DeepLearning_TermProject_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DeepLearning_TermProject_final.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lee-euijin/AppleFarm/blob/main/DeepLearning_TermProject_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mp2OCzwFfSvE"
      },
      "source": [
        "# 1. Kaggle data를 skeleton화 하여 Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LrXUCdj151QA"
      },
      "source": [
        "## Data upload"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "myhLqnncdFg6"
      },
      "source": [
        "* mount drive, copy and unzip the kaggle data in the local directory"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJakET3Xb0Pl",
        "outputId": "ff044417-c054-40ec-83ee-21535ba12785"
      },
      "source": [
        "# mount Google Drive\n",
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "# unzip\n",
        "import zipfile, shutil\n",
        "\n",
        "dataset = '/content/gdrive/My Drive/학교 수업 자료/딥러닝/Project/static_data.zip'\n",
        "dst_path = '/content/static_data'\n",
        "dst_file = os.path.join(dst_path, 'static_data.zip')\n",
        "\n",
        "if not os.path.exists(dst_path):\n",
        "  os.makedirs(dst_path)\n",
        "\n",
        "shutil.copy(dataset, dst_file)\n",
        "\n",
        "with zipfile.ZipFile(dst_file, 'r') as file:\n",
        "  file.extractall(dst_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jrqrFM0JdTfw"
      },
      "source": [
        "* 이미지의 경로를 imagepaths list에 저장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g_e8q-QCdZ-E",
        "outputId": "d2fdb711-37ed-435b-c952-95bb2e8006bc"
      },
      "source": [
        "import os\n",
        "imagepaths = []\n",
        "\n",
        "for dirname, _, filenames in os.walk('/content/static_data'):\n",
        "  for filename in filenames:\n",
        "    path = os.path.join(dirname, filename)\n",
        "    if path.endswith(\"png\"):\n",
        "      imagepaths.append(path)\n",
        "\n",
        "print(len(imagepaths))\n",
        "print(imagepaths[19999]) # index 20000부터는 path만 다르고, 똑같은 data가 반복됨"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40000\n",
            "/content/static_data/leapGestRecog/08/09_c/frame_08_09_0200.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6asV0bS-dgVp"
      },
      "source": [
        "* kaggle의 데이터를 skeleton으로 변형할 구글 드라이브 내의 directory 생성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAJ4PYSS19VW"
      },
      "source": [
        "# google drive에 directory 추가\n",
        "dir_path = '/content/gdrive/My Drive/학교 수업 자료/딥러닝/Project/static_skeleton_data'\n",
        "\n",
        "for path in imagepaths[:19999]:\n",
        "  splitted = path.split(\"/\")\n",
        "\n",
        "  # 없으면 directory 추가\n",
        "  dir = os.path.join(dir_path, splitted[4])\n",
        "\n",
        "  if not os.path.exists(dir):\n",
        "    os.makedirs(dir)\n",
        "\n",
        "  dir = os.path.join(dir_path, splitted[4], splitted[5])\n",
        "\n",
        "  if not os.path.exists(dir):\n",
        "    os.makedirs(dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4CdBGteCjej"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnfpfd40iWQ_",
        "outputId": "976735ff-8935-4800-ba10-27c0dc240cc9"
      },
      "source": [
        "!pip install mediapipe opencv-python"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting mediapipe\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5a/c6/cb43b4d35257270a428a7c8e8c10565bb9719eaa4a3a5f34442d77e02678/mediapipe-0.8.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (37.7MB)\n",
            "\u001b[K     |████████████████████████████████| 37.7MB 1.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (4.1.2.30)\n",
            "Requirement already satisfied: protobuf>=3.11.4 in /usr/local/lib/python3.7/dist-packages (from mediapipe) (3.12.4)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from mediapipe) (0.12.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mediapipe) (1.15.0)\n",
            "Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.7/dist-packages (from mediapipe) (4.1.2.30)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (from mediapipe) (0.36.2)\n",
            "Requirement already satisfied: attrs>=19.1.0 in /usr/local/lib/python3.7/dist-packages (from mediapipe) (21.2.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mediapipe) (1.19.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.11.4->mediapipe) (57.0.0)\n",
            "Installing collected packages: mediapipe\n",
            "Successfully installed mediapipe-0.8.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NYLfT5r8E78r"
      },
      "source": [
        "### Data Preprocessing and Saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRbbetnSd4uR"
      },
      "source": [
        "* kaggle 데이터를 skeleton data로 바꾸어 미리 생성해둔 directory 내에 저장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0W1twcFjguxn",
        "outputId": "7fc7cf13-a1c3-4179-be1b-430d3bc1d685"
      },
      "source": [
        "# 최초 save 이후에는 이 셀 실행하지 않음!\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2 as cv\n",
        "import pandas as pd\n",
        "import mediapipe as mp\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "new_path = []\n",
        "\n",
        "# 저장을 위한 path 생성\n",
        "for path in imagepaths[:19999]:\n",
        "  splitted = path.split(\"/\")\n",
        "  temp = '/' + splitted[4] + '/' + splitted[5] + '/' + splitted[6]\n",
        "  new_path.append(temp)\n",
        "\n",
        "# train data preprocessing and save\n",
        "mp_drawing = mp.solutions.drawing_utils\n",
        "mp_hands = mp.solutions.hands\n",
        "\n",
        "new_paths = []\n",
        "\n",
        "with mp_hands.Hands(\n",
        "    static_image_mode=True,\n",
        "    max_num_hands=2,\n",
        "    min_detection_confidence=0.5) as hands:\n",
        "  for idx in range(19999):\n",
        "    # image read\n",
        "    image = cv.imread(imagepaths[idx])\n",
        "    # bgr에서 rgb로 convert\n",
        "    results = hands.process(cv.cvtColor(image, cv.COLOR_BGR2RGB))\n",
        "\n",
        "    # draw hand landmarks\n",
        "    # print('Handedness:', results.multi_handedness)\n",
        "    if not results.multi_hand_landmarks:\n",
        "      continue\n",
        "    image_height, image_width, _ = image.shape\n",
        "    annotated_image = image.copy()\n",
        "\n",
        "    # landmark만 그릴 image\n",
        "    landmark_image = np.zeros(image.shape)\n",
        "    for hand_landmarks in results.multi_hand_landmarks:\n",
        "      '''print('hand_landmarks:', hand_landmarks)\n",
        "      print(\n",
        "          f'Index finger tip coordinates: (',\n",
        "          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].x * image_width}, '\n",
        "          f'{hand_landmarks.landmark[mp_hands.HandLandmark.INDEX_FINGER_TIP].y * image_height}'\n",
        "      )'''\n",
        "      mp_drawing.draw_landmarks(\n",
        "          landmark_image, hand_landmarks, mp_hands.HAND_CONNECTIONS)\n",
        "\n",
        "    '''# imshow\n",
        "    cv2_imshow(annotated_image)\n",
        "    cv2_imshow(landmark_image)'''\n",
        "    \n",
        "    # save\n",
        "    if len(new_paths) % 1000 == 0 or len(new_paths) == 19999:\n",
        "      print(len(new_paths))\n",
        "    cv.imwrite(dir_path + new_path[idx], landmark_image)\n",
        "    new_paths.append(os.path.join(dir_path, new_path[idx]))\n",
        "\n",
        "# 19999개 중 6765개는 skeleton이 생성되지 않았기 때문에 지워짐\n",
        "print(len(new_paths))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "1000\n",
            "2000\n",
            "3000\n",
            "4000\n",
            "5000\n",
            "6000\n",
            "7000\n",
            "8000\n",
            "9000\n",
            "10000\n",
            "11000\n",
            "12000\n",
            "13000\n",
            "13235\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FYRbS_30_dCw"
      },
      "source": [
        "* image plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 924
        },
        "id": "dlOukvYq_ebO",
        "outputId": "35c8e529-873c-47ea-b958-b32001e7f4f3"
      },
      "source": [
        "import cv2\n",
        "\n",
        "print(new_paths[0])\n",
        "def img_plot(img_path):\n",
        "  img = cv2.imread(img_path)\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  ('Shape : ', img.shape)\n",
        "\n",
        "  plt.grid(False)\n",
        "  plt.imshow(img)\n",
        "  plt.xlabel('Width')\n",
        "  plt.ylabel('Height')\n",
        "  plt.title('image : ' + img_path)\n",
        "\n",
        "plt.subplot(211)\n",
        "img_plot(imagepaths[0]) # 전처리 전 kaggle data\n",
        "\n",
        "plt.subplot(212)\n",
        "img_plot(new_paths[0]) # 전처리 후 kaggle data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/학교 수업 자료/딥러닝/Project/static_skeleton_data/05/07_ok/frame_05_07_0010.png\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 54617 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 44368 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 49688 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 50629 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 51088 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47308 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 46373 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 47084 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:214: RuntimeWarning: Glyph 45789 missing from current font.\n",
            "  font.set_text(s, 0.0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 54617 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 44368 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 49688 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 50629 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 51088 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47308 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 46373 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 47084 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py:183: RuntimeWarning: Glyph 45789 missing from current font.\n",
            "  font.set_text(s, 0, flags=flags)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAApkAAAEWCAYAAAAtoYzCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9d7xdR3ku/Lx779PP0TkqlpCsZiwZN9ywMTZu2AQbA/FNuaYFEIFryIUvAcIlQAgxITeUy5dcSmgJYEwNvdePIIwpsUFYLiq2ZCxbvZze23x/zMzWu19NW/sc+Rxb8/x++7f3XmvmnXdmzVrzrOedWYuUUsjIyMjIyMjIyMiYTZTm2oGMjIyMjIyMjIzHHzLJzMjIyMjIyMjImHVkkpmRkZGRkZGRkTHryCQzIyMjIyMjIyNj1pFJZkZGRkZGRkZGxqwjk8yMjIyMjIyMjIxZRyaZ8wxEdB8RXTXXfmQ8uiCiy4lo+yzaW0tEiogqs2UzUp4ionWPRlkZjw8Q0c1E9NnEtEREnyKiHiK643j7lpGRMTvIJHOeQSl1llJq41z7MRsgoh8S0bOOk+1ZJVFEtIGIbvfs205EpwXyXkVEuwuWV0PKlFI/V0o9qYiN2UI9/pt8lxDRL4+HT4EyG4no7eaYDBHRHiL6/kz7mas/mT4xRUSDRNRPRJuJ6Lkzr8XMQEQr7PEiokVE9HXTFruI6EUs3VVENG38t5+X1WnrrcLOiLG95DjX1V5DLgPwBwBWKqWeejzLnAkMGX4PER0xn/cQEbH9yrSvbcd/n4lNc3M6KD6KiP4kwe7riWi/6dufJKImtu+dRHQPEU0S0c2OvCcR0eeJqM8Q/8+xfe8lokeM3V1E9NaEpst4nCKTzIzjAiJqA3AhgJ/NtS8zARGdCqCslLp/rn2Zh3gOgO89ymV+BcANAF4KYCGAUwC83/hyPPArpVQ7gC4AHwbwRSLqOk5lpeJ6AD8wv/8VwDiAZQBeDOAjRHQWS7tXKdXOPp+ux5ZS6p+4HQDvAbBRKXX4eFQQOOYasgbAQ0qpIU/aR0WxT8BNAP4bgHMBnAPgeQBeJdKcy9rylTOxaW5O+XF5LoBBHD2mThDRtQDeDOAa6LZ9IoB3sCQ7ALwJwHc9Jr4GYD+A1QCWAngf2/cJAKcrpRYAuBTAi4nojxPqmfF4hFIqf+bRB8BDAJ5pft8M4MsAPgtgAMA9AE4D8BYABwE8AuBZLO/LAWw1aR8E8Cph+00A9gHYC+CVABSAdWZfE/SF4mEABwB8FEDLDOrxhwC+ZX6XAbwVwE7j228BrDL7LgVwJ4A+830ps7ERwDsB/MLk+xGAJWbfw8b/QfO5xGz/c9MGPQB+CGANs6cAvBrAAwB6oQdVAnAGgFEAU8ZWL8vzlwA+YH5fD2CL8WUPgDcCaAMwAmCa+bICwFMB/MqUsw/AhwA0Gju3GV+GTPrnA7gKwG5W7iroC/khAEcAfCjS3mVz/A6bY/8aU0Yl1Dfq8Z+VuQnABaxto30Jmhh+x9Srx/xeKY75uwDcAaAfwDcBLDL7nml8XRlpixUAvmrK+D2Av2T7ngrgN8b2AQD/7OtPADYAuJ3lbTVpLko5Z6DJ8F2mrJ0ArmP+fQtAN/Rg/j9YnhYAnzZtsxX6nN0t6vc1AH9sjt04gNPYvs8AeLf5fZXM62irJFsiD5k+9LKE60CorjcD+Kz53QDgC+a42fPkD03eV6D2/HyHrRuAv4EmO59J7Fv/COCXxs63ASwG8DlzjO4EsJalPx3Aj43v2wHcmFDfXwK4if1/BYBfi2vQuoLX0qBNkfZTAD6VYPPzAP6J/b8GwH5Hus8CuFlsexb0OFVOKOdk6HHrTZ79G6Cv7x+CHgO2AbhGHDPnGGD2vxTALuhr5N+BjZ/5Mz8+c+5A/ogDcizJHAVwLYAKgFuhB82/NRfl/wHg9yzvcwCcCj0IXAlgGEdJwHXmYnwW9GD5WdQSg3+BvqAvAtBhLsDv8vi4Gpp8rA7U46M4SmT+l7nQPMn4dq65uC+CHgxeYur3QvN/scm3EXpwPg168N2IowPoWjASZbbdAD2QnWHsvQ3AL9l+BT3wdJk6HMLRgX8DGKFgeX4A4Frzex+Ay83vhaxtr8KxROApAJ5m/FgLTRheJ3xZx/5XbUATxs3mmLQBaAZwWaTfvBr6Ar3KtOtPUUsyQ32jHv+XQxNtkvVBoC+Z4/4n0H2wA/om6hvM7kZj92xT96/iKBF5N7R6FmqHEvRNzNsBNEIrNA+yY/grAC8xv9sBPC3Qn6p9whyT10ATsaUJ9Xwq9KD5B8ank6HVHUDfZHzYHNfzoPvh1ayOP4PuXysB3I3am48G6BuJDgDnAxgW9X8jgG+z4zoOTYB/b/xtq8eW2H4FNElrT7iehep6M/R1qAVaMbsFjLig9hpSPRasbpPQimqTsZHSt3ZAnwed0DeM90PfvNjr66dM2jbom/iXm33nm7Y6M1LfPgAXs/8XAhgQ5/1e6Gvx18BIbb022fY2aCJ2VYLNzQCez/4vMb4tFulcJPPt0Dfwn4Umd3cCuFKkebPpIwr6/HPeGJrjOgng9aY/Pt/U195YboR/DDjTlHEZ9Ln+PgATyCRzXn3m3IH8EQfkWJL5Y7bveeakKpv/HeYk7vLY+gaAvzK/PwlGGgGsM3nXQROPIQCnsv2XgBHYOurxMI6qldsB3OBI8xIAd4htvwKwwfzeCOBtbN//BPAD83stjiUF3wfwCva/BE2m1pj/CoysAfgSgDeb3xsgSCb0YHUEQBOr06sALBDprkJcMXodgK+z/yGSeQn0YFwJ2RT2/xPAq9n/Z8n2CfSNevx/BYBPyPoU7UvQxKOH/d8Ipp5BDyTj0CTv3wF8ke1bBH2z0wdg1Gy7GMDDooy34Ch5uA1aCVsi0rj60wboAbAXevAagVGzYvUE8DEA/+Ko7ypoRa6DbXsXgFvM7yohNv9fiVqSeQ2An5jfl0OoT9A3nhvN7yeY9itBTyu4DcDH6rEltn/C+hvpM7G63gxN0n8G4AMwNywsLb+GbMCxJHMcQHOgfFff+lv2//8F8H32/3kA7jK/nw/g58LexwD8faTOUzA3E+b/etOv7M3YFdCEqAtavbsXkfM8ZpNtfwn0zQSF7Jm0VWXd/G8wNteKdC6S+XGT9hUm3wugzxF5ThE0OX8H7wMizQZo0k1s2x04eiO4Ef4x4O0AvsD2tZo+kUnmPPrkOZnzHwfY7xEAh5VSU+w/oBUZENGziejXRNRNRL3Q4V07MX8F9J25Bf99EvQJ+lsi6jV5f2C2FwYRPRlAn1LKlrEK+qImsQI61MGxC1r1sdjPfg/D1NWDNQDez+rQDX2hq9feNdBK6Jj5/yfQbbqLiH5GRJf4MhLRaUT0HTuxHsA/4eixiGEVgF1KqcnE9MCxx7emXSN9ox7/r4d7PmawLxFRKxF9zCwI6IcmPl1EVGY2ZD0aTNlHoBVUAIBSqlsp1QWtutpFC2sArLBlm/LfCj3PENAD42kAthHRnRRfyPNrU8ZCaEJ0eUo9Ee7z3UqpAVHHk9l+33kK1Lb7IIAFYv8CaDULSqn9SqktSqlppdTvoUPvfEFIsi0LImoF8N+hQ/oxxOoKaLX8HOgbC8XKkdcQFw4ppUa5bwl9S15P5X97PVgD4GLRj14MTdxDkO24AMCgrZtS6jal1LhSqhfAX0GT/zNmYpPhZQBudWxPtQmI4+3BCPT82E8opSaUUl+E7qdP54mUxu9M+nc47FjsET7vgu47Fr5rds25opQahr5GZMwjZJL5OIFZGfhV6JDBMjMwfg+aZAE61LuSZVnFfh+GvhCcpZTqMp9OpSeS1wNJQB6BDlFJ7IW+mHOshg6XxuC6kD4CHV7rYp8WpVTKCmiXvZp6KKXuVErdAD3R/RvQSqgv70egw9frlZ4A/1YcPRYxPAJgdcHFDPtQe0xX2x8JfaOQ/0TUAB1y/7EjX6wv/TX0tImLjd0rrJvMhqzHhLH7EwAXERHvxxKPQKuJvA90KKWuBwCl1ANKqRdCH8P3APiKWWASHJiVUoMA/gLAS4jIhk5D9Qz1+UVE1CHqaPt86DwFavvk/QAqRLSe7T8XwH2+aqD2ml+PrT+Cvnnb6CmDI1ZXQM+xexeAnxDRMrbddxPDIY9ZSt9KxSMAfib6UbtS6i8i+e6DbjeL0PEAjCI5U5tEtApa3b01Yitk84BSKoWk3Y1j2z50/lTgPhcsTuYr8KH7yN4EP2rOFSKyUyYy5hEyyXz8oBFazTkEYJKIng0dMrX4EoCXE9EZRo34O7tDKTUN4N8A/AsRLQUAIjrZrECsB9ejdlXivwN4JxGtN4/jOIeIFkMPIqcR0YuIqEJEz4cO730noYxD0ItVnsi2fRTAW+yKWCLqJKL/nujzAQAriaiRbXu2rQfpR+e8mIg6lVIT0AsFplnexUTUyfJ2mDSDRHQ6NEGR5T0RbtwBfQF9NxG1EVEzET3dk9biSwD+kohWEtFC6DlRFrG+UdT/ywDcrZTql04k9KUOaHLWS0SLAPy9oy5/RkRnmn76DwC+opSaUkr9CHqu6TeI6GJzTBqg1TCLOwAMENHfEFELEZWJ6Gwiusj48mdEdJLxs9fkmYa7P8m6dUP35bcn1PMT0OfbNURUMvtON8rcLwG8yxzXc6DVVfu8yC9B9+GFRHQygNfa8onoFOipG1uNP0PQ8/r+wfSTp0PPS/6MSf8MIlpjzrlV0PM9v1mPLYZktSyhrjbde6EXovyEjj4SSV5DUpDSt1LxHehr00uIqMF8LiKimOp4K4A3mOO9Apr43gIARHQWEZ1n+mQ7dLh+D/R857psMrwEOuriUs99Nl9hzrMu6PnrVZumvs3QHKFijp9VhL8OYCERvczU5U+hyd4vTF9/lem/RERPhZ7L/JOAL0uhr10N5np9BtKeWvEVAM8jokvNdftm1HdDkXE8Mdfx+vyp/eDYOZmfZfueCR2msP8r0HeQK83/10AThl7oweGLAP6RpX8LdOhhLzRpUDg656kZOiT6IDS52Aq2Klf4uBo63HLMwh/ouUY18wmh59O9DXq+0AD0RHHr82XQCzX6zDefM7kRwCvZ/w2onZf1D6asXhxdwPES6EVG/dBqxCdZejkP8hbbPtBE7LvQKs1h6IUn97K0jdDh0B4cXYnKff0kdKimFzqMcwW0EjgI4OfGV+77q6GJZC+AG3Hs6vLV0GrpEePPByL9pgK9sOOIaWe5ujzWN5L9h1ZE3yjKr7ZtqC8Z2xuN3fuh57hyPzeidnX5t1G7mtQOJg9Ah852Q8/F5U9ZWAG9Unm/OV6/xtFz6rPQT2YYhFZz/puvP8E9T3clgDHoEG/wnIFW/e6G7vM7cHTx0UpoEtMNHVLnc2nbzPHpNfbeBmCn2fdaiKcMQM9L/Qb0/NCHAbyI7XsDNIkZhj4XPgAzN66oLbP/ZOg5qsmroyN1vRm117d/hF6NvwjHXkNqjgXci9VS+tYrRXm3iOvrDvb/SdDXBPuEh/8EcF6kvgTgvaa+3ea3nY95NfT89CHoPvgN6EhBrA29NlmabWDz0ROPzRugrwn90KvSm8S1UYnPBrb/cujr7CD00xrsgsgS9HWymx2Ht3J/zXabfgNqV5ffj9pzWR4z2Q82QPdVu7p8j7WdP/PjYzt/xgkGc0d+L/SFpcjcv5jdGwH8qVLqxtmyORcgojdBk5s3zbUv8w1EtAX6GG85DrY3QhOP6EOqTwQQ0V8AeIFS6koi+h40MZzxs0ln09Zs4/FyDcmIg4g2QJPIy2bBVjv0zdl6pecgZ8wD5HD5CQQi+iMiajLh1PdAP55k1gimQS+0ovZYx0PQd/cZDCYsdevxIJgZABEtJ6Knm7Djk6BDo183uzdCTxeYDcymrdnG4+UaknGcQUTPI73gqw06wnIP9LU7Y54gk8wTC6+CDtPshH4sRmwSe2EopX6klPrVbNudTRDRdaRfS7iDiN7sSqOU+pIy89XmC4joo3TsK+QGieijj5YPSq+MffejVd4JiEboR+UMQIdnvwn9nEkopd6rlBoJ5E3GbNry9MlBIro8ntvp27y+hhDRfZ76vnie2fy+x+bj6TWPN0BP/9oL/WinF6gcnp1XyOHyjBMKZvL6/dAPyd4NPbfyhVmZy8jIyMjImF1kJTPjRMNToSf3P6iUGodeAHPDHPuUkZGRkZHxuEOR5/BlZDwecDJqH3C9G/otMV4Q0aMm9xMRfNEF3z5ij5jLkYn6EWr7jIwADiul6npxRUbG4x2ZZGZkOEBENwG4yf4vl8uuNAA0sbMERW6boQ9V0iNt8e2cGKWU60sjt/vKlumlLz4brnTShqx7kTZMIeex4+Tzx2c3pfxQfle9pV8xu6npZLmu9pc+hY6Rrw6PVdR7zk5NTcm3lmVkZBjkcHnGiYY9qH2Lyko43jCklPq4UupCpdSFch8R1QxG9ndRUhDbJ0mey67cViqVogNlaL8kGD4yKu3JNvDZ4OlihM5F4uwn5I8vvy+ttOkjU7IePn9i8LUrL9u1P3bcrF1fOrvPRyJDx6Lo9hBC/WW24eqPcjtvN9keEo8XQp2R8Wghk8yMEw13AlhPRKeQfhzPC6DfSR0EH3Q4sXCRjJjCVYQU8TI4sYmRlFRIolR00I+RLBcR86mKvjbmeVw++giLtM3Tcx9S6+xSjIuqiC5b3C+7L9RurvQ8ja/eIXu+4yjJ62yRQn7+HE/4zr1QO/DzTOJ4k+KMjMcbMsnMOKGg9HNBXwvgh9BvVPmSUir0bmGbD4Bb0fKpTqFQt2tfKoGMhZKLhM9dKmMMLlLoK1vmSw2Lu0LYpVL4cpVKlEPkytceKQooz+ciZq72kmSwiArtUiTlvhhRkm3vU+9cpDd07FPV8vmCFDVbps/IyIgjP8IoIyMCIlLlctkZ4nUN2L5wZFFwO6VS6RiS6fAT09PTwXBpSlhY1if0m9vx1T2EkPro84Pn8/mS4oNLCQy1rYvkx9rQHhO5PXYsi+xLaedUzKbd4+Xj8Ubo3HalnZ6e/q1yTKvJyMjISmZGRjI4sSiiQPrIRSpC4TuXf7H5ZC4fQgTIRfKkf/J/LCQdCg3zb9fvUP15eSltHCOtoTyh8LX9+AhmSpi93n31wBd6n0l4uIgyOJ8wk2kQGRkZtcgkMyMjAZK0hMKMIZKTShg5XESIf7vSh+yHCKjc7wtT+0ihyy+XbbktNUwcIqvyv4t0cjshwi/L8/kb8j1VUZb7ihK740WEZkIw51MZM0HqNIaMjAw38iOMMjISEQu7piI1fZH5efUiNVztSu8jabGwsi8EL/enKpGSOEol1ecvT+Oqd8yP0E1GqK5yu/TFZWc2w+Eue64pAylTHWLh/NjUgse6UpjJZ0ZGGFnJzMhIhFTDig6QvoG9iJ0QAZxpeFPad4WxJflw/ZdwpQ+FwV0qqc83SQRDKq2P0EqyG6pTLHxfVNWMQSqy0qZUVGejfBfZjanfvn2xqQX1wndDMBN7RSMMGRkZcWQlMyOjIELKjA9S0XKF11PsuVRCbifFRpF0Rf6H0oQU0pCampon5H9MQYwR1BTFsl7EbLva03fcY2ljyqNFigLpIufS1mwo/zE1V/avmLIa2pdVyYyM2UdWMjMyCqKeeVohNS3lv89eURxvtaao3ynh+pCtIvVxzav0qaZcKU1RnEPHL+Rj0TpY32U5LiUupia6bM0kT2ibb7/rhsuVNta+sRunevfFkJXPjIwwMsnMyJiHmImqEiLBqfMc7XcKAQqFdHm5PlUwpg5yP1JDxKmhbF9+l8JZrwIXyucijEWIm0sp9hE3V3tJMhlTdlNQ9ObLNzXBZddnux4lcqb1nGnejIwTAZlkZmQkIha6my/gxCGFNPjURfkdIjopIfiU8l1lSHJUtO1jali9KmQsL0dI/XOFuWWb+xS+UDmuNgvlcSm7PuKaMqVB2vb56PNfIkXRlLZi9lIwn8/1jIz5jkwyMzIiCM0786V1pSkSBp4txEKgPgWIEzupctUz783nm8uG3F8PyZN+82/fdlkPV7muvKF0rrrUozDzkDC34SKhsXar5xj5yKcLLnI324QuVZGPHZsUO6lTJjIyMo5FJpkZGRHUS5zqHZBiA/RMFD1p32VLKn/1kuUYUkProfJTQtGxOXtc8ZWEOqQyusrx+ZWiJsfITEzhlQiphy5VMmRffnxk3tV3Y2Fun88+m658ofq60hU9h3zqfkZGRhiZZGZkJCI2oBcNSxcp12W3qHKYmr7eATQl/OzbJhUzlzIXKxdwE40YQXAphHI/9y0lpO/y0UXcebmx8lPJUUiJ5f74ynGlCSmZqaqiJP0pfsXqEfK3SKi8qLqcFc2MjDTkRxhlZETgU30kaYsNbDH7RciUy7ciZUm7sfCvTBez7wpXp6iTM2k/l7+ziaLEypU3hdwUte86LjG7sf0+IukjxL7fKdMXXPUJQbal63z09WPXcYj195gSnpGR4UdWMjMyIvCRI9dAVUR58ZWRkrao7RCKqIYzsRNThnwD/3wMTdbrk0st9E1L4NtC4e6ihFSWG1KVfdtDx9p1LKWS6fIhdu74iLrL3xBxTCX6PtsZGRnpyCQzIyMBMRIZC5em2p7PCCmuM/Wdz/XzEe4QEfX5erxQ7zEL1Y3/no0bGBeRdCmKRSDnZMbsxG7K5O8Q6skT8ivV73r2Z2RkaGSSmZFRAD61KTRPbzZRD7kpoiDK9D7VyFVG0XB/KmngpCik4NWjZNUzRYEjhUTJ9NPT0zX/ffWSJNFVlo+AzuS3q272f4jocjXSRW5dmCuy5vKxyA3MfL8hzMiYL8hzMjMyEuGbAyYHJ1dYMGXRg7XN8/r21zs4c79dqpQsO0ZQi4RtQ/MGY3M1Y+B1Cc3144SOpy1KgkLl+MhZzKZvTmFKGxRpN9lOsh+HFOWUeZRF5z3OBTJRzMh4dJBJZkZGQYRCkHy7RSoRixHMVN9SSZbPp9AAzNOWSqVZCVv6VDRJXFJIV5F9PpJdL1JIdJGbDFf7zmQ+Jk/vUkVnYtfmCU13mE9IUW5DyCQ1IyMNOVyekTEDzAb54XaO51yzVJXU91/ieJEJX1i4SLiY2/KRJxdRSJmrlzINQqYJka9Uki23S8JYVEmWvsryipIumc93Q+OzXbTMepFyM+D7D9S/uC8j40REVjIzMhIRUtcAdzid7/ORGp633rKlvZAdn7+S0LlIUUj1kvtSw+4+MuYK50qS4iNdrlB+iOj5fA6FkFNCx646+ghkbPoBr7fvxobX/Xgp4vXAd67IMn3pY/ZiZVv7oT7CfYgp7KnnbUbGiY6sZGZkRBALdca2xchWbD6b739RlcVFekPKWIoNuU+SpJCtFOWRp/Nt5/lCdfApay647ITs23b33XTwT5F+JPcXnacpy01JL/2vFyFfpW+u9gz1n5QbrCJEUKqvkqj7bhIeDeU1I+OxjKxkZmQkIEaW+MDjI21FCYL8LRWU0NxPXp78H1IefUpNPaTIV5ei7eAj2C41U6apx7/Q/hQb9ZQ3G9MkXDZDCqoLoRuUlLyxG5QixyWkKrrU4JRpB5L4+8qUvzMyMupDVjIzMgrCpW7JUG5IvSkyeElS5iKwIfUvZttFTmJqZL2kK7WdQj748pVKpWB6aTtWrxDZ9NkvOldPHk/p10yIfYq/vuPv+p8y9UH6HVIhQ0qk6/jMBtEP9TfX/tk6DhkZJzIyyczIqANFiJdUOO1vF8Hw5ZW/Q+Vwmy4fuT/1KDexUGaISNt9LtIiCZv00Ud6XD7FCEVoTqPLfkyFSyHOPt999rhdn98pRMxH6uQc05jyHSrDlc/339pJTcd/x86TlHbhfTA2ZYO3ja+dMjIy/Mjh8oyMRBRVIXk+17dFjLxISELlIn0hIphaVoxExlQh7ovPhv3NH9fjGthdeeTvWH6+L0QYQuXzOqXYcLWBPS7chqtvyTr6fOP1T+2jRZU4181Bkbzyd+jYhKZx8O1Fzpl60vjSZxUzIyMdWcnMyEjEbCgYRQmAS/F02YupVaGyXYpSimpaz/w+H2EIKZch8pQa0nSRcr49lN9FMELbYu3t2lckrSx/pvARPpeyHENIZfSV51O2Xf9d/d8XIYj54Nsntx/v9s/IeDwjk8yMjFlAPYSrSNpUIugLcYbK9xEMl+0YuZRpUuoSAlf9Yn4XsRnKL33nZfNXQvryybyphCZElri9kKqYUlaKYs2PY8rx8h1vX51Cqq3Lp9R+5JteIPO5bnRcaXyYjRvOjIwTATlcnpFREL6wniuMnBLiiw36obCtqxyp1Pny+OrmCkmHfA+FdH3pfXX01VOqkK4y+OKf2HFwpZH2fcfZVUdOhiWJkcfDp+LKdvL9Tgk1c59cdZDHltuUv1Pg8kf6GXqDke+4FgmJyzJT+xTPm5KO+5eRkRFGVjIzMmYBrvBfCjmMIaaocNtFCaWvPNdvX5m+9PUou642s+XI3760Mh0njC7inaKQyrypxzR2PHz7pb+xcix8BNmnJs42kYoplbF6+MihbAtXvpjqmHLDEMNMzquMjBMVmWRmZBSEb+B2KSeh8GgKXEpbin9FbKbs8ylVfF/RQdinHIVC7T4Fy2ffF5YNEbyQTV8eSVpS+kDMroRPeZX5Y+qzTRNq5xR/XOl95D4W3g+pilItdvkVUj5jZFX65rspcd08ZuKZkRFGDpdnZCQgpIT4wo5SmUlR3nwKUCx8yCHDkkUJoctvV56QGuQLP6aQVb7Ptz+lHX3qriQ/ITs2nW3T6enpYGjZp5K60vB0sfZx2fb9d9XB1R9ifVLmlX2+CKktqhy6zqGiNmO+uAh7kZuOTDAzMuLIJDNj3oCIfqKUuia2bS4gBznf4Bwiny47oTLsNh8J8/ng8jVUBockFb68LoIWIm+c1Pjsczu+bSmqm4v0S1IUIhSlUgmlUglEhAULFmD9+vU46aST0N3djQceeABHjhzB1NSU02bq9Abpa4i4hvL4vovk9+23beJTIn03V3Kb3Bfqz3J7Pcq963jzfaG8Lt95vqLHNyPjREcmmRlzDiJqBtAKYAkRLQRgr+ILAJw8Z44x+NS9UPqQkuUiWjE102cz5GvMv5jKGVMrXXAN8CmqZ6bsjgUAACAASURBVCrp5MTHR3ZdimGsbqVSCS0tLVi9ejXWrFmD6elpLFy4EKeffjq6urowPDyMLVu24Kc//Sn27dtXVTXtinNfSDhFKQz55eszof4SU1VD5NZH8n11c+XzKaC+NpGE1bVISCLF39BNT6h+oXOLtxdfbJaRkXEsMsnMmA94FYDXAVgB4Lc4SjL7AXxorpySSCVfcrtUuOpRQ0KkkMNHUEM260Uq2bZ++VYXu75j9vhv1/QA128f4S2VSmhoaMDy5ctxySWX4KKLLsKiRYswNDSEvXv3YnJyEpVKBZ2dnVi5ciVWrVqFw4cPY3R0tIZgcgISIm4+JddHiFLbhtt0tatLwZSEPRWum6bU/hk6X1Jv4nz2ffZ8RDt2HvuIum9bRkZGLTLJzJhzKKXeD+D9RPT/KKU+WCQvEX0SwHMBHFRKnW22LQLwHwDWAngIwI1KqR7SI8b7AVwPYBjABqXUpsRykgbAkOIUQorCadPJ30XJr/Q3VAdXWklWpH8upSymZvmISYh0yby+ND5Fk4jQ0NCApUuX4oILLsBVV12FM844A+3t7ZiensbQ0BDK5TJ27tyJnp4eVCoVDA0Noa2tDQ0NDRgdHXW2jSzDB5+6VoS8y/SxvunyoV6iFCozpf/E7LmOs++c8p1nofOhCKnOYfKMjPqQSWbGvIFS6oNEdCk0Oayw7bcGst0CrXbyNG8G8BOl1LuJ6M3m/98AeDaA9eZzMYCPmO8kuBQlF6lKseHbFlI761GcXGX5fEwpLxZSlHVxtU2I2PpCrzZ/rExfemvTfkqlEtrb23H22Wfjuc99Lp7ylKdg0aJFaGhowNTUFEZHRzE2NoZKpYKxsTEMDw9DKYWBgQH09PRgdHTUedMhy5H1k+lku4byuBC7EXGlr7f/hEhaavjZV3aIQKbYSLk54dtTbl5CfobKyMjIOIpMMjPmDYjoMwBOBXAXgCmzWaGWQNZAKXUbEa0Vm28AcJX5/WkAG6FJ5g0AblV6ZPg1EXUR0XKl1L5Z8N2rTIWUF0kSYiqPy17KXDJXWtc+OZCnDOgu+OojCaAvXSoZcpELIsL09PQxRK5UKqFSqaCtrQ1r1qzB05/+dFx55ZU45ZRT0NbWhlKphMnJySqp3LNnD7Zu3Yr+/n60tLRgamoKO3fuxNatWzExMRH0iZMZO3fTBZeSKPuNTx3mbZRKdoqm9/ns638xddPV52N5QzcZoZuxWB2k/RTIPpvVzYyMMDLJzJhPuBDAmWrm8sAyRhz3A1hmfp8M4BGWbrfZdgzJJKKbANxUpFCfSuJSPn0kLqTipKpW9Q5+Lj9l+UWVthCpkOWEQpu+/b4BXyqK5XIZnZ2dOOuss3DFFVfgoosuwsqVK9He3l4llyMjIxgYGMCRI0ewc+dOPPjgg5iensZJJ52E3t5e3HnnndiyZQtGRka8RIj7w+ud2lahdgqV5bKVGjqXtmNh+5jS6EKMYMryXaTapf7y9PZ3UcKdCtkeedFPRkYcmWRmzCfcC+AJcJC+eqGUUkRUmLQqpT4O4OMAUCqVvPlDJNFFAmJEK7bPpwTyclIHTzloSlLEyykSWvSRVRchLBKidBFdCzvgu9TLpqYmrFmzBtdffz2uvvpqrFq1Cu3t7WhoaIBSqkowDx06hK1bt+Lhhx/G+Pg4Fi1ahJaWFuzbtw//9V//hS1btmBsbCxpukFI7QqRyBQyGsNM1DVXf5jNMlNvonzH2TetIFRePTdHvvxF+m1GRkYmmRnzAET0beiweAeALUR0B4Axu18p9YcFTR4gEwYnouUADprtewCsYulWmm0pPlpfkhwoMqiFQoC+wTSm9KUgFpqXiquLBPj8SK2vj8C6VMnYPvtsS6tcNjc3Y/HixTj77LNx3XXXVVeNNzU1VUnpxMQERkdHcfDgQWzevBkPPvggurq6sGrVKkxMTGDr1q247bbbsHPnTkxMTHh9lf3Dp8TxNL52CfWHkGpZVMmL+ZEajg8Rap7Gp56GbPL/KT6E+mPoXArdRLkUy6LXg4yMExWZZGbMB7xvlu19C8DLALzbfH+TbX8tEX0ResFPn0qcj+lTmoqqIj67sXSh8lxz5FIG/hhcJMOnMvkUHw4+WLuIrH0cUIryJ8ml/W5oaEBXVxfWrVuH8847D+eccw5OO+00LF26FG1tbahUKtXw+Pj4OLq7u7Fz507cc8896Ovrw4oVK9DW1ob9+/fjjjvuwKZNm9Dd3V0zr5KTRh8BCc1NlG3gq7MljKl9LNaXpErps+tSjmP9vui0jZhy6fo/E4TOARl29+WJ3fhlZGQcC8p3YhmPZRDRF6AX+SwBcADA3wP4BoAvAVgNYBf0I4y6SY8IHwJwHfQjjF6ulPpNrIxSqaSamppqBiK5sEQSENcg7hp0XQpKvcQiZSCNQZLTlHyyHSRBcaWz/11ENlR/l71yuYyGhgYsXLgQZ599Ni6//HI8+clPxooVK9DZ2YmWlpYquVRKYXx8HP39/di1axfuuecePPLII2hqasLSpUsxNDSE++67D3fccQf27t2L8fFxr3rpeu6nj8ilHE8XoSvSB3ztGVPdUvzzqX0p/rnawEUoYzdPMZ9S+2tKWquK8/Qyv8Xg4OBvlVIXRgvNyDgBkZXMjHkDIhqADptz9AH4DYC/Vko9KPMopV7oMXfMqyiVHpFeU6dvx3y7CFMotJZCOvm+In7JMGko3Ojzh/sUUpV85bvKdLUb328HcxepkuRJKqVEhEqlggULFuDJT34yrrvuOjztaU/DE57wBDQ3N6OhoQHlcrlqZ3p6GhMTE+jr68Pvf/973Hnnnejt7UVHRwdOPvlkEBEOHjyIwcFBlMtlNDU1YXp6uqpihgilr21kW/J9IdJnIYlsjIj5fqeEmn3+hfqvyw8XqXXdXKUQW1e5MeLpyiPr5evTcm6vVHN95WVkZLiRlcyMeQMieif0iu/PAyAAL4B+pNEmAH+hlLpqLvwql8uqubkZQHwlryRGPvjCizavL03IlkvJ8pHAkDKVQjDl4OtTLeU+F9m0RMqlaLr8teplS0sL1q5di2c+85m49tprsXbtWrS2tqJSqTjJ1tjYGAYGBrBv3z5s2rQJv//977Fw4UKsXr0aCxcuxMTEBFpaWtDQ0IDu7m5s27YN99xzD7Zt24Y9e/ZgeHi4+s5yWRefeug7VjaNnSIgfU3pO6GQsk+pC6mGLhTti658Lh99qqv0M8W+y17IB5cd/l9+ZDr5u7e3NyuZGRkeZJKZMW9ARJuVUueKbXcppc5z7Xu04CKZIUWkKLmsJ12KCuQjsimDdwwhxUwOzjGFM4UI2TTlchmtra1Ys2YNLrroIjzjGc/AWWedha6uLjQ2NqJcLtfYKZVKmJqaqiqYNkT+8MMPo7W1FaeeeioWL16MlpYWdHR0oKOjA3ZqxMTEBAYGBrB7925s2rQJv/jFL3Dvvfeir6+vhmy66iTrEatjkX7hO4Y+AulLG8sf62M+hHxw3fikqJqueoRugIr4Km+YXATT11+JCN3d3ZlkZmR4kMPlGfMJw0R0I4CvmP9/CsC+u29e3A3JASz0Pm4grnzaNLGB0ZWnnnB7rJxQqNaV1kUgub8xP12+cpJDRDULek499VRccMEFOO+887B+/frqgh77OCIJ+/aew4cPY8eOHbjvvvuwf/9+tLS0oLGxEVNTU2htbUVXVxempqZw6NAhLFiwAO3t7WhqakJTUxM6Ozuxdu1aXHzxxbj99tvxne98Bzt27MDk5KSzDpKQhELMsTb1kW2fOij3ScRIXUr4up4Qd+x30XC+3R7a5yOxsn1j7RVTMzMyMvzIJDNjPuHF0O8W/zA0qfw1gD8johYAr51LxwD/IFs0/BhTmlLVo9kc6FyKmy8sK7eFiBEf4H3KXkjtbGhowOLFi3HOOefgwgsvxOmnn46TTz4ZixYtwoIFC6qPI+K+WHKslML09DTGx8cxMDCAgYEBtLa2oqOjAwMDAyAi9Pf3Y//+/ejr68PBgwfxu9/9DhMTE7jwwguxbt06LFy4EO3t7ejo6MCaNWtAROjp6cHhw4fR19dXrQufuzk9PV2d2+e7yZBtw7f71E/Z/q72DpXlSp/ii6seIbKacmPFbfv8K9K/efqQb6mqrt0ulXlXWRkZGX5kkpkxb6D0wp7neXbf/mj6IpEyYPlIVwopjZVZRFFMCUGGlCZf2hSiGVLnfAO1HMytetnY2IhTTz0Vz3jGM3DuuedizZo1WLx4Mdrb26tzJ102pN+W/NlXQZbL5Sr5HBoawl133VUNoe/btw+Tk5P4/ve/j1WrVuE5z3kOrrvuuup7zTs7O/GkJz0J5557LlpaWrBkyRI0NDSgv78f27dvx/bt22veCGSfQuAKQft+uxRr