EyeContect_Team_Project.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#<아이컨택> Team Project\n",
        "## 이미지 분류를 통한 제주도 여행지 추천\n",
        "##### #include : 전혜지 박서희 심영민 이재훈 정다연"
      ],
      "metadata": {
        "id": "13atYMOCvgpg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 환경 설정\n",
        "\n",
        "*   구글 드라이브 마운트\n",
        "*   필요한 라이브러리 import\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "2oHy62ZUwCyl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UqANDC0TJEg5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfefe1d6-c62c-4af3-80e1-b9eedfca7b9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "#구글 드라이브 마운트\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3xzwJxP6JSt5"
      },
      "outputs": [],
      "source": [
        "#필요한 라이브러리 import\n",
        "import cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report,accuracy_score\n",
        "\n",
        "from IPython.display import Markdown, display\n",
        "import glob\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==2.8\n",
        "!apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2"
      ],
      "metadata": {
        "id": "YUlFPpp13mVH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##여행 선호도 시각화\n",
        "\n",
        "*   여행지를 제주도로 선정한 이유\n",
        "*   전체 만족도 시각화\n",
        "\n"
      ],
      "metadata": {
        "id": "VgRGXyHVuMpl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/satisfaction.csv\",encoding = 'CP949')\n",
        "df.drop('통계분류(2)',axis = 1,inplace = True)\n",
        "df.columns = list(df.iloc[0][:])\n",
        "df = df.drop(df.index[0])\n",
        "df=df.transpose()\n",
        "df.head()"
      ],
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        "outputId": "658933e0-8631-4cfe-b6aa-4eb07a392bbb"
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          "output_type": "execute_result",
          "data": {
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              "           1     2     3     4     5     6     7     8     9     10  ...  \\\n",
              "통계분류(1)    전체    성별    성별    연령    연령    연령    연령    연령    연령    연령  ...   \n",
              "서울       78.7  78.4  78.9  77.5  79.9  78.5  78.5  77.5    75  82.4  ...   \n",
              "부산       80.5  79.7  81.3  80.8  81.8  79.6  79.9  79.1  80.7  80.6  ...   \n",
              "대구         76  74.3  77.3  77.5  79.2  77.2  73.5  73.1  75.7  74.8  ...   \n",
              "인천       78.1  78.3  77.9  80.3  78.5  78.9  78.8  77.1  76.8  73.6  ...   \n",
              "\n",
              "           22    23    24    25    26    27    28    29    30    31  \n",
              "통계분류(1)  가구원수  가구원수  가구원수  가구소득  가구소득  가구소득  가구소득  가구소득  가구소득  가구소득  \n",
              "서울       80.2  78.8  78.4  81.1  78.6  77.6  78.3  77.9  79.5  78.9  \n",
              "부산       80.3  79.6  80.7  84.7  80.5  80.6  79.7  78.9  79.2  82.9  \n",
              "대구       79.2  76.6    75  76.1  76.1  79.1  76.4    73  75.5  77.5  \n",
              "인천       78.1  78.3    78  79.9  77.9  80.2  77.4  80.4  77.7  76.9  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "      <th>27</th>\n",
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              "      <th>31</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>통계분류(1)</th>\n",
              "      <td>전체</td>\n",
              "      <td>성별</td>\n",
              "      <td>성별</td>\n",
              "      <td>연령</td>\n",
              "      <td>연령</td>\n",
              "      <td>연령</td>\n",
              "      <td>연령</td>\n",
              "      <td>연령</td>\n",
              "      <td>연령</td>\n",
              "      <td>연령</td>\n",
              "      <td>...</td>\n",
              "      <td>가구원수</td>\n",
              "      <td>가구원수</td>\n",
              "      <td>가구원수</td>\n",
              "      <td>가구소득</td>\n",
              "      <td>가구소득</td>\n",
              "      <td>가구소득</td>\n",
              "      <td>가구소득</td>\n",
              "      <td>가구소득</td>\n",
              "      <td>가구소득</td>\n",
              "      <td>가구소득</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>서울</th>\n",
              "      <td>78.7</td>\n",
              "      <td>78.4</td>\n",
              "      <td>78.9</td>\n",
              "      <td>77.5</td>\n",
              "      <td>79.9</td>\n",
              "      <td>78.5</td>\n",
              "      <td>78.5</td>\n",
              "      <td>77.5</td>\n",
              "      <td>75</td>\n",
              "      <td>82.4</td>\n",
              "      <td>...</td>\n",
              "      <td>80.2</td>\n",
              "      <td>78.8</td>\n",
              "      <td>78.4</td>\n",
              "      <td>81.1</td>\n",
              "      <td>78.6</td>\n",
              "      <td>77.6</td>\n",
              "      <td>78.3</td>\n",
              "      <td>77.9</td>\n",
              "      <td>79.5</td>\n",
              "      <td>78.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>부산</th>\n",
              "      <td>80.5</td>\n",
              "      <td>79.7</td>\n",
              "      <td>81.3</td>\n",
              "      <td>80.8</td>\n",
              "      <td>81.8</td>\n",
              "      <td>79.6</td>\n",
              "      <td>79.9</td>\n",
              "      <td>79.1</td>\n",
              "      <td>80.7</td>\n",
              "      <td>80.6</td>\n",
              "      <td>...</td>\n",
              "      <td>80.3</td>\n",
              "      <td>79.6</td>\n",
              "      <td>80.7</td>\n",
              "      <td>84.7</td>\n",
              "      <td>80.5</td>\n",
              "      <td>80.6</td>\n",
              "      <td>79.7</td>\n",
              "      <td>78.9</td>\n",
              "      <td>79.2</td>\n",
              "      <td>82.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>대구</th>\n",
              "      <td>76</td>\n",
              "      <td>74.3</td>\n",
              "      <td>77.3</td>\n",
              "      <td>77.5</td>\n",
              "      <td>79.2</td>\n",
              "      <td>77.2</td>\n",
              "      <td>73.5</td>\n",
              "      <td>73.1</td>\n",
              "      <td>75.7</td>\n",
              "      <td>74.8</td>\n",
              "      <td>...</td>\n",
              "      <td>79.2</td>\n",
              "      <td>76.6</td>\n",
              "      <td>75</td>\n",
              "      <td>76.1</td>\n",
              "      <td>76.1</td>\n",
              "      <td>79.1</td>\n",
              "      <td>76.4</td>\n",
              "      <td>73</td>\n",
              "      <td>75.5</td>\n",
              "      <td>77.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>인천</th>\n",
              "      <td>78.1</td>\n",
              "      <td>78.3</td>\n",
              "      <td>77.9</td>\n",
              "      <td>80.3</td>\n",
              "      <td>78.5</td>\n",
              "      <td>78.9</td>\n",
              "      <td>78.8</td>\n",
              "      <td>77.1</td>\n",
              "      <td>76.8</td>\n",
              "      <td>73.6</td>\n",
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              "      <td>78.1</td>\n",
              "      <td>78.3</td>\n",
              "      <td>78</td>\n",
              "      <td>79.9</td>\n",
              "      <td>77.9</td>\n",
              "      <td>80.2</td>\n",
              "      <td>77.4</td>\n",
              "      <td>80.4</td>\n",
              "      <td>77.7</td>\n",
              "      <td>76.9</td>\n",
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              "    .colab-df-convert {\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# warning메시지 무시\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "bdZR7F1vuTCs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 나눔바른고딕 폰트 설치 - [런타임 다시 시작]되면 폰트를 다시 설치해야 한글이 보입니다.\n",
        "!apt-get install fonts-nanum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tr4Bv9V1up7P",
        "outputId": "ee2ce3ba-902c-4334-df50-bc52e8f8590c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'apt autoremove' to remove it.\n",
            "The following NEW packages will be installed:\n",
            "  fonts-nanum\n",
            "0 upgraded, 1 newly installed, 0 to remove and 20 not upgraded.\n",
            "Need to get 9,604 kB of archives.\n",
            "After this operation, 29.5 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 fonts-nanum all 20170925-1 [9,604 kB]\n",
            "Fetched 9,604 kB in 1s (10.2 MB/s)\n",
            "Selecting previously unselected package fonts-nanum.\n",
            "(Reading database ... 155676 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-nanum_20170925-1_all.deb ...\n",
            "Unpacking fonts-nanum (20170925-1) ...\n",
            "Setting up fonts-nanum (20170925-1) ...\n",
            "Processing triggers for fontconfig (2.12.6-0ubuntu2) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 한글폰트 설치하기 위해 필요함\n",
        "import matplotlib.font_manager as fm\n",
        "\n",
        "fm.fontManager.addfont('/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf')\n",
        "\n",
        "!sudo apt-get install -y fonts-nanum\n",
        "!sudo fc-cache -fv\n",
        "!rm ~/.cache/matplotlib -rf\n",
        "plt.rcParams['font.family'] = \"NanumBarunGothic\"\n",
        "plt.rcParams['axes.unicode_minus']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQikInNWuamc",
        "outputId": "1831aa8c-4fdd-4fab-87d2-6be162893ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "fonts-nanum is already the newest version (20170925-1).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 20 not upgraded.\n",
            "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
            "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 3 dirs\n",
            "/usr/share/fonts/truetype/humor-sans: caching, new cache contents: 1 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/liberation: caching, new cache contents: 16 fonts, 0 dirs\n",
            "/usr/share/fonts/truetype/nanum: caching, new cache contents: 10 fonts, 0 dirs\n",
            "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
            "/root/.local/share/fonts: skipping, no such directory\n",
            "/root/.fonts: skipping, no such directory\n",
            "/var/cache/fontconfig: cleaning cache directory\n",
            "/root/.cache/fontconfig: not cleaning non-existent cache directory\n",
            "/root/.fontconfig: not cleaning non-existent cache directory\n",
            "fc-cache: succeeded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "arr = pd.to_numeric(df.iloc[1:][1])\n",
        "arr =arr.sort_values()\n",
        "arr_y = arr.values\n",
        "arr_x = np.array(arr.keys())"
      ],
      "metadata": {
        "id": "_XySM5vBuuOr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colors = sns.color_palette('cool',len(arr))\n",
        "plt.figure(figsize = (13,8))\n",
        "plt.title(\"여행지별 전반적 만족도\",size = 20)\n",
        "plt.ylim(60,90)\n",
        "arr.plot(kind = 'bar',color = colors)\n",
        "plt.xticks(rotation = 0,size = 12)\n",
        "for i in range(len(arr_x)):\n",
        "  plt.text(i,arr_y[i],arr_y[i],horizontalalignment='center',verticalalignment='bottom')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pS-nG-AS32bl",
        "outputId": "b1834899-de94-4a7e-fddc-0ec6a3cd31e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 936x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##이미지 전처리\n",
        "\n",
        "\n",
        "*   카테고리 리스트 생성\n",
        "*   X에 image, Y에 label을 넣어 라벨링\n",
        "*   이미지 사이즈 및 스케일 조정 작업\n",
        "*   train/test set 나눔\n"
      ],
      "metadata": {
        "id": "TzxIGo1BwLHs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zZVXE_UVJS2E"
      },
      "outputs": [],
      "source": [
        "caltech_dir = \"/content/drive/MyDrive/project_data/project_data\" #img파일 경로\n",
        "categories = [\"낚시\",\"농장\", \"동굴\",\"레일바이크\",\"미로\", \"미술관\",\"민속촌\",\n",
        "              \"바다\",\"사찰\", \"산\",\"서핑\",\"섬\",\"성당\",\"숲\",\"스쿠버다이빙\",\"승마\",\n",
        "              \"역사\",\"오프로드\",\"요트\", \"자동차\",\"잠수함\",\"장성\",\"정원\",\"지질\",\n",
        "              \"짚라인\",\"차\",\"초콜릿\",\"카누\",\"카트\",\"캐릭터\",\"패러글라이딩\",\n",
        "              \"폭포\",\"피아노\",\"해녀\"] #모든 카테고리\n",
        "\n",
        "nb_classes = len(categories) #카테고리 개수\n",
        "\n",
        "image_w = 128 #이미지 가로\n",
        "image_h = 128 #이미지 세로\n",
        "\n",
        "pixels = image_h * image_w *3 #이미지 픽셀\n",
        "\n",
        "X = [] # feature\n",
        "Y = [] # taget\n",
        "\n",
        "#라벨링, 이미지 dir만들어줌\n",
        "for idx,cat in enumerate(categories):\n",
        "  label = [0 for i in range(nb_classes)]\n",
        "  label[idx] = 1\n",
        "\n",
        "  image_dir = caltech_dir +\"/\" + cat\n",
        "  files = glob.glob(image_dir+\"/*.jpg\")\n",
        "\n",
        "  for i,f in enumerate(files):\n",
        "    img = Image.open(f)\n",
        "    img = img.convert(\"RGB\")\n",
        "    img = img.resize((image_w,image_h))\n",
        "    data = np.asarray(img).astype('float32')/255\n",
        "    X.append(data)\n",
        "    Y.append(label)\n",
        "\n",
        "X = np.array(X)\n",
        "Y = np.array(Y)\n",
        "\n",
        "#train과 test set으로 나눔\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y) \n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##모델 생성\n",
        "\n",
        "*   model0 : 기본 모델 설명\n",
        "*   model1 : 은닉층 추가\n",
        "*   model2 : 컨볼루션, 폴링 레이어 추가\n",
        "*   model3 : inceptionV3 임베딩\n"
      ],
      "metadata": {
        "id": "Q3EziYUowUIT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###(0)CNN모델"
      ],
      "metadata": {
        "id": "eGkKQnwRr9pl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model0 생성\n",
        "model0 = keras.Sequential()\n",
        "model0.add(keras.layers.Conv2D(32,(3,3),padding = \"same\",input_shape = X_train.shape[1:],activation = 'relu'))\n",
        "model0.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model0.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model0.add(keras.layers.Flatten())\n",
        "\n",
