Extract_MAFA_and_Train_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Extract MAFA and Train model",
      "provenance": [],
      "collapsed_sections": [
        "DQTWAq1Jm_rN",
        "yACzJxyPHskX",
        "0QtHdJg9IfHg",
        "MIdvBMkLRSh-",
        "LUX0Ghe9Ri8W",
        "gurvwkPORv_E",
        "eEFmlpI1g2Yb",
        "N7RF0wkHg9Zt",
        "GLxZQhT4hb6w",
        "oTlOba-XjFqp",
        "0Ct8Q31DzxEI"
      ],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQTWAq1Jm_rN",
        "colab_type": "text"
      },
      "source": [
        "## **INIT MAFA DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBtEtnnlrreN",
        "colab_type": "code",
        "outputId": "2c09b6d6-ff72-4de3-9094-1f1902e50773",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gR49aN2WrvJg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp -r /content/drive/My\\ Drive/MAFA /content/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEL7zEl7rwlP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd /content/MAFA/\n",
        "!unzip train-images.zip\n",
        "!unzip MAFA-Label-Train.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VqiKYkzmrybf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip test-images.zip\n",
        "!unzip MAFA-Label-Test.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67XPScVevwpK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import scipy.io\n",
        "matTrain = scipy.io.loadmat('/content/MAFA/LabelTrainAll.mat')\n",
        "matTest = scipy.io.loadmat('/content/MAFA/LabelTestAll.mat')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-gqBRF9I4s6",
        "colab_type": "code",
        "outputId": "5cb77c10-982c-449a-e8ca-7ba8383e5e67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(matTrain.keys())\n",
        "print(matTest.keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['__header__', '__version__', '__globals__', 'label_train'])\n",
            "dict_keys(['__header__', '__version__', '__globals__', 'LabelTest'])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yACzJxyPHskX",
        "colab_type": "text"
      },
      "source": [
        "## **EXTRACT TRAIN SET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uqjuw8a6Kp85",
        "colab_type": "code",
        "outputId": "63b84bbf-1c51-4a3c-d40f-c8e0da91065a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "#Read train\n",
        "label_train = matTrain['label_train']\n",
        "print(label_train.dtype)\n",
        "print(label_train.shape,label_train.size)\n",
        "print(label_train[0,0]['imgName'],label_train[0,0]['label'])\n",
        "imgName = label_train[0]['imgName']\n",
        "label = label_train[0]['label']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('orgImgName', 'O'), ('imgName', 'O'), ('label', 'O')]\n",
            "(1, 25876) 25876\n",
            "['train_00000001.jpg'] [[ 95 160  91  91 113 177 158 172   7  26  82  89   1   3   1   1   3  -1\n",
            "   -1  -1  -1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYgnjfVgkPtn",
        "colab_type": "code",
        "outputId": "e2fbf983-2472-407c-8a3b-25737ca3f95f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print(imgName[0])\n",
        "print(label[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['train_00000001.jpg']\n",
            "[[ 95 160  91  91 113 177 158 172   7  26  82  89   1   3   1   1   3  -1\n",
            "   -1  -1  -1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7JhUrArnMZg",
        "colab_type": "code",
        "outputId": "42360777-5fdb-453f-b1c8-7bae3a1fd274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "n = [0,0,0,0]\n",
        "for i in range(label.size):\n",
        "  t = label[i][0][12]\n",
        "  n[t]=n[t]+1\n",
        "print(n)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 12730, 11126, 2020]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_dpQntCOnZw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Write Train\n",
        "import cv2\n",
        "import numpy as np\n",
        "import os \n",
        "pathIn = '/content/MAFA/train'\n",
        "pathOut = '/content/MAFA-Crop/train'\n",
        "numM1, numM2, numN = 0, 0, 0\n",
        "index = 1\n",
        "for i in range(label.size):\n",
        "  img_name = os.path.join(pathIn, imgName[i][0])\n",
        "  img = cv2.imread(img_name)\n",
        "  x,y,w,h = label[i][0][0:4]\n",
        "  crop_img = img[y:y+int(h), x:x+int(w)]\n",
        "  img_label = label[i][0][12]\n",
        "  if img_label == 1:\n",
        "    numM1 += 1\n",
        "    if numM1 > 1000: continue\n",
        "    img_label = 'M'\n",
        "  elif img_label == 2: \n",
        "    numM2 += 1\n",
        "    if numM2 > 1000: continue\n",
        "    img_label = 'M'\n",
        "  else: \n",
        "    numN += 1\n",
        "    img_label = 'N'\n",
        "  img_name_out = img_label + '_' + str(index) + '.jpg'\n",
        "  index += 1\n",
        "  img_crop_name = os.path.join(pathOut, img_name_out)\n",
        "  cv2.imwrite(img_crop_name, crop_img)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cK6R9DCnGvE-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd /content/MAFA-Crop/train/\n",
        "!ls\n",
        "print(index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QtHdJg9IfHg",
        "colab_type": "text"
      },
      "source": [
        "## **EXTRACT TEST SET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdgnIEWyIhV4",
        "colab_type": "code",
        "outputId": "5b3dd4e5-82d0-42b5-db77-03148e0d5b0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "label_test = matTest['LabelTest']\n",
        "print(label_test.dtype)\n",
        "print(label_test.shape,label_test.size)\n",
        "print(label_test[0,0]['name'],label_test[0,0]['label'])\n",
        "imgName = label_test[0]['name']\n",
        "label = label_test[0]['label']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('name', 'O'), ('label', 'O')]\n",
            "(1, 4935) 4935\n",
            "['test_00000001.jpg'] [[2694 1211  353  353    1    9  105  144  337    2    3    2    2    1\n",
            "    -1   -1   -1   -1]\n",
            " [1754 1449   68   68    3   -1   -1   -1   -1   -1   -1   -1   -1   -1\n",
            "    -1   -1   -1   -1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vGUe-D8K5T7",
        "colab_type": "code",
        "outputId": "523ac601-4130-4fd3-e047-452c8ccd6f51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "print(imgName[0])\n",
        "print(label[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['test_00000001.jpg']\n",
            "[[2694 1211  353  353    1    9  105  144  337    2    3    2    2    1\n",
            "    -1   -1   -1   -1]\n",
            " [1754 1449   68   68    3   -1   -1   -1   -1   -1   -1   -1   -1   -1\n",
            "    -1   -1   -1   -1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZGJokyIMGSS",
        "colab_type": "code",
        "outputId": "634ef331-44a4-4c9d-9469-b0b330a36e3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "n = [0,0,0,0]\n",
        "for i in range(label.size):\n",
        "  t = int(label[i][0][9])\n",
        "  n[t]=n[t]+1\n",
        "print(n)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 2698, 1677, 560]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRhKESaAN0CD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Write Test\n",
        "import cv2\n",
        "import numpy as np\n",
        "import os \n",
        "pathIn = '/content/MAFA/test'\n",
        "pathOut = '/content/MAFA-Crop/test'\n",
        "numM1, numM2, numN = 0, 0, 0 #Want test set has 1000 imgs\n",
        "index = 1\n",
        "for i in range(label.size):\n",
        "  img_name = os.path.join(pathIn, imgName[i][0])\n",
        "  img = cv2.imread(img_name)\n",
        "  x,y,w,h = label[i][0][0:4]\n",
        "  crop_img = img[int(y):int(y)+int(h), int(x):int(x)+int(w)]\n",
        "  img_label = int(label[i][0][9])\n",
        "  if img_label == 1:\n",
        "    numM1 += 1\n",
        "    if numM1 > 250: continue\n",
        "    img_label = 'M'\n",
        "  elif img_label == 2: \n",
        "    numM2 += 1\n",
        "    if numM2 > 250: continue\n",
        "    img_label = 'M'\n",
        "  else: \n",
        "    numN += 1\n",
        "    if numN > 500: continue\n",
        "    img_label = 'N'\n",
        "  img_name_out = img_label + '_' + str(index) + '.jpg'\n",
        "  index += 1\n",
        "  img_crop_name = os.path.join(pathOut, img_name_out)\n",
        "  cv2.imwrite(img_crop_name, crop_img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nXR-LeKuOyPE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd /content/MAFA-Crop/test\n",
        "!ls\n",
        "print(index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MIdvBMkLRSh-",
        "colab_type": "text"
      },
      "source": [
        "## **MOVE CROP DATA TO DRIVE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKRSAxdPRVWM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp -r /content/MAFA-Crop /content/drive/My\\ Drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUX0Ghe9Ri8W",
        "colab_type": "text"
      },
      "source": [
        "## **LOAD CROP DATA FROM DRIVE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEFoAze_yVFO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!apt update\n",
        "!apt-get install zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xD8vWGBgRm3F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp -r /content/drive/My\\ Drive/MAFA-Crop  /content/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gurvwkPORv_E",
        "colab_type": "text"
      },
      "source": [
        "## **IMPORT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGVXhn4qVi8b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HeErL-rUqJa",
        "colab_type": "code",
        "outputId": "9352efad-a42c-474a-f881-2d41eef3bf26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install Pillow\n",
        "!pip install scipy==1.1.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (4.3.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from Pillow) (0.46)\n",
            "Collecting scipy==1.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/0b/f163da98d3a01b3e0ef1cab8dd2123c34aee2bafbb1c5bffa354cc8a1730/scipy-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (31.2MB)\n",
            "\u001b[K     |████████████████████████████████| 31.2MB 1.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.8.2 in /usr/local/lib/python3.6/dist-packages (from scipy==1.1.0) (1.17.4)\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: scipy\n",
            "  Found existing installation: scipy 1.3.3\n",
            "    Uninstalling scipy-1.3.3:\n",
            "      Successfully uninstalled scipy-1.3.3\n",
            "Successfully installed scipy-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nau-icyrR0Vc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# defines\n",
        "\n",
        "DIR_IMG_TRAIN = '/content/drive/My Drive/MAFA-Crop/train/'\n",
        "DIR_IMG_TEST = '/content/drive/My Drive/MAFA-Crop/test/'\n",
        "DIR_TMP_DATA = '/content/drive/My Drive/Checkpoint/'\n",
        "DIR_OUTPUT = '/content/drive/My Drive/MAFA-Crop/models/'\n",
        "\n",
        "IMG_SHAPE = (150,150,3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9myNine2UDSa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# imports\n",
        "\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.ndimage import imread\n",
        "from scipy.misc import imsave\n",
        "import numpy as np\n",
        "import time\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eEFmlpI1g2Yb",
        "colab_type": "text"
      },
      "source": [
        "## **EXPLORING THE DATA**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckrHSd__RZjh",
        "colab_type": "code",
        "outputId": "7b1b64d6-339d-4854-8529-e16ed67cc4c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "#EXPLORING THE DATA\n",
        "sample_img_path = os.path.join(DIR_IMG_TRAIN,'M_1.jpg')\n",
        "sample_img = plt.imread(sample_img_path)\n",
        "plt.imshow(sample_img)\n",
