvzBmbNhEpfLQ+e/GDTFEEHH2wCI4SWaU2YtIbNm2Ho0K61SRCEmCKuPCa/9nPptr/z6VB4euoTjO4t8wm1m9aEaeFCOPBA+PzzrrVJEISYIq6E6eZPb+YltZK7lqVx8fhfdbc5PRNbwMluTpgKC8366ae7zh5BEGKOuBGmJ5Y9wT2L7+HKnf24uVQ8pQ6jlBGn5oSpqsqs33wTKiu71i5BEGKGuBGmw/sfzmWHXsY/5jtQQ3K725yejd3evDAFJ3CsqYHXX+9amwRBiBniRpgO7XcoT019HNv2HfVZH4QO0pYwHXggPPts19okCELMEDfCBMDOneD1mpBwoeO0Jkw2G1x+OSxeDOvWdb1tgiD0eOJLmIKh4OIxdY6WhKmqCpKT4cILwWKB555r/vrdu8Hni6iJgiD0XOJLmLZuNWvxmDpHax5TcrJJgnvKKSZb+74CVF5uxjm9+mrX2CoIQo8jvoQp6DENGtStZvR4WhOmlBTz8yWXmIS4X37Z9JwdO0xwxObNETdTEISeSXwJ09atZsp0mZ22c7TlMQEccohZb9vW9Jw9e8y6tDRy9gmC0KOJL2FqNA+T0AlCEaY+fcx69+6m5wSFqawscvYJgtCjiS9h2rpV+pfCQVvBDwAZGea8oBAFEWESBKEN4keY/P76CQKFThJKH5MKzG/VksckTXmCILRA/AhTYaGpTMVj6jyhNOWBac6TpjxBENpJ/AhTMFRcPKbOE6owBWcEbowIkyAIbRBRYVJKnaKUWquU2qCUmt3COWcrpX5SSv2olHo5YsbI4NrwEUofE7TuMUlTniBEFdFUX0dsokCllBV4FJgCFADfKKXe01r/1OicYcBNwCStdalSqnek7Kn3mGQMU+dpTph8PnC5GvqYwAjTnj1mAsHghIxBYSovN/1+lvhx2gUhWom2+jqStcIEYIPWepPW2g28Ckzb55zLgUe11qUAWut92n3CyG9+A9980/SLXugYzQlTTY1Z79uU53YbEQqyZ48RI62hoiLytgqCEApRVV9HUpj6A9sbbRcE9jXmIOAgpdRipdRSpdQpzRWklLpCKbVMKbXM6/V2zJqMDBg/vmPXCk1pTpiCmcX3bcqDBi8pKFLBABTpZxKErsQWrEcDyxWNjoWtvg6LoZEquB33HwYcCwwAFimlDtFaN6mxtNZzgbkAycnJuquNFPahOWEKThLYnDDt3g0HHQRFRWZ7+HCTkkiESRC6Eq/WujNf5yHV1+Egkh7TDmBgo+0BgX2NKQDe01p7tNabgXWYBxeimdY8psZ9TL0DTdDBAIig53TQQWYtARCCEC1EVX0dSWH6BhimlMpVSjmAc4D39jnnXYz6opTKwbiKmyJokxAOOtqUt68wicckCNFCVNXXERMmrbUXuAb4GFgNvK61/lEpdYdS6ozAaR8DJUqpn4DPgBu01iWRskkIE6EKU06Oicbb12MaPtysRZgEISqItvo6on1MWut5wLx99t3W6GcNXB9YhJ5CqMJks0F2dssekzTlCULUEO76OtD/tLIjtsggEqH9hBr8AE0H2e7ZAw4HDBhgPCnxmAQhlnlMKfW1UuoqpVR6ey4UYRLaT6jBD9A0keuePWbbYjHh++IxCULMorU+GjgfE1TxrVLqZaXUlFCuFWES2k+oTXnQkP0BGoQJjDCJxyQIMY3Wej3wR+BG4BjgEaXUGqXUWa1dJ8IktB+73WRu8Pka9rUmTPt6TCDCJAgxjlJqjFLqQUwwxfHA6VrrEYGfH2ztWhEmof3Y7Wbd2GuqqjL9R7Z94ml694bKSqitbSpMmZnSlCcIsc3fgeXAWK311Vrr5QBa650YL6pFujvzg9ATcTjMuq4OnE7zc+NJAhvTeCzTvh7T2rWRt1UQhG5Ba31MK8debO1aESah/WRkmHVpKaQHgm32nYspSFCYNmww2cfFYxKEuCCQjfyvwEjAGdyvtR7a1rXSlCe0n5wcsy5pNLauJWEKCtGqVU23pY9JEGKdZ4HHAS9wHPAC8K9QLhRhEtpPdrZZNxamfScJDBL0mFYGxtk1FqaaGpNxXBCEWCRRa/0poLTWW7XWc4DTQrlQmvKE9tOcMLXUx9SSx5SZadZlZQ37BEGIJeqUUhZgvVLqGkxS2GYqif0JyWNSSv1OKZWmDP9USi1XSp3UCYOFnkxLwtScx5SYCKmpzTflgTTnCULs8jsgCfgtMA64ALg4lAtDbcq7VGtdAZwEZAIXAne3304hJsjKMuvi4oZ9LQkTmOa84DinXr3MWoRJEGKWwFTts7TWVVrrAq31L7XWM7TWS0O5PlRhUoH1VOBFrfWPjfYJ8YbNZoQlFI8JGryk1NSG8PJgU55E5glCzKG19gFHdfT6UPuYvlVK/RfIBW5SSqUC/o7eVIgBsrNDC36AhgCIxn1J4jEJQqzznVLqPeANoDq4U2v9dlsXhipMvwLygE1a6xqlVBbwy45YKsQI+wpTS8EP0LwwicckCLGOEyjBpCAKooGwCdMRwAqtdbVS6gLgMODh9lopxBCN51nyek3Yd1tNeeIxCULcoLXusPMSqjA9DoxVSo0Ffg88jRks1WLKCSHGycmBn34yP7eUwDVIcx6T02lSG4kwCUJMopR6FuMhNUFrfWlb14YqTF6ttVZKTQP+obX+p1LqV+20U4glGjfltTRJYJDmPCalJC2RIMQ2HzT62QlMB3aGcmGowlSplLoJEyZ+dGDQlL1dJgqxRXa2ESS3u+VJAoM05zGBpCUShBhGa/1W422l1CvAF6FcG2q4+CygDjOeaRcwAPhbe4wUYozGg2zbasr72c9Ms93IkU33yyy2ghBPDANCSvMSksektd6llHoJyFdK/Rz4Wmv9QicMFHo67RGmfv2gqAjS0pruz8xsGtknCELMoJSqpGkf0y7MTLZtEpIwKaXOxnhICzADa/+ulLpBa/1m+0wVYoZghvHiYjOdBbQsTLC/KIHxmDZuDL9tgiB0O1rr1I5eG2of0y1AvtZ6D4BSqhfwCSDCFK80ly+vpT6mlpCmPEGIWZRS04H/aa3LA9sZwLFa63fbujbUPiZLUJQClLTjWiEWaU9TXktkZprgB71fRKkgCD2f24OiBKC1LgNuD+XCUD2mj5RSHwOvBLZnAfPaZaIQWzQWpuBg2fYKU0aGGZxbU9P+awVBiHaac15C0pxQgx9uUErNACYFds3VWr8TonFCLJKYCElJRpjsgZEDHfGYwDTniTAJQqyxTCn1APBoYPtq4NtQLgx5osBATPpbbZ4oxA/Z2Sb4Idi3lJTUvusbpyUaMCC8tgmC0N1cC9wKvIaJzpuPEac2aVWYmgn3qz8EaK11M6FWQtwQzP7Qq5fxoKzW9l0fFCYJgBCEmENrXQ3M7si1rQYwaK1TtdZpzSypIkpCvTC1NhdTazSeXl0QhJhCKTU/EIkX3M4MxCq0iUTWCR2ns8IkGcYFIZbJCUTiAaC1LiXEzA8iTELHyckxwtTaJIGtIU15ghDL+JVSg4IbSqkhNN81tB8iTELHyc6GvXuhsrL9g2vBCJPdDvPng88XfvsEQehObgG+UEq9qJT6F7AQuCmUC0WYhI6TnW0Gx+7Y0TGPyWaDu++GDz6Aq66SgbaCEENorT8CxgNrMWNgfw/UhnJtyOHigrAfwUG2W7fC0KEdK+P6603I+V//CllZZi0IQo9HKXUZ8DvMbBQrgInAEppOtd4s4jEJHScoTB0Nfgjy5z/DlVca7+kFSVovCDHC74B8YKvW+jjgUCCkSKeICpNS6hSl1Fql1AalVIvx7EqpGUoprZQaH0l7hDATzDAOHetjCqIUPPqomUhw4cLO2yUIQruJQH3t0lq7AtckaK3XAMNDsSViTXlKKSsmFcUUoAD4Rin1ntb6p33OS8Uo61eRskWIEEGPCTqfUshqNUInEXqC0OVEqL4uCIxjeheYr5QqBbaGYk8kPaYJwAat9SattRt4FZjWzHl3AvcArgjaIkSCcAoTNGQbFwShqwl7fa21nq61LtNaz8GkJvoncGYoxkRSmPoD2xttFwT21aOUOgwYqLX+sLWClFJXKKWWKaWWeb3e8FsqdIy0NBNZB+ETJvGYBCFS2IL1aGC5otGxsNXXzaG1Xqi1fi8gem0b2t4bhAullAV4ALikrXO11nOBuQDJyckSUxwtKGW8pt27O9fHFCQzE1au7Hw5giA0h1dr3aF+/PbU1+Egkh7TDmBgo+0BgX1BUoHRwAKl1BZMKOF7EgDRwwg254nHJAg9maiqryMpTN8Aw5RSuUopB3AO8F7woNa6XGudo7UeorUeAiwFztBaL4ugTUK4CbcwVVRIFghB6Hqiqr6OmDBprb3ANcDHwGrgda31j0qpO5RSZ0Tqvq3b5O+O28Y24RYmkAAIQehioq2+jmgfk9Z6HvtMwa61vq2Fc4+NpC3btt3Hpk1/YPLkWiyWhEjeKr6IhDCVljaN+BMEIeJEU30dN5kfrNZkQOPxSB9GWAkOsg1X8ANIP5MgxDlxI0w2m6n0vF6p9MJKpDwmQRDilrgRJrs9CwCvd283WxJjDB4MFkvT9EQdRYRJEATiSJhsNiNMHo8IU1iZMQN++AH69u18WSJMgiAQR8Jkt0tTXkSwWmHUqPCUJcIkCAJxJEziMfUAEhMhIUGESRDinDgSpnRASR9