1zHzbZPpQ30xFNZ2HUf522XP55O0HSOpIaLJb1xcZfsUTFd7pNzkpZLYjIyMWmSSmTHnIKI3KaXeS0QfhCMsrpT6yzlw6xi4BmDX4FmvLb7PpTS59vM03Cf+HRr0fSTER6AlQmW4ynPlc6lClUoFq1atwjOf+UxceOGFWLt2bXXuZGNjY41S6FPdLJEcHh7GkSNHcODAAfT392NwcBAAqu8i37RpE3bt2oWJiYkqIe3p6cHw8DCWLl2Kc889F62trWhubkZTUxPWr1+PU045BYsXL66uQO/v78fmzZvx+c9/Htu2bfOG0q3SmgLXsUi5OYitSJe2XMpzvZBETPov98VuXmJlpOSR7edCqjLMy8oEMyMjjkwyM+YDtprv6DMr5xN8ITOfuhLa5kIojOfb7iIkqeVYwudSBSVCIc0UAutTQEulEsrlMrq6uvCUpzwF5557Lk499dTqm3oaGhpq3uzDbfKV2vZVkb29vdi9eze2b9+OPXv2YGxsDER6Uc+uXbuwbds29PX11Txsnfu7detW/OAHP8C1116L1atXo1QqYenSpdX5mqVSCePj4xgZGcHw8DBGR0eDKp6LhPHtoePD1d6QGppKflxqZNGbBZ/NkHrqy8PzpZQT64OpvseU4JkS74yMExmZZGbMOZRS3zbfnwYAImpVSg3PrVe1CA2eoRBmSphd5pvpIC8VPZ9y5BtcU8oLERSf2hYjEJZANTc346yzzsKll16K9evXo6urq0oufXMdOcEcHR1Fd3c3du3ahR07dmDPnj04cuQIJiYm0NjYiKamJnR0dKCtrQ1tbW144IEHcODAgWMI4tTUFHp7e/Hzn/8c5XIZF198MZYtW4bOzs5qqH5sbAx9fX3YuXMnbr/9duzbt6/qi0/J87Wfr+1TSJ39HQsNy/bmJCrUt2S/iamPIfWUpw8RPJnH5VMsb6iPu5TQmF+xbRkZGbXIJDNj3oCILgHwCQDtAFYT0bkAXqWU+p9z69lRhAilRCgknjKIufL4BvoYWZE+uezOJAToU3XttlgbWXWysbER69atw9VXX40zzzwTCxYsQKVSQblcDhIb+20X4ExPT2NkZASPPPIIdu/ejcnJSTQ2NqKzsxMrVqxAR0cHKpUKzj//fAwNDWHHjh3YtGkTdu7cidHR0ep7z0877TRccMEFOPPMM9Ha2opyuYypqSkMDQ1henoaw8PD2LNnD37zm99g27ZtNURVkhbbxrGbELtd3hzweseOg+umwkX+Xf3I5ZN8lqmv3BBcxDGV1PpsSGLtSh+7UfL1/ZASnwlmRkYaMsnMmE/4vwCuhX7HOJRSm4noirl1KYzQ4B9TM31hSp+i4lN3QopZyG850PsGzlT7ckCXvksVkpMuIr2SfOXKlbj66qtx/vnnVxVDq2CWy+Vj5jO6fLMPal+2bBlWr16NQ4cOVRf9TE9Po6mpCUuWLKmGu8vlMk477TRcdtll2L59O/bu3YuTTjoJa9aswcqVK9HV1YWmpiYA+oHuVr0cGhrCwYMHsWPHDmzevBk9PT01iqokej6SLI+9bDufKpyqrsm+5CovtL0e+Pqqqw1c/13E2JfW1c4uki3zhhT9mGrrqktGRsaxyCQzY15BKfWIuHhPzZUvHD6FB0hb4euy5/svVyT78qYMnrEQtQ8u1StFFQr5ESJAlUoFy5cvx7Oe9SxcdtllWLx4MZqbm1GpVKrvHY+1CSes9rWQXV1daGtrw/DwcDW/fcRQY2NjlcQqpbBkyRIsWrQIg4ODmJqaQmdnJ9rb29Hc3FxdkU5EmJycRE9PD/bu3YsHHngAd955J7Zv346JiQnnTYGrLV3t5GtHX31jabj9WJ+SPs9EqZOEup5+F7sx8+0H3FMRZDunlsPz8zKkipqRkeFGJpkZ8wmPENGlABQRNQD4KxxdFDRniA04IWLgC0ny3y6VK+QLH+B43pBy5gqJuhBSs1yhV18b8P0uxVQSwpNOOglXX301rrzySixbtqy6ityGyuViH1muq01KpRKam5urbwKyZfG3AllY++VyGQsWLMDk5CSUUpiamsLY2BiUUhgbG0N/fz/27duH7du346677sLWrVvR3d19zBuAZPuFCHeIDLmOSypiangKYqTT1V98fc91HvBnmvrS8m08bB9TgKV/HD511OenL39GRkYYmWRmzCe8Gvph7CcD2APgRwBeM6ceMVg1SBIa4FjyB/iJnU/1Sxm45MAbgo8shvL6CFyKipjiqySYpVIJixcvxlVXXYVrrrkGy5cvR2tra5VgcnIZ8o+3u52TaffZRTqlUgmVSqWqkHLyakPytqzp6WkMDg7iyJEjKJfLmJycxJEjR/DAAw9g8+bNeOCBB9DT01MNw0tSFFKZQ30hdFx8bV5EoXTl9/kniVhI4XPZjLWJy/8iN3BFzoGi+V3tGVNSMzIyjkUmmRnzBkqpw9Bv/Zl38BFB/p8TG1coTW5zDa4u2z5S5VKpXGFBVz1idXXl8ak7rnJl+ZxY2v/lchkLFy7EZZddhquvvhrLly+vPmTdRTB9bcH38YU/9qbAPlfTEk5rn/tiSabNZ+dd2jf7HDlyBFu3bsW2bdvQ29uLqampY1aRA0cV0ebmZnR2dlYXCI2Pj1ffiz4wMIDR0VFMT0+jUqlgcnKyhhjXSzxD+1L6F9/m6pe+GyiX3Vj/9Cm6PjXYRQ7l+ZZa39T2dZ07vLxMMjMy4sgkM2POQZ6HsFuoefAwdt8A5RoEUwcjbtM3oLnK9G0PhWCtP/UQRZ4+pMT5/LTzH5ctWwalFA4cOIBSqYSuri487WlPw5VXXokVK1Y4FUYZKvWVYwmmJX/2WylVDZFbktnU1FRVSu3xsentqvT+/n50d3dX513efffdOHz4cHXeJf/YOsq2mZycrPps901NTVXrNz09jXK5DKWOvpXIdczqITM+gudCEfshki9tufqLzCPVTpnPVYbLf1lXF4H1le3zk5Nlmzek3GZkZByLTDIz5gP4Q9jfAeDv58oRH4oM0nbw4SuNY7Z95fkGMReJ4L6E1Ey5TZLQGOngvrnC2FKhtaSvr6+vuq1cLmPdunW48MILsWrVKjQ1NdWQS+6XJAEuBdgSAluWJICWUJbL5ZoV65b4TU1NYWJiAmNjY9Uw+Z49e3Dffffh7rvvRm9vb3WepmwLl7/j4+M16uTk5CQGBwdryKZVTPl7z0NhZR98xyvlxiEG301FPTbleeF785EknTHfZFpXnUPnQ6gc175MLjMy0pFJZsacQ5mHsAMAEb2O/58vSCGYod8pg5VLvUoJO/J0IaUmZC9WT5ePvnq6yrAKoc33hCc8ARdccAHWrl1bXb1t8/BQt6vOPgIvy7Nkky8uKZfL1VA5J3qjo6MYHh7G0NAQenp6sG/fPtx999249957MTAwcEwo29UOXLnmoXu739bJEkqXgplyDEJpXW1fLylykfmiCNWNh7vtt2zD0LnDf/tuTIr6HurDvnIyMjL8yCQzY75h3l+1fWHIFBXQRyxtfr7NFW4PEUJX2S4SGiOaPr9T0nG/fQpqR0cHzjnnHJx66qloa2urIZhTU1OYnJw8pi3sY4ek3zwcbUmdtWFXho+Ojlbf9mNXlltyZ/cPDAzUhMi3bNmCLVu2YHh4uHoMXCom91G2l8wjVdcYEUq5CXG1bwyx/PUSKJe/VjHmRJ/XQz5X1Nde0n5R9THmt83HfQgd76xkZmSkIZPMjIwEpCqKPG0KQmE+3yCXYlMOnDJvapgx5KslDi7fXCF1Iv08zCc+8Yk444wzsHDhQlQqlao9S/z4wh2bz85hdKledt/ExAQmJycxPj6O8fHxKnkcGBjA5OQk2tvbUalU0N/fX10xbt833tvbi+7ubhw4cABbtmzBtm3bMDw8XKM02jI52fCREl97S7iOeYhIFSU3rhsVF2lzlVHkhsnnFyeRMq09rr6yUrbxfiDThMi075yI3Shm5TIjoxgyycyYcxDRAI4qmK1E1G93AVBKqQVz41ktpPLkUlqAMOHi6eTvFCVU5ontd5FfVx1CtlwKq4/I+kiHfbPOkiVL8OQnP7m60IeTDE4up6amaggSf9SQ/eYqp1Uvx8fHq+pkX18fenp6MDQ0BABoaGjA9PQ0+vr6qqH0oaEhDA8Po7u7GwcPHsT999+P7du3Y2hoqKZsW4eGhoYa5dX6PTk5WTNn06VA24U+KccxpHKmkCBXX/Kp1z4lz0ccpSpulUrbprIcPv2B23QRTBcpDtVT/vbVNWYjxVaszIyMjGORSWbGnEMp1THXPqSi3sGFk6+UQdGVLkWh8SEUAgyRCkka7WDrUqV8vtp9TU1NeOITn4g1a9ZU3wNu93GiyPPw51daosq3W5I3NTWF8fFxjIyMYGhoCAMDA+ju7kZfXx/Gxsaq+W3ofHR0FESEwcFBDAwMVAnm/fffXyWlvH2sT1aZs6vCLcmyfvFV7fJGhLeRVEV97S6PUb3H3fdb+hAjmNK2VJb5cXOtmPcp/inqqc/nUFofipxDkjjnsHlGRjoyyczIqAOuAYcjpgL51CNpn9tLCecVhY94hNKlgtupVCpYunQp1q1bh4ULF1afXWltc1Vwamqqul0STNsGU1NTNc+1tMSRL97p7u7GwMBANc/ExESVQFqCOjAwgEOHDmHbtm146KGHMDIycoz/ltByXycmJlCpVNDV1YVKpYLBwcHq/E0XQXSp3qFQLi/fp7i5yKHvv+/Y+PptjEC5yrPb+DNHuV1JuPkNS+gGKzb1wNUWMnog8xUlhz5ynBXNjIwwMsnMyKgTkkxY+AYknq4oWYypnKn+hvL5wvx8X2ig9qmbpVIJra2tOOWUU7Bs2TI0NjbW5OMk0xJNm9eGzUulEiYnJ6vqJ99vw+SWZPb29uLw4cPo7e3F+Ph4tZyxsbHqg88BYHR0FLt378aOHTtw8ODB6tt7ZEjXPmfT+qaUQqVSwaJFi9DS0lJ9/NL09HSVaHKy7HpUD1f+LGl2tXvseMi2L9ovfDcUkmy6vkM2ZBkh330+pxJBF8EMpU0Jx8eQyWVGRhoyyczIqAOxgcyHmZAEGbqdSeg0lSiEiCf3xZWPSD/8vKurC6effjpOP/10dHZ21qzw5ookVzDtanKrZFpiODExUUMUJMm0YfKenh4MDw9Xw9eWXNrnYQ4MDGD37t3YvXs3RkdHq2Vbvy2x5A9y54qqfbf64OBg1a/Ozk4opZ+Rackxf5SSrZsrhOw6/jG1O3R8Y+lS1EFbtsznUxJlPUJKbYjc2psMV7oihDeF/PIyU/JkZGQUQyaZGRkFERskQ+lS1M9QualEo+ictVCdrL2QEsXVS7tAZtGiRVi9ejVWrVqF5cuX46STTqo+ExPAMQt9rC1OMDnRlESNL7gZHR3F4OBgTZh8dHS0Ok/T/h4eHsahQ4dw6NAhjIyM1CiI1v9KpYKmpqbqKyj5Yh9LSCzBbWxsrL7ZZ3p6Gq2trejo6KiSzdHR0eo8UF9bc5u+Y+M6LrEwsw+ukLiLtIWIL/+dcsMib0xkPWQ+VxhfEnKZP7XdYj5lZGTMHjLJzMiYJYQIpU894kpgCmGIhdtna7CU5YTIK1f5mpqasGTJEqxZswbLly9HR0cHOjo6sHDhwup7yTlplOFxSzClXfkmIP6WHv6oop6eHvT29mJwcBBDQ0Po7+9Hb28vent70dfXh/7+/qrCaW3xelQqFTQ3N6OxsRGNjY3V11ByYmyfwWn95q+ytIppY2Mj2tra0NzcXJ0nOjY2lkSEJFzha/67HgXTpwb6CKULPhWb53URapft2A1ZqN1S1X+etp5IgEQmpxkZcWSSmZFRECmhcpcSw+EbJH0kzmVzJvANvtwnX8jS5rekzBKqJUuWYPny5Vi6dCna29vR3NyMlpYWtLW1obW1teZ1jtbm5ORk1aYlbXy/bAObzobI+WrywcFB9Pb2Vh+qfvjwYRw5cgRHjhxBf38/RkZGal7fKNvdvnKyUqlUv+07zjnxtbDvMbdvEZqenq6Gym2IvFKpoKWlpdqm8tWUKSTFRTBdeUMKZCj9TCH7SajfuPx02ZO/U1RbHyn1kVxfyD0VmWBmZMSRSWZGRkH4FMSQ2uIaAGN5im4rAp9yFRqouarY0tKCjo4OLFq0CIsXL8aCBQvQ0dGBtra2qvrX0tJS/Vh10JI1OVdRKpiWaHJSYEPj/GHrIyMjGB4erpLMvr4+HD58GAcOHMChQ4fQ3d1dVS5DxIeXKcP0tnyroFolk5NSu69UKmFsbAwTExM1pNkSV/6echfJdxGwWLjad2xnuj0FVonmduS747lS7SLCsZso31SNUFjet5+nqafeKf5mZGQcRSaZGRmJkORQEhCZxpXXYqYqSixUV08oTw7gnCA1NjaipaUF7e3t6OzsrH5aW1uri2AaGhrQ2NiIpqYmNDU1VX9zNVASN/6gbqWOPvich9L5vM2JiQmMjY3VrCa3SqZdVb5v3z4cPHgQg4ODNavJrV3+bevKiSWvvzye9rtcLlfrYhVN/oxMIqqqtFbBrVQqGB8fr9bN2natPpfHsJ5+Eutf9YZ7DJI18QAAIABJREFUpQoo3yMvSaf8zRGrV0idjW3jUzL48UyZMiBJvjz+OVSekZGGTDIzMgrAFzp2kc6Y0hIqI5bORWJjKk8ov91mQ78NDQ3VcHd7ezva29vR1taGtra2qjJpCSRfIOP7b0kZgBoljw/snFha0saVw8nJySrJtAtq+IPXDxw4gH379qGvr69mhTdvIw5b11BonvsmbXCCXKlUUKlUqnM97UPlreLJiazNawmaVXEl2ZHEJwUxUlY0VO+Cj4CF0sXsuerOwQl5aig+pMy7/PSF+V1l8TdOZWRk+JFJZkZGnXApRSH1JmXA8+2LEc8iAzpXKflcxIaGBrS0tKC1tRWtra1oa2urqpE27M1XXVuFslKp1NiwIWS7XT7MnCtdklhaxdKqljZEPjExUSWYlmQODQ2ht7cXR44cwYEDB3DkyJGaRT28DF87WJWRP+aI+yOPCycxljzaevKQOYBq2HxsbKzaDjYtnyoA4Bg1MNaPfGQsFMp1kdcicKmAvv312PT5Cxytlyv0HsqbUs8iEYXQ+Z6RkXEsMsnMyEiAa9AqOqi65nMVVagAP0nwqTCWUJbLZTQ2NtYQHksKm5ubq6FvrlA2NjZW51NKAsnDwPzDH/djCZtV9qwCZAmZVSn5Cm1JMO0cTEsuh4eHq6+DtAt7+vr6qvMgiwz8NkTPiS9XWvncUU6QbdvK52haEtTQ0FBNb8PpNo/rkUyhfhRT+lzqpS+Pj3SntJOr78bUx1TV01WehUuhr+dGzGVbbotNY5HnbQ6ZZ2SEkUlmxmMaRLQKwK0AlgFQAD6ulHo/ES0C8B8A1gJ4CMCNSqke0qPC+wFcD2AYwAal1Kai5cow70wGuFj6kNLjGoC5SmeVyIaGhiopsgoknzfJQ9t2vwx/WwJpv+3HtVDG+scf9cMJAn8dpAyHT0xMVIklJ5iWZNrvnp4eHDhwAIODg9U3+/ja10V4LPm1++wx5GF61xQIW4YNc3PlVM7LLJfLNYuA+PGJHV/uqzzGIbLpy1tP/5RzEeVv+5+3D7fDt7kIp++88YWui/gu98mwuCsPP7ahsrOCmZGRhkwyMx7rmATw10qpTUTUAeC3RPRjABsA/EQp9W4iejOANwP4GwDPBrDefC4G8BHzXQihQbdeW77BNpaPq5V2gY5djGNVOvuAdE4quZLJQ95W7eSKZYhUSiLGVUEXseMEjz9fcnx8vCYkbsPNdr8loYODg9i/fz+6u7urjyUKtZ+PrFhCaevECYYlmDa/fU6mXIEulUE535QTU+snT+Pz13cDkdI3XGQqFSmE16depp4Tvrr5iHOqGirrW0QdDimyvvzc54yMDDcyycx4TEMptQ/APvN7gIi2AjgZwA0ArjLJPg1gIzTJvAHArUqPDr8moi4iWm7s+Mqo+Q4NZqmDnMs+/x8jR5YAWWXSzqNsamqqCf9asmgfLs5VSKtS8jA3J5epiqWLRLlCpzwczj+ukLgll1zxHBoawuHDh3H48GEMDAwc88zJlDaV+7mSaUP/lnzKh8bzVeCu+nGSyhVNq3baesj5mCH4FDe5LaTOFUEK4UolftJGqqpabyhaknrfMXeVxdOHVEyXWpuRkeFHJpkZjxsQ0VoA5wP4LwDLGHHcDx1OBzQBfYRl22221ZBMIroJwE32vyuM6BrUfQNqauhNkjdJNjlBtA86t2+k4c9glGFvF2nkC3f4ohT7nxNLrlhan+2Hv3/cl0aGoXl43JLLkZGRmtCyUvrZmCMjI+jp6cHhw4fR399ffXNOquIXOw423G0Jpj0efHW6L0RsCSQn2lzRddU/1DdS1Fhff6tHuZTlptzcyPS+/C4iGqqf/M3Li5G+EPlLbZPU9oudzxkZGUeRSWbG4wJE1A7gqwBep5TqFwOcIqJCo69S6uMAPg4ApVJJpYStWd7C4XQZ+uZKJVcXm5qaqo8PsvMs+fxBvsKbL8yxRNOSR/4gcb7NFwq39bLkyW7n8y0lebJppYIpV4vb94rzdpicnMTY2Fj18UQ9PT3VleMpK7Fdx4CT0pjyxsmhfLc6P1bSF0si+YIhXn/5jvKQH9wXV18J5ZMoQqD4d0o7yX0hslck1O7z12XDdUxDfvoQO1ezipmRUQyZZGY85kFEDdAE83NKqa+ZzQdsGJyIlgM4aLbvAbCKZV9pthWCiyCECEzA9yqp44SSkz4evrbE0BIcq6bxxwpxO5xk8geIuwgmJ7cSfJGLXMgj20CSSz7vkj/r0j5UXRITGxrv6+tDT09PzXu/iwzuPrLvUr94fUL57H+rXkoCY8kor7cMn6eEg13+hRS0mSqaMTXQ50OqX9amS8UNnUu+/9J2bLu8MZiJ6puRkZGOTDIzHtMgPXp8AsBWpdQ/s13fAvAyAO82399k219LRF+EXvDTpwLzMS1ig2tM0ZF5ieiYhTX8eZKcbFqiaAmhJJexeZSu3/w/Vy19yqWrjrK+nFxKBVO+DpK/iYerl/YVkX19fejv78f4+HihxxL50qWEaX0fXj8ANSRFvk2IL2ziH9lO0jcfSXT5HVPoUrfJ8lLU9xCBc4XMU0hvrB5F2sa3X/qY0oYx4puJakZGHJlkZjzW8XQALwFwDxHdZba9FZpcfomIXgFgF4Abzb7vQT++aAf0I4xeHivAFd6Ug6gkZxx2P3+bjg1lWxv8weg8ZM1D25a0cdLpC4W7wt+ctHJyKetjSRFfme6qmyRevjf18EU9llzy9pqamsLo6CgGBgYwMDCAoaEhjIyM1ISobTvGHi3jOw4uIsXrzI+jTcMfIO/Lz9vBR7RnQkRcRKZIaDnFvkSovqH0vpsw182Y7wZM+saPTSgN9yU1oiBvMlz+yfSy3hkZGWFkkpnxmIZS6nYAvtHkGkd6BeA1s1S2d+CxBEU+9JyIjiFtnAi6CKZ9m4xSqkoO+WOHXIRS/peP3pH+ArXzK/k+SfYkqbLEis+7tI8jsh9JLi0ZtQt+BgcHMTg4WF1Z7iILvL1tfXxv5pHHyKVg+QiK/M/T2TrI1wryduDt4iMrLpWN/3b55qpPipqWotaF/AylkfbljZdPbQzllXUOEV1+zHz++W4I5U2GrwyXLZe/GRkZxyKTzIyMBKSECYGj5IcrjHa+I1D7vmtJCu0AyLcDqD7LkYfQ+apwrk5yEutSLDkkcQmpPz5iKR+qzudd8udgcjt238jICIaHhzE0NFQTQpdE0OWjj8xY0u46RpJM2P1cSbb14nZ9eeSxD7Wxa5uLVIUUvhTVMVSehK8s3+8Q+eJpZ9oOPrjq6iKzKe0W8isTzIyM2UMmmRkZCfCF/iypI6Ia8scf8M1VLT7/UZJJvs2ql9aufBWkaz6lb1W4rAcnUT5i4FIAZTjchsTtw8v54h65ktqSzYmJCYyOjlbfPW7D4lKRlISGK7E+dc1Vb0kGudLI08vFVjatS83zqZ9SzXSlcZFXlxLn++3aFiKRISLkImnyN0+XYiOkdtr0vnSpZJ7/jxFzV19JaZPQf24rIyMjjEwyMzIicA1OkhBKgmLJmIVcvS1Ji+tNMjLsbW2EVC7XYCzVuBjBdL3H27WYR64at9v4Q9L5Q8jtivLh4eHqPE35CCBZHwA1SqxUjV15OImTpMkunJLtIB8yz28Q+EKrEEHiaV0KKC/Dp7y5SLMkxLw8SXClTyH4SKjPPx/Z9RGzECFLUUVTlcLQzYkrTQiu8yqk9GZkZISRSWZGRgRShfLNfZTkxkfmuB0XqZienq4hrL58/L/1k5dnSY+rLhxSZZJkST6OyPXcS/5MSBlGl+8dtyRUKnyyDryuLoLP/8fIkq+dfG0KHEti5aKjkJoolTZOVrgf0l/eJ3w3N9IH+UxOl23ul0+dlXZdx8YHl+IozwdfHleZLmVVbo/l9ZVTBC7CHCPIGRkZR5FJZkZGBJJYWkWMEwKrWkqykjKQc+JhV3NzhVOqbD64SCJX5FxqmCsv/1jywpVLrlhy4mnbwSqb9jmY9tva8pEL3mb2XeF8H1d8XeTDR8rst8uGPE42Lz8O3L4sR5JJTup9x8ulzHK7lsxy5dp+JLnmJNmnprrIvItoutrS1VdS0rvgUzztb5+66yuTb4uRW9mPUn311WsmxDUj40RCJpkZGQmwD0CXahMnFa4FJ/I3/+9aYMIVTNeqcKmuWVLB1TEeDpakRw60fHDn7x6XCqb8cPVMhs7tqyLHx8ed8zO577ztXKTP1bbSb77NR+Il2XTZtLDt6SIushyXeuZT9ew3f1qATO/qK7Zs+VxRVxoXcbN5+fQNbidFxQyRSJ+d1Hxym+vmwXdj4lI2Q9t85cj2i+XNyMhIQyaZGRkJkAtq5AAnB2PX4CjDpFKF4vMxuR0epuWDoSRDoVCeJCK8DpxkckKilKqZbykJpSWh8l3kdvW4VC6lGsjJnosESr/5Np+KySEXOHGy5morl7LLjw8/vpzwuciitGXb1nUcU+sqVWled/6fq+LWhpzOEVNcfQqhCy5S50KIjIbshnz0EVuf0ui6AbD/QzcpPE+qGpqRkZFJZkZGFD7VyhW6dEGSK07qOKl0EVVOiqwy6Vq17gvvuQZEqXDZ375XIcpFP3ZFuVQvuWrJy5G+SCVRksAYCXEN8C6yJgldKL3cbx96b4+TRIq6JtuY++YrX5Ic/s2PtYvwWDJp05ZKpZpjEVJQpX3pawrBdJF213ZXGt85JsuQ+6Qdl09F4eorrpu0jIyMODLJzMhIQGhg4+qWS4nz2eEkhhMASyT5m3e4+uQia1zBcpECTpQkabTbXOTS9S5u16IfnlaSFld7uRb0SDLhajufGiZVwlA+uU+SCAv+fnJpN3RcJclMIUkugir3S9s+8sx94yqsi9BJZTPkq89v6ZfdHiL3PjLqaxdf+b42id1YSPuhY8Nt+5TvjIwMNzLJzMgoAKlAye1yALcLWORAKBf6SIJp03PCKQmLJBMc3A+5EESqk/Yj91l1U6qcExMTNWTU+skJm4t0uBYv+dQuX1v7lDNJnkJEU26X5DzlhiJGXO23z5avXhwhQuOqn88u7z8Wvj4XsmP/p5A1V51DJC1GIF1lpabn5ct0oXZ1+WyxcOFCDA0NOee6ZmRkHAXlu7GMjDCIaADA9rn2I4IlAA7PtRMJeCz4mX2cHZwoPq5RSp00G85kZDzekJXMjIw4tiulLpxrJ0Igot/Mdx+Bx4af2cfZQfYxIyPD/QyPjIyMjIyMjIyMjBkgk8yMjIyMjIyMjIxZRyaZGRlxfHyuHUjAY8FH4LHhZ/ZxdpB9zMg4wRElmUR0HxFd9Sj4knGcQUQbieiVgf0vJqIfPZo+PRqYaR9WSs37geix4CNw/PwkotVENEhE5Znamsu2JKLvE9HLYumK+khENxPRZ+vwp658wKPXjkT0EBE9s568j5XzZjZARBuI6PYC6f+RiA4T0f7j6VfG4xtRkqmUOksptfFR8OW4g4h+SETPOk621xKRIqJZWUwVuiAQ0XYiOm02yuFQSn1OKTVr7WPb2wxUioj+Suz/K7P95gRbtn0HzecAEX2HiP4glnc2+zARXUJEvySiVxPR7Y7PnxPRxZ59H3TYW+1J+2Wz/5ue/U9w2PqYJ+35IX9Z/kYzqFzp89+kce27PeaDbEPzWxHRkDmme4jon+shikqph5VS7UqpGT1PxkdYiOiFRPT5SN7gTZwj/TEETin1bKXUp9M9nj8oWv+5gOlv6wrmsedFOxE1EdEniaifiPYT0RtYOnmNGiSiv6vT1ouFnWFj+ykzb4VgXT9GRDcR0WoAfw3gTKXUMdea+QQier1pv37Tnk1s30NENMLaMUlE8dmkozez/KOI6K8TbL6IiHaZ6903iGgR27eIiL5u9u0iohexfcuJ6FtEtNeUtVbY9faj+YATZnU5EbUBuBDAz+bal5mAiE4FUFZK3T/LditKqclZtMfb+1IA9wN4KYD3s2QvM9uLoEspNUmaZD0fwNeJ6LVKqVscPsxqnQyeA+B7ANYC2KCU2sHKOxvAnwLYC+AWpdS/C3++4rDXCmCjUuptnrQTSqnLxL73AWh22FrsSPtaAJ0Rfy2uAHAXgJaA/yUADyml/szjb8gHC9uGFucqpXYQ0ekANkL3iY8KG8fjWBaB9DnjxMEVAO5SSg0S0bsArAewBsATAPyUiLYopX7A0ncF+mqSLaXU5wB8zmYiog0A/g7AptmunMCzAbwT+npxRCl10JVoHpyP1o9rAbwZwNXQ192vA3iH2WbxPKXU/zcbNpVSDwNoZ2lPAbADwFcjNs8C8DHo68gm6GkaHwbwApPkXwGMA1gG4DwA3yWizUqp+wBMA/gBgHcB+KXD/M2I98k5Q0q4vHpnb+68v0xEnyWiASK6h4hOI6K3ENFBInqEmFJIRC8noq0m7YNE9Cph+01EtM8w9FcSu8s07Px9RPQwadXqo0TUMoO6XgPgF0qpMSIqE9FbiWin8e23RLTKlHspEd1JRH3m+1Lm70YieicR/cLk+xERLTG7bzPfvebu5hKT589NG/SQVvbWMHuKtML0ABH1EtG/ksYZ0IPsJcZWL6tHdbAjosVE9G1zB3Mn6fDG7cz+HxDRNlOXDwEgtm+Dqce/ENERADcTU0+J6COGzPDj9U17l0REK4joq0R0iIh+T0R/6Wtv8/9OAK3mZLMnXbPZbu3fS0TPY/8bSN/1nw8BpdR+pdT7oU+w9xBRyeR5iIj+hojuBjBERBXbh43PI1R7B3m+KaPBc7xeSlo53kFEbwZwPeaAbJC+Uz1IRPeybYuI6Mem//wYgK0DEdEHiGgHgLdAX4BSMKO6mXPoCiLaQnqKglWuWwG8j/n5PADfI6o+5fon5ni1Avg5gLPpqCr0CiJ6GMB/ElGJiN5G+k7/IBHdSkSdpuyaSAIRdRLRJ0hfX/aYc6NMRM1EdAfpa9WY+WwhoucS0WHoC/UPzHn3JtLXof+AHgxeT0RPIn39O2LO2TuJaBkR/W8AlwP4kMn7IePH+01Z/aSvM5eb7dcBeCuA55v0m832qhpo/H2Y9LVmwLTfPaYv/gcRNZp0Teb/DpN+v0m/nYiucRynBiL6gjl/GxPOZZ73aaSV/F4i2mzKuod0SPVKVv+Hjb93ENEmOno9/ZLx824i+g35r6ehfvYS0weOENHfin1PJaJfGf/2EdGHiOgkIvoKEQ2bZPcYH+8mPQYcIn0N6CEdHfkk8/EC1J4XLwPwTqVUj1JqK4B/A7Ah5jNDvbZeBuBWFXmwten3t5o67TLni3OcJ6L/QzrKYM+hcwD0AjgdwI8BrDDtdAs5zkeT58umD/QR0W1kru9m3y1E9GHSU0AGzXF+AhH9X9PW26g2wpHcD0W7fEIpdZ9SqgeaIPvaMBVFbL4UwG1KqYciNl8M4NtKqduUUoPQNwx/TEQdpAWZPwHwd0qpQaXU7QC+BeAlAKCUOqCU+jDYWOnwN6kfkeZwXzHXiwFzbp7L9j9ERG80fb/PpGtm+72czYd6Fv48D8BnACwE8DsAPzR2TgbwD9Bs3eIggOcCWADg5QD+xZy09iL7BgDPBLAOwFWinHcDOA2a1a8z9t/ucoi0hN1LWuL34XoA3zW/3wDghWbbAgB/DmCYNPn4LoAPAFgM4J+h7ygWMzsvMnVZCqARwBvN9ivMd5cJ2/2KiG6AHkj+GMBJ0APoF4RfzwVwEYBzANwI4FrTUV4N4FfGVpenHv8KYAj67uVl5mPbZAmArwF4G/QDh3cCeLoo+2IAD0LfPf1vse8L0AMgGXsLATwLwBfNRevbADZDH5drALyO9B2gy0+Lz0CflDC+fkbsvxUAV8iuB7BPKfU7+PE16GPxJLbthdBkvEZRUErtBfAr6BPa4kUAvqKUmnAcr9uh7zifDeBM6JP+ZOh+/2jjFgDXiW1vBvATpdR6AD/B0TZ4NjSxXA99HF+fWIbrmBXBJIC7lVJnAngagNcQ0ZkA/gDAJuPnHQCeCN2Gzzb5rgFwE3QdL0dt+14J4AwA10JfODcAeIax0Q7gQx5fbjH+rANwPnTffSWAMQAfhL5mXQmt3L4d+nx7LYBdAL4P4H8ppd4L4BXQ5P1OAP8HWl3qBLAK+hrxagAjSqm/hT6/X2vO2dcaP+6EvoYtAvB5AF8momajMvwTgP8w6asXeoZPQp+7d0Ffpx4A8DGl1DoAPcY3mO8e6D7fCuC/lFIdps0e4gZJ36h/w7TDjaaNYueyzXsydP/4R1OfN0KfJ39qQqo/M234YfO5GMDZ0GrQYmhi8kdm+03Q13ff9dQJ058+An0urjB2V7IkU9D9fQmAS0x9vgvgB0qpVpPmUuPf5wE8FXoM+zSA1dDH9jnQ585NpqzroceBhQCWm7ay2AzgLNRiFxHtJqJPOUhzUVsgLUxcAX19jOGDpg5PhO7fL4VuX26vRET/Bj3mPEsp1cd9M6rfswHsNX1zA8vOz0dAnyvroY/fJjD11eBGHB2DxqCvv5vM/69Aj7FIHFNcOAvHtuEyMWZ/zhDXH3FCNUObMGPjS6H7TiGbSqmd0MrlaeYzKaKTzr4gUaQfMdwA4Ms4ek36BhmRxeBG6LHmFOg+ssGUFeNsTtRDMn+ulPqhGby/DH2RebdSagLAFwGsJaIuAFBKfVcptVNp/AzAj6AHEVuRT5m7hWFoRQqmMgR9gr9eKdWtlBqAviBbabkGZj5Wl5GyfeB3kK8E8Dal1Hbj22al1BHoi8sDSqnPKKUmlVJfALANmlhbfEopdb9SagTAl6AHEB9eDeBdSqmtpr3+CcB5xNRM03a9xvefhuwRUSs0Id1Iet7anwD4e6XUsFJqC/7/9u483q753v/465OEkIGIECSRGHIRU6irxlaHS4KKtoSiXFerLW1pqeLXXxWtKjVWpVW0QkVJKVepIVRrSDSpIUjVlMgkkYQMEhk/94/Pd+Wss3P22efEOtn7nPN+Ph7ncfZee++1P3uNn/WdVv2N/VDgZXcfndbN1UBpA+4Z7v6L9FuXlLz2d8CpW19HEUnvjBTDZu5+kbsvc/c3iaun/PppqFTsNuBLaYM+Nj0vff1QM9soPf8yayaipWak/z1z065196kN/CaInepLsHo7OzZNgzXX1xgiwVjp7suIbeHtSqUJLcHd/wbMK5k8jLp1fgtxcM6mZ6Uek4lkrGtj87dohtHJ3df6zkbuPpMoCSHts5NSTLsR1T0Ac4nl6SlOiJPOg0QJyh3Ab3Oz/ZG7f5DW5fHAle7+ZioNOA841kraQZtZb2L7OzN9djZwFXBs+t4TgMuAF4n1O5VIirMq/4eBI9PjYek3PZBe34FIbLZ395XuPsHdFzSyTG5z97lpH7sC6Ez9C6IGmVlf4gL0ZiBLAvamrhnBLSUx3kIkWKuAg8xsPXefnE5mmY2I9fAGcLJH+9Wm7MuZE4AH3P0Bd1/l7o8QJ8rSNqxZPIcRd8oamPanHsBbRPXlWKK51l3NOJ5CHIfuTyVCS4kSoVXZi2l9jE3Le3KKY2fgptw8FmYxpuP+94DD0zY7C+iazgtjifNb57RfZNWk83Pzmg90T4/npOXZH/hYmp6v8s7vY5XmlXcicd59q7EFk84JxwLnufvC9PuvIJWIJesRF549ifWwOPdaU5qE5PdH3P3m9F1LiXP47lnJaHJPWicfEtXOH7r7yLTt/YG4AITmbYd53VhzGULdcjyeqPrvT5xfH8ryk48wz8wBRAFNQ82gKs0zm2/39FrpMaTcttDQfPMxNuWzE3J5wZVEjeI+udevdfcZ7j6PSPyzfbJsztaYtUkyZ+UeLwHmeF1j++yk3g3AzIaa2Vgzm2dR5XsocQUDcRU6NTev/OPNiCvyCamE8n3i4LhWt+4ys12B+e6efUc/4kBbaiuiJCNvCnUnb6ifqC0m1z6jAf2Ba3K/YR5RZb228/sM8HTaoTcjDtLllmG95ZtOrvnXS99fT3r/HaSEjChxyA6Y/YmqlPdzv+18YodraHln83ybaL9yCZHMl74+A3gK+GI6EAxlzSvjUtmyzCdgZX8X0XZmXzPbkigdWEUk1Nnvyq+vR4hEO/uOramt2+T1TokdxHaUNXjvQ/1l8C6VD1iHEoleISwap+8BjEvfna2f/UnV+tQt1z3dfRNiPdzm7qvq5rTGNp3fP6cQ+0Dvkq/vn75jZm5d/poobYHY/88galoeIY4F73tdqfecXGx9iLbFD6TXZ6c470hVRpeVlAKULoezLZpfzE9xbEzdMbAxVxMni+np+aYlMU4riXGqR3vbM4kSwXfN7A4z2yo3z32IhP/S3IVSo/tyif7A0SXv7Qycb2YTiBIVqNsutyL292xefYC3c3EvBZbn5l/p+AdrHtc+IC5cALBovnW/pU4bwAXEPv5bM8tKyDfMYkwX7j8CBqb3DwO6Wl3ns+VE6TvAovQ/uwjOHi9MsSxy9/EpwZ1FlOoebGbZvpffxxqdV4mmlpb1Irb70n0kf77ZnviNF6YLZwDS8XZHGm7zl7d62Vs057jUosnBAupKzfPbd2m+UPo8W9/N2Q7zFrHmMoS6dfKUuy9JBTE/JS4YD6Rxjc4z5yTgj+mCt5LSeWbzXVjhtabMNx9jUz6b339WEceS/HGiXE7SWM5WVouNk2nRG+uPwM+JHboHcZWUtcOaSf1qjn65x3OIDXBnjxLKHu6+sbtXOgCVU1qqNhXYroH3zSA29rytqTvQN6ah0q2pwNdyv6GHu2/o7pV25HLzy/+Od4mqrnLLcGb+eSq1y79e7jvyRgFHpZLXj1PXuHkq8FbJ7+ru7oc2EGepkUSvxXJVP7cQJSZHEyWnlZb954kTf74Eruzv8mhj8zDRaeg44I7cCbfe+iIOIr9z96dTIrEjcZKsObnfsLYKa2tqZt2IbeXMfClfWoafJEq/mir/u0r3z62JfWAW9U0lEpheue1zI3ffOff6NcS+szexXhv6PogkNqsKzFzu0SRgP6K08cSGPmtyaCKSAAAccElEQVTR/vIcogRgk7RNzafuGFhune1GbNOvUf/gX5G7304sp8Fp/j/Lvfww0XlgTCrthcr7ct5U4Nb8e4F+7t6HuCDsQ/32v9n6yv/O3jTteFpO6XGtC5GAZ0YQNQ4D3X0jolq8GzDC3bNSs3zfgLOIkuUF6f3PZLNO/zclLnyzY8dMIF/lujvwcplYs9+dnWdX72NNnZeZ7U9sA00pLZtDJMWl+0h+eU8iqs8fNLN8ifohwGNeeXSG/Lo8jkhYP0td50LItf1vhuZsh3kvs+YynJVKqBviTYiv4jxTs5OjaVryv8Y8zWxb4gLt3+mvk5nl953GtqvV1mKbhPr7TwfiODij/NtXayxnK6slB2Nfn1iI7wIrzGwo0S4qcydwspntlA4Uq4d6SNn1b4g2nJtDtAdqQvuMckrbmt0IXGxmAy3sltpbPAD8h8VQA53M7BiiLd79TfiOd4kr5m1z034FnGd1nV02NrOjmxjzLKCvpcb9ydDsd6SDwd1Eh50uFj1zT8y998/Azmb2hVSd+G2i7WaTebSFnEMsr4fcPeuA9Cyw0KKDzYbpinYXM/vP9Hpjbfv+QGwHd5Z5/U/AnkRJU9k2SBadLb5JlFScV1L6VcntxLI6irqqcihZX8RVb3Zf4wOIE//kZnxPS5uVSmRJ/7NOVtOpfwDYjEaubNP+tzdRnfRRGZFg/t7d707TFhLVcwcQCUDWY7U02ejbwLS8UUTnm21SIpu1aazXyzWVoj0MXGFmG1m0QdvOzD6Z3nIj0fZvO+I3Hw70TPvJLOIgncWxHPiHu2edinoBW6aSrgXp9Wzbm0X9/b87kQS/S5xEfkj9EodZRPOi0uPw9sARREnwaUQNxjUpxmz++WU1Hehn0SkpO+HPIC7U6+0XHu1MbycSzV5U3pfzbgM+Z2aHpPdtQJQA9vVokjCZ2F+y7fIBIoFbkpbdBmn5ZMfTzqTmFc0wGjjczA5Ix8aLqH8e606sl0XpmDgMWOru49Lr84iq2SzG7mkZzbZok1+awG5CdMLIjAR+YGabpPl/lWj/i8XQZTuk7W1Tom3/X919fpl9rOy8crLSsoqlWumccCfwE4sOJf2JNnSlw2SNIkoJH7Wowoe1a4/dnTjmzCVqHi9p5ufzmrMd5o0ETjGzQak09gfUrY+tzWx/i85tG5jZ94j996m1nWfO54l20E09Zv6e2HcOtOjocxFwd2pq8AFxLr/IzLqmC4th5JqKpX0tq6nqbLnOODRtO8r7WC4vOJNYh2Ob8BvK5myNabEkM+0U306BvUdc9dyXe/1BYid8nKhSyX5kdqL8fjY9FcU/Spm2TFY3dtUaHX/SRjKI+tUAV6a4HiYOSDcBG6YrlcOJq9u5RCnE4e5esYo0tVH4CfCURXH/Pu5+D1GScEf6DS9R19mhkseIq5F3LHo+7gIs8vrtTrOhYd4hNshRpOWXYj6a6EA1lyhhqLRzNeR24kp1dTKWDmaHE6Ulb1GXiG5cZnmT++wSd3/UG24vSZr+R6LR8d0NvOV9M/sAmEgcGI9295ub+ZvuI5bHO+6eb4xdur5+C2xrMUzF54hqtvsamF+13EddZ6+TqLsavQ840cyMKF34IP2V82mi1PjDjxJM+r69gEnufmXupYlEQ/LDiJK8e3NxZp/dh2hiMZPybia2878R292HwLfKvPdE4kL3FeL4M5pIDjcjqsh/QuwvFxBNQiYQFx0/BU4HDjSzs4kkMTu4H5XmN5o4bkwiOrtkJ4NriJL/98zsWqJDyV+IkoopKd58FdNd6f9cM8uXlN7t7n3dffM0z5XESacjUSoKsb7zy/GkFOevqDsmbE60W63H3S8mLuYeTe9tcF9u4HNTUxznE4nzVKLzWYd04lxFHKP7E53p5hLHjQ7EMag/sS3MTet7BWu2U2uUx5Aup6f5ziTW7bTcW84mzjULiYKK24GlVldq9wzRRrM/Uct2NbGPDCDOQfkChW8QJZyTc9MuIJpXTCHW/eVeN1TMtsT6Xkgc65dS19yooX2ssXllicVwml5aBrE/fEB06Hwy/f41jo8eY7FeRIzasA1RktncIW9GptinE/tFUxKVBjV2Tqnwub8Q7asfJ2qZphDLFSIJHkFsI9OJY9DQRko5mzLPzElEqX6TapDSdvt1ItmcnWI7LfeW04jzy2ziuPSN9JnMEuqqxv9FXdNEaGQ7KpMb3UvU5L1HtNf9gkf7zEq/oVLOVvaDNfFH9FhbSTSMLnK+w4E7q/37Cvgd5wCXVXjPz4jG7NWM8yMvb6K3723VXuYplkOJJGEZcHVu+qVE54/8e3ch2ncNAb7SwLxGNzBtR+DH5d6b+z+KOKkuJ05i5xBVeWOIatVHgXvTe40YeeAN4uD6tQrxXg+clpteNn6iNGqNdZNeO4CojnqR6BH9fFp+5xBJXNa7tGcDcU4E9voI62lbImmxCu/bjei9/iKRCPww9/lniYPnXUTC1ok42d2Tpj8LbLuOt7+DiI4uDcaYpm+Qnq/TGFM8L6S/l4H/l6aXbpeFr+9mxjkYGJ/W+Z+I0smmxDiXaL9aRAz19rFa+iNKWJ+tdhz6Wyfr+kcNHb/Xcl5NytksvbkqzOzzRJVKF+JqbZW7H9n4p5r9HQcDC939mYpvrmFmNhyY6DG8UTZtR6K0ZiJRBfQAkRz8qTpRfvTlnaqsngO+7NGjuupStdx33f3S3LRLiaF08leUXYkqp7FECVRpqdxyd693h6K0Dh8irt7zNnX3Xc3s1Qbmsx1woJeMzWZmT7BmO78+RPXJkEbinUGM4TYzzWdIufiJEt0pRCle3s7uvlkjMZxOdPC5lBZgZkcAV7l7Q22t13aemwNfdPcRRc1TWg8zO5XcflEr8yqame1NHG8K6/gntcni7nrbe8nNNJrx+WbnbNVOMv9CjGW2kijmPa0Wd8JaldqrjCIahs8ixnTM9xxtVczsq0T11a3u/vUW/J4sieoI3NhSiY+sGxY3CDgH+Ja731Xp/a1ZqvZ6pczLg7zxYdxaBTM7nvrjLWemeF3nrXbLzF5mzQ6qEJ0WK43G0eqY2YM03CP8Endfq3agLTTPX1F/nOfMbS15PmuOApLMZudsVU0yRda11GHj38QA4dOIwbK/5DHOqIiIiBSkJXuXi9SivYHXPQb0XkaMBTqswmdERESkmTpVfotIm1I6UPk0YgzQelIbqlPT04+tg7hEpHWa4+5rdaMQkbZOSaZIA9z9BqKNK2amNiUiUk7pXeJEJFF1ubQ3pQOVVxoAXERERNaCkkxpb/5B3KVkmzQ00bHU1gDrIiIibYKqy6VdcfcVFrejfIgYwuhmr39nBRERESmAhjASqUBtMkWkERPcfa9qByFSi1RdLiIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIlIlHYCugFU7EBGRFqAkU0SkCjoC3waeB75Q5VhERFqCkkypGWY2pinTRFqrTsBBwFYdYdOPw7mdYXvgB8B6VY1MRKR4SjKl6sxsAzPrCfQys03MrGf6GwD0qW50IsU5C3gIuHsLsMvg8h3gTeCnwPLqhiYiUjglmVILvgZMAHZM/7O/e4HrGvugmd1sZrPN7KXctJ5m9oiZvZb+b5Kmm5lda2avm9mLZrZni/0iEWB9YG+geyfgYNjlk1Fiud106DIUrn4RBgN3VTVKEZGWoSRTqs7dr3H3bYCz3X1bd98m/e3u7o0mmcDvgCEl084Fxrj7QGBMeg4wFBiY/k4FRhT2I0RKdAAuAp4EfrMLbHgafHclXAmcALy1GFYCCwGvYpwiIi3F3HV4k9phZvsBA4jmawC4+8gKnxkA3O/uu6TnrwIHuftMM9sS+Ku772Bmv06PR5W+r8L8tZNIs3XqBo93h/1nwhSDvdaHuUurHZW0gAnuvle1gxCpRZ0qv0Vk3TCzW4HtiA63K9NkBxpNMhvQO5c4vgP0To/7AFNz75uWpq2RZJrZqURpp0jzfQJWXAGnvgXnnQA3LlOCKSLtj5JMqSV7AYO8wOJ1d/e1KYl09xuAG0AlmdIM6xPdx28GZsCkc+DEZdUNSUSkWtQmU2rJS8AWBcxnVqomJ/2fnaZPB/rl3tc3TRP56DoDFwOjgduJwS8nx0DrJwM3AZtXLTgRkXVPJZlSdWb2v0S1eHfgFTN7FlhduejuRzRzlvcBJwGXpv/35qZ/08zuAD4OzK/UHlOkks2AY4A/fw/eOgW4AriE1WMS7doFrlsMGwKrOsNXV1DXGEREpA1Tkim14Odr+0EzG0VUUPYys2nABURyeaeZnQJMAYantz8AHAq8DiwmCphE1lon4FfAkcBxf4Ihf4YFE4EVQA9gOMw8DJ7/Kuw2G/52MXTuAmd/FxYsi+ENVlQxfhGRlqQkU6rO3Z/4CJ/9UpmXPtPAex04fW2/S2QNHWDVIOAl8JfSUEQpueS/gX/BuxfA8NnQH3jmFjhtJ7hwRRRmvkMUeE4lBoYVEWlLlGRKzTCzhoYMnA+MB85y9zfXfVQi5a3YCf7/r2HyFfDLx2HhF4kGGv8Gvgm8AF1WwoXAAcDJL8OrL8P7wOK+cEgvOOF5eBc4GJhUtV8iIlI8JZlSS64mhhW6negvcSwxpNE/if66B1UtMpFSXWGrr8HvvgPbPgePXACTtwS+BbwIrAI6waAd4MTXoNMq+NZB8MtPwbKLwRyGzor+Ql2ADar4U0REWoKSTKklR7j77rnnN5jZ8+7+fTM7v2pRiTTkUPiPAbDHs3GryIMvgYc/ZHVyyceBb8HETeD6L8OBc+C6zWDE72DLFdQb12Ac8No6/wEiIi1LQxhJLVlsZsPNrEP6Gw58mF7TWJVSO/oC58CT78APO8MdX4Cfb06Uv+9D3D7gZ8ADsP/RsMUc+Mp/wrhT4H8HwhLg18AjxIZ9MHGPcxGRtkS3lZSaYWbbAtcA+xLn3rHAd4gyn4+5+5NViks7idTpBPwUOA4YAr3ehus/DsvHwTcvhvd2JQbFvBc2Ww7jl0I/hyd3gk9vAfYkbLcc3gJ2BG4lBoj9OrCgWr9JPgrdVlKkDFWXS81IHXs+V+blqiSYIms4EDgN+D4wMXLNLz4MGDxxE9zwBrAI+BQs+j48cyNsMRqemAQrJ8XV07/SrF4A9geWkRsYtsQu6et+Cbzccr9KRKRwSjKl6szsHHe/zMx+QQPV4u7+7SqEJbKmbsDZRO/xUTHpQeBEYOVOMG4hdOwIKy+LiUteha++EQPBvkjDbT4WNvJ1XbvCyMUw2KM6/RPEAK8iIq2BkkypBdnILeOrGoVIJV8BPkkM6T83Jr0GfBY4YgDcPRZGHgOXHAPLrwF+CQsXrMWG3RHYO+Yx8WrYbRRM9NU3ERIRaRXUJlNqjpl1cfeaKbBRm0wBYAvgb8BEYrD13K0hO6wX7Tn2XQ7vdYLdt4Sp02h+dzUD+gBXAUOAp6Hb5bD3GHjWoxZeao7aZIqUod7lUjPMbF8ze4XUZM3Mdjez66sclkiULF5K3ID8O9S/9/gWsOpuuPgmeHUDuHwFzJxKsxLM/sA5HaHvN4C/A3sCPwaOhEWPwmNKMEWkFVJ1udSSq4FDgPsA3P0FM/tEdUMSAfYAhhFb6Nu56VsAtwG7w4MnwxNLY8ytVc2YdReD2xz2XwkHPw+HPQ5LzyfuOSki0oqpJFNqirtPLZm0ssE3iqwr3YARxDhDl6VpBhxBVJ+vInrk3A+LvXkJJv1h1c9g0Wei4HPR0+D/gxJMEWkTVJIptWSqme0HuJmtB5yBbucs1TYM2Bk4gRhFfcM07RfEGERfBmY1c54GHA2cBx9uDif/E4Z2gPtXxXBGIiJtgTr+SM0ws17EYOyfJU7DDwNnuPvcKselnaS92o7o0XMPcU/yDYktdDhwIXAzMK9ps+oAdAUWbQ5+GTEi7BjgB8SQSNJaqeOPSBkqyZSa4e5zgOOrHYcIEFnh14kbk18H7ApcQAxYeT5wPU1uzNEB+Bpwdke48Ltw60Hg3wXuQgNfikibpZJMqbpyg7Bnqj0Yu0oy26lDgNHEnX2eBe4gevUcA7xC03uPG/ToCy/Mga2XwKvdYM/usPidZsxDaplKMkXKUMcfqQXjgQnp74jc4+xPZN1an7izzxNAP+B+ItE8iri3Y1OTw+7AabDgUbjkc/AG8ONFsGRmM+YhItJKqSRTaoqZPefue1Q7jjyVZLZDZwEXAX8l7vBzI3AuUZLZFBsSl0tnAB8AF0KHcdBleTzVBtWmqCRTpAy1yZRao/OvVNcWwGlEorgncBzwELC08Y/1AD7sCB/uCpyZPns9cCcwL4Y20oDqItKeKMkUEUn2Bb64OVy1HkyfTQxPNIa6S59Ngb5rfm7vt2Hke/Dc1nDqLbDwMeAgmtzzXESkLVJ1uVSdmS2k7jTehbr+tga4u29UlcCyIFRd3i70BsYBWwN/PgyG3QirSocW6kncW7zEb74Op9wZnc0/1QeenIHK5NsPVZeLlKGSTKk6d+9e7RhElhAdc/oA/xoPfg1xmZP3b6IDUIkr5sZ47eOB8dNbNk4RkdZCJZkiFagks/3YHBhM3C2yqX18MusTJZm6D2q7o5JMkTJUkikikswmbjO1NnQ7SBGR+jROpoiIiIgUTkmmiIiIiBROSaaIiIiIFE5JpoiIiIgUTkmmiIiIiBROSaaIiIiIFE5JpoiIiIgUTkmmiIiIiBROSaa0ambWz8weN7NXzOxlMzsjTe9pZo+Y2Wvp/yZpupnZtWb2upm9aGZ7VvcXiIiItE1KMqW1WwGc5e6DgH2A081sEHAuMMbdBwJj0nOAocDA9HcqMGLdhywiItL2KcmUVs3dZ7r7P9PjhcAkoA8wDLglve0W4Mj0eBgw0sNYoIeZbbmOwxYREWnzlGRKm2FmA4A9gHFAb3efmV56B+idHvcBpuY+Ni1NExERkQJ1qnYAIkUws27AH4Ez3X2Bma1+zd3dzLyZ8zuVqE4XERGRtaCSTGn1zGw9IsH8vbvfnSbPyqrB0//Zafp0oF/u433TtHrc/QZ338vd92q5yEVERNouJZnSqlkUWd4ETHL3K3Mv3QeclB6fBNybm35i6mW+DzA/V60uIiIiBTH3ZtUiitQUMzsA+DswEViVJp9PtMu8E9gamAIMd/d5KSm9DhgCLAZOdvfxFb5DO4mIlDNBNR4iDVOSKVKBkkwRaYSSTJEyVF0uIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihVOSKSIiIiKFU5IpIiIiIoVTkikiIiIihetU7QBEWoFFwKvVDqKCXsCcagfRBK0hTsVYjPYSY/8iAhFpi5RkilT2qrvvVe0gGmNm42s9RmgdcSrGYihGEVF1uYiIiIgUTkmmiIiIiBROSaZIZTdUO4AmaA0xQuuIUzEWQzGKtHPm7tWOQURERETaGJVkioiIiEjhlGSKiIiISOGUZIo0wsyGmNmrZva6mZ1bxThuNrPZZvZSblpPM3vEzF5L/zdJ083Mrk0xv2hme66jGPuZ2eNm9oqZvWxmZ9RanGa2gZk9a2YvpBgvTNO3MbNxKZY/mNn6aXrn9Pz19PqAlo4xF2tHM3vOzO6v4Rgnm9lEM3vezManaTWzvtP39jCz0Wb2LzObZGb71lqMIm2VkkyRMsysI/BLYCgwCPiSmQ2qUji/A4aUTDsXGOPuA4Ex6TlEvAPT36nAiHUU4wrgLHcfBOwDnJ6WVy3FuRT4tLvvDgwGhpjZPsDPgKvcfXvgPeCU9P5TgPfS9KvS+9aVM4BJuee1GCPAp9x9cG68yVpa3wDXAH9x9x2B3YllWmsxirRJSjJFytsbeN3d33T3ZcAdwLBqBOLufwPmlUweBtySHt8CHJmbPtLDWKCHmW25DmKc6e7/TI8XEifzPrUUZ/quRenpeunPgU8Do8vEmMU+GviMmVlLxghgZn2Bw4Ab03OrtRgbUTPr28w2Bj4B3ATg7svc/f1ailGkLVOSKVJeH2Bq7vm0NK1W9Hb3menxO0Dv9Ljqcacq2z2AcdRYnKka+nlgNvAI8AbwvruvaCCO1TGm1+cDm7Z0jMDVwDnAqvR80xqMESJBf9jMJpjZqWlaLa3vbYB3gd+mpgc3mlnXGotRpM1SkinSBniMRVYT45GZWTfgj8CZ7r4g/1otxOnuK919MNCXKK3esZrxlDKzw4HZ7j6h2rE0wQHuvidRzXy6mX0i/2INrO9OwJ7ACHffA/iAuqpxoCZiFGmzlGSKlDcd6Jd73jdNqxWzsqq89H92ml61uM1sPSLB/L27312rcQKkatPHgX2JatFODcSxOsb0+sbA3BYObX/gCDObTDTR+DTRrrCWYgTA3aen/7OBe4ikvZbW9zRgmruPS89HE0lnLcUo0mYpyRQp7x/AwNSrd33gWOC+KseUdx9wUnp8EnBvbvqJqafsPsD8XNVgi0ntAG8CJrn7lbUYp5ltZmY90uMNgf8i2o4+DhxVJsYs9qOAx7yF72Dh7ue5e193H0Bsc4+5+/G1FCOAmXU1s+7ZY+Bg4CVqaH27+zvAVDPbIU36DPBKLcUo0pbpjj8ijTCzQ4n2cR2Bm939J1WKYxRwENALmAVcAPwJuBPYGpgCDHf3eSnZu47ojb4YONndx6+DGA8A/g5MpK4t4flEu8yaiNPMdiM6enQkLrLvdPeLzGxbotSwJ/AccIK7LzWzDYBbifal84Bj3f3NloyxJN6DgLPd/fBaizHFc0962gm43d1/YmabUiPrO8U5mOhAtT7wJnAyad3XSowibZWSTBEREREpnKrLRURERKRwSjJFREREpHBKMkVERESkcEoyRURERKRwSjJFREREpHBKMkWkXTKzq8zszNzzh8zsxtzzK8zsh2Z2bpnPL0r/B5jZcbnp/21m17Vk7CIirYGSTBFpr54C9gMwsw7EGKQ7517fD3jY3S+tMJ8BwHEV3iMi0u4oyRSR9upp4paSEMnlS8BCM9vEzDoDOwG7ZaWS6c5Pz5jZRDP7cW4+lwIHmtnzZvadNG0rM/uLmb1mZpeto98jIlJTlGSKSLvk7jOAFWa2NVFq+Qxxd6J9gb2IOxcty33kGmCEu+8K5G81eC7wd3cf7O5XpWmDgWOAXYFjzCx/P2wRkXZBSaaItGdPEwlmlmQ+k3v+VMl79wdGpce3VpjvGHef7+4fEvfK7l9YxCIirYSSTBFpz7J2mbsS1eVjiZLM/YgEtFRT78O7NPd4JXFvbxGRdkVJpoi0Z08DhwPz3H2lu88DehCJZmmS+RRwbHp8fG76QqB7SwcqItLaKMkUkfZsItGrfGzJtPnuPqfkvWcAp5vZRKBPbvqLwEozeyHX8UdEpN0z96bW/oiIiIiINI1KMkVERESkcEoyRURERKRwSjJFREREpHBKMkVERESkcEoyRURERKRwSjJFREREpHBKMkVERESkcP8HsQPDq5aVa9YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yb71YqF-_TTl"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpFm4Hx_UyzY"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import cv2 as cv\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers import Dense, Flatten, Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyG8P9vx_WdG",
        "outputId": "aaa33345-ed5c-4606-b0c0-f017521021e6"
      },
      "source": [
        "# Creating training set and labels\n",
        "X = []\n",
        "y = []\n",
        "\n",
        "print(new_paths[0])\n",
        "for path in new_paths[:]:\n",
        "  img = cv2.imread(path)\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "  img = cv2.resize(img, (128,128))\n",
        "  X.append(img)\n",
        "\n",
        "  # image 경로에서 label 추출\n",
        "  category = path.split('/')[9] # 9번째가 label! (07_ok)\n",
        "  label = int(category.split('_')[0][1])\n",
        "  y.append(label)\n",
        "\n",
        "# X, y를 numpy array로 바꿈\n",
        "X = np.array(X)\n",
        "X = X.reshape(len(new_paths[:]), 128, 128, 1)\n",
        "y = np.array(y)\n",
        "\n",
        "print(\"Images loaded: \", len(X))\n",
        "print(\"Labels loaded: \", len(y))\n",
        "\n",
        "print(y[0], new_paths[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/학교 수업 자료/딥러닝/Project/static_skeleton_data/05/07_ok/frame_05_07_0010.png\n",
            "Images loaded:  13235\n",
            "Labels loaded:  13235\n",
            "7 /content/gdrive/MyDrive/학교 수업 자료/딥러닝/Project/static_skeleton_data/05/07_ok/frame_05_07_0010.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FirA36YhJW_8",
        "outputId": "9c446b24-3062-480f-ca2c-b7678a4849aa"
      },
      "source": [
        "# Test train split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=10)\n",
        "\n",
        "# CNN Sequential Model\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (5,5), activation='relu', input_shape=(128,128,1)))\n",
        "model.add(MaxPooling2D((2,2)))\n",
        "model.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model.add(MaxPooling2D((2,2)))\n",
        "model.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model.add(MaxPooling2D((2,2)))\n",
        "model.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model.add(MaxPooling2D((2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 124, 124, 32)      832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 60, 60, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 12, 12, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               589952    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 832,010\n",
            "Trainable params: 832,010\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ftvl-hb6KPFu",
        "outputId": "4b8eab19-6a20-415d-8c06-730873e2770f"
      },
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Training\n",
        "model.fit(X_train, y_train, epochs=15, batch_size=64, verbose=2, validation_data=(X_val, y_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "166/166 - 300s - loss: 0.1839 - accuracy: 0.9444 - val_loss: 0.1953 - val_accuracy: 0.9350\n",
            "Epoch 2/15\n",
            "166/166 - 299s - loss: 0.0953 - accuracy: 0.9717 - val_loss: 0.1389 - val_accuracy: 0.9637\n",
            "Epoch 3/15\n",
            "166/166 - 299s - loss: 0.0683 - accuracy: 0.9793 - val_loss: 0.0801 - val_accuracy: 0.9751\n",
            "Epoch 4/15\n",
            "166/166 - 300s - loss: 0.0432 - accuracy: 0.9853 - val_loss: 0.0639 - val_accuracy: 0.9815\n",
            "Epoch 5/15\n",
            "166/166 - 299s - loss: 0.0347 - accuracy: 0.9886 - val_loss: 0.1292 - val_accuracy: 0.9675\n",
            "Epoch 6/15\n",
            "166/166 - 299s - loss: 0.0375 - accuracy: 0.9879 - val_loss: 0.0777 - val_accuracy: 0.9792\n",
            "Epoch 7/15\n",
            "166/166 - 299s - loss: 0.0223 - accuracy: 0.9929 - val_loss: 0.0907 - val_accuracy: 0.9792\n",
            "Epoch 8/15\n",
            "166/166 - 299s - loss: 0.0367 - accuracy: 0.9887 - val_loss: 0.0905 - val_accuracy: 0.9754\n",
            "Epoch 9/15\n",
            "166/166 - 298s - loss: 0.0213 - accuracy: 0.9930 - val_loss: 0.0857 - val_accuracy: 0.9781\n",
            "Epoch 10/15\n",
            "166/166 - 298s - loss: 0.0163 - accuracy: 0.9947 - val_loss: 0.1221 - val_accuracy: 0.9788\n",
            "Epoch 11/15\n",
            "166/166 - 298s - loss: 0.0256 - accuracy: 0.9918 - val_loss: 0.1034 - val_accuracy: 0.9777\n",
            "Epoch 12/15\n",
            "166/166 - 299s - loss: 0.0272 - accuracy: 0.9905 - val_loss: 0.1213 - val_accuracy: 0.9785\n",
            "Epoch 13/15\n",
            "166/166 - 298s - loss: 0.0321 - accuracy: 0.9902 - val_loss: 0.0873 - val_accuracy: 0.9830\n",
            "Epoch 14/15\n",
            "166/166 - 298s - loss: 0.0151 - accuracy: 0.9945 - val_loss: 0.1088 - val_accuracy: 0.9822\n",
            "Epoch 15/15\n",
            "166/166 - 298s - loss: 0.0192 - accuracy: 0.9937 - val_loss: 0.0779 - val_accuracy: 0.9830\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7be7a621d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmIuN1Bcew5-"
      },
      "source": [
        "* result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "RvPc83XJew6K",
        "outputId": "092fbb17-c69f-4d0a-d044-1cf029f86636"
      },
      "source": [
        "# plotting\n",
        "loss = model.history.history['loss']\n",
        "val_loss = model.history.history['val_loss']\n",
        "acc = model.history.history['accuracy']\n",
        "val_acc = model.history.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-nY__ARZKvOB"
      },
      "source": [
        "# save\n",
        "model.save('/content/gdrive/MyDrive/trained_kaggle/trained_kaggledata.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlkQgGLFK5k4"
      },
      "source": [
        "## Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ml_vi6BiLHkA"
      },
      "source": [
        "* 직접 만든 데이터로 test 진행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DGahomRsLLuO",
        "outputId": "08d53a65-8ed5-462f-a0fe-3d836e89fb2c"
      },
      "source": [
        "# test data\n",
        "X_test = []\n",
        "y_test = []\n",
        "\n",
        "test_paths = []\n",
        "\n",
        "for dirname, _, filenames in os.walk('/content/gdrive/MyDrive/10'):\n",
        "  for filename in filenames:\n",
        "    path = os.path.join(dirname, filename)\n",
        "    if path.endswith(\"png\"):\n",
        "      test_paths.append(path)\n",
        "\n",
        "print(len(test_paths))\n",
        "print(test_paths[len(test_paths)-1])\n",
        "\n",
        "for path in test_paths[:]:\n",
        "  # image data\n",
        "  img = cv.imread(path)\n",
        "  img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
        "  img = cv.resize(img, (128,128))\n",
        "  X_test.append(img)\n",
        "  if len(X_test) % 100 == 0 or len(X_test) == len(test_paths):\n",
        "    print('number of X test data : ', len(X_test))\n",
        "\n",
        "  # labels\n",
        "  category = path.split('/')[5]\n",
        "  label = int(category.split('_')[0][1])\n",
        "  y_test.append(label)\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "X_test = X_test.reshape(len(test_paths), 128, 128, 1)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "print('test 이미지 개수 : ', len(X_test))\n",
        "print('test 라벨 개수 : ', len(y_test))\n",
        "print(y_test[0], test_paths[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2347\n",
            "/content/gdrive/MyDrive/10/09_c/frame_00_09_0290.png\n",
            "number of X test data :  100\n",
            "number of X test data :  200\n",
            "number of X test data :  300\n",
            "number of X test data :  400\n",
            "number of X test data :  500\n",
            "number of X test data :  600\n",
            "number of X test data :  700\n",
            "number of X test data :  800\n",
            "number of X test data :  900\n",
            "number of X test data :  1000\n",
            "number of X test data :  1100\n",
            "number of X test data :  1200\n",
            "number of X test data :  1300\n",
            "number of X test data :  1400\n",
            "number of X test data :  1500\n",
            "number of X test data :  1600\n",
            "number of X test data :  1700\n",
            "number of X test data :  1800\n",
            "number of X test data :  1900\n",
            "number of X test data :  2000\n",
            "number of X test data :  2100\n",
            "number of X test data :  2200\n",
            "number of X test data :  2300\n",
            "number of X test data :  2347\n",
            "test 이미지 개수 :  2347\n",
            "test 라벨 개수 :  2347\n",
            "1 /content/gdrive/MyDrive/10/01_palm/frame_00_01_0002.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "idSA_uOgWItt",
        "outputId": "ea11eeae-d15c-44ff-f672-424008a0de04"
      },
      "source": [
        "# loss and accuracy on test \n",
        "\n",
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "\n",
        "print('Test accuracy : {:2.4f}%'.format(test_acc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "74/74 [==============================] - 16s 221ms/step - loss: 13.8318 - accuracy: 0.2318\n",
            "Test accuracy : 23.1785%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n5tuU2PlYyLC"
      },
      "source": [
        "# 2. Webcam을 통해 직접 만든 data로 training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0WFC6TQZH2h"
      },
      "source": [
        "## Data upload"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aamHsC8-aCtc"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2 as cv\n",
        "import pandas as pd\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers import Dense, Flatten, Dropout\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WuuUOiUQZ1Ez"
      },
      "source": [
        "* mount drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jWcWN9w6ZrT8",
        "outputId": "0e8824b4-c761-44e5-f53d-a625392a3233"
      },
      "source": [
        "# mount Google Drive\n",
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s9jprgj8Z3Dk"
      },
      "source": [
        "* 이미지의 경로를 skeletonpaths list에 저장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mdQP8dYrZNRg",
        "outputId": "cae00c57-f3bc-414e-ccee-c6a605540fed"
      },
      "source": [
        "dir_path = '/content/gdrive/MyDrive/static_skeleton_data'\n",
        "skeletonpaths = []\n",
        "\n",
        "for dirname, _, filenames in os.walk(dir_path):\n",
        "  for filename in filenames:\n",
        "    path = os.path.join(dirname, filename)\n",
        "    \n",
        "    if path.endswith(\"png\"):\n",
        "      skeletonpaths.append(path)\n",
        "\n",
        "print(len(skeletonpaths))\n",
        "print(skeletonpaths[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7838\n",
            "/content/gdrive/MyDrive/static_skeleton_data/12/01_palm/frame_00_12_0001.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9U4iB8k9BbY"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHRPAiX_eqLu"
      },
      "source": [
        "* X, y 변수 생성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jExavc8kHnq",
        "outputId": "8beb121d-62c6-4401-9aad-2f484b111f75"
      },
      "source": [
        "# Creating trainig set and labels\n",
        "X_made = []\n",
        "y_made = []\n",
        "\n",
        "for path in skeletonpaths[:len(skeletonpaths)]:\n",
        "  # image data\n",
        "  img = cv.imread(path)\n",
        "  img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
        "  img = cv.resize(img, (128, 128))\n",
        "  X_made.append(img)\n",
        "  if len(X_made) % 100 == 0 or len(X_made) == len(skeletonpaths):\n",
        "    print(len(X_made))\n",
        "\n",
        "  # labels\n",
        "  category = path.split(\"/\")[6] # path에서 6번째가 label\n",
        "  label = int(category.split(\"_\")[0][1])\n",
        "  y_made.append(label)\n",
        "\n",
        "X_made = np.array(X_made)\n",
        "X_made = X_made.reshape(len(skeletonpaths), 128, 128, 1)\n",
        "y_made = np.array(y_made)\n",
        "\n",
        "print(\"이미지 개수 : \", len(X_made))\n",
        "print(\"라벨 개수 : \", len(y_made))\n",
        "print(y_made[0], skeletonpaths[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n",
            "200\n",
            "300\n",
            "400\n",
            "500\n",
            "600\n",
            "700\n",
            "800\n",
            "900\n",
            "1000\n",
            "1100\n",
            "1200\n",
            "1300\n",
            "1400\n",
            "1500\n",
            "1600\n",
            "1700\n",
            "1800\n",
            "1900\n",
            "2000\n",
            "2100\n",
            "2200\n",
            "2300\n",
            "2400\n",
            "2500\n",
            "2600\n",
            "2700\n",
            "2800\n",
            "2900\n",
            "3000\n",
            "3100\n",
            "3200\n",
            "3300\n",
            "3400\n",
            "3500\n",
            "3600\n",
            "3700\n",
            "3800\n",
            "3900\n",
            "4000\n",
            "4100\n",
            "4200\n",
            "4300\n",
            "4400\n",
            "4500\n",
            "4600\n",
            "4700\n",
            "4800\n",
            "4900\n",
            "5000\n",
            "5100\n",
            "5200\n",
            "5300\n",
            "5400\n",
            "5500\n",
            "5600\n",
            "5700\n",
            "5800\n",
            "5900\n",
            "6000\n",
            "6100\n",
            "6200\n",
            "6300\n",
            "6400\n",
            "6500\n",
            "6600\n",
            "6700\n",
            "6800\n",
            "6900\n",
            "7000\n",
            "7100\n",
            "7200\n",
            "7300\n",
            "7400\n",
            "7500\n",
            "7600\n",
            "7700\n",
            "7800\n",
            "7838\n",
            "이미지 개수 :  7838\n",
            "라벨 개수 :  7838\n",
            "1 /content/gdrive/MyDrive/static_skeleton_data/12/01_palm/frame_00_12_0001.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m21DS__GevRm"
      },
      "source": [
        "* 생성한 변수 (X, y) 저장"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6761KZsyrON-",
        "outputId": "b4c3575e-678d-4384-889b-bf90e5d407ee"
      },
      "source": [
        "# data 저장\n",
        "np.save('/content/gdrive/MyDrive/made/input_data_made.npy', X_made)\n",
        "np.save('/content/gdrive/MyDrive/made/label_data_made.npy', y_made)\n",
        "\n",
        "# 저장 잘 됐는지 확인\n",
        "X_made_ = np.load('/content/gdrive/MyDrive/made/input_data_made.npy')\n",
        "\n",
        "print(np.array_equal(X_made, X_made_)) # array가 같은지 확인"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mr7dIWxcAFP",
        "outputId": "aa112a41-60ba-4c60-9813-233dcb65c463"
      },
      "source": [
        "# 저장한 image data 불러오기\n",
        "X_made = []\n",
        "y_made = []\n",
        "X_made = np.load('/content/gdrive/MyDrive/made/input_data_made.npy')\n",
        "y_made = np.load('/content/gdrive/MyDrive/made/label_data_made.npy')\n",
        "\n",
        "print(len(X_made))\n",
        "print(len(y_made))\n",
        "\n",
        "print(X_made.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7838\n",
            "7838\n",
            "(7838, 128, 128, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1lmIRohe5te"
      },
      "source": [
        "### Training (1st try) : test_acc = 41.5850%"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a66EnNwzBiPK"
      },
      "source": [
        "# test train split\n",
        "X_train_made, X_val_made, y_train_made, y_val_made = train_test_split(X_made, \n",
        "                                                                      y_made, \n",
        "                                                                      test_size = 0.2, \n",
        "                                                                      random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7FCq6oq1uJm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbb9baaf-bb4d-4232-b769-79d30ea95cf8"
      },
      "source": [
        "# model\n",
        "model_made = Sequential()\n",
        "model_made.add(Conv2D(32, (5,5), activation='relu', input_shape=(128,128,1)))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Flatten())\n",
        "model_made.add(Dense(128, activation='relu'))\n",
        "model_made.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_made.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_4 (Conv2D)            (None, 124, 124, 32)      832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 60, 60, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 57600)             0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               7372928   \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 7,393,546\n",
            "Trainable params: 7,393,546\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kcDwTjh10nZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cac78802-7ae6-48b8-911c-a8ad1c7d35d2"
      },
      "source": [
        "model_made.compile(optimizer='rmsprop',\n",
        "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_made = model_made.fit(X_train_made, y_train_made, epochs=10, batch_size=64,\n",
        "                              verbose=2, validation_data=(X_val_made, y_val_made))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "98/98 - 4s - loss: 2.5224 - accuracy: 0.6100 - val_loss: 0.6523 - val_accuracy: 0.8061\n",
            "Epoch 2/10\n",
            "98/98 - 2s - loss: 0.2249 - accuracy: 0.9330 - val_loss: 0.4984 - val_accuracy: 0.8705\n",
            "Epoch 3/10\n",
            "98/98 - 2s - loss: 0.0381 - accuracy: 0.9893 - val_loss: 0.5972 - val_accuracy: 0.8890\n",
            "Epoch 4/10\n",
            "98/98 - 2s - loss: 0.0210 - accuracy: 0.9946 - val_loss: 0.8514 - val_accuracy: 0.8744\n",
            "Epoch 5/10\n",
            "98/98 - 2s - loss: 0.0117 - accuracy: 0.9971 - val_loss: 0.8238 - val_accuracy: 0.8986\n",
            "Epoch 6/10\n",
            "98/98 - 2s - loss: 0.0060 - accuracy: 0.9981 - val_loss: 0.9204 - val_accuracy: 0.8954\n",
            "Epoch 7/10\n",
            "98/98 - 2s - loss: 0.0037 - accuracy: 0.9990 - val_loss: 0.9916 - val_accuracy: 0.8858\n",
            "Epoch 8/10\n",
            "98/98 - 2s - loss: 0.0092 - accuracy: 0.9981 - val_loss: 0.9833 - val_accuracy: 0.8980\n",
            "Epoch 9/10\n",
            "98/98 - 2s - loss: 0.0064 - accuracy: 0.9994 - val_loss: 0.8968 - val_accuracy: 0.8960\n",
            "Epoch 10/10\n",
            "98/98 - 2s - loss: 3.7869e-04 - accuracy: 0.9997 - val_loss: 0.9616 - val_accuracy: 0.8954\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4y2NKQP15Uy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "26be5307-b27e-46cf-e178-7c4514720ec5"
      },
      "source": [
        "# plotting\n",
        "loss = history_made.history['loss']\n",
        "val_loss = history_made.history['val_loss']\n",
        "acc = history_made.history['accuracy']\n",
        "val_acc = history_made.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BPIdFXhpzEqn"
      },
      "source": [
        "* 직접 만든 데이터로 test 진행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e-mdPyCzEqq",
        "outputId": "a4c2d1c2-c70e-4aed-c44a-89ae3fa1b15f"
      },
      "source": [
        "# test data\n",
        "X_test = []\n",
        "y_test = []\n",
        "\n",
        "test_paths = []\n",
        "\n",
        "for dirname, _, filenames in os.walk('/content/gdrive/MyDrive/10'):\n",
        "  for filename in filenames:\n",
        "    path = os.path.join(dirname, filename)\n",
        "    if path.endswith(\"png\"):\n",
        "      test_paths.append(path)\n",
        "\n",
        "#print(len(test_paths))\n",
        "#print(test_paths[len(test_paths)-1])\n",
        "\n",
        "for path in test_paths[:]:\n",
        "  # image data\n",
        "  img = cv.imread(path)\n",
        "  img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
        "  img = cv.resize(img, (128,128))\n",
        "  X_test.append(img)\n",
        "  if len(X_test) % 100 == 0 or len(X_test) == len(test_paths):\n",
        "    print(len(X_test))\n",
        "\n",
        "  # labels\n",
        "  category = path.split('/')[5]\n",
        "  label = int(category.split('_')[0][1])\n",
        "  y_test.append(label)\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "X_test = X_test.reshape(len(test_paths), 128, 128, 1)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "print('test 이미지 개수 : ', len(X_test))\n",
        "print('test 라벨 개수 : ', len(y_test))\n",
        "print(y_test[0], test_paths[0])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n",
            "200\n",
            "300\n",
            "400\n",
            "500\n",
            "600\n",
            "700\n",
            "800\n",
            "900\n",
            "1000\n",
            "1100\n",
            "1200\n",
            "1300\n",
            "1400\n",
            "1500\n",
            "1600\n",
            "1700\n",
            "1800\n",
            "1900\n",
            "2000\n",
            "2100\n",
            "2200\n",
            "2300\n",
            "2347\n",
            "test 이미지 개수 :  2347\n",
            "test 라벨 개수 :  2347\n",
            "1 /content/gdrive/MyDrive/10/01_palm/frame_00_01_0002.png\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HL3urnC_3bTG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbe11c3e-abe0-4404-ef88-bb24b683f439"
      },
      "source": [
        "# loss and accuracy on test \n",
        "\n",
        "test_loss, test_acc = model_made.evaluate(X_test, y_test)\n",
        "\n",
        "print('Test accuracy : {:2.4f}%'.format(test_acc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "74/74 [==============================] - 0s 6ms/step - loss: 6.9861 - accuracy: 0.4159\n",
            "Test accuracy : 41.5850%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8SBhxORuccF"
      },
      "source": [
        "### confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DTq6Ms0tUnQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb9f326d-710f-4ae0-d64a-352f5a69972e"
      },
      "source": [
        "pred2 = model_made.predict_generator(X_test)\n",
        "y_pred2 = np.argmax(pred2, axis=-1) # 모델이 예측한 class label"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1976: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0dM1sQQ83sKt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "outputId": "cc8c9558-4b86-4aa0-b7f7-803957b1e6e6"
      },
      "source": [
        "labels = ['palm', 'l', 'fist', 'fist_moved', 'thumb', 'index', 'ok', 'palm_moved', 'c', 'down']\n",
        "\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "cm = confusion_matrix(y_test, y_pred2)\n",
        "plt.figure(figsize=(15, 15))\n",
        "ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_title(\"model1_madeData\", fontsize=30)\n",
        "ax.set_ylabel('Actual', fontsize=25)\n",
        "ax.set_xlabel('Predicted', fontsize=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 185.99374999999995, 'Predicted')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ng8YzWij0XLb"
      },
      "source": [
        "### Training (2nd try) : test_acc = 74.7763%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVLiYZ_p2Mgw"
      },
      "source": [
        "* with a more Conv2D (128 filters)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Yza-MLSxgUs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b54b4040-e973-463d-b66c-3050086aba73"
      },
      "source": [
        "# model\n",
        "model_made = Sequential()\n",
        "model_made.add(Conv2D(32, (5,5), activation='relu', input_shape=(128,128,1)))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Flatten())\n",
        "model_made.add(Dense(128, activation='relu'))\n",
        "model_made.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_made.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 124, 124, 32)      832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 60, 60, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 12, 12, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               589952    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 832,010\n",