        "model0.add(keras.layers.Dense(128,activation = 'relu'))\n",
        "model0.add(keras.layers.Dense(64,activation = 'relu'))\n",
        "\n",
        "model0.add(keras.layers.Dense(nb_classes,activation = 'softmax'))\n",
        "\n",
        "# model0 요약\n",
        "model0.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zc0skmLxsBuU",
        "outputId": "afdddb00-fbb8-49a4-f7aa-cabef455a03f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_189 (Conv2D)         (None, 128, 128, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 64, 64, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 131072)            0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 128)               16777344  \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 34)                2210      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16,788,706\n",
            "Trainable params: 16,788,706\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model0 compile\n",
        "model0.compile(loss = 'categorical_crossentropy',optimizer = 'adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "T2jPECvvsf63"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train데이터로 학습시킴\n",
        "history = model0.fit(X_train,Y_train,batch_size = 32,epochs = 30,validation_split=0.25)"
      ],
      "metadata": {
        "id": "kMka9tieslcq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#손실, 정확도\n",
        "test_loss,test_acc = model0.evaluate(X_test,Y_test)\n",
        "print('\\n테스트 정확도:', test_acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bDX_AXwsvpn",
        "outputId": "b6caf032-f7a8-452e-a7cd-884c8145c555"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75/75 [==============================] - 1s 9ms/step - loss: 4.9313 - accuracy: 0.3793\n",
            "\n",
            "테스트 정확도: 37.933918833732605\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history.history['accuracy'], linewidth=\"2\")\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Accuracy Trend')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['train','test'])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "x0HBG2KJtXXH",
        "outputId": "b1c2e492-7274-460a-c12b-4da2a1f07d00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###(1) 은닉층 추가 모델"
      ],
      "metadata": {
        "id": "rYjvOdqR2fj7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#model1 생성\n",
        "model1 = keras.Sequential()\n",
        "model1.add(keras.layers.Conv2D(32,(3,3),padding = \"same\",input_shape = X_train.shape[1:],activation = 'relu'))\n",
        "model1.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model1.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model1.add(keras.layers.Conv2D(64,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model1.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model1.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model1.add(keras.layers.Flatten())\n",
        "\n",
        "model1.add(keras.layers.Dense(1024,activation = 'relu'))\n",
        "model1.add(keras.layers.Dense(512,activation = 'relu'))\n",
        "model1.add(keras.layers.Dense(256,activation = 'relu'))\n",
        "model1.add(keras.layers.Dense(128,activation = 'relu'))\n",
        "model1.add(keras.layers.Dense(64,activation = 'relu'))\n",
        "\n",
        "\n",
        "model1.add(keras.layers.Dense(nb_classes,activation = 'softmax'))\n",
        "\n",
        "# model1 요약\n",
        "model1.summary()"
      ],
      "metadata": {
        "id": "i8TH8O6yzimk",
        "outputId": "6610d354-ccdd-444f-ad04-0a0331fd3482",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_1 (Conv2D)           (None, 128, 128, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 64, 64, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 64, 64, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 65536)             0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1024)              67109888  \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 512)               524800    \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 34)                2210      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67,828,770\n",
            "Trainable params: 67,828,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model compile\n",
        "model1.compile(loss = 'categorical_crossentropy',optimizer = 'adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "rt1h_aE6tJ_6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sv__RoBCJTEI"
      },
      "outputs": [],
      "source": [
        "# train데이터로 학습시킴\n",
        "history = model1.fit(X_train,Y_train,batch_size = 32,epochs = 40,validation_split=0.25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5HCQaj_mm9l",
        "outputId": "070fb04e-d9b4-45e4-e066-7c059367d03c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75/75 [==============================] - 1s 12ms/step - loss: 4.0695 - accuracy: 0.4207\n",
            "\n",
            "테스트 정확도: 42.07444489002228\n"
          ]
        }
      ],
      "source": [
        "#손실, 정확도\n",
        "test_loss,test_acc = model1.evaluate(X_test,Y_test)\n",
        "print('\\n테스트 정확도:', test_acc*100)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history.history['accuracy'], linewidth=\"2\")\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Accuracy Trend')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['train','test'])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "qpBaL1PMtZgT",
        "outputId": "187ceff3-620a-4ef5-8153-d2da67290fdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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LAbkpbfrnmZGcwKcn9ePTk/rR6Auw+ONS5q0p4Y31+9hWWsefF2zlzwu2kpeZxMxRvclKTcBjDPFxBk9cHJ44DrqOjzPExTU/bkj0xJGVmkBuaiK5ae7iui3a/o+9st7LhhLXLbKhpJoNJTVsKqmhro1jUZIT4khPcsGoORylh1q8Gn3BT7Qq1Hv91DUd/OFVWtvmcsNnU/u73ob0TGPKwOyWFq6ReRl4OhiAPHGGfqGxYCcN7tAmoiYzOYFzx+Rx7hg3DKW8zsvSrS6MLdlaxpZ9tSzYtJ8Fm/Yf9vk90xMZ2y+Lcf0yQ5csBuWmHhQmbztzKHVNfhZu2s/8dXt5c/1eNu+rZfO+Wv7w9sf0zUrm/LF5XDC2D6cMzSXBE0e918+8NSU8t7KYdz8ubfny1DM9kcsm9+eqqfmMbUcXnTGGlETXrdij47vrhKIAJiIA+ANBHnt3O79+fRMNvgCJHhjSM4Ps1ARyUhPJSUsgOzWRnFR3nXvQskSyUhI6/AF7qOQED+eMzuOc0Xn4A0GWba/gtbUlvLa2hD1VjTyzbFdYXicxPo4eaa7+5lCW23w/PZHtRT6WzF3vAteeGkqqDz9SondGEqP7ZjK6TwbDe6fTIy3RhavkeDJDLVRpSfEkxndskHIg6Lov65v81IWCWW2jnzqv/6BWtppG3ye7OJvcstomF+ZautwS4kiOd11sSfEekg+5ToqPIznBw949xYwcOpjkBA8pCW5ZSqKHpHh3nRwf564T3LiepIQ40hLdzyuflJuWyEUT+nLRhL6AGxu3ZGsZi7eUsWZ3Ffk5KYwLBa7xoS76tnxJSEuKb9muLxDk/W3lzF9bwvx1e9lT1cjfl+zg70t2kJkcz5SBOSzbXt4yiD0xPo7zx+Zx1dT+nDWiV0QG08sn6S9EJEqKKxtYuGk/CZ440hI9pCa5bqWDupmS3IdapJvfP9xVyb3PrWbdnmoALpnYl/NyK7l8VuendumseE8cpw7rwanDevDDT49ldXEV728rxxsIEgxa/EHbch0IXVpuW0sg4K6b/MFQF6qX8lovZXVemvxB9lQ1sqfqaENQt7bcSknwuG6RvAxG981gVJ8MRvfJjPhYFE+caWkt62qFhfs71PUmbdM7M5nLJvfnssn9w7bNBE8cpw/vyenDe/KjS8exuriK+Wv3Mn9dCZv21ra0tk0blMNVU/P51IS+be4OlvBRABPpYttL63i48GPmrCxqObLoaIyB1IQDAS07NZELxuVx5ZR8+mR17qinmkYfv5q/iSeWbMda6J+dwk+vGM/MUb1jZr6f1owxTMzPZmJ+dli21+ANUFbXREWdz13Xeymv81Fe10R5nY9tu4qZMW4oo0NBa+AhXT8isa7138zdF45iW2kdq3ZWMGVgTocG6kv4KICJdJEt+2r4w9sf88IHxQQtxBk4f2weGUnxhz2iqPlIIzc2KECdN8B+gLJ6PthVyQOvbeT04T25elo+F47r0+5D2+etKeFHL66lpLoRT5zhtrOG8LVzR5CaeOL8W0hJ9JCfmEp+zuEfLywsU+uPHFeG9ExT8IoRJ85/WpEoWbe7mj+8vYVX1+zBWjcv0FVT+/OlmcPb9I/w0PE/H++r5flVxby+bi+LNpeyaHMpGUnxXDKpL1dPy2fqwJyjdlnurmzghy+u5fV1ewGYNCCb+66Y0K4BtyIi0jkKYGHwwAMPcPfdd0f8OdK9fLirkt+/tYU31rugk+iJ49qT8rnjrGHtmmT00PE/Q3qmcd7YPCrrvbz04W6eXVHEh0VV/PP9Xfzz/V0M6ZnG1dPyuWJKf/plp7RsJxC0PL54O7+av5F6b4D0pHi+PWsUN54yKGyD50VEpG0UwMJAAez4UlHnZfV+P9m7KumVkUTP9ESS4tvevbdsezm/f2sLC0MDXZMT4rjh5EHcftbQTo/Zai07NZHZpw5m9qmD2by3hmdXFvH8ymK2ldbxy9c28sD8jZw+zHVRDshN4UcvrmN1cRUAF43vw48uHUdeZvjqERGRtju+Atjce6Bkdac2kRLwg6fVbukzAS66/4jrX3TRRZSXl1NQUMCPf/xj7r//fpqamggEAvzhD39g4MCBXHXVVTQ0NJCQkMAvfvELfvCDHxz0nLPPPrtTNUvnBYKWd7aU8u/lu3h97V68gSC/WvFuy+NZKQn0ykiiV3qSu26+tLq/v6aJP7y9haXbygE38eTsUwdz6xlDIn6OwxF5Gdx70Ri+dcEoFm0p5dkVRby+bi/vbCnlnS2lLev1z07hx5eO47yxOjWWiEg0HV8BLArmzp1Lnz59KCwsZObMmdx3333MmDGDNWvW8M1vfpPf//731NbWsnDhQqy1lJaWHvQcia6dZfU8u2IXz64oYndoKgJjYHh2HClpGeyvcac/aT7H3JZ9x54FMyM5nptPH8LNpw0mp4tPlRHviWPmqN7MHNWbqnofL33kuig3763hMycP5Bvnj9T8TCIiMeD4+k98lJaqtmqoqSEjI6NDz/3ggw+45557Wu57vV5GjhzJgw8+yD333ENGRgZ33XVXp2uUzmnwBpi3dg//XlbEkq1lLcsH5qZy7fR8rpyaz6YPllJQcAbgTjVT2eBjf02Tu9Q2Hrhd00RprZf9NU34AkGumpbP7FMHkZkc/Tl1slITuGnGIG6aMSjapYiIyCGOrwAWZWPHjuWxxx5jyJAhAGzfvp3i4mImTpzIjBkzePTRR/npT3/KL37xiyhXeuKx1vJRURX/Xr6LFz/YTU2TO79ackIcF4/vy7UnDeDkwbktczxtavXcuDjTMkP6qD4dC+ciIiKtKYCFweTJk7n44ouZNWsWd9xxBz6fD7/fz+23386oUaO44YYbMMbg9Xr57W9/e9BzbrjhBm666aYo/wTHr5pGH/9atov/LC9i496aluWTB2Rz7fQBXDKpb0y0VomIyIlFASwM5s2bd9THFyxY0O7nSOct3LSf78z5qOU0Mz3SErlyan+umT6AkXlqyRIRkehRAJPjTm2Tn5+9sp5/vr8TgEn5WXyxYDjnjO7d4ZMhi4iIhJMCmBxXFm8p5VvPfkRxZQOJnji+fv4Ibj9zKPEeBS8REYkdCmByXKj3+rl/7gb+vmQHAOP7Z/KrayZr0LyIiMQkBTDp9t7fVs7d//mQneX1xMcZ/uecEXxp5jAS1OolIiIx6rgIYNbao558uDuz1ka7hJjV6Avwy9c28rd3t2EtjO6Twa+uncS4flnRLk1EROSoun0AS05OpqysjB49ehx3IcxaS1lZGcnJOl/foVburODuf3/I1tI6PHGGL80cxv+cM0KD7EVEpFvo9gEsPz+foqIi9u/fH5btNTY2xlTgSU5OJj8/P9plxIxGX4AH39jEowu3ErQwonc6v7p2EhPzs6NdmoiISJt1+wCWkJDQMvN8OBQWFjJlypSwbU/CZ/2ear76z1Vs3ldLnIE7zx7G188bQXKCJ9qliYiItEu3D2ByYli2vZxbHltGTZOfob3SeOCaSUwdmBPtskRERDpEAUxi3tsb9vHFp1fQ6Aty0fg+PHjdZLV6iYhIt6YAJjHtxQ93c9e/PsAftFw3fQA/v3ICnrjj62ALERE58SiAScx66r0d/O8La7AW7jhrKPdcNPq4O9JVREROTApgEnOstfyx8GN++dpGAL49axRfKhge5apERETCRwFMYoq1lvvmbuCRhVsxBn56+XhuPGVQtMsSEREJKwUwiRmBoOW7z63mX8t3ER9nePC6yXx6Ur9olyUiIhJ2CmASE5r8Ab7+zAfMXVNCckIcD980jZmjeke7LBERkYhQAJOoq2vyc+dTK1i0uZSM5Hj+9vmTOGlwbrTLEhERiRgFMImqynovNz++jFU7K+mZnsgTt5ysk2mLiMhxTwFMomZfdSOz//o+G/fW0D87hSdvPZmhvdKjXZaIiEjEKYBJVOwqr+fGvyxlZ3k9w3ql8dRtp9A3KyXaZYmIiHQJBTCJip+/up6d5fVMzM/i8ZtPJjctMdoliYiIdJm4aBcgJ556r5+3N+4D4E83TVP4EhGRE44CmHS5BRv30+gLMnlANv2y1e0oIiInHgUw6XLz1pYAcNH4PlGuREREJDoUwKRLNfkDvLXedT/OUgATEZETlAKYdKl3t5RS0+RnTN9MBvVIi3Y5IiIiUaEAJl1q3hp1P4qIiCiASZfxB4K8vm4voO5HERE5sSmASZd5f1s5FfU+hvZKY0RvzXgvIiInLgUw6TJzQ92Ps8b1wRgT5WpERESiRwFMukQwaHmtZfqJvlGuRkREJLoUwKRLrNpVwb6aJvpnpzC+f2a0yxEREYkqBTDpEnNXh7ofx6v7UURERAFMIs5aq9nvRUREWlEAk4hbu7uaoooGemUkMXVgTrTLERERiToFMIm45slXLxyXR1ycuh9FREQUwCTi5q7ZA8CscTr6UUREBBTAJMI2763h4/11ZKcmcMrQ3GiXIyIiEhMUwCSimrsfzxuTR4JHbzcRERFQAJMIm6uTb4uIiHyCAphEzM6yetbtqSYt0cPpw3tGuxwREZGYoQAmETNvrRt8f86YPJITPFGuRkREJHYogEnEzFP3o4iIyGFFNIAZY75ijFlijHnPGHPdIY/1Msa8bIxZaIxZboz5YiRrka5VUtXIyp2VJMXHcfbIXtEuR0REJKbER2rDxphhwC3ADCAJeN8YM99aWxFa5W5ggbX2l8aYVGCdMebf1tqySNUkXee10KmHzh7Zi7SkiL3NREREuqVItoCdA7xorfVaa2uAhcBprR4vAXqEbmcC9UBjBOuRLtTc/ThL3Y8iIiKfYKy1kdmwMd8Fqq21D4Xu/wzYbK19PHQ/HngFGAJkA1+01s45zHZuB24HyMvLm/bMM89EpN5mtbW1pKenR/Q1Yl1n90G11/K1t+qJM/C7c1JJS+h+px/S+0D7ALQPQPugmfaD9gG0fx/MnDlzhbV2+uEei2TfUB2Q1ep+FlDR6v7/AW+EuiAzgVeNMeuttetab8Ra+wjwCMD06dNtQUFBBEuGwsJCIv0asa6z++Bfy3ZiWc3pI3rxqfNPDl9hXUjvA+0D0D4A7YNm2g/aBxDefRDJLsg3gYuNMR5jTApQACwPhS2AUcC20O0aoAoYFsF6pIto8lUREZGji1gAs9auAV4GFgNvA7/GhbCnQqt8H/iqMWYhsAzYAcyNVD3SNaobfby7pZQ4A+ePzYt2OSIiIjEpooenWWvvA+47ZPHTocfWAWdF8vWl6721fh++gOWUIbn0TE+KdjkiIiIxSROxSljNXeNmv1f3o4iIyJEpgEnY1Hv9LNi0H4ALFcBERESOSAFMwmbBxv00+oJMHpBN36yUaJcjIiISsxTAJGzmrdXkqyIiIm2hACZh0eQP8Nb6fQDMGqcAJiIicjQKYBIWi7eUUdPkZ3SfDAb3TIt2OSIiIjFNAUzC4sDRj32jXImIiEjsUwCTTvMHgry+bi8AF01Q96OIiMixKIBJp72/rZyKeh9De6YxoveJfaJWERGRtlAAk0576aPdgDv60RgT5WpERERinwKYdMqeqgbmrCjGGLhiSv9olyMiItItKIBJp/x5wVa8gSAXj+/LiLyMaJcjIiLSLSiASYftq27kH+/vBOB/zh0e5WpERES6DwUw6bA/LdiK1x9k1rg+jO6TGe1yREREug0FMOmQfTWNPL10B6DWLxERkfZSAJMOeXThVpr8Qc4fm8e4flnRLkdERKRbUQCTdiutbeKp99zYr6+eMyLK1YiIiHQ/CmDSbo8u2kqDL8C5o3szIV+tXyIiIu2lACbtUl7n5cklbuzXV89V65eIiEhHKIBJu/z1na3UewMUjOrFpAHZ0S5HRESkW1IAkzarrPfyxOLQkY8a+yUiItJhCmDSZn97Zxu1TX7OHNGTaYNyol2OiIhIt6UAJm1SVe/jsXe3A/A1jf0SERHpFAUwaZPHFm+jpsnPacN6MH1wbrTLERER6dYUwOSYqht9/O2dbYBav0RERMJBAUyO6Yl3t1Pd6OeUIbmcMrRHtMsRERHp9hTA5Khqm/z8pbn16zy1fomIiISDApgc1ROLt1PV4OOkwTmcqtYvERGRsFAAkyOqa/Lzl0VbAfjauSMxxkS5Ih3/p98AACAASURBVBERkeODApgc0ZPv7aCi3sfUgdmcPlytXyIiIuGiACaHVe/18+jCUOvXeWr9EhERCScFMDmsfyzdSVmdl0kDsjlrRM9olyMiInJciY92ARJ7mgKWP73jWr++fu4ItX6JiIiEmQKYfMKCXX5Ka71M6J9Fwahe0S5HRETkuKMuSDlIoy/Aq9t8AHxVrV8iIiIRoQAmB/n38l1UNlnG9s3kvDG9o12OiIjIcUkBTA7y8od7ALizYJhav0RERCJEAUxaNPoCfLCrEgOcPUJjv0RERCJFAUxarNxZgTcQZEBGHFmpCdEuR0RE5LilACYt3ttaDsDoXL0tREREIkmftNJi6dYyAEbleqJciYiIyPFNAUwAN/5r1a5KjIFROQpgIiIikaQAJgB8sKsSrz/IqLwM0hN19KOIiEgkKYAJAO+Fuh9nDO0R5UpERESOfwpgAsDS0AD8GUNzo1yJiIjI8U8BTGjyB1i5swKAk4eoBUxERCTSFMCED3dV0RQa/5WblhjtckRERI57CmDSavyXuh9FRES6ggKYsHSbC2CnaAC+iIhIl1AAO8F5/UFW7Gge/6UWMBERka6gAHaC+6iokkZfkBG90+mZnhTtckRERE4ICmAnOM3/JSIi0vUUwE5wS7e5+b9O0QB8ERGRLqMAdgLzBYIs3+7Gf52i+b9ERES6jALYCeyjoioafAGG9UqjV4bGf4mIiHQVBbATmMZ/iYiIRIcC2AnswPgvBTAREZGupAB2gnLjv0In4Nb8XyIiIl0qogHMGPMVY8wSY8x7xpjrDvP4FGPMYmPMO8aYlyNZixxsTXEV9d4AQ3um0TszOdrliIiInFDiI7VhY8ww4BZgBpAEvG+MmW+trQg9ng38GbjcWrvbGBOxWuST3tuq7kcREZFoiWQL2DnAi9Zar7W2BlgInNbq8ZuA94BHjDHvAFdEsBY5hE7ALSIiEj3GWhuZDRvzXaDaWvtQ6P7PgM3W2sdD9x8CBgDXAFnAEuBUa+3+Q7ZzO3A7QF5e3rRnnnkmIvU2q62tJT09PaKvEW2BoOXLb9bTGIAHC1LIST44h58I++BYtA+0D0D7ALQPmmk/aB9A+/fBzJkzV1hrpx/usWN2+xljHgEet9YubnuJANThglWzLKCi1f0A8B9rrRfYb4xZAYwGDgpg1tpHgEcApk+fbgsKCtpZRvsUFhYS6deItg92VdI4/10G90jlilkzP/H4ibAPjkX7QPsAtA9A+6CZ9oP2AYR3H7SlC3IOcJsxZqkx5rvGmPw2bvtN4GJjjMcYkwIUAMuNMZmhx98BzgUwxqQBE4FN7apeOmSp5v8SERGJqmMGMGvta9baW4AzcAHpHWPM68aYq4/xvDXAy8Bi4G3g17gQ9lRolTlAlTFmOS6s/dhau7ejP4i0XfP4L53/UUREJDradOShMeYk4GbgJOAPwCvAdcaYT1lrbz7S86y19wH3HbL46dBjQeDrHSlaOs6v8z+KiIhEXVvGgH0EbAH+ZK39UquHfmiMWRCxyiQi1u2ppqbJz6AeqfTLTol2OSIiIiektrSA3Q/Ms9aWG2N6AVOtta+FHpsVudIkEpY2z/+l2e9FRESipi2D8G+z1pYDhKaI+E7zA9bahkgVJpGhE3CLiIhEX1sC2KGtZAmRKEQiLxC0vL9dM+CLiIhEW1sCWLEx5nZjTI4x5vNAWYRrkghZv6eamkY/A3JT6K/xXyIiIlHTlgD2FeBU3KmEzgfujGhFEjEt00/o6EcREZGoOuYgfGttGW4KCunmmk/ArfFfIiIi0XXMFjBjzBeMMWuMMVubL11RmIRXMGhZtl1HQIqIiMSCtkxDcSdwCzAFNxP+BRGtSCJifUk1VQ0++menMCA3NdrliIiInNDaMgasElgBDLTWvg3MiGxJEgkt83/p9EMiIiJR15YAthcYBAwxxlwP9IlsSRIJmv9LREQkdrQlgH0DKAK+j2v9+tLRV5dYE2w1/9cMHQEpIiISdW0ZA/a8tfY0YCs6eXa3tHFvDZX1PvplJTMgV/N/iYiIRFtbWsBWGGPGRbwSiZilzfN/De2BMSbK1YiIiEhbAtjngNXGmBJjzB5jzO5IFyXhdWD+Lw3AFxERiQVtmYg1sysKkcgIBi1Lt2kGfBERkVhyzABmjPnBocustT+JTDkSbpv31VJR76NPZjKDemj+LxERkVjQlkH4e0PXHuBioDhy5Ui4tZz/cWiuxn+JiIjEiLZ0Qf65+bYx5k/A8xGtSMKquftR83+JiIjEjrYMwm8tGRgYiUIk/Ky1B2bA1/kfRUREYkZbxoDtASxgAB/w80gXJeGxZV8tZXVeemckMaRnWrTLERERkZC2jAE7GSi31tYZY7KArAjXJGHynub/EhERiUlt6YL8C+AN3W4AHolcORJOCzeXAup+FBERiTVtCWBJ1lofgLXWixsHJjGuos5L4cZ9xBk4f2xetMsRERGRVtoSwHzNpyIyxozGjQWTGPfyR7vxBSxnjOhFXqYys4iISCxpyxiwu4E5xpgmIBWYHdmSJByeXemma7tqav8oVyIiIiKHaksAM8AM3ESsBhgQ0Yqk07bsq+XDXZWkJ8Vzwdg+0S5HREREDtGWLsgHrLWV1toyoAz4ZYRrkk56flURABdP6