TtCPZHwQh7okbYVLKgs2WIU150Y4IkyDEPXEjTGCa86QpL8oRYRKEuCeuhMluz5KmvGhH5mQShLgnroTJZssUjynaEY9JEOKeuBIm4zFJpRfViDAJQtwTV8IkfUw9gIwMKC8Hv2SCF4R4Ja6EKegxyfQXUUxmJmhtxEkQhLgkroTJJHL14/NVdrcpQktI9gdBiHviTJgk+0PUI8IkCHFPXAmTZBjvAYgwCULcE1fCFJyTSSYLjGJEmAQh7okrYRKPqQcgwiQIcU9cCZP0MfUARJgEIe6JM2GSOZminqQksNtbFqbf/MZMTigIQsxi624DuhKr1YnFkihNedGMUq1nf1iyBIqKutYmQRC6lLgSJpDsDz2C1oRp61Yz+NbtBoeja+0SBKFLiKumPDBTrEtTXpTTkjBVVJjM41rDjh1db5cgCF1C3AmTeEw9gJamvti6teHnbdu6zh5BELqUuBMmmZOpB9CSxyTCJAhxQdwJk80mTXlRTyjC1PhnQRBiCgl+EKKPYFOe3w+WRt9O27aZgIe0NPGYBCGGiTuPyW7Pwu+vwe+v625ThJbIzDSiVLlPFvitW2HQIBgyRIRJEGKYuBOmhuwP0pwXtbSU/WHrVhg82IiTCJMgxCxxJ0x2ezD7gzTnRS2hCpPWXW+bIAgRJ+6ESfLl9QCaE6a6OigsNKI0aBBUV0s+PUGIUeJOmCTDeA+gOWHavt2sgx4TSHOeIMQocSdMksi1B9CcMAXDwwcPNguIMAlCjBJRYVJKnaKUWquU2qCUmt3M8euVUj8ppX5QSn2qlBocSXtAmvJ6BBkZZt2SMInHJAhhJ5rq64gJk1LKCjwKnAqMBM5VSo3c57TvgPFa6zHAm8C9kbIniM2WBlikKS+aSUkBq7WpMG3bZjKPDxgAvXpBQoIMshWEMBFt9XUkPaYJwAat9SattRt4FZjW+ASt9Wda65rA5lJgQATtAUApCzZbhoSLRzPNTX2xdSsccIAZYKuUhIwLQniJqvo6ksLUH9jeaLsgsK8lfgX8p7kDSqkrlFLLlFLLvF5vpw2TfHk9gAMOgLVrG7aDoeJBOiJMRUVwyy0mwk8Q4g9bsB4NLFc0Oha2+jocREXwg1LqAmA88Lfmjmut52qtx2utx9tsnc+iJGmJegDTpsHChSZEHMIjTC+/DH/5C/z3v+GzUxB6Dt5gPRpY5nakkLbq63AQSWHaAQxstD0gsK8JSqkTgVuAM7TWXfIpK4lcewDnnmvSEr3+ullv376/MBUWmgkDQ+Xrr81ahEkQ9iWq6utICtM3wDClVK5SygGcA7zX+ASl1KHAk5iH3BNBW5ogTXk9gBEjIC8PXnnFCJDH0xCNB+bn9k4Y+M03Zi3CJAj7ElX1dcSESWvtBa4BPgZWA69rrX9USt2hlDojcNrfgBTgDaXUCqXUey0UF1akKa+HcN558NVX8L//me19PSYIvTlv715Yvx4GDoR162DLlrCaKgg9mWirryM67YXWeh4wb599tzX6+cRI3r8ljMdUhtZ+lIqKbjahOWbNgj/8Ae4NRKU2Fqb2DrJdtsysb7wRrrnGeE1XXNH6NYIQR0RTfR0T8zF5PB4KCgpwuVwhne/1nkZ6+pGsXr067oXJ6XQyYMAA7HZ7d5uyP4MGwdFHw+efm+3GwjQgEKkaqjAF+5cuuADuuUeESRCimJgQpoKCAlJTUxkyZAhKqTbP93j24nJtIjFxMDZbShdYGJ1orSkpKaGgoIDc3NzuNqd5zj3XCFNWlhl4GyQxEXr3Dn2Q7TffwMEHQ3o6nHQSvPkmeL0QhihPQRDCS0y4Cy6Xi+zs7JBECYLZH1TcR+YppcjOzg7Z0+wWZs404jG4mewnoYaMa236qvLzzfbJJ0N5eUMwhCAIUUVMCBMQsiiZc21YrWl4vaXoOJ/Tpz3vrVvIyYGrr4bp0/c/NmgQbNrUdhkFBbB7N0yYYLZPOMFkj5DoPEGISmJGmNqLzZaJ1m78/pq2Txa6l4cegltv3X//kUeaSLu2xCnYvxQUpqws4z19/HF47RQEISzEsTBlEK7mvLKyMh577LEOXTt16lTKyso6bUNcMmOGWb/xRuvnffMN2O0wdmzDvpNPNs17MtmgIEQdcStMFosNqzUVj6fzzXmtCVNbuf3mzZtHRnCaB6F9DBliPJ8332z9vK+/NqKUkNCw74wzTEaJyy4zQRBBPB549VUzQ64gCN1CzIUkXXcdrFgR2rlaD8Hvr8Ni8WOyvjdPXp5pTWqJ2bNns3HjRvLy8pgyZQqnnXYat956K5mZmaxZs4Z169Zx5plnsn37dlwuF7/73e+4IhCqPGTIEJYtW0ZVVRWnnnoqRx11FF9++SX9+/fn3//+N4mJiU3u9f7773PXXXfhdrvJzs7mpZdeok+fPlRVVXHttdeybNkylFLcfvvtzJgxg48++oibb74Zn89HTk4On376aWgvp6fwi1+YsUmbN0NzkYU+nxnDdOGFTfePH29+qdddB7/+NTz1lMkiMWsWfPkl/O1v8P/+X9c8gyAITYg5YWoPStmAOrT2tipMbXH33XezatUqVgQUccGCBSxfvpxVq1bVh2E/88wzZGVlUVtbS35+PjNmzCA7O7tJOevXr+eVV17hqaee4uyzz+att97iggsuaHLOUUcdxdKlS1FK8fTTT3Pvvfdy//33c+edd5Kens7KlSsBKC0tpaioiMsvv5xFixaRm5vL3r0xmO1i5kwjTG++CTfcYPbdey/8+c8wdSpMnAiVlQ39S4353e+gpATuvNN4SJ98Ai6X6YP68suufQ5BEOqJOWFqzbPZHws1NTvR2kNy8uiw2jFhwoQmY4MeeeQR3nnnHQC2b9/O+vXr9xOm3Nxc8vLyABg3bhxbmkmbU1BQwKxZsygsLMTtdtff45NPPuHVV1+tPy8zM5P333+fyZMn15+TlZUV1meMCnJzYdy4BmH6+mu4+WYYNcoITfCdNCdMAH/6kxGnxx6D0aNNOX/+s4nY09pE7wmC0KXEbR9TEJstE7/fhddbGdZyk5OT639esGABn3zyCUuWLOH777/n0EMPbXbsUEKjPhCr1dps/9S1117LNddcw8qVK3nyySejewxSV/GLXxhBWr3aZHY44ABYsMAkf50/H557zgyubQ6l4O9/h3nzYOlSGD4cjjjChJdLPj1B6BbiXpjs9myUSsDl2ozJY9h+UlNTqaxsWdjKy8vJzMwkKSmJNWvWsHTp0o6aS3l5Of37m/m7nn/++fr9U6ZM4dFHH63fLi0tZeLEiSxatIjNmzcDxGZTHhhhApgyBTZsgOefNzPg2mxw4olw8cWtez4WC5x6KgQ/Jo480qylOU/oanbtggcegM8+axqUE2fEvTApZcXpzEVrNy7Xtg5F6GVnZzNp0iRGjx7NDcF+jkaccsopeL1eRowYwezZs5k4cWKH7Z0zZw4zZ85k3Lhx5OTk1O//4x//SGlpKaNHj2bs2LF89tln9OrVi7lz53LWWWcxduxYZs2a1eH7RjU/+5mJUNmxA66/Ho47rnPljR5t0h+JMAkt4fGYsXX33GOafDuL1vDPf5rpXn7/ezj+eOjTx3xUffhh++YdiwFUT8t8kJycrKv3CeVdvXo1I0aM6FS5dXU7cbt34nTmYrdnt31BDBGO99ft/OtfZlLBN95oGhbeUaZMgeJi+O67hn3FxSYwwhL333OxTU0NPP44ZGebsXKpqU2Pl5TA2Wc3TMdy990mACdIYaEZ9J2fDw6H2VdXB//5D3zxhQm0qa01YpOQAE4nrFpljk2eDA8/bKJM330X3n/fjLXLyjK2XHWV+QjrAEqpGq11cttndj8xF/zQURyOfvh8FbhcW7FYkrBaE9u+SIgeLrjALOHiiCNMEERVlfGeioth2DA47TQjgkJssny5+TtavdpsX301nHWW+XvIzjZC8vvfmzRXzz5rgmRmzzapsy6+2PRX3nqrEZ/UVNOUnJ0Nb71lBCYx0ex3Os2gb7fbiJbDAU8+acbVWSxGfKZPN8fnzzcTZr78ssm230Fh6kmIMAVQSuF05lJTs4ba2nUkJR2MxRKGL2+hZ3LkkWYA7tdfm2aVhx+GsjJ46SW45BJT4Qg9i4oK81HxzjvGAznwQBPVabUaAdiyxfTv9OplBCc52fRXvvZa04+Rvn1h4UIzFOG888wklFdcAfffbwRt6lS46CLjUf3nP8bDmj7dCN6JJ7Yvo73DYT6GTjvNeHJx4q1LU94++Hw11NSsRSkbSUnDsVgcYSk3momJprxwU1ZmAijuvBOuvdZkNz/6aFizxlQOP/wQniZDoXn8fvj3v81oeYvFLBUVsHGjaSZzOODSS+H8840H4vfD2rUm/dRPP8GPPxqvplcv6N/f9OG8+abxZEaMMH1EW7bsH2AwcyY88YQRriBerxGXkhIjQiNHNj1eXW1SXG3aBI88YprcgsE2WhvbrB0fJxkuelJTnghTM/h8VdTUrMNicWC1ZgA+QGO398Jq7RG/13YhwtQCo0cbQTrqKDM2avlyKCoyldAddzSfWFYw2TbaUxHv2mX6XPr0MU1d//kP/PGPTfv3wHwI5OYaT2fHDiNaqammL+e77xryHtrtJux/8GDz+9q50wyyPuss+M1vGqY/8XpNf5DWRuicTuhoejCfz5QTxfN79SRhit632I1YrSkkJv6M2tqN+P27ASvgx+PZS2Ligdhs6d1totAVHHGE+cr+5hsTTn7ooWb/zJmm/+m880wlKRjcbpgzx2TeOOII0xdz+un7i5TWpsnr3XfN0nherMREI1JDh8KLL5qJIi0W43Uo1dCUFZxj67HHYOVK46UceSQcfrjpCwxlRmabDQYODM+zR4FHFEuIx9QKWuv6+Yr8fje1tRvw+2tISBiCw5HTxtU9B/GYWuDZZ01zEZhZdI86yvy8Y4dpDjrgAHjvPTjooPaVq7VpFkpPD60CbY2SEvOV37hi1BqWLDEeRl5e85VmRQUsWmS8iZQUswwaZJLd7jvmy+s1XsxzzxlBuflm04TW+LwffzR9KCtWmP6U5cvN7MI/+5lpAj3wQNOk9u23Jvw5MLaOww+HadNMv83u3WYZNcoEEnT23QhNEI8pRmg8iZ7F4iApaTi1tRupq9uCz1cWaNpL69BkeykpKVRVVYXTXCHcBAfaTp7cIEpgKtgPPjBf6RMmmM7xk08Orczt201C2YULzXZGhlmC/Shamz6L6mrTPHTYYebeRx5phKNPH9Pk9PbbRji/+MJkWb/iClOZL15swpeXLzflZ2bCsccaz8DjMRFgP/5oEtv6fPvbN3CgEYqxY01/zrp1pszdu01/Tb9+xv7nnoO77jLeynvvmWCB9HTTL3TGGUbM3n4b5s6Fjz4yTWZgPKITTzTh1T//uXmXgrAP4jG1E639uN2FuN1FgBelHNjtvbDbc7BYQv/CiyZhEo+pBbQ23sE55zSdyynIli2mEl+1Cm6/3VS2wYCI3bvNYN+1a01F/YtfmE75yy83AnHDDcbjKC42gRZ+f8NAzeRks/j9prnq22+bzwIwfLhpVly82GQKCDJsmCk/OdlEhn32mfGsHA6zDB5sZvE9/njj0dTUmD6YlStN09p//2ua02w24+mMGWM8pKlTjfc1d64JkS4vN/cbMgTOPBNuugl6927+XdbUGFEeNMiIk9Dl9CSPKeaE6bqPrmPFrhDnvQiRvL55PHRK0+ywWvvxesvweIr44x//Qv/+fbnqqiuw2TK5884HSE1N5ze/+Q3Tpk2jtLQUj8fDXXfdxbRp04CWhaml6TGam76ipaku2osIUyeorjbeyssvm0r+oYdMJX/NNWadl2f6UIL/Z/n5DeeGSk2NaSIrLDSCV1pqRGXixIbmtDVrTMLaUaNMJ39n+jxqakxAwqBBLXfm79plBn9OnGiCRCTZbdQjwhRBokWYGrNs2VL+7/+uY968xwEf+fln8847j9K//2Dq6uxkZfWnpKSCI4+cxOrVywFNZuYAyst30zQrlKakZA8ZGcnU1tYwadJUFixYiNaaww47rMn0FVlZWdx4443U1dXxUCClemlpKZmZme1+PhGmMDB/vgkrX7vWbE+YYJraRo40suFoDAAADyhJREFUgvLOO8YDuvJK6TsRuoWeJEwx18fUmoBEivHjJ1JcXE5FRS927y4gKyuH3NyRuFx7ueWWOXz55XdYLIodO3awdesS+vTJAfzU1Kzer6wHH5zLBx8sAGD79p388MNH7N3rYtKk8RxwQAJu9x7S0hLx+73NTnUhdBNTppixTY8/bravvrrB2+jXz6SSEQQhJGJOmLqLmTNn8tZbb7Nr1y7OOecCnM7BvPrqZ5SVab7+ehE2m+KggyYAA0hKygUsOJ0/A/z1ZSxcuJhFi1ayZMlXJCUlcdxxx+PxKPz+Gvx+F273zib39Ptrqa5eS1VVDQBWazI2WyY2W0anJj4UOojDYSYfFAShU8RHfosuYNasWbz66qu8+eabzJw5EzBTVPTp05ekpH4sXryarVu3Y7Ol1A/StdszsNuz6pfqaj9ZWTmkpGSybt1mvvrqWxIS+nPcceeyZMkqiooySU4eQ21tDg5Hf044YTJPP/0WVms6VmsaxcWFuFybqapaQXX1Kmpq1lFbu5m6up14PHvx+Wrw++sCiwufrxafrxq/30V5+Ze43bvbnV3d7/fg8ZSF/X0KghC/iMcUJkaNGkVlZSX9+/enX79+AJx//vmcfvrpHHLIIYwfP56DW5qsLsApp5zCE088wYgRIxg+fHj99BjB6StmzPgFfr+f3r17M3/+fObMuY+rr76a/PyfY7Vaue2225g27WS83jK0duH3e/D7a/F6Pa3e1+0u5rvvTgXAYknG6RyE3d4bh6M3oHC5tuBybcHrLcfh6I3D0ReLJRGXayt1ddsBPykph5GTM42srFMCHpsdpezY7VlYrUmdfr+CIMQPMRf8IOyP1r56L0lrf6MAKgtKWVm7dhN9+xZSW7sxME