Canine_Classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "Canine_Classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vrI77P0XkFAG",
        "colab_type": "text"
      },
      "source": [
        "## Tweakables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "490FZ8qokIFN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "onGoogle = False\n",
        "\n",
        "# Data set parameters\n",
        "DATA_SET_URL = \"http://vision.stanford.edu/aditya86/ImageNetDogs/images.tar\"\n",
        "\n",
        "if onGoogle:\n",
        "    DATA_DIRS = [ \"/content/Images\", \"/content/train\", \"/content/validation\" ]\n",
        "else:\n",
        "    DATA_DIRS = [ \"/content/Images\", \"/content/train\", \"/content/validation\" ]\n",
        "\n",
        "IMG_HEIGHT = 256\n",
        "IMG_WIDTH = 256\n",
        "\n",
        "# Validation set parameters\n",
        "PERCENT_FOR_VALIDATION = 20\n",
        "\n",
        "# Training parameters\n",
        "BATCH_SIZE = 64\n",
        "EPOCHS = 50\n",
        "MODEL_SAVE_FILE = \"drive/My Drive/model (1).hdf5\"\n",
        "TMP_IMAGE_FILE = \"/content/temporary_image_file\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zF9uvbXNVrVY"
      },
      "source": [
        "## Import packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VddxeYBEVrVZ"
      },
      "source": [
        "Basic packages...\n",
        "* os for file access\n",
        "* matplotlib to display images and graphs of results\n",
        "* random for making random validation sets\n",
        "* requests for pulling data and images from the web\n",
        "* shutil to move data files\n",
        "* tarfile to extract the data tarball"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eXuqExYYOi0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import random\n",
        "import requests\n",
        "import shutil\n",
        "import tarfile\n",
        "import cv2\n",
        "import matplotlib.cm as cm\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Jlchl4x2VrVg"
      },
      "source": [
        "Import Tensorflow."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "E82grprdYPI0",
        "outputId": "702e2edd-8f0b-4a57-e31c-2efc6428f45a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  !pip install tensorflow\n",
        "  !pip install tf-nightly-gpu-2.0-preview\n",
        "  !pip install tf.image\n",
        "  !pip install opencv-python\n",
        "  !pip install keras\n",
        "  \n",
        "except Exception:\n",
        "  pass\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: Could not find a version that satisfies the requirement tf.image (from versions: none)\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OK8TB6WeHAF1",
        "colab_type": "code",
        "outputId": "61adebf9-9c91-4ccd-a9b4-d389d8f19667",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.version.VERSION)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0-dev20191002\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-OCFmohGuDJ",
        "colab_type": "code",