            "Trainable params: 832,010\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zcO1wnFyZUm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a2702dc-4510-4711-e1fd-f3bf68eec897"
      },
      "source": [
        "model_made.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_made = model_made.fit(X_train_made, y_train_made, epochs=30, batch_size=64,\n",
        "                              verbose=2, validation_data=(X_val_made, y_val_made))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "98/98 - 47s - loss: 1.7350 - accuracy: 0.4810 - val_loss: 0.6188 - val_accuracy: 0.8023\n",
            "Epoch 2/30\n",
            "98/98 - 3s - loss: 0.3366 - accuracy: 0.8904 - val_loss: 0.2159 - val_accuracy: 0.9324\n",
            "Epoch 3/30\n",
            "98/98 - 3s - loss: 0.1039 - accuracy: 0.9671 - val_loss: 0.1548 - val_accuracy: 0.9592\n",
            "Epoch 4/30\n",
            "98/98 - 3s - loss: 0.0580 - accuracy: 0.9828 - val_loss: 0.1276 - val_accuracy: 0.9681\n",
            "Epoch 5/30\n",
            "98/98 - 3s - loss: 0.0433 - accuracy: 0.9856 - val_loss: 0.1629 - val_accuracy: 0.9598\n",
            "Epoch 6/30\n",
            "98/98 - 3s - loss: 0.0409 - accuracy: 0.9872 - val_loss: 0.1127 - val_accuracy: 0.9681\n",
            "Epoch 7/30\n",
            "98/98 - 3s - loss: 0.0142 - accuracy: 0.9959 - val_loss: 0.1617 - val_accuracy: 0.9700\n",
            "Epoch 8/30\n",
            "98/98 - 3s - loss: 0.0299 - accuracy: 0.9906 - val_loss: 0.1649 - val_accuracy: 0.9624\n",
            "Epoch 9/30\n",
            "98/98 - 3s - loss: 0.0164 - accuracy: 0.9951 - val_loss: 0.1012 - val_accuracy: 0.9802\n",
            "Epoch 10/30\n",
            "98/98 - 3s - loss: 0.0085 - accuracy: 0.9978 - val_loss: 0.1093 - val_accuracy: 0.9770\n",
            "Epoch 11/30\n",
            "98/98 - 3s - loss: 0.0105 - accuracy: 0.9965 - val_loss: 0.1373 - val_accuracy: 0.9739\n",
            "Epoch 12/30\n",
            "98/98 - 3s - loss: 0.0104 - accuracy: 0.9976 - val_loss: 0.1149 - val_accuracy: 0.9758\n",
            "Epoch 13/30\n",
            "98/98 - 3s - loss: 0.0115 - accuracy: 0.9971 - val_loss: 0.1059 - val_accuracy: 0.9790\n",
            "Epoch 14/30\n",
            "98/98 - 3s - loss: 0.0188 - accuracy: 0.9936 - val_loss: 0.0935 - val_accuracy: 0.9809\n",
            "Epoch 15/30\n",
            "98/98 - 3s - loss: 0.0054 - accuracy: 0.9990 - val_loss: 0.0773 - val_accuracy: 0.9828\n",
            "Epoch 16/30\n",
            "98/98 - 3s - loss: 3.5842e-04 - accuracy: 1.0000 - val_loss: 0.0764 - val_accuracy: 0.9872\n",
            "Epoch 17/30\n",
            "98/98 - 3s - loss: 8.1602e-05 - accuracy: 1.0000 - val_loss: 0.0738 - val_accuracy: 0.9872\n",
            "Epoch 18/30\n",
            "98/98 - 3s - loss: 5.0142e-05 - accuracy: 1.0000 - val_loss: 0.0747 - val_accuracy: 0.9872\n",
            "Epoch 19/30\n",
            "98/98 - 3s - loss: 3.8814e-05 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 0.9872\n",
            "Epoch 20/30\n",
            "98/98 - 3s - loss: 3.1588e-05 - accuracy: 1.0000 - val_loss: 0.0766 - val_accuracy: 0.9872\n",
            "Epoch 21/30\n",
            "98/98 - 3s - loss: 2.6468e-05 - accuracy: 1.0000 - val_loss: 0.0774 - val_accuracy: 0.9879\n",
            "Epoch 22/30\n",
            "98/98 - 3s - loss: 2.2516e-05 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 0.9879\n",
            "Epoch 23/30\n",
            "98/98 - 3s - loss: 1.9597e-05 - accuracy: 1.0000 - val_loss: 0.0789 - val_accuracy: 0.9879\n",
            "Epoch 24/30\n",
            "98/98 - 3s - loss: 1.7021e-05 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9872\n",
            "Epoch 25/30\n",
            "98/98 - 3s - loss: 1.5042e-05 - accuracy: 1.0000 - val_loss: 0.0802 - val_accuracy: 0.9872\n",
            "Epoch 26/30\n",
            "98/98 - 3s - loss: 1.3397e-05 - accuracy: 1.0000 - val_loss: 0.0809 - val_accuracy: 0.9872\n",
            "Epoch 27/30\n",
            "98/98 - 3s - loss: 1.1996e-05 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 0.9872\n",
            "Epoch 28/30\n",
            "98/98 - 3s - loss: 1.0807e-05 - accuracy: 1.0000 - val_loss: 0.0820 - val_accuracy: 0.9872\n",
            "Epoch 29/30\n",
            "98/98 - 3s - loss: 9.7607e-06 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 0.9872\n",
            "Epoch 30/30\n",
            "98/98 - 3s - loss: 8.8367e-06 - accuracy: 1.0000 - val_loss: 0.0830 - val_accuracy: 0.9872\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ZdquM2j0w1r"
      },
      "source": [
        "* result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BOutN8rNyZ9W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "c27deb1d-dce7-4b6f-a56b-f65eef0c7fc3"
      },
      "source": [
        "# plotting\n",
        "loss = history_made.history['loss']\n",
        "val_loss = history_made.history['val_loss']\n",
        "acc = history_made.history['accuracy']\n",
        "val_acc = history_made.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deZwV1Z3//9ebZguCCoIbW6NRjMreQKTVYBaD0RHXKGFUwkQCo3GbUUmYBGLCPPKNzvwcf1ETNGoWFP2ahMFRo3FBVNxAEUFRESFiVBCiwADK8vn+UdV4ae7t/fbtvv1+Ph71uFWnqk6duhfq0+ecqlOKCMzMzCprVegCmJlZ0+QAYWZmWTlAmJlZVg4QZmaWlQOEmZll5QBhZmZZOUBYo5D0oKQLGnrbQpK0UtJX85BvSPp8Ov9LST+sybZ1OM5YSQ/XtZxV5DtS0uqGztcaX+tCF8CaLkmbMhY7AJ8AO9Ll70bEzJrmFREn5WPbYhcRExsiH0mlwNtAm4jYnuY9E6jxb2gtjwOE5RQRHSvmJa0EvhMRj1TeTlLriouOmRUPNzFZrVU0IUi6WtL7wO2SOkv6H0lrJf09ne+Rsc9cSd9J58dJekrSdem2b0s6qY7b9pE0T9JGSY9IulHS73OUuyZl/Imkp9P8HpbUNWP9eZJWSVonaUoV389wSe9LKslIO13S4nR+mKRnJH0k6T1Jv5DUNkded0j6acbylek+f5M0vtK2J0t6SdIGSe9Impaxel76+ZGkTZKOqfhuM/YfIekFSR+nnyNq+t1URdIX0v0/krRU0qkZ674h6dU0z3cl/Wua3jX9fT6StF7Sk5J8vWpk/sKtrg4EugC9gQkk/5ZuT5d7AVuAX1Sx/3DgdaAr8HPg15JUh23vBJ4H9gOmAedVccyalPFbwLeB/YG2QMUF60jg5jT/g9Pj9SCLiHgO+F/gy5XyvTOd3wFcnp7PMcBXgH+uotykZRiVludrwGFA5f6P/wXOB/YFTgYmSTotXXd8+rlvRHSMiGcq5d0FuB+4IT23/wTul7RfpXPY47uppsxtgPuAh9P9vgfMlNQ33eTXJM2VnYCjgcfS9H8BVgPdgAOAHwAeF6iROUBYXe0EpkbEJxGxJSLWRcQfImJzRGwEpgNfqmL/VRFxS0TsAH4DHERyIajxtpJ6AUOBH0XEpxHxFDAn1wFrWMbbI+KNiNgC3AMMTNPPAv4nIuZFxCfAD9PvIJe7gDEAkjoB30jTiIiFEfFsRGyPiJXAr7KUI5tvpuVbEhH/SxIQM89vbkS8EhE7I2Jxerya5AtJQHkzIn6XlusuYBnwDxnb5PpuqvJFoCPws/Q3egz4H9LvBtgGHClp74j4e0S8mJF+ENA7IrZFxJPhgeManQOE1dXaiNhasSCpg6RfpU0wG0iaNPbNbGap5P2KmYjYnM52rOW2BwPrM9IA3slV4BqW8f2M+c0ZZTo4M+/0Ar0u17FIagtnSGoHnAG8GBGr0nIcnjafvJ+W499JahPV2a0MwKpK5zdc0uNpE9rHwMQa5luR96pKaauA7hnLub6basscEZnBNDPfM0mC5ypJT0g6Jk2/FlgOPCxphaTJNTsNa0gOEFZXlf+a+xegLzA8IvbmsyaNXM1GDeE9oIukDhlpPavYvj5lfC8z7/SY++XaOCJeJbkQnsTuzUuQNFUtAw5Ly/GDupSBpJks050kNaieEbEP8MuMfKv76/tvJE1vmXoB79agXNXl27NS/8GufCPihYgYTdL8NJukZkJEbIyIf4mIQ4BTgSskfaWeZbFacoCwhtKJpE3/o7Q9e2q+D5j+Rb4AmCapbfrX5z9UsUt9yngvcIqkY9MO5Wuo/v/PncClJIHo/1YqxwZgk6QjgEk1LMM9wDhJR6YBqnL5O5HUqLZKGkYSmCqsJWkSOyRH3g8Ah0v6lqTWks4BjiRpDqqP50hqG1dJaiNpJMlvNCv9zcZK2icitpF8JzsBJJ0i6fNpX9PHJP02VTXpWR44QFhDuR74HPAh8Czw50Y67liSjt51wE+Bu0me18imzmWMiKXARSQX/feAv5N0olalog/gsYj4MCP9X0ku3huBW9Iy16QMD6bn8BhJ88tjlTb5Z+AaSRuBH5H+NZ7uu5mkz+Xp9M6gL1bKex1wCkktax1wFXBKpXLXWkR8ShIQTiL53m8Czo+IZekm5wEr06a2iSS/JySd8I8Am4BngJsi4vH6lMVqT+73sWIi6W5gWUTkvQZjVuxcg7BmTdJQSYdKapXeBjqapC3bzOrJT1Jbc3cg8EeSDuPVwKSIeKmwRTIrDm5iMjOzrNzEZGZmWRVVE1PXrl2jtLS00MUwM2s2Fi5c+GFEdMu2rqgCRGlpKQsWLCh0MczMmg1JlZ+g38VNTGZmlpUDhJmZZeUAYWZmWeWtD0LSbSSP7q+JiKOzrL+Szx6rbw18AegWEeuVvL1sI8n4K9sjoixf5TSzutu2bRurV69m69at1W9sBdW+fXt69OhBmzZtarxPPjup7yB5Gctvs62MiGtJhvRF0j8Al0fE+oxNTqjvODBmll+rV6+mU6dOlJaWkvt9T1ZoEcG6detYvXo1ffr0qfF+eWtiioh5wPpqN0yMIX2ZSmObORNKS6FVq+Rzpl/hblZjW7duZb/99nNwaOIksd9++9W6plfwPoh02OJRwB8ykoPkRSELJU3I17FnzoQJE2DVKohIPidMcJAwqw0Hh+ahLr9TwQMEyVDAT1dqXjo2IgaTDBF8kaTjs+8KkiZIWiBpwdq1a2t14ClTYPPm3dM2b07SzcxauqYQIM6lUvNSRFS8bWoN8CdgWK6dI2JGRJRFRFm3blkfBszpr3+tXbqZNS3r1q1j4MCBDBw4kAMPPJDu3bvvWv7000+r3HfBggVccskl1R5jxIgRDVLWuXPncsoppzRIXo2loAFC0j4kL1T574y0vdKXvCNpL+BEYEk+jt+r8gsbq0k3s/pp6D6//fbbj0WLFrFo0SImTpzI5Zdfvmu5bdu2bN++Pee+ZWVl3HDDDdUeY/78+fUrZDOWtwAh6S6SN0H1lbRa0j9JmihpYsZmpwMPpy+Ar3AA8JSkl4HngfsjIi9vJ5s+HTp02D2tQ4ck3cwaVmP1+Y0bN46JEycyfPhwrrrqKp5//nmOOeYYBg0axIgRI3j99deB3f+inzZtGuPHj2fkyJEccsghuwWOjh077tp+5MiRnHXWWRxxxBGMHTuWitGwH3jgAY444giGDBnCJZdcUm1NYf369Zx22mn079+fL37xiyxevBiAJ554YlcNaNCgQWzcuJH33nuP448/noEDB3L00Ufz5JNPNuwXVoW83eYaEWNqsM0dJLfDZqatAAbkp1S7G5s+hTFlStKs1KtXEhwq0s2s4VTV59fQ/+dWr17N/PnzKSkpYcOGDTz55JO0bt2aRx55hB/84Af84Q9/2GOfZcuW8fjjj7Nx40b69u3LpEmT9nhm4KWXXmLp0qUcfPDBlJeX8/TTT1NWVsZ3v/td5s2bR58+fRgzptpLH1OnTmXQoEHMnj2bxx57jPPPP59FixZx3XXXceONN1JeXs6mTZto3749M2bM4Otf/zpTpkxhx44dbK78JeZRUQ3WVxdjxzogmDWGxuzzO/vssykpKQHg448/5oILLuDNN99EEtu2bcu6z8knn0y7du1o164d+++/Px988AE9evTYbZthw4btShs4cCArV66kY8eOHHLIIbueLxgzZgwzZsyosnxPPfXUriD15S9/mXXr1rFhwwbKy8u54oorGDt2LGeccQY9evRg6NChjB8/nm3btnHaaacxcODAen03tdEUOqnNrAVozD6/vfbaa9f8D3/4Q0444QSWLFnCfffdl/NZgHbt2u2aLykpydp/UZNt6mPy5MnceuutbNmyhfLycpYtW8bxxx/PvHnz6N69O+PGjeO3v8367HFeOECYWaMoVJ/fxx9/TPfu3QG44447Gjz/vn37smLFClauXAnA3XffXe0+xx13HDPTzpe5c+fStWtX9t57b9566y369evH1VdfzdChQ1m2bBmrVq3igAMO4MILL+Q73/kOL774YoOfQy4OEGbWKMaOhRkzoHdvkJLPGTPy38R71VVX8f3vf59BgwY1+F/8AJ/73Oe46aabGDVqFEOGDKFTp07ss88+Ve4zbdo0Fi5cSP/+/Zk8eTK/+c1vALj++us5+uij6d+/P23atOGkk05i7ty5DBgwgEGDBnH33Xdz6aWXNvg55FJU76QuKysLvzDIrPG89tprfOELXyh0MQpu06ZNdOzYkYjgoosu4rDDDuPyyy8vdLH2kO33krQw14CorkGYmdXTLbfcwsCBAznqqKP4+OOP+e53v1voIjWIFn8Xk5lZfV1++eVNssZQX65BmJlZVg4QZmaWlQOEmZll5QBhZmZZOUCYWbN1wgkn8NBDD+2Wdv311zNp0qSc+4wcOZKK2+G/8Y1v8NFHH+2xzbRp07juuuuqPPbs2bN59dVXdy3/6Ec/4pFHHqlN8bNqSsOCO0CYWbM1ZswYZs2atVvarFmzajRgHiSjsO677751OnblAHHNNdfw1a9+tU55NVUOEGbWbJ111lncf//9u14OtHLlSv72t79x3HHHMWnSJMrKyjjqqKOYOnVq1v1LS0v58MMPAZg+fTqHH344xx577K4hwSF5xmHo0KEMGDCAM888k82bNzN//nzmzJnDlVdeycCBA3nrrbcYN24c9957LwCPPvoogwYNol+/fowfP55PPvlk1/GmTp3K4MGD6devH8uWLavy/Ao9LLifgzCzBnHZZbBoUcPmOXAgXH997vVdunRh2LBhPPjgg4wePZpZs2bxzW9+E0lMnz6dLl26sGPHDr7yla+wePFi+vfvnzWfhQsXMmvWLBYtWsT27dsZPHgwQ4YMAeCMM87gwgsvBODf/u3f+PWvf833vvc9Tj31VE455RTOOuus3fLaunUr48aN49FHH+Xwww/n/PPP5+abb+ayyy4DoGvXrrz44ovcdNNNXHfdddx66605z6/Qw4K7BmFmzVpmM1Nm89I999zD4MGDGTRoEEuXLt2tOaiyJ598ktNPP50OHTqw9957c+qpp+5at2TJEo477jj69evHzJkzWbp0aZXlef311+nTpw+HH344ABdccAHz5s3btf6MM84AYMiQIbsG+Mvlqaee4rzzzgOyDwt+ww038NFHH9G6dWuGDh3K7bffzrRp03jllVfo1KlTlXnXhGsQZtYgqvpLP59Gjx7N5ZdfzosvvsjmzZsZMmQIb7/9Ntdddx0vvPACnTt3Zty4cTmH+a7OuHHjmD17NgMGDOCOO+5g7ty59SpvxZDh9RkufPLkyZx88sk88MADlJeX89BDD+0aFvz+++9n3LhxXHHFFZx//vn1KqtrEGbWrHXs2JETTjiB8ePH76o9bNiwgb322ot99tmHDz74gAcffLDKPI4//nhmz57Nli1b2LhxI/fdd9+udRs3buSggw5i27Ztu4boBujUqRMbN27cI6++ffuycuVKli9fDsDvfvc7vvSlL9Xp3Ao9LLhrEGbW7I0ZM4bTTz99V1NTxfDYRxxxBD179qS8vLzK/QcPHsw555zDgAED2H///Rk6dOiudT/5yU8YPnw43bp1Y/jw4buCwrnnnsuFF17IDTfcsKtzGqB9+/bcfvvtnH322Wzfvp2hQ4cyceLEOp1Xxbuy+/fvT4cOHXYbFvzxxx+nVatWHHXUUZx00knMmjWLa6+9ljZt2tCxY8cGebGQh/s2szrzcN/NS5MZ7lvSbZLWSFqSY/1ISR9LWpROP8pYN0rS65KWS5qcrzKamVlu+eyDuAMYVc02T0bEwHS6BkBSCXAjcBJwJDBG0pF5LKeZmWWRtwAREfOA9XXYdRiwPCJWRMSnwCxgdIMWzswaTDE1UxezuvxOhb6L6RhJL0t6UNJRaVp34J2MbVanaVlJmiBpgaQFa9euzWdZzayS9u3bs27dOgeJJi4iWLduHe3bt6/VfoW8i+lFoHdEbJL0DWA2cFhtM4mIGcAMSDqpG7aIZlaVHj16sHr1avzHWdPXvn17evToUat9ChYgImJDxvwDkm6S1BV4F+iZsWmPNM3Mmpg2bdrQp0+fQhfD8qRgTUySDpSkdH5YWpZ1wAvAYZL6SGoLnAvMKVQ5zcxaqrzVICTdBYwEukpaDUwF2gBExC+Bs4BJkrYDW4BzI2nI3C7pYuAhoAS4LSKqHvzEzMwanB+UMzNrwQryoJyZmTVvDhBmZpaVA4SZmWXlAGFmZlk5QJiZWVYOEGZmlpUDhJmZZeUAYWZmWTlAmJlZVg4QZmaWlQOEmZll5QBhZmZZOUCYmVlWDhBmZpaVA4SZmWXlAGFmZlk5QJiZWVYOEGZmlpUDhJmZZZW3ACHpNklrJC3JsX6spMWSXpE0X9KAjHUr0/RFkvySaTOzAshnDeIOYFQV698GvhQR/YCfADMqrT8hIgbmepm2mZnlV+t8ZRwR8ySVVrF+fsbis0CPfJXFzMxqr6n0QfwT8GDGcgAPS1ooaUKBymRm1qLlrQZRU5JOIAkQx2YkHxsR70raH/iLpGURMS/H/hOACQC9evXKe3nNzFqKgtYgJPUHbgVGR8S6ivSIeDf9XAP8CRiWK4+ImBERZRFR1q1bt3wX2cysxShYgJDUC/gjcF5EvJGRvpekThXzwIlA1juhzMwsf/LWxCTpLmAk0FXSamAq0AYgIn4J/AjYD7hJEsD29I6lA4A/pWmtgTsj4s/5KqeZmWWXz7uYxlSz/jvAd7KkrwAG7LmHmZk1pqZyF5OZmTUxDhBmZpaVA4SZmWXlAGFmZlk5QJiZWVYOEGZmlpUDhJmZZeUAYWZmWTlAmJlZVg4QZmaWlQOEmZll5QBhZmZZOUCYmVlWDhBmZpaVA4SZmWXlAGFmZlk5QJiZWVYOEGZmlpUDhJmZZeUAYWZmWeU1QEi6TdIaSUtyrJekGyQtl7RY0uCMdRdIejOdLshnOc3MbE/5rkHcAYyqYv1JwGHpNAG4GUBSF2AqMBwYBkyV1DmvJTUzs93kNUBExDxgfRWbjAZ+G4lngX0lHQR8HfhLRKyPiL8Df6HqQGNmZg2s0H0Q3YF3MpZXp2m50vcgaYKkBZIWrF27Nm8FNTNraQodIOotImZERFlElHXr1q3QxTEzKxqFDhDvAj0zlnukabnSzcyskRQ6QMwBzk/vZvoi8HFEvAc8BJwoqXPaOX1immZmZo2kdT4zl3QXMBLoKmk1yZ1JbQAi4pfAA8A3gOXAZuDb6br1kn4CvJBmdU1EVNXZbWZmDSyvASIixlSzPoCLcqy7DbgtH+UyM7PqFbqJyczMmqgaBQhJe0lqlc4fLulUSW3yWzQzMyukmtYg5gHtJXUHHgbOI3lK2szMilRNA4QiYjNwBnBTRJwNHJW/YpmZWaHVOEBIOgYYC9yfppXkp0hmZtYU1DRAXAZ8H/hTRCyVdAjweP6KZWZmhVaj21wj4gngCYC0s/rDiLgknwUzM7PCquldTHdK2lvSXsAS4FVJV+a3aGZmVkg1bWI6MiI2AKcBDwJ9SO5kMjOzIlXTANEmfe7hNGBORGwDIn/FMjOzQqtpgPgVsBLYC5gnqTewIV+FMjOzwqtpJ/UNwA0ZSasknZCfIpmZWVNQ007qfST9Z8Wb2yT9B0ltwszMilRNm5huAzYC30ynDcDt+SqUmZkVXk2H+z40Is7MWP6xpEX5KJCZmTUNNa1BbJF0bMWCpHJgS36KZGZmTUFNaxATgd9K2idd/jtwQX6KZGZmTUFN72J6GRggae90eYOky4DF+SycmZkVTq3eKBcRG9InqgGuyEN5zMysiajPK0dV7QbSKEmvS1ouaXKW9f+fpEXp9IakjzLW7chYN6ce5TQzszqoaR9ENlUOtSGpBLgR+BqwGnhB0pyIeHVXBhGXZ2z/PWBQRhZbImJgPcpnZmb1UGWAkLSR7IFAwOeqyXsYsDwiVqR5zQJGA6/m2H4MMLWaPM3MrJFU2cQUEZ0iYu8sU6eIqK720R14J2N5dZq2h3Rspz7AYxnJ7dOntp+VdFqug0iaUPGE99q1a6spkpmZ1VR9+iAa0rnAvRGxIyOtd0SUAd8Crpd0aLYdI2JGRJRFRFm3bt0ao6xmZi1CPgPEu0DPjOUeaVo25wJ3ZSZExLvp5wpgLrv3T5iZWZ7lM0C8ABwmqY+ktiRBYI+7kSQdAXQGnslI6yypXTrfFSgnd9+FmZnlQX3uYqpSRGyXdDHwEFAC3BYRSyVdAyyIiIpgcS4wKyIyO8O/APxK0k6SIPazzLufzMws/7T7dbl5KysriwULFhS6GGZmzYakhWl/7x6aSie1mZk1MQ4QZmaWlQOEmZll5QBhZmZZOUCYmVlWDhBmZpaVA4SZmWXlAGFmZlm1+ACxfTvcfz8sWlTokpiZNS0tPkAAnHsu3HproUthZta0tPgA0bo1fPGL8PTThS6JmVnT0uIDBMCIEbB4MWzcWOiSmJk1HQ4QQHk57NwJzz1X6JKYmTUdDhDA8OEguZnJzCyTAwSwzz7Qrx/Mn1/okpiZNR0OEKkRI+CZZ2DHjuq3NTNrCRwgUuXlSSf10qWFLomZWdPgAJEaMSL5dD+EmVnCASLVpw8ceKD7IczMKjhApKSkmck1CDOzRF4DhKRRkl6XtFzS5Czrx0laK2lROn0nY90Fkt5MpwvyWc4KI0bA22/De+81xtHMzJq2vAUISSXAjcBJwJHAGElHZtn07ogYmE63pvt2AaYCw4FhwFRJnfNV1grl5cmnm5nMzPJbgxgGLI+IFRHxKTALGF3Dfb8O/CUi1kfE34G/AKPyVM5dBg2C9u0dIMzMIL8BojvwTsby6jStsjMlLZZ0r6SetdwXSRMkLZC0YO3atfUqcNu2MHSo+yHMzKDwndT3AaUR0Z+klvCb2mYQETMioiwiyrp161bvAo0YAS++CFu21DsrM7NmLZ8B4l2gZ8ZyjzRtl4hYFxGfpIu3AkNqum++lJfDtm2wYEFjHM3MrOnKZ4B4AThMUh9JbYFzgTmZG0g6KGPxVOC1dP4h4ERJndPO6RPTtLw75pjk0/0QZtbStc5XxhGxXdLFJBf2EuC2iFgq6RpgQUTMAS6RdCqwHVgPjEv3XS/pJyRBBuCaiFifr7Jm6toV+vZ1P4SZmSKi0GVoMGVlZbGgAdqGxo+HOXNg7drkATozs2IlaWFElGVbV+hO6iapvBzWrYM33ih0SczMCscBIgsP3Gdm5gCRVd++0KWLO6rNrGVzgMiiVaukFuEahJm1ZA4QOYwYAcuWJX0RZmYtkQNEDhUD9z3zTGHLYWZWKA4QOZSVQevW7ocws5bLASKHDh1g8GD3Q5hZy+UAUYURI+D555OxmczMWhoHiCqUl8PWrfDSS4UuiZlZ43OAqELFA3Pz58PMmVBamtwCW1qaLJuZFbO8DdZXDA4+OAkGd90FS5bA5s1J+qpVMGFCMj92bMGKZ2aWV65BVGPECFi48LPgUGHzZpgypTBlMjNrDA4Q1Sgvhx07sq/7618btyxmZo3JAaIaFf0Q2fTq1XjlMDNrbA4Q1ejXD9q3Tx6ay9ShA0yfXpgymZk1BgeIapSUwHHHJR3WvXsnLxDq3RtmzHAHtZkVN9/FVAMjRsCjj8Lf/w57713o0piZNQ7XIGqgvBx27oTnnit0SczMGk9eA4SkUZJel7Rc0uQs66+Q9KqkxZIeldQ7Y90OSYvSaU4+y1md4cOTB+Q8cJ+ZtSR5a2KSVALcCHwNWA28IGlORLyasdlLQFlEbJY0Cfg5cE66bktEDMxX+Wpj772TzmoP3GdmLUk+axDDgOURsSIiPgVmAaMzN4iIxyOi4hG0Z4EeeSxPvYwYAc8+m/uZCDOzYpPPANEdeCdjeXWalss/AQ9mLLeXtEDSs5JOy7WTpAnpdgvWrl1bvxJXobwcNm5MhtwwM2sJmkQntaR/BMqAazOSe0dEGfAt4HpJh2bbNyJmRERZRJR169Ytb2XMHLjPzKwlyGeAeBfombHcI03bjaSvAlOAUyPik4r0iHg3/VwBzAUG5bGs1SothYMOcj+EmbUc+QwQLwCHSeojqS1wLrDb3UiSBgG/IgkOazLSO0tql853BcqBzM7tRicltYinnnI/hJm1DHkLEBGxHbgYeAh4DbgnIpZKukbSqelm1wIdgf9b6XbWLwALJL0MPA78rNLdTwVx8snJUN9f+hK88UahS2Nmll+KiEKXocGUlZXFggUL8pZ/BPz+93DJJcmb5n76U7jssmQ4DjOz5kjSwrS/dw9NopO6uZDgvPPg1VfhxBPhX/8Vjj0Wli0rdMnMzBqeA0QdHHQQzJ6dvHb0jTdg4EC49lr3TZhZcXGAqCMJvvUtWLoUTjoJrroq6cR+teA9JWZmDcOjudbTgQfCH/8Id98NF18MgwbBj3+cND9VfodEfWzdCo8/DkcemQw33lBeey3peN+yJfu0detn8926wZVXekRbsxYjIopmGjJkSBTS++9HnHlmBESUlUXcf3/E5s31y/OllyIuvjiic+ck37ZtIy6/POLDD+uX76uvRpx+epJnVVPr1hGdOkXsv39Eq1YRpaURTzxRv2ObWdMBLIgc11Q3MTWgAw6Ae++Fe+5J/io/+WTYbz849VT41a9g9eqa5bN+PfziFzB4cFIjueUWGDUK/vu/k07y//ovOPRQ+D//J/nLvjb++lcYPx6OPhoeeQSuuSZ5Ovyll5LO9lWrYM0a2LABtm1Lpg0b4IMP4Mknkzu2Ro5Makhbt9b6KzKz5iRX5GiOU6FrEJm2bIn485+Tv/779PnsL/IBAyKmTIl45pmI7ds/23779oiHHoo455yklgARQ4ZE3HhjxPr1u+e9ZEnEKack2/ToEXHbbbvnlc2aNUnNo23biHbtIq64ImLt2tqf18aNERMnJsc++uikhpNvy5dH3HxzxB//WLcym1luVFGDKPhFvSGnphQgMu3cGbF0acTPfx5x/PERJSXJN9+1a8T550dMnhzRs2eS1qVLxKWXRixaVH2+c+dGDB362cX6/vuTY2XasCHixz9OmolatYoYP+RThFEAAAupSURBVD5i1ar6n9MDD0QceGBEmzYR//7v1Qeo2ti+PWL+/OR7OfLIPZu9jjoqYtKkiLvuinj33YY7rllL5ADRyH7/+4jevSOk5PP3v999/fr1ycVt7NgkIEgRo0ZF3HNPxNattTvWzp3JfocemvyaI0dGPP98ks9//VdEt25J+hlnJP0ODenDDyPOPjvJf8SIiDffrHtemzZFzJ6dBLD990/yLCmJ+PKXI66/PuL11yOefjoJRqNGJQGvImB8/vPJfnfcEfH223sGSTPLraoA4SepG9jMmTBhAmze/Flahw4wYwaMHbvn9jt2JMOI77tv/Y776afJMX78Y/jww+SOo7Vr4YQT4Gc/g2HD6pd/LhFw551w0UWwfTv8x38k5y/l3n7TpqSfY80aeOUVuO++pD9k61bYZ5/ktuFTT036XTp3zp7P9u3w8svwxBMwb17SP7J+fbKuWzfo3j15XiXXdOCB0L59fr4Ts+akqiepHSAaWGlp0tFbWe/esHJl/o+/YUPy0N6iRcmQIF/9au6LdUN65x349rfh0UeTC/zXvvZZEFizJunkrpiv3LFeWgqjRydB4bjjoE2b2h9/587kGZQnnkgCx3vvJdPf/pYce+fOPffp3Dk59iGHJNOhh3722bNn3cph1tw4QDSiVq2Sv5Irk7JfpIrJzp3J3VdXX53UBlq3hv33T+7u2n//3ecrPktLoW/f/AaxHTuS2lRF0MgMHitXwooV8PbbSS2sQkkJ9Or1WdA47LDkzq9+/eDggxsn6Jo1BgeIRlToGkRTsGFDclHed9/mcyHdsSMJGCtWJNNbb302v2JFEmAqdO6cBIqKqX//JHh06lS48lvdVfRm7dy5+5SZVjFf3Wd1+1VOq8l8TT7btk1q7XVRVYDwk9QNbPr07H0Q06fvue3MmTBlSvJsQq9eyTbZ+imam+b4pHVJSdKs1LNnMpx7ZevXJ6+bfeUVWLw4+fztb5P+owqlpUmg2HffJL9WrXb/rJzWujW0a1ezSUqCWMVFJnM+cznzM3OqnJZt21z55DpmTdJylbOqKdvFurZTrgt2tuVicMAB8P77DZ+vA0QDq7jAV3fhr9yZvWpVspyZhzUdXbrA8ccnU4WI5Hd75ZXPpqVLk6kmF97t23dv1moKKge1Vq1yp1VeJ1W9b+ZyxbatW+++TtpzOdsxs00V22bmkS2/ynlW3iYzLVte2T6rOnbltFz7V7W+us+2bfPz78FNTAVS26aomtY2irVWUqwikiDxySdVT1CzC2+2Wkt1y5n7WMvjJqYm6K9/rXl6TWsbrpU0P9JnzUhmTY3/ZiiQXr1qnj5lyu59GpAsT5lSt+1qa+bMpMbTqlXyOXNm/fIzs+bBAaJApk9POq8z5erMrmltoza1EqjZhb+iVrJq1Wdt7hMm5A4SxRZMiu18zGol1yPWzXFqKkNt1FR1Q3JU6N37s2ElMqfeveu2XcWxO3TYfbsOHfYsQz7yrM2512bbhs6zNudT2+ObNRUUaiwmYBTwOrAcmJxlfTvg7nT9c0Bpxrrvp+mvA1+vyfGaW4CoqZpeqGpzQavphV/Kvp1U9zxrG0ga+twdHJ1nS8wzl4IECKAEeAs4BGgLvAwcWWmbfwZ+mc6fC9ydzh+Zbt8O6JPmU1LdMYs1QEQ0/D+Yml74a3ORzEee+ag9OTg6z5aWZ1UKFSCOAR7KWP4+8P1K2zwEHJPOtwY+BFR528ztqpqKOUA0tHxc0PJx4a3ptvnIsyUHR+dZXHlWpVAB4izg1ozl84BfVNpmCdAjY/ktoCvwC+AfM9J/DZyV4zgTgAXAgl69etXum2nB8tEkko+mm0L+B2zJwdF5FleeVSnqAJE5uQZRO/noVG3ozt9CVuFrej61ybO5BEfnWVx5VsVNTNbkFLrDzsHRebakPKtSqADRGliRdjJXdFIfVWmbiyp1Ut+Tzh9VqZN6RUvvpLbi0VyCo/MsrjxzqSpA5HUsJknfAK4nuaPptoiYLumatEBzJLUHfgcMAtYD50bEinTfKcB4YDtwWUQ8WN3xmtNYTGZmTYHfB2FmZllVFSA81IaZmWXlAGFmZlk5QJiZWVYOEGZmllVRdVJLWgtkvqetK8mzFcWk2M6p2M4Hiu+ciu18oPjOqT7n0zsiumVbUVQBojJJC3L1zjdXxXZOxXY+UHznVGznA8V3Tvk6HzcxmZlZVg4QZmaWVbEHiBmFLkAeFNs5Fdv5QPGdU7GdDxTfOeXlfIq6D8LMzOqu2GsQZmZWRw4QZmaWVdEGCEmjJL0uabmkyYUuT31JWinpFUmLJDXLEQkl3SZpjaQlGWldJP1F0pvpZ+dClrE2cpzPNEnvpr/TonRE42ZDUk9Jj0t6VdJSSZem6c3yd6rifJrt7ySpvaTnJb2cntOP0/Q+kp5Lr3l3S2pb72MVYx+EpBLgDeBrwGrgBWBMRLxa0ILVg6SVQFlENNuHeyQdD2wCfhsRR6dpPwfWR8TP0kDeOSKuLmQ5ayrH+UwDNkXEdYUsW11JOgg4KCJelNQJWAicBoyjGf5OVZzPN2mmv5MkAXtFxCZJbYCngEuBK4A/RsQsSb8EXo6Im+tzrGKtQQwDlkfEioj4FJgFjC5wmVq8iJhH8t6PTKOB36TzvyH5z9ss5DifZi0i3ouIF9P5jcBrQHea6e9Uxfk0W+l7fjali23SKYAvA/em6Q3yGxVrgOgOvJOxvJpm/o+C5B/Aw5IWSppQ6MI0oAMi4r10/n3ggEIWpoFcLGlx2gTVLJpispFUSvIyr+cogt+p0vlAM/6dJJVIWgSsAf4CvAV8FBHb000a5JpXrAGiGB0bEYOBk4CL0uaNopK+/rC5t3neDBwKDATeA/6jsMWpG0kdgT+QvM1xQ+a65vg7ZTmfZv07RcSOiBgI9CBpMTkiH8cp1gDxLtAzY7lHmtZsRcS76eca4E8k/yiKwQdpO3FFe/GaApenXiLig/Q/707gFprh75S2a/8BmBkRf0yTm+3vlO18iuF3AoiIj4DHgWOAfSW1Tlc1yDWvWAPEC8Bhaa9+W+BcYE6By1RnkvZKO9iQtBdwIrCk6r2ajTnABen8BcB/F7As9VZxEU2dTjP7ndIO0F8Dr0XEf2asapa/U67zac6/k6RukvZN5z9HcjPOaySB4qx0swb5jYryLiaA9La164ES4LaImF7gItWZpENIag0ArYE7m+P5SLoLGEkyNPEHwFRgNnAP0ItkqPZvRkSz6PjNcT4jSZotAlgJfDej7b7Jk3Qs8CTwCrAzTf4BSbt9s/udqjifMTTT30lSf5JO6BKSP/LviYhr0uvELKAL8BLwjxHxSb2OVawBwszM6qdYm5jMzKyeHCDMzCwrBwgzM8vKAcLMzLJygDAzs6wcIMyqIWlHxqifixpydGBJpZmjwZo1Ja2r38SsxduSDmtg1qK4BmFWR+k7On6evqfjeUmfT9NLJT2WDgT3qKReafoBkv6UjuP/sqQRaVYlkm5Jx/Z/OH06FkmXpO8xWCxpVoFO01owBwiz6n2uUhPTORnrPo6IfsAvSJ7cB/j/gd9ERH9gJnBDmn4D8EREDAAGA0vT9MOAGyPiKOAj4Mw0fTIwKM1nYr5OziwXP0ltVg1JmyKiY5b0lcCXI2JFOiDc+xGxn6QPSV5Ssy1Nfy8iukpaC/TIHP4gHYL6LxFxWLp8NdAmIn4q6c8kLySaDczOeAeAWaNwDcKsfiLHfG1kjpezg8/6Bk8GbiSpbbyQMVKnWaNwgDCrn3MyPp9J5+eTjCAMMJZksDiAR4FJsOuFL/vkylRSK6BnRDwOXA3sA+xRizHLJ/9FYla9z6Vv76rw54iouNW1s6TFJLWAMWna94DbJV0JrAW+naZfCsyQ9E8kNYVJJC+ryaYE+H0aRATckI79b9Zo3AdhVkdpH0RZRHxY6LKY5YObmMzMLCvXIMzMLCvXIMzMLCsHCDMzy8oBwszMsnKAMDOzrBwgzMwsq/8HjuCVdZz/wNoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-yc5gv7N03k8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "901ec986-9b62-42ff-bda7-5de91aa3827f"
      },
      "source": [
        "# loss and accuracy on test \n",
        "\n",
        "test_loss, test_acc = model_made.evaluate(X_test, y_test)\n",
        "\n",
        "print('Test accuracy : {:2.4f}%'.format(test_acc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "74/74 [==============================] - 1s 8ms/step - loss: 1.8044 - accuracy: 0.7478\n",
            "Test accuracy : 74.7763%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fb-tUSivXS1F"
      },
      "source": [
        "### confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmjMKgwutALp"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhTkqvmx1nQ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2be2c491-a2bf-4804-a451-2c22805096ca"
      },
      "source": [
        "pred1 = model_made.predict_generator(X_test)\n",
        "y_pred1 = np.argmax(pred1, axis=-1) # 모델이 예측한 class label"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1976: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmU5cqN81nTj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "outputId": "f8d7d7f5-43fd-4405-b434-2bbbfe9a4ec9"
      },
      "source": [
        "labels = ['palm', 'l', 'fist', 'fist_moved', 'thumb', 'index', 'ok', 'palm_moved', 'c', 'down']\n",
        "\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "cm = confusion_matrix(y_test, y_pred1)\n",
        "plt.figure(figsize=(15, 15))\n",
        "ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_title(\"model2_madeData\", fontsize=30)\n",
        "ax.set_ylabel('Actual', fontsize=25)\n",
        "ax.set_xlabel('Predicted', fontsize=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 185.99374999999995, 'Predicted')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2rHob2IC48nu"
      },
      "source": [
        "### Training (3rd try) : test_acc = 80.5709%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acSZPU1Q2Joy"
      },
      "source": [
        "* with dropout"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSCgCxYX4-r9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d0b2f5c-003c-4f55-aded-1fcb27c9ae37"
      },
      "source": [
        "# model\n",
        "model_made = Sequential()\n",
        "model_made.add(Conv2D(32, (5,5), activation='relu', input_shape=(128,128,1)))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Flatten())\n",
        "model_made.add(Dropout(0.3)) # dropout\n",
        "model_made.add(Dense(128, activation='relu'))\n",
        "model_made.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_made.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 124, 124, 32)      832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 60, 60, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 28, 28, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 12, 12, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               589952    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 832,010\n",
            "Trainable params: 832,010\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nYJHfyXg4_Vo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "958f0a30-84e7-4a39-8c9f-3714a03a2758"
      },
      "source": [
        "model_made.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_made = model_made.fit(X_train_made, y_train_made, epochs=30, batch_size=64,\n",
        "                              verbose=2, validation_data=(X_val_made, y_val_made))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "98/98 - 4s - loss: 1.4296 - accuracy: 0.5424 - val_loss: 0.4007 - val_accuracy: 0.8763\n",
            "Epoch 2/30\n",
            "98/98 - 3s - loss: 0.2985 - accuracy: 0.9067 - val_loss: 0.1977 - val_accuracy: 0.9432\n",
            "Epoch 3/30\n",
            "98/98 - 3s - loss: 0.1364 - accuracy: 0.9542 - val_loss: 0.1074 - val_accuracy: 0.9713\n",
            "Epoch 4/30\n",
            "98/98 - 3s - loss: 0.0742 - accuracy: 0.9753 - val_loss: 0.0930 - val_accuracy: 0.9732\n",
            "Epoch 5/30\n",
            "98/98 - 3s - loss: 0.0534 - accuracy: 0.9833 - val_loss: 0.0856 - val_accuracy: 0.9770\n",
            "Epoch 6/30\n",
            "98/98 - 3s - loss: 0.0414 - accuracy: 0.9858 - val_loss: 0.0885 - val_accuracy: 0.9739\n",
            "Epoch 7/30\n",
            "98/98 - 3s - loss: 0.0410 - accuracy: 0.9872 - val_loss: 0.0719 - val_accuracy: 0.9828\n",
            "Epoch 8/30\n",
            "98/98 - 3s - loss: 0.0154 - accuracy: 0.9949 - val_loss: 0.1191 - val_accuracy: 0.9777\n",
            "Epoch 9/30\n",
            "98/98 - 3s - loss: 0.0233 - accuracy: 0.9919 - val_loss: 0.1044 - val_accuracy: 0.9790\n",
            "Epoch 10/30\n",
            "98/98 - 3s - loss: 0.0381 - accuracy: 0.9887 - val_loss: 0.1052 - val_accuracy: 0.9783\n",
            "Epoch 11/30\n",
            "98/98 - 3s - loss: 0.0168 - accuracy: 0.9957 - val_loss: 0.1402 - val_accuracy: 0.9688\n",
            "Epoch 12/30\n",
            "98/98 - 3s - loss: 0.0290 - accuracy: 0.9917 - val_loss: 0.1036 - val_accuracy: 0.9790\n",
            "Epoch 13/30\n",
            "98/98 - 3s - loss: 0.0261 - accuracy: 0.9920 - val_loss: 0.0579 - val_accuracy: 0.9866\n",
            "Epoch 14/30\n",
            "98/98 - 3s - loss: 0.0140 - accuracy: 0.9963 - val_loss: 0.0647 - val_accuracy: 0.9847\n",
            "Epoch 15/30\n",
            "98/98 - 3s - loss: 0.0134 - accuracy: 0.9957 - val_loss: 0.0670 - val_accuracy: 0.9853\n",
            "Epoch 16/30\n",
            "98/98 - 3s - loss: 0.0134 - accuracy: 0.9947 - val_loss: 0.0784 - val_accuracy: 0.9834\n",
            "Epoch 17/30\n",
            "98/98 - 3s - loss: 0.0256 - accuracy: 0.9919 - val_loss: 0.1175 - val_accuracy: 0.9796\n",