ENKoifK1YiIiMih2hLAEppvWGtt6/sSe4JBy/Oh7scrp+ZHuRoRERE5nLYEsGpjzPkAxpgCoCmiFUmnvLetjN1VjfTPTuHkwZp+QkREJBa1ZQzY/wCPGWMeB7YDt0SyIOmcOSuaW7/6ExenA1ZFRERiUVtawMYD9cAm3ISsD0e0Iumweq+fuWv2AOp+FBERiWVtaQH7Me78jyOBEmBiRCuSDnttbQn13gBTB2br3I8iIiIxrC0tYJXAS7gZ8R9DASxmPafB9yIiIt1CmwbhAznAaGPMDECf7jGopKqRd7aUkuiJ45KJfaNdjoiIiBxFWwLYHbixXw8A9wA/imRB0jHPryrGWjhvbG+yUxOjXY6IiIgcxTHHgFlr94VuLgcuj2w50hHWWp5b6SZfvXKKGihFRERiXVtawCTGrSmuZvO+WnLTEjl7VK9olyMiIiLHoAB2HJgTav26dFI/Ejz6lYqIiMQ6fVp3c75AkBc/3A3AVTr6UUREpFtQAOvmCjfup7zOy8i8dMb3z4x2OSIiItIGCmDdXMvg+6n5GKNTD4mIiHQHCmDdWGW9lzfX78MYuHxy/2iXIyIiIm2kANaNvfzRHryBIGcM70mfrORolyMiIiJtpADWjTV3P2rwvRzVrmXw78/ChlcgGIx2NSIiQttOxi0xaOv+WlburCQt0cMF4/KiXY7Eqi1vwr9uAn8jrHsBeo2BM++CcVeCR3/+IiLRohawbur5Ve7E2xdN6Etqoj5I5TDWPAf/uA56DINvrIUrH3XLn/sCPDQNlv8N/E3RrVFE5ASlANYNBYOW51a6AHblVA2+jwlb3oQnLoVti6JdibP8b/DsLZA/HT73MmT2g4nXwhcXw/X/gJRcePkb8JuJsPghaKrt+GsF/FC8wm3n5W+44NeZ7YmInADUdNINvb+9nOLKBvpnpzBjSI9olyOb34BnboCgH7YtgOm3wvk/hqSMrq/FWnjn1/DmT2DEhXDN45CYeuDxuDgY/SkYdbGrddGvYP73YNEDcMoX4eQvQGru0V/DWw/Fy2HHEti52I0x89W5xxJSXfjzJMHwc2HMpTBqFqTkROxHFhHpjhTAuqHmwfdXTOlPXJzm/oqqTfPhXzdC7zFw/T9hyR/gvT/C5vnw6d+6ENJVrIX534clD8GEa+HyP4In4fDrGgNDC9xl1zIX2gp/Dot/B9NvgVO/fGDd+nLYtRR2LIadS2D3BxD0AQbyxsHkG2DQqTDwNEjvDTvfg/UvwvqXYOOrEBcPQ86CMZ+G0Ze4dURETnAKYN1MgzfAq6tLALhC3Y/Rtek1N8C99xiY/V/XcjTr5zD2Mnjhy/DUlTBlNlz4M0jOimwtAT+89FX44Gk4+Q6Ydb9r7WqLASfBZ/4JJWvgnQddgFv6Z8blTIV198K+dW69uAToP9WFs0GnwYBTICX7k9sbfLq7zLofileGwtiLrnvy5btg4Kkw9lIXyLJ0BK+InJgUwLqZ+etKqG3yM3lANsN6pUe7nBPXxnnw79nQeyx89r8Hd7ENPAXuXASF98Hi37vxYZ/+DYy8MDK1+Bphzq2w4WUouBfO/o5r4WqvPuPh6r/CzO/Cu78lc82LMGCKO2Jy0KnQfxokpLR9e8ZA/jR3Oe9HsHetaxVb/yLMu8dd+k11YWzCtZClLxQicuJQAOtm5oQG31+l1q/o2TjPtXz1GQ+znz/8+KaEFDj/J6417L9fhn9cCxOvh1n3HXuMVXs0VrvxZ9sXwUW/gFPu6Pw2ewyDS3/HkswrKSgo6Pz2wIWxPuPdZea9ULrlQMvYGz9yY9aGzoQpN8KoT0GCJhaOCXs+ZMy6X4Nd7FouB5wMiWnhf52ADyp2QHySGzuZlAFxnvC/jkgMUQDrRvZWN/LO5v0keAyfntQv2uXEtvKtsOFVNwapbIsbXD7jywcPSO+IjXPhX7ND4eu/h++Ca63/NLhjASz8JSz6NWx9Gz71axhzSefqAKgrhaeugpLVboqJidd2fptdpedwNx/ZmXe539UH/4AP/umO3EzOggnXwOQbod+UjrXmSed98E94+ev0sMDCRWCDbjxf30kujA06zV239wuFr9F1a+/5APZ86C5710LAe/B6iemhMJZ5IJQlZUBy5oFlab1g0mcgSb0B0g6+Bnj3d+4L67H+h0eQAlg38sIHxQQtXDA6j+zUxGiXE1uCQdi9Cja+4mZ837/BLc+b4AaKv/VTWPZXmPk9N2i8I9+uN7zqZpTvMyHU8tXGP9z4JDjn+27M03+/7Abtj78KLvolpHXwKNaqIvj75VC1y43filT3ZlfIHer2T8G97sjMD/4Bq56CZX9xXbyTb4CJ12nw/qFq90Vmn/i98Nq9bv8PPpOl/W7j9LPOgV3vu6NedyyB9x91YwUBeo0+OJBlDziwLW897F3jQtbuUODav94dMQyQnO0C3Sl3ut910A9N1dBU41p3m283X1fvPnDfG5rqZOmf4ZrH3N+lyLHsXOrG6JZthpxBMOn6qJWiANZNWGuZs6IL5v7at4ERm/4Eja8d/M0zufl21sHLEtLaPtg73HyNruttwyuuZaq2BIzHfRBM+7ybaiFnkFt3xxJ4/X/hxa+4IxXP/wmMOL/trSsbXoF/fw76ToSbnuvYt6a+k+D2t91A9wW/gK0LXB29RruWuYRU172TkALxKUfer/s3wZNXuA+h2c+7n/d4EOeBYee4S0MlrH0OVj3tjux840cw4gKYcpO7PtLRnScCfxPM/TaseNx11876OeQMDs+2q3e793nR+3DqV+C8H+Nb9I77Wx9xnruA+9vbvSoUyBbDmjmw4jH3WNYAF4bKt0HpRtdyBpDaE/pNhpEXQN/J7u8he2DHWziDAff3/9wd8Oi5cNH9MO1mtZiGW8AP1UVQVUxO+Qew2ef2fdAPNnQdDB5yP+Aunng3hjSKrUwtvHXui/h7D7v36Oz/wrCZUS1JAaybWLu7mo17a8hJTaBgVIRaAopXwlNX0qepDkqTwFvThieZA10Bw2a6uaeGnNm+wdrtUV/upnjY8Ap8/Jb7FpyY7qZ7GPUpF6oO1yUy6FS49fUDY47+cQ0MPtMFoP5Tj/6a61+G/3zOfWjMfq5zRzR6EuDsb7u5uF74MrzwpSOvm5B64JLY6va+ta4r6POvuEB4PErJdtNhTL8F9m1wR3d+9C/XpZzaE8Zd4cJ1So5rRUnJOfgSjTFk/iYXHG3ATXwbCdV7XCts0fuuRXXLm/DQyXD61+CMb3Sui337u/Cfz7sPqqsfg/FXHnndhGT3NzXoVDjzm+7Ddu+aA3PD7VsPucPcGMi+k9wls194w1Gcx02jcuc78Pzt7ijbbQvd9C+RPur4eBIMuOBduQMqdx64VITuVxe79zQwCeCjdm5/0YNw5SPuvRIt2xbBi/8DFdvgpC/AeT+MzjyNh4hoADPGfAW4ETDAg9bafx1mnWRgGTDHWvujSNbTnTWfeuiyyf1JjI9Ai9OOJfD0NZCaw7KJ9zPjouvdH6a3tlV3QE3oUvXJZZU73JiRZX9xrTdDz3YtFSMv7PhUA9a67e5a5uahKnrfTZVgA5Dex40TGv0pN8dUfNKxt2eM+0AYdbFrPSi8Hx6dCeOvhnP/9/CtCOtfch9K/abATXPC9489bxzc+gYULQt1p9SBr96NTWi+7a1z91tu17sunX5T4eJfusHyJ4Leo+GC/4Nzfwhb3nBhbOXfIXCU0yjFpxwSyrJdUItPgvhkiE90k8W2XIcuBy1LJLtiLayrgoaKVpfKw99unowWInPAxc6l7sjbplq45gkYdzlUFbuW3YW/gA//CRf81L3H2xN0rHWtAvO/D7lD4HMvuqlV2iPOcyBozbizfc/trPRecOMcePc3roVj9yoXII/1xepE1VABb//cDdOo2OECVnOXMAAGMvq6LziDTnWtlNmDIKs/q1ZvYMq0k1zrfFy8uxhP6HbcIfc9ULoZ/nsnPH4xnHm3O0K7K89B21TjvnAv+wvkDHFfWgef0XWvfwwR2xPGmGHALcAMIAl43xgz31pbcciqPwHeiFQdxwNrLXNX7wHg0skR+Gb98VvwzxtcUPrsCzSu2uyWx3lc4EjOgrbkDn8TbH/HzY+1aZ67vALkjT8QxvJPOvL4K1+jG5i7a6kbb1K0DGr3uscS090/1DO/CSNnuUDU0a5PT4IblD/xOjfx6OKH3ImqT74dzrr7wIfmuhfh2ZtD4es51w0TTp746H4r7G488W5W/VGzXGhoqoHGykPC0aGXSncp3+bW9Te6MU6Bpk8O+j6MyQAftloQl+DeH83BLnuAa4VsDnkpOa7VYMkfXFi86P+58X6dbflZ/jd49dvub3T2fyFvrFue1R+u/ptrKXz1266ldshZ7ojYtoQob51rGVgzx02Se/kfu2frUVycO6Bj0Gnw7K3w1wtcGD3ljq7tkvQ1utNy7Vzsxrz5Gw/ukjtcN13rZfEp7svV0LMjU5+3Hv5xvaux3xT3/zjn6lDIag5a+Uf8Qlu1K87NG9hWaT1dC+Wr33ZfEra+7Q4Yyh0Sph/oKD5+C178mhsnO+NLbpxpJI7g7YRIRtFzgBettV7Aa4xZCJyG+0gGwBhzCtAbeBwoiGAt3drq4ip2VzXSJzOZyflh7kvf8Kr7p91zpBtPlN4b2NyxbcWHTj8z/Fz3wVO6KRTGXoN3f+tmW0/JheHnuTDWdzKUfOSC1q6lsOej0AzruG8rQwvcYe/5J7sBuuH+5pSc6f4op9/i5uxa+rAb/H3mXfTeWwULf+dam26aE/7wJZ1jjPudJGe6D46OCAZdCAs0HQhl/tAltGzVyuVMmXFWq67N1LZ9oE+41gWbObfC6v+4I187Ms+Zvwle/RasfML93Vz1l8NPezL4DLhjoRuH9dZP4eHT3ReKgnuOPP6m7GM3ncr+DXDuD+D0b0RvPGe4DJzh5uD775dg3ndcl+RlD4W3JbK1xirXMtl8cMLulQeCfY/hbhxtc0tRnMe1rrZuIYrzHNxqVLQM/nm9+8IX7i9nAZ/7QrlrqTtoYdwV4d3+kSRlwBUPu/GDL30D/nQmfOoB9wU4EuG4scq15q78u/sd3DLPvS9ikLHWRmbDxnwXqLbWPhS6/zNgs7X28dD9JOBV4Gpc13LB4bogjTG3A7cD5OXlTXvmmWciUm+z2tpa0tNj65DmZzd5eXmrj3MHxjN7bBu62tqo996FjFn/IDUZw/ho4g/xJ7g+8Ujsg3hfLTkVH9CjbBm55StJ9FW3PBaIS6QmYzjVmaOpyhpNdeYofIldP2gztW4nQ7f+nZ5lywCoyhzNRxN/SCC+k1NXdFOx+LfQ1Tq1D2yA/KKXGbLtKazxsHXo59jd70IwbQs5iU1ljFv7/8iq3siOgVezbcgN7oP6GBK81QzZ9hR998zHl5DJ1qGfpaTPOQe9bo/S9xmz/kGs8bBu7N1U5E4+4va65fvAWvKLXmLo1ifwJuawbuzdVGeN7tQma2tryU3wkVW1jqyqtWRXriOtbjsGS9B4qE0fSlXWOCqzx1KVNQZ/Qvu/tCV4K5my6l4SvRV8OOkn1GSO7FTNLaxl9Ibf0WfvW2wacSe7+1/Uoc109r2Q1LiPMesfJLtqHXt7n8WmkXcSiA9fq1Ru2XJGbfwjid4Kdg24nO2DryfoCd9nJrR/H8ycOXOFtXb64R6LZAD7GpBurf1Z6P5DwOvW2hdC9+8HFllrXzHGFHCEANba9OnT7fLlyyNSb7PCwsLwTT4ZBtZazv3VAraW1vGPL5zCacN6hmfDK59039AHnQafeeagFp6I74NgwDWB71vnjpbKm+C+GcaKbYvY+dbfGHjjb0/olq9Y+1uIhrDsg/Jt7jRR2xa682Ve+jvoOeLoz9n5nhts31TrugXHXd7+1939gTtactfS0LjBB9xRiIX3uXnp+k6G6548Zgtit34fFK+A/9zspm059wdw2lfb1srXVOsGbJdvdZf9m6jf9DapDW4oCAmpkD/d/T4Hneq68sLVvVVVDI9d5LrMP/dyeA60mf+/brhFwXeh4Dsd3kxY3gvBgJsTsfA+yOwPVz3a+Raq+nJ47btuHGSvMXDZH9wZOCKgvfvAGHPEABbJLsg3gT+HglYirovxPmNMprW2GpgADDbGzAb6AznGmL3W2ocjWFO3s2VfLVtL68hJTeDkwWFqRl/6Z/ePedg5cN3TnZ+ctL3iPK5rccDJXfu6bTXkTLYOCzDwBA5fEka5Q+CzL7ru7fnfc92DBd9xYeDQ6TSsdeO95n7HjS/77AvtHxDfrN9kuOU1d/To6z+Av5zjhhqUbnLTeVz8q+P/jAP9p7mu2Ze+Cm/80I1RveJPbmxSQ+XBIau81e3msafN0npRnzqE1DO+5L609p0UualQsvrD515yIezJy+Hzr7oDUTrq3d+68HXSF9wR2NEW54Gzv+WGmDx3m/s5z/oWnPXttg8zaaoNzS230oXsrQtc1+NZ33KXthyUFQMiFsCstWuMMS8DiwEL/BoXwq4DLrXWfqp5XWPM54HBCl+fNG+NO/H2eWPyiPeEYXzGol+5076MvsQN3u0mb1SRbs0YmDrbTZPy6rfc3+Da5+HS37vB0BAa73W3G7sy/HzXMnC48V7tfd1J17ujhRf8wh1Beslv3Dx5J8p8WSnZ7qjR5X+Fed+F309z3bEN5Qevl9HPheUR57vJgZsvOUMgOZM1hYUUnF7QNTXnDDoQwv5+Kdw8t2NHPa962oXvcVe6AzNi6Xc+4CS4Y5FrDFjw/+Djt917/tCj0QM+N61J8YrQZaWbzLd5frmsge6ghTO+4YJxNxLR40GttfcB9x2y+OnDrPd4JOvozuatdQFs1vg+nduQtfDW/7kANuEauPzhE3syS5FoyOjjuv3WvwSvfNNNIHrql10geu52KF7uDtef+d3wngsxKcNN5XHB/4Vvm92JMXDSbe6Annd+7aYkOShkDe76noBj6THMtZw+fjE8cSnc/OqBiaXbYuNcN8xk6Ey44s+xeYBFcqZrkRx+Hrx8Fzx8Blz4M9edWxxq3drzIfgb3PopOa5Vc8wl7rrfVDcNSTeliVhj2K7yetburiYt0cPpwzsx9stamHevO8pv6mfdN2Cd6FYkesZ82k0E/HpobM7i37mzSlz7JIy9NNrVHb/6ToRrHo92FW3Xe7SbduSJS+CJT7sj+toyye+OJW7+wr6TXOCPpTG2hzPhajck5bnbXXcxuPn6+k52R6n3n+ouOUNiqxWvkxTAYthrodavmaN7k5zQwcAUDMDLX3fdGqd80U0OeRy9gUW6rZRs1wU5/mo3zcRZ3+r4eC85fvWdCDc9D3+/7EBL2NHOAVqyBv5xnTvdzo3/iYkZ39ske6A76GDr25Ce5/4WjvNemhhsk5Rmr3W2+zHgg+fvcOHrrG8pfInEoqFnu/GYCl9yJPnTXJiqLnZBrK7s8OtVbIenrnJdeLOfcwcbdCeeeDcGr+/E4z58gQJYzNpX08jyHRUkxsd1/NyPr33PTQJ57g/dhKMKXyIi3dOgU+Ez/3QT6D55uTuKs7Xa/fDkFW72/dnPdXyCYukyCmAx6vV1e7EWzhzek/SkDvQUr38J3v+z63Y8867wFygiIl1raAFc95Q7KvDpq93puMCdl/fpq6CmxLWUqTW1W1AAi1HN009c2JHux4od8MKX3eHt5/8kzJWJiEjUjLzAnUqoeKUb69VQAc/cAHvXwrV/j935FeUTFMBiUFW9jyUfl+GJM5w3Jq99Tw744Nlb3JGPV/8t9o9+ERGR9hnzabjyEdixGH47CbYvclMLjTg/2pVJO+goyBj01sa9+IOW04b1IDetnQHqzZ+4uYSuedzNbyMiIsefCVe7yXtf+hrMuh8mXhvtiqSdFMBiUHP3Y7uPftw0380nNP3WrjvTvYiIRMeUG2H8lZCQEu1KpAPUBRlj6r1+FmzaD8AFY9sRwKqK3ZQTeePhwp9HqDoREYkpCl/dlgJYjFm4aT+NviCTB2TTJ6uNJ8oN+GHOba45+prHj/8T7IqIiHRz6oKMMR3qflxwP+xcDFc8Aj1HRKgyERERCRe1gMUQrz/Imxv2AXDhuDYGsI/fhoUPwOSbYNJ1EaxOREREwkUBLIYs2VpGTaOf0X0yGNIz7dhPqNnrTl7aaxRc/IvIFygiIiJhoS7IGNIy+WpbWr+CAXjuC24m5M+96M79JSIiIt2CAliMCAQtr69rx/ivRb+GbQvg0t/rtBMiIiLdjLogY8SKHRWU1noZmJvK6D4ZR195+7tQ+HOYcA1Mmd01BYqIiEjYKIDFiNZHfCQ3YAAAEUNJREFUPxpjjrxiXSnMuRVyBsMlD8LR1hUREZGYpC7IGGCt5bW1bRj/FQzC83dCfRnc9gYkHaOlTERERGKSAli0NNWCiYP4JNbuqaW4soHeGUlMGZB95OcseQi2vA4XPwB9J3VdrSIiIhJWCmDR8OEz7rRBIWOMh7VJ8ZhgEnG/ToX4RPAkQXwSeBIhPtkt2/4OjLkUTrotisWLiIhIZymARcPKJyF7EEy/Gfxenlm8mbr6ei4ZlUtqugG/FwJN7trfCAEveOth5Cx31KPGfYmIiHRrCmBdrXo37HgXCu6FM77Bln01fG/eQrJSErj5uvPAo+MiREREjnf6tO9qa58HLIy/CoDX1u4F4LwxeSQofImIiJwQ9Inf1dbMcQPoew4HOnjybREREenWFMC6Uvk2KF7R0vpVVFHP6uIqUhM9nDmiZ5SLExERka6iANaV1j7nrsddAcD8UPfjzFG9SU7wRKsqERER6WIKYF1pzXMw4BTIHgjAvNDkqxeMy4tmVSIiItLFFMC6yr4NsHdNS/fj/pomlm0vJ9ETxzmje0e5OBEREelKCmBdZc0cN/P92MsBeGP9Xv5/e/ceZHdZ33H8/c1uNvcLCUkg5IZcNQtICAmgYAAHKjc1oUWxglqGWuroqCN2ajsj43hrHW9TbYstpVRCRIMxoKXcGjBNIKQlgQ03iRJyVQjkftls8u0f52zYLBuS6Dnnt9nzfs1k2PM7h91vvvNk97PP85znlwnvOH44g/r2Lrg4SZJUSwawWsgsBbAJ58Kg0nKj736UJKl+GcBqYe1SeHX53uXHTTt2sWD5K/SK0vlfkiSpvhjAaqFlNvRqhLdeDsB/P/s7du1Ophw7jOED+xRcnCRJqjUDWLXt2VM6/f64C6H/MADmLlkDwMUTXX6UJKkeGcCqbdUi2Lhy7/Ljyle38dBzv6OpoReXnza64OIkSVIRDGDV1jIbGvvCyZcA8MNHV5AJl516NEe6/ChJUl0ygFXT7rbS8uOJF0OfQWxv3c2sx1cCcM05E4qtTZIkFcYAVk0r5sPWl/cuP85dupqN23dx2tihvH3s0IKLkyRJRTGAVVPLbGgaCCdcRGZy64IVAFx79viCC5MkSUUygFVLWys8PRdOvhR692Pxitd4Zu0mhg9o4tJTjy66OkmSVCADWLUsfwh2bNi7/PjvC14E4INTxtGnsaHAwiRJUtEMYNXSMhv6DoW3nM9vN+3g3pZ1NPQKPnTWuKIrkyRJBTOAVUPrNnjuF/C290JjE7c/9hJte5KLJ47i6CH9iq5OkiQVzABWDb+6D1q3QPMMWtv2MPOxlwC45uwJxdYlSZK6BQNYNbT8BAaMhAnv5D9b1vLKlp2cfNQgph47rOjKJElSN2AAq7Qdm+D5+2Di+6FXA7eWN99fc/YEIqLY2iRJUrdgAKu0534Bu3dC8wyeXLWBJ17awOC+jbzvdO/7KEmSSgxgldYyG4aMhTFnctvC0sGrfzJ5LP2bGgsuTJIkdRcGsEra9mrp/K/m6azftou5S9cQAR/25HtJktSBAaySnpkLe9qgeQY/WryS1rY9nH/SSMYPH1B0ZZIkqRsxgFXSUz+B4cfTNqKZH5aXH69x9kuSJHViAKuUzevgxfnQPIMHnn2ZNRt3cOyRAzjvhBFFVyZJkroZA1ilLJsDJEycvve+jx8+azy9enn0hCRJ2pcBrFJaZsOoZp7PY1j46/X0b2rgysljiq5KkiR1QwawSnhtBaxaBM0zuG3hiwBMn3QMg/v2LrQsSZLUPRnAKmHZXQBsPuEK7vq/1YD3fZQkSftnAKuEltlwzGR+/EID21p3c/ZbhnPiqEFFVyVJkrqpqgawiPhERCyMiEcj4qpOz42IiNsj4rGIWBwRn6hmLVXz8vOw7in2TJy+d/nx2nMmFFmRJEnq5qp2f5yIOA74GHAW0AdYFBH3ZeZr5ZeMBL6amS0R0Q/4TUR8LzOzWjVVxS+/AdHAo/3O5cX1Kxk9pC/vfuvIoquSJEndWDVnwC4A5mZma2ZuBh4Bzml/MjOXZWZL+eFwYNVhF76W/RSe/BG860Z+sGQHAH969ngaG1zZlSRJ+xfVyjwR8dfApsz8h/LjLwO/ysxbO71uAHA38JnMXNLF57keuB5g1KhRZ8yaNasq9bbbsmULAwcOPODrmna+ypmPf5Lt/Y7i3hO/wuf/ZxcNveBb0/ozqOnwPvvrYHvQk9kDewD2AOxBO/tgD+DQe3D++ef/b2ZO7uq5qi1BAluBIR0eDwFe6/iCiBgE3Anc1FX4AsjMm4GbASZPnpzTpk2rSrHt5s2bxwG/Ribc/sdAG72vvYPnH91F8hved/oYLr/otKrWVwsH1YMezh7YA7AHYA/a2Qd7AJXtQTXXyh4ELomIhvIer2nA4ogYDBARQ4A5wNcz8+Eq1lF5i2+BF+6Hi77EtsHHcufilQBc69ETkiTpIFRtBqy8uf4eYAGQwDcphbCrgCuALwAnA1+M2Ltk96HMXF2tmipi/XK472/guAvgzOuYs2glm3a0cfq4oZwyZsiB/39JklT3qrkESWZ+Ffhqp8u3l5+7Ebixml+/4na3wV3XQ0MTvPd7JPAfj64AnP2SJEkHr6oBrMeZ/y1YvRhm/CsMHs3SlRt4Zu0mjujfm/ecclTR1UmSpMOE5yUcrDVPwMNfg+YZcMqVANzx2EsAXHnGGPo0NhRZnSRJOowYwA7Gru2lpccBI+GSbwCweccu5i5dA8AHpowrsjpJknSYcQnyYDxwE7zyPHz4p9B/GAA/W7KG7bt2M/XYYRw3or7PRZEkSYfGGbAD+fU8eOwfYcr1pXc+ApnJzPLy49VTnf2SJEmHxgD2ZrZvgDk3wPAT4N037b381OqNPF3efH/xRDffS5KkQ+MS5Jv5xedg8zq47n5o6r/38h2LSrNfMyaNoW9vN99LkqRD4wzY/iz7KTx1J7zrRjjmjL2Xt+xs42dL3HwvSZJ+fwawrmxeB/d8GkZPgnM/u89Tc5esYVvrbqYcO4zjR7r5XpIkHToDWGeZ8LO/hF07YPrN0NB7n6dnLiqdfH+1s1+SJOn35B6wTkavuRdeeADe8/dw5An7PPfUqo20rN7EkH69+aNmN99LkqTfjzNgHa1fznHL/23vjbY7m+nme0mSVAEGsI5+dT97epVutE2vfVuzZWcbc5esBuDqqWOLqE6SJPUQLkF2dNbHWbRpNO8YPPoNT929dA1bW3czZcIwjh85qIDiJElST+EMWCe7mgZ3eb397K8POvslSZL+QAawg9CyeiNPrtrIkH69eU/z0UWXI0mSDnMGsIPQPvs1fdIxbr6XJEl/MAPYAWztcPL9Bz37S5IkVYAB7ADuXrqGLTvbmDz+CE4c5eZ7SZL0hzOAHcDezffOfkmSpAoxgL2JltUbWbpqI4P7NnLpqW6+lyRJlWEAexOzHm/ffO/J95IkqXIMYPuxrbWNOU+4+V6SJFWeAWw/7lm6li072zhj/BGcdJSb7yVJUuUYwPZjppvvJUlSlRjAuvD0mk0sWbmBQX0bufQUN99LkqTKMoB1of3oiRmTxtCvyc33kiSpsgxgnexsS+Y8sRqAD0zxxtuSJKnyDGCdLFrXxuadbUwaN5STjxpcdDmSJKkHMoB1Mm9lG+Dme0mSVD0GsA6eWbuJ5Rv3MKhvI5edOrrociRJUg9lAOtgVnnz/ftPP8bN95IkqWoMYB28srWVwOVHSZJUXY1FF9CdfO/qSdw17CHeerSb7yVJUvU4A9bJsL62RJIkVZdpQ5IkqcYMYJIkSTVmAJMkSaoxA5gkSVKNGcAkSZJqzAAmSZJUYwYwSZKkGjOASZIk1ZgBTJIkqcYMYJIkSTVmAJMkSaoxA5gkSVKNRWYWXcNBi4iXgRVV/jJHAq9U+Wt0d/bAHoA9AHsA9qCdfbAHcOg9GJ+ZI7p64rAKYLUQEYszc3LRdRTJHtgDsAdgD8AetLMP9gAq2wOXICVJkmrMACZJklRjBrA3urnoAroBe2APwB6APQB70M4+2AOoYA/cAyZJklRjzoBJkiTVmAGsg4j4REQsjIhHI+KqouspQkRsiIh5Hf4MLLqmWoiIkyJiQUTM6nDty+VrCyNiWoHl1UTnHkTEhIhY12Es3FN0jdUUEQMi4vsRsSgiHo+Ir5Sv18046KoHdTgOhkbEnR1+FnymfL2exsEbelBv46BdlNwfEbeWH1dsHDRWosCeICKOAz4GnAX0ARZFxH2Z+VqxldXcksycVnQRBZgKfBd4H0BEXAC8PTPPiYjRwEMR0ZyZbUUWWWX79KDs3sz8SDHl1NxQYGZm3hARvYBnIqKF+hoHb+gBMJf6Ggd9gC9m5tMR0UhpHKyivsbBG3oAPEJ9jYN2NwAtwBGV/rngDNjrLgDmZmZrZm6mNNjOKbimIkyMiEfKf/6s6GJqJTNvA9Z1uHQh8OPyc2soHQB8UgGl1UwXPQC4MCLmR8RDEXFFEXXVSmauzsz55YcDgFbgDOpoHOynBxuor3Hw28x8uvxwBNBG6ZeTehoHXfVgK3U0DqC0CgBcQukXU6jwzwVnwF43gn1Pt32lfK3ejMrMPRExHPh5RLyYmQ8WXVQBRgALOzyux/GwAhiXmRkR44D7I+K5zHyu6MKqKSIagNuAzwHTqcPvC5168Bz1OQ6+BlwPfB44k/ocBx178Cx1NA4iIigFr08C7e9WrOjPBWfAXrcVGNLh8RCg3pYfycw95f+uB2YDpxVbUWHqfjxkWfnjl4AHgInFVlVdEdEb+CEwKzPvpQ7HQece1OM4AMjMvwLGAtcAJ1Bn4wDe0IMz62wcfBz4r8xc3uFaRb8fGMBe9yBwSUQ0REQ/YBrwWLEl1VZEjI+IoeWP+wGXA78stqrCPAhcARARR1KaZu6Rv+ntT0ScWB4HRMQRwLnA48VWVT0R0QTMorQV4Ufly3U1DrrqQR2Og5Mion1WYxuwEfg29TUOuurB6fU0DijNep5XflPSPwHvohS2KjYOXIIsy8yW8rs6FlCabvxmZnbeD9PTDQZuLS8/9Ab+JTN78j+wN/Nz4KKIWEDpF5VPZeaOgmuqtdHALRGxm9J4+EJmriy4pmq6jtIvXsMj4s/L1z4L/LaOxkFXPbgbeH8djYM24J8jYgjQH5hP6Y0IF9bROOiqB89TWnasi3GQmR9r/7j8bsePAF8CvlOpceBBrJIkSTXmEqQkSVKNGcAkSZJqzAAmSZJUYwYwSZKkGjOASZIk1ZgBTJIkqcYMYJK0HxFRb2cBSqoRA5gkSVKNeRK+pMNeRPQC/g6YSun72kzgDGAz0AwcAdySmd+NiHdSOtE6gZ3ApzPz2Yi4ALgJ2E3pnm9XlT/3l4DzgD6Ubs7dQOl2PXuA7cDHM/PXNfqrSuohPAlf0mEvIj4CTM3MvyiHsfmU7mH3ZGZ+JiL6A09Sun/dI8B5mbk2Ik4Fvg9cAiwBzs3M1RExiFI42wlMy8yHI+Jvy59zCXADcCUwEGgq37xekg6aM2CSeoLJwDsiYl758WBgC3APQGZui4hlwFhga2auLV9/MiLeApwILMvM1eXrmwEiYn1mPlz+nCuBkzLzwYhopHSD5lXAd2rxF5TUs7gHTFJP0AL8ODOnZeY04DJgLaXlRyJiMDAReAEYEBFHl6+fCvymfL05Ika1vz4i+lG6KXFHERETgIcy81PAUEo3sJakQ+IMmKSe4AfAtyPil5T2cK0FWoHzIuJS4Ejgi5n5akR8FLgjIvZQCljXZeaGiPgUMCciWoFNwIf287XeBtxefl0v4KNV/ZtJ6pHcAyapR4qIW4FZmXlv0bVIUmcuQUqSJNWYAUySJKnGXIKUJEmqMWfAJEmSaswAJkmSVGMGMEmSpBozgEmSJNWYAUySJKnGDGCSJEk19v9qfoP3si+viAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###(2) 컨볼루션 레이어, 폴링 레이어 추가 모델"
      ],
      "metadata": {
        "id": "EIfetCoM2m77"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUos3Kuelqph",