        "outputId": "777b9410-1dd1-4e30-9ac0-2ab6c53767ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        }
      },
      "source": [
        "pip install hyperas"
      ],
      "execution_count": 0,
      "outputs": [
        {
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            "Requirement already satisfied: widgetsnbextension~=3.5.0 in /usr/local/lib/python3.6/dist-packages (from ipywidgets->jupyter->hyperas) (3.5.1)\n",
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            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->jupyter-console->jupyter->hyperas) (0.7.5)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->jupyter-console->jupyter->hyperas) (42.0.1)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->jupyter-console->jupyter->hyperas) (0.8.1)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->jupyter-console->jupyter->hyperas) (4.7.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->jupyter-console->jupyter->hyperas) (2.6.1)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.6/dist-packages (from jupyter-client->jupyter-console->jupyter->hyperas) (17.0.0)\n",
            "Requirement already satisfied: ptyprocess; os_name != \"nt\" in /usr/local/lib/python3.6/dist-packages (from terminado>=0.3.3; sys_platform != \"win32\"->notebook->jupyter->hyperas) (0.6.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YniFOA_bHCVk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czMU-oCcYFsw",
        "colab_type": "text"
      },
      "source": [
        "Keras is TensorFlow's API to build and train deep learning models."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "L1WtoaOHVrVh",
        "outputId": "19951cf8-64d9-4d92-cdc0-6e974d6b362c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from keras import backend as K\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, ZeroPadding2D, BatchNormalization\n",
        "from keras import metrics\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
        "from keras.layers import LeakyReLU\n",
        "from tensorflow.keras.models import load_model\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "print(tf.keras.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.4-tf\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TPKW7NX7zER7",
        "colab_type": "text"
      },
      "source": [
        "# Data Source"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6AH2uj60tkE",
        "colab_type": "code",
        "outputId": "a7709f58-401e-48f2-9866-67f212627c01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "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": "markdown",
      "metadata": {
        "id": "oxZkxP9OkUja",
        "colab_type": "text"
      },
      "source": [
        "Download the data from Stanford and un-tar it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iaIcLOi2IXvY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if onGoogle:\n",
        "    # FOR USING ONLINE\n",
        "    data_file = requests.get(DATA_SET_URL).content\n",
        "    file_name = DATA_SET_URL.split(\"/\")[-1]\n",