            "Epoch 18/30\n",
            "98/98 - 3s - loss: 0.0251 - accuracy: 0.9907 - val_loss: 0.0720 - val_accuracy: 0.9834\n",
            "Epoch 19/30\n",
            "98/98 - 3s - loss: 0.0047 - accuracy: 0.9982 - val_loss: 0.0805 - val_accuracy: 0.9834\n",
            "Epoch 20/30\n",
            "98/98 - 3s - loss: 0.0196 - accuracy: 0.9925 - val_loss: 0.1320 - val_accuracy: 0.9700\n",
            "Epoch 21/30\n",
            "98/98 - 3s - loss: 0.0258 - accuracy: 0.9919 - val_loss: 0.0680 - val_accuracy: 0.9853\n",
            "Epoch 22/30\n",
            "98/98 - 3s - loss: 0.0246 - accuracy: 0.9917 - val_loss: 0.1448 - val_accuracy: 0.9700\n",
            "Epoch 23/30\n",
            "98/98 - 3s - loss: 0.0225 - accuracy: 0.9935 - val_loss: 0.0727 - val_accuracy: 0.9841\n",
            "Epoch 24/30\n",
            "98/98 - 3s - loss: 0.0073 - accuracy: 0.9968 - val_loss: 0.0765 - val_accuracy: 0.9853\n",
            "Epoch 25/30\n",
            "98/98 - 3s - loss: 0.0072 - accuracy: 0.9974 - val_loss: 0.0941 - val_accuracy: 0.9847\n",
            "Epoch 26/30\n",
            "98/98 - 3s - loss: 0.0157 - accuracy: 0.9954 - val_loss: 0.1098 - val_accuracy: 0.9815\n",
            "Epoch 27/30\n",
            "98/98 - 3s - loss: 0.0108 - accuracy: 0.9967 - val_loss: 0.0735 - val_accuracy: 0.9847\n",
            "Epoch 28/30\n",
            "98/98 - 3s - loss: 0.0070 - accuracy: 0.9979 - val_loss: 0.0793 - val_accuracy: 0.9847\n",
            "Epoch 29/30\n",
            "98/98 - 3s - loss: 0.0027 - accuracy: 0.9990 - val_loss: 0.0675 - val_accuracy: 0.9853\n",
            "Epoch 30/30\n",
            "98/98 - 3s - loss: 0.0044 - accuracy: 0.9982 - val_loss: 0.0688 - val_accuracy: 0.9853\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MtlHvrxB5Don",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "c2f5d203-5779-4c6f-a862-c6ee46c5979f"
      },
      "source": [
        "# plotting\n",
        "loss = history_made.history['loss']\n",
        "val_loss = history_made.history['val_loss']\n",
        "acc = history_made.history['accuracy']\n",
        "val_acc = history_made.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQkHGwjz5Jhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f69e5ad-5cb6-4d16-952f-9eec9491bf72"
      },
      "source": [
        "# loss and accuracy on test \n",
        "\n",
        "test_loss, test_acc = model_made.evaluate(X_test, y_test)\n",
        "\n",
        "print('Test accuracy : {:2.4f}%'.format(test_acc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "74/74 [==============================] - 0s 6ms/step - loss: 1.1577 - accuracy: 0.8057\n",
            "Test accuracy : 80.5709%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5Y29_fxu9ij"
      },
      "source": [
        "### confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OgMpQ_ba5cFu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec7b988b-fe27-44f0-bbdd-ba8e2e6c7c57"
      },
      "source": [
        "pred3 = model_made.predict_generator(X_test)\n",
        "y_pred3 = np.argmax(pred3, axis=-1) # 모델이 예측한 class label"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1976: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9ywmdMg5p4m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "outputId": "71bdbbcb-1c55-43a8-8000-5c6423c246f3"
      },
      "source": [
        "labels = ['palm', 'l', 'fist', 'fist_moved', 'thumb', 'index', 'ok', 'palm_moved', 'c', 'down']\n",
        "\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "cm = confusion_matrix(y_test, y_pred3)\n",
        "plt.figure(figsize=(15, 15))\n",
        "ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_title(\"model_last_madeData\", fontsize=30)\n",
        "ax.set_ylabel('Actual', fontsize=25)\n",
        "ax.set_xlabel('Predicted', fontsize=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 185.99374999999995, 'Predicted')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAANDCAYAAACJ6Q+rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3wU1frH8e9D6EiXBKWjWABFBWyIgtiwUEQFe0FRRLkqKGIBFFFU1Gv5qSAqqGC7oNcKekW6ihQRxIYNQekgvSXn98dM2CUk2QBJZrLzeb9eebE7OzvzzMlsmGfPc86Yc04AAAAAEGXFgg4AAAAAAIJGYgQAAAAg8kiMAAAAAEQeiREAAACAyCMxAgAAABB5xYMOAAAAAEDBstNrhnoqavfpYgs6BnqMAAAAAEQeiREAAACAyCMxAgAAABB5jDECAAAAkp0FPoQn9OgxAgAAABB5JEYAAAAAIo9SOgAAACDZ0R2SEE0EAAAAIPJIjAAAAABEHqV0AAAAQLJjVrqE6DECAAAAEHkkRgAAAAAij8QIAAAAQOQxxggAAABIdgwxSogeIwAAAACRR2IEAAAAIPIopQMAAACSHdN1J0SPEQAAAIDIIzECAAAAEHmU0gEAAADJju6QhGgiAAAAAJFHYgQAAAAg8iilAwAAAJIds9IlRI8RAAAAgMgjMQIAAAAQeZTSAQAAAMmOSrqE6DECAAAAEHkkRgAAAAAij8QIAAAAQOQxxggAAABIdsUYZJQIPUYAAAAAIo/ECAAAAEDkUUoHAAAAJDsq6RKixwgAAABA5JEYAQAAAIg8SukAAACAZGfU0iVCjxEAAACAyCMxAgAAABB5lNIBAAAAyY5KuoToMQIAAAAQeSRGAAAAACKPUjoAAAAg2RWjli4ReowAAAAARB6JEYACZWYTzcyZmSuEff3u7+v3Ath2q8zjMLMB+b19hJuZDYj7/bcKOh4AQP6jlA4AkoyZ3SKpkqS1zrl/Bx0P9oyfePfP5iUnaYOkfyStljRP0mxJ451z3xVagJLMrK6kq/ynE51zEwtz/wD2ApV0CZEYAUDyuUVSHUl/SCIxSh4mqbz/U1PSkZIulfSYmU2XNMg591EhxVJXuyZvEwtpvwBQYEiMAAAIrzclvRH3vJSkypJqSTpOUktJJSWdKOlDM3tBUg/n3PbCDhQAijoSIwAAwusH59y7Ob1oZgdIukPSv+T1KF0X9y8AYA8w+QIAAEWUc+5v59ytki6UNwZJkq41s4sDDAtAGJmF+ycESIyAgGQ3y5mZHWJm/2dmP5vZJjP7y8zeN7MW2bz/HDP7wMz+NLMtZvaHmT1rZtXzsO8qZnavmX1hZivMbJuZ/W1m/zOzm82sdB6PoZyZ3WVms81snZn9Y2bfmtl9Zrb/XrRJbTMbZGYz4uJaamafmll3Myu5p9ssTOZp6R/DBP/3t9XMNprZb2b2hpmdl8dtVTKzPmY2ycyW+22xzsx+9X9v/2dmbc1i/5tkzsonb3yRJNWJO8fif67Kx2PO3ObEuLjvMbNv/PNhjZlNN7PLzKxYlvc2MbOX/PN9s3+cY82saR7229Q/h8eZ2SL/M7DZ/zy86+8vJY/HYP76n5nZSn87v5jZUDNrtBdtsp+Z3eKft5nnwGoz+9rM7jezanu6zUScc2MkPRK36N6s7R0X32FmdruZveefT5v8GP/22/PGnP4GmP93S9LncYv7Z3eeZfPe2mZ2k5m9bWY/mtkG/7xebt7slX3MrOK+tAMA7AtzrsBn0AWQDfOm/M28uLhP3gxTr0gqm83qTlJX59zLZlZC0lBJV+ew6aWSWjrnFuaw3/aSRsibtSwniyR1cM7NySX+BpLGS6qXwyqLJZ0nb/D/KZLknMvxKyEz6ytvMHepXOL6WdK5zrmfctjG7/InHXDO1c1lO3ss6+/LOTcgm3VeVmymrtyMk9TZObcuh301l/SBpNQ8bKuyc26t/77fFUuKcnO1c25EHtZLKO4CeJKkG+XFndM58bK889iZ2Q2Snlb2Jd07JF2YUwmZmfWXNCAP4c2S1M4591cu8ZeV9I6kM3JYZYu8srSDFZtsoHVOs7CZWVt5n6/cfnfrJV3mnHsvh20MiNtXtudaDu+rKulPSWX8RSc556ZlWedKP75EfpF0nnPu+yzvb6Vdk6IcxX/e/fdNUOJ5sVZIOt85NzUv+wCQd9bl4FBf9Ls3FgbebcQYIyAcmkq6U9I2eYnETHk9umdJuljexcQLZjZVUk95SdG3kl6TN/NYmqRukhpLqi7vwuekrDsxs7MljZGU+U36ZEn/kbRM3gX15ZKOkFRb0iQzO9Y590M226ki7yKnpr/oN0kvyUtcqkg6X9JpksbKm1o4V2b2hLyZ1CRprbzB5l/Lu4A8QFIHSa0lNfDjOto5tzTRdgNQRtJWeUnCDHkXlxslVZN0iLz2rSLv9/qKvOPaRdyFeuaF9WR5ycYiSRmS9pf3e24j6dAsb+8mL7Ee5u9zhb8sq9l7eXy5qSjpv/LOozclfSpvauljJPWQVE7eeTvFzNZJek7eefeSvC8FSku6QNLZ8v5vetnMpjnnVmSzrzLykqcvJE2TtFDSOnltW0/SZZJqyPtcvWtmLXKZjOAtxZKi9ZJelPf5KyEvob/MX/ZpogYws07+sadI2i7pPXmztS2TVEHeOXyRvFnl3jGz051zExJtN6+cc6vMbLxi59Up8tonXhl5X7TMkndu/ShpjR9fHUmd5Z2rB0n62MyOyky8ffMldZR3Dg70l2WdICI7peX9HftOXmL1vaRV/vJafsxN5Z23H/j7/T2vxw4gD4oFnneEHj1GQECy+eb1F0mnOucWZVnvLkmD/KezJR0t6XlJNznnMuLWKyvpS3mJjSQd55ybEfd6BXmJS+YFdy/n3ONZ9lVc0rOKDdye6Zxrnk3swyV19Z+Ol/cN76Ys6/xLWaaKzq7HyO/ByuwZ+J+kLs65Vdmsd71/3JL0pnOuSzbr/K5ge4xaSpqX5UIy/vVy8npNLvQXtXLOTcqyzgWS3vafPuecuzGXmI6XNMc5tzXL8t9VQO2QTQzx/4lskdfL8L8s65wk7yLc5CXy+8k7F9tmbSsze1HSNf7TPs65+PKwzHWaS/ozp+TYvJLLR+RNSCBJVznnRmaz3mWSXvWfLpL3+/gtyzot5J3j5eIW79ZjZGa15CUNFfxtneucm5fNPo+V9Im8RHKxpPpZk7a97THy33unpIf8p+865zpmeb2RpE1ZjzPu9WKSbpP0qL9ogHPuvmzWa6UEn4cs69eRVCG7Nolb52J5X/YUkzTCOZdTrziAvWCXNAj1Rb8b/XPgmRtjjIDwuCxrUuR7TN432ZL37ft8STfHJ0WS5Ccmg+MWnZllO1cplhS9lTUp8rexQ1J3eb1RktTMzE6LX8fMUuX1fEjeN76XZE2K/G09Ka83KpH7/X//lFe+t1tS5G9vqGIXsRf4F6Kh4pybklNS5L++UV5CudFfdHk2qx0c9/iFBPv7MmtSFLD7siZFkuSXRWX2uNSRlxhdlENb9VdsEoGs53Dm9r7OrcfQObdNUi95PZlS9u0sf51MV2SXLPilaH1y2lec2+UlRemS2ueUAPhfVtzmP62pWJKcX36Pe7zbWCbn3Hc5JUX+6xnOuSHyElkp57bbI865P3JLivx1XpeXGElSZ79sGAAKDYkREA6znHNfZveCf+E7M27RUOdceg7bia/Lb5jltfPjHj+cUyD+th+NW3R+llXOkXffFMn7Vnd1TtvKsp3dmFkTeTeplLzekY25ra/YRVOKvFKyIsc5t15e6Zjk3Ycmq/gkc48H/gcoXbEevezEl3S975z7M7uVnHOLFbu4z3oO55l/Hn/lPz3WbNcpj8ysnqSj/KezsvbcZTFcXolntvxtX+o//cw5902C8N6UVwoo5Ty2aW+tiXtcdR+2M93/9yDbi4lU8mG/ZRT72wAgP1jIf0KAMUZAOHyV4PVlcY9n5LjWrutVznzgX7hllsStdM4lGmPySdzjrBfv8aV1nyXYTuY4ofI5vN4y7nEpM9ttzE0WNeIeH55g3UCYWSl540jaS2oib/zXfsr+z37NbJb9T16PiUl63swOkjTaOfdzwUScb37MrbdMeT+HM9etp7hzOCu/5KuDpE7yyksPlHeeZfeFX3l5vTnx493yfB4757b64/vOzWGVRvLGN0nS+jycx5I3/qqS8v88jj/+HMtm/J7gLvLaoba8NsppFr8aklbmR3Bmdpy8cVvHS6rv7zennqGa8sZCAUChIDECwiHb8rE48eVSOa7rX8BlPo2fbreCYrPdJbzAds4tN7N/5I2DOCDLywfGPc525ru47Tgz+0Wxb+azqhv3uH8O6+Qkx4vmoJjZEfImt2iQx7dUyLrAObfAzAZL6itvXMsASQPM7E9536ZPlvShc+6PfAk6/+TLOZxl3WxnKDSzmvLGpSWc1jtO1sQoz+dxHtapG/e4k/+TV/l9HsfPNrlbb64/HXb8hBN5sdt5uqf8cV/DtWelefu8XwDYEyRGQDhkJF5lr9bNFN9jk6hcLdMGeYlR1t6e/eIe7za2KBu57W9f7lkSqnsa+TP1/U+xcVx/yptN7gd5s8NtUewb/Afk9TJkW87snLvLzL6WN7Yls8eulrwZwzpLesbMxkm6JaepywNQ0OewJMkfdzJesTK7lfJmf5svr6dpS9z2e8qbCU7avTckWc/junGPs5vR7z/yZoyUvN7c9yV9I+lvee2Q2XZd5J1rUs49SXvi/xRLirZK+khej/ISeW2bWR58qqSb83G/ADKF5CaqYUZiBETD+rjH5XJca1eZF47rsyzfEPc4u3suZZXb/uK3dapzLk/3RwmpmxRLikZKutafzGI3ZnZ3oo05596RN6XzgfJKDk+U1EreuAuT1FbSiWZ2QtZ7zSS5ixVLij6V1DGnsWlmdml2y30FdR7f75zb097P/BRf+rpLyaKZnaxYUjRX0uk5TIeeORtfvjCzuorNYrlY0inOuV9zWLdGdssBoDAw+QIQDesU+1b84NxWlCQzq6bYt+BZb44Z/zzXbfljm+rnssqSuMfZjbcpSjIvOHfI68nJNiny5eUmrJIk59xfzrk3nXP/cs41kXePmcyZ3yoqdi+ZqIifJfHWBBN25NbOeT6P87BOKM5j/wav8SVyWSeUiG+7u3NKinx5Pkfz4FTFxtgNzikpKoD9AsAeITECIsB5Nyz72n9azcxyGvOTKf7iKutA+fjnpybYTnPlPk4g/sItv2fnKmxp/r+rcpuEwMyOVjbTKOeVPwnDBYqVHu12I1/FyqGSsW4iLe7xLzmt5E8rn9t5nufz2J9QI7t2zjRH3pcPktTGnxgiCHfIm81NkhbIuwFuvLy2XUnFShBzEl8Omeg8y9N+fdlO0Q4gHwQ961wRmJWOxAiIjjFxj2/PaSUzS5HUO4f3SdKHkrb5j68ys9wGj/fK5TXJm4b8O/9xZ//mk0VVZo9cqpnlNAufJPXb1x055/5RbFrm7EqiM0u78lo2WZTEjwc6KJf1+irn2c7knPtd3tgaybtfV8uc1pV3w9lKOb3oTw0+yn9aR9K1uWyrQJhZJ+36uR7odr+De17brrukRFN0x5cPJjrP8rRf/2bPTNENIDAkRkB0jJC03H98iZn1zLqCnxQ9o9g37V8753aZytgvv3nFf7q/pFFmVkZZmNlN8qatzpF/4dbXf1pC0kdm1jyXt8jMGprZc7mtE5DMHjmTN7nCLswzUN4U0zkys55m1im3m1ua2YWKXbjOzWaVzBt4VjWz2gkjL1q+jns8MLveGTPrJm/ihUSGxD1+xcx2K+MysxOUy32/4jyo2L2OnjKzK3Jb2cxSzexeM9unRMDMqpvZ45LeVuw71xedc29ks3p82/Xze8Kybu887Xqj6JzE3yT2mATrxu+3d3ZfpvjTeL+Uh/0CQIFh8gUgIpxz683sankzeKVIetLMOsqbpWqFvHuZXK7YN7brJeV0cddH0lnyxlO0lTTfzF6SN6VxZXk3hT1d3sXTP8qlpMk5976Z3S+vJ6W2pK/M7BN542gWy5vJraq8WdxayRt4ny7vW+0weVZez0KKpJ5+ueJYSUvlzSh3ibz77SyQtFk5TzV9jKQnJa3x22GWvDEsGZKqyys5zCw3cpIeymYbn0lq5z8e6yeSfytW/jTPObckm/cVBS9LukteL0VHSbPN7FV550qavHPvFHntPk/eeZgt59woM7tE0tnyZnP71sxelNeTWULSyfI+AxnyekrPyWVbi82si7zPVylJI83sNv/5z/J+5xXljRE7XlILeedKoglHDstyX6SS8nqvavvbaaldZ7YbLunGHLb1jrxzqYakYyUt8I/3V3+bZ0s6T94scWOUy7Tjzrk1ZjZH3jnd2syel3ferY9bZ5z/8At553FTee38g7/+j/JK/05VbAa8UYrdLBcAChWJERAhzrmP/JKbkfIu0lr5P1ktkjfb1w85bGe1mbWRN21yXXkTLGTtJVks78L1yTzE1d+/T89j8sYknancxxosTrTNwuac+8bMbpbX41ZM3kX1yVlW+17ejV+H57Yp/9/Kik3PnZ2Nkro75/6XzWsvSeoh7yK8aTb7u1peD2KR45z7259t7g159+pq4v/EWyLv3OuRh01eJC9hOF3euXdrlte3yCuNa6BcEiM/tvFmdoq8i/v6OcQWb4N2vbdSdnI7B+J9Ka987qNc4ttsZhfImyq7sh/joCyrrZWXmByrxPdjulvedN8pkq73f+KZv1/nJ40T5H1JkKrdS0q3yPt9ZYjECCgYxUIykCfEKKUDIsY59195df79JH0l72ab2+WV2U2Q9C9JhznnZifYzk+SGsu7OPpG3kXeenn3kxko6WjnXHZlXjltb7i88Rm9JH0ib9awrf7PUnk3Nn1UUhvlPtNdYJxzz8nrCXhbXsyZ7Tpd0m2SmjnnEt1MtLu8XrhH5PUmLJHXBjvk3bNnqryb4R7inHs1hzg2yOtNGCRptryL772+d1DY+OfwMfKSuz/ltfMqeb0S/SQ1cc59neMGdt3WRnlJ+BXy2nu1vIv0XyW9IO93NirnLey2vS8lHSrpMnk3Uv1N3mdjh7/tmf52O0uq7pybl9dty0uaN8j7bMyTNFreeMBGzrkTckuKssTXRF4C/4u88YL/yPvcPiyv7RJux9/Wx/LO99HyjnNzLusulNe79JC8Lwi2+Mfyox9LU+ccpXQAAmW7j80EAAAAkEzsykNDfdHvRv4YeJcWpXQAAABAsgs87Qg/SukAAAAARB6JEQAAAIDIo5QOQFIzs8MkHbYPm/jEObcp8WpFh5mVlTft9976IacZCwEAIWXU0iVCYgQg2XWRN4vb3qon6ff8CSU0UuVNUb237pM0IH9CAQAgHJIyMer0QbdQz7pRmPqdeF3QIYRGgwqHBx1CKGzP2Bp0CIWqzz236+EHHt3r9/+wcMFvW9KTqsNIPyxcoMMObrjX7+9zz+39125buS/JZqiVTikbdAihkeHSgw4hNLiwiClmjMSIVyalHF0xSSIpEyMAyNT33j7qe2+fbF8rlVKmkKMJhzp162jzjo27LEu25A8AkAX5bEI0EQAAAIDIIzECAAAAEHmU0gEAAADJjlnpEqLHCAAAAEDkkRgBAAAAiDwSIwAAAACRxxgjAAAAINkxxCgheowAAAAARB6JEQAAAIDIo5QOAAAASHZM150QPUYAAAAAIo/ECAAAAEDkUUoHAAAAJDu6QxKiiQAAAABEHokRAAAAgMijlA4AAABIdsxKlxA9RgAAAABCzcxqmdnnZrbAzL4zs3/5y6uY2adm9rP/b2V/uZnZU2a20My+NbNjEu2DxAgAAABA2O2Q1Ms511DS8ZJ6mFlDSXdK+sw510DSZ/5zSWorqYH/003Sc4l2QGIEAAAAJDsL+U8Czrm/nXOz/cfrJX0vqYak9pJG+quNlNTBf9xe0ivO86WkSmZ2QG77IDECAAAAECgz62ZmM+N+uuWybl1JR0v6SlKac+5v/6WlktL8xzUk/Rn3tsX+shwx+QIAAACAQDnnhkkalmg9M9tP0hhJtzjn1lncpBLOOWdmbm9jIDECAAAAkl2xoj8rnZmVkJcUjXLOjfUXLzOzA5xzf/ulcsv95Usk1Yp7e01/WY4opQMAAAAQauZ1Db0o6Xvn3ONxL70n6Ur/8ZWS/hu3/Ap/drrjJf0TV3KXLXqMAAAAAIRdC0mXS5pnZt/4y+6SNFjSW2bWVdIfki7yX/tI0tmSFkraJOnqRDsgMQIAAAAQas65qcp5/ro22azvJPXYk32QGAEAAADJzor+GKOCxhgjAAAAAJFHYgQAAAAg8iil89145JVqlnaE/tm6XrdOvm+315unNdHFh7ZXhnNKd+l6+bu39MOahfu0z/1KlNVtx3RTatmqWr5plR6bPUwbt29SyxrHquNBZ0kybd6xRcPmjdIf6xfv074KW0Z6hu68+l5VqVZZdz7WW/O+nq/XnnldGc6pdJnS6nFPN1WvVT3oMAvN1q1b1fWK67Rt2zalp6frtDPaqPtNNwQdVqEZeO8gTZ08TZWrVNYb74ySJD312DOaMnGqSpQooRq1aqjfwLtVvkL5gCMtfNOmTNPDDz2qjPQMdbygg7ped03QIRWagfc+qGn+efH6O6/t8tqoka/rqSHPaPzkD1WpcqWAIgxOlM+L7KSnp+vSiy5Xalqqnnr230GHE5hzTj9P5cqVVbFiKUopnqJRb70adEiB6H/3AE2eNEVVqlTRmPfeDjqcooNKuoToMfJNXDxdA796KsfX5638QbdNvl+9pwzUs3NH6sYmV+R5242qHqKbmly12/KOB7fVvJU/6KbP79W8lT/4yZC0fNNK3fvFEN02+T795+cPdcORl+/x8QTto7fGqUbdA3c+H/7oCN1834169JUHddIZJ2jMiP/m8u7kU7JkSQ176Xm99c4bemPMaE2fOl3fzp0XdFiF5pz2Z+vJ557YZdmxJzTX6++8ptFjX1XtOrU0YvgrAUUXnPT0dD34wGA9O/QZvfP+GI37aJx+WfhL0GEVmnPbn61/P/f4bsuXLV2mr6bPUPUD0rJ5V/KL+nmRndGvvq569esFHUYoDH15qN4YOzqySZEktet4np4d9kzQYSAJhT4xMrMKZlYl86eg9rNg9c/asH1jjq9vSd+683GplFLyJrrwtK9/hh4+6S49fnI/dT7kvDzvs3laE32++AtJ0ueLv9Cx1Y+SJP245ldt3L5JkvTT2l9VtUzR+rZ01fJVmj3tG7Vp1yq20KTNGzdLkjZt2KzK+xetY9pXZqay5cpKknbs2KEdO3ZEagzkMc2OVoWKFXZZdvyJx6l4ca/TunGTxlq+bEUQoQVq/rz5qlW7lmrWqqkSJUvorLZnauKEiUGHVWiObnbUbueFJD3xyFO66bYbZVH6kMSJ+nmR1bKlyzR18jR17NQh6FAQEk2bNVWFihWDDgNJKLSldGZ2vaT7JG2RlJmFOEn1g4rp2OpH6bLDzleFkuX14IynJUlN9m+oA8qlqs/UB2Uy3dm8hxpWaaAFq39OuL1KpSpo7dZ/JElrt/6jSqV2v0BoU6uF5iyfn78HUsBG/Ps1XXbTxdq8afPOZTf0vVYP3TZEJUuVUJlyZTRo+IDgAgxIenq6LrnwMv256E91vvgiHXHkEUGHFBrvv/OBTj9zt5k2k97yZctVvXqsVyS1eprmfVu0Pu/5bdKEKaqWWk2HHNog6FACw3mxq0cHP6Z/9eqpTRtz/vIyKsxMPa7rIZmp04Xnq9NF5wcdEoqQqH7ZtCdCmxhJ6i2psXNuZV5WNrNukrpJ0tE9TlK9sw7P94BmLP1GM5Z+o4ZVGujiQ9vrvq+eUJNqDdWkWkMNaXmvJKl08VI6oFyqFqz+WQ+16KsSxYqrdPFS2q9EuZ3rvPbDGH2zYsFu24/vhZKkxlUPVZtaJ+nu6Y/k+7EUlFlT56hi5Qqqf1g9fTc7dowfvjFOfR/vrQaNDtZ7r32gV54cpRvuui7ASAtfSkqK3hz7utavW6/bevbSwp8X6uAGBwcdVuBeGjZCKSkpOuvcM4MOBQHbsnmLRg5/RU8NfSLxyoiEyRO9cSQNGx2umTNmBh1O4F56dbhS01K1etVqdb+2h+rWr6umzY4JOiwgaYQ5MfpF3l1q88Q5N0zSMEnq9EE3l2D1fbJg9c9KK7u/ypfYTyZp7MJx+nTR5N3W6zvtIUneGKPWNU/UM3NH7PL62q3rVKlURb+3qKL+2bZ+52t1ytdQ9yOv0AMznsy1xC9sfvz2J82cMltzps/Vtm3btXnjZj3U61H99cffatDISwJOPO14Dbq16CR7+a18hfJqdmwzTZ86PfKJ0Qfvfqipk6bp2eFPR/KbrNS0VC1dumzn8+VLlykttVqAEQVr8Z9L9NeSv3TZBVdKkpYvW6ErLrpGL7/+gqruXzXg6AoP50XMN3PmatLEyZo6ZZq2bd2mjRs36O4+92rQwwODDi0QqWmpkqQqVauo9Wmt9N2870iMgHwU5jFGfSVNN7OhZvZU5k9QwVQvG/tPqV6F2iqeUlzrt2/QNysW6NRaLVQ6pZQkqUrpSqpQMm8za81cNleta54gSWpd8wR9vWyuJGn/0lV0e7PueuqbF/X3xuX5fCQF65IbO+v5957W/73zb90ysIcaN22oOx6+TZs2bNJfi/6WJH07Y75q1K0RcKSFa/XqNVq/zkt8t2zZoq+++Ep169UNNKagfTH1S7368ig99vQjKl2mdNDhBKJR40Za9MciLV68RNu3bde4j8frlNatgg4rMAcfcpDGTfpQ744fo3fHj1FqWjW98tZLkUqKJM6LeD1vvUnjJ3ykjz59X4OHDFLz45pHNinavGmzNvrlhJs3bdaX07/SQQcfFHBUKErMLNQ/YRDmHqOhkiZImicpo6B3duvR16pR1UNVvuR+GtbmYb3503tKsRRJ0ieLJuv4A45Rq5onaEdGurZlbNPjs16QJM1duUA1y1fXgy3ulCRtSd+iJ+e8pHVxvT85GbtwnHo17aY2tVtoxabVemz2UEnShYeco/Ilyum6xpdKktJduvpMfZcIhc4AACAASURBVLAgDrtQpBRP0fV3dtVjfZ9UsWLFVK58WXW/u1vQYRWqlStWqt9d/ZWRka6MDKfTzzxNJ7c6OeiwCs09d/TTrK/naO3atTq3TXtd1+NajRz+irZt266but0iSWp8ZCP17XdHwJEWruLFi6vv3X3U/boblZGRoQ4d2+vgBtG50Lnnjv6avfO86KBuPbqq3fl5n8AmWUX9vED2Vq1apV49b5fkjVk965wz1aLliQFHFYw7e/fVzBmztHbtWp3R+ix1v+kGJudAvrCs41rCwszmOOeO3pv3FnQpXVHS78RojePJTYMK+T/urCjanrE18UoRUSqlTNAhhMaW9DxXLie90illgw4hNDJcetAhhAYXFjHFLMwFR4WvTEq5cHR3JJByS5NQn8bp/54beDuGucfoY39Chfcl7bySc86tDi4kAAAAoOgJSbVaqIU5MbrY/7dv3LJAp+sGAAAAkJxCmxg557jFNQAAAIBCEbrEyMxyvVuZc25sYcUCAAAAIBpClxhJym1KIieJxAgAAADYA8UYZJRQ6BIj59zVQccAAAAAIFpClxjFM7NzJDWStPPuj865+4OLCAAAAEAyCm1iZGbPSyorqbWk4ZIukDQj0KAAAACAIsgopUsozHfoOtE5d4WkNc65+ySdIOmQgGMCAAAAkITCnBht9v/dZGYHStou6YAA4wEAAACQpEJbSifpAzOrJOkRSbP8ZcMDjAcAAAAokiilSyzMidEQSd0ltZT0haQpkp4LNCIAAAAASSnMidFISeslPeU/v0TSK5IuCiwiAAAAAEkpzIlRY+dcw7jnn5vZgsCiAQAAAIooSukSC/PkC7PN7PjMJ2Z2nKSZAcYDAAAAIEmFuceoqaTpZrbIf15b0o9mNk+Sc84dGVxoAAAAAJJJmBOjs4IOAAAAAEgGVNIlFtrEyDn3R9AxAAAAAIiGMI8xAgAAAIBCEdoeIwAAAAD5g1npEqPHCAAAAEDkkRgBAAAAiDwSIwAAAACRxxgjAAAAIMkxxigxeowAAAAARB6JEQAAAIDIo5QOAAAASHImSukSoccIAAAAQOQlZY/Ra22fCDqE0Khy58lBhxAaqwdPDjqEUNiasTXoEEKjVEqZoEMIjdIpZYMOASHk5IIOITSKWUrQIQAoYEmZGAEAAACIYVa6xCilAwAAABB5JEYAAAAAIo9SOgAAACDJUUmXGD1GAAAAACKPxAgAAABA5FFKBwAAACS5YtTSJUSPEQAAAIDIIzECAAAAEHkkRgAAAAAijzFGAAAAQJIzxhglRI8RAAAAgMgjMQIAAAAQeZTSAQAAAEmOUrrE6DECAAAAEHkkRgAAAAAij1I6AAAAIMlRSZcYPUYAAAAAIo/ECAAAAEDkUUoHAAAAJDlmpUuMHiMAAAAAkUdiBAAAACDyKKUDAAAAkhyldInRYwQAAAAg8kiMAAAAAEQeiREAAACAyGOMEQAAAJDkGGOUGD1GAAAAACKPxAgAAABA5FFKVwD63z1AkydNUZUqVTTmvbeDDmePPX9hP7Vt2FIrNqxWs8c67/Z6pTLlNfSi/qpXtaa2bt+q69+6XwuW/bJP+yyZUkIvdrlfR9c8XKs3/aPLXrtTi9b8rVMbHKeBZ9+skikltC19u+764ElN+uXrfdpXEJb+vVT39O2n1StXSWbqdNH5uvTyS4IOq9AsW7pcg+5+SKtXr5FJanfBubrw0gv0f48/r+mTpqt4iRKqUfNA9b2/j8pX2C/ocAvVtCnT9PBDjyojPUMdL+igrtddE3RIgaEtYmiLmPXr1uv+fg/ol4W/SGbqP/BeNTnqyKDDKnRF/doiP9EWe4dSusToMSoA7Tqep2eHPRN0GHvt1Znvq/3wm3N8/Y5Tr9Hcv37UsY93Udc3+mtI+9553nbtygdo/A1Dd1t+1bEdtGbzOjV+uIOenjxKg87uKUlatXGtLnj5FjV/vLOue6O/Xrr4/j0/oBBIKZ6iXnfcqrEfjNGrb4zUm6Pf0i8Lfw06rEKTkpKiHr2767V3Rmjoa89q7Bv/1W+//K7mxzfVyDEva+R/XlStOjX12oujgg61UKWnp+vBBwbr2aHP6J33x2jcR+O8i78Ioi1iaItdPfrQYzrxpBM09oP/6M0xo1W/fr2gQwpEUb+2yE+0BQoKiVEBaNqsqSpUrBh0GHtt2m9ztHrTPzm+flhafU1a6PXa/LTid9WpcqBS96siSepyTFtNuXmkvrx1tJ7udJeKWd5OsXMbnaJRsz6QJI2d95laNThWkjT3rx/197qVkqQFy35R6RKlVDKlxF4fW1CqVaumwxseLkkqV66c6tevp+XLlwccVeHZv1pVHXr4IZKksuXKqm792lq5fKWOPbG5ihdPkSQ1OrKhVixfEWSYhW7+vPmqVbuWataqqRIlS+istmdq4oSJQYcVCNoihraIWb9+g2bPmqMOndpLkkqULKHyFcoHHFUwivq1RX6iLVBQQpcYmdl6M1uXzc96M1sXdHyQ5v31k9o3PlWS1KxWI9WuVF01Kqbq0NS6uqDJGWr9f111/BOXKD0jQ12OaZunbR5YsZoWr10mSUrPSNe6LRtUtWylXdbpeEQbfbPkB21L356/B1TIliz5Sz98/6OOOLJx0KEE4u8lS/XTDwvV8IjDd1n+4bsf67gWxwUUVTCWL1uu6tXTdj5PrZ6mZRFLDjPRFjG0Rcxfi5eocuVKGnD3fbq406W6v98D2rxpc9BhAUWSWbh/wiB0Y4ycc3v1VZCZdZPUTZKefu6pSNdjF7Qhn4/QkPa99eWto/Xd3ws1968fle4y1PrgY3VMjcM19V+vSJLKFC+lFRtWS5LevHKI6lQ5UCVTSqhWper68tbRkqT/m/K6Xp35fsJ9Hp5WXw+c01PnvtCj4A6sEGzauEm9/9Vbt/ftpf32i9ZYGknatGmz7unVTz1v76Fy+5XbufyVF15TSkqKzjjntACjAxA26enp+uH7H3XH3bfriCMb69GHhujl4SN0Y8/uQYcGIAmFLjHaW865YZKGSdLm9I0u4HCS2vqtG3X9W/ftfP5D3/f126olalHvaL026wP1+3j3ut/OI71xSLUrH6AXOg/Qmc9fv8vrf/2zQjUrpWnJP8uVUixFFUrvp1Wb1kqSalRM1ZtXDtG1b/TTb6sWF+CRFazt27er1y29dfa5Z6vN6W2CDqfQ7di+Q/fc1k+nn32aTjnt5J3LP/rvOE2f/IX+PeyxyA0MTU1L1dKly3Y+X750mdJSqwUYUXBoixjaIiY1LVWpaak7e9jbnNFGI4aPDDgqAMkqdKV0CL+KpfdTiRQvp7762I6a+ttsrd+6UZ//PEMdj2ijauUqS5Iql6mg2pWq52mbHy6YpEubnitJOv+INjvHMFUsvZ/GXvOk7v3oaX3x+9wCOJrC4ZzTfffer3r16+nyqy4LOpxC55zT4AGPqG79OupyxUU7l381bYZGj3hDDz05SKXLlA4wwmA0atxIi/5YpMWLl2j7tu0a9/F4ndK6VdBhBYK2iKEtYvavtr/Sqqfp999+lyTN+PJr1TsompMvAPvKzEL9EwbmXPJ1rgTdY3Rn776aOWOW1q5dqypVq6j7TTeoY6cOgcRS5c6TE6+UxchLBqnlQc20f7lKWr5+lQZ+MnRnIjT8yzE6rs4ReqHzfXLO6ftlv+qGt+/X2s3rJUkXNDldvU+9WsWsmLan79Ct7wzWjEXzd247px6jUsVL6qUuA9WkxqFas+kfXT7qLv2+eon6tOmq20+9WgtXLtq57nnDemjFxjV7fFyrB0/e4/fklzmz5ujqy7uqwSEHy/wJKW6+5Sa1POWkQo9l/facJ9YoKN/OnqceV/dU/Qb1VayY98ev283X6smHn9b2bdtVoVIFSVKjIxqq9723FVpcFUpUSrxSAZsyaYoeGTxEGRkZ6tCxva674dqgQwoMbRETlrZIdzsC2W+8H7//Uff3H6Tt27erZs0aGvBAP1WoWKHQ4yhmKYW+z3hhurYIWtjaokxKuXBc1Sdw4AOnhPqi/697JgXejiRGSW5vEqNkFWRiFCZBJEZhFYbECAizMCRGYRF0YoTwIjHKH2FIjJJmjBEAAACA7IWlXC3MGGMEAAAAIPJIjAAAAABEHqV0AAAAQJIrRildQvQYAQAAAIg8EiMAAAAAkUdiBAAAACDyGGMEAAAAJDmGGCVGjxEAAACAyCMxAgAAABB5lNIBAAAASc6opUuIHiMAAAAAkUdiBAAAACDyKKUDAAAAkpyJUrpE6DECAAAAEHkkRgAAAAAij1I6AAAAIMkxK11i9BgBAAAAiDwSIwAAAACRRykdAAAAkOQopUuMHiMAAAAAkUdiBAAAACDySIwAAAAARB5jjAAAAIAkxxCjxOgxAgAAABB5JEYAAAAAIo9SOgAAACDJMV13YvQYAQAAAIi8pOwxci4j6BBCY83gKUGHEBplOjcOOoRQ2PjG3KBDQAht2rEh6BBCo1RKmaBDCI0US8rLBOwjJxd0CECB4C8eAAAAkOQopUuMUjoAAAAAkUdiBAAAACDyKKUDAAAAkhyldInRYwQAAAAg8kiMAAAAAEQepXQAAABAkqOSLjF6jAAAAABEHokRAAAAgMijlA4AAABIcsxKlxg9RgAAAAAij8QIAAAAQOSRGAEAAACIPMYYAQAAAEmOMUaJ0WMEAAAAIPJIjAAAAABEHqV0AAAAQJKjlC4xeowAAAAARB6JEQAAAIDIo5QOAAAASHJU0iVGjxEAAACAyCMxAgAAABB5lNIBAAAASY5Z6RKjxwgAAABA5JEYAQAAAIg8SukAAACAJEcpXWL0GAEAAACIPBIjAAAAAJFHYlRA0tPT1aXTJep54y1BhxKoaVOmqd3ZHXTume304gsvBR3OHqtZ9QBNGDBK3z0xXvOfGKeeZ1+12zqXtGyvuY99pG8f+1jTBr2tI+scts/7LVm8pN649Sn9/PQEffnQWNWpVkOSdNqRJ2nmw//Vt499rJkP/1etG5+wz/sKCp8RT1H/jOyLB/o9pLNPOU+Xdrxil+Vvj/6POre7VJd0vFzPPP5sQNEF65zTz9NFHTqry/mX6NKLLg86nEBF+TOSFW3h6X/3ALU+qY06tbsw6FCQZBhjVEBGv/q66tWvp40bNwYdSmDS09P14AODNXT4c0pLS9MlnS9Vq9an6KCDDwo6tDzbkb5DvUY+qDm/faf9SpfTrEfe06ffTtX3ixfuXOe35X/qlH5dtHbjOp119CkadsODOr7v+Xnafp1qNTTipkfVuv8luyzv2uYirdm4Tg1uPlWdW5yrhy/roy5P9NTK9at13uDr9Pea5WpU6xCNv2eEal5/Yr4ec2HhM5Icn5F9cU67trqwy/m6/+5BO5fNmjFbkz+fqlf/87JKliyp1avWBBhhsIa+PFSVK1cKOoxARf0zEo+2iGnX8Tx1ubSz7rmzX9ChFCmMMUqMHqMCsGzpMk2dPE0dO3UIOpRAzZ83X7Vq11LNWjVVomQJndX2TE2cMDHosPbI0rUrNOe37yRJG7Zs1PdLFqpGleq7rPPFj7O1duM6SdKXP81RzbjXL23ZXl899I7mPPqBnu/2gIoVy9tHrn3z0zRy4hhJ0n+++FhtjvCSn29+W6C/1yyXJH33508qU7K0ShYvuW8HGQA+I55k+Izsi6ObHaUKFSvssmzsW+/q8q6XqWRJ77yuUrVyEKEhJKL+GYlHW8Q0bdZUFSpWDDoMJKFQJkZmtlvfaHbLwurRwY/pX716qlixaGfmy5ctV/XqaTufp1ZP07LlKwKMaN/UqVZDR9dtpK9+/ibHdbq2uUgfz5kkSTqsxkHq3OJctbjnQh19+7lKz8jQpS3b52lfNaqk6c+Vf0uS0jPS9c+m9apaftcLxE7Ht9Xs377Tth3b9vKIgsNnxJNsn5H88Ocff2rurLnqekk3db/6Ji2Y/33QIQXCzNTjuh665MLLNOatsUGHExg+IzG0BVDwwlpK11fS23lYFjqTJ05RlSpV1LDR4Zo5Y2bQ4SCflCtdVmN6P6tbRgzU+s0bsl2nVaPj1fXUi3TSPRdJktoccaKa1m+srwe/K0kqU7K0lq9bJUkae/tzqpdaSyWLl1Dt/Q/UnEc/kCQ9+dEIjfj8PwnjaVizgR6+7A6dMfDK/Di8QsVnBLlJ35GudevWafiooVow/3vd07u/xnz8ZuRKQF56dbhS01K1etVqdb+2h+rWr6umzY4JOiwARVjE/ozulVAlRmbWVtLZkmqY2VNxL1WQtCPBe7tJ6iZJTz/7pK657uoCizM338yZq0kTJ2vqlGnatnWbNm7coLv73KtBDw8MJJ4gpaalaunSZTufL1+6TGmp1QKMaO8UTymuMb2f1agp7+mdr8Znu84RdQ7T8O4Pqe2ga7R6w1pJ3je+IyeO1V2jH91t/fMf7S4p5zFGS1YvU639D9CS1UuVUixFFcuW16r13liLGlWq6507ntcVT/fWr8sW5eehFgo+IzHJ8hnJT9XSqqlVm1NkZmp0REMVK2Zau2atKleJVkldalqqJKlK1SpqfVorfTfvu0gmRnxGYmgLoOCFrZTuL0kzJW2RNCvu5z1JZ+b2RufcMOdcM+dcs6CSIknqeetNGj/hI3306fsaPGSQmh/XPJIXfJLUqHEjLfpjkRYvXqLt27Zr3MfjdUrrVkGHtcdevHGwvl/8i5744MVsX6+1/4Ea2/tZXf50L/389287l382b7ouOKGtqlWoKkmqvF9F1d7/wDzt872Zn+nKVp0kSRec0FYT5n8hSapYtrw+vOtF3TnqEU3/cda+HFZg+IzEJMtnJD+dfGpLzfp6tiRp0e+LtH37DlWK2AQEmzdt3jkpyeZNm/Xl9K8iOcBe4jMSj7YACl6oeoycc3MlzTWz0c657ZJkZpUl1XLORXdqoiKqePHi6nt3H3W/7kZlZGSoQ8f2OrhB0frPvcVhzXTFKefr2z9+2FnudtfoIapdzUtwhn4yWv0uuFlVy1fWs9feL0nakZGu5n3a6/vFC3XP64/pk3tHqlixYtq+Y7t6DO+vRSv/SrjfFz97U6/2fFw/Pz1Bqzf8oy5P9JQk3dT2Ch1cvY76XXCz+l1wsyTpjIFXaoVfooeiJRk+I/ui3x0DNHvmHK1d+4/anXa+rr3xGp3X8RwN6veQLu14hYqXKK57H7grcmV0q1atUq+et0vyZiI765wz1aJl0Zx9cl9F/TMSj7aIubN3X82cMUtr167VGa3PUvebboj8ZD55EbW/pXvDnHNBx7AbM5soqZ28xG2WpOWSpjvnbs3L+zftWB++gwpIMUsJOoTQKNO5cdAhhMLGN+YGHUJo8PmI2bQj+7FzUVQqpUzQIYRGCp8RZMOJy6x4ZVLKFYmM46jn24f6F/fNDf8NvB3DVkqXqaJzbp2k8yW94pw7TlKbgGMCAAAAkKRCVUoXp7iZHSDpIkl3Bx0MAAAAUKRRSpdQWHuM7pc0XtJC59zXZlZf0s8BxwQAAAAgSYWyx8g597bi7lnknPtVUqfgIgIAAACQzEKVGJnZHc65R8zsaWn3kX3OuZ4BhAUAAAAUacxKl1ioEiNJfSQ9IukXSUzPDQAAAKBQhC0xWmZmB0q6WlIrSaS2AAAAAApc2BKj5yR9Jqm+vPsXZTJ5pXX1gwgKAAAAQHILVWLknHta0tNm9pxzrnvQ8QAAAADJgCFGiYVyum6SIgAAAACFKZSJEQAAAAAUplCV0gEAAADIf0zXnRg9RgAAAAAij8QIAAAAQORRSgcAAAAkOUrpEqPHCAAAAEDkkRgBAAAAiDxK6QAAAIAkRyldYvQYAQAAAIg8EiMAAAAAkUcpHQAAAJDkqKRLjB4jAAAAAJFHYgQAAAAg8iilAwAAAJIcs9IlRo8RAAAAgMgjMQIAAAAQeSRGAAAAAELNzF4ys+VmNj9u2QAzW2Jm3/g/Z8e91tfMFprZj2Z2Zl72wRgjAAAAIMklwRijEZKekfRKluVPOOeGxC8ws4aSukhqJOlASf8zs0Occ+m57YAeIwAAAACh5pybLGl1HldvL+kN59xW59xvkhZKOjbRm5Kyx6iYpQQdAkJo85vzE68UAWXOOiToEEJj07gfgw4hNMoULxd0CKFhKvLfqgIFis8IQuYmM7tC0kxJvZxzayTVkPRl3DqL/WW5oscIAAAASHJmFvafbmY2M+6nWx4O6zlJB0k6StLfkh7blzZKyh4jAAAAAEWHc26YpGF7+J5lmY/N7AVJH/hPl0iqFbdqTX9ZrugxAgAAAFDkmNkBcU87SsocN/GepC5mVsrM6klqIGlGou3RYwQAAAAkuaI+K52ZvS6plaT9zWyxpP6SWpnZUZKcpN8lXS9JzrnvzOwtSQsk7ZDUI9GMdBKJEQAAAICQc85dnM3iF3NZf5CkQXuyD0rpAAAAAEQePUYAAABAkivilXSFgh4jAAAAAJFHYgQAAAAg8iilAwAAAJJcUZ+VrjDQYwQAAAAg8kiMAAAAAEQeiREAAACAyGOMEQAAAJDkGGOUGD1GAAAAACKPxAgAAABA5FFKBwAAACQ5SukSo8cIAAAAQOSRGAEAAACIPErpAAAAgCRHJV1i9BgBAAAAiDwSIwAAAACRRykdAAAAkOSYlS4xeowAAAAARB6JEQAAAIDIo5QOAAAASHaU0iVEjxEAAACAyCMxAgAAABB5JEYFYNqUaWp3dgede2Y7vfjCS0GHEyjaIqaot0XNagdowqNv6bvhEzT/hc/Us2PX3dZpd8IZmjv0U815fry+/r8P1aJR833eb+XylfTJ4NH6acQUfTJ4tCrtV1GSdMmpHTV36Kf6dtj/NO3f7+rI+ofv874KW/+7B6j1SW3Uqd2FQYcSONpiV0X970V+oi1iaIsY2mLPmVmof8KAxCifpaen68EHBuvZoc/onffHaNxH4/TLwl+CDisQtEVMMrTFjvR09Rp6vxpde6qO79lOPdpdqcNrN9hlnc/mTFWT60/X0TecqWuG9Nbw2x7N8/ZPOfIEvXz747stv7NzD302Z5oOuaqlPpszTXd26SFJ+m3pIp3S6wId2e00DRz1pIbd8si+HWAA2nU8T88OeyboMEKBtohJhr8X+YW2iKEtYmgLFBQSo3w2f9581apdSzVr1VSJkiV0VtszNXHCxKDDCgRtEZMMbbF09XLNWThfkrRh80Z9v+hn1di/+i7rbNyyaefjcqXLyMntfN77whs045kPNHfopxpwRa8877f9iWdo5KdvS5JGfvq2Opx4piTpiwWztHbDP5KkL7+frZrVDti7AwtQ02ZNVaFixaDDCAXaIiYZ/l7kF9oihraIoS1QUEiM8tnyZctVvXrazuep1dO0bPmKACMKDm0Rk2xtUSetpo4+uLG++mHObq91aHGWvn9xoj584BVdM8RLgE5verIa1KinY286V0fdcIaaNjhCLY84Lk/7Squ8v5auXi7JS87SKu+/2zpdz+qij7/+fB+OCAiPZPt7sS9oixjaIoa2QEEJ1XTdZva+FPcVcxbOuXaFGA6AbJQrXVZj+g3TLc8N0PpNG3Z7/d1p4/TutHFqecRxGnjV7Tq9z8U6o+nJOqPpyZrz/HhJ0n6ly6lBjXqaMu8rffnU+ypVsqT2K11OVcpX2rlOn+EP6pOZk3bbvnO7/olo1eREdW3bRSfd0rEAjhYAgORQLBzDeEItVImRpCH+v+dLqi7pNf/5xZKW5fZGM+smqZskPfPc0+p63TUFFWOuUtNStXRpLNTlS5cpLbVaILEEjbaISZa2KJ5SXGP6D9OoCe/onakf57rulHlfqf4BtVW1QmWZmR564xkN+3DUbusd3/M8Sd4Yo6vOvFBXP3rbLq8vW7NS1aukaunq5apeJVXL167a+doR9Q7X8NseUdu7Ltfq9Wvz4QiB4CXL34v8QFvE0BYxtAUKSqhK6Zxzk5xzkyS1cM51ds697/9cIqllgvcOc841c841CyopkqRGjRtp0R+LtHjxEm3ftl3jPh6vU1q3CiyeINEWMcnSFi/2GqLvFy3UE2NeyPb1gw6su/Px0Qc3VqkSpbRq3RqNnzlJ15zZReVKl5UkHVi1uqpVqpqnfb73xae68nRvprIrT79Q/53+iSSpVrUDNbb/C7r84X/p5yW/7cNRAeGSLH8v8gNtEUNbxNAWKChh6zHKVM7M6jvnfpUkM6snqVzAMeVJ8eLF1ffuPup+3Y3KyMhQh47tdXCDg4IOKxC0RUwytEWLRs11xekX6Ntfv99Z7nbXSw+rduqBkqShH7ymTi3P1hWnddL29B3avHWLOj/QXZL06azJOrx2A33x1HuSvMkbLhvcUyvien9yMviNZ/TWvc+ra9su+mPZYl3kb7Pf5beqaoVKerbng5KkHek71LzHOfl+3AXpzt59NXPGLK1du1ZntD5L3W+6QR07dQg6rEDQFjHJ8Pciv9AWMbRFDG2xd8IyJXaYWdZ6/TAws7MkDZP0qySTVEfS9c658Xl5/5b0TeE7KCAkypx1SNAhhMamcT8GHQJCyMTFA4C8K51Stkj80ThtzJWhvj7+X6eRgbdjKHuMnHPjzKyBpMP8RT8457YGGRMAAACA5BXKxMjMykq6TVId59x1ZtbAzA51zn0QdGwAAABAUVOMUrqEQjX5QpyXJW2TdIL/fImkB4ILBwAAAEAyC2tidJBz7hFJ2yXJObdJougbAAAAQMEIZSmdpG1mVkb+zV7N7CBJjDECAAAA9gKz0iUW1sRogKRxkmqZ2ShJLSRdFWRAAAAAAJJXKBMj59wnZjZL0vHySuj+5ZxbGXBYAAAAAJJUKBMjM3tf0mhJ7znnNgYdDwAAAFCUhXVigTAJaxsNkdRS0gIz+4+ZXWBmpYMOCgAAAEByCmWPkXNukqRJZpYi6VRJ10l6SVKFQAMDAAAAkJRCmRhJkj8r3XmSOks6RtLIYCMCAAAAkKxCxZDmBQAAIABJREFUmRiZ2VuSjpU3M90zkiY55zKCjQoAAAAomooxXXdCoUyMJL0o6WLnXHrQgQAAAABIfmFNjCZI6mFmJ/vPJ0l63jm3PcCYAAAAACSpsCZGz0kqIelZ//nl/rJrA4sIAAAAKKKMUrqEwpoYNXfONYl7PsHM5gYWDQAAAICkFtb7GKWb2UH/z959x0dV5X0c/5wkIF1AKSqIgOiioqti72JBXAEFsbu6iGUVy4qFdQXb2td17V0URdRF7KKuilKsa4O1PIIdKdKRTnKePxIgWBgSktybmc/b17wmc+fOvd853hnyyzn33OUPQghtAM83kiRJklQp0tpjdB7wWgjhSyAArYATk40kSZIkVU/OSpdZKgujGOMrIYR2wOYliz6PMS5OMpMkSZKk7JXKwiiEkA8cCGxCccb9QgjEGG9INJgkSZKkrJTKwgh4BlgEjAO8sKskSZK0FpyVLrO0FkYtYoxbJx1CkiRJUm5I66x0L4QQDkg6hCRJkqTckNYeo7eA4SGEPGApxTPTxRhjg2RjSZIkSdVPWntD0iSthdENwC7AuBhjTDqMJEmSpOyW1uLxO2C8RZEkSZKkqpDWHqMvgZEhhBeAFdcvcrpuSZIkSZUhrYXRVyW3miU3SZIkSeWU53TdGaWyMIoxXrq650MIN8cY+1ZVHkmSJEnZLZWF0RrYLekAUnU1+em3ko6QGh3+eWjSEVJj/DlPJh1BSrWfls5NOkJq1Cmom3QEqVJU18JIkiRJ0hoKDqXLKK2z0kmSJElSlamuhZElryRJkqQKk8rCKIRweIZl/6rCOJIkSVK1lhdCqm9pkMrCCOi/umUxxkFVF0WSJElStkvV5AshhIOALsBGIYSbSj3VAFiWTCpJkiRJ2S5VhRHwA/Ae0BX4b6nl84BzEkkkSZIkVXPpGKyWbqkqjGKMHwEfhRCGxBiXAoQQGgEtY4yzkk0nSZIkKVul9Ryjl0MIDUIIjYH3gbtDCP9MOpQkSZKk7JSqHqNS1o0xzg0hnAQ8GGMcGEL4OOlQkiRJUnWUlpnf0iytPUYFIYQNgF7As0mHkSRJkpTd0loYXQa8CEyIMb4bQmgDfJFwJkmSJElZKpVD6WKMjwOPl3r8JdAjuUSSJElS9eVQusxSVRiFEM6PMV4bQrgZiD9/PsZ4ZgKxJEmSJGW5VBVGwAXAtcBEwOm5JUmSJFWJtBVGU0MIGwInAnvjtagkSZIkVYG0FUa3A68AbYD/lloeKB5a1yaJUJIkSVJ1FjzHKKNUFUYxxpuBm0MIt8cYT0s6jyRJkqTckMrpui2KJEmSJFWlVPUYSZIkSap4TtedWSp7jCRJkiSpKlkYSZIkScp5DqWTJEmSspwD6TKzx0iSJElSzrMwkiRJkpTzHEonSZIkZTlnpcvMHiNJkiRJOc/CSJIkSVLOcyhdJRgzagzXXHUdRYVFHNqzO737/CnpSImxLVbK5baYNmUaf//b1cyaOYtA4JAeB9PzmB7cf/sDPPvEczRs1BCAPn17s/MeOyWcNrOrDvwL+7bdmRkLZtNl0Mm/eP6kHQ6na/t9ASjIy6dt45bseFsv5iyaV+591syvwXUHncdWzdoxa9E8znrm70yaO5XdWm3HeXv0pkZ+AUsLl3H163fz1ncflns/Scrlz8jP2RYr5XJbTJ0yjcsv+juzZs4CAt16HkKvY3oyd85cLj7/Eqb8MIXmGzbn8usupUGD+knHrXKFhYUc0+s4mjZryk233Zh0nNRzKF1mFkYVrLCwkCuvuJo777mdZs2acfQRx7D3PnvRdtO2SUercrbFSrneFvn5+Zx+7qls1n4zFsxfQJ+jTqXjztsDcPixPTnyj70STlg2T/zvZR764Gmu63L+rz5/z7uPc8+7jwOwb5udObHjYWtcFG3UoBnXHtSPYx49b5Xlh3fozJxFP9Hp3hM5ePO9OX/P3pz17JXMWjiHk4dfzLT5M2m3/ibc3+NKdr/z6LV7gwnI9c9IabbFSrneFvn5+fTtdzqbt9+M+fMX0PvIPuywc0eef/oFOu64Pcf1PobB9z7MQ/c+zJ/POTXpuFVuyOBHaN2mNfPnz086irKEQ+kq2Phx42m5cUtatGxBjZo16HzQgYx8dWTSsRJhW6yU622xXpP12Kz9ZgDUqVuHVm1a8eO06QmnKr93vx/H7DUsdP7Qfm+e/fS1FY+7te/EsGNu4unjb+fy/c8iL6zZ1/B+bXdh+P9eBmDE/73BLhtvC8An0yYybf5MAL6Y/jW1CmpSM79GWd5OKuT6Z6Q022KlXG+L9Zusx+Yl3511V3x3/sio18ZwUNfOABzUtTNvvDY6yZiJmDplKqPfGMOhPbonHUVZxMKogk2bOo3mzZuteNy0eTOmTvsxwUTJsS1Wsi1WmjxpCl98NoEtOrQHYPjQJznx8JO4euB1zJtb/qFmaVSrYB323KQjI74o/qWlbeOWHPy7vTjikXPo+uBpFMWiFUPuMmlWf30mzys+ZgpjET8tmU+j2g1WWafzZnvwv2kTWFK4tGLfSBXwM7KSbbGSbbHS5EmT+eKzL9iywxbMmjmL9ZusB8B66zcuGWqXW667+h+cde6Z5OU5PEwV5zeH0oUQvqygfcQYY5n6vEMIbYB/AbsARcCbwDkxxorKJCkBCxYsZEC/S+h73p+pW68u3XodwvEnH0sIgXtvvZ9b/3EHF156XuYNVRP7tt2Z93/4ZMUwul1bbcuWzdrxxLG3AFCroCYzFswG4LZuA2mxbnNq5hewQf2mPH387QA88P5who1/KeO+2q3XivP37M0Jj/evpHcjKSkLFizgonMHcOZ5falbr+4qz4UQyLXS4I2Ro2jcuDFbbNme9955L+k41UbwHKOMVneO0SYVtI9YjtcMAW4FDi15fCTwCPCbZ2WHEE4GTga45fabEzs5s2mzpkyZMnXF42lTptKsaZNEsiTNtljJtoBlS5cx4NxL2K9LJ/bstAcAjddrvOL5Pxx2MP3PvCipeJXiD7/bm2dKDaODwPD/vcz1o+77xbp/fupS4LfPMZo6bzob1G/ClJ+mkx/yqFezLrMWzgWgeb31ua3bQPo9fy3fzplcae+nMvkZWcm2WMm2KP7uvOgvAzigy37svd+eADRq3IjpP85g/SbrMf3HGTRs3CjhlFXrww8+4vWRbzB61BiWLF7C/Pk/cdEFF/P3ay5POpqqudUNpTuxgm7lqVDqxBgHxxiXldweAmqt7gUxxrtijB1jjB2TnLFmy6225NtvvuX77yexdMlSRrzwInvts3dieZJkW6yU620RY+SaS6+nVeuNOeK4w1csn/HjjBU/j3p1NK033SSBdJWjXs067NiiA/+Z+OaKZW9++wGdN9uDxnWKZ+Fbt1Z9NmzQdI2298rENzl0y/0B6LzZnitmnqu/Tl3uPuxyrht1L+//8EkFv4uqk+ufkdJsi5VyvS1ijFx1yTW0atOKI48/YsXy3ffejReeHgHAC0+PYI99dksqYiLOPOcMXnz1eZ5/+Rmuvv7v7LDTDhZFqhC/2WMUY3ygKoMAhBCW//n4hRDChcBQinucjgCer+o85VFQUED/iy7gtD5/pqioiO6HdmPTdrkxe87P2RYr5XpbjPtwPC89+zJt2rWmd6/i6a379O3Nf0a8yoTPJxICNN+wOf3+dk7CSdfMPw/uz04tt6ZR7XUZfcrD/GvMYAry8wF45KPnADig3W6M/uZ9Fi5dtOJ1E2Z8yw2jBzGo51XkhcCywkIueeVmfpg7LeM+Hxs3gn90uYBXet/P7EXzOPvZKwE4bttutGq0EWfscixn7HIsACf8uz8zS4boVRe5/hkpzbZYKdfb4uMPxjHi2Zdo264Nf+zVG4BT+vbhuD8dzcXnXcKzTz5H8w2ac/l1lyQbVNWC03VnFmIsz0i3yhFC+IriQujX/s/FGGObNdnOosIF6XlTUsrMXjIz6QipsfutuXM9lEzGn/Nk0hGkVPtp6dykI6RGnYK6mVfKIXUK6leLiuPkV89O9e/Hd+17Y+LtmKrrGMUYWyedQZIkSVLuSVVhtFwIIR84mOIJIFZkjDHekFQmSZIkqbpKvDumGihXYRRC2AY4HdgdaAGsrk81xhjLup9ngEXAOIqn65YkSZKkSlPmwiiEcAZwA5BP5RWfLWKMW1fStiVJkiRpFWUqjEIIO1F84VWA24DnKJ4tbibQC2gO7AccDcwFzgTKc1GNF0IIB8QYM1/VUJIkSdJqOStdZmXtMTqT4l6iG2OMf4EVV9FdEmN8tWSdISGEm4AXgcuB7cqR6y1geAghD1hass8YY2xQjm1JkiRJ0mqt7gKvv2Y3iqfT/tfPlq9SgsYYPwT6Am2B8yi7G4BdKL7Qa4MYY32LIkmSJEmVpaw9Rs2AxTHGb0otKwJq/cq6wynu7TkMGFDG/XwHjI9pusiSJEmSVE05lC6zshZGCyjuMSptHtAghLBOjHHx8oUxxqUhhAVAq3Lk+hIYGUJ4ASi9TafrliRJklThyjqUbhLFRVDpgmpiyf0OpVcMIWwIrEv5Zq77CngFqAnUL3WTJEmSpApX1h6jT4EtgQ7AByXLRlI8wcKAEELXGOOiEEJN4KaS58eVNVSM8dKyvkaSJEnSrwsOpcuorIXRS8DhwCGsLIxupfhir52A70MInwObAY0pHnZ3S1lDhRBe45dD9ogx7lvWbUmSJElSJmUtjIYBLYAfli+IMX4VQjgauJ/iYmiXkqeKgOtijA+XI1e/Uj/XAnoAy8qxHUmSJEnKqEyFUYxxNvCLYW4xxuEhhNeBLkBLYA7wUoxxQnlCxRj/+7NFY0II75RnW5IkSZKUSVl7jH5TjHEm8FBFbCuE0LjUwzxge4oncpAkSZJURmWdcS0XVVhhVMH+S/E5RoHiIXRfAb0TTSRJkiQpa6WyMIoxtk46gyRJkqTcUabCKITwajn2EWOMncr6ohDCrsAmlMoYY3ywHPuXJEmScprTdWdW1h6jvddwveVTbQd+ZdrtTEIIg4G2wIdAYaltWhhJkiRJqnBlLYwyXXh1XWAniqfsngHczsrCpiw6AlvEGMtcVEmSJElSWZV1uu5MhREAIYR9gScoLm56liPXeKA5MLkcr5UkSZJUSp5D6TKqlMkXYoyvhhDOAu4LIZwUY7xnTV4XQniG4iFz9YFPSq5dtLjUdrtWRl5JkiRJua0yZ6V7FLgbOAlYo8IIuJ7i85KuAbqXWr58mSRJkiRVuEorjGKMi0II84H2ZXjN6wAhhBrLf14uhFC7giNKkiRJOcGhdJlVWmEUQtiI4skYfirDa04D/gy0CSF8XOqp+sCYik0oSZIkScUqpTAq6d25reThuDK8dAjwAnAVcGGp5fNijDMrKJ4kSZIkraKsF3gdkGGVWkBL4EBgPYonUrh1TbcfY5wDzAGOKksuSZIkSb/NC7xmVtYeo0tYswu2BqAIuCLGOKSsoSRJkiSpKpW1MHqD1RdGy4BZwEfAYzHGL8obTJIkSZKqSlkv8Lp3JeWoUEuLliQdITVq5NVMOkJqxDXq7Mx+dQrqJh0hNcaf82TSEVKj9hk7JB0hNRbe8m7SEVKjMBYmHSE16tVokHSE1Php6dykI6RKnYL6SUdQBanM6xhJkiRJSoE8PMcok7yyrBxCGBBC+EsZ1j9zDSZskCRJkqRElakwonjyhX5lWP8cYGAZ9yFJkiRJVcqhdJIkSVKWc7ruzMraY1RWjYFFlbwPSZIkSVorlVYYhRAOB+oD31bWPiRJkiSpIqx2KF0I4SzgrJ8tbhJC+HJ1LwMaAg0ovubRc2uVUJIkSdJayXMoXUaZzjFqCGzys2X5v7Lst7wCXFa2SJIkSZJUtTIVRk8CX5f8HID7gDnA2at5TREwFxgfY5y4tgElSZIkqbKttjCKMX4EfLT8cQjhPmBhjPGByg4mSZIkqWIEL/CaUZmm644xVvYsdpIkSZJU5Sx0JEmSJOW8MhVGIYSdQwjvhxBuXYN17ylZt2P540mSJElaWyGEVN/SoKw9RkcD2wCj1mDdt4Dfl7xGkiRJklKrrIXRXiX3L63BusNL7vcp4z4kSZIkqUqVtTBqAcyJMc7MtGKMcQbFU3tvVJ5gkiRJklRVyjQrHVAbWFKG9QNQv4z7kCRJklSB8lJyHk+albXHaBpQP4SwYaYVQwgbAQ2A6eUJJkmSJElVpayF0Vsl96evwbrL13m7jPuQJEmSpCpV1sLoXoqHx50fQjj5t1YKIZwCnA/EktdIkiRJSkggL9W3NCjTOUYxxpdDCP8GegK3hxBOB54FvilZpRVwCLAlxQXUsBjjCxWYV5IkSZIqXFknXwD4I8U9QYcDHYCtfvb88jO7hgK9yx9NkiRJkqpGmQujGONC4IgQwp3An4BdgeYUF0tTgLHAvTHGkRWYU5IkSVI5OStdZuXpMQIgxvgq8OpvPR9CyAMOBnrHGLuXdz+SJEmSVNnKXRj9lhBCO4qH0B0PNKvo7UuSJElSRauQwiiEUAfoRXFBtOvyxSX3n1bEPiRJkiSVT3AoXUZrVRiFEHamuBjqBdRbvhj4DHgceDzGOH6tEkqSJElSJStzYRRCaELxMLk/Ab9bvrjkPgI7xBj/WzHxJEmSJKnyrVFhFIr73rpQXAz9oeR1AVgIPAk8AIwoWT3nh849/OAjPDXsKQiBTdu1ZeAVF7POOuskHSsRY0aN4ZqrrqOosIhDe3and58/JR0pEQMvuoQ3Xh9F48aNGfb040nHSdwjgx/lyWFPEWOke89uHH3ckUlHSky2fUZaNGrOg3+8imYN1iPGyF2jH+em1x5aq20ev3M3/nbQKQBc8cKdPPjWU9SuUYvH+9xA2yYtKSwq4plxI+n/5D8r4i2kQrYdF2vj4P0PoW7dOuTl5ZNfkM/Djw1OOlJicvm4uHLA1Yx5400aNW7EQ08MAuCLzydw3RX/YOGChWywYXMGXnUxdevVTTZoigUcSpfJai8zG0JoG0L4O/Ad8DRwKMVF0WigD9A8xnhMjPGlSk9aTUybOo1HH36UBx8dxGNPPkJRUREvvfBy0rESUVhYyJVXXM1td97C8GeGMeL5EUycMDHpWInoeugh3HbXLUnHSIUJX0zkyWFP8cAj9zFk2GBGvz6a7779LulYicjGz8iywmWcO+xatrysKztfexSn73UU7Zu3XaPXvnbO/bRqvOEqyxrVWZeBB5/GTtccxY7XHMnAg0+jYZ0GAFz/n0G0v/QQtr2yJ7u13ZbOW+5e4e8nCdl4XKytO++/k6FPDMnpoijXj4su3Q7ihtuvW2XZ1Zdey2lnncLgYYPYc989eHjQ0ITSKVustjACvgAuBDYEvgYuBTaNMe4VY7w3xjivkvNVS4XLClm8eDHLli1j0cJFNGmyftKREjF+3HhabtySFi1bUKNmDTofdCAjXx2ZdKxEbN9xexqsu27SMVLh6y+/ZqsOW1Krdi0KCgrYruN2vPafkUnHSkQ2fkamzJ3OB98VDxz4afECPp3yJRs1bEqb9Vvywhl38l7/x3jj3AfZvFnrNdregVvsxsufvsmsBXOYvWAuL3/6Jp232J2FSxcx8v/eAWBp4VLe//YTWjRsXmnvqypl43GhtZfrx8Xvt9+GBg3qr7Lsu2++5/fbbwPADrvswOuvvJ5ENGWRTIXRcjcB7WOMl8YYv6rMQKsTQqiZ1L7XVNNmTTn2hGP4w37d6LzPwdSrX4+dd9s56ViJmDZ1Gs2br5yxvWnzZkyd9mOCiZQGbTdtw4fvf8js2XNYtHARY0eNZeqUqUnHSkS2f0ZaNd6QbVu25+2vP+auYy6h76N/p+NVveg37DpuO+riNdrGRg2b8t2sKSsefz9rKhs1bLrKOuvWrs8hW+/NK5+/VaH5k5Ltx0VZhRA4vc/pHH34sQx77Imk4yTG4+KXWrfdhFGvjQbgtZdeY+qUaQknSre8EFJ9S4NM5xgtBtYB+gLHhhAeBQbHGCv9X58QwkjghBjj1yWPdwTuBrap7H2vjblz5vL6a2/w9IvDqV+/Phec25/nn3mBLocclHQ0KRVat23N8X86jr4nn0nt2rXZbPN25OXlJx1LFazuOnUYdsqNnP341RTFyK5tfs/jfVaeA7ROQQ0ATtilO2ftcxwAmzbZmOfPuIMly5by1YzvOezOszLuJz8vn0d6X8dNrz3MV9O/r5w3o0TdN/gemjZryswZMzntpNPZpM0mbN9xu6RjKQX+eukF/PPqmxh014Psvvdu1KhRI+lIquYyFUYbAMdSPCX3NsBpwKkhhAkUT7jwUIzx20rKdhUwIoRwE7ARcBBw4m+tHEI4GTgZ4F+3/ZMTTzqhkmKt3jtvvcuGG21Io8aNANin0z58/OG4nCyMmjZrypRSPQHTpkylWdMmCSZSWnTr0ZVuPboCcOuNt9O0eW4eF9n6GSnIK2DYyTfy8DvPMfzD/1C/Vl1mL5zHtlf2+MW6g958kkFvPgkUn2N0wgMX8c3MH1Y8P2n2NPbebIcVj1s0asbI/3t3xeO7jrmEL6Z9w79ezZ5zT7L1uCivps2Kewgbr9eYffbbm/+N+19OFkYeF7/UqnUrbrzzHwB8+/V3jH3jzYQTqbpb7VC6GOPsGOMtMcZtge2B24E5QDvgcuDLEMKrIYTfLFjKK8b4InAq8C+KZ8PrEmN8fzXr3xVj7Bhj7JhUUQTQfINmjP94PIsWLiLGyLtvv8smbTZJLE+SttxqS7795lu+/34SS5csZcQLL7LXPnsnHUspMHPGTACmTJ7Ca6+MpHOXAxNOlIxs/Yzce9xlfDrlS/75ygMAzFs0n6+mf0/P7Q5Ysc7WG22+Rtt68ZMxHNB+VxrWaUDDOg04oP2uvPjJGAAu73om69auz9mPX13xbyJB2XpclMfCBQuZP3/+ip/fGvs2bTdds8k8so3HxS/NmjELgKKiIh64+0G6H9414USq7tb4OkYxxg+A00MIfwF6UtyLtBewd8n9cgeEEJ6NMS5bm2AhhIspvnDsnsDWwMgQwrkxxufWZruVbautt6LT/vtyTK/jyc/PZ/PfbcZhh3dPOlYiCgoK6H/RBZzW588UFRXR/dBubNouN/9Bu7Bff95757/Mnj2bA/bpzGlnnMqhPXLzuAC44Jz+zJk9h4KCAs6/qB/1f3ZCba7Ixs/Ibm234/idu/Hx95/zwV+HAfDXp27kmPsv4PajBvC3g06lRn4BQ997gY8nfZ5xe7MWzOHy5+/g3QseBeCy529n1oI5bNSwGX876BQ+nTyR9/v/G4BbXh/CvWOGVd6bqyLZeFyU14wZMzj3zPOA4lnZOh98ILvtsWvCqZKR68fFwAsu5YP3is9P7b5/T3qfdiILFy7kiaHDAdir054c3L1LwinTLaTkPJ40CzHG8r84hNYU9+b8EWhRsjhS3Kv0FPA48FJ5iqQQwo1A/xjjwpLHrYB7Yoz7Z3rtvKWzy/+mskyNvNTPV1FlIh4WAEuLliQdITVq5uXm9cV+Te0zdsi8Uo5YeMu7mVfKEYWxMOkIqZEfPBdyuZ+Wzk06QqqsX6t5tag4/v7fK1L9i9BF2/8t8XZc01npflWM8asY48VAK4ovAPsEsAxoCBwPPAOUa7qpGOPZACGEzUsef7MmRZEkSZIkldVaFUbLxWIjYow9KZ4ooR/wKRAoLpLKLIRwCPAhMKLk8e9DCE9XRF5JkiQpl+Sl/L80qPAUMcbpMcYbYoxbAbsC95ZzU5cAOwKzS7b7IdCmQkJKkiRJUilrPPlCeZRc76i81zxaGmOc87MTxYrWPpUkSZIkrapSC6O19L8QwtFAfgihHXAmMDbhTJIkSVK146x0maVjQN+v6wtsCSwGHgHmAmcnmkiSJElSVkptYRRjXBBjvCjGuEPJhVsvijEuSjqXJEmSpKoVQrgvhDAthDC+1LLGIYSXQwhflNw3KlkeQgg3hRAmhBA+DiFstyb7SN1QuhDCM/DbF5yJMXpZY0mSJKkMsmAo3SDgFuDBUssuBF6JMV4dQriw5PEFwEFAu5LbTsDtJferlcYeo+uBfwBfAQuBu0tuPwETE8wlSZIkKQExxjeAmT9b3A14oOTnB4DupZY/WHJJobeAhiGEDTLtI3U9RjHG1wFCCP+IMXYs9dQzIYT3EoolSZIkqZKEEE4GTi616K4Y410ZXtYsxji55OcpQLOSnzcCviu13vclyyazGqkrjEqpG0JoE2P8EiCE0Bqom3AmSZIkqdrJI91D6UqKoEyF0OpeH0MIv3k6zppIc2F0DjAyhPAlEIBWwCnJRpIkSZKUElNDCBvEGCeXDJWbVrJ8EtCy1HotSpatVmoLoxjjiJLrF/2uZNFnMcbFSWaSJEmSlBpPA38Eri65f6rU8jNCCEMpnnRhTqkhd78ptYVRie2BTSjOuU0IgRjjg6t/iSRJkqRsEkJ4BNgbWD+E8D0wkOKC6LEQQm/gG6BXyerPA12ACcAC4MQ12UdqC6MQwmCgLfAhUFiyOLLqFH2SJEmSMqju03XHGI/6jac6/cq6ETi9rPtIbWEEdAS2KHljkiRJklRp0ngdo+XGA82TDiFJkiQp+6W5x2h94JMQwjvAikkXYoxdk4skSZIkVT951XwoXVVIc2F0SdIBJEmSJOWG1BZGMcbXk84gSZIkKTekrjAKIYyOMe4eQphH8Sx0K56ieJKJBglFkyRJkqqlgEPpMkldYRRj3L3kvn7SWSRJkiTlhjTPSidJkiRJVSJ1PUaSJEmSKlZesD8kE1tIkiRJUs6zMJIkSZKU8xxKJ0mSJGW54AVeM7LHSJIkSVLOszCSJEmSlPOycihdjbyaSUdIjaJYlHSE1Fiw7KekI6RCzfx1ko6gFFp4y7tJR0iNxv33SDpCasy8alTSEVIjrnLN+dxWr0aDpCOoHLzAa2b2GEmSJEnKeRZGkiRJknKehZEkSZKknJeV5xhJkiRJWinP6bozssdBcEyVAAAgAElEQVRIkiRJUs6zMJIkSZKU8xxKJ0mSJGU5p+vOzB4jSZIkSTnPwkiSJElSznMonSRJkpTlnJUuM3uMJEmSJOU8CyNJkiRJOc+hdJIkSVKWC8H+kExsIUmSJEk5z8JIkiRJUs5zKJ0kSZKU5bzAa2b2GEmSJEnKeRZGkiRJknKehZEkSZKknOc5RpIkSVKWywueY5SJPUaSJEmScp6FkSRJkqSc51A6SZIkKcsFh9JlZI+RJEmSpJxnYSRJkiQp5zmUTpIkScpyeTiULhN7jCRJkiTlPAsjSZIkSTnPoXSVYMyoMVxz1XUUFRZxaM/u9O7zp6QjJWLx4sX0Pr4PS5YsobCwkP0O6MRpZ5yadKwqc+WAqxnzxps0atyIh54YBMAXn0/guiv+wcIFC9lgw+YMvOpi6tarm2zQBDwy+FGeHPYUMUa69+zG0ccdmXSkxPh9sVJ1b4s7Dh9A5/Z78ONPM9nhhiN+8XzD2vW54/CBtF6vBYuXLubUxy/jk6kT12qfNfNrcM+Rl7HtRu2ZuWAOxz18Id/Omsy+7Xbi8oP6UiO/BksLl/LX5/7F6xPfXat9JaW6HxcVZcrkKfyt/wBmTp8BIdCj12Ecc9zRScdKjMdF2TkrXWb2GFWwwsJCrrziam678xaGPzOMEc+PYOKEtfuHr7qqWbMmd913B48NH8rQYUMYO3osH380LulYVaZLt4O44fbrVll29aXXctpZpzB42CD23HcPHh40NKF0yZnwxUSeHPYUDzxyH0OGDWb066P57tvvko6VCL8vVsqGthj83jN0v7fvbz5/3r5/4uMfPmenfx7JSY8O5Lqu/dZ42xs32oARp9z5i+Un7Nid2Qvn0uHa7tw86mGu6HImADPmz6bnoLPZ8Z9H0OfRgdx75GVlf0MpkA3HRUXJL8jn3PPP4YlnhzF46AM8OuQxJk74MulYifC4UGWxMKpg48eNp+XGLWnRsgU1atag80EHMvLVkUnHSkQIgTp16wCwbNkyli1bRi79seL3229Dgwb1V1n23Tff8/vttwFgh1124PVXXk8iWqK+/vJrtuqwJbVq16KgoIDtOm7Ha/8ZmXSsRPh9sVI2tMWYrz5g5oI5v/l8+6ZtGDmhuNfm/378mlaNN6RpvcYAHLntQbxxxgO8dfYQbj7sr+SFNfvn+eAt9uKh954FYPi4V9h70x0B+OiHz5k8dzoAn0ydSK0a61Azv0a531tSsuG4qChNmjSh/RbtAahbty5t2rRm2rRpCadKhseFKktqC6MQwva/suwPSWQpi2lTp9G8ebMVj5s2b8bUaT8mmChZhYWFHHHYUXTaY3923mVnOmzdIelIiWrddhNGvTYagNdeeo2pU3LvH7W2m7bhw/c/ZPbsOSxauIixo8YydcrUpGMlwu+LlXKhLcZN/j+6ddgXgI4tt2Tjhs3ZaN2mbN50E3pucwD73tabnW88msKiIo7c9qA12uaG6zZh0pziz09hUSFzF/3EenUarrJO9w6d+HDSZywpXFqxb6gK5MJxUR6TJv3AZ59+Toett0o6SiI8LsonhLxU39IgzecY3R1COD7GOB4ghHAUcDbwbLKxVBb5+fk8+sQjzJs7j7+ceS4TvpjApu02TTpWYv566QX88+qbGHTXg+y+927UqFH9/oK7tlq3bc3xfzqOviefSe3atdls83bk5eUnHUuqdNe/Nojru/bjrbOHMH7yBD764XMKi4rYZ9Md2bZFe0af+SAAtWqsw4/zZwIw9Pjr2aTxhtTIr0HLhs156+whANw6+hEGv/dMxn22b9aGK7qcySF3n155b0xVasH8BfQ7qx/n9T+XevXqJR1HyippLox6Av8OIRwN7AEcDxzwWyuHEE4GTga45fabEzsJr2mzpkwp9dfvaVOm0qxpk0SypEn9BvXpuGNHxo4em9OFUavWrbjxzn8A8O3X3zH2jTcTTpSMbj260q1HVwBuvfF2mjbPzc+I3xcr5UJbzFs8n1Mev3TF408vfIavZk5i19bb8tB7zzJwxC2/eM2RDxafh7Rxow24q9cldL7zlFWe/2HOj2y0bjMmzZlGfl4+DWrVY8aC2QBstG5Thh5/PScNHcBXM7+vxHdWeXLhuCiLpUuXcu7Z/ejyhy502r9T0nES43GhypKOfqtfEWP8EjgSeALoARwQY/zNwdsxxrtijB1jjB2TnJlky6225NtvvuX77yexdMlSRrzwInvts3dieZI0c+Ys5s2dB8CiRYt4+8232aT1JolmStqsGbMAKCoq4oG7H6T74V0TTpSMmTOK/xo+ZfIUXntlJJ27HJhwomT4fbFSLrTFurXqUSO/+O+RJ+54KKO/ep95i+czcsI7HLp1J5rUbQRAo9oNaNmw+Rpt8/lPXufYjsWjzA/t0InXS85hWrdWPYad+C8GvHAzb33zUSW8m6qRC8fFmooxcunFl9G6TWuOO+HYpOMkyuNClSV1PUYhhHFALLWoMZAPvB1CIMa4dTLJ1kxBQQH9L7qA0/r8maKiIrof2o1N27VNOlYipv84nQF/HUhRUSFFRZH9D9yPPffeM+lYVWbgBZfywXvF59J0378nvU87kYULF/LE0OEA7NVpTw7u3iXhlMm44Jz+zJk9h4KCAs6/qB/1fzZJRa7w+2KlbGiLQUf/nT3bdGS9ug354q/Pc8XLd64ohO55axibN23N3UdcSiTy6ZQvOe3fxTPFfTbtKy598Tae6XMrIeSxrHAZZz95Nd/NnpJ5n+8+xb1HXs64859k1oI5HD/krwCcuusRtF2/Jf3360P//foAcMjdp/Pj/FmV9O4rRzYcFxXlw/c/5Nmnn6PdZpvS69DiSxz0PfsM9thr94STVT2Pi/LJI4dmwCqnEGPMvFYVCiG0KvWwEcXD6CIwCpgdY/wm0zYWFS5I15tKUFEsSjpCaixY9lPSEVKhZv46SUdIjZp5toV+qXH/PZKOkBozrxqVdITUiPirxXLBX7BXUSu/TrVokMH/d2+qD+LjNuudeDumbihdjPGbkuKnGzAYWB9oWvJzbo47kiRJklSpUjeUrpSTgJ1jjPMBQgjXAG8CNyeaSpIkSapmQi5dTLKcUtdjVEoACks9LixZJkmSJEkVKs09RvdTPOHC8JLH3YF7E8wjSZIkKUultjCKMd4QQhgJLJ9u5cQY4wcJRpIkSZKqJSfNyCy1hRFAjPF94P2kc0iSJEnKbmk+x0iSJEmSqkSqe4wkSZIkrT1npcvMHiNJkiRJOc/CSJIkSVLOcyidJEmSlOXynJUuI3uMJEmSJOU8CyNJkiRJOc+hdJIkSVKWC8H+kExsIUmSJEk5z8JIkiRJUs6zMJIkSZKU8zzHSJIkScpywem6M7LHSJIkSVLOszCSJEmSlPMcSidJkiRluRAcSpeJPUaSJEmScp6FkSRJkqSc51A6SZIkKcs5K11m9hhJkiRJynkWRpIkSZJynkPpJEmSpCznrHSZ2WMkSZIkKedZGEmSJEnKeVk5lG7OkllJR0gNu01XqltQL+kIqfDjoqlJR0iNprU2SDpCavhdsdKMq95IOkJqHPh4n6QjpMYzPW5JOkJq1MirmXQElUOes9JlZI+RJEmSpJxnYSRJkiQp51kYSZIkScp5WXmOkSRJkqSVPJc0M3uMJEmSJOU8CyNJkiRJOc+hdJIkSVKWC/aHZGQLSZIkScp5FkaSJEmScp5D6SRJkqQs56x0mdljJEmSJCnnWRhJkiRJynkOpZMkSZKyXMChdJnYYyRJkiQp51kYSZIkScp5DqWTJEmSslyes9JlZI+RJEmSpJxnYSRJkiQp5zmUTpIkScpyzkqXmT1GkiRJknKehZEkSZKknGdhJEmSJCnneY6RJEmSlOWC03VnZI+RJEmSpJxnYSRJkiQp5zmUTpIkScpywf6QjGwhSZIkSTnPwkiSJElSznMoXQWYOmUaV/7tambOnEUADunxBw4/pgf33Hofo0eOJS8EGjZuyF8vu4D1m66fdNxKNXXKNP5+0VUr2qJrzz9w+DE9ufWGOxj7+lgKatRgoxYb0v+yC6jfoF7ScavUvLnzuGzAFUycMBFCYODlF7PN77dOOlaVWLJ4Cef2uYClS5ZSWFjEHp124/hTj1nx/G3X3smLT7/MU6P/nWDKqrd48WJ6H9+HJUuWUFhYyH4HdOK0M05NOlYiBl50CW+8PorGjRsz7OnHk46TqCmTp/C3/gOYOX0GhECPXodxzHFHJx2rTC7YsQ+7bLgtsxbN5cQRF/7mer9r3IZb97uEy8bewuvfv7NW+6xfsy6X7NqX5nWbMGX+jwwccxM/LV3Afq125ej2hxAILFi2kBveu5+Js79dq30l5ZHBj/LksKeIMdK9ZzeOPu7IpCMlwu+L8nFWuszsMaoA+fn5/PncUxn8xP3cMfhWhj/6FF9P/Jqj/ngEgx6/h/seu5td99yFQXcNTjpqpcvPz+f0fqfx0PBB3PnQbTwx9Cm+mvg1O+y8PQ8Mu58H/n0vLVu14KF7H046apW77qp/sOvuu/DEs//m0WFDaNOmddKRqkyNmjW49o4ruWPoLdw+5CbeG/tfPh33GQD/98kX/DTvp4QTJqNmzZrcdd8dPDZ8KEOHDWHs6LF8/NG4pGMlouuhh3DbXbckHSMV8gvyOff8c3ji2WEMHvoAjw55jIkTvkw6Vpm88NUoznv92tWukxcCp2xzJO9NKdsx//um7blwp1N+sfyY9l3579T/ccxz5xbfb9EVgMk//ciZr1zOiSMu5MH/PUm/HXqXaX9pMeGLiTw57CkeeOQ+hgwbzOjXR/Pdt98lHSsRfl+oslgYVYD1m6zH5u03A6BO3Tq0arMxP06bTt16dVess2jhInKhUP95W2zSZmOmT5vOjrvuQEFBPgBbbr0FP077McmYVW7evJ94/78f0L1HN6C4UKjfoH7CqapOCIHadWoDsGzZMgqXFRIIFBYWcveN99H7zBMTTpiMEAJ16tYBittl2bJlOfE98Wu277g9DdZdN+kYqdCkSRPab9EegLp169KmTWumTZuWcKqy+fjHz5i3ZPV/8Dis3YG8/t27zFo8d5XlR/7uYO7c/zLu63wVJ27VY433udtG2zHiq1EAjPhqFLtvtD0A/5vxBT8tXVD88/QvaFK7cVneSmp8/eXXbNVhS2rVrkVBQQHbddyO1/4zMulYifD7QpXFwqiCTZ40hS8+m8AWHYr/Ubv75nvpceARvPz8f+h9Wm798jd50hT+r1RbLPfcky+w0247JZQqGT98P4lGjRpyyUWXclSPY7hswBUsXLAw6VhVqrCwkNOO6ssR+x/Ltjv/nt912JynH32WXfbaifWaVM9fVCpCYWEhRxx2FJ322J+dd9mZDlt3SDqSUmTSpB/47NPP6bD1VklHqVDr127EHi068tSE/6yyvGPzDrSo35xTXh5A7xF/ZbNGrdm6ye/WaJuNaq3LzEWzAZi5aDaNav3yF+eD2+zN25M/Wuv8SWi7aRs+fP9DZs+ew6KFixg7aixTp0xNOpaqkTxCqm9pkLrCKIQwL4Qw97duSedbnQULFnJxv4H0Pe/PK3qL+vTtzbAXH2X/LvvxxNAnE05YdRYsWMjfzh3AmeedvkrP2YN3P0R+fj4HHLxfgumqXmFhIZ99+jk9j+zJI8MepnbtWtx/z6CkY1Wp/Px8bn/kZh5+YRCfj/8/xr0/nlH/GUO3Iw5JOlqi8vPzefSJR3jx1RcYP248E76YkHQkpcSC+Qvod1Y/zut/LvXqZdc5mX23PY47PxpKJK6yfIfmHejYvAP3HHgldx/4dzZusAEt6jcD4Pb9L+WeA6/kvB1OYrcNt+OeA6/kngOvZIfma/bHhG2bbsHBbfbmzo+GVvTbqRKt27bm+D8dR9+Tz+TMU89ms83bkZeXn3QsKaukbvKFGGN9gBDC5cBkYDAQgGOADX7rdSGEk4GTAa67+WqO631s5YctZdnSZVx87kD277Ife3Xa8xfP79+lE+ef0Z8//fmEKs2VhGVLl/G3vwwobov9VrbF80+NYOwbb3LjXf/IuRMAmzZrStNmTVf81bfTAZ0YdM8DCadKRr369dim49Z89N7H/PD9D5zYvQ8Aixct5oRufRj01N0JJ0xG/Qb16bhjR8aOHsum7TZNOo4StnTpUs49ux9d/tCFTvt3SjpOhdu8cWsG7HoGAOvWrM/OG2xDYSweYvvwJ0/zzMRXf/Ga014eCBSfY9S59Z5c/fadqzw/a9EcGtdqyMxFs2lcqyGzFs1Z8VybdVty3o4ncf7r1zI3wxC/NOvWoyvdehSfO3XrjbfTtHmThBNJ2SV1hVEpXWOM25R6fHsI4SNgwK+tHGO8C7gLYOrCSfHX1qksMUauufQ6WrXemCOOO3zF8u+++Z6WrVoAMHrkGDZuvXFVxkpEjJGrL7mWTdq04sjje61Y/vaYdxgyaCg333sjtWrXSjBhMtZvsj7Nmjfj66++ZpPWm/DOW+/Sum3uTL4we9YcCgryqVe/HosXLeb9tz+g1x97MvSlh1as0233njlXFM2cOYsaBQXUb1CfRYsW8fabb3NC7z8mHUsJizFy6cWX0bpNa447oWr/yFdVjnz2nBU/X7jTKbw56QNGT/oviwqX0LtDT/7zzRgWLlvM+rUbsayokNmLMw8YGTPpfTq33oMhnz5D59Z7MGbS+wA0rbMel+9+Nn9/83a+nzel0t5TVZg5YyaN12vMlMlTeO2Vkdz/8D1JR1I1kmt/lC6PNBdG80MIxwBDgQgcBcxPNtKvG/fheF589mXatGvDn3oV//W7T9/ePPfkC3z39XeEvDyab9CUcy86J8OWqr9xH6xsixN7nQTAyX1P4l/X3MzSJUv5y6n9ANiywxb0u/gvSUatchf8tR8XXTCApUuX0qLFRlxyxa/W+Flp5vSZXD/wnxQVFlEUi9hzvz3Yec8dk46VuOk/TmfAXwdSVFRIUVFk/wP3Y8+9f9njnAsu7Nef9975L7Nnz+aAfTpz2hmncmiP7knHSsSH73/Is08/R7vNNqXXocXTMfc9+wz22Gv3hJOtuQG7nM7vm7Zn3XXq83jXm7l//L8pCMW/cjw98ZXffN17U8bRqsGG3LbfpQAsXLaIK966bY0KoyGfPsMlu/Xl4DZ7M2X+dC4ZexMAf9zyUNZdpz7ndCw+z7cwFnLKSxev5TtMxgXn9GfO7DkUFBRw/kX9cmoSn9L8vlBlCTFWaefKGgshbAL8C9iN4sJoDHB2jPHrTK+t6h6jNPOvAyvVLciuMfrl9eMiT9Zdrmmt3xydm3P8rtCv6fz4yUlHSI1nejg99HI18momHSFVaufXrRZfoP+Z9Fyqfz/eb6ODE2/H1PYYlRRA3ZLOIUmSJCn7pW5WuuVCCJuFEF4JIYwvebx1COFvSeeSJEmSqpuQ8v/SILWFEXA30B9YChBj/Bg4MtFEkiRJkrJSmgujOjHGd362bFkiSSRJkiRltdSeYwRMDyG0pXjiBUIIPSm+rpEkSZKkMnCSnczSXBidTvF1iX4XQpgEfEXxRV4lSZIkqUKluTD6Jsa4XwihLpAXY5yXdCBJkiRJ2SnNhdFXIYQRwKPAq0mHkSRJkqqrkOqpBdIhzS30O+A/FA+p+yqEcEsIofpc9luSJElStZHawijGuCDG+FiM8TBgW6AB8HrCsSRJkiRloTQPpSOEsBdwBNAZeA/olWwiSZIkqfrJc1a6jFJbGIUQvgY+AB4Dzosxzk82kSRJkqRsldrCCNg6xjg36RCSJEmSsl9qzzECGoQQhocQppXchoUQWiQdSpIkSapuQsr/S4M0F0b3A08DG5bcnilZJkmSJEkVKs2FUZMY4/0xxmUlt0FAk6RDSZIkSco+aS6MZoQQjg0h5JfcjgVmJB1KkiRJUvZJ8+QLfwJuBv4JRGAscGKiiSRJkqRqKDhdd0apLYxijN8AXZPOIUmSJCn7pbYwCiG0BvoCm1AqZ4zRYkmSJElShUptYQQ8CdxL8Wx0RQlnkSRJkqqttEyJnWZpLowWxRhvSjqEJEmSpOyX5sLoXyGEgcBLwOLlC2OM7ycXSZIkSVI2SnNh1AE4DtiXlUPpYsljSZIkSWvIWekyS3NhdDjQJsa4JOkgkiRJkrJbmi/wOh5omHQISZIkSdkvzT1GDYHPQgjvsuo5Rk7XLUmSJJVBXqr7Q9IhzYXRwKQDSJIkScoNIcaYdIZyCSG8GWPc5deeW1g4v3q+qUpQXf//VgZPOpS0przex0oR/x0BqHPMNklHSI0FD3+UdIRUqZ1ft1p8Ybw5dWSqP8y7NNs78XZMc49RJrWSDiBJyj4WRStZFEnZwz8QZ1adBxv6bS1JkiSpQlTnwkiSJEmSKkR1Hkpnf6AkSZK0BhwmnFnqC6MQQgNK5Ywxziz58bhkEkmSJEnKNqktjEIIpwCXAotYeT5RBNoAxBjHJxRNkiRJUpZJbWEE9AO2ijFOTzqIJEmSpOyW5sJoIrAg6RCSJElSded03ZmluTDqD4wNIbwNLF6+MMZ4ZnKRJEmSJGWjNBdGdwKvAuOAooSzSJIkScpiaS6MasQY/5J0CEmSJKm6c7ruzNJ8gdcXQggnhxA2CCE0Xn5LOpQkSZKk7JPmHqOjSu77l1q2YrpuSZIkSaooqS2MYoytk84gSZIkZQOH0mWWusIohHDY6p6PMT5RVVkkSZIk5YbUFUbAIat5LgIWRpIkSZIqVOoKoxjjiUlnkCRJkrKKF3jNKHWFUWkhhIOBLYFay5fFGC9LLpEkSZKkbJTa6bpDCHcARwB9gQAcDrRKNJQkSZKkrJTawgjYNcZ4PDArxngpsAuwWcKZJEmSpGonpPy/NEhzYbSw5H5BCGFDYCmwQYJ5JEmSJGWpNJ9j9GwIoSFwLfDfkmX3JJhHkiRJUpZKc2F0PXAasAfwJjAKuD3RRJIkSZKyUpoLoweAecBNJY+PBh4EeiWWSJIkSaqGQhZM1x1C+Jri+qAQWBZj7BhCaAw8CmwCfA30ijHOKs/201wYbRVj3KLU49dCCJ8klkaSJElS0vaJMU4v9fhC4JUY49UhhAtLHl9Qng2nefKF90MIOy9/EELYCXgvwTySJEmS0qUbxSPNKLnvXt4NpbnHaHtgbAjh25LHGwOfhxDGATHGuHVy0SRJkqTqIy1TYv+WEMLJwMmlFt0VY7zrZ6tF4KUQQgTuLHm+WYxxcsnzU4Bm5c2Q5sKoc9IBJEmSJFW+kiLn54XQz+0eY5wUQmgKvBxC+Oxn24glRVO5pLYwijF+k3QGSZIkSekQY5xUcj8thDAc2BGYGkLYIMY4OYSwATCtvNtP8zlG1dbAiy5hn9070aPr4UlHSdTixYs59ojj6XXokfToeji333JH0pESM2XyFE464WQO+0MPDjukJw8PHpJ0pMTYFiv5XbGSbbGqMaPG0LVLd/5wYFfuvfu+pOMkprp/X7RYbwNevfgh/nf9CMZf9wJnHvTHX6yz+YZtGHvZ4ywa/Ann/qF3hey3ZkFNhp71L7648RXeuuLftGqyEQD7ddiN9658ko+vfY73rnySfbbcOcOW0snvi/IJKf8vY/4Q6oYQ6i//GTgAGA88DSz/cP0ReKq8bWRhVAm6HnoIt911S9IxElezZk3uuu8OHhs+lKHDhjB29Fg+/mhc0rESkV+Qz7nnn8MTzw5j8NAHeHTIY0yc8GXSsRJhW6zkd8VKtsVKhYWFXHnF1dx25y0Mf2YYI54fwcQJE5OOlYjq/n2xrHAZ5w6+ii37dWbni3ty+gHH0n6jTVdZZ+ZPszlz0GVc/2zZr2HfqslGvDbg4V8s773P4cz6aQ7tzu7EP5+7n2uOPh+A6fNmcch1J7P1+Qfzx9vOY/Dp15fvjSXM74uc1QwYHUL4CHgHeC7GOAK4Gtg/hPAFsF/J43KxMKoE23fcngbrrpt0jMSFEKhTtw4Ay5YtY9myZWTBFPrl0qRJE9pv0R6AunXr0qZNa6ZNK3dPb7VmW6zkd8VKtsVK48eNp+XGLWnRsgU1atag80EHMvLVkUnHSkR1/76YMvtHPvj6fwD8tGg+n06ayEaNVz0v/Me5M3nvy3EsLVz2i9cfs3s33r5iGB9c/TR3nHQ5eWHNfm3r1nE/HnhjOAD/fvv/27vzeDvG+4Hjn68QIpFIhEQsiaI01FIUtdfSUPteqvgpVVvtjaUaRey6abVamlDbz5Lyay1tqV0Ru6CI2tImIXtCJG6e3x8z1z3iJndx752553zeeZ3XObOe7zy5Z2a+8zzzzN1su9amADz75kv8d0pWfmPefY1uXZeg66JdW719RXF/UZtSSm+klNbNX2ullM7Lx09KKW2bUlo9pbRdSmlya7+jtIlRRIyMiKUrhntHRO22J+ik6urq2G/Pb7HtFtuzyaab8OV1vlx0SIUbN+4/vPLyv/jyOmsXHUrhLAvpsyZOmEj//g0nz8v178eEie8VGFE5dPb9xcBlV2D9QYN5/PXnmjX/mgNWZb9Nv8lmP96P9YfuSt28eRy4+a7NWnaFPv14Z1LWSVfdvDqmfTiTZZbq/al59tp4CE//ewxzPp7Tsg1RpxURpX6VQWk7XwDWSSlNrR9IKU2JiPUXNHNlF3+/vOIXHHb4/3RAiGpKly5duOm2G5gxfQYnHncSr7/2OqutvlrTC1apD2Z9wMk/OJlTTjuJHj16FB1OoSwLSc3V2fcX3RdfkltP+BXHjzyXGR/ObNYy2375a2ywylo8ed5tAHTrugQTp08C4LYTf80qy61I10W7snLf5XnmgjsA+PldIxnxwK1Nrnvwiqtz4QGnssPwQ1q3QVKVKnNitEhE9E4pTQGIiD4sJN7KLv4+rJvV6m761D6W6rkUG351Qx59+NGaTYzmzp3LScefzE4778S2229bdDiFsiykBVuu33KMHz/hk+GJ4yfQb7llC4yoWJ19f7Fol0W59a3+rSIAACAASURBVMRfcd3DdzDqyb82e7kARj44itNv/Ox9QHtedhSQ1UKN+P5FbPOTAz81fdzkCay0zPKMmzyeLot0oVe3HkyaMQWAFfr0Z9RJv+Y7vzqZNya8/Zl1S7WstE3pgEuBxyLinIg4B3gUuKjgmNQCkydPYcb0GQDMnj2bxx97nEGrDCo0pqKklDj7Rz9hlS+swkGHfLvocAplWUgLt9baa/H2W2/z7rvjmDtnLnffdQ9bbbN10WEVohr2F1d973xeHvc6P72zZXcD3PviY+y98RCW7dkHgN7de7Fy3wHNWvaOp+7l4C33AGDvjYdw35h/AtBryaX4yw9/x9DrL+bRV59uUTzq/Irude7z9krXESKl8lauRMRg4Ov54H0ppZeas1zRNUZDTz6N0U88xdSpU+mzTB++f8yR7LHX7oXEUuT/76v/eo2zTv8x8+bVMW9eYvtvbMf3jjqi6QXbSZHtV5956hkOPegwVv/iakR+8+yxxx/DFlttXlhMRbEsGpRpX1G0MpVFGQ7QDz3wEBddcAnz5s1j9z124/Ajv1tIHIlizxHKtL9Y8sB1W7zMZmtswMNn38Tzb73CvDQPgNNvvPSTBOe3f7+Bfr36Mnr4n+jZrQfz0jxmzv6AwScPYcaHM9l30504bbcjWSQWYW7dxxx99TAef/3ZT9a/oBqjxRfryrVHX8r6gwYzeeZU9v/F8fx74jucscdRnLbbkbw2/s1P5t1h+CG8N71l96p/cF3z7pNqL2XaXwB069K9+J1GMzw/eXR5T/qBdfpsWHg5ljoxaq2iE6Myqcb/39Yqy419ksqtDIlRWRSdGJVJaxKjalV0YlQ2JkZtowyJUZnvMZIkSZLUBrzo07Qy32MkSZIkSR3CxEiSJElSzTMxkiRJklTzvMdIkiRJqnJ2QtU0a4wkSZIk1TwTI0mSJEk1z6Z0kiRJUpWzu+6mWWMkSZIkqeaZGEmSJEmqeTalkyRJkqqcvdI1zRojSZIkSTXPxEiSJElSzbMpnSRJklTl7JWuadYYSZIkSap5JkaSJEmSap5N6SRJkqQqZ1O6plljJEmSJKnmmRhJkiRJqnkmRpIkSZJqnvcYSZIkSVUuwnuMmmKNkSRJkqSaZ2IkSZIkqebZlE6SJEmqcnbX3TRrjCRJkiTVPBMjSZIkSTXPpnSSJElSlbMpXdOsMZIkSZJU86qyxsiMuIF91qsx81Jd0SGUxivTxhQdQims1nONokMoja6LLF50CCqhD697vugQSqPbQesWHUKppOtfKzoEtZGqTIwkLZhJUQOTIklSrfBiedNsSidJkiSp5pkYSZIkSap5NqWTJEmSqp5N6ZpijZEkSZKkmmdiJEmSJKnmmRhJkiRJqnneYyRJkiRVObvrbpo1RpIkSZJqnomRJEmSpJpnUzpJkiSpyoXddTfJGiNJkiRJNc/ESJIkSVLNsymdJEmSVOVsStc0a4wkSZIk1TwTI0mSJEk1z6Z0kiRJUpXzAa9Ns8ZIkiRJUs0zMZIkSZJU82xKJ0mSJFU5e6VrmjVGkiRJkmqeiZEkSZKkmmdTOkmSJKnK2ZSuadYYSZIkSap5JkaSJEmSap6JkSRJkqSa5z1GkiRJUpWL8B6jplhjJEmSJKnmmRhJkiRJqnkmRu3gkYceYdeddmfnb+zKVb+7uuhwCmVZNLAsPq2uro799zqA4446vuhQOtyxe57Eqd8+g6EH/4jT/+fHAPzzvic4+cDTOGCzQxj78r8LjrBj/OTMc9lhyx3Zb/cDPhk3bdo0jv7usey5094c/d1jmT5teoERFsf9RebHZwxjm823Za9d9yk6lFLozH8XK/bpz31nXMuYi+7ixYvu5LghB39mnjUGfIFHz/5fZo8cw0nfPKxNvrfrol258dif8dplf+efP7mFgX1XAGC7tTdj9HmjeP6CPzP6vFFsM3iTNvm+MouS/ysDE6M2VldXx/BzL+DXv72cUf93K3ffeTdjXx9bdFiFsCwaWBafdf21N7DKF1YpOozCnHn5UC4YeQ7Drz4bgJW+sCInDj+ONddbo+DIOs7Ou3+TX/zmp58aN/L317DRJhtx2523sNEmGzHyqmsKiq447i8a7LrHLvz6ysuLDqMUOvvfxcfz6jjpuvNZ69Qd2eSsfTh6+wP50gqrfWqeyTOnctzIc7jkL79v8foH9l2Bf5z5x8+MP2zrvZkyazqrn7gdP73rD1z4rVMAeH/GFHa5+HusM3RnDr7iVK496uLWbZiqiolRG3vxhRdZaeWVWHGlFVms62IM2fEb3H/f/UWHVQjLooFl8WkTxk/g4QcfYY+9di86lNJYYdAABgxcvugwOtRXNlyfnr16fmrcA/94iJ132wmAnXfbifvve7CI0Arl/qLBBhtuQM9evYoOoxQ6+9/F+Knv8cybLwEwc/YsXh43lhV69/vUPO9Nn8zoN15gbt3Hn1n+wM125fFzbuGZ4Xfwm8POYZFo3insbhtux8iHbgPglsfvZtu1NwXg2bde4r9TJwIw5t3X6NZ1Cbou2rXV26fqYGLUxiZOmEj//g0/9OX692PCxPcKjKg4lkUDy+LTLr7gUn5w0nEsskg5qs47WgScf/zFnH7oWdz7p38UHU6pTJ40mb7L9gVgmb7LMHnS5IIj6njuL9SYavq7GNh3BdYfNJjHxz7XrPnXHLAq+236TTYbtj/rn74rdfPqOHDzXZu17Aq9+/HOpPEA1M2rY9oHM1lmqd6fmmevrw7h6TfHMOfjOS3bkE4mIkr9KoPSdtcdEXsCFwLLAZG/Ukqp5wLmPwI4AuDyK37JYYf/T0eFKqkFHrz/Ifr06cPgtb7E6CdGFx1OIYb95gz6LNuHaZOnM/z4ixgwcHm+tP6aRYdVOmU6WEpqG90XX5JbT7ic4689jxkfzmzWMtuuvSkbrLIWT56T1fx067o4E6dPAuC2E37FKsuuRNdFF2PlvsvzzPA7APj5PSMZ8cCtTa578AqrceG3TmGH8w9t5RapmpQ2MQIuAnZJKb3cnJlTSlcCVwLMrvsgtWdgC7Ncv+UYP37CJ8MTx0+g33LLFhVOoSyLBpZFg2efeY4H7n+Qhx96hDkfzWHWrJmc8cMfcd6F5xQdWofps2wfAHr16clGW27A2JffMDHK9VmmD++/9z59l+3L+++9T+8+vZteqMq4v1BjquHvYtEui3LrCZdz3SN3MOrJvzZ7uSAY+eAoTr/p0s9M2/OnRwNZLdSIIy9km3O//anp46ZMYKVl+jNu8ni6LNKFXkv2YNKMKQCs0Kc/o078Nd+54hTemPj259gyVYsyN6Wb0NykqEzWWnst3n7rbd59dxxz58zl7rvuYattti46rEJYFg0siwbHnXAM99x3J3f+7f+44JLz2GjjjWoqKZr94Ud8OOvDTz4//8SLrPiFFQuOqjy23HoL/nz7nQD8+fY72WqbLQqOqOO5v1BjquHv4qojhvPyuLH89M4/tGi5e8c8xt4bD2HZntlFpd7de7Fy3wHNWvaOp+7l4C32BGDvjYdw35h/AtBryaX4yylXMvTGS3j01adbFE9nVXSvc52hV7oy1xiNjoibgD8BH9WPTCndVlxITVt00UU57Ywf8v3Dj2LevHnsvsdurLb6qkWHVQjLooFloXrTJk/jstN+AWS9TG22/aast8k6PPnAaEZc9kemT53BRSdfxqDVV+a0n51ScLTt64xTfsRTTz7N1KlT+ea2u3DEUYdz8He/w2knncEdt91B/wH9Of/S84oOs8O5v2gw9OTTGP3EU0ydOpUdthnC9485smY7bensfxebrbEB39liD55/+5VPmrud/r+XsvIyWYLz23tvoF+vvow+dxQ9u/VgXprH8UMOYfCpO/LyuNc5839/yl+HjmCRRYK5dR9z9B/O5u33/