        "print ('Image have shape: {}'.format(sample_img.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image have shape: (91, 91, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD7CAYAAACscuKmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nOy9W7BsWXYVNla+M0+e1z3nvu+tR1dV\nV/Vb/VB3i26hRsJAgAM5wgTIEIRMyNF82Bgbhw3mR/7AESbsMNaXwx0GIzuwhUIQSAhhTAjUSEgq\ndamrH+quqq7qW69b933eefKduf2x5thz7Mx9b91WFac6dNb8OXn23rn2WmvvXHOuOcccM2RZhiRJ\nkvzBl8p73YEkSZKcjKQfe5Ikp0TSjz1JklMi6ceeJMkpkfRjT5LklEj6sSdJckrkHf3YQwh/IoTw\nUgjhlRDC33i3OpUkSZJ3X8LvN84eQqgC+A6Afw/AdQBfAfAfZVn27Xeve0mSJHm3pPYOvvtpAK9k\nWXYNAEIIPwfgxwHc98feXV3LzmydQ6VSzY+FEP+Ohv382HBgn20hGg0G3uFqxf6G/Fi1Eqwt+ws/\nF3LbxRe1WrVm5yr2fe9PxY6FirQh7RU6LefyQyXndEHlaR4KC01rHxa6vSTz+dwu8Yvy/tu953Lv\nbB4/69j4VbZRmLuFvhb6aNfrvYNNduHyUPyg5/i8prPZUvvsYygYn/Hb0+k0PzKfxTmoVqv2vQcb\nq1V7f/J3RR4Ax8I2M5tfwOd6nsmxWVY4xu8BwIxtyPW94QgAMJlMZTR272x5Puf2vNY3NgEAnZXV\n/NxKtwsAqNfr+bE333gdw34P4/Go5K16Zz/2ywDelP+vA/jMg75wZusc/uuf/h+x0lzLj9Vt8l/5\nztfyY9/59nPxw3gMALj2rRfzc9vrbQDA1rp3fbUTB9wIdWvTz9Ub/FVN8mObm1sAgFYzTt7amven\n1WoBAJrtVn6sVm0UxqE/xmq1bn/jy1ar1Zaum0z83uwbXx5tiw+80+n4vefFc7pwDEZDAMWXv92O\n81Opxf6MbQ4BYDSM/Wg0ZDxZ8QdUrXj/OaZMXmLev1WJx8Yzv3ejGe89mcuPt2YLQIhtzWW87OP+\nwZFfb+8DX+J6rZmfmtmisLOzkx/r93oAgNXV+Cyb7W5+rmwhXV1dsb7GcepcTKdxro6PjwEAw2NR\nQMM410NRPP3jeGwwGFlfjvNzBzYmnf/f/M7LAICbt+4CAEZTn4vxND6bGXzu+qP43S/8sT8NAPjE\np7+Qn/vBH/o8AODyxbP5sb/2V/4ynv31f7E8aJN38mN/KAkhfBHAFwFga/scVldXMRn4yz8ZxgFV\n5cncvX0HAHDj1e8CADZlRWu34sPprPhL0LQXKsxjG3zhASBDfBC1uk8sH/Daxnr82/HrK7m29/7U\na0VtMZWXeTrlj48/Yn/5+UPSH/vc7h1s1S5ocTs2kZd0aFpgbiu+LiauNVSjTOzcrPDXbhqvnnl/\nFr+nfc21pWo/++EfDwfWvlgVprnmYh3UWvE5DW0x6dsPQ8dEjQcA9+7dK4xbx8uFqFlzbcZF8y37\nAVXq/uNlv6tqBdqYZvactjc38nMXL50HAGysxfdCF93ewaG16VbgeBTbWFuz/sx9LrhgZJlf37J3\nl898dcXfu+NRvO7e3r382HAcn8Xu7i4AoN/3xee1116ze/pi8tnPfhbf+upv4H7yThx0bwG4Kv9f\nsWMFybLsS1mWfSrLsk+t2iQmSZLk5OWd/Ni/AuCpEMLjIYQGgJ8A8EvvTreSJEnybsvv24zPsmwa\nQvjPAPwLAFUAfy/Lsm896DshBDRrdQwnw/zYvbvR/Lpx/Xp+7NlnfwsA8OEn3wcAuHj2XH6uVTWz\nVPaRnbVobk36U/YtP1c1k29zU7YCHXNu2H6wXnVTK98bi9NoNC+arDPxrOT3miyvm2x3OnbTmOby\nnGaq3Jtm4HTiZu1wPCrchz4FwM3UuTiSJtNR4ZzOBa+bZLJvXvArcG8KuD9Ctxq8bjSaFO4DADP2\nX5xM7W6c92Mz1e/cczO1b20dHvXyY71+NH9pIuued2YTr/ts7u3Zj0rNz7W4ZQreH/Y/2Nbnzetu\njP7eC9E3RNP+zJkz+bkPPPP+eK7ppnerEd8fmtkrXd0+xu/SnAeAzc3oaMv37PJejMdx3ttt354G\ne3evv/4GAGB/fz8/t3UUTfpvfsN/cj/6R/4ofv7/+hLuJ+9oz55l2a8A+JV30kaSJElORv6dO+hU\nJpMpbt++jUxU49BW8lfNGQcAW5txbx9MQzTFuXZ+a9Macw9uMO1FJ1yz6atjpW5ttNzZUjftODUH\n12AomtfuqRru6DhqngG1WVW0eK5B4191KNFRKIol1ygz87S2G97XqoWuVHNNZq7ZAGA4cq2fe8vV\nksm18ByLUjEHl2rjPPxlDrTxRDTpqG/nvK3cK22WjN77kPM09DbmFtY8tnDq3pE/t9HYnHYjn+uJ\n3cutFY180GHojioKoxwVmf+uhafWuuKhN2tlxTRoTZyvDP8OxsvOyue+8lUAwLlt1/bU1Py7t+tW\ny8gcbs2mOxM75qCb2hw3myv5uZk9knHP547vypHNWRAHII+12v6utFe6qFTvvzNPcNkkSU6JpB97\nkiSnRE7UjJ/NZjg4OEBDnCh0WPWP3bzrWmy23Ygm0GzqZl67ZYCYFQfCjM38ahkQplr3YTUa8XNd\nHFt00O3cOwAA3Dh0BxHNx+O+gyd2D6JjpDcyR52alvWiaawmctNivtUgMX4zM+dmjncEE9Buxj6u\ntNTRYziEyvIWhVuGhphuzUYRAFSIg1cMOCNOQY6XgBVF3I2sj3uHB/mx4+M4B9NK3BZNJ27i39uL\njqq9A3ck7R/G50pHYxDQDrdM88znjNuJMucjP6tTk3NAR50CoKqGMci3fnBnI032rOH94fOq2fM6\nFsfh+94XncXDvh+jKU0cxsqqbxcmBjZSwNOZM3F7ymeYyXvBNhqyDZmYE7pnc3hksX4AOHtxXhg/\nAOzuH2A6XUYj5mO/75kkSZL8gZIT1ewBccXu9z0ccXiwBwAYHPuKeXAQNcnHP/g0AGC9I+EmQ68p\nrJCOsFYzahtFOXVstZ1LuOn1N24AAF559TUAQN99Xrlmm4szZGSrNLXTWBxWVEqqLfO2uMqKduqY\n1g7WZkscdB2zaLrqUBrHuaLmWmlr6CceW11xR8/KiiHzynD59lEtH2qemTkr1TI5MsTW7q5r6oPD\nqF2y5hkbmo/twMJMo4lrM7ZbNWuu1lR46rL2666v2jjpQPO+cu5aLXHAWn85P1VF79l317v+/gwM\n7hoMTdgUpCTRmet8ZwQNee2V79i9vf+E6G6sLyPuKnm+Rn4IZwO/F8exs+u/A87ZiqDqJmZtHfZi\nn+/cvpmfe/KZjwAAhgN36PX6g8LzWJSk2ZMkOSVyopp9Pp9j0B9iIJr92PbqBCYAwKULFwEAAzt3\n9ayHOzqW9DLo+T6SeOiqaclG3Vfy3iDu92++fjs/9trrr9s9o1XRWD2fn2MGlmrEmWmLiWmgkYan\n2AfbC6ommlkIRwE6E4uxZKb1W00Ja9n+dyDw+tCPWpXhuH7bzZCOaUnH5wOE7XNfWynLiBtL1hgI\nFIr3nopmGJjJMxx5CIpgmv74sNAvwLV8e8U1XKcbrQ7Zlnv7TCqRe66bllwxay4THD9x+dOeWxr0\nNQSzeOri06hXbG88dH9Qy/px7py/UxRmo1Xtqc4lyYd7as2Eo3+kb+NoiQVat2ezuu6+pablEzTN\nClEATcWel4YhZxb6WzVLr9/zcRyaf+T8lSv5sePeALOk2ZMkSZJ+7EmSnBI5WTM+y3A8HGEuDhma\nO/2Bm/aDOp0VlwAAs7mbchXE6wvmo5lmNGH2jtzEf+6rzwMomt7VejSfVtdjSGagKauGWGOuMuDp\nnMRmqzOuQuQWQzozR3eRaEOvp/lO50+l4qYc7DJ1PhJalY2Z/+5zEewLTXHSNKqx30SU1ep+76Z9\nnkleAdGAbKtADAE6v8TcNEcbHYAdcSjNLdSlhAqeUrpMCNG0LY+i2G6+FSkSXrUw02wsKbElTk0i\nyOqG1JuMff7JU3Dx4kU5FnHvs9YymrBpYbuOheMmEtIcGkZ/RZyh7YVQb0M4EIjpDzWf/7MWAtwy\n7P3+ofeV865pr2Pbgk5si7V/767f20LJvZ47ti9ePFsYz6IkzZ4kySmRE9XsUSoFZw2BCYpDnk5j\nt7g6dsTxMcviCtiQ7CM6ae7di06L75oDLl4f/1brAlQx5xtx1M0gobFudC61RNtsW8YcnW/ZXLSB\nOYR4RLO0uFqrs89JJaL2ywQw1Gib46bh7VdMO4USxwvv1R8uE3NUWqaxxUgYZ9a3ivRnxrmgZvf+\n85nMpP8cb9vCYEoMsXY2sqZMxLFFJxypxu7ede3EjLCBsL8QgLSxGcfdqDoHAh1ibdGga5ZVR407\nG7um43uhpA+3bt0C4A5hDZddOB8tyRyLLpq9Ze+bPku2Syechr0aecadz0/X5urS5WhpvHnTsfRn\nO7H/+/t7+TEaBZmFeqcjHwfzFmoShhwMUugtSZIkSD/2JElOjZy4g248mWAsZtutWxEVpI6hReJF\nZehsmmNiJiitmzdjDP3Q4pBKtnDWTMu5rGsk98u3B2K60qRc7zjZRdPi9rmJJGg8xkxp2quZN55x\nG+Km4hHxAYaw6nb9HLHoaoq1ut3CsWpQRFw8VhdzMzPn3nRKkgY/Nze+sgLhhJm61Xl8FdQZWq3G\ne22sOaJvmhlKsbL86hwZ4WLvyJ2txNXfeCuaz4dCykhTt6axcZvPJnMfxJm4trUNAFhf92dDMo+u\nmcFXLm7n5zjOycifL9GZJIK4/sYb+bnhKDoH+c5sb3tbsxJSk9kCK25NsO5VvhfyPjQM0/DEY48C\nAJ599nkfh21D2oLQm4/j54ltKSfixD7Yj6SbF4XqbW93F7OEjU+SJMkJY+MDqtV6gRhiZyc6bJQR\nllqbzprR2C2BujnTGjW/fsWcNA0LW4ghgPZK1Eqh6tpjaBlPJCkNEt6pWFhE+zhiaMvCO5rpdWgO\nFVoEY0Gn5Uyv4pCkA4zjPe5JGMm0FDHXADC1Nqi868Ks6pz1foM5jI2WXObw0E9O8KAOugqJP+L/\nYyEWoZWgXOYZnW/2d2ffw5y0Pvb33Ml08+Yd62zsB4keYkfisYZk+ZFkpGdZh4c9d0r1jHZ3+KpT\nSdFhmP+tuGY7dy7SmT3+6CP5sfPnLwMAztjfasMtq/3dqC179uyzHUd1btp7oXkLi1Tbqunrxirb\nlNDb3ObzCcugWxGLb2K0XQ1xDGdt0mlHrT/J3DH52isvAQCuPv54fiw+k/sXGkiaPUmSUyInHnqr\nIOBIQC879+LKrznZI9Ih2Z53fdWJ8Ou219XtEkNizFSrNpYz0KaCOea+lwpuNFymepqJhpvYPujG\njZgtd/u2h486lhu/apjusQJWzDLhPhHwcNnUQj8zwX6T4PDy5cv5sZZpumaeN++PjPo8ExAOt4ik\n3Kpq2I8WgFTAca5zVovx/tfMJKnWNf89avSe+Ud0XqnllZaKe/DhmASb3v+ahUNv33NL4Hhws3B9\nQ0JL+8fxOe3sucZlyI37/40V136HoziWF7/r+3LSSn3g/U8BAM6f87wI7ssnxltQa7gVxdAkJEsu\nrzFgITX1nfA9Uv9L7o+omn9BcO0vvhQ19UpHsupaa3YsWhVTuAX08nciOeanPvtD3v5KtwBaWpSk\n2ZMkOSWSfuxJkpwSOWEzPsNsPsF45A63Xi9ioM+turMCsyKXuRIGzM3B1aoKocVavH5o7aoZT3/S\nRExdhkims3i9Elvs70ZTVOuJMYN0bTOGYtbOOta6Z/XAyJTa0HGYrAqOf2R45xHNXzGzj4zy6Vsv\nvJwfu3g+0nAxtLQqYTBuaRpiZlerluppLKqhhFRDHXqLtE5T8W5mNRY6lC2NhbFImqDX08F4KKE3\n1n1rtuO8DJXYwii3nnr66fzYi995JY6jQQej35tlos5IHQGa0gNzqv3AJz7t/Te04q5tFQFP4X32\nK78DAHjkimyZzDlGDPt8rtujIikIoHNHog3hszecfSbO0IrBGZnK/NT7nsjPsZxTW9hoH7l8AQCw\n0ooO21deu5WfG/bj72b3jqdur5+vJARdkiRJTlizZ/MMk+GosDqSbUEzpepGZkjQiAI9JpaBVpEV\nkBlAGUhzJOQStoo2KkrFFDXE3dsxhLO7I9fbPS9c8nDN2Fb1W4br7vXdoXTHCDByksVMQSxGfSSk\nhmsWCuxamLAhDquOnVMs99g47UdVs3Ikwy2rL1dZZXiN1WjqVXEYEjcvtFQsklitFp2cgGvB3sDx\n5vv7UaMMpsv0XYeseqpUUrX4bFgRZmXFLZNzl6JW3T7rTrIjw8tfey3mN2hFlRodXDUBBRm3+ppR\nPV296pqaxJ2Vp5/Kj5EQ5dnf/HUAwIE4+wamha3Aa4H8cWqOv0pVLCuzBGhdqFbNLQF1SJrDkxaZ\nOmJJRDIW/PuTj9s7aGG8r/zuN/JzDXMKvvbqK/mx97VamJeUv6YkzZ4kySmR9GNPkuSUyAnzxk+x\nv7tTiLOTvxsz5+qaEWVmpnpHGElnZjI16u4Iy7nMJ8SWC87enEuZwNhGxmQ7MnO5UnFn3/Z2dIhp\ngcBb5gS5afH1uxI3H5jDiY6wlY4ww7I0lJh3h4YMI0ouN30hfPlNH9vYHDHVijkm68ucb5mgtIbV\n4nWhI/XNzWlXrciWyVI36Ueq18ThZmOaDAUZZt8dWxMHwhFfsS1BNvHrezbHHcO6b57Zys8RRVgX\nU/fRqzH2vLcfzet2q750/Z4g9Oq2NXn88fi9DUmHbtp7s7frDrqdO9HJ9biZ+2eFU/7oMLbLbddZ\n6StZX3X+ufXMOesV3VhZdox6v+Jz3pZ752Pb8bGxIOUxGX2nQlySxc9vvv5qfuzSY48XnIqLkjR7\nkiSnRE68Iszh4SF6h8t46rqEy457MYxSVmWF5OdDyZwjKo2hn6kQQhwfxVVxJMf2D2JYjZbAhXOP\n+jnr2++9+NX8WMMYUs+ejyGfoeKPLZTG6h49CSuSWKEtxBl3bZV+8ulnAABVCV29ee0165ev4ETQ\n1U2jVMTRWKksF3Ykbn9ijrMg2Hi21RCH4cZGRGcxMlmruaWRl6/OvBIJsf9z05qNpmtSWliZ8Pav\nrFjeAkk+9FmSj1+cUpcuRmddtfoxAB6SAhxR2W56Ntq5c/Hz0xa+C6LZ3jDn1VwoyWrm0Ouuxmfa\nlPeubgVFq/Z8lcOdFFf8CwCd1fi5VufYXHeydoGyDZNJZG4oSMXZr5mlNzj2+dy5Ey3Jm5YxuCHZ\nfjs9WgD+/ly9dBENQf0tStLsSZKcEjlRzV4JFXQabUyERqnbjsCB2cT3Qlwpp6aVqnUn+ZsaF3u1\n5qt1pRI1w3xm5YElXEPCv71D31se2VdDI+6Znv/ONT9nlEQXrr4vP/bpH/wMAOf0Hhz/Tn6ufxDx\n8tRcddFqs160HAYVx95vrkZNSn70/pFrzUeeeiz2Vfakb9yIWPF121TPRAtum8XQbftqnk3i+clx\n1Jr18xv5ubXt+LnR8flk1lfXrJDmkdS4uxtpk+oN10DtRvzufBwtoE4Q/4iFPmez5RLMJII8HHj/\nj/fJre595FuwtRb7s/3RH8jPEWCjFGbMNOvtxWc+3XGrcf1MfL6jkVS5sYc/s3DWeOY/gZWV+EyY\no7AmACZi8HUrnplfx+fTtSz7NZaS2wyzVs2iuSzjvnohUmLtHjo3/Gs3om/p/Fr0R6w0va1796yG\nXubz+Ze/+Bfwj/7R38f9JGn2JElOiaQfe5Ikp0RO1Iyv12q4eHYT3/hdN6k3Vi1EBDfNuhZu2Vo3\n4onMzzVZxlkiDDnDq5nQRz03RSdjYtG9jcEgmtVMQ6xmbopudKNp/Nijjm6azux6Q6y972k38Y+n\n8V63jdObDikAqAbjNJfQ0uZGNBVntiXoCAc6OdLrMrZtQ5wxlbQuyMEuw3yZf2FsjsiVVSv/LIi1\nuvVfw00ta4OhyZY4lJwiyc3TvYMYEhsdz+yvz/UUJPkQ3nhrLi/PLGFIMub2pBTxDQtlZoYyUwcX\nw1PKw8/PORWZmM3Y8Weej9e2dWuGiNvcdFqnrm0dyF7bkZwMfhbGM793jvT09ygQQSdfCDYtVbvs\nqOfbtY998qMAgK98+2v5sUY79ufq1ZiLkck7fMbKV33jjTfzY+12My8oWSZJsydJckrkhLPeZkB2\nDEzdCXFxO66s2cTx14Rzb50xLSkED6xq0hu5E44rFmmshJMxx6ozCwwA2g1SGZmmEKw+CRJ3dzzD\nqGMlf9sWHnn6Gc9WmplTqvdcHNNIHGibRgaoxBwf/eAH4jFbgW9fv5Gfu3szft7f9RWfWP2uhf+0\n5PFkZA4xyayqGha9biGxRsNDOVvmeOrIMZZ9plVUafsrcVCJGrfV9RDU9uUYGst26ET17MCjoRFU\nlLxWU3PUaZiQWlvDcRlDZ/YOVMSqyMzPORFLJjOPWV6yWW5dsWo6XSFlZHi2bvkBVZm7lr1bbQtN\nClYpv041ddWeIZVpEK3KYaqmnRkxCMtMZ0Lqf/FyDOvOa24d3L4X34f6E/F9q0p9gwuPXY1tSDnq\nRrNS6MOiJM2eJMkpkfRjT5LklMjbmvEhhKsA/k8A5xGJyr6UZdnPhBDOAPiHAB4D8BqAP5tl2d79\n2gGA8XiEG2++gpowgLasTFFVUlzHx9FcYZG7glPErBRNG3XOdkMtCcKNn5vi1BnT+WNtrW84Fj2z\nPUTI3NFz7ZVvAwA2tyMX3pntC/m5cxsRRfW5T38SAHDntpf06Rp7qDroBhZXf+HadwEAbcFa05xd\nWfU4+IVz8V4EZ00mHrMnoL0upv3cnIk0+GpCnMG69avCiU8UF5FxM5k7xoU1N2HTnFijaeyr8s01\njQlW+RNotocQt2IF8gciAGWbVjPnHjn39DnniEpJI6Y53rK8gpXm8itdkRRppotW7d4taZ+fieZU\nDncWn9QcC/aHpn0ByWjbES2FNZvyvbMthyDu+I5vbjpC78hw8r296PxtjaTopm2tPvbBJ/1YNirU\nWFiUh9HsUwD/VZZlHwTwWQD/aQjhgwD+BoBfzbLsKQC/av8nSZLk+1TeVrNnWXYTwE37fBRCeAHA\nZQA/DuALdtnPAvg1AH/9QW1VKhW02m1oKIclZ1da4lgwLZxzgQuXNhFkxeyv+Je48BUp/FezJbMu\nq2h9wYfRr/qKvGlFHHvCV75t+PGBoZtuS7ipa9lcwTTcmiDoapb9dSShpaE5gYjNVq5x4t5XVj0c\nd2BZcc0WSSm833PTWBMpJskSxusbLHgolW2MrTQIVn8ejPgjxDaUt3zNDIzJus9FxSyL6Tx2JIjm\nygyDfnjo42WVm4lZUxp6q5rjUrH0JCxhOFGVMstPq6WUWwD1klfZ2p9raoVp47aFHBWfXjfroGLO\n3Lo4MlmWWfvP55WRPz5T3UlCC9HG9lVmHw6GPq/VtfhM9H1Ytfe4b9l46023+OaDOMfnN/35Do93\n3cFZIt/Tnj2E8BiAjwN4FsB5WwgA4BaimV/2nS+GEJ4LITzHB58kSZKTl4cOvYUQugD+EYD/Isuy\nw0K4JMuyEAoBLz33JQBfAoBHH3k0+8N/5Efxwgsv5OcPDiNII5u5pt4yTHLdVrKKAG5GpEGqLIdr\n5gaO0WwlhlpaDcdrt01LcvFpCOB5ZnRLqy1f8fsG0lmzrLG9PcdfX78ZQ08EfJBYEQBGhtHflkyp\nvlkmFWod4WnvsRKOlEHesJzqmVFpaUnimpEabsk+r2uEl8R3b57xDLGKcQD0Z5IZNbdQlOG7eR8A\nGE4si01ek7pRPW1vGi5c+lqzV2C3o2WELZ/AshR1wZ/ndclEs9t7RbqsUPDXmJUm/p3F/fJcsgK7\nBijSakN8H9pm8VQla69as/yGBjW8cAGYv6OQpZ7H3MxqEa0cWH1HfBS0CkZmiVUg4zBfwtkNz6Gf\nGTc/zJ8yzXzuZkawuXvXc/UPdu5iNl0GEuX3uO8ZkRBCHfGH/g+yLPvHdvh2COGinb8I4M79vp8k\nSZL3Xt72xx7iUvt3AbyQZdn/LKd+CcBP2uefBPCL7373kiRJ8m7Jw5jxnwPwFwF8M4RA4O7fBPA/\nAPj5EMJPAXgdwJ99u4a63S4+94c+j1/5p7+cH3vlxWiWTsZuAq2vRVOm3ojm17jvpkmlRo5uQSYR\nT20UVA1Bgc0sJXYy8esbZsZPrYTx5MDNI6K6JkKt1KwWy++e3/ZyVBurxv7KLUFrGZ026Dvabzox\njLuZohooWTOHpJqdfTPl1tcsjNfxtEhGxGoSVmR56bqlrk5li1LvRiRZa+1MfmylG9sjzvtI2FZD\nl2yr3gZdSmutYn6BipZsGhqrLNOO1Xk3MOejluvO+5oj4lwfMVTXaCg1lDlzSREl2yjfWrWXjrVX\n4nUdSfdtM1TKe0veQoGEwoQpt+y/pt5SNBxHpObcMO4NQXVObPv4w5/5XH5s563rAIC734qlnkZS\nNLRlzR7uyXweHmM+u3/o7WG88b8BINzn9I+93feTJEny/SEnio2vVqvYWl/H1YsOSrnxaqx+kk09\nnNWxFZYOGV35O+SIF/BADjE2UkbVLOQtqEx9FWWFmYxACXFqcCWeVCVkYu3zNn2hxOL1bQM5jIay\nuhuWWYEbXRbpsxBWpSJYd9OCNanA0lk3Dn1zAp2Rksdblr02lv6vbkat/cQzMYvq6Q87+UP3TAyY\njOauLXPCTCMM6Zz3oMr5lcfi+Oce2nvzjVgkcXQz/g1Nt1pmtLpWhDx0wkKQ0YKrt9xy6FHLC/d8\nv28OSOLf58t+X/UF56HVetH6Atx5V6B/MmcprYOGkETSQso1m8wrtbi274OMfa1o1ltYDjWy3PXq\nqgGZBuIMNSvn/KY76Dr21WvPRoP63GUvBFntxHE0O477r8wauL9eTnDZJElOjZxsRZjZDIOjfXz6\nk65tMgslPf87/zY/NjMKI0Z1qoKXJblkQ/epTe7j43AU8lkzYMRKoSKMQUMN+ph1HNzAsMhMLAfW\nM6OyaQqMckbaJduvKSglJ0SKgyoAACAASURBVB0UUsMcRGR7S63+wmMaWso1u4VymgJ1bW5E38En\nf+AT+bHHn45ZdSPbu42rbuXcO4j7fw03UWO1Lcx5b89De3UDnuwfSb7/pZiB1TG/hWq6YwMd3brl\nGYOsDff6G5HyeJgJIMYounbveQnsCsNxc/phBLRjU1vTvbT5RViOuiU+E74PLdnjc76peVXrTxZw\nINrW3N6ZeXZ/7V2ssxY/1yR2OLF8/+HE5kzARC0LVw6O3JrIa8gZZdihaO0r74tVbrYf8cpF6xsX\ncsBOmSTNniTJKZH0Y0+S5JTIyZrxyDDPxvjEJz+WH/vKb3/Zzrl51O1EZxfDTh1xuE1HZuZpSCOv\nEkPKKg+bHZvZxhCcXs/CfyvnfRqIiT8+dpOpYqGSzMIjM3GgBfK5m1NxJqGPXj+G2ZTrfcW4xmn2\nb19y+isP3/l492bRrP7hz38eAPDH/+SP5+fGhk/f6zk68PYRKbRiv4bCtJuboELz1TVCDjLaVsTs\nv96P5AkkxACA4cgcVUYdps7TzLD3j33AHbA0Zz/+uR8B4EUNAeSlu29biAkArr0Sud5vvBELO373\nlRfzcyzCeGvPyU9ool82p2+oef87tk2QqC5WV9dtnNbmkbeVm/1mvmsojea/hhrZBslQqjK2qb0r\nlbpkthm+/qAXn0lNyo63mbcg7wpLdzcNVXfpmQ/k5xpn43g3L3nNg2pjLd86lknS7EmSnBI5Uc0+\nR4b+bFSg4/nwxz8CAPjqc7+VH3vlWlzd/9SfiGH8G294Pas106BatD6n/jFHWk1ooOoGnsg6Dqzg\nik2O+CPBmzeYDx0Ut2yY5lwj6goeNdvYQkwNAXBk5lTbVyec8cVPzLp4SzLizl+O3OHv/4Cv4D/0\nR/8wAGBjI4bU7g1ck5JY86ivYA6rTGOZUkFgO2e24r0lQpQ7gcj13pW8goPDqIGqVXdwEe9/d8w8\ndZ8LhkpHc8mSs3HuHMZjdQGnMDvxzOXH82MXHov52R17Xr19D9V1zBIbDdxaefmllwAA9+5FHoH9\nW24l8PlWxVq5Y1YB+7W+4WHCu/dizsPaGsNsQn9lFpv4NjGxugDHDSG5NKGV1pH5rHbjO1WxHIvx\nzLXw3dsxx+KscCW8/Ga0rD72sY/Hvj5yNT/3A5+P4Jv5TByAM6AkMJhL0uxJkpwSST/2JElOiZyo\nGR8qFTRWVnHcc9P1gx+LMeKnPvzh/NjOW9E58/pb0Yw5u+FY7pk5dQoxTYO4MaapDKAFgnmTvMSu\nxVgbU79+OmVbkt5YiWZgw5yDvb4j6EbGqDq3rcOuoKJWt2O/Hzl/KT924crVwt+VVY/zbmzHdFSN\nXY/NIfT67QM752bzvpmkeozOw3OGhFtb9/aZOquMp8QobFR4jbDwkpdepnpoqZU1204daPlqIgvF\nSTQwk5uItb0j3zK1jqOJG4ThlVRY7FdF0HINssuO3GzuXnwMAHDhqfj+tOfCYz8lYs2xCfv7sWYB\nsfrnzp3Lz+3sRFN6YiQchedg7506f8nlz2MzYdrgVlHbmNvU0mHblTTqy0/Ed+XiRUfJ/fTjsfgn\nOfQzodw6tns2ZVtUqYQHAeiSZk+S5LTIyYbeMmA0AyaCzd4xVNd/+Gf+XH7sb/61vwoAaLUjqutQ\nKKIapgUaorCrJDogD7lqdpbJFUtgShIBlimuCt2PkR9Opf0DC231jIih1XWnzgc/Gp1pjz/1fgDA\nhSuOaGpZRlVHeMsPjkjKaBziokkZ/lJNdPuuhcRsyaYjCgBWzBk4FeTXh81CGlp1mUxCgftGp8WM\nLwCYWWHGpoXSZkPXjCSQqNSEW73KjEErTnjJsfRzsyqmQo300ksvQ+XGbac92NqKIaXdHecpVUQb\nUJwL8jppuI9ZbHcPY7/PdgXdaNrvuzfcwXue2H/LjLx96GHLSjPea26FOAvORyP+kEha3leG5SQR\nMy+FrZp9OI/3mlgolxYjAGSmd+8duOWzthUtvZ5V4dFy0XvD+LsZjN1Z2W2uJM2eJEmSE68IE1dL\npSbiCtiSoMGP/bE/DgCYWDhrc81zuI8P4p5rkum+0PZHRl9VVxCOaZvByFdwVoLpbMb92krTtQmB\nD4+L9u5uxOyyjq2stZaE8Wz1HxrNb09CIYdH8Z79W17bjis9K7XoXBD3f+OmY8U3N+LqftyPK/nj\nVwULvR77cyTAkOvXY+0vbuPrEitas3HUhayTOdzsV7Xh/c/4TERbcD5Jrby35xVhujZnYxnTE088\nXhjv2QtuCfCeLF8NOKCFee+tps81j42FerpqJZdZL242dEtpapqd5ZYB4KVrr1lfu0vnuM8mcGYg\n2Y20xHRfXjMtrLkMlFsWapwKGSh9E6TCXumIFWPzqXNxaDwIbfNfzMf+DrcM0XNw5PPfqATM3yGV\ndJIkSf4ASPqxJ0lySuRkzfgMyMZT1CVUxHTFQ0lz/PN/7icAAP/qX/wKAKAuaxJxzke7HvKpmVl6\nSAZTcUqdvxRDXM983NNqrzwWTUuSZGTC8smQjDqBmAq7ZyGfqTixGmbSVwznrKGZQ0v51DRWmuq8\nTmmaPvbRiJTq1/368+ej6T0YxPYHQzflDo7iOAdjN5uZQUqMdFeqy7BMNPndAaBpj4K4cK3Mg5zr\nXVlTm3oqD5EBsWQw4HMIACtmlt++HVnHle6LIdJNMV1pQncam9amm/HntmwbJWmjNK/3rUS10psx\n1VadfgeWb8G0ZeWU5zMPFkbVsTH9dU3YX2nm1+kolXdmaOdWN5xshNz2E3P0QnjmSZ3VEy75i9vx\nu03bttwUJOmLhjI9d8XDumF9FeEBGLqk2ZMkOSVywpp9DkzGRbofC+GcO+MrYP8wOrQuX4yr1oGU\nMKY23hY+9ItXHwMAnL8Ur984dzE/17BV/UjALkdGIHFgYZep1J4b09knC2RmWphlhxuCFT+0NvZ2\nIwDouCehK9MMCtw4zqJmefojETCh4R1quu1tH1vv6DYA4M6dGLJqr7rjsGmkkufPubY52I/tU5sd\n7Di2fHs9znFNPG4N05IMs/VlnqqWf9Af+jESas4tZKf8/Yckx9xwhyr7zbCRhkDH1u5EnKd3d2N/\nR3ZMqxFwPtfXXFPTobdlGPe5AHouXY44891dn4Mf/ULMNXjFHHVvvvlmfu7KlQhoufZa1KBPP/10\nfu6Fb0cM/pNPem21kb3HfasGpFlyM9PGazIXb1lJ7k3jrFdw0LFx9G+e99/Bof0O1gwM9fXffS4/\nV7e8gpaQgTZrdYRES5UkSZL0Y0+S5JTICcfZq0BYwTxzZ1x30xx0B26vtc99EADw2Go0yxs1d/is\nrUeH02QixQkR29gbRTPyxvXX83MNK2J4dOgmU61G/nGa71KyOSNBhZv2d+/G/tLkVnONCC9yiD39\ngffn5+iYUxTYeBydUTu7sU2Ns9MkVVO3Z6QbU1j5oYmQIfRJRuGoK7K59ktiwIPpeOnYxOLGc3sV\nmmtSvsr6JpYiZmPbphi+W9Nf2e9hX7DrxoLKLZAWUGpKoULK5Uc2rK34vzo32Z/9vpv9fUMUErM/\n1xi8OftYCgsAdvaiic5nubXlNQB4zw88Hd8/fQc+8+lPAyg+ez4vOuoWSqLFPogTbtPutcoikVV3\nzq7Ye33v+rX82LWXYqnwN776FQDAE487UcVKN5KetOeCjZ+EB+a4Js2eJMkpkZPNegsZapU5Ll50\nB9rXvxGdDpevPJEfu3srcpKvrnawKHfvxpX82Bd8ZFnUqr1+XCk7Kz6sIyNS6LTdUTKy0EffsOJH\nPUchEY2myCqu2B/7WKTT0ookizzimgXGc0MJl1FTUWtotRJSRKk2q1vzq9YfRXXRuVSXOs7U2mtd\nlmz2cWR1ot80U2qZm5/CcaslkFsdZOYVgodZSTUSts90Bc3QywsdinXDUOSqOfTOnPHnxnmcTn1M\n1Wp0flLj9kXr374dnZuvXntdro9z9fQzkZ1V54ehOn5P0WwMJ2pYMQ8LljDbcs4OJLRaacZ7j4w6\n7IwQW9y8GUOTzz/7bH7sklmLZ8/HMQYxsRgOHomzeyUgYeOTJEly4qG3DNl8hLHUrOJq/cqL38qP\nPWpaPliVmGMJzRB6viohqOHQcrg7EXc9mfv11Fi6l2u1o2YmbPnMGd9TM0SkYA5qOGp9zZdniIt8\n8Arg4NgOe76npmZZt8ouugc8POwtHeMmjJpCgR78rPdUnnX9HuCWhlojnJ+yc3nNMyFZ5FzULFtP\n/Quz2WipjVrNCDkXSivrvdWq4BxT26slQ4uqrO4aceeaGcbrGFIDPJ99Zzf+1ed85eqjhb4OSir/\nrEqeRl4DoMTqCqwGtCL4dwvx5v0XTc1+rMmzDCQINf/IXKjSSH9Wafix/niEeUkFnfz29z2TJEmS\nP1CSfuxJkpwSOXEHXb06R0XMsE0rRPiN576SH5sacuh9T0YE05HQQLXt+kNJ6xwcR3t83eirlPt8\n25B586lSBkVzamDsoIOJm9kkh1Dz9/3vj+E0ovfU4TZboL26dcfJGWheF03LhvWBpaT9+zRTC6Gx\nWdHUVRM2L/sspvTEtkikoCoQMJg5rlmQwRB0VcODV4W3PHfC6fUomuO6pWH/i05LlseaLZ3j56Yw\nBfMzKaJu3r6xNF4NZfKZcF7mGC6dU4fqWUt/5r337rlz9g0rWkmzXJ13xNCrg46lp/aPohNOtyPc\nYs2G/ny7Gx3rY3wOWhK624ooyI0VH9twJzoKQ73ISgsAt3fib6T7iPLSVxGSgy5JkiQnyxufzTEe\nHQOZECoYXnu146vv6y89DwCozKLGfvITn8/PTY0EQcNT29tRg86Nh5tEEgDwnRsxO6hRF653O183\n0oogfEIf+chHrE3HpxNUw9CMalICNqhFpnKO1Ue0r3t7MTRHLavOL2bEaQinuujggmuK0UIhQhVa\nEDUpNEkrpKysMS2AMgdambAttULYllofvG4yGWJRSG6pc0CqKpKaHB