        "    with open(file_name, 'wb') as handler:\n",
        "        handler.write(data_file)\n",
        "        \n",
        "    tarf = tarfile.open (file_name)\n",
        "    tarf.extractall()\n",
        "\n",
        "else:\n",
        "    data_file = \"content/Images\"\n",
        "    file_name = \"/Images\"   \n",
        "    tarf = tarfile.open(\"drive/My Drive/Images.tar.xz\")\n",
        "    tarf.extractall()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g_Fhkb6KYMo",
        "colab_type": "code",
        "outputId": "1d8e2848-c396-4ef6-ebb3-158b0c30a744",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!ls Images/"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "n02085620-Chihuahua\t\t\t  n02100583-vizsla\n",
            "n02085782-Japanese_spaniel\t\t  n02100735-English_setter\n",
            "n02085936-Maltese_dog\t\t\t  n02100877-Irish_setter\n",
            "n02086079-Pekinese\t\t\t  n02101006-Gordon_setter\n",
            "n02086240-Shih-Tzu\t\t\t  n02101388-Brittany_spaniel\n",
            "n02086646-Blenheim_spaniel\t\t  n02101556-clumber\n",
            "n02086910-papillon\t\t\t  n02102040-English_springer\n",
            "n02087046-toy_terrier\t\t\t  n02102177-Welsh_springer_spaniel\n",
            "n02087394-Rhodesian_ridgeback\t\t  n02102318-cocker_spaniel\n",
            "n02088094-Afghan_hound\t\t\t  n02102480-Sussex_spaniel\n",
            "n02088238-basset\t\t\t  n02102973-Irish_water_spaniel\n",
            "n02088364-beagle\t\t\t  n02104029-kuvasz\n",
            "n02088466-bloodhound\t\t\t  n02104365-schipperke\n",
            "n02088632-bluetick\t\t\t  n02105056-groenendael\n",
            "n02089078-black-and-tan_coonhound\t  n02105162-malinois\n",
            "n02089867-Walker_hound\t\t\t  n02105251-briard\n",
            "n02089973-English_foxhound\t\t  n02105412-kelpie\n",
            "n02090379-redbone\t\t\t  n02105505-komondor\n",
            "n02090622-borzoi\t\t\t  n02105641-Old_English_sheepdog\n",
            "n02090721-Irish_wolfhound\t\t  n02105855-Shetland_sheepdog\n",
            "n02091032-Italian_greyhound\t\t  n02106030-collie\n",
            "n02091134-whippet\t\t\t  n02106166-Border_collie\n",
            "n02091244-Ibizan_hound\t\t\t  n02106382-Bouvier_des_Flandres\n",
            "n02091467-Norwegian_elkhound\t\t  n02106550-Rottweiler\n",
            "n02091635-otterhound\t\t\t  n02106662-German_shepherd\n",
            "n02091831-Saluki\t\t\t  n02107142-Doberman\n",
            "n02092002-Scottish_deerhound\t\t  n02107312-miniature_pinscher\n",
            "n02092339-Weimaraner\t\t\t  n02107574-Greater_Swiss_Mountain_dog\n",
            "n02093256-Staffordshire_bullterrier\t  n02107683-Bernese_mountain_dog\n",
            "n02093428-American_Staffordshire_terrier  n02107908-Appenzeller\n",
            "n02093647-Bedlington_terrier\t\t  n02108000-EntleBucher\n",
            "n02093754-Border_terrier\t\t  n02108089-boxer\n",
            "n02093859-Kerry_blue_terrier\t\t  n02108422-bull_mastiff\n",
            "n02093991-Irish_terrier\t\t\t  n02108551-Tibetan_mastiff\n",