6rAI+nCI9nD1r7cDqH4HQOwWZLx+3eg9u9G7+/moSEwSQm5qKUg717/0NFxVJg/78niyUJuz0Hmy0NqzUFqzUVi8WJUg4sFidO52CSk0eSlDQKuz0bi8WBUnZstvSINklqramt3UhFxZdUVX0XsNWJxZJMcvJI0tIOJyFBxtkIsYEEPwhRhVJWrNakFj0Xi8VJdvbUTt1jyJBbcbt3U17+RUAAvfj9dXg8e/F4ivF4ivH5KvD5qvD5KvF4igJiWUtR0WstzB5sJSFhAE7nIGy2TJSyY7HYUSoBqzURiyURuz2bxMSDSEoaTkLCAMCE8pv7V+PzVaG1DqSSSkVrTVXVd+za9Rx79ryOx7M78A4SUcoWsL3Bw3Q4+pOSMpbk5FEkJ48mJSWPpKSRWCwN/zp+vxelLCglLeOCEA5EmISw4XD0oVev9o+jMume1lNd/VOgGdKD3+/G4ymmrm4bLtdWXK4taO0NHHPh99fi89Xi91e3fYN6+/pjtSZRW7sepRLIyTmdzMwppKUdSXLyiHrvzOdzUV39PRUVX1FR8TXV1SspLf0Erc0sohaLk+TksShloa5uO3V1O1HKgsPRPyCkg3E6c0lMzMXh6I/NloHNlo7NZvoUZToVQWidmBGmxnnthNCJhqZci8UR8EhGtftar7eK2tr11Nauw+3eBShAoZQNqzUZqzUF0NTUrKemZg0eTxEDBlxP796zsNubD6+3Wp2kpR1OWtrh9fv8fi+1teupqvqOysplVFYuRykLGRkn4HQORGsfdXUF1NVtp6JiCXv2vIbJSt/c8yYFxCoNqzUNqzU1YGsSFktSI8/Qgd2eg8PRG7u9VyDiMh2bLR2LJbneaxRPTYg1YqKPafPmzaSmppKdnS3i1A601pSUlFBZWUlubm53mxNT+P1e6uq243YX4vWWB5ZSvN5SPJ69eL2l+HyVeL2medM0O1bj89WgtaeJZ9gWwX4xqzUZuz0bh6MPdnvvQKqsbOz2bKzW5EBAigPQje7hDtynDovFUd+fmJAwAKs1tdn/J619eL2VaF2H3d5LhLGH0JP6mGJCmDweDwUFBbhcrm6yqufidDoZMGAAdslGEJX4fDV4PEW43UV4vWV4vWX4fOWB0P/aQJNmTaDvrgqPpwSPZzdu9248nuKQhK1lLAHvLCnQjOoO9AvW1J+hVAKJibk4nbn1QmizpePz1eD9/+3da4xcZR3H8e9v5uxsh93SZiu0tEWhLajVSLkEq6hpQJOixPICBQUlRFNfYASjUTAalcREEiNiIFzCxVYbRGvRxhfeCqnygtJyUblo5FLLEtptoS297O5c9u+L55ntdDvblnVnduac/ydpds6Zk+nz7LN7/vtczv+p7KVafRNQXNDSTXf3XIrFhUybtpBC4WSSJDzq0CjPZLg32VEDX5hPLPvw6HHwwNREjQKTc66xanWQcvl1RkYOjvaQQqDoGu1BhR7XNEZGDo7O5w0Pv0q1ujcGwgOjy/9zue44BDkDKWF4eBuDgy8yNLSVcnkXlcobVKv7kQqjw5VmFoPaEOXyzoblPDS8OSPmoQy9SrMKUhLLWCSfP5EkmQ5AqbSDUmkAqFIozKVYXEBX18mxd/oGlcoeRkZKcTGLxd7jbAqFkwiPcI7EVao5II+Uj0Onp1AozKFc3hWHiV9ESigU5lAozI7BOnzPwGKPcwgpiY9ZzD7skYlcrpt8fjpJMp18vnfCK01r9+qJjgp5YKp9uLQcuJWw0949ZvbDMe93A6uBc4HXgcvNbOvRPtMDk3Ptzaw67s23Wh1kaOhlBgdfGl2pWetZhaTIu2OA6CNJ+sjlCnUPhR+kUtlHtfomZiMxUMwhlyswNLSVwcEXKZcH4lxcH0kyk1yuOwYIUS7vij3JWnDMEeYkRzCrYlYZLVNNksyiWFwEGKXSdkql7aOLYCZO8Q+CLqQEKQHymA2PDquGa3LUB1AY4cwz72Tu3C9N7H89RmBqxv16opq2+EHhJ/N24GNAP7BZ0noze67usi8Au81skaQrgJuBy5tVJudc8x2tR5DPF+npWUxPz+IWluitqVYPUCptJ0n6jlggc6j3NxyHSXN1Pc5hyuUBSqWBuLq0top0mGp1X3xc4sBozzXM81VHv4bP6UYqICkOU1bjowh5IEdv7zlNqXO73a+buSrvfOAFM3sJQNIvgRVAfUVXAN+Lr9cCt0mSddr4onMuNfL5HorFhQ3fk4TUHee0TjzsvVyuiyTppVhc0IJSTrq2ul83MzDNA16pO+4H3j/eNWZWkbQXmAXsqr9I0kpgZTw0SROd0U2ARk9ypl0W653FOkM2653FOsNbr