15KJO0XWMhcWDoj21MhIud\nYVC1PhYtEp3r1Q0PtQHAzp6TjtBKa4t23T+M7XMOOkIUwvlc33Dr4M5OfH9o6dXFOTgxcpJxCZkJ\nHbcDobE6Y3kgNSFqqbUaHuMskaTZkyQ5JZJ+7EmSnBI5UTO+EgKajRoycZjQ1F0RJ83uLDrkvvaV\nWBLq0Y98Jj+30yOnt5uztTbhWdF0ms3cLF81FFS3I7Wwa8YU2ornQm3ZNH7JygqpXDLTSU1dXj8x\nc7UlZh7jsGpu03zM+c7q/ghY0FELO+6aI4ZorkpY5jvTNFDyrvGe1ar3h9x7yg3vprrFhVvqAIzP\nSZ18iw5JxQRwe6NYAJr0NHV17qrGVKtt0AolKvDsWceubxo2Ye/AzWtukdh+I0ipKpsD7XPFHJJ0\nsiri8cjMZTr01sSsZx37QV/YXBfYZSuKcLN5He55ei1TWuv2HEYylU0sb+tGRjLSn8R5PSsY/2c+\n+sl4z44QbEwmS4hOlaTZkyQ5JXLCvPEZJpMJGh1BdZkToqmMp7a6Nbaic0OdKE1qg5GH4wZGuEAe\nhb446NbX4so3FhRe3bjkhxa+U81OTafhMoZ6qAU0JMPV/Y6F7FSzU0No6IefqQVWOh5qya0EoVbi\nvakt1Wk2tRV/MnJtQy2pfaRQwxVCdQuMqmXotEqJ04fHChVSLLOtWvX2PQRoYb9MEXckBfEx0YoY\nGbc6qa4A57tXXnpq6LxSTcvfra7x9hfKSpvm4zul491f2bc+k7nVce3sl84r+99di89Iw3I8p+9R\na6GkeK/A0R/n80jQltsXYg7J1acj0cnjz3w0P9dosjikWFaY4UFpb0mzJ0lySuSEQTUVtJpd7Oz7\nirm1HUMsHeHQ7rTi6nk8XQ4H7e5H7bq16fsXGEnfsWWxKR65xyyomWv242Or+MFc7HD/bC3AaY24\nZ9T+7Fg4qG3Va1SLcwXXtrj3Gw/Momm41pmXVDzhdxl2UqmZBuquu3Wg+2WguCdlG9pHarbF3GzA\ntavu2dl+q3V/q0X7wP5zbGUZd9oGryPpYyG33yoD6fWXL8e8bmrVnuTS5zh+ueciJ4GeoxXF9hU3\nX8k5EPzebKNppKNlFGMamuRefWDh06bUbhvTOpNy43/oC1+IY7NswrGE3kiqSr8HANSr1XeHliqE\nUA0hPB9C+GX7//EQwrMhhFdCCP8wBPGMJEmS5PtOvhcz/q8CeEH+/9sA/k6WZU8C2APwU+9mx5Ik\nSfLuykOZ8SGEKwD+FID/HsBfC9Fe+VEAf94u+VkA/x2A//VtWsJsXkWnKzRNRqPU7ojTazWa6CMz\ntysSnqIZ3DuWgnxG3dSgEysTJ5Y5cxoSsmKJo4GZYWPButMEJSIOWCguCKAniDg6nnLzswSLPhOn\nFLcAdNyUhbU0VMfzRJStSgoknVfqJHv99UjUQGILPcdxlIVnaM7uyxar7DpuaXLE2kRzDiaF++jn\nUELwsEj+ALjZy+sadSWEmC2NyecnvjPrm8KhVSJ8ToWS3yYeJmzYGP0cS4wFMbNpqr/6aqS60i0T\n+9WR97pijtd5w96LiRB5WLg5yLs+tXvNKnFOpkJJ1jIkaWXu78pkpqnRy/Kwmv1/AfDfwCt1bwHY\nz7L8V3UdwOWyL4YQvhhCeC6E8Nxuyb4zSZIkJyNvq9lDCP8+gDtZlv1uCOEL3+sNsiz7EoAvAcCH\nP/ihLMsaaEiY7XgQNcXlK4/lx17/eiSyuLcbM8iEyQjnz0eQxd1bvoqu1iyTycgBpjN30swDq6ZI\nOGhqZICtuMJurTjvOldk1bjjKTPJ4vX1ujtKcg1UQqyQO6ckHBJQ1DzqvCvLtqoYYoY46ZdfdrAP\nM8NUS1EbTxf6DLjFoA5A3oths1CiZbU/HB/po8pCdcOBz/90UsSn1xruAKwa4aL2n/PIeWXoTtvQ\nOWMf2Y9KiWKvVupL15eFDnvGdTY0MIsqf87/0bGHxgim4Rw3xWphf0YS8t0yENfYQpMdCUEH457v\nCWinydChpdxVBGw1OorXNeS3UatWH8RK9VBm/OcA/OkQwp8E0AKwBuBnAGyEEGqm3a8AeOsh2kqS\nJMl7JG9rxmdZ9t9mWXYly7LHAPwEgH+VZdlfAPCvAfwZu+wnAfziv7NeJkmS5B3LO4mz/3UAPxdC\n+FsAngfwd9/+KwGoVgtOnZywoS3IpGp03KxuRQRRQ8yjyb6hzLTUDtNAzYYJYvqRjCGbuSk9n0eT\nqWYm61zWPMYpK1I8kE47OtXK6olTxlKPmya1mvHsN+PZSoSRI+hkbMNBNNfK0G9lZi3jzWxD48Jr\nhj/QNFzek/1QM57H1IzPY9HEd4szMUexiWnPuSvjuCtLq11MuVUcfxnKj8e831LKy57FcOhOR24x\neK4MXUeiCtaUB3ze602fTzpx2YfpTLcjdJ76MVLnHx7ErWuz5ug6WArwxlnfUrLU154VMR0K4s4o\n97Q2JCoPYq7A9/hjz7Ls1wD8mn2+BuDT38v3kyRJ8t7JySLoKgH1ZgNZcG0wsxWzs+YF7T766R8C\nAPzmVyKH9o4UJ+x043WXL17Ij9XNAdOw+sOhqdRH8W8D7txoGHKp0ognw8S9OmXIJ3e2xH4rmo0a\nokzLUrsqlpuapMxZtngOAPq9qKmpIdXhljPnlji4qIGVUIHX696NFgbb2Nx0zcJjZU7EjdpyRhzn\nQvtPbcnrynD5ZQy4eXUZsXycysstn0V23JmEnxYz9PRetMgU7cfrx6aN9fv5vYW9t2Jh16mxGRcz\n+owjXsOohsJjliIJKwDg2svRKa1zPZwaG22VBCZ+72rd3rdCWHeKBxHHJ2x8kiSnRE6Wlmo+x2A8\nxByS3121qilCCPnUR2J2T6VrZXglk+lwYDj4I91r2ZplbdVbAj6w7KDQ8aG2jZCybgCGWmM5R/xI\nCC1JQkktUghF1Ytav6CpDexT5FYnTdOk8L9+zmSf1+3G/i+G+ADXlqoNuI/cNL9CAd8dakvX37N8\na1or6oMg5Za2wX7fvRvJEDXTi/n7zFHQ68tw8GU57ov7ZpUyQAytrtyi6Xhf+SzUj0JLgZmSmZSc\nXqQKazW9rcw0Zu/QQ28tuxcpwKoCuBmMiJt3cNZkHMfWtroFwyOvebhjxJqXrj6SHxtZ/jtLhGd1\ntRrjOaFuQMgqDyr1ljR7kiSnRdKPPUmSUyInW/4pANVqhro4mdqdaDbu3HGnV+8omkBXHn8SAHB7\n17nYZ0fRjBKLCbO5Yd0NhTQYCM1UMx7bEyffVMw6oEggUeZoCwshjd5Rf/l6G5KamA/CYVPU2URE\nlqaZkh22zMFFc5OptwDQNNOYWw81s/OticRr6i1uaeL33njjtfzc0OZTzXiyv9bsei3SSWfXHeHO\nJzMvCzrqXE7mJK8Q2iibz2YJIpFjV7N80aFaEd57OuvGsi0iT36rXbItMt1Hh6o6VjntdXF4MjR3\nnDs581P5FqKwjarSOWj5HcJeu2rbr07H258ZOQl3GlWBknJMWUVDmQ/W3UmzJ0lySuTECztOZ0NA\nQ1EHVhBR6HtgqfEHhlVWZ1nbcPUjCVtkpi0ahlmvClc6FcNs7ho0B2xg2TFHB06xuGLxe+pQorbJ\nQSMC6sD8/kCSMk3tmVLu1KFmpwbSIo68Tttg5pUTTroVxcy5ghPRvspjqnnLsuTy75pGOZbnUDMH\n0hkhRqS2H4xZqHGZF72sgOKDMvPKeOApE3m3agaAWW8v87kzYjUXrT/L+MxpTUmGm9UW6FRdU3NM\nzCvodKU8dlbMaQCARtuITq109lToxObWlqLb6fDcXDVHoDiSx1MDBQnbaAZ3JJZJ0uxJkpwSST/2\nJElOiZwwB12MbWcSWyZ/165wgdcb0Xxc6cRY8ZrUxL596zoAYF4gqDA+buODnwzddLq5F+PBasav\ndmO7HSv/dHjsDre8D5IruVhAUU2zRfO3Ibxi9fry9PL6Mqw7zVQ148n1RkdPGdqswIu+4LAqQ9yp\n8Lt5/wucckxn9dgyMQbsa4FdlmhI6X/DTOiObSuYGgu4Sa9mP01X/lUEXRlbr44PWMDZG8KtzOyf\nW90BzVugcy/H2ytRhWEUGrIlmB4UefKHI3/HWAde+/+hT8X67/PD+E6OBeuOeXw26vCc5+zBbNf7\nytoCs8niu5jYZZMkOfVysrzxAKYzIFSkasfAUEVSZrlljp5RL4aPViV8ccdW34oUY7zxZkQfndmO\nxBZFcomovbc2PDw16EetEQzn3KwJiYBpuLHw0nOBz1FvU3cy5Zlz5hiqSPhlNicWPSxdP7HihKur\n6oyLY1tb82PVGtlcjYZItNRoaCu++ATrtThXLGQ5Hrs2n02Xse6ZHSPxwkjYWTNj923WfD5ZFHNq\nFFHK095nSFL6w363LQusKRVeqPVUGzP0RG1ZwNnb3OqxwZSZdmQKdk3fMPqxuYSsmuyH5VEUmXyt\nicDKQkJ/ZYPqH3qIuGLPfG5zuCIlmFfObRXGAQBn6nF+RlbBqHnpsfzcm9+J45hW3cnXMGaKrD5a\nGvdKNVq/Awmt1isVhKTZkyRJcrKhtxIpw0AvltXVcBBDT9Oea17iutsrrL0l1V9s7zqWbCWCb85u\nx71iT1ZHcshr3jJz28u4z1fE6gCKOce8fqK0URbqWS/BrhMco9qG4Rbu/3s97yvJH1kZBnBN6n4G\nGbeFuLR9HmN/dA8cLKwzmUiWogFVWOZar6/bXroM6845m0qWFvuooTdqwnPn2nbvZdDRZKzauPhs\nxhO/99DaHQzk+Vo4kWHIEJbDiot+AMB9DUoeyv31+fPnARR9CaQMe+stJ3Diq7FqOR+PPOJW0Xdf\nfhlAESA1N8siryVXWf65qg9nOsuQst6SJEmSfuxJkpwWOXEzPgvzhRI1ZkZJqmEejrJjauqvrkcT\naChOIIZ6XnjpRQDAD3/+R/JzR2Z+qTlIZNihMXTWCkC65bJDiwQVzqi9XOKpjOlVJfcV2di0ZDNN\ndmXTrTZIkBDbHU2lvJGh67pa+NLM4HzLMRWHnjkd1UxtWqls/i0WarTy2OJxq9eKXPVqgpO3X7c5\nRNgd9SKybyxbCCfacJovPqfhMD6bMuKMrLOMYKRpn2W+5WhtxHel3fYH7LRgsf1CmNN4+HM6K3GI\nXb4Sy3WfO3cuP8bQHEN8mhNA/v5iuajYxwN77w4FG0/gX1kaBRF6LUnd5rirNW9/OhvhQZI0e5Ik\np0TecwddWTlgAh3KQiA5yd+BO10+98OfBwA899XnAfiqCojTSzDT1EZ0Zk2FOCMnDxQNNLbz60YA\nqA4rZjwx20zx1CQzUEKIdbNMmpY9peSVThHlmmsRZEKwRhQLUYr2mB5EZyUzuDT0s7m9ae27+qDz\nZwZSMrnlUAvLr0eweWm2ly0Z4vKpxQG3ogicCWJV5DRQY3eoLgKX1AqhM5OaDpACjfV43WrNQ1ds\nYzIVggrL+JuY1XJ45M+elsPmZnwmjz56dalfKkcH0Wq8uxNDxPpsGk1m4fm9GSqlQy+TcXz+R75g\n9/H2KwbsKgtD8nejmv3tJGn2JElOiaQfe5Ikp0TeEzN+Lg4umiqZeqVyvnVDOUmMvNFeXp9axun1\nyU9+EgDwC//4n+TnaAaSdAFwXDFN2NUVN3XpXCor0Ehni5qdNEVn/J70i2YnY7qAm+1lzrucjVbj\n4HmqajQLz549n5/jlmYm5iDJIkhaoffJ64mLab/IY6fbKo67jJCD6LeuFHEsi0+7qW75C3JvHtO5\n5hz795YxASqMk7PbDUGx1RssCbUcS+f2Yk1SqxedrOrUJb5Dse63bt2w64pOWu2rOvScfTe2e+fO\nvfzcxz8WeRfHQ9+ezsyhRwSnOj7ZVkt49cu2xCpJsydJckrk5DV7yBBK4LsF1BUvLTmXZ4tJ1hu1\n5XQWHSSf/tQn8nO//E9/BQDwmc981q83R1vX/h4fOWUVV0/VfjzGonsjyW6i841/C85E0wZKOJHz\nnFs88eDIsdbUalp6YZQmMQAAIABJREFUt9lYrDjj4z40J2WxSkzsK8NZRebZZc1Lx5+3q5Vw2kvt\n52WlTbOsSfucA31eI0Mr7s2i045OwtifztL1i6QhhVwAs3IKCEZzqpXTicV2L1/2AsO0PjhuFZ4r\n47+/du0agGLWHq0Cls6GGB5NI1k52NNsToYm47lO298LWpkNpeGyzDz+EAo1CRayJ2O7tSUKNZWk\n2ZMkOSVy4po9ZAs0TaxZ9QDqJl3RqBl1r/Xit78NAHjiyacBAD/4gz+YnyOZ5O/93rfyY6uGoecK\nXkYHWVZ/rKze18Za1KDMO1ffQ24dCKY5szaILdexcZXWfSHXY16n+3lGEzXc12q6tgCK2ok5AIWc\neLMwglF0acWT4ch47AtzUcwRL0akeEyyxUIRQ8/9bbxuOeuN42Mf19aWK9qUkW7y2PGh02Qd9Zct\nH1oCZbRjfB+Ya0H/B+AhXLXScn/NAieAHtMaA2PLYdjb3S9cAzhnwPYZD8X6+eU8BJ9/f171ejtp\n9iRJkqQfe5Ikp0beGwSdIMTIe62mXM1MFB5RjvjMzMyqmK4DS/t88YVoql+5+mh+7uM/EEMaH/rg\nB/36PksARY9KwDLzrJIC5CbicbF8MuBONYbGFAvNMZUVOiTaTM1gsugWTLGs6FQrw96XbTnYjwLO\nnua7hmiIxCICUFNWzcmkJm+WFf/eueOmbq1GZ+NyiiuLGhZKSNtW7KwQWpB4xENq3lea0NofOsxo\nevcODpeuHwrt2GKasjrqyLXPbdSqhBW5hdD0afa/UmF9gPxUvg3RLRmJSHjPmqDrOEwtTOnUYvG6\nwyN39nF7oOy4KfSWJEkSACeu2TMgmxUrV9DpVQLICORdL/E5tBqSLWYrX7C2vvXN38vPvWWUVY8/\n/kR+7KhXzKjK5o4Hp9ZuC56dRATENF+96phpOnVuXI8kBQr84KquBJXU8qy2Ukb0oNpgPCqWHS4r\nMVzMYovnj457hTECronUycfw13ihSg7gpYIzSTtcdAAp7t8z4YZL1wfTQMc917I792LI88ZbN/Nj\nn7SwKbW9WlE3bsRn+eabb+bHmGn24Q9Ey+2ZT70/P1dGEHrQi9YTnW8HB+7Q471oEVQFsMJnonPN\nZ62Zc4v3VoceX+TDgzjurLr8bGr6MzAe+929+P5pHgXbV8BZuavZJWn2JElOiaQfe5Ikp0RO3EEX\n/XFqbiyb7+XfiUKLUvmyV1ci0mvPEGUba2467e1Ec00dW8SXV6vGi95305KOG42P0vR+9dVXAbgj\nB3CT/tKlS0v3If/Yy8YvBriTb2Vlden63GFYQL3FsZWVnhqNjJFUUnRnWRGLrs6s/f09u95NP5ql\njJ+rA63ZNHIPQXVxm9BtRLMzE2drsGdJ81/vxfg9CRx0TFrei6buKy9/FwDwbcNQAI4yWxfH2eUL\nkQfuypUrAIqcfrdvx2ev8fLb94q5A4rXoJlMc/6eFAPNTfxOSYknQ1RqDkReRkwQj5m99w0z39sd\n35JVbCs6lJJQjVoxr0Ofc1luxWg0Qpbd35RPmj1JklMi70no7UEoHwDlHjmTClcu0X5XLkXs83QS\nHTd7e06esL5q4Zqhh7/eeiNet7oeV+JafRnBpRhoOlnooFPN/uUvfxkAMOxHraxakKLjpSZnG5US\ncgbVrnMLxczMwVWo6hKWkVtT00AssrgiDiKiCcuy2Lyv1fuei+fjX1o76vyi9iuG6oqhNz1XVnzy\nl37pl2P/cwvLNTWtqKeecGcr+3jz5i0AwPPPu7OP99JxkN2WRUDnMyEKOSaLa6XQd8CdcMOxv0fE\nv5ehG/ldRSvu3ouVYC5dvhD7IN64iVHP1mQuFrH6g4FboKuW16FzN5lMSjU+JWn2JElOiZw8Nv7t\ntPpDt+MrWN1WyEeuRg2v2mMOYqc9hDO0qib7uzGkoW6DyWg5nPLII48Ujj0hmmV7exsAsHsvtqUr\nK2mKymqxkeoKma+3izXl4neLOeihkDI4L3wP8FBYs91aOuf98GP0E3DOApY1e7G2WrHUtI63rL5c\nn36FnEjSw3JlOfcEsnzmM58BUORRv3c7htm++c1vevvcx9t+uybaku0rzVfbfDF8lmqJkROe/dFx\n87kpQIrj5XVl4Cmdf5YPnI7jXKzInr1tZKMVeb59q1zECF23676Ksvkfj8eFsS7KQ2n2EMJGCOEX\nQggvhhBeCCH8UAjhTAjhX4YQXra/m2/fUpIkSd4reVgz/mcA/L9Zlj0D4GMAXgDwNwD8apZlTwH4\nVfs/SZIk36fytmZ8CGEdwB8G8B8DQJZlYwDjEMKPA/iCXfazAH4NwF9/u/aWyj2Z1VE4vHBNpcQy\nCXM1X6Jp2DLG1ksXnbrp+lvRKdKX8FqzZc4WQ7E1KorGiybZoO8mGdlq6SjREAuddzymDiuaoOrs\no2m53orXq1MnD0HJ+Om0I+iwJYQHNHnVbM5LHY/GS/3JiTPEAcrzecktYZR1hJ5SH1npYlJ77Ti2\nfDgslnqK11cKxxRRRsomDZfxOiLjNGxZy6mn3Lwma22ZE4umfUXKb5PmK9/SCD6dx8q2I3xu2tfF\nVGwFhk7MVO8d+5ay02KRTqPeGvu5gW3rul0p9Gn3rxtzrm4Hfacnzt8HFHUEHk6zPw7gLoD/I4Tw\nfAjhfw8hrAA4n2UZXZ+3AJwv+3II4YshhOdCCM/t7e2XXZIkSZITkIdx0NUAfALAX8my7NkQws9g\nwWTPsiwLoYxsCsiy7EsAvgQAH/rQBx6w9NzfcVe0Bu6f6UU+dw3XvP/9TwIAfu/bL+XHGGIhsGUk\n5Zm5qusqqlYB4Fh5ALhz63ahD2VEDJrV9b73vS9eX1vmp18kZwSAkWmqMscmvzsSgFHNtHCnxUoy\nPg5q9ELhyGoxfDSbKWjHtJMUk+Rzarenhe8BwOpmdH4pIGmO4rwUK84sUyvRycXxqqP0jFV40fyD\nRQfjxobjx2lRseAn4CCanI+/72Ojc5PWhTp6czCRWCbU5A3T1L3DZQedPjfWFqibx02tlpnx2H/0\nwx/yNswBOxotl9oOeanw/BDq9eY7Jq+4DuB6lmXP2v+/gPjjvx1CuGg3vgjgzn2+nyRJku8Dedsf\ne5ZltwC8GUJ42g79GIBvA/glAD9px34SwC/+O+lhkiRJ3hV52Dj7XwHwD0IIDQDXAPwlxIXi50MI\nPwXgdQB/9mEaWrLjw8LfEinfIEibeVy0affwNazdiWabOtUms4h5rreiWaVmfBm3+nxuDqpAkgKp\nh94oFlIsQ53dvOmoLjqeumtxC6EECWVc5nPDYi860gB3Gg0Gji3n/XP+PsGpb53ZsPEu868Roaem\na6u5sjxeM3W7q3GrpKZlzSpkHouZTXPZHXXL5Yo0Fs17bW1t8kB+jvXo1XHG50rc+LGQjuTINiGE\noGlPH6VuOXIiEiu2qeNetWdSL+SgRuGcaVkw34ZIjoWVxWL/P/KhD+fn+sdHhT7bCOI9bX4KeRG2\ndSsQqTTbxfTxBXmoH3uWZV8D8KmSUz/2MN9PkiTJey/veWHHhxGFylPL6wroFEBRo6g2O7h1y44t\nVx3hKq9c3WXZZXTQdVejs0i132ABT11ATNkx1R65trGwi+LsqbkKoSsU2VzVQZSz44oxsb6+WejH\nVFZ+4t4rGmo0bTwcxLZqVXHe1ZaRYYvVUtTZlzv5xH6jJVCGDiTTrKLqaNXwuo6QY1CLq/amP4rX\nMxSnfZyIk5JYeDpIHxQmVPRebj1KiW32Y3f3nvXVHcNlTLhNYyV+8dsvAACuPuJ89mQp1oxQvouc\nn+K7FfuhDtV6vZ7YZZMkSXLimj2gMg+lq08QbfBgcp0oGSRcM7EwB0HEQbKcJoZ1b/hea1S3+1vE\naiymAxV6sfSyES9OWSbXw0ENqwZSrS1jpyfT2Mem8Mwz/7vdXiZPpAKaCBVVu10MB+3t+/6c2qbV\ncv/C7s6+3Tv2o7inXt6L5tV3QnXp+kWO+Phd2DiXCSFZhnpeUh6bAO+uhNJyS0By3PndlSYx7FqX\nLv7d3FiVY7TE4rFp8PmshdjGcc9Dk4265Q40lzMMR9PY1+56fDYF3Dwz+iQ//fqb0Rdz/nwMrWpG\nXM3ei4k4nC5buPLrr3wdAPDhZx7Lz9Wb8V7jifwODPBT4bsYJIxqpbUbdak7MB3iQb+epNmTJDkl\nkn7sSZKcEvm+cdA9jOmuUiibZF+m2TUTLm0isI6PPRy0WJa5JiyfRJlpSIPGLvnsJzNNl43tNrJo\nDqoThaPKMjG/rGt0JpZxDVQFy83tymBIZJlflxeCHLmZyoKXnhK7jINXMz6Yo6dSIcJNeen5oQy5\nVWS91fZVGA5luEmdcXmRTi3nFIpOskI9ATumjLbVKhmCYxtV2a4R03/unKPqgr3yLGRZFcdts9os\njENDsnfvRtTkvhKjGPae4UV1xHIr2dLy2NmgcL1y1lesNFRnxfvKLWGrzj77/LLf+nyzLCt9n/J7\n3P9UkiRJ/iDJiWv2OcrxM++E0sKL2y07oKjhBoNlwEOe6SVWAldPViYp3seywGp+PVdztjWd+upL\nLV+vqLYp0jNp6GRsWXghCCGkOROzeby+JpZDMMthNl8GyfA+qhk5L7Op5BXUWCFlOdOLGr2MxooW\nhF7/IFoqWiFlRBsrK66pj45iOPSY2WualWZmUX/oGpchrjxsJhWu6YybC617JcT7M+xaF0vg8iOR\ntPLYAC5fl2KgFy5EKqmWOFtphaxvxnDfUc+zG8+d2y60BQCjeXwHHYDl1lGrZP6r5qzj6xMy/bku\n28IPoqQCkmZPkuTUSPqxJ0lySuQ9cdCV1WIvO/YgKTiZ7G/RqVYUNY/ye4WiSapt1Ove/iJjbCam\nZW4az4iPlri5bS+mJTxt+XZB0H4ZpjYeuZ+VAGpb3FYRayNzzFW18qWt32XOsroVFgwQhlozf/M5\nF2cc56IsDZe3nMuzyvnvmstboJxjXfrFGHwZGpJTdu+epxPzOi2D1DLUGp9Dtbq85RhKCjDn88xW\nROP1xWF47dorAICemePnz27n58gerH3lvNBxuy4ccTetHNjVK5fyY/0bkQSl3TS+ObG6uR086is6\nMM4t36layc+iUD7sXSCvSJIkyR8A+b4Jvb0TodZm+dp6zZ1ADMmQ4xtwx0rdKsJMFGVmiCQNEVGI\n2x4IndDBQayyQu2/JtVoshmx31oy2LS3LcKNhmKz41/lbg8V0x41EnQoNRHDbEIWkWPX43VKM1XP\n7yXhGlbYMacdUWSA0FgVSgubJUB2U0F8oZHH6vJDZNGlxi4rgqjaidqe41CHHp+zklfQUqA1MRPn\nJsfeaHj7Z8yZRlz7iy+9kJ8jJRStKCUpOXNm2/ogDlIzInjvXclzYCFL1d7U1MxvKFgJM4b73JFM\nS4nvimruSmXZIn47SZo9SZJTIu+5Zi+j7yk79iDx6/g9P8fwRq2u+8JixtNUsPGOlfZ9Xv84anlm\nv6nmZRt5SK3AHb7sQ+DKTTy4hw0d0KP+hVY7apKJhfQ0XZkapQAwmhXDibWqP2JyihdCaaT5moel\nc9ToRby8tRtIY6VZdfH6Is+8jaWy3D7ns5BDb9qMxI4aJjzuRY0+lXpmvFdmz6TT8GdJTv+axONe\neSXuy/f2okVWb7imXjGNTs2uVgifuXK3Y4HmS5/91csxjKekkuMRq76YdSThs6N9A+uUEH62zS8x\nGWsp7GU9/XZKPmn2JElOiaQfe5Ikp0RO2IzPolcje3fXGJo7NLWKpiXvJRhoM39zEgRxetGU1vDR\nZExHm6UVipm62iF3e/zeeKKmVs3a8r7SdKWTULcqdEYpdRPDcTndlKzPBbTbgrCMk7ZP3Lvyi+db\nJmu2Lm3WSrZRi7RXegnx/mX4d/LM67zS4anjyMNl1kZPmH2Jajy7uZUfo7OLZn+r6WN7480Y/trd\ndQrzDSP3IDGFWsN0epG2K0gYr9UxBtlj70+7HR2je0Y1tiXUZ3OWeG77VuCmUalPLWTaO3R03axm\n20ZxLq9WY3seAlVs/HJ48+0kafYkSU6JvDcOOiGX+P1q+aLGMu1h4bUi3zfpkPy71Mx0Ns0FWz6d\nWjFDaZ9ag21o6IfOGWqpFSVPzBg2UyeZhWSwzOFeBoipNyxMaNq+CHBh8UAJ1fEcAT1TISS0cYe5\nZvkVnXZTcSqSNKHgoDMtXJkXx6OfVVOvWsnsenO5/16hRkN1Fk60Ob9wyUEpSjlFIa3X7duRSGI0\n8EwyWkjdFXeqLZZXXtEKLKbZ140cQ9HnLBDaFOqpnZ3o5As23pkSQpozbn3Vr69aDK1pIVAWbgSA\n5ioz26RMdLuYuajvUXnI7cEO7aTZkyQ5JXLimn1pRSpZoRYhtGUhOG2H4Rruh5uN9tJ1Wsq21W7Y\nsbh2ax7ywUHcRympYbdb3FuOJZxCq4J12nS/yhCO7lNZhYZati/VVthGYY4yAkmKGVOAay5NLafm\n0vpmlLzaSnV5jc8zsAphy+XacHn71N6zZSutAOG0r3JeVOtzv63Pd7MTnx0r22jO91tvvVVoC3Ar\ni1qvVhHQi1kmbcl/n4PhxOX3LifptNxyJaqc0XciobG9gxgu6zRYutkJKrtNAmL8PjffugEAyOh7\n2HLfw6H5hRq1ZRARn0Px2ZO7QUBW2RwlZO25JM2eJMkpkfRjT5LklMh7jqBzZ939w0hvJ3S2lIWi\naJ6W0Ru1jbl0KElRG1Y88OjIzWtuDxrGSKpmOc0o/l1d9WouE8u2Um549pXVXzSD68zW2aVxTM3Z\nmBdGnC6baUX21yK9lDorWemkWHwyns8rzoh568UqNTRp2Wth2YzkvPR6HlI6tCooly5FRNnly86V\nTrP0+s0b+bGjWzcL91nMOAQWCkda3zos0tlz5ym3SloBp2lsvZz/+VSzFDnGUPgLAG1D09244yUN\nt8/F5zW1kON4IjkQVVYnkkKWM+YJLNOJdRnClZLci87TMkevSnxeiTc+SZJTL++9Zi+RRcdcWZhB\nfXZ5jnWpZucK6F/gdXQQ9e85nRC1k2ajUTNwJWZYSPuYZ5uJppuYdm0IsKJt9w6mgRXwQU2qlgM/\nc1WvSFJznkuv82Ona+Y0qkhtsrJ8f84t56QpPO2cHwURsTL3fLIcZqMVsSKhLjpP6Uj72te+5veu\nLJMm8l4Md5aV5tZxLGq/tj4bI9O8ePFcfmxgZlxOEtl25x0tOGa7nbtwMT93+7aV5pZcA7axvW5W\nwtg1e6cRn/lzv/Ub3lcLg1YDwTtuhWwaUGhl1a2W8bxYnrwq3AesK6fVkt5OcyfNniTJKZH0Y0+S\n5JTIe2DGz/Gwa8z3murqZp6jtDwdVVNozaljTpdazWO5Y0sjFDpxdDrR1KM5rwi3HI1nDqu5UD6x\nfTU7+4Po+MsLFxbSKONNjyTGfzyI/WnWS9hfHyBlc1fktC/2301Fv4bj7fc9rp2n5NqQ1Dm4uKXR\ndjlnFSmMuHhvwLcCZX2lib6/71sfprFyjjtNn8+h9f/Ovbv5sUqVRUDjmA73hQfeTOiNtYhJPzrw\nc0QiNjpuShO5GAwpuSEm+K03XwUA3L3xhvefiER7F2fiPPWUZ6Fby7jNWWYK5mfd4EZyixRnT5Lk\n1MvJavasSNOzdNJkUZPPS8goCyvg4vWKNKouD5HhLzrhNtZ9Rd63TCSlf2rmJIhWQnfgmn3KUs0t\n01KCbaYDR1dkFj/MyQTHyxzr1cpyiGVkWqAiVkLOSy9aMF/xbYxqVeQIurCMiMsrsEhEZ1pSsYWl\ngiv1ZWcZK9loVZm+VbJh+xvrHlpiv1VTMyuNuQAsYw14xp/2kSWYeU5pnfqjqL1vC71UMM3esmdf\nVyKSSjFTcCAZbg1D9tXF5KvaXLfNWunv+X2uvxpJMqY9tw7q9p6VOZ5zOq5DdxavrMawbE6yUsh6\ns7lWAGPijU+SJAnwHuSzx9Xne6ON1hVpnu9VlkMyZUCDsvvkVFUkiRRtw7ZuTxwIM84pmy2cJdtm\nglJqtv/UPXWeLaflqK2PAwNiqGZcpLgCgPoCEEYqAEtOuY8tr+5rGlhz0lsZSyr7POVcAAZqYtZf\nsW/+BGgN+Zx7f8Z5OWO/J0FGbEs1L/t97