            "n02094114-Norfolk_terrier\t\t  n02108915-French_bulldog\n",
            "n02094258-Norwich_terrier\t\t  n02109047-Great_Dane\n",
            "n02094433-Yorkshire_terrier\t\t  n02109525-Saint_Bernard\n",
            "n02095314-wire-haired_fox_terrier\t  n02109961-Eskimo_dog\n",
            "n02095570-Lakeland_terrier\t\t  n02110063-malamute\n",
            "n02095889-Sealyham_terrier\t\t  n02110185-Siberian_husky\n",
            "n02096051-Airedale\t\t\t  n02110627-affenpinscher\n",
            "n02096177-cairn\t\t\t\t  n02110806-basenji\n",
            "n02096294-Australian_terrier\t\t  n02110958-pug\n",
            "n02096437-Dandie_Dinmont\t\t  n02111129-Leonberg\n",
            "n02096585-Boston_bull\t\t\t  n02111277-Newfoundland\n",
            "n02097047-miniature_schnauzer\t\t  n02111500-Great_Pyrenees\n",
            "n02097130-giant_schnauzer\t\t  n02111889-Samoyed\n",
            "n02097209-standard_schnauzer\t\t  n02112018-Pomeranian\n",
            "n02097298-Scotch_terrier\t\t  n02112137-chow\n",
            "n02097474-Tibetan_terrier\t\t  n02112350-keeshond\n",
            "n02097658-silky_terrier\t\t\t  n02112706-Brabancon_griffon\n",
            "n02098105-soft-coated_wheaten_terrier\t  n02113023-Pembroke\n",
            "n02098286-West_Highland_white_terrier\t  n02113186-Cardigan\n",
            "n02098413-Lhasa\t\t\t\t  n02113624-toy_poodle\n",
            "n02099267-flat-coated_retriever\t\t  n02113712-miniature_poodle\n",
            "n02099429-curly-coated_retriever\t  n02113799-standard_poodle\n",
            "n02099601-golden_retriever\t\t  n02113978-Mexican_hairless\n",
            "n02099712-Labrador_retriever\t\t  n02115641-dingo\n",
            "n02099849-Chesapeake_Bay_retriever\t  n02115913-dhole\n",
            "n02100236-German_short-haired_pointer\t  n02116738-African_hunting_dog\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Giv0wMQzVrVw"
      },
      "source": [
        "The dataset has the following directory structure:\n",
        "\n",
        "<pre>\n",
        "<b>Images</b>\n",
        "    |______ <b>n02085620-Chihuahua</b>: [pic0.jpg, pic1.jpg, pic2.jpg, ...]\n",
        "    |______ <b>n02085782-Japanese_spaniel</b>: [pic0.jpg, pic1.jpg, pic2.jpg, ...]\n",
        "    |______ <b>n02085936-Maltese_dog</b>: [pic0.jpg, pic1.jpg, pic2.jpg, ...]\n",
        "    |______ <b>...</b>\n",
        "</pre>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CC6Xa3V9i3L-",
        "colab_type": "text"
      },
      "source": [
        "## Create Training and Validation Sets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DldfFhdHph3R",
        "colab_type": "text"
      },
      "source": [
        "Delete the current training and validation sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0CBSXT8jDW1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Start with a clean training directory\n",
        "if os.path.isdir(DATA_DIRS[1]):\n",
        "  shutil.rmtree(DATA_DIRS[1])\n",
        "os.mkdir(DATA_DIRS[1])\n",
        "    \n",