        "outputId": "a28cd07c-6260-45ee-b598-e50427fcb59f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_8 (Conv2D)           (None, 128, 128, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d_8 (MaxPooling  (None, 64, 64, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_9 (Conv2D)           (None, 64, 64, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_9 (MaxPooling  (None, 32, 32, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_9 (Dropout)         (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " conv2d_10 (Conv2D)          (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_10 (MaxPoolin  (None, 16, 16, 128)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_10 (Dropout)        (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 16, 16, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_11 (MaxPoolin  (None, 8, 8, 128)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_11 (Dropout)        (None, 8, 8, 128)         0         \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 8, 8, 256)         295168    \n",
            "                                                                 \n",
            " max_pooling2d_12 (MaxPoolin  (None, 4, 4, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_12 (Dropout)        (None, 4, 4, 256)         0         \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 4, 4, 256)         590080    \n",
            "                                                                 \n",
            " max_pooling2d_13 (MaxPoolin  (None, 2, 2, 256)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_13 (Dropout)        (None, 2, 2, 256)         0         \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 2, 2, 512)         1180160   \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 1, 1, 512)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_14 (Dropout)        (None, 1, 1, 512)         0         \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 128)               65664     \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 34)                4386      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,376,290\n",
            "Trainable params: 2,376,290\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#model2 생성\n",
        "model2 = keras.Sequential()\n",
        "model2.add(keras.layers.Conv2D(32,(3,3),padding = \"same\",input_shape = X_train.shape[1:],activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(64,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(128,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(128,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(256,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(256,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(512,(3,3),padding = \"same\",activation = 'relu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size = (2,2)))\n",
        "model2.add(keras.layers.Dropout(0.25))\n",
        "\n",
        "model2.add(keras.layers.Flatten())\n",
        "model2.add(keras.layers.Dense(128,activation = 'relu'))\n",
        "\n",
        "\n",
        "model2.add(keras.layers.Dense(nb_classes,activation = 'softmax'))\n",
        "\n",
        "# model2 요약\n",
        "model2.summary()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model compile\n",
        "model2.compile(loss = 'categorical_crossentropy',optimizer = 'adam',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "vEUPSFEmtRk5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train데이터로 학습시킴\n",
        "history = model2.fit(X_train,Y_train,batch_size = 32,epochs = 50,validation_split=0.25)"
      ],
      "metadata": {
        "id": "LPKzT_uT01_Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#손실, 정확도\n",
        "test_loss,test_acc = model2.evaluate(X_test,Y_test)\n",
        "print('\\n테스트 정확도:', test_acc*100)"
      ],
      "metadata": {
        "id": "8Sp6n1ZLz1-8",
        "outputId": "8540f52b-3179-4479-d1c1-bc65ba5b211f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75/75 [==============================] - 1s 14ms/step - loss: 1.9769 - accuracy: 0.5483\n",
            "\n",
            "테스트 정확도: 54.83061671257019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history.history['accuracy'], linewidth=\"2\")\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Accuracy Trend')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['train','test'])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "J_i1JimvkVD0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "outputId": "e4cd34d9-8a5a-4b56-bb82-3ff14cd46985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###(3)inceptionV3 임베딩"
      ],
      "metadata": {
        "id": "9ZVFkjgi30KS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.applications.inception_v3 import preprocess_input\n",
        "from keras.applications.inception_v3 import InceptionV3\n",
        "from keras import models, layers\n",
        "from keras.models import Model"
      ],
      "metadata": {
        "id": "pYlyMTLi6A6i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pre_trained_model = InceptionV3(input_shape=(128, 128, 3),\n",
        "                                include_top=False,\n",
        "                                weights=None)\n",
        "\n",
        "pre_trained_model.summary()"
      ],
      "metadata": {
        "id": "2LYfwM0e6Hsy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_layer = pre_trained_model.get_layer('mixed7')\n",
        "last_output = last_layer.output\n",
        "\n",
        "my_layer = layers.Flatten()(last_output)\n",
        "my_layer = layers.Dense(256, activation='relu')(my_layer)\n",
        "my_layer = layers.Dense(128, activation='relu')(my_layer)\n",
        "my_layer = layers.Dense(nb_classes, activation = 'softmax')(my_layer)"
      ],
      "metadata": {
        "id": "UOIiEu0I6MdC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model3 = Model(pre_trained_model.input, my_layer)\n",
        "\n",
        "model3.compile(optimizer='adam',\n",
        "              loss='mse',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CclMMSnExvq0",
        "outputId": "1ae745b6-b354-4a05-be91-ab2fbe862709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 128, 128, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d_94 (Conv2D)             (None, 63, 63, 32)   864         ['input_2[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_94 (BatchN  (None, 63, 63, 32)  96          ['conv2d_94[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_94 (Activation)     (None, 63, 63, 32)   0           ['batch_normalization_94[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_95 (Conv2D)             (None, 61, 61, 32)   9216        ['activation_94[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_95 (BatchN  (None, 61, 61, 32)  96          ['conv2d_95[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_95 (Activation)     (None, 61, 61, 32)   0           ['batch_normalization_95[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_96 (Conv2D)             (None, 61, 61, 64)   18432       ['activation_95[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_96 (BatchN  (None, 61, 61, 64)  192         ['conv2d_96[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_96 (Activation)     (None, 61, 61, 64)   0           ['batch_normalization_96[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_4 (MaxPooling2D)  (None, 30, 30, 64)  0           ['activation_96[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_97 (Conv2D)             (None, 30, 30, 80)   5120        ['max_pooling2d_4[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_97 (BatchN  (None, 30, 30, 80)  240         ['conv2d_97[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_97 (Activation)     (None, 30, 30, 80)   0           ['batch_normalization_97[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_98 (Conv2D)             (None, 28, 28, 192)  138240      ['activation_97[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_98 (BatchN  (None, 28, 28, 192)  576        ['conv2d_98[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_98 (Activation)     (None, 28, 28, 192)  0           ['batch_normalization_98[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_5 (MaxPooling2D)  (None, 13, 13, 192)  0          ['activation_98[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_102 (Conv2D)            (None, 13, 13, 64)   12288       ['max_pooling2d_5[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_102 (Batch  (None, 13, 13, 64)  192         ['conv2d_102[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_102 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_102[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_100 (Conv2D)            (None, 13, 13, 48)   9216        ['max_pooling2d_5[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_103 (Conv2D)            (None, 13, 13, 96)   55296       ['activation_102[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_100 (Batch  (None, 13, 13, 48)  144         ['conv2d_100[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_103 (Batch  (None, 13, 13, 96)  288         ['conv2d_103[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_100 (Activation)    (None, 13, 13, 48)   0           ['batch_normalization_100[0][0]']\n",
            "                                                                                                  \n",
            " activation_103 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_103[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_9 (AveragePo  (None, 13, 13, 192)  0          ['max_pooling2d_5[0][0]']        \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_99 (Conv2D)             (None, 13, 13, 64)   12288       ['max_pooling2d_5[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_101 (Conv2D)            (None, 13, 13, 64)   76800       ['activation_100[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_104 (Conv2D)            (None, 13, 13, 96)   82944       ['activation_103[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_105 (Conv2D)            (None, 13, 13, 32)   6144        ['average_pooling2d_9[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_99 (BatchN  (None, 13, 13, 64)  192         ['conv2d_99[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_101 (Batch  (None, 13, 13, 64)  192         ['conv2d_101[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_104 (Batch  (None, 13, 13, 96)  288         ['conv2d_104[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_105 (Batch  (None, 13, 13, 32)  96          ['conv2d_105[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_99 (Activation)     (None, 13, 13, 64)   0           ['batch_normalization_99[0][0]'] \n",
            "                                                                                                  \n",
            " activation_101 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_101[0][0]']\n",
            "                                                                                                  \n",
            " activation_104 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_104[0][0]']\n",
            "                                                                                                  \n",
            " activation_105 (Activation)    (None, 13, 13, 32)   0           ['batch_normalization_105[0][0]']\n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)           (None, 13, 13, 256)  0           ['activation_99[0][0]',          \n",
            "                                                                  'activation_101[0][0]',         \n",
            "                                                                  'activation_104[0][0]',         \n",
            "                                                                  'activation_105[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_109 (Conv2D)            (None, 13, 13, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_109 (Batch  (None, 13, 13, 64)  192         ['conv2d_109[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_109 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_109[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_107 (Conv2D)            (None, 13, 13, 48)   12288       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_110 (Conv2D)            (None, 13, 13, 96)   55296       ['activation_109[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_107 (Batch  (None, 13, 13, 48)  144         ['conv2d_107[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_110 (Batch  (None, 13, 13, 96)  288         ['conv2d_110[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_107 (Activation)    (None, 13, 13, 48)   0           ['batch_normalization_107[0][0]']\n",
            "                                                                                                  \n",
            " activation_110 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_110[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_10 (AverageP  (None, 13, 13, 256)  0          ['mixed0[0][0]']                 \n",
            " ooling2D)                                                                                        \n",
            "                                                                                                  \n",
            " conv2d_106 (Conv2D)            (None, 13, 13, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_108 (Conv2D)            (None, 13, 13, 64)   76800       ['activation_107[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_111 (Conv2D)            (None, 13, 13, 96)   82944       ['activation_110[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_112 (Conv2D)            (None, 13, 13, 64)   16384       ['average_pooling2d_10[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_106 (Batch  (None, 13, 13, 64)  192         ['conv2d_106[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_108 (Batch  (None, 13, 13, 64)  192         ['conv2d_108[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_111 (Batch  (None, 13, 13, 96)  288         ['conv2d_111[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_112 (Batch  (None, 13, 13, 64)  192         ['conv2d_112[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_106 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_106[0][0]']\n",