pyHxpiTwPFyT65tqJ+D9FJ53oJUi5nT6hLLp8Jnbrnn\nax2pz2Z+hYHlKxQq+QTus90ns2rvzWRofR44mOj1l1+M7TeXqwFVic+XeSJ4aia5Ek3LlyfeX59b\nTrldwk1wP0maPUmSUyLpx54kySmR9whBp+b2cijpYUJuZWy0ZXhqOsDqUpCPKZ4sFFgRXnSa9EpQ\nsTux1NNAsggJT5npSvNUzxGHreYXrysrP1xWpnixcKG2NTHSjelw2clXRniQl6qGC0OGvL6qnPWg\nY1JTbkmnxXDQMipPU1AXed0V4cZt0Z07vmXi/JGYQ+/Nqjs6JqYFE+veWfUtGXMBhkKYsWFIta6Z\n77t3bufnJlbWu2vPpCphxdWV2G5PCCfm4/gsOyuxz7/9/Ffzc+uGRDy76luOAYqUXip5+W05ludk\nTC28W/ipsG+zhWMJG58kyamXh9LsIYT/EsB/guhZ+yaAvwTgIoCfA7AF4HcB/MUsy5YLjL2NuCZS\nbf/2a9CDeObVwdWfxdW3Oq8tfZcOn0xqvZFEULPRRmOWCqZmEW1j7TLMo1lmxKeXaVfeezh2h1Vl\nuqy5Kgtj0nLRZQ5JOvC8Bp0QMHC8+rX8NENwioO3EFHQexZBMrNCKJA89suhQyrosv6odbNYX65I\nMWZVX6TMNXH4vHdNcgemdm91APbNYnvjjRgm67Y8F4BWBZ2EW0IuQcoqCAnK3EJhv/Pbz8W2pTzz\nGYYmR1JZKCemWH5ueT6EnKJmdwott4p8XtUp9w5pqUIIlwH85wA+lWXZhxHt7p8A8LcB/J0sy54E\nsAfgp96urSRJkrx38rBmfA1AO0Ru5A6AmwB+FMAv2PmfBfAfvPvdS5Ikybslb2vGZ1n2VgjhfwLw\nBoABgP8P0Wzfz9z+vQ7g8n2aEAlmGj54jQl5MLmEqIEfxNyhqcvLp0IKQHQTBC+fo8YqNJHdPBpZ\nqaempLg+djmagbesDG+75QQV3331OgDgzHq85m7PzfKdo/h5pevbChYZHM/jua0z3lbv0FBUUixx\n2ojm3XhaNIcBoFGP5m9NTHumUVZQTF0FgFqVsXSfn/mkyPCawftPs1BRiPkWo2K4AjH7822FIvR4\ne3PsjQSZ2LeClyR1AICWIRFZrqshBTnZfzVd51a2in7F4cwdpI9cuQAAuHXjLR+RTVXLkHBzYd+d\nWltbFx4B4HkSAJCZiX521dt/6eu/AwA4eus1AMB227cELZuKYd/nc+WMPUtLbR1NPaZ+9krcMtzY\nccRdC0YGshNRdVUhIl7dMnyAbGlmtfAgaPxDmfGbAH4cwOMALgFYAfAn3u578v0vhhCeCyE8t7e/\n97BfS5IkybssD+Og+6MAXs2y7C4AhBD+MYDPAdgIIdRMu18B8FbZl7Ms+xKALwHAhz74gexhdg4P\nU4b2YTPhHkZmolkqDWLWfRm9fCkaLb/1678OAHj80cfzcxtrcYWdhKgh1iQrqj8xlJxSH5lVweys\n2UwcjTk3ubChVjkXxjQqYaTxKGqG8dDnq7rA9T6X9oezInIQABq1IiIuw3IorTSzzRyqBSy9jXNS\nqLBTfJbF52aEE6360jHi1OtiVdApWGCotRBabWEcAHD9jdcBAMfHHkYllpwVYSaiGfMalJa5GKZa\njcYq1ew7/v2NVyJKbtOyIoPExu4apv/JJ57w/hvSjlz6mnFHq0iJQvb3juzehtyUjMFdU5wbW86l\nHyrhgb+Lh9mzvwHgsyGETogt/RiAbwP41wD+jF3zkwB+8SHaSpIkyXskD7NnfzaE8AsAvgpgCuB5\nRE39zwD8XAjhb9mxv/swN3wQfn1RHrRKZSWZcN+r5NhyCaXlZXhnvq+9fj3mJLdbcbr+7b/51fzc\nx3/gkwCA0Sjuq1pdD9dc3or46909X8FJ2T7mXlk0L4IBSoKC76M2oBujKutzhxVeSsKQZaCUMvLG\nnBvd/qpmpAbV0Btb476/UBGG9wrCZT4vZs4FAfcTGz4c+L6WIc+mhS2DhLq6Of+AhCZZ025Oi8zD\nX0PLOlxb82cysznuj+L814W2a9OstP170Ui9siV12q69BAB48Wu/mx/bNnJSlnoeCs3U2tmI6T+U\n0GrV/EbE708aPrb9nWgJtNa8Xhy1fNN8M8f7DuipGhhK3631rTMP2rI/XJw9y7KfBvDTC4evAfj0\nw3w/SZIk770kBF2SJKdEThQbn+Uprg95/fdwbZT7r11lpi6N0iwTLPcomp3r617KZ2oEDCuGr793\n63p+7p//00g/9Of+/F8EAMzFLLxlZtf5DQ+v9c1kHTHUNRdmVSsaqGZzGBK/v5z+yjFp8UYSTpRd\nvzx+l7J02bK01ByFF5Zx3rUqyy1JSmmlWJywquQPRAVKH0d9K/t8HJ19nY5j3XPnlcxZXqqJ5B7B\nnaF07qmDjiHPy5euAgDu3fFn2diM7Z+9HM3+3/7Vf5afG9yL3PYd+cXUzbE7G8X+tCVdlvVK9wRV\nd9kcc8c2hxreff3aNQDAMx/7RH6sbWHIljlze4ceqquzfJgg9Nrj8QN/M0mzJ0lySuSEs95CzhX+\nPcnvs6xz8dYa4uKhZXogZqppRZhVW0U3rZRvuyn9MaX0//z9/w0AcO4RD7U89YHovGs4jTrqppXm\nFuJSUMpwHDVQX6iq1htFzTyeLmPL1eeZZ87ZOj6Qcjdl2YR5QUSzSObCc8RQV7GsNNtarrQzLsF8\n8zrHxCvF1fJzZRHNplXfIa0VAIytGk5Niity7GxrMPL2W1ZdpVPxOR6ZY3R6HENoj571h7PzxncA\nAP/mq78Zr+l5mG3LaKnOSM4ErQmGbqtVn4vBND7LpoTLmAdRt8w/dT7uGylmNvHn1WoQLx+vY2Yf\n4CSgxVDdToEbq1RvAAAKAElEQVRsdVGSZk+S5JRI+rEnSXJK5ETN+BACqpVlPvDfb1vv9DpHgzmm\nuVaNZnwmqDflZQOAlqRRVs38HR1H8+n43s383G/8y18GAPzQD/+x/NjlR58EABwexzbuHTqEuLIS\nHTENib/OrD47HW86nqoVDwz6FAMZXuP148myWV7gdTez2vnQJG7O2LgcW0wjLvLlLaf0MiWXZrZC\nCMqciCSjaFh/6kJ20Tvq2/XeH5aoIsssqlqay/oomdeNeTSvV2xr+Oo3X8zPfeO5aL5fPh9RaRsX\nvfwTWZFv3XW226uPRGQlzfnjnjPD5vx0YpZzh9Gc2zxJVJwztnvXC0defd9TAICDYyPJ6Diyks9h\nKGQao+Mestn9cSxJsydJckrkxGmp7qdpv1cU3MNr9rdfzyrBtQG54auCye5aCGTHVk1mZAHAzFbd\nbjteM564Q6llhfh+61/98/zY9oUY8nn6Ix8GAGydcWwzjIn0WBxi0wmRZPF/dehRW6oTKy9fbY6h\nhpQ3bpoWVAKGPC/Axqb+09x/VkINRQi90nBVG6xas/xsspLHxW6MhIe/2YkOsGDWQV8djOycWAfD\niZBDYIF9lzh7yeSbDKLm/PKvfTn2X5CSZ7uGrtuNjrnGXHDn9r5tbJ/Njx0Yr3zDQo5VedXGfVoh\nUva5Gy036l4xEBFssvdv38qPbZ6JKLx6I76fFWmrZRz0B1MPK6JZRYnPM5ek2ZMkOSXyfVOy+XvF\nuj883ubtL5zPl4EqbSnle3QY92nHlrWk4Y75NO6bpxYu6wqwojKMWrYm3OF7d18DAHz5X0YQxVxq\nq338c58HADz6xJP5sTtGhTWbLofBuOfVfRotEo5jJGSUpXXUmL1mIZtaUOx9y673/pM7IGMYTPH2\nJSWVJwwF8TGI6RBs31xvuM+Ec9szMEpf9qTcs1YFez+0MskMHZ6pOZ79aCcCYX7+n/zf+bF1SzS/\nsGHZikMPsW5un49tGZBHM+IqJWSgzvMfjzXE6mIdt27Xw2WHx3EspOEaDvzeK92NpbG99nLE4z/2\n1NMAgND0ezP0tt7193R6tIOQar0lSZIk/diTJDklcsKFHTPM5/NiuaISTLbLMsJNGrvvbcpx8OXn\nAaAaxPzKSlhNjQP8uB9NSw1dMZRIDnQtD9Qwk3o6chKEZiWaaXToHUp657ee+y0AwP5dd9I8/dEv\nxD7T9BuJWT5jmqY/xpn1LWeIEsqqmjGwHvfcqRMqNEHrNm53WN2xFNGqmN4r5vAjumsiJns+rwUv\nkW0nxsslickI2xIsfd/Ca2Njge0K33/Tmg+Zz0GbZCOTaBLfMRQcAPzOb/9bAMBWx7chZ1jE0zDy\nGxueUspnnjG8qGFiY+mYSnkpGLrx/2/vbGKrKqI4/vu/1pbSAm3RkEJRICJCNIphAdGF8SMiMbpx\noXHBwqWJaEwMxJVLE6OyMCZG48IYNSJR0oVGkY0bFAIxyIe0gfBhS1ukhVDo68dxMXPfm8ZHSyu5\n973c+SUvfXfm9s155955c+6ZM2fqGp3+x4LlygU/LToyWsns9/8XLh2edHpPHHsADc3uvNPd7jvd\ns3ZtqW6Rv3+uXi5PBU5ev1xaqlyJOLJHIjkhIwddOLLPLkf8nFuc5jMmLHR6ub/FYvkXfEGrm/q4\neHHwP581UZqycqPA1DByP/UTTJkkCi9MuhObCsH2xiNudOrt6SmVnel2iRRaFrqY8XDqp2OpS4zY\n3FZOzjDprYmidy6NBim35KNvFrTMD8r89sR+JLUgtVJra7s/J0iA6Ue/a96xNTkl+ePUz4QwVVOy\nyWKQlNFbBxOjgf6THOl+9FvYWD6/IDcaD/SeLZVduODeD/npsuu9p0p1yc4uTUFu+KK3zpJR9nox\nsNK8tVg/z6f0Cu6LOj93KKurUJZsJV2uS65DmIikYFMDXiaCadqCT4DREOxpbr5+1K9s6/NJVAAW\nt7kpypHhcvz+yHA/kzE2PhKJxM4eieSEdM14CalQ0aSeyak23flzFyfJNx+Ycj6sqRgsM53X5Eym\noaHEGRLEjydBXd5BFwSnlZxR4bx24sCb8Akhkig7gHpvbo5PBL/Bo95M846rwSsDpaqeI4cAuBqY\nou1LOgFYc/96ADruXFWqGyn6LaeCXGlJRtvEaVQpeUVIsu97Yi6G8+zJhpnh9k+lBBtjU/PZu8b9\n9k9Bwo+GRjfH/ffpbgAO/3qoVPdPnzNj6wtl83exny9v8s6ylnKeEAo+t+D4RPn8xrok3717NAvX\nPdR5J2XyWBeuCShYkrM+cKolZd58VwXH5HTO6GKwv0Gdf1RScC8m0YryjwtXLpU3wDQfX3BtuFw2\n3H+e8bEb78AWR/ZIJCdo9qmf/kdj0gBwFRic6dwq5nai/FlRy7JDOvLfZWZ3VKpItbMDSDpgZhtS\nbfQWEuXPjlqWHbKXP5rxkUhOiJ09EskJWXT2jzNo81YS5c+OWpYdMpY/9Wf2SCSSDdGMj0RyQqqd\nXdJmSSckdUvanmbbs0XSckn7JB2V9Kekbb68XdJPkk76v20zfVaWSKqTdEhSlz9eKWm/vwZfS8GS\nvypDUqukXZKOSzomaVMt6V/S6/7eOSLpS0nzstR/ap1dLtXnh8DTwDrgRUnr0mp/DowDb5jZOmAj\n8IqXdzuw18xWA3v9cTWzDTgWHL8DvG9mdwOXgJczkerm2An8YGb3Ag/gvkdN6F/SMuBVYIOZ3YfL\nnPcCWerfzFJ5AZuAH4PjHcCOtNq/BfJ/DzwJnAA6fFkHcCJr2aaRuRPXIR4DunAxnINAfaVrUk0v\nYBFwCu9XCsprQv/AMuAs0I4LS+8CnspS/2ma8cmXTzjny6oeSSuA9cB+YImZJcnh+4AlGYl1M3wA\nvEk5mH8xMGRWWrtZzddgJTAAfOYfQz6R1EyN6N/MzgPvAmeAXmAYOEiG+o8OuhmQ1AJ8C7xmZpfD\nOnM/z1U5nSHpGaDfzA5mLcscqQceAj4ys/W4MOspJnuV678NeA73o7UUaAY2ZylTmp39PLA8OO70\nZVWLpNtwHf0LM9vtiy9I6vD1HUD/jf4/Yx4GnpV0GvgKZ8rvBFql0h4y1XwNzgHnzGy/P96F6/y1\nov8ngFNmNmBmY8Bu3DXJTP9pdvbfgdXeG9mAc1bsSbH9WSG3DvFT4JiZvRdU7QG2+vdbcc/yVYeZ\n7TCzTjNbgdP1L2b2ErAPeN6fVs3y9wFnJa3xRY8DR6kR/ePM942S5vt7KZE/O/2n7LTYAvwF9ABv\nZe1EmUHWR3Am4h/AYf/agnvu3QucBH4G2rOW9Sa+y6NAl3+/CvgN6Aa+ARqzlm8auR8EDvhr8B3Q\nVkv6B94GjgNHgM+Bxiz1HyPoIpGcEB10kUhOiJ09EskJsbNHIjkhdvZIJCfEzh6J5ITY2SORnBA7\neySSE2Jnj0Rywr/3J1BaUtvMswAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TD3VfeI3nHP6",
        "colab_type": "code",
        "outputId": "c40464ef-2b19-4cc6-c9ca-cafe5ef1bb56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "#EXPLORING THE DATA\n",
        "sample_img_path = os.path.join(DIR_IMG_TRAIN,'N_1216.jpg')\n",
        "sample_img = plt.imread(sample_img_path)\n",
        "plt.imshow(sample_img)\n",
        "print ('Image have shape: {}'.format(sample_img.shape))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image have shape: (150, 150, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7RF0wkHg9Zt",
        "colab_type": "text"
      },
      "source": [
        "## **PREPROCESS DATA AND SPLIT TRAIN SET, VALID SET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ln8ISV1vpMuN",
        "colab_type": "code",
        "outputId": "c1da71ad-4e23-4f30-b5f0-32dc08b09aa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "#PREPROCESSED DATA\n",
        "# as long as images have different sizes, let's resize!\n",
        "\n",
        "from keras.preprocessing import image\n",
        "from keras.applications.imagenet_utils import preprocess_input\n",
        "\n",
        "def loadAndResizeImage (img, w, h):\n",
        "    '''\n",
        "    loads the image in 'img' path and returns a PIL image of size (w,h)\n",
        "    '''\n",
        "    return image.load_img (img, target_size=(w,h))\n",
        "\n",
        "# Mask example resized\n",
        "mask_index = 1\n",
        "sample_img_path = os.path.join(DIR_IMG_TRAIN,'M_'+str(mask_index)+'.jpg')\n",
        "sample_img = loadAndResizeImage (sample_img_path, IMG_SHAPE[0], IMG_SHAPE[1])\n",
        "plt.imshow(sample_img)\n",
        "print ('Mask #'+str(mask_index)+' as image:')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Mask #1 as image:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng0BAwJgjDAIBAWOOMAgEBIw5/h++SiWnPCXAuQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHtQyIEyskH6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#GET LABEL\n",
        "from PIL import Image\n",
        "\n",
        "def getLabel (X):\n",
        "    \"\"\"\n",
        "    Return the label for an element based on the filename: \n",
        "        mask -> 1 \n",
        "        non_mask -> 0\n",
        "    \"\"\"\n",
        "    if ('M' in X):\n",
        "        return 1\n",
        "    else:\n",
        "        return 0\n",
        "    \n",
        "def getLabels (X):\n",
        "    \"\"\"\n",
        "    Returns an array that contains the label for each X\n",
        "    \"\"\"\n",
        "    return np.array([getLabel(X[i]) for i in range(len(X))])\n",
        "\n",
        "def getLabelFromScore (score):\n",
        "    \"\"\"\n",
        "    Returns the label based on the probability\n",
        "    if score >= 0.5, return 'mask'\n",
        "    else return 'non_mask'\n",
        "    \"\"\"\n",
        "    if (score >=0.5):\n",
        "        return 'M'\n",
        "    else:\n",
        "        return 'N'\n",
        "\n",
        "def normalizedArrayFromImageInPath (image_path, img_shape):\n",
        "    \"\"\"\n",
        "    returns an the image in 'image' path normalized in an np array\n",
        "    \"\"\"\n",
        "    img = loadAndResizeImage (image_path, img_shape[0], img_shape[1])\n",
        "    return image.img_to_array(img) / 255.\n",
        "\n",
        "def loadResizeNormalizeImages (basepath, path_array, img_shape):\n",
        "    \"\"\"\n",
        "    Loads the images from the path \n",
        "    and returns them in an array\n",
        "    \"\"\"\n",
        "    images = np.empty ((len(path_array), img_shape[0], img_shape[1], img_shape[2]), dtype=np.float32)\n",
        "    for i in range (len(path_array)):\n",
        "        images[i] = normalizedArrayFromImageInPath (os.path.join(basepath,path_array[i]), img_shape)\n",
        "    return images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dsaKtWfJWF4",
        "colab_type": "code",
        "outputId": "87e8d841-a499-40f8-9f9a-dd50ccf4ce0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "#CREATE TRAINING AND VALIDATION SETS\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "train_imgs = os.listdir(DIR_IMG_TRAIN)\n",
        "train_imgs.sort()\n",
        "\n",
        "#TRAIN VALID TEST: 70 10 20 => TOTAL ~ 5000\n",
        "\n",
        "#Training data have 3500 img\n",
        "train_x = np.append(train_imgs[:1750],train_imgs[-1750:])\n",
        "train_y = getLabels (train_x)\n",
        "\n",
        "print (\"Number of training examples = {}\".format(len(train_x)))\n",
        "print (\"Number of training labels = {}\".format(len(train_y)))\n",
        "\n",
        "#Validation data have 500 img \n",
        "validation_x = np.append(train_imgs[1750:2000],train_imgs[-2000:-1750])\n",
        "validation_y = getLabels (validation_x)\n",
        "\n",
        "print (\"Number of validation examples = {}\".format (len(validation_x)))\n",
        "print (\"Number of validation labels = {}\".format(len(validation_y)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training examples = 3500\n",
            "Number of training labels = 3500\n",
            "Number of validation examples = 500\n",
            "Number of validation labels = 500\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0BWnN9XMSpIl",
        "colab_type": "code",
        "outputId": "3ef4a880-987a-4557-ea3b-fc989f55139c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "#CHECKPOINT \n",
        "img_train_x = loadResizeNormalizeImages (DIR_IMG_TRAIN, train_x, IMG_SHAPE)\n",
        "np.save (os.path.join(DIR_TMP_DATA,'train_x'), img_train_x)\n",
        "print (img_train_x.shape)\n",
        "print ('Input X for training saved!')\n",
        "\n",
        "img_validation_x = loadResizeNormalizeImages (DIR_IMG_TRAIN, validation_x, IMG_SHAPE)\n",
        "np.save (os.path.join(DIR_TMP_DATA,'validation_x'), img_validation_x)\n",
        "print (img_validation_x.shape)\n",
        "print ('Input X for validation saved!')\n",
        "\n",
        "print ('Checkpoint 1')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3500, 150, 150, 3)\n",
            "Input X for training saved!\n",
            "(500, 150, 150, 3)\n",
            "Input X for validation saved!\n",
            "Checkpoint 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-bZPQB031jmk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Save in drive\n",
        "!cp /content/dataset/train_x.npy /content/drive/My\\ Drive/Colab\n",
        "!cp /content/dataset/validation_x.npy /content/drive/My\\ Drive/Colab"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqVlBi-Iza6X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#PROPORTION\n",
        "def MaskVsNon_MaskProportion (labels):\n",
        "    N = 0\n",
        "    M = 0\n",
        "    for label in labels:\n",
        "        if (label == 1):\n",
        "            M = M + 1\n",
        "        else:\n",
        "            N = N + 1\n",
        "\n",
        "    N_percent = (N/len(labels))*100.0\n",
        "    M_percent = 100.0 - N_percent\n",
        "\n",
        "    return M_percent, N_percent"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryJkzyp40SXV",
        "colab_type": "code",
        "outputId": "c27c91da-f641-4535-ac89-996a7ddb5ab7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "import numpy as np\n",
        "img_train_x = np.load (os.path.join(DIR_TMP_DATA,'train_x')+'.npy')\n",
        "img_validation_x = np.load (os.path.join(DIR_TMP_DATA,'validation_x')+'.npy')\n",
        "\n",
        "M, N = MaskVsNon_MaskProportion (train_y)\n",
        "print (\"--> Training set: Non Mask represent \"+str(N)+\"% of the total\")\n",
        "print (\"    Mask represent \"+str(M))\n",
        "\n",
        "M, N = MaskVsNon_MaskProportion (validation_y)\n",