        "# Start with a clean validation directory\n",
        "if os.path.isdir(DATA_DIRS[2]):\n",
        "  shutil.rmtree(DATA_DIRS[2])\n",
        "os.mkdir(DATA_DIRS[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Zv65gzPTqquz"
      },
      "source": [
        "For every file in the training set, generate a random number. If it is less than PERCENT_FOR_VALIDATION, then move the file to the validation set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "rvahIFWRqqu0",
        "outputId": "074fc958-a4a7-421a-f944-ac0ae09fa0f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "source": [
        "# Counts for total, training, and validation\n",
        "file_counts = [ 0, 0, 0 ];\n",
        "class_counts = [ 0, 0, 0 ];\n",
        "known_classes = [];\n",
        "\n",
        "# Get all the files from REMOTE_DATA_DIR\n",
        "for data_path, data_dirs, data_files in os.walk(DATA_DIRS[0]):\n",
        "\n",
        "  for data_file in data_files:\n",
        "    \n",
        "    # Choose where this file is going\n",
        "    index = 2 if random.randint(0,99) < PERCENT_FOR_VALIDATION else 1\n",
        "    \n",
        "    # Set up the destination directory\n",
        "    file_class = os.path.split(data_path)[1]\n",
        "    dest_dir = os.path.join(DATA_DIRS[index], file_class)\n",
        "    if not os.path.isdir(dest_dir):\n",
        "      os.mkdir(dest_dir)\n",
        "      class_counts[index] += 1\n",
        "      \n",
        "      if not file_class in known_classes:\n",
        "        class_counts[0] += 1\n",
        "        known_classes.append(file_class)\n",
        "      \n",
        "    os.symlink(os.path.join(data_path, data_file), os.path.join(dest_dir, data_file))\n",
        "    file_counts[index] += 1\n",
        "    file_counts[0] += 1\n",
        "      \n",
        "print('Total images:', file_counts[0])\n",
        "print('Total classes:', class_counts[0])\n",
        "print('Training set images:', file_counts[1])\n",
        "print('Training set classes:', class_counts[1])\n",
        "print('Validation set images:', file_counts[2])\n",
        "print('Validation set classes:', class_counts[2])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total images: 19047\n",
            "Total classes: 120\n",
            "Training set images: 15198\n",
            "Training set classes: 120\n",
            "Validation set images: 3849\n",
            "Validation set classes: 120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "INn-cOn1VrWC"
      },
      "source": [
        "## Prepare generators"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5Jfk6aSAVrWD"
      },
      "source": [
        "ImageDataGenerator is a helper class that will generate data for a batch of 32 images for TenserFlow from a series of images on disk. Categories are determined by the file's subdirectory.\n",
        "\n",
        "In addition, the following flags are added:\n",
        "\n",
        "1. rescale=1./255 - Has all RGB values divided by 255 so neural network input values are between 0 and 1.\n",