            "                                                                                                  \n",
            " activation_108 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_108[0][0]']\n",
            "                                                                                                  \n",
            " activation_111 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_111[0][0]']\n",
            "                                                                                                  \n",
            " activation_112 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_112[0][0]']\n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)           (None, 13, 13, 288)  0           ['activation_106[0][0]',         \n",
            "                                                                  'activation_108[0][0]',         \n",
            "                                                                  'activation_111[0][0]',         \n",
            "                                                                  'activation_112[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_116 (Conv2D)            (None, 13, 13, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_116 (Batch  (None, 13, 13, 64)  192         ['conv2d_116[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_116 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_116[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_114 (Conv2D)            (None, 13, 13, 48)   13824       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_117 (Conv2D)            (None, 13, 13, 96)   55296       ['activation_116[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_114 (Batch  (None, 13, 13, 48)  144         ['conv2d_114[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_117 (Batch  (None, 13, 13, 96)  288         ['conv2d_117[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_114 (Activation)    (None, 13, 13, 48)   0           ['batch_normalization_114[0][0]']\n",
            "                                                                                                  \n",
            " activation_117 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_117[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_11 (AverageP  (None, 13, 13, 288)  0          ['mixed1[0][0]']                 \n",
            " ooling2D)                                                                                        \n",
            "                                                                                                  \n",
            " conv2d_113 (Conv2D)            (None, 13, 13, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_115 (Conv2D)            (None, 13, 13, 64)   76800       ['activation_114[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_118 (Conv2D)            (None, 13, 13, 96)   82944       ['activation_117[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_119 (Conv2D)            (None, 13, 13, 64)   18432       ['average_pooling2d_11[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_113 (Batch  (None, 13, 13, 64)  192         ['conv2d_113[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_115 (Batch  (None, 13, 13, 64)  192         ['conv2d_115[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_118 (Batch  (None, 13, 13, 96)  288         ['conv2d_118[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_119 (Batch  (None, 13, 13, 64)  192         ['conv2d_119[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_113 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_113[0][0]']\n",
            "                                                                                                  \n",
            " activation_115 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_115[0][0]']\n",
            "                                                                                                  \n",
            " activation_118 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_118[0][0]']\n",
            "                                                                                                  \n",
            " activation_119 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_119[0][0]']\n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)           (None, 13, 13, 288)  0           ['activation_113[0][0]',         \n",
            "                                                                  'activation_115[0][0]',         \n",
            "                                                                  'activation_118[0][0]',         \n",
            "                                                                  'activation_119[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_121 (Conv2D)            (None, 13, 13, 64)   18432       ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_121 (Batch  (None, 13, 13, 64)  192         ['conv2d_121[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_121 (Activation)    (None, 13, 13, 64)   0           ['batch_normalization_121[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_122 (Conv2D)            (None, 13, 13, 96)   55296       ['activation_121[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_122 (Batch  (None, 13, 13, 96)  288         ['conv2d_122[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_122 (Activation)    (None, 13, 13, 96)   0           ['batch_normalization_122[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_120 (Conv2D)            (None, 6, 6, 384)    995328      ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_123 (Conv2D)            (None, 6, 6, 96)     82944       ['activation_122[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_120 (Batch  (None, 6, 6, 384)   1152        ['conv2d_120[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_123 (Batch  (None, 6, 6, 96)    288         ['conv2d_123[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_120 (Activation)    (None, 6, 6, 384)    0           ['batch_normalization_120[0][0]']\n",
            "                                                                                                  \n",
            " activation_123 (Activation)    (None, 6, 6, 96)     0           ['batch_normalization_123[0][0]']\n",
            "                                                                                                  \n",
            " max_pooling2d_6 (MaxPooling2D)  (None, 6, 6, 288)   0           ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)           (None, 6, 6, 768)    0           ['activation_120[0][0]',         \n",
            "                                                                  'activation_123[0][0]',         \n",
            "                                                                  'max_pooling2d_6[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_128 (Conv2D)            (None, 6, 6, 128)    98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_128 (Batch  (None, 6, 6, 128)   384         ['conv2d_128[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_128 (Activation)    (None, 6, 6, 128)    0           ['batch_normalization_128[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_129 (Conv2D)            (None, 6, 6, 128)    114688      ['activation_128[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_129 (Batch  (None, 6, 6, 128)   384         ['conv2d_129[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_129 (Activation)    (None, 6, 6, 128)    0           ['batch_normalization_129[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_125 (Conv2D)            (None, 6, 6, 128)    98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_130 (Conv2D)            (None, 6, 6, 128)    114688      ['activation_129[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_125 (Batch  (None, 6, 6, 128)   384         ['conv2d_125[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_130 (Batch  (None, 6, 6, 128)   384         ['conv2d_130[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_125 (Activation)    (None, 6, 6, 128)    0           ['batch_normalization_125[0][0]']\n",
            "                                                                                                  \n",
            " activation_130 (Activation)    (None, 6, 6, 128)    0           ['batch_normalization_130[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_126 (Conv2D)            (None, 6, 6, 128)    114688      ['activation_125[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_131 (Conv2D)            (None, 6, 6, 128)    114688      ['activation_130[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_126 (Batch  (None, 6, 6, 128)   384         ['conv2d_126[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_131 (Batch  (None, 6, 6, 128)   384         ['conv2d_131[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_126 (Activation)    (None, 6, 6, 128)    0           ['batch_normalization_126[0][0]']\n",
            "                                                                                                  \n",
            " activation_131 (Activation)    (None, 6, 6, 128)    0           ['batch_normalization_131[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_12 (AverageP  (None, 6, 6, 768)   0           ['mixed3[0][0]']                 \n",
            " ooling2D)                                                                                        \n",
            "                                                                                                  \n",
            " conv2d_124 (Conv2D)            (None, 6, 6, 192)    147456      ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_127 (Conv2D)            (None, 6, 6, 192)    172032      ['activation_126[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_132 (Conv2D)            (None, 6, 6, 192)    172032      ['activation_131[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_133 (Conv2D)            (None, 6, 6, 192)    147456      ['average_pooling2d_12[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_124 (Batch  (None, 6, 6, 192)   576         ['conv2d_124[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_127 (Batch  (None, 6, 6, 192)   576         ['conv2d_127[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_132 (Batch  (None, 6, 6, 192)   576         ['conv2d_132[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_133 (Batch  (None, 6, 6, 192)   576         ['conv2d_133[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_124 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_124[0][0]']\n",
            "                                                                                                  \n",
            " activation_127 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_127[0][0]']\n",
            "                                                                                                  \n",
            " activation_132 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_132[0][0]']\n",
            "                                                                                                  \n",
            " activation_133 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_133[0][0]']\n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)           (None, 6, 6, 768)    0           ['activation_124[0][0]',         \n",
            "                                                                  'activation_127[0][0]',         \n",
            "                                                                  'activation_132[0][0]',         \n",
            "                                                                  'activation_133[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_138 (Conv2D)            (None, 6, 6, 160)    122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_138 (Batch  (None, 6, 6, 160)   480         ['conv2d_138[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_138 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_138[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_139 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_138[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_139 (Batch  (None, 6, 6, 160)   480         ['conv2d_139[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_139 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_139[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_135 (Conv2D)            (None, 6, 6, 160)    122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_140 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_139[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_135 (Batch  (None, 6, 6, 160)   480         ['conv2d_135[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_140 (Batch  (None, 6, 6, 160)   480         ['conv2d_140[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_135 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_135[0][0]']\n",
            "                                                                                                  \n",
            " activation_140 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_140[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_136 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_135[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_141 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_140[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_136 (Batch  (None, 6, 6, 160)   480         ['conv2d_136[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_141 (Batch  (None, 6, 6, 160)   480         ['conv2d_141[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_136 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_136[0][0]']\n",
            "                                                                                                  \n",
            " activation_141 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_141[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_13 (AverageP  (None, 6, 6, 768)   0           ['mixed4[0][0]']                 \n",
            " ooling2D)                                                                                        \n",
            "                                                                                                  \n",