        "print (\"--> Validation set: Non Mask represent \"+str(N)+\"% of the total\")\n",
        "print (\"    Non Mask represent \"+str(M))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--> Training set: Non Mask represent 50.0% of the total\n",
            "    Mask represent 50.0\n",
            "--> Validation set: Non Mask represent 50.0% of the total\n",
            "    Non Mask represent 50.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLxZQhT4hb6w",
        "colab_type": "text"
      },
      "source": [
        "## **BUILDING THE MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eumRWhI2d54M",
        "colab_type": "code",
        "outputId": "1193d751-71f2-4dd7-fefb-5261ada6e5f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "#BUILDING THE MODEL\n",
        "#Hide Warning tensorflow\n",
        "#--------\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
        "import tensorflow as tf\n",
        "tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n",
        "#---------\n",
        "from keras.applications.inception_v3 import InceptionV3\n",
        "from keras.preprocessing import image\n",
        "from keras.models import Model\n",
        "from keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from keras import backend as K\n",
        "\n",
        "# create the base pre-trained model\n",
        "base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(150,150,3))\n",
        "\n",
        "# add a global spatial average pooling layer\n",
        "x = base_model.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "non_dense_predictions = Dense(1, activation='sigmoid')(x)\n",
        "# let's add a fully-connected layer\n",
        "x = Dense(1024, activation='relu')(x)\n",
        "# add dropout\n",
        "x = Dropout (0.5)(x)\n",
        "# one fully connected layer more\n",
        "x = Dense(256, activation='relu')(x)\n",
        "# add dropout\n",
        "x = Dropout (0.3)(x)\n",
        "# one fully connected layer more\n",
        "x = Dense(32, activation='relu')(x)\n",
        "# and a logistic layer --\n",
        "predictions = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "# this is the model we will train\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "# first: train only the top layers (which were randomly initialized)\n",
        "# i.e. freeze all convolutional InceptionV3 layers\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# compile the model (should be done *after* setting layers to non-trainable)\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.5/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "87916544/87910968 [==============================] - 8s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bKiTUA-1AzS",
        "colab_type": "code",
        "outputId": "9a0f98bd-4f86-438a-8e98-f14931a49ece",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 150, 150, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 74, 74, 32)   864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 74, 74, 32)   96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 74, 74, 32)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 72, 72, 32)   9216        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 72, 72, 32)   96          conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 72, 72, 32)   0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 72, 72, 64)   18432       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 72, 72, 64)   192         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 72, 72, 64)   0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 35, 35, 64)   0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 35, 35, 80)   5120        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 35, 35, 80)   240         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 35, 35, 80)   0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 33, 33, 192)  138240      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 33, 33, 192)  576         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 33, 33, 192)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 192)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 16, 16, 64)   192         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 16, 16, 64)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 16, 16, 48)   9216        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 16, 16, 96)   55296       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 16, 16, 48)   144         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 16, 16, 96)   288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 16, 16, 48)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 16, 16, 96)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 16, 16, 192)  0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 16, 16, 64)   76800       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 16, 16, 96)   82944       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 16, 16, 32)   6144        average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 16, 16, 64)   192         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 16, 16, 64)   192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 16, 16, 96)   288         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 16, 16, 32)   96          conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 16, 16, 64)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 16, 16, 64)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 16, 16, 96)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 16, 16, 32)   0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 16, 16, 256)  0           activation_6[0][0]               \n",
            "                                                                 activation_8[0][0]               \n",
            "                                                                 activation_11[0][0]              \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 16, 16, 64)   192         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 16, 16, 64)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 16, 16, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 16, 16, 96)   55296       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 16, 16, 48)   144         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 16, 16, 96)   288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 16, 16, 48)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 16, 16, 96)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 16, 16, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 16, 16, 64)   76800       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 16, 16, 96)   82944       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 16, 16, 64)   16384       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 16, 16, 64)   192         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 16, 16, 64)   192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 16, 16, 96)   288         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 16, 16, 64)   192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 16, 16, 64)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 16, 16, 96)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 16, 16, 64)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 16, 16, 288)  0           activation_13[0][0]              \n",
            "                                                                 activation_15[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "                                                                 activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 16, 16, 64)   192         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 16, 16, 64)   0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 16, 16, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 16, 16, 96)   55296       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 16, 16, 48)   144         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 16, 16, 96)   288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 16, 16, 48)   0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 16, 16, 96)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 16, 16, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 16, 16, 64)   76800       activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 16, 16, 96)   82944       activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 16, 16, 64)   18432       average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 16, 16, 64)   192         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 16, 16, 64)   192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 16, 16, 96)   288         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 16, 16, 64)   192         conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 16, 16, 64)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 16, 16, 64)   0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 16, 16, 96)   0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 16, 16, 64)   0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 16, 16, 288)  0           activation_20[0][0]              \n",
            "                                                                 activation_22[0][0]              \n",
            "                                                                 activation_25[0][0]              \n",
            "                                                                 activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 16, 16, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 16, 16, 64)   192         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 16, 16, 64)   0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 16, 16, 96)   55296       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 16, 16, 96)   288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 16, 16, 96)   0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 7, 7, 384)    995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 7, 7, 96)     82944       activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 7, 7, 384)    1152        conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 7, 7, 96)     288         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 7, 7, 384)    0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 7, 7, 96)     0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 7, 7, 288)    0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_27[0][0]              \n",
            "                                                                 activation_30[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 7, 7, 128)    384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 7, 7, 128)    0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 7, 7, 128)    114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 7, 7, 128)    384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 7, 7, 128)    114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 7, 7, 128)    384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 7, 7, 128)    384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 7, 7, 128)    0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 7, 7, 128)    0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 7, 7, 128)    114688      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 7, 7, 128)    114688      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 7, 7, 128)    384         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 7, 7, 128)    384         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 7, 7, 128)    0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 7, 7, 128)    0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 7, 7, 768)    0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 7, 7, 192)    147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 7, 7, 192)    172032      activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 7, 7, 192)    172032      activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 7, 7, 192)    576         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 7, 7, 192)    576         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 7, 7, 192)    576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 7, 7, 192)    576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 7, 7, 192)    0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 7, 7, 192)    0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 7, 7, 192)    0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 7, 7, 192)    0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 7, 7, 768)    0           activation_31[0][0]              \n",
            "                                                                 activation_34[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "                                                                 activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 7, 7, 160)    480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 160)    0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 7, 7, 160)    179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 7, 7, 160)    480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 160)    0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 7, 7, 160)    179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 7, 7, 160)    480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 7, 7, 160)    480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 160)    0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 160)    0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 7, 7, 160)    179200      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 7, 7, 160)    179200      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 7, 7, 160)    480         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 7, 7, 160)    480         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 160)    0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 160)    0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 7, 7, 768)    0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 7, 7, 192)    147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 7, 7, 192)    215040      activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 7, 7, 192)    215040      activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 7, 7, 192)    576         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 7, 7, 192)    576         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 7, 7, 192)    576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 7, 7, 192)    576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 192)    0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 192)    0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 192)    0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 7, 7, 192)    0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 7, 7, 768)    0           activation_41[0][0]              \n",
            "                                                                 activation_44[0][0]              \n",
            "                                                                 activation_49[0][0]              \n",
            "                                                                 activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 7, 7, 160)    480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 7, 7, 160)    0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 7, 7, 160)    179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 7, 7, 160)    480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 7, 7, 160)    0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 7, 7, 160)    179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 7, 7, 160)    480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 7, 7, 160)    480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 7, 7, 160)    0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 7, 7, 160)    0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 7, 7, 160)    179200      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 7, 7, 160)    179200      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 7, 7, 160)    480         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 7, 7, 160)    480         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 7, 7, 160)    0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 7, 7, 160)    0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, 7, 7, 768)    0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 7, 7, 192)    147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 7, 7, 192)    215040      activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 7, 7, 192)    215040      activation_58[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 7, 7, 192)    576         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 7, 7, 192)    576         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 7, 7, 192)    576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 7, 7, 192)    576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 7, 7, 192)    0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 7, 7, 192)    0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 7, 7, 192)    0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 7, 7, 192)    0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 7, 7, 768)    0           activation_51[0][0]              \n",
            "                                                                 activation_54[0][0]              \n",
            "                                                                 activation_59[0][0]              \n",
            "                                                                 activation_60[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 7, 7, 192)    576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 7, 7, 192)    0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 7, 7, 192)    258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 7, 7, 192)    576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 7, 7, 192)    0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 7, 7, 192)    258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 7, 7, 192)    576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 7, 7, 192)    576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 7, 7, 192)    0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 7, 7, 192)    0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 7, 7, 192)    258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 7, 7, 192)    258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 7, 7, 192)    576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 7, 7, 192)    576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 7, 7, 192)    0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 7, 7, 192)    0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, 7, 7, 768)    0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 7, 7, 192)    258048      activation_63[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 7, 7, 192)    258048      activation_68[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 7, 7, 192)    576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 7, 7, 192)    576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 7, 7, 192)    576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 7, 7, 192)    576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 7, 7, 192)    0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 7, 7, 192)    0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 7, 7, 192)    0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 7, 7, 192)    0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 7, 7, 768)    0           activation_61[0][0]              \n",
            "                                                                 activation_64[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "                                                                 activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 7, 7, 192)    576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 7, 7, 192)    0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 7, 7, 192)    258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 7, 7, 192)    576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 7, 7, 192)    0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 7, 7, 192)    258048      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 7, 7, 192)    576         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 7, 7, 192)    576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 7, 7, 192)    0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 7, 7, 192)    0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, 3, 3, 320)    552960      activation_71[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 3, 3, 192)    331776      activation_75[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 3, 3, 320)    960         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 3, 3, 192)    576         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 3, 3, 320)    0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 3, 3, 192)    0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 3, 3, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 3, 3, 1280)   0           activation_72[0][0]              \n",
            "                                                                 activation_76[0][0]              \n",
            "                                                                 max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 3, 3, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 3, 3, 448)    1344        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 3, 3, 448)    0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 3, 3, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 3, 3, 384)    1548288     activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 3, 3, 384)    1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 3, 3, 384)    1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 3, 3, 384)    0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 3, 3, 384)    0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 3, 3, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 3, 3, 384)    442368      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 3, 3, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 3, 3, 384)    442368      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, 3, 3, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 3, 3, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 3, 3, 384)    1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 3, 3, 384)    1152        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 3, 3, 384)    1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 3, 3, 384)    1152        conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 3, 3, 192)    245760      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 3, 3, 320)    960         conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 3, 3, 384)    0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 3, 3, 384)    0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 3, 3, 384)    0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 3, 3, 384)    0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 3, 3, 192)    576         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 3, 3, 320)    0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 3, 3, 768)    0           activation_79[0][0]              \n",
            "                                                                 activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 3, 3, 768)    0           activation_83[0][0]              \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 3, 3, 192)    0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 3, 3, 2048)   0           activation_77[0][0]              \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate_1[0][0]              \n",
            "                                                                 activation_85[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 3, 3, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 3, 3, 448)    1344        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 3, 3, 448)    0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 3, 3, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 3, 3, 384)    1548288     activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 3, 3, 384)    1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 3, 3, 384)    1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 3, 3, 384)    0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 3, 3, 384)    0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 3, 3, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, 3, 3, 384)    442368      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 3, 3, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 3, 3, 384)    442368      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_9 (AveragePoo (None, 3, 3, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 3, 3, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 3, 3, 384)    1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 3, 3, 384)    1152        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 3, 3, 384)    1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 3, 3, 384)    1152        conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, 3, 3, 192)    393216      average_pooling2d_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 3, 3, 320)    960         conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 3, 3, 384)    0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 3, 3, 384)    0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 3, 3, 384)    0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 3, 3, 384)    0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_94 (BatchNo (None, 3, 3, 192)    576         conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 3, 3, 320)    0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_88[0][0]              \n",
            "                                                                 activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 3, 3, 768)    0           activation_92[0][0]              \n",
            "                                                                 activation_93[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_94 (Activation)      (None, 3, 3, 192)    0           batch_normalization_94[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 3, 3, 2048)   0           activation_86[0][0]              \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_2[0][0]              \n",
            "                                                                 activation_94[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_1[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 1024)         0           dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 256)          262400      dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 256)          0           dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 32)           8224        dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 1)            33          dense_4[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 24,171,617\n",
            "Trainable params: 2,368,833\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTlOba-XjFqp",
        "colab_type": "text"
      },
      "source": [
        "## **TRAINING**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVdWiRrTh5BA",
        "colab_type": "code",
        "outputId": "b650443e-a0d7-46f1-a3f2-8b5f5a27fb69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "model.fit (x=img_train_x, y=train_y, \n",
        "           batch_size=16, epochs=10, \n",
        "           validation_data=(img_validation_x, validation_y))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 3500 samples, validate on 500 samples\n",
            "Epoch 1/10\n",
            "3500/3500 [==============================] - 25s 7ms/step - loss: 0.5294 - acc: 0.7469 - val_loss: 0.2745 - val_acc: 0.8960\n",
            "Epoch 2/10\n",
            "3500/3500 [==============================] - 16s 4ms/step - loss: 0.3638 - acc: 0.8466 - val_loss: 0.2562 - val_acc: 0.9120\n",
            "Epoch 3/10\n",
            "3500/3500 [==============================] - 16s 4ms/step - loss: 0.3222 - acc: 0.8640 - val_loss: 0.2554 - val_acc: 0.9180\n",