9Pk9151/81ce9QlvHbZ35k8ayr7//IEAI7Z4SBW6zeQs/Y4hrP2OAaAHS44hPem1959jWoQKRXW6myhIqKxywkppdTkzUNFNqWTym5eqis6hNJ4ZdqYokMojdV61k434U3pusjiRYdQGgkPp/XKckW7DLodtG7RIZRKuv61TvHH8Z8P3ir1D3rAkgMLL8cy1xh9P6U0u+ggJEmSpM6v8Lyj9MqcGL0YEROAh/LXwymlaQXHJEmSJKkKlbbzhZTSasC3gBeAbwLPRcSzxUYlSZIkqRqVtsYoIlYENgO2ANYFxgAPFxqUJEmSpKpU2sQIeBt4EhieUjqy6GAkSZKkzso7jJpW2qZ0wPrANcABEfFYRFwTEYcVHZQkSZKk6lPaGqOU0nMRMRYYS9ac7tvAVsBVhQYmSZIkqeqUNjGKiNHA4sCjZL3SbZlSeqvYqCRJkqTOJ8LGdE0pbWIE7JhSeq/oICRJkiRVvzLfYzQnIi6LiNH569KI6FV0UJIkSZKqT5kTo6uBGcC++Ws68IdCI5IkSZI6pSj5q3hlbkq3akppr4rhs33AqyRJkqT2UOYaow8jYvP6gYjYDPiwwHgkSZIkVaky1xgdCVxTcV/RFODgAuORJEmSOqVyNFYrt9IlRhFxYsXgNUD3/PMsYDvg+Q4PSpIkSVJVK11iBCyVv68BbATcTpbkfht4oqigJEmSJFWv0iVGKaWzASLiQeArKaUZ+fAw4C8FhiZJkiR1Ujama0qZO1/oB8ypGJ6Tj5MkSZKkNlW6GqMK1wBPRMSofHh3YERx4UiSJEmqVqVNjFJK50XEXcAW+ahDU0rPFBmTJEmS1BlF2JSuKaVNjABSSk8DTxcdhyRJkqTqVuZ7jCRJkiSpQ5gYSZIkSap5JkaSJEmSap6JkSRJkqSaZ2IkSZIkqeaVulc6SZIkSZ9fYHfdTbHGSJIkSVLNMzGSJEmSVPMipVR0DFUrIo5IKV1ZdBxlYFk0sCwylkMDy6KBZdHAsmhgWTSwLBpYFi3z/uzxpT7p77tE/8Lb+llj1L6OKDqAErEsGlgWGcuhgWXRwLJoYFk0sCwaWBYNLAu1KRMjSZIkSTXPxEiSJElSzTMxal+2e21gWTSwLDKWQwPLooFl0cCyaGBZNLAsGlgWalN2viBJkiRVOTtfaJoPeJUkSZKqXETheUfp2ZROkiRJUs0zMWpHETEiIvYuOo6iRcTMomPoaBFxXES8HBFTImLoQuZbLyJ26sjY1PYiYumIOCr/vHVE/LmdvufNiOjbHutuLxHxaAvnb7fy62wiYlBEvFh0HFIRImJYRJxcdByqLSZGUvs4Ctg+pdQ7pXTBQuZbDyhVYlQtSV0Hn2AvTfZ/rvmklL5WdAwqRjVcHKzFC3tSLTMxaoH86t0rEXFdfuJ4S0QsGRFnRcSTEfFiRFwZjTTizK/0nh8Rz0bE6Ij4SkTcExFjI+LIIrZH7SMifgN8AbgrIk6IiMvz8fvkfyPPRcSDEdEV+AmwX/53sV+RcVfotEldgS4AVo2IZ4GLgR75/qF+fxHw6RqfiNgwIu7PPw+LiJER8VBEvBURe0bERRHxQkTcHRGLVXzXqfn4JyJitQ7ezharP7HME9X7F1AuQ/JxTwN7VizbPSKuzrf1mYjYLR//84g4K//8jfz31OmPZxFxYr6PeDEijp9v2hfyMtioqPhUvIj4TkQ8nx9Hri06nrYWEWdExKsR8TCwRj5uvYj4Z77doyKid0QsFxFP5dPXjYgUESvnw2Pzc7MREfGLiHg0It7o7Em6OkanP5AUYA3g1ymlLwHTyU4iL08pbZRSWhvoBuy8gGXfTimtBzwEjAD2BjYBzm73qNVhUkpHAv8BtgGmVEw6C/hGSmldYNeU0px83E0ppfVSSjd1fLSf1tZJXXNP+CNi2/yk74X8RHjx/GT55op1fVIDFBE7RMRjEfF0RNwcET3y8Y2eYHeAocDY/Pd9CrA+cDwwmKw8N2vGOlYFvg7sCvwR+EdK6cvAh8A3K+ablo+/HPhZm21Bx/hMuUTEEsDvgF2ADYD+FfOfAdyXUvoq2e/p4ojoDpxG9re3DfAL4NCU0ryO24y2FxEbAIcCG5MdFw4HeufT1gBuBQ5JKT1ZYIwdfnEw/90/EBG35ye3F0TEgXmy/EJErFoR2335yfO9EbFyRPTK9zuL5PN0j4h3ImKxiFg13wc9le+f1sznWSXft7wQEee2V1m2RkSsBZwJfD0/jvyg4JDaVP4b2J+Gi271FwGuAX6YUloHeAH4cUppIrBERPQEtgBGA1tExEBgYkrpg3zZ5YHNyc7LFnahTwJMjFrjnZTSI/nnP5L94LaJiMcj4gWyE5u1FrDsHfn7C8DjKaUZKaX3gI8iYul2jVpl8AgwIiIOB7oUHUxj2impW+gJf35iPALYLx+/KPB94O/AxvmJMMB+wI2R1bicCWyXUvoK2QHxxCZOsDvaEymld/OT9WeBQc1Y5q6U0lyy/UMX4O58/AvzLX9DxfumbRJtx2msXNYE/p1Sei1lz4/4Y8X8OwBDI6uJux9YAlg5P+k5HPgb2YWpsR24De1lc2BUSmlWSmkmcBvZCd+ywO3AgSml54oMMFfExcF1gSOBLwEHAV/Mk+XfA8fm8/wSGJmfPF8H/CKlNI3s72yrfJ6dgXvy39mVwLEppQ2Ak4Ff5/P8HLgi3xf9txnl0ZG+DtycUnofIKU0ueB42toWZL+BD1JK08nOmboDS6eUHsjnGQlsmX9+lOyi05bA8Px9C7K/r3p/SinNSym9BPTrgG1QJ2di1HLz9wGfyHaoe+c70t+RHbwb81H+Pq/ic/2wXadXuTzpOBNYCXgqIpYpOKSW+DxJXVMn/GuQnRi/mo8fCWyZUvo4n3eXiFiUrNbkdrITqcHAI/kJ88HAQBZ+gt3RKn/fdTT8vj+mYb87/37iI4A8aZibGh4yN//+IS3gc2ewoHJZkAD2ypPv9VJKK6eUXs6nfRmYBAxohzjLZBrwNlniVAZFXBx8MqX035TSR8BY4K8V6xmUf94UuD7/fC0N5XUT2UUVyGojbsprmL8G3JzvQ35LVrMA2Yn2DRXrUXk9SJYIDSQ7NqxL9v9emRhV7nNqvq/qKPm/MjAxarmVI6L+Ku0BwMP55/fzna1tWNWoiFg1pfR4Suks4D2yBGkGsFSxkTXtcyZ1zT3hb8yNwL5kJ1ujU0ozyA5uf6s4WR6cUjqsBfG0h+b+P75JVpsFsFcrv2u/ivfHWrmOMnkFGFTfJAr4VsW0e4Bj65tmRcT6+ftA4CSypnk7RsTGHRhve3kI2D1vmtYd2CMfNyf//J2IOKDIAHNFXBycf97K9TS1D7kDGBIRfch+e/eRnftMrdiHrJfXgFVuUxndB+xTv//Nt6maPEj2G+gWEUuR1f7PAqZExBb5PAcB9bVHDwHfBl7Ljy+TyZrgPYzUSiZGLfcv4OiIeJms/fcVZAeCF8kO4oW1/1bpXZy3W3+RrAnAc8A/gMFRrs4XPqOdk7p/kZ0Y13ckUHngewD4ClmzqRvzcf8kuzdltTy27hHxRRZ+gt2uUkqTyGqwXiTrfGFBzgZ+HhGjyWpMWqN3RDxPdn/BCa1cR2mklGYDRwB/ye8Nm1gx+RxgMeD5iBgDnJMnSVcBJ6eU/gMcBvw+b0rZaaWUniZrXvYE8DhZM7Ep+bRZZM3AToiIXYuKMVfWi4OPktUIARxIXmuQN0t8kqyJ3J9TSnV5M61/R8Q+AJFZN1/2kfnWUxoppTHAecADEfEccFnBIbWp/DdwE9mx8S4azqcOJjt+Pk92/9FP8vnfJLtQ9mA+38NkCW9lM3CpRaLh4q2aEhGDyHasaxccitRuIuJNYEOyE7ENU0rHRMRtwOpkB6F7yW6g7012MWAx4PzG7jOKiGHAzJTSJfnwzJRSj/mnRcS2wCVkV3+fBL6fN5shsg4gDgGWq7+hNiK+DlwILJ5/1ZkppTsiYghZhwQfkJ0YrZpSWtD9DpJaID8G3k12X98GwEtkFzJOJ7sQMR54FXgrpTQsIkaQHTNvqd+vpJTej4hD8s/H5Ov9ZFoj37k1WRK8cz58fz48unJaXov4B6Av2cWbQ1NKb+fL7A3cDGxdf69KRKxCdmFzebJ92I0ppZ/k468HepA1zzq+fp8ldXaTP3qv1Cf9fRZftvD2dCZGLWBiJEmqVR4Dpc7NxKhp3vDfAnm1rQcESZIkqcqYGEn63CLiUD77TI1HUkpHFxGPpLbXnhcHI+LLfLYXuI9SStXQsYZUCoVXx3QCNqWTJEmSqtyUkjel612CpnT2SidJkiSp5tmUTpIkSapy+SPhtBDWGElSjYiI+yMi5V2lzz/tzXzaIR0fWfvKtyvl3TtLktQoEyNJaqaIGFZxkl35mh0R70bEHRGxb3hZjogYlJfXsKJjkSSpOWxKJ0mtM6Hicy9ghfy1C3BIROxR/5DaTmIsMBuY1kbrGwT8OP88rI3WKUlSu7HGSJJaIaXUv/4FdCfrxvhv+eQdgXMLC64VUkrbppTWTCmNKjoWSVJ7iJK/imdiJEmfU0ppXkppDLAr8Ho++nsRYa28JEmdhImRJLWRlNJs4OZ8cClgzfxem/p7kQZFxKoRcWVE/DsiPoqINyvXERGLRMSBEXFnREyIiDkR8V5E/DUivrWw+5cioktEHBsRT0fErIiYnHe4sHdTsTen84WI2Dgi/hARr0fEBxExPSJeioirI+IblesC/lExPP89WSMaWfdSETE0Ih7L4/4oIt6JiBsjYtMmYu8dERdHxNj8fq//RsTNEbFBU9stSVI9r2ZKUtt6t+JzT2BmxfDXgN8CPYAPgLmVC0ZEH2AUsGXF6GlAX2D7/LV/ROyTUpoz37KLA7cD9QnKPGBOvq6tIuLC1m5QRHQBLgOOqxg9C/gYWBP4ErAnsHQ+7T2ybe+dD1fej1W/TZXrXw/4P2DFfFQdWfmsCOwH7BsRZ6SUzm8ktkHA/cDAfNQcYElgb2DXiNin2RsqSVWsHI3Vys0aI0lqW4MqPk+eb9pvgTHARiml7imlHsAO8EnycRtZIvMsWScO3VNKS5MlUgcDE8ma6zWW5JxPlhQl4Eygd0qpN9AfuAL4IbBeK7dpOA1J0dXAGimlHimlPmTJz+7A3fUzp5Q2IkuU6of7z/f6Qf20iFgeuIcsCboN2BDollLqCfQDziFLlIZHxO6VQeVldjNZUjQF2JeszHoBawGPAyNbuc2SpBpjYiRJbSQiegIH5oOTgVfnm2USsF1KaXT9iJRS/TwHAFsBrwBbp5T+nFL6IJ9nVkrpGmAnssTnqIhYruJ7BwDH5oPnppTOSylNz5edmFI6CriBrPe8lm7TF4GT88GLUkqHVcRMSmlaSun2lNL+LV13fbzAcsD1KaW9UkpPpZTmVsR+FnBqPu+w+ZbdiyyRAtgnpXRzSunjfNmXgCFkZS5JUpNMjCTpc4qIpSNiW+A+YEA++ucppXnzzXp5SmkmjTssf78ipdRol9kppafIapy6AttUTNqbrGn0h8AlC1j/sIVuxIIdTHasmERD99ttIiKWIEsIofFasHrX5O/rRkS/ivH1ydgjKaV7518oTywv+tyBSlJVKLrXufL3Suc9RpLUChGRFjL5j8B5jYx/ZAHr6gJskg8Oi4jTF7LuPvn7wIpx9bUmo+triuaXUno1IsaRPWupJb6Wv/8t71yiLW0ALJF//mszn4s7kIZ7luq3+76FzL+waZIkfcLESJJap7JDgY+A94FngOtSSv9ofBEmLmB8H2Dx/HPvBcwzvyUrPtc3qxvXxDLv0vLEqH/+/lYLl2uOARWf+y1wrk9r6Xa/u5BpkiR9wsRIklohf7BrS9UtYHyXis87ppTuXsB8RVhYzdjnVbnd3dqhRkqSlGtmrXxN8x4jSSreJLKur+HTTeSaq74mqqnaoJbWFgGMz99bE1dz193a9Tdnu1uzzZKkGmRiJEkFy3theyIf3KUVq6jv5W7DiOjR2AwRsToNzwlqiUfz9+3zzhKa65OOJxbyUNonyZ47BJ9vu7dZyDxfb8V6JUk1yMRIksrhyvx9p4jYaWEz5g+CrXQrWTO9bjR0rT2/s1oZ14h83csAZ7dgucpOIJZubIaU0izg+nzwhxGx8sJW2Mh235S/bx4RWzcyfzfglGZFK0mqeSZGklQOfwT+TtZn6aiIODN/PhEAEdE9IraJiF8Bb1QumFIaB/wqH/xRRJwWEUvlyy0bEZcD3wYa7QZ8YVJKrwMX54OnRsTv89qn+rh6RsR+ETFqvkVfpaE26LsLqTU6HfgP0Bd4LCIOqo+9Iv698vXfMN+ytwJP13/O5+uSL/cl4C5g2RZtsCSpZpkYSVIJpJTqyB5Y+mey5xSdA4yLiGkRMQWYQdb19FFA90ZW8UOyxGoRYDgwJSImk/WedzTZc4KebWV4Z9KQeB0GvBoRM/L1TwVuZL7mbPkzhK7NBy8CZkbEWxHxZkRcUjHff4HtyBKpAWTPLJoaEZMiYibZfUS3ALsz3zErf5jrPsA7ZD373QLMioipwEvApmTPYZIkqUkmRpJUEiml6SmlXYCdyJqJvU3WjfeSZF1S/xU4DVijkWVnAzsCPyBLgOaQ1T49BOybUhr6OeKqSykdA2wOXJfHtVi+/peAq8iSuvkdTfZg2Rfy4ZXJOlnoO9/6XwbWAb6Xb+P7QM98/a8DNwNHAPs2EtsbwHrAZcC/82VmkyVJX0sp3dG6rZak6hIl/1cGkVJ79sQqSZIkqWgz5k4t9Un/UostXXh2ZI2RJEmSpJpnYiRJkiSp5i1adACSJEmS2lvhLdVKzxojSZIkSTXPxEiSJElSzbMpnSRJklTlbEjXNGuMJEmSJNU8EyNJkiRJNc+mdJIkSVKVi7AxXVOsMZIkSZJU80yMJEmSJNU8m9JJkiRJVc+mdE2xxkiSJElSzTMxkiRJklTzbEonSZIkVTkb0jXNGiNJkiRJNc/ESJIkSVLNMzGSJEmSVPO8x0iSJEmqet5l1BRrjCRJkiTVPBMjSZIkSTXPpnSSJElSIi8D1wAAATpJREFUlYuwKV1TrDGSJEmSVPNMjCRJkiTVPBMjSZIkSTXPxEiSJElSzTMxkiRJklTzTIwkSZKkKhcl/9esbYgYEhH/iojXI2JoW5eRiZEkSZKkUouILsCvgB2BwcC3ImJwW36HiZEkSZKksvsq8HpK6Y2U0hzgRmC3tvwCH/AqSZIkVbkluixZ6ie8RsQRwBEVo65MKV1ZMbwC8E7F8LvAxm0Zg4mRJEmSpELlSdCVTc7YjmxKJ0mSJKnsxgErVQyvmI9rMyZGkiRJksruSWD1iFglIroC+wN3tOUX2JROkiRJUqmllD6OiGOAe4AuwNUppTFt+R2RUmrL9UmSJElSp2NTOkmSJEk1z8RIkiRJUs0zMZIkSZJU80yMJEmSJNU8EyNJkiRJNc/ESJIkSVLNMzGSJEmSVPP+H263Fal+ZNTiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhpVmynI5qKS"
      },
      "source": [
        "### Training (4th try) : test_acc = 80.9118%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GN6MgFZ2evi"
      },
      "source": [
        "* with callback and more epochs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVVryFd05sPZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99ec9f8d-fe0b-41d5-d0d2-c31c4e5beaac"
      },
      "source": [
        "# model\n",
        "model_made = Sequential()\n",
        "model_made.add(Conv2D(32, (5,5), activation='relu', input_shape=(128,128,1)))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_made.add(MaxPooling2D((2,2)))\n",
        "model_made.add(Flatten())\n",
        "model_made.add(Dropout(0.3)) # dropout\n",
        "model_made.add(Dense(128, activation='relu'))\n",
        "model_made.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_made.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_14 (Conv2D)           (None, 124, 124, 32)      832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 60, 60, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_15 (MaxPooling (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 28, 28, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_16 (MaxPooling (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 12, 12, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_17 (MaxPooling (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 128)               589952    \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 832,010\n",
            "Trainable params: 832,010\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgvA59dc5s71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a331bb79-19d4-4b55-cd5b-6fba6e85cbaf"
      },
      "source": [
        "from tensorflow.keras import callbacks\n",
        "\n",
        "callbacks_list = [callbacks.EarlyStopping(monitor='accuracy', \n",
        "                                          patience=7) # 다음에폭에서 증가되지 않으면 멈추겠다. \n",
        "            ] \n",
        "\n",
        "\n",
        "model_made.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_made = model_made.fit(X_train_made, y_train_made,\n",
        "          epochs=100,\n",
        "          batch_size=64,\n",
        "          callbacks=callbacks_list, # 이렇게 callback list 를 넘겨줄수 있다.\n",
        "          verbose=2, \n",
        "          validation_data=(X_val_made, y_val_made))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "98/98 - 4s - loss: 1.5679 - accuracy: 0.5053 - val_loss: 0.5471 - val_accuracy: 0.8246\n",
            "Epoch 2/100\n",
            "98/98 - 3s - loss: 0.3737 - accuracy: 0.8743 - val_loss: 0.2507 - val_accuracy: 0.9273\n",
            "Epoch 3/100\n",
            "98/98 - 3s - loss: 0.1497 - accuracy: 0.9525 - val_loss: 0.1140 - val_accuracy: 0.9700\n",
            "Epoch 4/100\n",
            "98/98 - 3s - loss: 0.0923 - accuracy: 0.9710 - val_loss: 0.1526 - val_accuracy: 0.9547\n",
            "Epoch 5/100\n",
            "98/98 - 3s - loss: 0.0713 - accuracy: 0.9766 - val_loss: 0.0982 - val_accuracy: 0.9745\n",
            "Epoch 6/100\n",
            "98/98 - 3s - loss: 0.0421 - accuracy: 0.9855 - val_loss: 0.1448 - val_accuracy: 0.9636\n",
            "Epoch 7/100\n",
            "98/98 - 3s - loss: 0.0421 - accuracy: 0.9852 - val_loss: 0.1090 - val_accuracy: 0.9777\n",
            "Epoch 8/100\n",
            "98/98 - 3s - loss: 0.0519 - accuracy: 0.9847 - val_loss: 0.0876 - val_accuracy: 0.9834\n",
            "Epoch 9/100\n",
            "98/98 - 3s - loss: 0.0225 - accuracy: 0.9925 - val_loss: 0.0879 - val_accuracy: 0.9860\n",
            "Epoch 10/100\n",
            "98/98 - 3s - loss: 0.0247 - accuracy: 0.9928 - val_loss: 0.1216 - val_accuracy: 0.9790\n",
            "Epoch 11/100\n",
            "98/98 - 3s - loss: 0.0204 - accuracy: 0.9920 - val_loss: 0.0690 - val_accuracy: 0.9866\n",
            "Epoch 12/100\n",
            "98/98 - 3s - loss: 0.0205 - accuracy: 0.9944 - val_loss: 0.1078 - val_accuracy: 0.9809\n",
            "Epoch 13/100\n",
            "98/98 - 3s - loss: 0.0369 - accuracy: 0.9887 - val_loss: 0.0648 - val_accuracy: 0.9853\n",
            "Epoch 14/100\n",
            "98/98 - 3s - loss: 0.0157 - accuracy: 0.9949 - val_loss: 0.0830 - val_accuracy: 0.9866\n",
            "Epoch 15/100\n",
            "98/98 - 3s - loss: 0.0200 - accuracy: 0.9936 - val_loss: 0.0910 - val_accuracy: 0.9821\n",
            "Epoch 16/100\n",
            "98/98 - 3s - loss: 0.0185 - accuracy: 0.9943 - val_loss: 0.0779 - val_accuracy: 0.9879\n",
            "Epoch 17/100\n",
            "98/98 - 3s - loss: 0.0217 - accuracy: 0.9927 - val_loss: 0.0874 - val_accuracy: 0.9828\n",
            "Epoch 18/100\n",
            "98/98 - 3s - loss: 0.0223 - accuracy: 0.9931 - val_loss: 0.0632 - val_accuracy: 0.9866\n",
            "Epoch 19/100\n",
            "98/98 - 3s - loss: 0.0057 - accuracy: 0.9978 - val_loss: 0.0750 - val_accuracy: 0.9853\n",
            "Epoch 20/100\n",
            "98/98 - 3s - loss: 0.0184 - accuracy: 0.9951 - val_loss: 0.0655 - val_accuracy: 0.9841\n",
            "Epoch 21/100\n",
            "98/98 - 3s - loss: 0.0048 - accuracy: 0.9986 - val_loss: 0.0649 - val_accuracy: 0.9885\n",
            "Epoch 22/100\n",
            "98/98 - 3s - loss: 0.0025 - accuracy: 0.9992 - val_loss: 0.0534 - val_accuracy: 0.9898\n",
            "Epoch 23/100\n",
            "98/98 - 3s - loss: 0.0050 - accuracy: 0.9989 - val_loss: 0.0656 - val_accuracy: 0.9904\n",
            "Epoch 24/100\n",
            "98/98 - 3s - loss: 0.0039 - accuracy: 0.9989 - val_loss: 0.0922 - val_accuracy: 0.9847\n",
            "Epoch 25/100\n",
            "98/98 - 3s - loss: 0.0227 - accuracy: 0.9936 - val_loss: 0.1476 - val_accuracy: 0.9783\n",
            "Epoch 26/100\n",
            "98/98 - 3s - loss: 0.0298 - accuracy: 0.9907 - val_loss: 0.1048 - val_accuracy: 0.9821\n",
            "Epoch 27/100\n",
            "98/98 - 3s - loss: 0.0225 - accuracy: 0.9931 - val_loss: 0.1070 - val_accuracy: 0.9815\n",
            "Epoch 28/100\n",
            "98/98 - 3s - loss: 0.0182 - accuracy: 0.9949 - val_loss: 0.0957 - val_accuracy: 0.9872\n",
            "Epoch 29/100\n",
            "98/98 - 3s - loss: 0.0177 - accuracy: 0.9951 - val_loss: 0.0738 - val_accuracy: 0.9879\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_wRVLC_R7AJG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "outputId": "776aacc7-7f05-42e0-bb47-d48c0cca870a"
      },
      "source": [
        "# plotting\n",
        "loss = history_made.history['loss']\n",
        "val_loss = history_made.history['val_loss']\n",
        "acc = history_made.history['accuracy']\n",
        "val_acc = history_made.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pofm6TKi5x9D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c265712a-d9c1-47ab-f2ce-b2c1836c8030"
      },
      "source": [
        "# loss and accuracy on test \n",
        "\n",
        "test_loss, test_acc = model_made.evaluate(X_test, y_test)\n",
        "\n",
        "print('Test accuracy : {:2.4f}%'.format(test_acc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "74/74 [==============================] - 1s 7ms/step - loss: 1.0587 - accuracy: 0.8091\n",
            "Test accuracy : 80.9118%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MLyUXFvd50vj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e927bcb-b1ee-43b8-eef6-fabe5619a7fa"
      },
      "source": [
        "pred4 = model_made.predict_generator(X_test)\n",
        "y_pred4 = np.argmax(pred4, axis=-1) # 모델이 예측한 class label"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1976: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uryi-B0AvCII",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "outputId": "6c867fad-6912-4bb5-fb64-fc15fbb67d69"
      },
      "source": [
        "labels = ['palm', 'l', 'fist', 'fist_moved', 'thumb', 'index', 'ok', 'palm_moved', 'c', 'down']\n",
        "\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "cm = confusion_matrix(y_test, y_pred4)\n",
        "plt.figure(figsize=(15, 15))\n",
        "ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_title(\"model_last_madeData\", fontsize=30)\n",
        "ax.set_ylabel('Actual', fontsize=25)\n",
        "ax.set_xlabel('Predicted', fontsize=25)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 185.99374999999995, 'Predicted')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlI5bLnpfGOm"
      },
      "source": [
        "* model save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "91O81pECC3DK"
      },
      "source": [
        "# model save\n",
        "model_made.save('/content/gdrive/MyDrive/made/model_made.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkPMFkngfMhh"
      },
      "source": [
        "# 4. 직접 만든 data에 Augmentation 적용 후 training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MKpdgLFvsFNB"
      },
      "source": [
        "## Data upload"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WOB0rinGn-R"
      },
      "source": [
        "* mount drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRKM3mQLGn-S",
        "outputId": "3a7bdb17-47bc-43dc-a7be-63a25e0d8989"
      },
      "source": [
        "# mount Google Drive\n",
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0q_VsItGh25"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2 as cv\n",
        "import pandas as pd\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers import Dense, Flatten, Dropout\n",
        "from tensorflow.keras import callbacks"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_5fM9CyjsFNC",
        "outputId": "fc29d53b-69c8-46ca-9569-f3e90822109d"
      },
      "source": [
        "# 저장했던 data load\n",
        "X_aug = []\n",
        "y_aug = []\n",
        "X_aug = np.load('/content/gdrive/MyDrive/made/input_data_made.npy')\n",
        "y_aug = np.load('/content/gdrive/MyDrive/made/label_data_made.npy')\n",
        "\n",
        "print(len(X_aug))\n",
        "print(len(y_aug))\n",
        "print(X_aug.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7838\n",
            "7838\n",
            "(7838, 128, 128, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpainmyQsFNC",
        "outputId": "5cff6e30-73ee-4a26-fbd7-383119743dea"
      },
      "source": [
        "# test train split\n",
        "X_train_aug, X_val_aug, y_train_aug, y_val_aug = train_test_split(X_aug,\n",
        "                                                                  y_aug,\n",
        "                                                                  test_size = 0.1,\n",
        "                                                                  random_state=10)\n",
        "\n",
        "# model\n",
        "model_aug = Sequential()\n",
        "model_aug.add(Conv2D(32, (5,5), activation='relu', input_shape=(128,128,1)))\n",
        "model_aug.add(MaxPooling2D((2,2)))\n",
        "model_aug.add(Conv2D(64, (3,3), activation='relu'))\n",
        "model_aug.add(MaxPooling2D((2,2)))\n",
        "model_aug.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_aug.add(MaxPooling2D((2,2)))\n",
        "model_aug.add(Conv2D(128, (3,3), activation='relu'))\n",
        "model_aug.add(MaxPooling2D((2,2)))\n",
        "model_aug.add(Flatten())\n",
        "model_aug.add(Dropout(0.3))\n",
        "model_aug.add(Dense(128, activation='relu'))\n",
        "model_aug.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model_aug.summary()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 124, 124, 32)      832       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 62, 62, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 60, 60, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 30, 30, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 28, 28, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 12, 12, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 4608)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               589952    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 832,010\n",
            "Trainable params: 832,010\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI72qQfcsFNC"
      },
      "source": [
        "## Data augmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CV9D1WRwsFNC"
      },
      "source": [
        "* 사람마다 폴더로 나눠져있으므로, 폴더별로 augmentation data를 생성한 후 MergeDI class를 이용하여 합함"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6-OUDxAsFND"
      },
      "source": [
        "# DirectoryIterator (train_generator) 합치기\n",
        "class MergeDI():\n",
        "    def __init__(self, *gens):\n",
        "        self.gens = gens\n",
        "\n",
        "    def generate(self):\n",
        "        while True:\n",
        "            for g in self.gens:\n",
        "                yield next(g)\n",
        "\n",
        "    def __len__(self):\n",
        "        return sum([len(g) for g in self.gens])"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oU4jZKZAsFND",
        "outputId": "1447edcf-1e63-461e-dee3-ef4c1c412499"
      },
      "source": [
        "# data augmentation\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "                             rotation_range=20, # range 내에서 random하게 data 회전시킴\n",
        "                             width_shift_range=0.1, # range 내에서 translation\n",
        "                             height_shift_range=0.1, # range 내에서 translation\n",
        "                             shear_range=0.1, # range 내에서 affine\n",
        "                             zoom_range=0.1, # range 내에서 zoom\n",
        "                             horizontal_flip=False, # 거울모드는 오른손만 detecting해야하므로 false\n",
        "                             fill_mode='nearest', # 빈 공간을 채울 방법 : 가장 가까운 값\n",
        "                             validation_split=0.1) # validation split 가능\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255) # test 시에는 변화 x\n",
        "\n",
        "train_generator_12 = train_datagen.flow_from_directory('/content/gdrive/MyDrive/static_skeleton_data/12',\n",
        "                                                       color_mode='grayscale',\n",
        "                                                       target_size=(128,128),\n",
        "                                                       batch_size=32,\n",
        "                                                       subset='training')\n",
        "\n",
        "train_generator_13 = train_datagen.flow_from_directory('/content/gdrive/MyDrive/static_skeleton_data/13',\n",
        "                                                       color_mode='grayscale',\n",
        "                                                       target_size=(128,128),\n",
        "                                                       batch_size=32,\n",
        "                                                       subset='training')\n",
        "\n",
        "train_generator_14 = train_datagen.flow_from_directory('/content/gdrive/MyDrive/static_skeleton_data/14',\n",
        "                                                       color_mode='grayscale',\n",
        "                                                       target_size=(128,128),\n",
        "                                                       batch_size=32,\n",
        "                                                       subset='training')\n",
        "\n",
        "validation_generator_12 = train_datagen.flow_from_directory('/content/gdrive/MyDrive/static_skeleton_data/12',\n",
        "                                                        color_mode='grayscale',\n",
        "                                                        target_size=(128,128),\n",
        "                                                        batch_size=32,\n",
        "                                                        subset='validation')\n",
        "\n",
        "validation_generator_13 = train_datagen.flow_from_directory('/content/gdrive/MyDrive/static_skeleton_data/13',\n",
        "                                                        color_mode='grayscale',\n",
        "                                                        target_size=(128,128),\n",
        "                                                        batch_size=32,\n",
        "                                                        subset='validation')\n",
        "\n",
        "validation_generator_14 = train_datagen.flow_from_directory('/content/gdrive/MyDrive/static_skeleton_data/14',\n",
        "                                                        color_mode='grayscale',\n",
        "                                                        target_size=(128,128),\n",
        "                                                        batch_size=32,\n",
        "                                                        subset='validation')\n",
        "\n",
        "train_generator = MergeDI(train_generator_12, train_generator_13, train_generator_14)\n",
        "validation_generator = MergeDI(validation_generator_12, validation_generator_13, validation_generator_14)\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory('/content/gdrive/MyDrive/10',\n",
        "                                                  color_mode='grayscale',\n",
        "                                                  batch_size=32,\n",
        "                                                  target_size=(128,128))\n",
        "\n",
        "print(len(train_generator_12))\n",
        "print(len(train_generator_13))\n",
        "print(len(train_generator_14))\n",
        "print(len(train_generator))\n",
        "print(len(validation_generator_12))\n",
        "print(len(validation_generator_13))\n",
        "print(len(validation_generator_14))\n",
        "print(len(validation_generator))\n",
        "print(len(test_generator))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2366 images belonging to 10 classes.\n",
            "Found 2342 images belonging to 10 classes.\n",
            "Found 2139 images belonging to 10 classes.\n",
            "Found 257 images belonging to 10 classes.\n",
            "Found 257 images belonging to 10 classes.\n",
            "Found 232 images belonging to 10 classes.\n",
            "Found 2347 images belonging to 10 classes.\n",
            "74\n",