        "2. rotation_range=45 - provide images that are up to 45 degrees rotates -- image augmentation helps prevent overfitting\n",
        "3. width_shift_range=.15, height_shift_range=.15 - move the center of the image around -- image augmentation helps prevent overfitting\n",
        "4. horizontal_flip=True - present a mirror image -- image augmentation helps prevent overfitting\n",
        "5. zoom_range=0.5 - scale the image -- image augmentation helps prevent overfitting\n",
        "2. shuffle=True - shuffles the order in which the images are returned -- this makes every epoch of training unique\n",
        "3. target_size=(IMG_HEIGHT, IMG_WIDTH) - scales all images to IMG_HEIGHT and IMG_WIDTH dimensions\n",
        "4. batch_size=BATCH_SIZE - controls how many images are fed to the trainer at a time. Tweak for performance.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Pw94ajOOVrWI",
        "outputId": "e20f3f19-9579-42d1-8423-d86b9e747790",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Create a generator for training data\n",
        "image_generator = ImageDataGenerator(rescale=1./255, rotation_range=40, horizontal_flip=True,width_shift_range=0.1,height_shift_range=0.1,brightness_range=(0.8, 1.2), fill_mode = 'wrap')\n",
        "train_data_gen = image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=DATA_DIRS[1], shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH))\n",
        "\n",
        "classes = [\"Unknown\"] * class_counts[1]\n",
        "for name, index in train_data_gen.class_indices.items():\n",
        "  classes[index] = name\n",
        "\n",
        "# Create a generator for validation data\n",
        "image_generator = ImageDataGenerator(rescale=1./255)\n",
        "val_data_gen = image_generator.flow_from_directory(batch_size=BATCH_SIZE, directory=DATA_DIRS[2], target_size=(IMG_HEIGHT, IMG_WIDTH))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 15194 images belonging to 120 classes.\n",
            "Found 3853 images belonging to 120 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hyexPJ8CVrWP"
      },
      "source": [
        "## Test access"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EI8pm9Xgl2NR",
        "colab_type": "text"
      },
      "source": [
        "# Attempt to create blobs\n",
        "Instead of comparing pixel values, instead find all lines in the photo using regionprops\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "60CnhEL4VrWQ"
      },
      "source": [
        "Pull a few images from the train_data_generator and display them to confirm everything's working."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3f0Z7NZgVrWQ",
        "outputId": "8d36f700-9ab7-4bca-f567-e3d02ec8c1d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        }
      },
      "source": [
        "sample_training_images, _ = next(train_data_gen)\n",
        "fig, axes = plt.subplots(1, 3, figsize=(10,10))\n",
        "axes = axes.flatten()\n",
        "for img, ax in zip(sample_training_images, axes):\n",
        "    ax.imshow(img)\n",