            " conv2d_134 (Conv2D)            (None, 6, 6, 192)    147456      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_137 (Conv2D)            (None, 6, 6, 192)    215040      ['activation_136[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_142 (Conv2D)            (None, 6, 6, 192)    215040      ['activation_141[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_143 (Conv2D)            (None, 6, 6, 192)    147456      ['average_pooling2d_13[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_134 (Batch  (None, 6, 6, 192)   576         ['conv2d_134[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_137 (Batch  (None, 6, 6, 192)   576         ['conv2d_137[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_142 (Batch  (None, 6, 6, 192)   576         ['conv2d_142[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_143 (Batch  (None, 6, 6, 192)   576         ['conv2d_143[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_134 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_134[0][0]']\n",
            "                                                                                                  \n",
            " activation_137 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_137[0][0]']\n",
            "                                                                                                  \n",
            " activation_142 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_142[0][0]']\n",
            "                                                                                                  \n",
            " activation_143 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_143[0][0]']\n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)           (None, 6, 6, 768)    0           ['activation_134[0][0]',         \n",
            "                                                                  'activation_137[0][0]',         \n",
            "                                                                  'activation_142[0][0]',         \n",
            "                                                                  'activation_143[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_148 (Conv2D)            (None, 6, 6, 160)    122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_148 (Batch  (None, 6, 6, 160)   480         ['conv2d_148[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_148 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_148[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_149 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_148[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_149 (Batch  (None, 6, 6, 160)   480         ['conv2d_149[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_149 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_149[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_145 (Conv2D)            (None, 6, 6, 160)    122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_150 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_149[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_145 (Batch  (None, 6, 6, 160)   480         ['conv2d_145[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_150 (Batch  (None, 6, 6, 160)   480         ['conv2d_150[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_145 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_145[0][0]']\n",
            "                                                                                                  \n",
            " activation_150 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_150[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_146 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_145[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_151 (Conv2D)            (None, 6, 6, 160)    179200      ['activation_150[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_146 (Batch  (None, 6, 6, 160)   480         ['conv2d_146[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_151 (Batch  (None, 6, 6, 160)   480         ['conv2d_151[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_146 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_146[0][0]']\n",
            "                                                                                                  \n",
            " activation_151 (Activation)    (None, 6, 6, 160)    0           ['batch_normalization_151[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_14 (AverageP  (None, 6, 6, 768)   0           ['mixed5[0][0]']                 \n",
            " ooling2D)                                                                                        \n",
            "                                                                                                  \n",
            " conv2d_144 (Conv2D)            (None, 6, 6, 192)    147456      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_147 (Conv2D)            (None, 6, 6, 192)    215040      ['activation_146[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_152 (Conv2D)            (None, 6, 6, 192)    215040      ['activation_151[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_153 (Conv2D)            (None, 6, 6, 192)    147456      ['average_pooling2d_14[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_144 (Batch  (None, 6, 6, 192)   576         ['conv2d_144[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_147 (Batch  (None, 6, 6, 192)   576         ['conv2d_147[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_152 (Batch  (None, 6, 6, 192)   576         ['conv2d_152[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_153 (Batch  (None, 6, 6, 192)   576         ['conv2d_153[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_144 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_144[0][0]']\n",
            "                                                                                                  \n",
            " activation_147 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_147[0][0]']\n",
            "                                                                                                  \n",
            " activation_152 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_152[0][0]']\n",
            "                                                                                                  \n",
            " activation_153 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_153[0][0]']\n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)           (None, 6, 6, 768)    0           ['activation_144[0][0]',         \n",
            "                                                                  'activation_147[0][0]',         \n",
            "                                                                  'activation_152[0][0]',         \n",
            "                                                                  'activation_153[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_158 (Conv2D)            (None, 6, 6, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_158 (Batch  (None, 6, 6, 192)   576         ['conv2d_158[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_158 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_158[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_159 (Conv2D)            (None, 6, 6, 192)    258048      ['activation_158[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_159 (Batch  (None, 6, 6, 192)   576         ['conv2d_159[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_159 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_159[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_155 (Conv2D)            (None, 6, 6, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_160 (Conv2D)            (None, 6, 6, 192)    258048      ['activation_159[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_155 (Batch  (None, 6, 6, 192)   576         ['conv2d_155[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_160 (Batch  (None, 6, 6, 192)   576         ['conv2d_160[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_155 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_155[0][0]']\n",
            "                                                                                                  \n",
            " activation_160 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_160[0][0]']\n",
            "                                                                                                  \n",
            " conv2d_156 (Conv2D)            (None, 6, 6, 192)    258048      ['activation_155[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_161 (Conv2D)            (None, 6, 6, 192)    258048      ['activation_160[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_156 (Batch  (None, 6, 6, 192)   576         ['conv2d_156[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_161 (Batch  (None, 6, 6, 192)   576         ['conv2d_161[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_156 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_156[0][0]']\n",
            "                                                                                                  \n",
            " activation_161 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_161[0][0]']\n",
            "                                                                                                  \n",
            " average_pooling2d_15 (AverageP  (None, 6, 6, 768)   0           ['mixed6[0][0]']                 \n",
            " ooling2D)                                                                                        \n",
            "                                                                                                  \n",
            " conv2d_154 (Conv2D)            (None, 6, 6, 192)    147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_157 (Conv2D)            (None, 6, 6, 192)    258048      ['activation_156[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_162 (Conv2D)            (None, 6, 6, 192)    258048      ['activation_161[0][0]']         \n",
            "                                                                                                  \n",
            " conv2d_163 (Conv2D)            (None, 6, 6, 192)    147456      ['average_pooling2d_15[0][0]']   \n",
            "                                                                                                  \n",
            " batch_normalization_154 (Batch  (None, 6, 6, 192)   576         ['conv2d_154[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_157 (Batch  (None, 6, 6, 192)   576         ['conv2d_157[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_162 (Batch  (None, 6, 6, 192)   576         ['conv2d_162[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " batch_normalization_163 (Batch  (None, 6, 6, 192)   576         ['conv2d_163[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " activation_154 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_154[0][0]']\n",
            "                                                                                                  \n",
            " activation_157 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_157[0][0]']\n",
            "                                                                                                  \n",
            " activation_162 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_162[0][0]']\n",
            "                                                                                                  \n",
            " activation_163 (Activation)    (None, 6, 6, 192)    0           ['batch_normalization_163[0][0]']\n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)           (None, 6, 6, 768)    0           ['activation_154[0][0]',         \n",
            "                                                                  'activation_157[0][0]',         \n",
            "                                                                  'activation_162[0][0]',         \n",
            "                                                                  'activation_163[0][0]']         \n",
            "                                                                                                  \n",
            " flatten_1 (Flatten)            (None, 27648)        0           ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " dense_3 (Dense)                (None, 256)          7078144     ['flatten_1[0][0]']              \n",
            "                                                                                                  \n",
            " dense_4 (Dense)                (None, 128)          32896       ['dense_3[0][0]']                \n",
            "                                                                                                  \n",
            " dense_5 (Dense)                (None, 34)           4386        ['dense_4[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 16,090,690\n",
            "Trainable params: 16,071,874\n",
            "Non-trainable params: 18,816\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model3.fit(X_train, Y_train, epochs = 50, validation_split=0.25)"
      ],
      "metadata": {
        "id": "meR6cBudxy3E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss,test_acc = model3.evaluate(X_test,Y_test)\n",
        "print('\\n테스트 정확도:', test_acc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEnrcueayf-v",
        "outputId": "3a074c2a-f509-47b1-aea6-b453d62ea602"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "75/75 [==============================] - 2s 31ms/step - loss: 0.0249 - accuracy: 0.3492\n",
            "\n",
            "테스트 정확도: 34.92262661457062\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history.history['accuracy'], linewidth=\"2\")\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('Accuracy Trend')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epochs')\n",
        "plt.legend(['train','test'])\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "el1L9SBxydr7",
        "outputId": "14476123-3b34-490c-e9f8-c3c05047f983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##모델비교"
      ],
      "metadata": {
        "id": "cRPpaQbPIqGD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![model0.png](data:image/png;base64,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)![model1.png](data:image/png;base64,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)![model2.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "aKM42-PbJKzS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##내 이미지로 찾기"