            "Epoch 4/10\n",
            "3500/3500 [==============================] - 16s 5ms/step - loss: 0.3218 - acc: 0.8723 - val_loss: 0.6494 - val_acc: 0.8020\n",
            "Epoch 5/10\n",
            "3500/3500 [==============================] - 16s 4ms/step - loss: 0.3028 - acc: 0.8800 - val_loss: 0.2451 - val_acc: 0.9040\n",
            "Epoch 6/10\n",
            "3500/3500 [==============================] - 16s 5ms/step - loss: 0.2837 - acc: 0.8857 - val_loss: 0.3075 - val_acc: 0.9240\n",
            "Epoch 7/10\n",
            "3500/3500 [==============================] - 16s 4ms/step - loss: 0.2928 - acc: 0.8771 - val_loss: 0.4161 - val_acc: 0.8660\n",
            "Epoch 8/10\n",
            "3500/3500 [==============================] - 16s 5ms/step - loss: 0.2855 - acc: 0.8834 - val_loss: 0.2220 - val_acc: 0.9180\n",
            "Epoch 9/10\n",
            "3500/3500 [==============================] - 16s 4ms/step - loss: 0.2819 - acc: 0.8840 - val_loss: 0.2234 - val_acc: 0.9200\n",
            "Epoch 10/10\n",
            "3500/3500 [==============================] - 16s 4ms/step - loss: 0.2645 - acc: 0.8894 - val_loss: 0.3200 - val_acc: 0.8780\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f1c48d4b160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dWXdJmkyk_Wr",
        "colab_type": "text"
      },
      "source": [
        "## **SAVING AND LOADING KERAS MODEL**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oo5sydcOjSEP",
        "colab_type": "code",
        "outputId": "3dfc8cff-b22a-4ec7-df97-76137b52ddbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#Save model\n",
        "print ('Saving the model...')\n",
        "model.save(os.path.join(DIR_OUTPUT,'model-10ep.h5'))\n",
        "print ('Model saved! Filename: {}'.format (os.path.join(DIR_OUTPUT,'model-10ep-.h5')))\n",
        "print ('Checkpoint 2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving the model...\n",
            "Model saved! Filename: /content/drive/My Drive/MAFA-Crop/models/model-10ep-.h5\n",
            "Checkpoint 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWJESzX0lLKI",
        "colab_type": "code",
        "outputId": "6ce27df1-5a9d-41f3-808a-96b80608f1f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Load model\n",
        "from keras.models import load_model\n",
        "#model = load_model(os.path.join(DIR_TMP_DATA,'model-10ep.h5'))\n",
        "model = load_model('/content/drive/My Drive/MAFA-Crop/models/model-10ep.h5')\n",
        "print ('Model loaded!')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model loaded!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVGsX7VylQn3",
        "colab_type": "text"
      },
      "source": [
        "## **EVALUATION**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WVz6-nWlPX3",
        "colab_type": "code",
        "outputId": "38beba07-273e-4e43-86cd-63e807e44515",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "# Evaluation with test images the model didn't see before\n",
        "\n",
        "#Test set original have 500 img\n",
        "\n",
        "test_imgs = os.listdir(DIR_IMG_TEST)\n",
        "test_imgs.sort\n",
        "test_x = np.append(test_imgs[:500],test_imgs[-500:])\n",
        "test_y = getLabels (test_x)\n",
        "\n",
        "print (\"Number of test set images= \"+str(len(test_x)))\n",
        "print (\"Number of test set labels = \"+str(len(test_y)))\n",
        "\n",
        "print (\"First element of test set \"+test_x[0])\n",
        "print (\"Label of first element of test set = \"+str(test_y[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of test set images= 1000\n",
            "Number of test set labels = 1000\n",
            "First element of test set M_1.jpg\n",
            "Label of first element of test set = 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJqyyIwDmUte",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(test_x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsqtVqiinTK5",
        "colab_type": "code",
        "outputId": "de7e12fa-d518-479e-d93d-630c28721044",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "img_test_x = loadResizeNormalizeImages (DIR_IMG_TEST, test_x, IMG_SHAPE)\n",
        "print (img_test_x.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 150, 150, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqPiZrvvnqFX",
        "colab_type": "code",
        "outputId": "c01859ef-0314-4eae-a3e8-e9eed5da3cfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print ('Saving test set...')\n",
        "np.save (os.path.join(DIR_TMP_DATA,'test_x'), img_test_x)\n",
        "print ('Input X for test saved!')\n",
        "print ('Checkpoint 3')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saving test set...\n",
            "Input X for test saved!\n",
            "Checkpoint 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRdw9hZx2ALp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Save in drive\n",
        "!cp -r /content/Checkpoint /content/drive/My\\ Drive/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4m2pVrVnuln",
        "colab_type": "code",
        "outputId": "505f8ba8-0255-4718-e0aa-df6a078d001f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "img_test_x = np.load (os.path.join(DIR_TMP_DATA,'test_x')+'.npy')\n",
        "print ('Input X for evaluation loaded!')\n",
        "\n",
        "# check the proportion organic vs recyclable\n",
        "M, N = MaskVsNon_MaskProportion (test_y)\n",
        "\n",
        "print (\"--> Test set: Non Mask represent \"+str(N)+\"% of the total\")\n",
        "print (\"    Mask represent \"+str(M)+\"%\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input X for evaluation loaded!\n",
            "--> Test set: Non Mask represent 50.0% of the total\n",
            "    Mask represent 50.0%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LS7zTqq1aZqE",
        "colab_type": "code",
        "outputId": "14823d82-71a3-4605-c435-a7f1adb8be59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "preds = model.evaluate (x=img_test_x, y=test_y, batch_size=10)\n",
        "print()\n",
        "print (\"Loss = \" + str(preds[0]))\n",
        "print (\"Test Accuracy = \" + str(preds[1]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1000/1000 [==============================] - 2s 2ms/step\n",
            "\n",
            "Loss = 0.5346075860979818\n",
            "Test Accuracy = 0.8589999970793724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Ct8Q31DzxEI",
        "colab_type": "text"
      },
      "source": [
        "## **PREDICT**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gI0Xw9bzwOq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predictFromPath (img_path, img_size=(150,150)):\n",
        "    img = normalizedArrayFromImageInPath (img_path, img_size)\n",
        "    return predictFromImg (img)\n",
        "\n",
        "def predictFromImg (img):\n",
        "    x = np.expand_dims(img, axis=0)\n",
        "    return model.predict(x)[0][0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtktRD0rdiYW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cd /content/MAFA-Crop/test\n",
        "!ls"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkAwZOCdz43w",
        "colab_type": "code",
        "outputId": "73b509b1-a13a-4141-acf3-5e02672c9e24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        }
      },
      "source": [
        "img_path = DIR_IMG_TEST+'N_776.jpg'\n",
        "display_img = image.load_img(img_path)\n",
        "start = time.time()\n",
        "score = predictFromPath (img_path)\n",
        "end = time.time()\n",
        "print(\"Prediction took {:.3f} seconds\".format (end - start))\n",
        "print(\"It's a {}! (with a score of {}) 0 -> Non Mask / 1 -> Mask\".format (getLabelFromScore (score), score))\n",
        "plt.imshow(display_img)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction took 0.052 seconds\n",
            "It's a N! (with a score of 0.07839389145374298) 0 -> Non Mask / 1 -> Mask\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1bee9e10b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD7CAYAAABqkiE2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMHDl+P8Alyolh51vH/AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GDOYM-31uxfl",
        "colab_type": "code",
        "outputId": "219029ae-4cc8-4995-97f8-b92068940c75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "img_path = DIR_IMG_TEST+'N_776.jpg'\n",
        "display_img = image.load_img(img_path)\n",
        "img = np.array(display_img)\n",
        "img = cv2.resize(img,(150,150))\n",
        "img = img/255.0\n",
        "score = predictFromImg(img)\n",
        "print(score)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.108090445\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f_fmmJuD0_tI",
        "colab_type": "text"
      },
      "source": [
        "## **CONFUSION MATRIX**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTmrN0NeM9tk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict_y = model.predict(x=img_test_x, batch_size=16)\n",
        "pred_y = [getLabelFromScore(pred) for pred in predict_y]\n",
        "pred_y = getLabels(pred_y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2oZagb3IUn_",
        "colab_type": "code",
        "outputId": "b7e6b57e-95c8-492a-d2c5-a87d6488d299",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        }
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn import svm, datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if not title:\n",
        "        if normalize:\n",
        "            title = 'Normalized confusion matrix'\n",
        "        else:\n",
        "            title = 'Confusion matrix, without normalization'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    # Only use the labels that appear in the data\n",
        "    classes = unique_labels(y_true, y_pred)\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    fig, ax = plt.subplots()\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    ax.figure.colorbar(im, ax=ax)\n",
        "    # We want to show all ticks...\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # ... and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='True label',\n",
        "           xlabel='Predicted label')\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=0, ha=\"right\",\n",
        "             rotation_mode=\"anchor\")\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    fig.tight_layout()\n",
        "    return ax\n",
        "\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "class_names = ['recyclable', 'organic']\n",
        "# Plot non-normalized confusion matrix\n",
        "plot_confusion_matrix(test_y, pred_y, classes=class_names,\n",
        "                      title='Confusion matrix, without normalization')\n",
        "\n",
        "# Plot normalized confusion matrix\n",
        "plot_confusion_matrix(test_y, pred_y, classes=class_names, normalize=True,\n",
        "                      title='Normalized confusion matrix')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[483  17]\n",
            " [124 376]]\n",
            "Normalized confusion matrix\n",
            "[[0.97 0.03]\n",
            " [0.25 0.75]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT4AAAEYCAYAAADFzZobAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATsAAAEYCAYAAAAj5FFfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-AmkRVwANiL",
        "colab_type": "text"
      },
      "source": [
        "## **PRECISION + RECALL CURVE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oB9uoFc2o65W",
        "colab_type": "code",
        "outputId": "b7a918c0-9c88-4abc-ee0c-4c50f522dd3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import print_function\n",
        "import numpy as np \n",
        "# confusion matrix to precision + recall\n",
        "def cm2pr_binary(cm):\n",
        "    p = cm[0,0]/np.sum(cm[:,0])\n",
        "    r = cm[0,0]/np.sum(cm[0])\n",
        "    return (p, r)\n",
        "\n",
        "cm = confusion_matrix(test_y, pred_y)\n",
        "p,r = cm2pr_binary(cm)\n",
        "print(\"precision = {0:.2f}, recall = {1:.2f}\".format(p, r))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "precision = 0.80, recall = 0.97\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM2X5B9MBIqH",
        "colab_type": "code",
        "outputId": "5b15b05c-3357-4a38-c067-8d5d01803fb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import average_precision_score\n",
        "average_precision = average_precision_score(test_y, predict_y)\n",
        "\n",
        "print('Average precision score: {0:0.2f}'.format(\n",
        "      average_precision))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Average precision score: 0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dk7S7mitATXe",
        "colab_type": "code",
        "outputId": "0e58f7f1-6123-4929-8b26-30bea639fd1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "import matplotlib.pyplot as plt\n",
        "from inspect import signature\n",
        "\n",
        "precision, recall, _ = precision_recall_curve(test_y, predict_y)\n",
        "\n",
        "# In matplotlib < 1.5, plt.fill_between does not have a 'step' argument\n",
        "step_kwargs = ({'step': 'post'}\n",
        "               if 'step' in signature(plt.fill_between).parameters\n",
        "               else {})\n",
        "plt.step(recall, precision, color='b', alpha=0.2,\n",
        "         where='post')\n",
        "plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)\n",
        "\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n",