        "    ax.axis('off')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ozGDyHmksVTbiwvQixqQ4usgL7ty+zYN7D8nzit/+6tdBav7wm3/E859+nt95\n8bss6wVKSTKjybRkfjxnY22NuqmZz1dU5YhCZcwODwkhUm5uEbWity0xeGIIg1xRkGWGZtmgTcFq\ntWJtMkYQUDriRH7qV4yShOAwmaYsK9rOnsqVTGaQImJtj5IS70ApxfbWlLLIsbZnuVyyWCwJ3iFl\n2seqImctq8ilIM8NptDoTFOOcpABk2meeuI615+8hlGarmuZL5bMljXTjQ0mk3VMNk4lHjFhqv/5\nf/0H3Lhxg4uXLvLiH/wBeVFw/fp1bt68SfCB49l8CGYV3nq889TLJZ/4xHP843/ym1y4dJFl2/Lk\nsz/EL/6nv8SvfOkP+b3v3mXFBDt7hWtPf4Z3di1q42kOas+ya/FZIJYp874mPVurO7zxy3/j42uQ\nE4txhs0V4l2FPCe38XRj5H3xwJ+qVPP/B3vxd/7x6f13nUKUGPXuzT0xu/L0XUauAElA4BF4ofBC\n40Ry7gBCDICCCNEh8CjhqfryFEhISFovIPT9QAwLZgeHHOzucnxwwPzwiOOjQ1zbUheWo6OjJKlQ\nilFVYpShaVouXryMFILLV69y7amn2Nq+wLQo2WsMcqQRvkebCQJYLpYslgvGozF7u3v44IaDjqey\nCUhMh84Utg5keU5ZVhRZTiSlnn1wtF1LhFT4YjKEkINuMlDXNSbLsX2PyTOcdUwma8znMwSPWMkQ\nLSGk3/U+4JyjaWsyVeKdp+06VqsFq2bFok4Md9/b0wsuxJh0u1EgwiMNKIBQIaVuh1k+yyOfmeFh\nzv7sGrx8PwM7sLdKJg3uxzARDFqVWAx95wjCgwIhAsiks8vkIPuRjqTESKzxSZByUmyX2Mw0rsAp\ncDvLGp8CryGAPHnfCfN8Fui97/GStHwnZxkHKVJ6VhKFxg2PAxKpMko3p3RLpFT0IhAwWG+YtZJO\nG0Jb4YsK4T3HywPu7B2SdTPKZcWzTz2GFjHpsUUkzzKkEHR9jwsBY5J8SWqdCrO8TefrQ5I1GIN0\nLgX8J6yuD0itki5QJk3xI2lDxNukHz7ZvE6AGQAhyUoISWIl9RkC4kR7TEgSJCGGOgY5kA8SMQD4\n74vW3s9iAtgCklYfgckKcgF1F4kWOhdpWkvT9XQ20LtAUDJlm4qCLDMYnRF8Q9c1dG2H7Xt8SDKz\nEFyqrfCQa5UYYSK5VuRZTi6TZEYKyDJFpgsEjt5aMu+ZTApi1HSdPZWnCsJQbCso85x21eCtRSPQ\nBBQBGT2xd/RNS55VLNqWXGYI5+m8RUsBSqMzM2jCh2JzlYpKU3oapIxD0BIGkmIY3hBwPoJSCCWR\ng84qhhMpjRwkgnysa7bIc4QV1fYAACAASURBVIJ3LBZLNre2qcqc2eyQ8Shn5+JVfAgcHx6mOhGp\nKPIxJsuIeZH8bvR455K0MIJQMc2R0VzY2uZzL2jefOtNbt+5y44U6FJzMBAm44NjZFaBUDAqGGnJ\n1e1t1A//EF/7vRcJzrJ0c8pqijaaqqpwoUfZSF5qjg96+pBzNF/RO89vf+UPKLKCCxc2mE7LVJui\nGlbLOYJI224g5RD9x0Hn7yPYDso0PymqDCB61iY50+kYZQq01lSjksevPsaDhw84Pjri+PiYzc1N\n1tbWUEUBg5Z8fWPK+vomzbJhvqwpVUkZxhRmhCoU3aomusB4LTHZRaaR4iKZjhwe1swWlnqxItiA\nH3lMkbFxcSjAFXA822NrcxuQhEgqpNtwfPnLX+V//0e/yqXL1yBGfuvLX2fe2SQZFKRCPqHQpuTB\n/iEmyxBS44BZ2+AzTaULZnWf9sf1MUVh6PuOvDDY3jFftGQmp6wmdH2ks7C1ucPx0QFRO8CdEhuJ\n1ZVI6VAqp+ubtL8LgzaaEAJt22J0AcDh4RKlG8ajkvF4PemhtcH1PdPpFNsnnyGKgtGkZGtcMtmc\nsmrmlGXOaFSxsbnOxQsXcM6zmAXaVqFx9Ks5nSkRIk91TDplAv+9v/FzWOv4Z197kWtPPEHwnr3d\nXTY3Nzk6OmI1n3P18WtsbW7xve99FyUkn/jEJzjY32M6GXHp0mW++b1X+D9/5R/y7Tfe4F/+N/8W\n1nbMixuES9d42xd0a4LYZ6ggKMSYiKIVHdHOGckF8ze/84HX50fOBZ0AjfcDu/H0LyCFep93cMo6\n/Xk0Kd//uET4/ufj0AFAhLTBnWhTPYCQINImqIQk4B99V4xpyw8RQiQER5EpYpA4Z1Onis7iupbl\nbM6dd96CCPVqSb1c0K5q2mVNW69QCHSZszlZS1/uI0pJNAqc55033koyiQd7vPXWO0y3tlnf2WHn\nwiWe2H6Wic7oukOaecvEdhQZ4C1PXZumIhaSFCTTBoEihoD3Hf1RTYyCuo00h4MYIaR0cAhxYNbh\nQhWBluiatGZywcUywxO4OBmdGc05N65MAcv6RA9ra/zo5fEjLbUP4TQTIQaKNcaIHZies/b3/t5/\nOcDfpFf8wHX352g5vhckCyFOZTwfx4QQp5Ie7z0uWmzboU2kqkzSdKswBGo61cvEwMbGBkVRMJvN\n6LruFOSeAN8ka8owJnUaWCwWp51H4FHRntYaY8wpcD557QRkf995cyp8OZXCiChSESGKiEADTiRm\n/yTQlAJEcAjlUSrHeomb1axM6oCxV/f0rWdNFBTR8/pbcyazJZtrhs3pGmWRoZVCikjA4xtHCKCM\nQWuF1AYZGOQNKVDTWqGROCnxLp4GFJBY2SSL0RDlUJA3SChsyqBIJYfagUdyIiSIKJEiDsW8JwFa\nwhF+0C4LAUKqJM8Yiu1EeLeU4iOtj+HWeQ9CYfJUpCadI1pPbx2rumPRtDQ2MaUyU6jM4GJkuazx\nvSUzhuA9vmuTUlzIpN+XIjFeWDKpyJQi15BrQS4lmYrkmUEisGR464fOMQIXQsoQNR1d54