3dR0pa647vN7O74etLu15OhI55jit+8u4954TFI2mJm501CkTpKFuudxTpDNuudxTpDuuvdzAcQXgVOrTueH881vEZhBnAGYVLNOedc67TV/bqZgWkzcIak0xWe6rsCWD/mmvXA1fH1ZcDDPr/knHMt11b366YN5cUxyC8DfyQsP7zPzJ6VdBOwxczWA/cCP5f0AvAG4ZvRTP/3cGCHymK9s1hnyGa9s1hnmMR6t9v9uuMesHXOOZdunuTKOedcW/HA5Jxzrq1kJjBJWi7p35JekHTDVJenGSSdKukRSc9JelbSdfF8n6Q/S/pP/Np4v4cOJikv6SlJv4/Hp0vaFNv7wTihmyqSZkpaK+lfkp6X9IGMtPVX48/3M5IekDQtbe0t6T5JA5KeqTvXsG0V/DTW/R+SmpMeooUyEZjq0m1cDCwGPiOpfXOiTFwF+JqZLQaWAtfGet4AbDCzM4AN8ThtrgOerzu+GbjFzBYBuwnpVNLmVuAPZvYu4CxC/VPd1pLmAV8BzjOz9xIm6mvpcdLU3j8Dlo85N17bXgycEf+tBO5oURmbJhOBibp0GxYyMNbSbaSKmb1mZk/G1/sIN6p5hLquipetAi6dmhI2h6T5wCeAe+KxgAsJaVMgnXWeAXyEsFIKMyuZ2R5S3tZRQshikAAnAK+RsvY2s78SVr7VG69tVwCrLXgMmCnplNaUtDmyEpgapduYN0VlaQlJpwFnA5uA2Wb2WnxrOzB7iorVLD8BvgGMxONZwB4zq6VrSWN7nw7sBO6PQ5j3SOoh5W1tZq8CPwK2EQLSXuAJ0t/eMH7bpu7+lpXAlCmSeoHfANeb2Zv171lt97WUkHQJMGBmT0x1WVosAc4B7jCzs4EDjBm2S1tbA8R5lRWEwDwX6OHIIa/US2Pb1stKYDqedBupIKmLEJTWmNm6eHpHrWsfvw5MVfma4ALgk5K2EoZoLyTMvcyMQz2QzvbuB/rNbFM8XksIVGlua4CPAi+b2U4LOwCuI/wMpL29Yfy2Td39LSuB6XjSbXS8OLdyL/C8mf247q36VCJXA79rddmaxcxuNLP5ZnYaoV0fNrMrgUcIaVMgZXUGMLPtwCuS3hlPXUTYoiC1bR1tA5ZKOiH+vNfqner2jsZr2/XA5+PqvKXA3rohv46UmcwPkj5OmIuopdv4wRQXadJJ+hDwN+CfHJpv+RZhnulXwNuB/wKfNrOxE6sdT9Iy4OtmdomkBYQeVB/wFHCVmQ1PZfkmm6QlhAUfBeAl4BrCH5upbmtJ3ydsUFchtO0XCXMqqWlvSQ8Ay4C3ATuA7wK/pUHbxgB9G2FI8yBwjZltafS5nSIzgck551xnyMpQnnPOuQ7hgck551xb8cDknHOurXhgcs4511Y8MDnnnGsrHpicayFJy2oZ0J1zjXlgcs4511Y8MDnXgKSrJD0u6WlJd8X9nvZLuiXuBbRB0knx2iWSHot74TxUt0/OIkl/kfR3SU9KWhg/vrduH6U18QFJ51zkgcm5MSS9m5BZ4AIzWwJUgSsJCUO3mNl7gI2Ep/EBVgPfNLP3EbJu1M6vAW43s7OADxKyYUPI+n49YW+wBYRcb865KDn2Jc5lzkXAucDm2JkpEhJmjgAPxmt+AayL+yLNNLON8fwq4NeSpgPzzOwhADMbAoif97iZ9cfjp4HTgEebXy3nOoMHJueOJGCVmd142EnpO2Oum2g+r/ocblX899C5w/hQnnNH2gBcJulkAEl9kt5B+H2pZbD+LPCome0Fdkv6cDz/OWBj3EG4X9Kl8TO6JZ3Q0lo416H8LzXnxjCz5yR9G/iTpBxQBq4lbMZ3fnxvgDAPBWELgjtj4Kll+YYQpO6SdFP8jE+1sBrOdSzPLu7ccZK038x6p7oczqWdD+U555xrK95jcs4511a8x+Scc66teGByzjnXVjwwOeecaysemJxzzrUVD0zOOefayv8AAwe1euaNokEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCbq9zJ0YA4t"
      },
      "source": [
        "## 테스트 결과 보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lP2EfC--LZND"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plotPredictions(model, train_x, train_y, test_x, test_y):\n",
        "\n",
        "    ix = np.random.randint(0, len(train_x))\n",