            "74\n",
            "67\n",
            "215\n",
            "9\n",
            "9\n",
            "8\n",
            "26\n",
            "74\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMS4cSFh8dc1"
      },
      "source": [
        "* 첫 번째 input data (image)에 대한 augmentation 결과"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OVvSu56r-K-s",
        "outputId": "308cb4ba-835f-4c0d-edf7-c9774797a4ac"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.preprocessing import image\n",
        "\n",
        "x_augmentation = X_aug[0].reshape((1,) + X_aug[0].shape) # 첫 번째 axis 추가 : batch\n",
        "\n",
        "i=0\n",
        "for batch in train_datagen.flow(x_augmentation, batch_size=1):\n",
        "  # 주어진 x에서 batch size만큼 꺼낸 후 전처리 수행하여 return\n",
        "  plt.figure(i)\n",
        "  imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
        "  i += 1\n",
        "  if i%4 == 0: break # 4번 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD7CAYAAABqkiE2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3deXRk53nf+e9zt9oXFJYGGmiiFzZ3iRRFk5Qox4woRRKtsRxF0ZHiJJSthLN4PHY858TSaE58MnNmRk4ytpUzziQ8thMlkbVYXkhp5CiUREmWTVFqLiKbS5O9sLvRjR0oFGq/yzN/VJEEm91soBagu+v9nIMD1EXh3rdvV/3qve99F1FVDMMYXNZOF8AwjJ1lQsAwBpwJAcMYcCYEDGPAmRAwjAFnQsAwBlzfQkBE3i8iR0TkqIh8ql/HMQyjO9KPfgIiYgMvAu8FZoAfAx9X1ed6fjDDMLri9Gm/twNHVfU4gIh8CfgQcN4Q8CSmcVJ9KophGADrrC6p6ui52/sVApPA6Q2PZ4A7Nj5BRO4H7geIk+QOuadPRTEMA+Bb+tWT59u+Yw2DqvqAqt6mqre5xHaqGIYx8PoVAmeAPRseT7W3GYZxielXCPwYOCgi+0TEAz4GPNSnYxmG0YW+tAmoaiAi/yPwTcAG/lBVn+3HsQzD6E6/GgZR1W8A3+jX/g3D6A3TY9AwBpwJAcMYcCYEDGPAmRAwjAFnQsAwBpwJAcMYcCYEDGPAmRAwjAFnQsAwBpwJAcMYcCYEDGPAmRAwjAFnQsAwBpwJAcMYcCYEDGPAmRAwjAFnQsAwBpwJAcMYcCYEDGPAmRAwjAFnQsAwBlzfZhs2LlOWjdg2YltEjQb0YcFa49JiQsB4lbgeVj4Ho0MEQ0mcZ44Tlko7XSyjz8zlgPEaS5CYhz+cojwVh0R8p0tkbAMTAsarxLbRRIzKZIziQQtJJna6SMY26DgERGSPiDwiIs+JyLMi8qvt7QUReVhEXmp/H+pdcY1+0jBEqnXqeaF+dYMwn0Zcb6eLZfRZNzWBAPifVfUG4E7gl0XkBuBTwLdV9SDw7fZj43IQhmi9TpAUJsZXCdMe4rk7XSqjzzoOAVWdVdUn2j+vA88Dk8CHgM+3n/Z54Oe7LaSxPTQICFdWaQwr/8O+77F2II5MT4LIThfN6KOe3B0Qkb3A24DHgF2qOtv+1Ryw6wJ/cz9wP0CcZC+KYfSCKlZDmGkWUAvUc0As0LB/x7RsxHUQx2nVRsII9Zv9O57xOl2HgIikgT8Bfk1VS7LhU0NVVUTOe6NZVR8AHgDISsHcjL6EpGaV//ji7WQairp2fw9m2ViJONZQHk0lkEYT6g2Cufn+Htd4VVchICIurQD4gqr+aXvzvIhMqOqsiEwAC90W0theI0+uU6xmGHqmiLVYJNCob8eyc1nCg1NUdieo52xyL9dxl6owv2A6Km2TjkNAWh/5fwA8r6q/veFXDwH3AZ9tf3+wqxIa204PHSZ3CCJaX/0kqSTl6SSlaZvGiOLWYqT8CNMKsX26qQncBfwD4BkReaq97X+h9eb/ioh8EjgJfLS7IhpXNFWsEOwGOBUhd3gZPT1LZGoB26bjEFDVH8AFA/ueTvdrDKYwDn5akWqdcH19p4szUEyPQWNniRA5Qvmgz9vfdYQon97pEg0cEwLGmxPBymSQWKw/+w8C3HIIbsS16XnUMS/J7WbOuPGmxHGxhoewMv35hNZGE2+1idjKjYkZon7fkjTewISAcUH26CjhnTey/G88Tv7ja1udeXrce1BrNdyzK6BwjbtAs+BhD5nhJtvJhMAVRhwHKx7HSqWwUqnWACCrs09X8VwaBZf/+7qvUD3YbF0SSG9fMhoE6FoJDS2SVoCftJB0qqfHMN6cmVTkCmPt3YM/mac65mGFkDlSRJaLHfXAC87OEl8YpRLFsOMBMj2JdXqWqIet9xoEhGsl7FWHv6weoJGzCMeHYOaM6Sy0TUxN4EpjWUS21aq2KxBFaKdvJlXsapMvL91BtBxD3da0Yz3XHq9wqjlMGBeCtBm5uJ1MTeBKo4pEilcKsRshUq6hjUbHu7OKZb73l28hd1JQy+pbTz6nIjxbmqCZgXrBJdXvQUvGq0wIXGnml4jVGjT3jhLGbWrXjxM/nYDiWmf7a/rElwSnokQJB8vuT+v98PMhh71rGD0Skjxbgz6OVzBez4TAFSYslWB9HWt3AT/rUhl3sKupjq/7NIpw18EKIUw4OE5/XjLpYyUkzJA8VcFeqxD05SjG+ZgQuBKJRW13grW9DrU7KoRektEfdLgr16U2rtSA8mSMq+YLrRF+PaYvHCd9IoY2m4RhZBoFt5FpGLyCRQ5Mj63gZ7q4khdBbQgS0BiJ0HiPPzdEENdDPA9sG4nHkHifeica52VC4ArlFQOcOvzM6Es0hrr4VFXFagpRMsKeqhKkezvxqDguViEPB/bQuHU//lv2w4E9ZkqzbWRC4EqkEW6xjl1TronPEia04w5DBAFeCQhhJFcm8nr7krEScXR8mOL1WRZujdHMu0Se6Tq8nUwIXKGstQpuVbnGXSBKRIjbWZdfDQK8NUUCYSJVInJ6/Akdi9EcSbJ2wKJyU51mxjKDiLaZOdtXIlXCs3MkFn0q6iLxEOuqSazk1id01WqNwuEyY4fg8CMHSZyt9LasVmsocW0q4L3XP0/6TAPn8AnTMLiNTAhcobTRwKmFvOyPoqGgqTjSwT1+9QOchTXSpxvkXmrVMHoqDLGbEZIMeFv6FNKMzKQi28yEwBXMXq/zuaPvxptzCVMeuFtv2Ve/SXDyNPajzzD0pccJXj7V0zJqrY43U0Qs5aeTRwkyLlbCLH+2nUwIXMGslXXq3x5l5KkIZ6GE1uqd7Ui1NdrPb/a8mq5NH4rrRA2bijr4KRsrl+3pMYw3ZzoLXcGCmTNM/PYZAC7VXvjqNwkXF5HKfhbDTKthsJCDuXnTLrBNTAgMgvYKP1Z7irCwVNrhAr2RU7E4VNlPGIMgGzdTjm8jEwIDQFwHK5l8bbKOcgWiS6tu4K0Jf720Hwm48BzWRl+YEBgA9ugI/vQo5T1xAHJ/vkRUv7RCYPrzx9Gv5Rg98xxRrd75HAjGlpkQGCDNVGscAFbn7cFWMol4LpJIoPUG4epqT8oWrqwipXWiatW0BWwzEwIDQBtN7PU6YTxJkAC6mBPA2jVKmE9Tn0gSW6rDj3oTAtpodDX5idG5rm8RiogtIk+KyNfbj/eJyGMiclREviwivR1xYnRGhOLbmsi7VpF4vOPdNKcKFG/MMPM3HRZuy/SwgMZO6UU/gV8Fnt/w+LeA31HVq4FV4JM9OIbRA9fsm+MXDhxCYp3ncqPgUtltMf6Wecp7e1Rtt+zWcOJYzIwe3AFdhYCITAE/C/x++7EA7wa+2n7K54Gf7+YYRveiUgmZmSdQixFnnXCigD0y3NG+4osNknOKa4dEbvchYA8X0He8heJHb2Xuv307zviurvdpbE23NYHfBf4pr61gPQwUVfWV2aFmgMnz/aGI3C8ih0TkkI+5Fuwn9QO0UuX4yTH+6MztSLPzybvscoNYKaRYTWA1evCpbdkEKYf6sEV1QqGLWorRmY4bBkXkg8CCqj4uIndv9e9V9QHgAYCsFExzcD9FIVE95PrPrqCJJHrkOFGz2dGu5MwC6SDi7EsF0melNU9Bl30OJFD8FPiFAO1gfIPRnW7O+F3Az4nIvUAcyAKfA/Ii4rRrA1PAme6LafTE0iriOER+0PFtOK1UsSo1rEBQATubJqrUWuMKOtFo4K3U8Eox6lUbCS6t/guDoOPLAVX9tKpOqepe4GPAd1T1F4BHgI+0n3Yf8GDXpTR6IlxdJVxc7OqTO6rX0XKldQFogWQyiNf5YiFRo4E1v0JsLcIpC0RmqvHt1o9RhL8B/LqIHKXVRvAHfTiGsYMkHqdwyyKltzaoXT+OVeh8AVH1A6LSOrURC7m2jL+7YBYk3WY9uQBT1e8C323/fBy4vRf7NfpABCudRmIeEo8Tray2eultcR+702tUGh6NfJZ4oovZgTVCmz5BAiYLa/i5XbjpFPSoJ6JxcWY+gQFjJZOU33MDZz9+Lc9/aormnddvfSdhyNGVEeo1j8q4RZTpvPMRqqjfxM8p94wdoXSVg79n2PQX2EYmBAaNZRE54GcgM1XCz3bQhViVaiVOGFg0MxB53Vco3TXhB8sHsPyud2VskQmBAeWnlffsOUKjgxDQMCJa9dCGTbMQESa6D4H0jPLC4T14ldaCqsb2MTdlL3Hieq1VeZIJRIRweRUNw85b+H2fxJKP3Yxza/ok34vdseVdaKXC5LdBbQunEhI/vtj12oHDj86RPZHDO7OKlquEZiThtjEhcImzEnGkkCfKpVBVpFKFegPtMAQ0jHCKDex66zpeO7j0juoNsk/NtR7UG0Rr3c9UFB49gXUUsxDpDjAhcInz33aA2TsTNAqK1YR9D7nYc6sEp2c62p8GPvax00w86vHPsx9l+kgdcb2tdfaJQsLTrT5gGqlZRvwyZ0LgEmdXfOIrcSp7Q7CV+licZDXV+Q5ViWp13NkSw4djOMU6kogTwZYuMzQwn9lXCtMweInTJ55n9As/AYFdU6usXuNSm+puHL/6TcIXj5H9ox8iZ5eQQh4rncLaydWARTpfL9HoiqkJXOqiEG02scsWa5UEVpyeLApqxeNYwwWCPSP4WY/4TByrVNl6x6Eu2dksun+K4vVZKhMWe754nGB2blvLMOhMTeAyoGGIu25RL8VQi54sCiqJBNFonspkgtIel+ZYGs2le1DaLZYjk2b9QIb5dyrDHzhDNJrf9jIMOlMTuByosu9LC0TJGHZxsTXIZmqysy6/r3AcwqRLebdNdVJp5uJkZjzSz8m2TvSpTR9vPURTcM+uI/wg9lPbdmyjxYTAZUJPziCui8bjMFagujdL6mnpPASCAKsWgEDoKfURcKpCJ3UBe2gISSXRWg1t+kRbWVA0CnHKPuLYXB2b5/uejfRgjgJj88zlwGUiqtcREdbftZ9jHy9w8J89R/EdUx3vL1xdRZ96jtRchFMR2FelvBeQrb8kKu86yKm/N03pbx4kuPnAlvr9a6WKe3oZy1belThNM+9iZ7f/smSQmRC4jGgY4lRDELglc4og1mXbgCqxFZ/knBAsxXHXpKN7/vW8TXU8Yn3Spja+tTsMGgToWomw6jAfevgpG8nntlwGo3PmcuByEobEVhpI6HJd7CxR53N5vCq2UCEXs/DTLvGlztoCmjnBGq9RieJYoU1KLNDN9zcISyWsss1LzV00U0KUT7dqE6br8LYwIXAZiZo+9vEz7P3aJL9x6n7GHl0i3Fj17uRNc+IMqYUkyaMZpNEk6GAf7roSrsTQbEgjbyGWbLlC4VSEJ6vT+BmhMZLA3UKQGN0xIXA5iULClVWsps+uuVyr/0BhCK3VWzMKdzDPX7S+3mrIm5sHy8bO59B6g6jpb7pxzmkodsUizPoEyc5eUm5ZeGplChTUMVep28mEwOVGtfXGLZdpfOA21qccRp8sYy+VCE6c7GrXzuQEC+/dQ/5YHfe5GaLV1U11D44VQ+JLLs51VYrVzl5S4z+ss7S6h5EjdbyFMqEZj7BtTAhcrlRRS4gcIYw7WF1M9vmKKJdm+WYF4owsDyOVyqZCwK6HuGWHZKzJWiLo6A6Dd3aNIXJ4syWk3L9FScX1ENsC123d0jRjIEwIXM6sQLF9JUjaOInuF+1ojKe47+7v83nnp0msZEnPJWAT/RCctQbxFY/xVIm1bGdTjYUvHsN+EfraCiCClU4h8RiaSSELS4TFtX4e8bJgQuAylji2jLeSwllaRyvd9/m3/Yjny+OoKNVhm4y7udqFvVYhvhxnKllkNZNEbAu9BKcJE9uGiVGaoynW98Qo/FjAhIAJgcuZzi1ir64RNZoQRVjJJNpsdlzFFT/ibDmHqBCkBJxNvjyqNby1JkNOleF4hbLnwRYaFoHWoqSW9Ld6btuE2Ti1EY/1PRa5FxP9O9ZlxITAZSwql5Gmh1x/gPquJMWrPcZ+vA4/eqaj/dnlBjNHx/CWLdjCJXm4tIwtQjmMkXKarO+dxJ5dbi10chHiOFiZDIyPEOYSWM8cI6pUOir/RUWKVW0iUYIgrUSebXrLYXoMXt5UIQwJEy71gsPaNRGN0c6n/7bWa2RetEmfhNRciNY3t1CsBgHabFL0k9RClyAbRzY5N4HEYjAyxPp1BRZuTWNl+thlWCOs9RrxpSapGXDWzUK4YGoClz+xiGI21XGLD971Y/7yyE8x1uGughMnGf/Xp159vKXJPv2Al9ZGEVH88Rju6uZmP7KyGdZvGGHm5wI+cvOPeOZHN7b6LPSBBgHBiZNYJ04y+gMhMj0SgS5rAiKSF5GvisgLIvK8iLxDRAoi8rCIvNT+btaU6iMNw9bttSM+33jkNvLHu2yRa78xxLZxxnfhTIxvakCQqrJQSrNSSeInrE2vRaC+T2zFBxUmvLW+dRQS18OZ3M2pf/ZOlv/xO7AzGcQ1y6BD95cDnwP+i6peB9wMPA98Cvi2qh4Evt1+bPRLFKKzC6SeneOqbzZJHF1qvbi7WMFHbBvxPMLJEcLdw5u77x9F1FYTlNfjhB6o2/6bV6YNs2zEcVpfrtf6isVALOyaDwppu45a0pfVh8RziUbzfOLvPkz1fetIJo2YZdABEO2wSiQiOeApYL9u2ImIHAHuVtVZEZkAvquq177ZvrJS0Dvkno7KYdB6g7kOVjLJwoevY/m2kOmvKckTRcIXjm654429awwdH+bEZxwsS5n+xZMXnSNAHIfm3TcTxSy8lSZ2pYFUGzSuKuBnbSq7bPyU4GehmYuIEhGxQo1sqs41Q4ucKBWYX85x9e8GWEdOEpa6n8b89QUU7FyW1XuvJ74cEP/e4S11jb4SfEu/+riq3nbu9m6icB+wCPx7EbkZeBz4VWCXqs62nzMH7DrfH4vI/cD9AHGSXRTDIArRRkjYaBAmhPR4mWY2SzzZWXVXRIgci9umTpNyGpyOp6FSvfAbRgTEIjZXIUq6RDEHv5CE4SRr+zyaOaE+pgSJCE0FeJkm8ZjPRLZEPlZjIr7GT+Z3owsxIIBY7yc8tcdGkVSS/HMlpFwjbDTMKMW2bkLAAW4FfkVVHxORz3FO1V9VVUTOe6ZV9QHgAWjVBLooh7FB7oTP4o/yxJcaWDW/ox544fIqVhBQDVwKXgXGCthBQHiBlYKtRAJJpahOZ6jscli7GtSByFHi0yUyiQZRPUZY9WDNg9kUYUVYns+zXlGWFprEro4RjbXCx052scDpBcx+5GoqU8rkd30SzcAEwAbdhMAMMKOqj7Uff5VWCMyLyMSGy4GFbgtpbJ5TDfFKLrUxlzCeJ9XcC6trhMsrm96HhiFarfHCw7dwOKEcLJ4karzxdpqVySC7dxGMpPEzLpavpOYC7IYNAipC/VSOigd2E3INxa2AV46waxFesYld87GKZYZ0mMSKh7NYQsu97yfglhVvTVqTsvhmvMBGHYeAqs6JyGkRuVZVjwD3AM+1v+4DPtv+/mBPSmpsilP2ia21esRJaIGMkjwRgy2EAFFIVK0y/S+fACCo18/7NKuQZ/WWESq7LZpZ2PNwBXdujcTiMlpvbGpoc9T+cuYWyHgeYbnSl+v01HyAFdg4azWkZvoHbNRt8+ivAF8QEQ84DvwirTsOXxGRTwIngY92eQxjC+zFNbKuRWUiRTOnzL7TZszLkTq89X1FjcZrdwZeaXzM55BEHH9iiGrWJXJbk4q4ZXAWSujKamt0Xri1N3LUaCBB0PMAsEdH0d0jOLWA9JkQObNA1INxFleSrkJAVZ8C3tDaSKtWYOwAXS/jLDo4tRR+Bpq5kCDR4Z1gVaA1JkE8t9VgN5wnyMYpTyeIbEDBW2+NZmS9QlTZ4vBcy8ZKxMGyWqsur6/39HpdMimqU2ksX7GrAVGp3NHkK1cyc6P0ChOurCKlMhOPuAS5OI3hGMmTRTqdosPO5yndcw1BTAg9IUgIYQLWDwTE5xymvlPFm1khWlgirNW2/AZ2JidYedcUQUKIHNj14DHC+d41I9X3DnP2rtbL3F33mH46TmhC4HVMCFxpVFG/iSwXcRpJJMrgF5IE9/4UqSdOEXTYJddPCfVhQZRWx56XHRLzirtYRotrHa9/oDGXRl6oF4QwoYz3cj1EEdSitWpTTJEIsM16h+cyIXCFCubmW11l66PMf3iaqQ+foPp/7sFdWNrSdbcGAV4xoDIeo7rXx112iC8JU79/mLBU6n4SEK8VArW9PumRCprq3fBesW2sUHFqQiMdEZqRw+dlQuAKpoFPtFrErl9FEFkECYv4UI5wZXVT1Xb74H4ae4aYeY8HqsRnXUaeDkmdqhDVzn/HYKukVCF/dAi76dIo5JDKTG/2G4thTU9R3eXhZyJiCzaxVbOk+vmYocRXMlWiSgWnDuvNGEHcgqEc4nkXXQZcHIfmZJ7SXo/YtWv4uZD4ImQOL6GHDvescU3LZTIvV8mdCMkeA632KFwch2A0Qz0vhJmQWBEyZ0LwL8Epj3aYqQkMgMITq6yGYxRvsFh62zjjPxwlebKCPvnseZ9vDxdgeIjFW+I0hsD+cZ5dpyKGnlqBuYtPFLIVYamM9ewxMi+5ZByHcKXYk/2KbeNnXMrT8LO3Ps1TD99C5jsvtLoLG69jagIDwFouknupQhRT/FGfypiNP3ThrrnieUSpONVxpTkckp5R0jNNZHHlvD0HuyGugyQTaL1BuLTc034Clh8Rucpb06eRiNakoqa78BuYEBgAwewcHHoO8cFNNSlfBdXxN5lENB4jyMcYu2Weq66ZZ+T7Z3B/fIRwfgHtYQiI42CPjVK+6wBy1WTP9gugzSbefAWJhJ9OHG2t29iHIcpXAhMCgyIKyR0F+/k0CFQmLGo/fzvOvunXP8+yiVIJGkMuS2tpZpbyUG/0rUGtOT3C2Y/6LN8xhr1rDNns5KYXoUGALK4wdki597/8GtljFVMLuAATAgOk8FyZkWdCUKiOK2futmhMD7/2BBHEdQizMWoFi+ZKHJ2Lo74PUe/fQBop5T1xvnbX77H8VmBkqKchEC4uk3vkGNf9PyWs51/uyX6vRCYEBoh15CTZJ2ZJnwIJ4aobZ6mPvHZZIJ6HPTLM3DtSWH97idHHbA78ca3V1TboQ6t6FBIrhfzhyl04NQiy8Z515nGmJjn9mTtY+tmrwWrNkWCcnwmBARKWSkSLyyRWItxK602hlrz66Wtl0lTfMkl9RPHskMypJs6Lp1u3A/tUlXbXfP6/4zcSXxIsP+zZcTSVoHl9lfKUEMVd01PwTZgQGDDaaJB9aoHdP6hTfHCS5Hzj1Wvx2tv38fd/52uECSX5v2WIHXppS/MQdML+4WH2/qPTTDzwBPrE8x13Pz6Xxl1umz5FbV+T0oEUkt7c7MeDyPQTGDAahlAs4VkWBUuo7opR3D+N6DS1MeH3XvoZskct3FNLhD3qFfim5QmCvqwHKLUmPzq2l9gZF68cgukpeEEmBAaNaut+/NIyzktC8VPvIP8zc7x95DSPzu3D+/wQuWeXCGbO7HRJu7NaYvi7Y8SLEYnZKlqr7XSJLlkmBAaZKk4dVtZTPGPvZnk5zfR61FpHsM8kFqP+nrfSTNuEMaHw1CrR0y/0ZN/OvmmiXIrkQkhivoZ14ixRxYTAhZgQGHBOVSmtxVhyUmjNAY2QPtwOPJc4DmvTLo0h8DNK6myKza2BfLEdC8FoFj8fQ8LW2oP9bte43JkQGHBjPyySms9w+v1pUFi91sNdzyHH+3xgyyJywc8qsYMl6k9mehICYtvM3ZGhOqn4uZCxRwvkzz9EwmgzdwcGnLW0RnKmilVtrUTcKECQ3IbPhjAktqa4JaFW9bCCHk4pFtJaVdmLiMzH3EWZUzTggjNnsYpreKWbacSU+p4mjYLbm6r5m1A/IH+kileOs+wkiC/35k6ERkp6LkRtm1LaxjIjhy/KhICBBgG5oxH1ZZsgZZNY3IZbg2GIO7tKupkB0nizPZilCEAjMkeKJObi5I95JE6tdTy/4qAwIWBAGJI9XiOR9WjkbZxiA2IxtNm/noJohK4UsesNsjUflnrYeHd2AecsyOqqCYBNMCFgoEGA/ZOjOOkU8dECqzfnKX3w7ez7z2cITpzs+fHE9bCyaVbfdw1eKSL5ncOtwOnSKwupHvlEDgnh4P/6FNEFFk4xXmMaBg0AokqFqLiGVa7SyFnUr64TZZP9GYNvCeJ5rF4nrO13enYMEUEtCy00CYd9M15gk0wIGK+JFBpN6mPw/hueozmSxEr2fsVo9QOIedz/4W8y+qHTyN4prFy26/1GpXXs2SW8l+N4Mx7ieT0bmnwl6yoEROSfiMizInJYRL4oInER2Scij4nIURH5cnuJMuMyoGFIVK6QeVn5i5/chNUIkWSy9UbqZY1AIwhCfrS2l7lShijpIW739yOsoTzh1CgSgeULEo+1JlU13lTHISAik8D/BNymqjcBNvAx4LeA31HVq4FV4JO9KKixDaKQaH2dwrNlxr7nYtd8JJVAHAfpZdVaFW00eXJmivJCijDpQLchIEI0kqNyVWu0oOW3hhNLrIeLmVyhur0ccICEiDhAEpgF3k1rmXKAzwM/3+UxjG1mHTnJyPdnKO9Ls3zXboI7rsfad1VPj6GVCrlvpBj/ro27WEG7HUKsSvGGHDPvaS2OmjkVwWrnKyMNko5DQFXPAP8KOEXrzb8GPA4UVfWVcZszwHlnkBSR+0XkkIgc8jHTQF9KwlKJ4PRZ/IRFvWDRGHLRZG8/UdUPyL9YJXusgqxX0W4GLVk2ViZDIy+4hTpOVYmvhGit3mp/MN5Ux60mIjIEfAjYBxSBPwbev9m/V9UHgAcAslIwM0BegpyGEtZAAiDs7X+RBj7OsyfQMCSoVrvqj+DsGmXtndMECUFfTpF/qUn8VLGjBVIHUTdNp+8BTqjqIoCI/ClwF5AXEaddG5gCLvOB6QNKI1Kna8RSDk4lQMIQe7jQk6W97WuvJhhJU0/YeMUGHDrcXVEzKYoHbSIb3LK09rnW2yXOr2TdhMAp4E4RSUcDE8QAAA+JSURBVAI14B7gEPAI8BHgS8B9wIPdFtLYAarIoz/BtWzs4QKSThLtGcd6+QxhsbsQWHznKMXrIUxFZI7GGD/UXVGD4TTh29fxX06TOSHYsysEPVze/ErXTZvAY7QaAJ8Anmnv6wHgN4BfF5GjwDDwBz0op7FDxHVgdIjyTbuYeW8edu/qep92U7GaYOWbNDNdls9xsPyQxnwSryTYvkJkOgtvRVc9KVT1N4HfPGfzceD2bvZrXDrEtomSHpUxm/KBgCCXoNseA7YPVlPwkg0ayQsvh7ap8sViqCruqoW7Dk5NURMCW2K6UxlvSsMQq1TDbmZAlK4TAIit+MRXYuzKrXEkn251ROrg+l0cB71+H818jKEXlPyLZawjJwnLle4LOUBMt2HjzYUhUqmRXPBJHXcJ4w7O/r1YqRTidtYbzys2SC5EvHh6F/E5p/MGPLFoDMepjbrUCxaRYxGWSj1d1HQQmJqA8aY0CAjOnMU7c5bJ/yqU/+4dLN+4m93fiSFLq4QdNMBZL54iP5chvjyOW1qn0zZ8cR1K0y7lPWBdt05pJUXu0Q53NsBMCBibp0rxgEX16ibZUzlSjgVbCAFxnNYsw3deQ3XUofDUKrKyRifdeZypScJdeSqTghVC7sEUQ4fNBCKdMJcDxpZU9wS87eBJyhM2fiGxtT+2bSQRZ/l6j6VbBFkuEszNd1SOcDRPZTpNY9wHhdwf/ZjoJ893tK9BZ0LA2JLsiw5PPbWfWEmxmlv73FU/QMsV1t/W4O+851Ga1+7GGe/slqOoogL7D8xTn+5+QpJBZkLA2JL0mZDMcRu3GoEq9tDQ5kfqaYQGAW7C58bEDM2si6a2WJsQQVyPIBOjkbMo1eNIzUwe0g3TJmBsSebPHicbi8GBPQTZOOt3X0PmmQXCoycu/seqaBDQXIvxRGUvtWGb2GgWObr541uJBNZwgbO3J1g/GFB4aJjpU0FrjgKjI6YmYGyJBkHrjsFQguruOMWrbcJCekv7iM05PHL6IHZDkbCDN28UUdkT8ZbrTxEvKvE50y+gGyYEjC0T26Y65rE+ZVG+rkljZGu9/jIvQ+25PF45QvwttiuEIRoE5A+s8H9N/xnxZR+ZmTeDhbpgLgeMLdNmk9yzqySW0nilGImZLUztLcLo9+cYfjKJPbeM1mpbWm/AHh2hetNuVl+2+PvRLzJW8UHMZ1k3TAgYW6ZhCAvLeH5AToaQagMrlSKq1d+8t54I4rhEp88CEDS2PpmM5tIUD7i4JWX9pTzjtTXTHtAlE6HG1qkSLq8QnTiF99fPEoxmWX//TTi7x990dmIrkcAeKWDt2Y01PdXRTMCrbx3i3b/0QwAm/kqRk2fNqsNdMjUBozPtln4NAuq7YhQP2mReyiCNJlxgXj8rl6V59QTVcQ+1hdzsArq+vqXDSgTlMIblC3Yzak0fZtoDumJqAkbXVq924PY1ansyMHTh9QPCyRFm35ng7AdCFv6bBlZ2i5MJiOCVQ/5qZj92DSJXwDIv4W6ZmoDRtcnvlqgcTxM/W251IBodRSuVN8z0K80Ab02px0IK+fKW3sBWPE749usI4hb2X+YYebpB/MRyax5BoysmBIyu6aHDJB8XrAN7wXOhkGtNO3BuCNR9YiUlCgVLFKzNTU4groeVz7F8MIHlK8PPNom/MEtw5mzP/y2DyISA0RPiuBRv20UjJ6gFI0+nkcXF1z1HZ2YpVOvkXhwiiqUJ505vat+lv3Mra/stmjdV0TMJJh61SXjdr1hktJgQMHrGTwjNjBDGwE87nDvliIYhWqlinWxiAeFFViK2UimsfI7aiEUzpwTrLomS4JYC6GadAuN1TAgYPRW50BiN8NP260NAWisRSypJtLK6uZWBrr6KpbfmqY2CFcC+P46Iza3AyTOmLaCHTAgYPaFhyPAz62RPxagXHBJLTeyD+9HTZ4nq9dZzrr6KM+/KMf7oEPbR04RrpTfc3rNSqVfDojGUIPQgvgxWE+InV6G4TlSrtTosGT1hQsDojShEDx3GESGbTMK+PVQPDpNcXYN2CFT2pgnfXWRtKUthMQulMujr38xWJg3JBMFYlmbOQW1ILEbEiiHRyzNdL3xivJG5yWr0lHge1kiBlz9c4Jb//Unqt+5rTUiqSiNr8beueoHSXovm3hGscxr3xPWo3nIVK3eMs3RziiBuMfRSg8JjcyR/dAwNTDtAP5gQMPqiWYj4peEf0MjZSLw16YgVKqWgNYlI6L7+pWdns9jjY5R3O9RGLSQCtxLhLlaJFpdbXYNNz8C+MJcDRk9po0Fwaob4wh6+V7mWMCZYIwWicpncixX++s9vZvi5gNhSDd0wl0D99oMs3Rxj/fomzrLLwf/jWaJqlShSM4V4n100BETkD4EPAguqelN7WwH4MrAXeBn4qKquiogAnwPuBarAJ1T1if4U3bhkqTLyTMDn/uIDTM+2qvBWOo0Uq4z+JE7iTBlrrYLu24PGXYJcgsi1yJ0IsWsesVLUWkDEvPm3hehFqlgi8jeAMvAfN4TAvwBWVPWzIvIpYEhVf0NE7gV+hVYI3AF8TlXvuFghslLQO+SeLv8pxqXImRiHmAdhhJYrhKurQKsbcOV9b6U6ZrO+FyZ+EJD8qyNos/nqwCSjt76lX31cVW87d/tFawKq+n0R2XvO5g8Bd7d//jzwXVoLkX6IVlgo8EMRyYvIhKrOdl5047Ilwvy9+6jtErwSpM+GZJ5eABE0GWP1WgcVyJxQ4gtVomoVjdTMD7DNOm0T2LXhjT0HvDJv9CSwsS/oTHvbG0JARO4H7geIc+Ex6MZlTCzqo0J1KqBRsYlsm9jqEOJHRJ5NI684VSE1F2KtVQnNp/+O6LphUFVVRLbcbKuqD9BaypysFEyz75UoCpn+8hmak0Ocel+C8r6I0s0WuafiJBcinKqQnFOS3zlMdJEuxEb/dBoC869U80VkAnhlLaozwJ4Nz5tqbzMGlC6v4omQnEtSU2jkWnkvoZI5qSQX/M11ITb6ptN+Ag8B97V/vg94cMP2fygtdwJrpj1gsIWlEtHZOXb99RqFZxV3zsNdV7xSSOFPfoL3zUM7XcSBt5lbhF+k1Qg4IiIzwG8CnwW+IiKfBE4CH20//Ru07gwcpXWL8Bf7UGbjMqNhhFWp4zRSWL5N+qxP8sgCQX3rE40avbeZuwMfv8Cv3nBPr31X4Je7LZRxhdEIqTWwaxF2zSE+XyV4+dROl8poMz0Gjb7TICCcWyBRrrD3hTTR4vJOF8nYwISAsS3UbxIWfWR93QwDvsSYEDC2T3uacuPSYkYRGsaAMyFgGAPOhIBhDDgTAoYx4EwIGMaAMyFgGAPOhIBhDDgTAoYx4EwIGMaAMyFgGAPOhIBhDDgTAoYx4EwIGMaAMyFgGAPOhIBhDDgTAoYx4EwIGMaAMyFgGAPOhIBhDDgTAoYx4EwIGMaAMyFgGAPuoiEgIn8oIgsicnjDtn8pIi+IyNMi8mcikt/wu0+LyFEROSIi7+tXwQ3D6I3N1AT+A/D+c7Y9DNykqm8FXgQ+DSAiNwAfA25s/82/ERG7Z6U1DKPnLhoCqvp9YOWcbf9VVV9ZReKHtJYgB/gQ8CVVbajqCVoLk97ew/IahtFjvWgT+CXgL9o/TwKnN/xupr3tDUTkfhE5JCKHfMzqtIaxU7oKARH5DBAAX9jq36rqA6p6m6re5hLrphiGYXSh47UIReQTwAeBe9pLkgOcAfZseNpUe5thGJeojmoCIvJ+4J8CP6eq1Q2/egj4mIjERGQfcBD4UffFNAyjXy5aExCRLwJ3AyMiMgP8Jq27ATHgYREB+KGq/neq+qyIfAV4jtZlwi+rqlmH2jAuYfJaTX7nZKWgd8g9O10Mw7iifUu/+riq3nbudtNj0DAGnAkBwxhwJgQMY8CZEDCMAWdCwDAGnAkBwxhwJgQMY8BdEv0ERGQRqABLO10WYARTjo1MOV7vci7HtKqOnrvxkggBABE5dL6ODKYcphymHP0th7kcMIwBZ0LAMAbcpRQCD+x0AdpMOV7PlOP1rrhyXDJtAoZh7IxLqSZgGMYOMCFgGAPukggBEXl/e52CoyLyqW065h4ReUREnhORZ0XkV9vbCyLysIi81P4+tE3lsUXkSRH5evvxPhF5rH1Oviwi3jaUIS8iX22vKfG8iLxjJ86HiPyT9v/JYRH5oojEt+t8XGCdjfOeA2n51+0yPS0it/a5HP1Z70NVd/QLsIFjwH7AA34C3LANx50Abm3/nKG1fsINwL8APtXe/ingt7bpPPw68EfA19uPvwJ8rP3zvwX++20ow+eBf9T+2QPy230+aM1OfQJIbDgPn9iu8wH8DeBW4PCGbec9B8C9tGbaFuBO4LE+l+NvAU7759/aUI4b2u+bGLCv/X6yN32sfr+wNvGPfQfwzQ2PPw18egfK8SDwXuAIMNHeNgEc2YZjTwHfBt4NfL39olra8B/+unPUpzLk2m8+OWf7tp4PXpu2vkBr+ruvA+/bzvMB7D3nzXfecwD8O+Dj53teP8pxzu/+NvCF9s+ve88A3wTesdnjXAqXA5teq6BfRGQv8DbgMWCXqs62fzUH7NqGIvwurYlbo/bjYaCory3wsh3nZB+wCPz79mXJ74tIim0+H6p6BvhXwClgFlgDHmf7z8dGFzoHO/na7Wi9j/O5FEJgR4lIGvgT4NdUtbTxd9qK1b7eQxWRDwILqvp4P4+zCQ6t6uf/q6pvozWW43XtM9t0PoZorWS1D9gNpHjjMng7ZjvOwcV0s97H+VwKIbBjaxWIiEsrAL6gqn/a3jwvIhPt308AC30uxl3Az4nIy8CXaF0SfA7Ii8grs0FvxzmZAWZU9bH246/SCoXtPh/vAU6o6qKq+sCf0jpH230+NrrQOdj21+6G9T5+oR1IXZfjUgiBHwMH262/Hq0FTR/q90GlNVf6HwDPq+pvb/jVQ8B97Z/vo9VW0Deq+mlVnVLVvbT+7d9R1V8AHgE+so3lmANOi8i17U330Jo6flvPB63LgDtFJNn+P3qlHNt6Ps5xoXPwEPAP23cJ7gTWNlw29Fzf1vvoZyPPFhpA7qXVOn8M+Mw2HfNdtKp1TwNPtb/upXU9/m3gJeBbQGEbz8PdvHZ3YH/7P/Io8MdAbBuOfwtwqH1O/hwY2onzAfxz4AXgMPCfaLV6b8v5AL5Iqy3Cp1U7+uSFzgGtBtzfa79unwFu63M5jtK69n/l9fpvNzz/M+1yHAE+sJVjmW7DhjHgLoXLAcMwdpAJAcMYcCYEDGPAmRAwjAFnQsAwBpwJAcMYcCYEDGPA/f/cNdln1co4XAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7jQtPPDsFNE"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aRKbafprsFNE"
      },
      "source": [
        "* steps_per_epoch = train data 개수 (6847) / batch size (32) = 약 215\n",
        "* validation_steps = validation data 개수 (746) / batch size (32) = 약 24"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uz0tI4BMsFNE",
        "outputId": "2c27565c-2bec-4860-eef8-383953ea9c9b"
      },
      "source": [
        "callbacks_list = [callbacks.EarlyStopping(monitor='accuracy',\n",
        "                                          patience=5)]\n",
        "\n",
        "model_aug.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "history_aug = model_aug.fit_generator(train_generator.generate(),\n",
        "                                      steps_per_epoch=215, epochs=20,\n",
        "                                      callbacks=callbacks_list,\n",
        "                                      validation_data=validation_generator.generate(), validation_steps=24)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1915: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "215/215 [==============================] - 2334s 11s/step - loss: 1.9401 - accuracy: 0.2925 - val_loss: 1.0278 - val_accuracy: 0.6546\n",
            "Epoch 2/20\n",
            "215/215 [==============================] - 75s 349ms/step - loss: 0.6982 - accuracy: 0.7461 - val_loss: 0.5356 - val_accuracy: 0.8275\n",
            "Epoch 3/20\n",
            "215/215 [==============================] - 47s 222ms/step - loss: 0.3840 - accuracy: 0.8687 - val_loss: 0.5943 - val_accuracy: 0.8226\n",
            "Epoch 4/20\n",
            "215/215 [==============================] - 47s 219ms/step - loss: 0.2868 - accuracy: 0.9090 - val_loss: 0.3416 - val_accuracy: 0.9106\n",
            "Epoch 5/20\n",
            "215/215 [==============================] - 47s 218ms/step - loss: 0.2149 - accuracy: 0.9278 - val_loss: 0.3701 - val_accuracy: 0.8783\n",
            "Epoch 6/20\n",
            "215/215 [==============================] - 46s 217ms/step - loss: 0.1837 - accuracy: 0.9409 - val_loss: 0.3365 - val_accuracy: 0.8988\n",
            "Epoch 7/20\n",
            "215/215 [==============================] - 46s 213ms/step - loss: 0.1302 - accuracy: 0.9589 - val_loss: 0.2227 - val_accuracy: 0.9326\n",
            "Epoch 8/20\n",
            "215/215 [==============================] - 46s 216ms/step - loss: 0.1267 - accuracy: 0.9594 - val_loss: 0.2300 - val_accuracy: 0.9413\n",
            "Epoch 9/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.1021 - accuracy: 0.9706 - val_loss: 0.2438 - val_accuracy: 0.9296\n",
            "Epoch 10/20\n",
            "215/215 [==============================] - 46s 214ms/step - loss: 0.0846 - accuracy: 0.9735 - val_loss: 0.3181 - val_accuracy: 0.9296\n",
            "Epoch 11/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.0841 - accuracy: 0.9700 - val_loss: 0.3187 - val_accuracy: 0.9413\n",
            "Epoch 12/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.0742 - accuracy: 0.9797 - val_loss: 0.2597 - val_accuracy: 0.9399\n",
            "Epoch 13/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.0744 - accuracy: 0.9769 - val_loss: 0.1595 - val_accuracy: 0.9516\n",
            "Epoch 14/20\n",
            "215/215 [==============================] - 46s 214ms/step - loss: 0.0623 - accuracy: 0.9793 - val_loss: 0.1664 - val_accuracy: 0.9593\n",
            "Epoch 15/20\n",
            "215/215 [==============================] - 46s 217ms/step - loss: 0.0597 - accuracy: 0.9813 - val_loss: 0.1823 - val_accuracy: 0.9530\n",
            "Epoch 16/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.0583 - accuracy: 0.9800 - val_loss: 0.1250 - val_accuracy: 0.9560\n",
            "Epoch 17/20\n",
            "215/215 [==============================] - 46s 214ms/step - loss: 0.0550 - accuracy: 0.9819 - val_loss: 0.1203 - val_accuracy: 0.9633\n",
            "Epoch 18/20\n",
            "215/215 [==============================] - 46s 214ms/step - loss: 0.0458 - accuracy: 0.9855 - val_loss: 0.1624 - val_accuracy: 0.9589\n",
            "Epoch 19/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.0525 - accuracy: 0.9846 - val_loss: 0.1750 - val_accuracy: 0.9560\n",
            "Epoch 20/20\n",
            "215/215 [==============================] - 46s 215ms/step - loss: 0.0597 - accuracy: 0.9797 - val_loss: 0.1453 - val_accuracy: 0.9589\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDfMcklWsFNE"
      },
      "source": [
        "* result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "xMKKebEKsFNE",
        "outputId": "341c10b3-3f77-43bb-c89e-c5d27ae77bbc"
      },
      "source": [
        "# plotting\n",
        "loss = history_aug.history['loss']\n",
        "val_loss = history_aug.history['val_loss']\n",
        "acc = history_aug.history['accuracy']\n",
        "val_acc = history_aug.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l3RJkAfTsFNF"
      },
      "source": [
        "* model save"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaaJeQ5vsFNF"
      },
      "source": [
        "# model save\n",
        "model_aug.save('/content/gdrive/MyDrive/model_new/model_made_augmentation.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RVNxsmMFzYdu"
      },
      "source": [
        "## Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXjWy2Zn0JFW"
      },
      "source": [
        "* Test data의 경우 위에서 ImageDataGenerator로 생성했음\n",
        "* steps : test data 개수 (2347) / batch size (32) = 약 75"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8CQiw0HG0zWg",
        "outputId": "5c92336f-5e12-4b98-83ff-19eb914c7e10"
      },
      "source": [
        "# loss and accuracy on test \n",
        "test_loss, test_acc = model_aug.evaluate_generator(test_generator)\n",
        "\n",
        "print('Test accuracy : {:2.4f}%'.format(test_acc*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1973: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  warnings.warn('`Model.evaluate_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy : 88.7942%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZAPDEgYrNLXH"
      },
      "source": [
        "## confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoIfbF3eNqny",
        "outputId": "5293db0f-6185-4fcc-dc45-a4709a190d33"
      },
      "source": [
        "pred_aug = model_aug.predict_generator(X_test)\n",
        "y_pred_aug = np.argmax(pred_aug, axis=-1) # 모델이 예측한 class label"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:2001: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  warnings.warn('`Model.predict_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGeJpVMdZb5q"
      },
      "source": [
        "* 라벨을 y 변수에 할당할 때에는 폴더명에서 split하여 할당하므로 1부터 시작 (palm -> 1)\n",
        "* image generator를 수행하는 과정에서는 라벨링을 palm부터 0으로 할당 (palm -> 0)\n",
        "* 그러므로 y 변수와 비교하기 위해서는 image generator의 라벨 순서를 바꿔주어야 함"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VvjXg2iATf66",
        "outputId": "b375d83c-2478-47be-8e73-e99804d745b8"
      },
      "source": [
        "print(len(y_pred_aug))\n",
        "\n",
        "y_pred_aug_label = []\n",
        "\n",
        "for i in range(len(y_pred_aug)):\n",
        "  if y_pred_aug[i] == 9:\n",
        "    y_pred_aug_label.append(y_pred_aug[i] - 9) # 'down'\n",
        "  else:\n",
        "    y_pred_aug_label.append(y_pred_aug[i] + 1) # else\n",
        "\n",
        "print(len(y_pred_aug_label))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2347\n",
            "2347\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 869
        },
        "id": "JxGU6HkWNqn0",
        "outputId": "24712640-68f6-43f7-d0c9-ffca9ccbde1b"
      },
      "source": [
        "labels = ['palm', 'l', 'fist', 'fist_moved', 'thumb', 'index', 'ok', 'palm_moved', 'c', 'down']\n",
        "\n",
        "plt.rcParams.update({'font.size': 10})\n",
        "cm = confusion_matrix(y_test, y_pred_aug_label)\n",
        "plt.figure(figsize=(15, 15))\n",
        "ax = sns.heatmap(cm, cmap=plt.cm.Greens, annot=True, square=True, xticklabels=labels, yticklabels=labels)\n",
        "ax.set_title(\"model_augmentation_madeData\", fontsize=30)\n",
        "ax.set_ylabel('Actual', fontsize=25)\n",
        "ax.set_xlabel('Predicted', fontsize=25)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 185.99374999999995, 'Predicted')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}