      ],
      "metadata": {
        "id": "OgmGuRZmwZD-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "jfj0amdXp8KH",
        "outputId": "def8edbc-5a93-4d59-c4c3-36aa4876b085"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7e7694eb-8ea3-4f5e-9b1a-9324a9c47008\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7e7694eb-8ea3-4f5e-9b1a-9324a9c47008\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving beachtest.jpg to beachtest.jpg\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "for fn in uploaded.keys():\n",
        "      name=fn\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ql3mNzdGp9YK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "outputId": "0b704838-4238-4950-ed70-620b9b8c40e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7eea4dab10>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.image as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "img=mpimg.imread(name)\n",
        "plt.imshow(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kQNrCd1vqGzw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "dddc0e3a-31ff-4aff-f331-d61b8aea4507"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7eea5183d0>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import cv2\n",
        "img = cv2.imread(name)\n",
        "img = cv2.resize(img,(128,128))\n",
        "img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(img)  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i6BD9OLKpZlf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce427c7d-ee08-48c3-fa9e-761d5c0c0760"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model0(기본) 판정 :  요트\n",
            "model1(은닉층) 판정 :  미술관\n",
            "model2(레이어) 판정 :  바다\n"
          ]
        }
      ],
      "source": [
        "y_hat0 = model0.predict(img[np.newaxis ,:,:])\n",
        "#print(y_hat0)\n",
        "category = categories[y_hat0.argmax()]\n",
        "print('model0(기본) 판정 : ',categories[y_hat0.argmax()])\n",
        "\n",
        "y_hat1 = model1.predict(img[np.newaxis ,:,:])\n",
        "#print(y_hat1)\n",
        "category = categories[y_hat1.argmax()]\n",
        "print('model1(은닉층) 판정 : ',categories[y_hat1.argmax()])\n",
        "\n",
        "y_hat2 = model2.predict(img[np.newaxis ,:,:])\n",
        "#print(y_hat2)\n",
        "category = categories[y_hat2.argmax()]\n",
        "print('model2(레이어) 판정 : ',categories[y_hat2.argmax()])\n",
        "\n",
        "# y_hat3 = model3.predict(img[np.newaxis ,:,:])\n",
        "# #print(y_hat3)\n",
        "# category = categories[y_hat3.argmax()]\n",
        "# print('model3(inceptionV3) 판정 : ',categories[y_hat3.argmax()])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##해당 위치 나타내기"
      ],
      "metadata": {
        "id": "C9aWjk_GwlCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import folium\n",
        "import pandas as pd\n",
        "from folium.plugins import MarkerCluster\n",
        "\n",
        "mc = MarkerCluster()\n",
        "file = \"/content/drive/MyDrive/location.csv\"\n",
        "df = pd.read_csv(file, encoding='cp949')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "id": "PV4re1z3U7i_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7fc6daff-c480-41a6-fa30-36877ec84511"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     name theme               location   Latitude   Longitude\n",
              "0     마라도     섬    제주 서귀포시 대정읍 가파리 600  33.118537  126.267348\n",
              "1      우도     섬             제주 제주시 우도면  33.506491  126.953361\n",
              "2  협재해수욕장    바다  제주 제주시 한림읍 협재리 2497-1  33.392440  126.239036\n",
              "3   월정리해변    바다    제주 제주시 구좌읍 월정리 33-3  33.555118  126.798624\n",
              "4   용머리해안    바다  제주 서귀포시 안덕면 사계리 112-3  33.232861  126.314063"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-377b2599-223a-4717-9b02-4a1179e7b646\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>theme</th>\n",
              "      <th>location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>마라도</td>\n",
              "      <td>섬</td>\n",
              "      <td>제주 서귀포시 대정읍 가파리 600</td>\n",
              "      <td>33.118537</td>\n",
              "      <td>126.267348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>우도</td>\n",
              "      <td>섬</td>\n",
              "      <td>제주 제주시 우도면</td>\n",
              "      <td>33.506491</td>\n",
              "      <td>126.953361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>협재해수욕장</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 제주시 한림읍 협재리 2497-1</td>\n",
              "      <td>33.392440</td>\n",
              "      <td>126.239036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>월정리해변</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 제주시 구좌읍 월정리 33-3</td>\n",
              "      <td>33.555118</td>\n",
              "      <td>126.798624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>용머리해안</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 서귀포시 안덕면 사계리 112-3</td>\n",
              "      <td>33.232861</td>\n",
              "      <td>126.314063</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-377b2599-223a-4717-9b02-4a1179e7b646')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-377b2599-223a-4717-9b02-4a1179e7b646 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-377b2599-223a-4717-9b02-4a1179e7b646');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df =df.groupby('theme').get_group(category)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ruG28UDNSHvr",
        "outputId": "fac8d54e-b708-4ba9-9c79-73edfe18ee82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name theme               location   Latitude   Longitude\n",
              "2     협재해수욕장    바다  제주 제주시 한림읍 협재리 2497-1  33.392440  126.239036\n",
              "3      월정리해변    바다    제주 제주시 구좌읍 월정리 33-3  33.555118  126.798624\n",
              "4      용머리해안    바다  제주 서귀포시 안덕면 사계리 112-3  33.232861  126.314063\n",
              "5     금능해수욕장    바다         제주 제주시 한림읍 금능리  33.359857  126.246073\n",
              "126  함덕 해수욕장    바다    제주특별자치도 제주시 조천읍 신북로  33.538670  126.645843"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>theme</th>\n",
              "      <th>location</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>협재해수욕장</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 제주시 한림읍 협재리 2497-1</td>\n",
              "      <td>33.392440</td>\n",
              "      <td>126.239036</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>월정리해변</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 제주시 구좌읍 월정리 33-3</td>\n",
              "      <td>33.555118</td>\n",
              "      <td>126.798624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>용머리해안</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 서귀포시 안덕면 사계리 112-3</td>\n",
              "      <td>33.232861</td>\n",
              "      <td>126.314063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>금능해수욕장</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주 제주시 한림읍 금능리</td>\n",
              "      <td>33.359857</td>\n",
              "      <td>126.246073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>함덕 해수욕장</td>\n",
              "      <td>바다</td>\n",
              "      <td>제주특별자치도 제주시 조천읍 신북로</td>\n",
              "      <td>33.538670</td>\n",
              "      <td>126.645843</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          const element = document.querySelector('#df-ccf3af24-a6d4-49a2-8429-07af1ca76573');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          "metadata": {},
          "execution_count": 46
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    },
    {
      "cell_type": "code",
      "source": [
        "#마커\n",
        "map = folium.Map(location = [33.3080, 126.57423], zoom_start=10) #제주도 서귀포시로 열기\n",
        "\n",
        "for name in df.index:\n",
        "  lat = df.loc[name, 'Latitude']\n",
        "  lng = df.loc[name, 'Longitude']\n",
        "  mc.add_child(\n",
        "      folium.Marker(location=[lat, lng],popup = df.loc[name,'name'],tooltip = df.loc[name,'name'])\n",
        "            \n",
        "  )\n",
        "map.add_child(mc)\n",
        "map"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R66x_ETiKbxs",
        "outputId": "56f240c3-302d-4e04-a4f6-19facec7ae35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<folium.folium.Map at 0x7f7eea64a890>"
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              "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
              "&lt;head&gt;    \n",
              "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
              "    \n",
              "        &lt;script&gt;\n",
              "            L_NO_TOUCH = false;\n",
              "            L_DISABLE_3D = false;\n",
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              "    \n",
              "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
              "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
              "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
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              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/leaflet@1.6.0/dist/leaflet.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
              "    \n",
              "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
              "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
              "            &lt;style&gt;\n",
              "                #map_3aeee3b967821d157215fa22c9a45f15 {\n",
              "                    position: relative;\n",
              "                    width: 100.0%;\n",
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              "                }\n",
              "            &lt;/style&gt;\n",
              "        \n",
              "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.1.0/leaflet.markercluster.js&quot;&gt;&lt;/script&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.1.0/MarkerCluster.css&quot;/&gt;\n",
              "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/1.1.0/MarkerCluster.Default.css&quot;/&gt;\n",
              "&lt;/head&gt;\n",
              "&lt;body&gt;    \n",
              "    \n",
              "            &lt;div class=&quot;folium-map&quot; id=&quot;map_3aeee3b967821d157215fa22c9a45f15&quot; &gt;&lt;/div&gt;\n",
              "        \n",
              "&lt;/body&gt;\n",
              "&lt;script&gt;    \n",
              "    \n",
              "            var map_3aeee3b967821d157215fa22c9a45f15 = L.map(\n",
              "                &quot;map_3aeee3b967821d157215fa22c9a45f15&quot;,\n",
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              "                    crs: L.CRS.EPSG3857,\n",
              "                    zoom: 10,\n",
              "                    zoomControl: true,\n",
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              "            );\n",
              "\n",
              "            \n",
              "\n",
              "        \n",
              "    \n",
              "            var tile_layer_d7b7ef15d1a31405e1b1a36c27afb6ae = L.tileLayer(\n",
              "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
              "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
              "            ).addTo(map_3aeee3b967821d157215fa22c9a45f15);\n",
              "        \n",
              "    \n",
              "            var marker_cluster_e65449731ac391a9ec929f4a49293e1f = L.markerClusterGroup(\n",
              "                {}\n",
              "            );\n",
              "            map_3aeee3b967821d157215fa22c9a45f15.addLayer(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "            var marker_c2dabae4a07275ce8c9a7838ebbc17d4 = L.marker(\n",
              "                [33.39244, 126.2390363],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_7adfc3f2a174475a7f107c0b0cb382a5 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_baf314bb83cee1484abe91094067d0d3 = $(`&lt;div id=&quot;html_baf314bb83cee1484abe91094067d0d3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;협재해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_7adfc3f2a174475a7f107c0b0cb382a5.setContent(html_baf314bb83cee1484abe91094067d0d3);\n",
              "        \n",
              "\n",
              "        marker_c2dabae4a07275ce8c9a7838ebbc17d4.bindPopup(popup_7adfc3f2a174475a7f107c0b0cb382a5)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_c2dabae4a07275ce8c9a7838ebbc17d4.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     협재해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_d9017f07ebf897d4e3d38580743b84f3 = L.marker(\n",
              "                [33.5551183, 126.7986235],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_3a28d25dbbe8b2e03a7cc623327ff45c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_a03da4838047e73507639c821cd84189 = $(`&lt;div id=&quot;html_a03da4838047e73507639c821cd84189&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;월정리해변&lt;/div&gt;`)[0];\n",
              "            popup_3a28d25dbbe8b2e03a7cc623327ff45c.setContent(html_a03da4838047e73507639c821cd84189);\n",
              "        \n",
              "\n",
              "        marker_d9017f07ebf897d4e3d38580743b84f3.bindPopup(popup_3a28d25dbbe8b2e03a7cc623327ff45c)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_d9017f07ebf897d4e3d38580743b84f3.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     월정리해변\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_9aa9902cbb2e7422af1758db242a8e6f = L.marker(\n",
              "                [33.2328609, 126.3140629],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_88eed74bcba1642018176e787ff7526e = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_c4b0b96d7ecdba56e996770f201f3a45 = $(`&lt;div id=&quot;html_c4b0b96d7ecdba56e996770f201f3a45&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;용머리해안&lt;/div&gt;`)[0];\n",
              "            popup_88eed74bcba1642018176e787ff7526e.setContent(html_c4b0b96d7ecdba56e996770f201f3a45);\n",
              "        \n",
              "\n",