        "          average_precision))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '2-class Precision-Recall curve: AP=0.95')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0\ndHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3de7wdZX3v8c83e+/cSEggCbckEAJB\njFw1BSxUUCwCR8Fqq1BRaCnU9tDaU2uPPW01Yq21HvXYI1Zp8aCIIvCyNmIoVi4iFSThYiDBYAgh\nF4IJIRdy3bff+eOZYU1W9p69srPXXmuF7/v1Wq+1ZuZZM888e6/5zfM8M88oIjAzM+vPiEZnwMzM\nmpsDhZmZlXKgMDOzUg4UZmZWyoHCzMxKOVCYmVkpB4oWJ+kKSQ80Oh9DTdJiSecMkOZISVsltQ1T\ntupO0gpJb80+z5X0zUbnycyBogEkjZJ0g6TnJL0s6XFJFzQ6X7XIDmQ7sgP0ryTdKGncUG8nIl4X\nEfcNkGZlRIyLiJ6h3n52kO7K9nOTpJ9KeuNQb+fVIvs/6ZZ0eNX8ISlnSb+b/Z62SfqepINL0r5D\n0pPZNn8qaXZh2RWSerJl+eucvc3P/saBojHagVXA2cAE4G+AWyXNaGCe9sY7ImIc8HpgDin/u1HS\n6v9f38n2czJwL3Bbg/Mz5CS1D8M2DgDeDWwGLusjSV7OU4AHgO9K0l6s/3XAV4H3A4cC24Ev95N2\nFnAz8EFgIvB9YF5VOTyYnYDkr/tqzcv+qtV/yC0pIrZFxNyIWBERvRFxB/As8Ib+viNpuqTvSlov\naYOkL/WT7ouSVknaIukRSb9RWHaapIXZsl9J+nw2f7Skb2br3SRpgaRDa9iPNcCdwAnZeu6T9ClJ\n/0X6sc6UNCGrPa2VtEbS3xWbiiRdJemprGa1RNLrs/nFJpj+8j1DUuQ/cklHSJon6SVJyyRdVdjO\nXEm3SvpGtq3FkuYMtI/ZfnaTDi5TJU0prPPtWW0wPxM+qbCsz7+XpGMk3ZPNe1HSzZIm1pKPapIu\nzra/RdIzks6vLrvCvn+zqsyulLQSuEfSnZKuqVr3zyW9K/t8vKT/zMp1qaT37GVW3w1sAq4FLu8v\nUUR0AV8HDgMm7cX63wd8PyLuj4itwN8C75I0vo+0bwN+EhEPZH/XzwBTSSdt1g8HiiaQHZSPAxb3\ns7wNuAN4DphB+se+pZ/VLQBOAQ4GvgXcJml0tuyLwBcj4kDgGODWbP7lpJrNdNIP9IPAjhryPR24\nEHisMPv9wNXA+Cy/NwLdwLHAqcB5wB9k3/8dYC7wAeBA4CJgQx+b6i/f1W4BVgNHAL8N/L2ktxSW\nX5SlmQjMA/oMtn3s58gsjxuAjdm8U4GvAX9IKrOvks5MRw3w9xLw6SyPryWV+dxa8lGVp9OAbwAf\nyfbnTcCKvVjF2dn23wZ8G7i0sO7ZwFHAD7LawH+S/pcOAS4BvpylyZt8Fg2wrcuzbdwCHC+pzxMi\nSaOAK4BVEfGipLOyINzf66zsq68Dfp6vJyKeATpJv6k+N1X1WWQnO5lTsyD+tKS/1TDUuppeRPjV\nwBfQAfwI+GpJmjcC64H2PpZdATxQ8t2NwMnZ5/uBTwCTq9L8PvBT4KQa8rsC2Eo6Q3yOVMUfky27\nD7i2kPZQYFe+PJt3KXBv9vku4EMl23nrAPmeAQSpKW860AOMLyz/NHBj9nku8KPCstnAjpL9nEs6\n2GzK1rsBOKew/J+BT1Z9ZynpANzv36uP7bwTeKyf/Z4LfLOf730V+MJAZVe9nkKZzSwsHw9sA47K\npj8FfC37/F7SGXj1tj9e4//3kUAvcErhb/7Ffsp5HXAP8Ia9/A3dDXywat6a4t+rMP/4bF/PAUaS\nah+9wF9ly2cCR5NOok8EluTLXs0v1ygaSKkN/ybSD+Wawvw7VelIex/pIPhcpKryQOv8i6wpZ7Ok\nTaSawuRs8ZWks6xfZM1Lb8/m30T6Ad8i6XlJ/yipo2Qz74yIiRFxVET8cUQUax+rCp+PIgXCtflZ\nIOkgc0i2fDrwzED7VJLvoiOAlyLi5cK850hn87kXCp+3A6MltUt6X6G87yykuTUiJpIC3pPs3jR4\nFPDh4hlutj9HUPL3knSopFuyZrgtwDep/H32Rq1l159X/k5Zmf2AVFuAFMxvzj4fBZxetZ/vIzUP\n1eL9wFMR8Xg2fTPwu1X/X7dm/0+HRMRbIuKRvdyXraQaadGBwMvVCSPiF6QazpeAtaSyX0KqiRIR\nyyPi2UhNwk+Qmst+ey/zs99xlapBJAm4gXQQujBS+ywAEXFBVdo3AkdKai8LFkr9EX8JnAssjohe\nSRvJqtoR8Uvg0ixAvQu4XdKkiNhGOmP/hFKH+nzS2fENg9i14nDEq0g1isn95HsVqSmpfIX95Lsq\n2fPAwZLGF4LFkaQzy4HWfzOVA2Nfy1+UdDWwUNK3ImJtlvdPRcSnqtMP8Pf6e1IZnRgRL0l6JzU2\ngVUpK7ttwNjCdF8H9epho78NfFzS/cBoUud9vp0fR8RvDiKPkJrsjpSUB+l2UlPdhcC/l30x+3++\nsyTJBRHxE1KT7cmF780ERgFP9/WliLgduD1LO5F0IrKgn20EuzdVvSq5RtE4/0xqI35H1Rl5Xx4m\nnf38g6QDlDqfz+wj3XhSf8B6oF3SxyicaUm6TNKUiOglVfUBeiW9WdKJWdv6FqCLVB3fJ9kB9YfA\n5yQdKGmEUmdu3nH4r8BfSHqDkmMlHVW9nv7yXbWtVaTms09n5XMS6QAwJPchRMRSUq3rL7NZ/wJ8\nUNLpWd4PkPTfsg7Usr/XeNIZ8GZJU0l9DINxA/B7ks7NynWqpOOzZY8Dl0jqUOqwr+WMeD6p9nAt\n6SqkvHzvAI6T9P5sfR2Sfk3SawdaYRYwjwFOI/WbnULqC/gWKYCUioifxO5XH1W/fpIlvRl4h6Tf\nyPpUrgW+W1W7LObrDZLalC5MuB6Yl9U0kHSBsgs5svL8WwYIaK8GDhQNkB0M/5D0w3mhqplpD5Hu\nE3gHqUN4Jama/N4+kt4F/AfpTOo5YCe7NwWdDyyWtJXUQXxJFqQOI51hbQGeAn5Mao4aCh8gtQUv\nIfWX3A4cnu3XbaT28G+Rmgm+R+qEr9ZfvqtdSmqDfx74N1I7+o+GaD8APgtcLemQiFgIXEWqDWwE\nlpH6iwb6e32CdFnxZlJzz3cHk5GIeBj4PeAL2bp+TDrQQzq4HZPl6xOk8h1ofbuyvLy1mD472J5H\napZ6ntR89xnSGTtZs12fF2GQmnj+PSKeiIgX8hfpb/h2ldzrsDciYjHpAoybSf0c44E/zpdnTbn/\nq/CVL5JOOJaSyuiqwrJzgUWStpGC53dJtcBXNUX4wUVmZtY/1yjMzKyUA4WZmZVyoDAzs1IOFGZm\nVqrl7qOYPHlyzJgxo9HZMDNrKY888siLETFl4JR7arlAMWPGDBYuXNjobJiZtRRJzw32u256MjOz\nUg4UZmZWyoHCzMxKOVCYmVkpBwozMyvlQGFmZqXqFigkfU3SOklP9rNckv5J6dnGi5Q9K9nMzJpL\nPWsUN5KGh+7PBcCs7HU16fkMZmbWZOoWKCLifuClkiQXA9+I5CFgoqTDB1rvtm1DlUMzM6tFI/so\nprL7Q3VWs/vzjV8h6WpJCyUtfPHFjezaNSz5a3oR0NubXpbKw2ViNvRaYgiPiLie9MhCjjlmTks9\na6mzs3LwioDubujpqUzv2AEjRqTPO3dCe3tatnMntLVVPhe/39VVmc5fxx8PRxwxdPnO89zTs+cB\nuLjdiLSPeV7zdF1daV4x3a5d0NGx5/d37Ur7Xdxub28qm46O3beb56mnJ6Uv5if/fv4+axYcemhl\nef6dYpq8TKXd5+VpOjtT3qrn5/udLyt+t2x5X+mmTIEJE/ZcJlXKxayRGvlvuAaYXpiels1revnB\nu7MTtm9PP+wtW9KBsacnNY/19KQDUGdn5ey2s7Pynh8g83epMt3WltJ1daUDJaR1tbendL29KY2U\n3p9/PuVj/Pi0LF9fHpSkPQNPe3slr1IlH3lgyA/APT0pkHV37z6/eJDN0xXf84NvfnDO89HVldan\nqsfVd3XByJGVg2Suu7tSBpCWjxiR1pcfhPN9ycstAl54AZ57DqZOrfzN8vzkZZjnq/heDEh9TfeV\nLg/0ed7ztPnfIA+OeTkX7dgBY8fCzJm7bzMvn2OPBY+BaY3WyEAxD7hG0i3A6cDmiFjbqMzs2JEO\noNW2bUuvrq50kNixIx1Q84NqZyds3pzSbt2aDnb5D37UqLR8/Pjdz7jb29P0uHHpc3t75WDY1pYO\nPMVXmYiUv1/9Cu6/v3I2391dCRT5wbyzs3LQKh5oe3pSXru70zpHjKikg7RPxQDV21sJWnl+OzrS\n5zyAjRhRWZavs/pd6vs1FMaOhQ0bYP36Si0tD+T5fud5yA/0+T7neczT5fuUv/KAlOe1et/y+dX7\nVD0f0t9t2TJYvXr35V1dsHYtrFwJp59e2YeuLpg4MdVCijWl6tpKPj1x4sD/Q2YDqVugkPRt4Bxg\nsqTVwMeBDoCI+ArpweUXkh5Kv530oPhh09tbOTACPP10+lFWt21HpECwfXvlgJmfJY4dm37Ahx4K\nBxyQfpCjRg3nXqT8TJ2aahVbtqQ8jhiRgpBUmW5v3zMA7c8HkJkz06vZHXZYevXloINgyRJ48MHK\nvG3b0v/a9Ol71o7y/+f8vbcXJk2Cww9P84q1lmITYbFGVGwKzKe7utJ38hpR8XX44el3YPs3tVSD\nP6mPYvHihYwevW/refJJWLeucubV3Q1r1sAhh+yZdtSo9GPY122aDUbebAXppGXx4lSjgN1rMXnw\nzz8vWlTp+xgxonJCkzefFQNLsW+o2IxYTFedl127Ur/Yb/1W/fbdho6kRyJizmC+23JdZRHwyCP7\nfua+bRs880zlhySls6O+AoVZIxWb4yZMgF//9dq+l/fP9PSkmsnOnZVaSN581t6eap1581qxSTEP\nOMX3/DViBCxcmGqyixbB5MkpoOT9K8ULD44+2p3yra7l/nxSaiYaimaTKVNSFd5sf9bWBieeOPTr\nPe44ePhhWLCgckFBsek2b7I67DA46aTKhRb5xQi9valJN+87yvvT2tpScOnpSYFxzJhKs681RssF\nivZ2mD3bZyhmjXbwwfCmN6ULPPIDeXUN5OGHYfny1Myb12LyCwqg0sSVX8yxZUv63tKlKZCMHJlq\nRiNHphp/XlN5+eV0kjdqVOrLcRCpLx9uzWzQxo4t78w+66xUsyheITZQa8DWrSn9hg2paevxx1Og\neOqpFFB27kw1jyVLUm3joIPgda9LtZX83p28hnLYYSmwdHam7eZXHo4bl/LtAFMbBwozq6vifTC1\nGDcuvR9wABx5ZPqc35eT11Q2bkwd+089lS4/X7kyLevqSrWVPDBMmpS2n/ef5O+9valGMmVK6uTv\n6EjfO+CAod33/YUDhZk1veqm5oMOSq8ZM1IzFOzeCR8Bzz5b6VBva0tXLba1peatZ55J97AccECq\nWeQXtUyYkNKdeqqvcixyoDCzljZ+fN/zZ8/ue/6UKXDMMal28fzzKdBs2wYrVqTg0N6ebnY89NA0\nnY9sEJHulD/44L2vJbU6Bwoze1Vqb680bUEaL627Gx59NHWmr1mTAkU++kF+xeW0afCa16T3/fmm\n1SIHCjOzTHs7nHZa38s2bEgd6y+8kJq1jj02dbIff3xa3ttb6STf3zhQmJnVYNIkOPdceOkleOKJ\ndKNhR0caTywf0bmjI10ynN85v79woDAz2wsHHwxnn52urHr66TRvzJjUVPXyy6mz/Oij0ygPRxwB\nBx7Y+k1UDhRmZoMwciSccEJl+ogj0o2FjzySgsXo0akpauLE9GyUww/vv+O92TlQmJkNkUMOgQsu\nSHerr1mThpAfOTJdRTVlCrz5zamG0WocKMzMhtiYMamz+9hjU9B4/PF0J/lxx7VmoGjxljMzs+Y2\nZkzlyqiHHkrNUq3GgcLMrM4OOijVLtatg/nz070a+ei6rcBNT2Zmw2DmzHRp7dq1lfe3vKVy93cz\nc43CzGyYzJmT7rPYujUNZHjXXXDPPbs/RbAZOVCYmQ2zs89OD21asiR1dD//fKNzVM5NT2Zmw6y9\nHc45J92g98QTaSyp4vPIm41rFGZmDbJrV7rD+9FH0416O3c2Okd9c43CzKxBJk1KrxUrUq1i06Y0\nnlSz1SxcozAzaxAJTjwRzjgjBYqnnmrOy2YdKMzMGmzixDTER08PLF/e6NzsyYHCzKwJTJ+eahUP\nPFB5vGuzcKAwM2sC48alR7SuX58GFGwmDhRmZk1i8mRoa4OHH06XyzYLBwozsyZx0EFpdNkNG9Jl\ns83CgcLMrImMG5fef/nLxuajyIHCzKyJzJyZOrWb6eonBwozsyYyalR6bOr69c3T/ORAYWbWZCLS\nXdrz58Ozz6bnWDRSXQOFpPMlLZW0TNJH+1h+pKR7JT0maZGkC+uZHzOzVnDyyemu7RUr4Ic/hDvu\naOxQ5HULFJLagOuAC4DZwKWSZlcl+xvg1og4FbgE+HK98mNm1ipGjEhDkZ9yShppdtOmxl4yW88a\nxWnAsohYHhGdwC3AxVVpAsgfNT4BaPJR2c3Mhs/48alze8eO9NyKtWsbk496BoqpwKrC9OpsXtFc\n4DJJq4H5wJ/0tSJJV0taKGnh5s3r65FXM7OmdOCBcOaZsHlz44Yhb3Rn9qXAjRExDbgQuEnSHnmK\niOsjYk5EzJkwYcqwZ9LMrJGkyv0VjVDPQLEGmF6YnpbNK7oSuBUgIh4ERgOT65gnMzPbS/UMFAuA\nWZKOljSS1Fk9ryrNSuBcAEmvJQUKty2ZmfVh+3bo7R3+7dYtUEREN3ANcBfwFOnqpsWSrpV0UZbs\nw8BVkn4OfBu4IqKZhsIyM2u8rq50893jj8MvfjH826/ro1AjYj6pk7o472OFz0uAM+uZBzOzVjdx\nIhx8MKxcCWPHwuzqGw3qzM/MNjNrchKcdFJqdtqyJT0Jr61t+Lbf6KuezMysRhHw0kupZjGcHCjM\nzFrErFnpTu1t24Z3uw4UZmYtoqcndWo/+ujwXv3kQGFm1iImTEh3am/cOLxDkDtQmJm1kCOOgJEj\nh3ebDhRmZlbKgcLMzEo5UJiZWSkHCjOzFtPTM7zbc6AwM2shmzalK56WLRu+bTpQmJm1kBkz0iiy\nwzk4oAOFmVkLGTMGpk2Djo7h26YDhZmZlXKgMDOzUg4UZmYtpqcHdu4cvu05UJiZtaBNm+CZZ4Zn\nWw4UZmYtZvJkWLcO7rsPurvrvz0/4c7MrMVMm5YCxKpV6b29zkdy1yjMzFrQ6NHDN4qsA4WZWYvq\n6YFdu+q/HQcKM7MW1NGRrnx65JH6b8uBwsysBU2aBKNGwbPP1n+QQAcKM7MWNWkSbNkCK1fWdzsO\nFGZmLerII6G3FxYtgg0bIKI+23GgMDNrUaNHp/eVK+GOO2Dt2vpsx4HCzKxFjRgBZ50F48bB6tWw\ndWt9tuMb7szMWlh7Oxx7LGzbll714BqFmVmLa2uDHTtSX0U9roByoDAza3GjRsH06fDSS+kxqUPN\ngcLMbD8wfnz9hvSouY9C0lTgqOJ3IuL+emTKzMyaR02BQtJngPcCS4C8BSyA0kAh6Xzgi0Ab8K8R\n8Q99pHkPMDdb388j4ndrzbyZmVV0d8OSJXDqqemKqKFSa43incBrIqLm4acktQHXAb8JrAYWSJoX\nEUsKaWYBfwWcGREbJR1Se9bNzCw3cSJs3w4PPwxTpqSb8YZKrTFnOdCxl+s+DVgWEcsjohO4Bbi4\nKs1VwHURsREgItbt5TbMzIx0891ZZ6UO7aG+TLbWGsV24HFJdwOv1Coi4k9LvjMVWFWYXg2cXpXm\nOABJ/0VqnpobEf9RY57MzKxASjffLV0Kr33t0K231kAxL3sNtXZgFnAOMA24X9KJEbGpmEjS1cDV\nAIceOoT1KTOz/cjo0Wm8p9Wr030VY8YMzXprChQR8XVJI8lqAMDSiOga4GtrgOmF6WnZvKLVwM+y\ndT0r6WlS4FhQtf3rgesBXvOaOXUa9srMrLVJMGsWrFgxtAME1tRHIekc4JekzukvA09LetMAX1sA\nzJJ0dBZkLmHPWsn3SLUJJE0mBaLltWbezMzqr9amp88B50XEUgBJxwHfBt7Q3xciolvSNcBdpP6H\nr0XEYknXAgsjYl627DxJ+WW3H4mIDYPfHTMz6+6GdetgxoyhWV+tgaIjDxIAEfG0pAGvgoqI+cD8\nqnkfK3wO4M+zl5mZ7aP8Mtm774bLL0+DBu6rWi+PXSjpXyWdk73+BVi475s3M7OhNHo0TJuWOrN/\n8IOh6auoNVD8Eemu7D/NXkuyeWZm1mSOPx5efhmWLx+ahxnVFCgiYldEfD4i3pW9vrA3d2mbmdnw\nGTECzjwTdu6En/5039dX2nol6daIeI+kJ0hjMe0mIk7a9yyYmdlQO+AAmDoVNm7c96HHB+rm+FD2\n/vZ924yZmQ23sWNh/frUBLUvSpueIiJv3XoRWBURzwGjgJOB5/dt02ZmVk+HHZYearSvT72rtTP7\nfmB09kyKHwLvB27ct02bmVkrqDVQKCK2A+8CvhwRvwO8rn7ZMjOzZlFzoJD0RuB9wA+yeW31yZKZ\nmTWTWgPFn5EeMPRv2TAcM4F765ctMzNrFrWOHvtj4MeF6eWkG+/MzKzJvfzyvn1/oPso/k9E/Jmk\n79P3fRQX7dvmzcysXtrb0013ixdDGoR8kOsZYPlN2fv/HuwGzMysMTo60nAeS5YAjBk12PWUBoqI\neCT7uBDYERG9AJLaSPdTmJlZEzv88PxTx4Ajfven1s7su4GxhekxwI8Gu1EzMxs+kyfDvowjW2ug\nGB0RW/OJ7PPYkvRmZrafqDVQbJP0+nxC0huAHfXJkpmZNZNan330Z8Btkp4HBBwGvLduuTIzs6ZR\n630UCyQdD7wmm7U0Irrqly0zM2sWNTU9SRoL/E/gQxHxJDBDkoceNzN7Fai1j+L/AZ3AG7PpNcDf\n1SVHZmbWVGoNFMdExD8CXQDZSLKDvsvPzMxaR62BolPSGLJhPCQdA/iZ2WZmrwK1XvX0ceA/gOmS\nbgbOBK6oV6bMzKx5DBgoJAn4BemhRWeQmpw+FBEv1jlvZmbWBAYMFBERkuZHxIlUHlpkZmavErX2\nUTwq6dfqmhMzM2tKtfZRnA5cJmkFsI3U/BQRcVK9MmZmZs2h1kDxtrrmwszMmtZAT7gbDXwQOBZ4\nArghIrqHI2NmZtYcBuqj+DowhxQkLgA+V/ccmZlZUxmo6Wl2drUTkm4AHq5/lszMrJkMVKN4ZYRY\nNzmZmb06DRQoTpa0JXu9DJyUf5a0ZaCVSzpf0lJJyyR9tCTduyWFpDl7uwNmZlZfpU1PEdE22BVL\nagOuA34TWA0skDQvIpZUpRsPfAj42WC3ZWZm9VPrDXeDcRqwLCKWR0QncAtwcR/pPgl8BthZx7yY\nmdkg1TNQTAVWFaZXZ/NekT2He3pElA4NIulqSQslLdy8ef3Q59TMzPpVz0BRStII4PPAhwdKGxHX\nR8SciJgzYcKU+mfOzMxeUc9AsQaYXpiels3LjQdOAO7LhgY5A5jnDm0zs+ZSz0CxAJgl6WhJI4FL\ngHn5wojYHBGTI2JGRMwAHgIuioiFdcyTmZntpboFiuy+i2uAu4CngFsjYrGkayVdVK/tmpnZ0Kp1\nUMBBiYj5wPyqeR/rJ+059cyLmZkNTsM6s83MrDU4UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmV\ncqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbK\ngcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkH\nCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlaqroFC0vmSlkpaJumjfSz/c0lLJC2SdLek\no+qZHzMz23t1CxSS2oDrgAuA2cClkmZXJXsMmBMRJwG3A/9Yr/yYmdng1LNGcRqwLCKWR0QncAtw\ncTFBRNwbEduzyYeAaXXMj5mZDUI9A8VUYFVhenU2rz9XAnf2tUDS1ZIWSlq4efP6IcyimZkNpCk6\nsyVdBswBPtvX8oi4PiLmRMScCROmDG/mzMxe5drruO41wPTC9LRs3m4kvRX4a+DsiNhVx/yYmdkg\n1LNGsQCYJeloSSOBS4B5xQSSTgW+ClwUEevqmBczMxukugWKiOgGrgHuAp4Cbo2IxZKulXRRluyz\nwDjgNkmPS5rXz+rMzKxB6tn0RETMB+ZXzftY4fNb67l9MzPbd03RmW1mZs3LgcLMzEo5UJiZWSkH\nCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwo\nzMyslAOFmZmVcqAwM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAw\nM7NSDhRmZlbKgcLMzEo5UJiZWSkHCjMzK+VAYWZmpRwozMyslAOFmZmVcqAwM7NSDhRmZlaqroFC\n0vmSlkpaJumjfSwfJek72fKfSZpRz/yYmdneq1ugkNQGXAdcAMwGLpU0uyrZlcDGiDgW+ALwmXrl\nx8zMBqe9jus+DVgWEcsBJN0CXAwsKaS5GJibfb4d+JIkRUSUrXjXLujuHvoMm5ntjzo7ATTo79cz\nUEwFVhWmVwOn95cmIrolbQYmAS8WE0m6Grg6m+o8++zxz9Qny62m6yDo2NjoXDQHl0WFy6LCZZFI\nsPXIwX67noFiyETE9cD1AJIWRrw8p8FZagqpLHa6LHBZFLksKlwWFZIWDva79ezMXgNML0xPy+b1\nmUZSOzAB2FDHPJmZ2V6qZ6BYAMySdLSkkcAlwLyqNPOAy7PPvw3cM1D/hJmZDa+6NT1lfQ7XAHcB\nbcDXImKxpGuBhRExD7gBuEnSMuAlUjAZyPX1ynMLcllUuCwqXBYVLouKQZeFfAJvZmZlfGe2mZmV\ncqAwM7NSTRsoPPxHRQ1l8YhfbJ0AAASHSURBVOeSlkhaJOluSUc1Ip/DYaCyKKR7t6SQtN9eGllL\nWUh6T/a/sVjSt4Y7j8Olht/IkZLulfRY9ju5sBH5rDdJX5O0TtKT/SyXpH/KymmRpNfXtOKIaLoX\nqfP7GWAmMBL4OTC7Ks0fA1/JPl8CfKfR+W5gWbwZGJt9/qNXc1lk6cYD9wMPAXMane8G/l/MAh4D\nDsqmD2l0vhtYFtcDf5R9ng2saHS+61QWbwJeDzzZz/ILgTtJt2mfAfyslvU2a43ileE/IqITyIf/\nKLoY+Hr2+XbgXEmDv0e9eQ1YFhFxb0RszyYfIt2zsj+q5f8C4JOkccN2DmfmhlktZXEVcF1EbASI\niHXDnMfhUktZBHBg9nkC8Pww5m/YRMT9pCtI+3Mx8I1IHgImSjp8oPU2a6Doa/iPqf2liYhuIB/+\nY39TS1kUXUk6Y9gfDVgWWVV6ekT8YDgz1gC1/F8cBxwn6b8kPSTp/GHL3fCqpSzmApdJWg3MB/5k\neLLWdPb2eAK0yBAeVhtJlwFzgLMbnZdGkDQC+DxwRYOz0izaSc1P55BqmfdLOjEiNjU0V41xKXBj\nRHxO0htJ92+dEBG9jc5YK2jWGoWH/6iopSyQ9Fbgr4GLImLXMOVtuA1UFuOBE4D7JK0gtcHO2087\ntGv5v1gNzIuIroh4FniaFDj2N7WUxZXArQAR8SAwGpg8LLlrLjUdT6o1a6Dw8B8VA5aFpFOBr5KC\nxP7aDg0DlEVEbI6IyRExIyJmkPprLoqIQQ+G1sRq+Y18j1SbQNJkUlPU8uHM5DCppSxWAucCSHot\nKVCsH9ZcNod5wAeyq5/OADZHxNqBvtSUTU9Rv+E/Wk6NZfFZYBxwW9afvzIiLmpYpuukxrJ4Vaix\nLO4CzpO0BOgBPhIR+12tu8ay+DDwL5L+B6lj+4r98cRS0rdJJweTs/6YjwMdABHxFVL/zIXAMmA7\n8Hs1rXc/LCszMxtCzdr0ZGZmTcKBwszMSjlQmJlZKQcKMzMr5UBhZmalHCjMqkjqkfS4pCclfV/S\nxCFe/xWSvpR9nivpL4Zy/WZDzYHCbE87IuKUiDiBdI/Of290hswayYHCrNyDFAZNk/QRSQuysfw/\nUZj/gWzezyXdlM17R/aslMck/UjSoQ3Iv9k+a8o7s82agaQ20rAPN2TT55HGSjqNNJ7/PElvIo0x\n9jfAr0fEi5IOzlbxAHBGRISkPwD+knSHsFlLcaAw29MYSY+TahJPAf+ZzT8vez2WTY8jBY6Tgdsi\n4kWAiMifBzAN+E423v9I4Nnhyb7Z0HLTk9medkTEKcBRpJpD3kch4NNZ/8UpEXFsRNxQsp7/C3wp\nIk4E/pA0EJ1Zy3GgMOtH9tTAPwU+nA1lfxfw+5LGAUiaKukQ4B7gdyRNyubnTU8TqAzhfDlmLcpN\nT2YlIuIxSYuASyPipmyI6gezUXq3ApdlI5V+CvixpB5S09QVpKeq3SZpIymYHN2IfTDbVx491szM\nSrnpyczMSjlQmJlZKQcKMzMr5UBhZmalHCjMzKyUA4WZmZVyoDAzs1L/H8nqCYtXLlmZAAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SswA1pZqCmbc",
        "colab_type": "text"
      },
      "source": [
        "## **F1-SCORE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ufnk38Yn864Z",
        "colab_type": "code",
        "outputId": "cb9f2251-9fd3-4cee-992e-9e773fcf5958",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "f1_score = metrics.f1_score(test_y , pred_y)\n",
        "print('F1_score: ',f1_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1_score:  0.8421052631578948\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QJfS7hBMSa9",
        "colab_type": "text"
      },
      "source": [
        "## **ROC + AUC**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGwv3YmqMVSC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from itertools import cycle\n",
        "\n",
        "from sklearn import svm, datasets\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.multiclass import OneVsRestClassifier\n",
        "from scipy import interp\n",
        "\n",
        "def plot_roc_curve(fpr, tpr):\n",
        "    plt.plot(fpr, tpr, color='orange', label='ROC')\n",
        "    plt.plot([0, 1], [0, 1], color='darkblue', linestyle='--')\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel('True Positive Rate')\n",
        "    plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iIrWbcCfM8Pl",
        "colab_type": "code",
        "outputId": "06514774-5b08-4fec-d841-dec152e921f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "auc = roc_auc_score(test_y, predict_y)\n",
        "print('AUC: %.2f' % auc)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AUC: 0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StqJlQSnM0H8",
        "colab_type": "code",
        "outputId": "5aea3448-a655-4930-88cf-a3ccbb19c4d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "fpr, tpr, thresholds = roc_curve(test_y, predict_y)\n",
        "plot_roc_curve(fpr, tpr)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}