        "\n",
        "    input_ = train_x[ix:ix+1]\n",
        "    mask_ = train_y[ix:ix+1]\n",
        "\n",
        "    preds_train = model.predict(input_)\n",
        "    preds_train_t = (preds_train > 0.5).astype(np.uint8)\n",
        "\n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.subplot(1,3,1)\n",
        "    plt.title(\"train_x\")\n",
        "    plt.axis('off')\n",
        "    plt.imshow(input_[0])\n",
        "    plt.subplot(1,3,2)\n",
        "    plt.title(\"train_y\")\n",
        "    plt.axis('off')\n",
        "    plt.imshow(mask_[0][:,:,0], 'gray')\n",
        "    plt.subplot(1,3,3)\n",
        "    ret = model.evaluate(input_, mask_)\n",
        "    plt.title(\"Prediction: %.4f\" % (ret[1]))\n",
        "    plt.axis('off')\n",
        "    plt.imshow(preds_train_t[0][:,:,0], 'gray')\n",
        "    plt.show()\n",
        "\n",
        "    ix = np.random.randint(0, len(test_x))\n",
        "    input_ = test_x[ix:ix+1]\n",
        "    mask_ = test_y[ix:ix+1]\n",
        "    preds_test = model.predict(input_)\n",
        "    preds_test_t = (preds_test > 0.5).astype(np.uint8)\n",
        "    \n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.subplot(1,3,1)\n",
        "    plt.title(\"X_test\")\n",
        "    plt.axis('off')\n",
        "    plt.imshow(input_[0])\n",
        "    plt.subplot(1,3,2)\n",
        "    plt.title(\"test_y\")\n",
        "    plt.axis('off')\n",
        "    plt.imshow(mask_[0][:,:,0], 'gray')\n",
        "    plt.subplot(1,3,3)\n",
        "    ret = model.evaluate(input_, mask_)\n",
        "    plt.title(\"Prediction: %.4f\" % (ret[1]))\n",
        "    plt.axis('off')\n",
        "    plt.imshow(preds_test_t[0][:,:,0], 'gray')\n",
        "    plt.show()"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ViYIUw06JxE9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "e1bc6755-17bd-4771-c997-2ebdf0798dca"
      },
      "source": [
        "plotPredictions(model, train_x, train_y, test_x, test_y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 31ms/step - loss: 0.0072 - accuracy: 0.9969\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 36ms/step - loss: 0.0605 - accuracy: 0.9859\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzdSWI67VIWl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}