              "        marker_9aa9902cbb2e7422af1758db242a8e6f.bindPopup(popup_88eed74bcba1642018176e787ff7526e)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_9aa9902cbb2e7422af1758db242a8e6f.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     용머리해안\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_bda0e6b267ebd48aebfc992608e7e571 = L.marker(\n",
              "                [33.3598575, 126.2460727],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_c20bd84701715700de92a791e69c52dd = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_589988f48433c9c5a9503e443c921679 = $(`&lt;div id=&quot;html_589988f48433c9c5a9503e443c921679&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;금능해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_c20bd84701715700de92a791e69c52dd.setContent(html_589988f48433c9c5a9503e443c921679);\n",
              "        \n",
              "\n",
              "        marker_bda0e6b267ebd48aebfc992608e7e571.bindPopup(popup_c20bd84701715700de92a791e69c52dd)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_bda0e6b267ebd48aebfc992608e7e571.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     금능해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_337a56bf94276dca9d954a8ae90f32da = L.marker(\n",
              "                [33.5386704, 126.6458435],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_9fb521056a11fd1c53b294d6dd21348f = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_e1f9cd1ca4fa2f283f7d2dd077d1d039 = $(`&lt;div id=&quot;html_e1f9cd1ca4fa2f283f7d2dd077d1d039&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;함덕 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_9fb521056a11fd1c53b294d6dd21348f.setContent(html_e1f9cd1ca4fa2f283f7d2dd077d1d039);\n",
              "        \n",
              "\n",
              "        marker_337a56bf94276dca9d954a8ae90f32da.bindPopup(popup_9fb521056a11fd1c53b294d6dd21348f)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_337a56bf94276dca9d954a8ae90f32da.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     함덕 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_2408e7853f37b7658a5ddfdae5850be7 = L.marker(\n",
              "                [33.4461558, 126.3144749],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_0c7f82c1cc4f3c6a0cdd4cf07bce16ea = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_8c33d29b87c33bdbd1454ab5b2ade3aa = $(`&lt;div id=&quot;html_8c33d29b87c33bdbd1454ab5b2ade3aa&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;곽지 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_0c7f82c1cc4f3c6a0cdd4cf07bce16ea.setContent(html_8c33d29b87c33bdbd1454ab5b2ade3aa);\n",
              "        \n",
              "\n",
              "        marker_2408e7853f37b7658a5ddfdae5850be7.bindPopup(popup_0c7f82c1cc4f3c6a0cdd4cf07bce16ea)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_2408e7853f37b7658a5ddfdae5850be7.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     곽지 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_3e9232a9d15bd302e0977375c7f24782 = L.marker(\n",
              "                [33.5271456, 126.5895044],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_e4f2fdcf4aac701e0913e645dbb2f268 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_f245ee0269dc6515327da4ddf5480eda = $(`&lt;div id=&quot;html_f245ee0269dc6515327da4ddf5480eda&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;삼양 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_e4f2fdcf4aac701e0913e645dbb2f268.setContent(html_f245ee0269dc6515327da4ddf5480eda);\n",
              "        \n",
              "\n",
              "        marker_3e9232a9d15bd302e0977375c7f24782.bindPopup(popup_e4f2fdcf4aac701e0913e645dbb2f268)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_3e9232a9d15bd302e0977375c7f24782.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     삼양 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_e50fbb9064128c53f79e361ed86847a4 = L.marker(\n",
              "                [33.5132931, 126.8984448],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_d2daa25f617b380a78b2fda51164afeb = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_3c87a0394820a05ded61b1277f5269bf = $(`&lt;div id=&quot;html_3c87a0394820a05ded61b1277f5269bf&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;하도 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_d2daa25f617b380a78b2fda51164afeb.setContent(html_3c87a0394820a05ded61b1277f5269bf);\n",
              "        \n",
              "\n",
              "        marker_e50fbb9064128c53f79e361ed86847a4.bindPopup(popup_d2daa25f617b380a78b2fda51164afeb)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_e50fbb9064128c53f79e361ed86847a4.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     하도 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_2161954a121ec58e98bd26fd52b2ea7c = L.marker(\n",
              "                [33.2814107, 126.4111118],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_e12532f55b7299686905fdf9f0f5264d = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_34e4551508e2b3c555628772070540bd = $(`&lt;div id=&quot;html_34e4551508e2b3c555628772070540bd&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;중문 색달해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_e12532f55b7299686905fdf9f0f5264d.setContent(html_34e4551508e2b3c555628772070540bd);\n",
              "        \n",
              "\n",
              "        marker_2161954a121ec58e98bd26fd52b2ea7c.bindPopup(popup_e12532f55b7299686905fdf9f0f5264d)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_2161954a121ec58e98bd26fd52b2ea7c.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     중문 색달해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_23023ae27e4e25af338c61b1c56506b7 = L.marker(\n",
              "                [33.3225332, 126.8228665],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_8e3ec3324baf491e2f535fe6d63f1252 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_09552b46d78f7f39645c0a2dd6d2891b = $(`&lt;div id=&quot;html_09552b46d78f7f39645c0a2dd6d2891b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;표선 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_8e3ec3324baf491e2f535fe6d63f1252.setContent(html_09552b46d78f7f39645c0a2dd6d2891b);\n",
              "        \n",
              "\n",
              "        marker_23023ae27e4e25af338c61b1c56506b7.bindPopup(popup_8e3ec3324baf491e2f535fe6d63f1252)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_23023ae27e4e25af338c61b1c56506b7.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     표선 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_d6ec32069e0cc63450979107ae99e680 = L.marker(\n",
              "                [33.5074342, 126.954269],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_64f7a8266f3731d4754e3bc8e51fc438 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_0ceb7ad7108fbdd88ce19ff40693678d = $(`&lt;div id=&quot;html_0ceb7ad7108fbdd88ce19ff40693678d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;검멀레 해변&lt;/div&gt;`)[0];\n",
              "            popup_64f7a8266f3731d4754e3bc8e51fc438.setContent(html_0ceb7ad7108fbdd88ce19ff40693678d);\n",
              "        \n",
              "\n",
              "        marker_d6ec32069e0cc63450979107ae99e680.bindPopup(popup_64f7a8266f3731d4754e3bc8e51fc438)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_d6ec32069e0cc63450979107ae99e680.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     검멀레 해변\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_f673f8fa3402025c8fe4fc449e9350a6 = L.marker(\n",
              "                [33.5252938, 126.8611117],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_29844df19194651f95b50f12ea4a0bf2 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_2670a832171ae1d23af5831457a936f5 = $(`&lt;div id=&quot;html_2670a832171ae1d23af5831457a936f5&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;세화 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_29844df19194651f95b50f12ea4a0bf2.setContent(html_2670a832171ae1d23af5831457a936f5);\n",
              "        \n",
              "\n",
              "        marker_f673f8fa3402025c8fe4fc449e9350a6.bindPopup(popup_29844df19194651f95b50f12ea4a0bf2)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_f673f8fa3402025c8fe4fc449e9350a6.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     세화 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_66ec304845c0588272c586642b94c7c2 = L.marker(\n",
              "                [33.514491, 126.9582418],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_8479157b81b324a98324951cc256c038 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_7b534e6977a6a48c769955daf3b28a9f = $(`&lt;div id=&quot;html_7b534e6977a6a48c769955daf3b28a9f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;하고수동 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_8479157b81b324a98324951cc256c038.setContent(html_7b534e6977a6a48c769955daf3b28a9f);\n",
              "        \n",
              "\n",
              "        marker_66ec304845c0588272c586642b94c7c2.bindPopup(popup_8479157b81b324a98324951cc256c038)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_66ec304845c0588272c586642b94c7c2.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     하고수동 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_ec0bc0da9a7f923da58975732fee4aa4 = L.marker(\n",
              "                [33.5582327, 126.759787],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_5ab3424d48b6a5afb5f73f775fefa57c = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_47b950c0ed47253a291cf7ce8344c6d2 = $(`&lt;div id=&quot;html_47b950c0ed47253a291cf7ce8344c6d2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;김녕 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_5ab3424d48b6a5afb5f73f775fefa57c.setContent(html_47b950c0ed47253a291cf7ce8344c6d2);\n",
              "        \n",
              "\n",
              "        marker_ec0bc0da9a7f923da58975732fee4aa4.bindPopup(popup_5ab3424d48b6a5afb5f73f775fefa57c)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_ec0bc0da9a7f923da58975732fee4aa4.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     김녕 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_20749f73082238a246284a12624e2a18 = L.marker(\n",
              "                [33.4979678, 126.4531463],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_cd3b3260b4a4b1a53473683f3756a6c8 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_474fdcfe9d1f388c7579418874a85255 = $(`&lt;div id=&quot;html_474fdcfe9d1f388c7579418874a85255&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;이호테우 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_cd3b3260b4a4b1a53473683f3756a6c8.setContent(html_474fdcfe9d1f388c7579418874a85255);\n",
              "        \n",
              "\n",
              "        marker_20749f73082238a246284a12624e2a18.bindPopup(popup_cd3b3260b4a4b1a53473683f3756a6c8)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_20749f73082238a246284a12624e2a18.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     이호테우 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_cb014e45097b462d344f7a6f431aa27b = L.marker(\n",
              "                [33.4880673, 126.8804954],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_4543cc3ddcf136ddcd1e92457d2fddec = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_c85cce4ab30c80cd7de68b101663843a = $(`&lt;div id=&quot;html_c85cce4ab30c80cd7de68b101663843a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;종달리 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_4543cc3ddcf136ddcd1e92457d2fddec.setContent(html_c85cce4ab30c80cd7de68b101663843a);\n",
              "        \n",
              "\n",
              "        marker_cb014e45097b462d344f7a6f431aa27b.bindPopup(popup_4543cc3ddcf136ddcd1e92457d2fddec)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_cb014e45097b462d344f7a6f431aa27b.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     종달리 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_d464613e4f6ee82d4e911c203ba60bd3 = L.marker(\n",
              "                [33.2110709, 126.260619],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_428ab040202d1b4862845147adde8268 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_536a2286d32d1814684ad20da23c297d = $(`&lt;div id=&quot;html_536a2286d32d1814684ad20da23c297d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;하모 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_428ab040202d1b4862845147adde8268.setContent(html_536a2286d32d1814684ad20da23c297d);\n",
              "        \n",
              "\n",
              "        marker_d464613e4f6ee82d4e911c203ba60bd3.bindPopup(popup_428ab040202d1b4862845147adde8268)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_d464613e4f6ee82d4e911c203ba60bd3.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     하모 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "    \n",
              "            var marker_f4938d91352366b2a7774d1725a718cb = L.marker(\n",
              "                [33.5024293, 126.9435597],\n",
              "                {}\n",
              "            ).addTo(marker_cluster_e65449731ac391a9ec929f4a49293e1f);\n",
              "        \n",
              "    \n",
              "        var popup_80933cec6f5d8f77f6fd2a7a42b0ec94 = L.popup({&quot;maxWidth&quot;: &quot;100%&quot;});\n",
              "\n",
              "        \n",
              "            var html_f60a10c532f187ed04a4c52a0ab1b44c = $(`&lt;div id=&quot;html_f60a10c532f187ed04a4c52a0ab1b44c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;서빈백사 해수욕장&lt;/div&gt;`)[0];\n",
              "            popup_80933cec6f5d8f77f6fd2a7a42b0ec94.setContent(html_f60a10c532f187ed04a4c52a0ab1b44c);\n",
              "        \n",
              "\n",
              "        marker_f4938d91352366b2a7774d1725a718cb.bindPopup(popup_80933cec6f5d8f77f6fd2a7a42b0ec94)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            marker_f4938d91352366b2a7774d1725a718cb.bindTooltip(\n",
              "                `&lt;div&gt;\n",
              "                     서빈백사 해수욕장\n",
              "                 &lt;/div&gt;`,\n",
              "                {&quot;sticky&quot;: true}\n",
              "            );\n",
              "        \n",
              "&lt;/script&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "VgRGXyHVuMpl",
        "EIfetCoM2m77"
      ]
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}