lCI1RG\nkBJ8krxkYigFDkmfbpQk1zJ1k0CQS0NZjnERosmIRtGGwEZV4ZaG9iAVEhqTgUiBgree4N3gs1M9\nhBkyekkC7yHKIUCMRBkfrUUhk9RMnBQSxo/lVrbW108D0a6paZqaz3/+88ybFVJr3nj9dfquZ2N9\nHYCyKKlXNbPFgsQ8R7x3ZEYRoh3AveXwaI9MSqqq5OlnnuHZFz7F/uFDXGNZvnWf+uE+40aDNtB0\nQA4C1sqCPSV4/vkb2BC48+CAzrbkoqBpOyKeEB3OW1xokD6yexQxs5pJOWJ9DVw4YDbPmS+WbG8q\ntIz0bZLYoXOQOnWyQEGZgeoh2rR/SgHBAx2Z6FCi49Y7d6iqMU8/c4PJZMTm+rOslsfUh/fw3jOf\nz1kDVJ6BMmAURMELP/wcy2WLQCdJQNCM84LCaLyLRJ8K58ui4MpjBUWhOZotOTqq+fa371DXiXUt\nvCJb5FRlSXCCcjzh5ZdfITcZb739Dj54fvf3v84ffeMV1tZ3aFvPYjGnl5FlvULpVCzqfaDv0+PM\n5ERpwAhcFEitCT7gMkOZqZQdkIK1tQlSDlLFzKJNTt87eh+YTDdYrZbsHx4RQ8S5jrIs0VrjnKPr\nki66bVvW1tbJ8xLvUwZVYBL7W4xwzjKdThOwFoNk0wvKaopSkuP2iFXdkWmd6rm0YSEEVTVie22K\nVJGNzSmjUUVZjjk+WqZOVaMpMUru3LpFnls6xoxdarRgtEm4IQZCdHzx858mxshXfvclpJQsZzNe\neuklnnj8KmvTMa+++h1W9Yq/+JM/iRSS3/zN3+DSlevM5wd0zQoTI4vDh1zZcPxbP/Mc/9OXOu7z\nBMI66BtK21A5RUUgignz1lNgmcYZd++++oHX54cCZDlEZGfBcW/d8NzgcIcB1QM4POusTxzFWWbp\nz6Op93FpEYaCrfdnGONA2fhwomQVICMxeoIUiSUZPiFD0lcqAUqapE0OCvqOvmmZLRbM53MWs2MW\nyyWzw0OOj49PdbUxhKEAJ5CVBWujMbP2MIEVH5FikAD4gOsD4zKBzK6z3L99j3v3dylHIzZ3LtB1\nLRc2d1C5ISsyijynMBkhRmZdQ5al9HAIka5p8T4ihvZTo7LCtQk8KVJUjBpac0V3Os+RmDaW4RZI\nxTd/zPS//0ifPDjzaNB9fxBp9ku/9Hff/b1nmU0erVl5hkn6Qey94eJ7z198QOD1kb5bCFI59Mez\nKARISXSpACox8j1RJ7awyDJ87HHhhOmFGOXQitBQ13Wqij7TuUKplNbPsozxeIz3SSpzAnpPGGcY\numcodcpi/HEMMu9XAAiPFsPQ8eFE1CK8J8icw2GdytixRsdYQDQD64wAH7AuMheRWgle8dvcPww8\neLHl+o7i2k7k8jRjWkSMCPgAmU4ATgqfWGaZ1u3ZICFlFgTOdnifumGcnOcJOxyDIoqYWNVBriJE\nklYorVK9AxE3pOZPtMYnsrTwnmAphsRQvquIkhREnIzu2ZX2AYm8d1kq7tMD253O03lwLmC9x7lA\nCApP6g6RD+xxCAEXXCp07FPbOuccMYahoDsRAyZ6VAQTPCoI5FCCK+H0fCOR3qcCbGOGorsIeVkh\npBvkeontNyLiYkxFa75L+nQPweXgDFJolDGoIPA+suw9I2vTBpfluKE3oxByCOgDwj8apZP1630Y\nMioKJXXK6A1dfrSSdC5grT9dt1opokwdSYxOAZe3H/26nUwmKfjUmr29PfZ2H3Lz5mtcf/YG9arm\nwsUL7O7tYYNHR8Hx0VEq3s00bdsMwQWpJiQjgfzgqJcrDuI+m+ubRAWffPaT7P3+AwiOS2tTqFso\nRnC0gBOiRUlUWaCM4aknr/Pw4IBZvaLrBUJ5jDZ0bSrQIgasX+FDhwsluckQGvwiMFtJxnnG7p7j\n+ELF+qRCm4JrLqYF2zuICkwG0g/tQ1z6UwqcBQQqy7m0PUJLuH1nlwf37zAZV0zWJqxNSjK5je9T\nR5a6aRgJiZSBE/2DGRVsGEW9bJMsAyhGilGVJ12+ViCyxFhHy/qkYjoZc+WSp297FivHYtGzbOYs\nbyciyGQFX//q1/ijP/oG3kWctaxtrKO0IcSM+bJFKfBCkWtJsVGQ5TnVuOKNN9/kwqXLzGcLhDRE\nIZGmGIZeUpo8SX0UCBlQSrNcLlO7urUxXd9St3XKnPUtXV+ftnIVypMNxY8xxtN2m5Bki+CpVzOy\nomI83iTLMppmyf7efS5cuEhd10wmk9N1WY0qnEvBh1Y5Ruf4GFjVHVF1hIXn4OCQh7sHTEvFbDkn\nOse1p55EX8mpG0uR5UDOeLJJbSPRWmJd42OgzAuUEvjoUUamonkf+akvfpa2bfmnX36RqizYWBuz\nuTnlTRF55sbTeAI3X3+dL3zhL3A8W/CNb72MsB2f/MSTiLLkD1/8fXae+1me3Zxjd19iVj5GVJLK\nHLEtZ8Tde+TrT1OO1qhkzfzNb/DJ6ewDr88P72IhxCNne1qMll47ccwnKss/p9j3I5ny7ylCEo82\nmPAuduZ9QJ5Jm10Mg+wiknp/ekl+UqwRQirScT227Zgvl+zvPUT2Hc6lPr/L5YLFYknbNXRNP2ii\nBUoBUhBcTJ05guNgeYwkaRubpmM6mZArmapmyxGjMi30etBDt13PcXPE7t4Bd27fZmdnh/WtLa48\nfpWLly8xWt9AZoZMqdMWsD4ERIxUOk8tc4SkXq2GFlkidR4YNmohEkh6pIcURBHfBSA9cZA38L67\nd3gPCybPrr33WBze/4NkJU5+5WRN/0nsg35bvEs08GdsUiZIERIrJlSkLHPyQpMZgxo6JmghU4tG\nBHIQT5+wv1LKYRPOBlZUDSk7fQqcq6pCSknXdQO7qk/fq5TCWnvKKv9ghXuP7GyA80H9pOWAqkOw\nSCQ+9Y3BRnBe0OqCfZFx59U9Nt484PH1GTcuFjy5lfHElubqesW1iwXCzYluH4XgzbUJt0uJWvVM\njWZzzyG7QKUMWkkWwhK1RGiVUvTBE70D54bbgAgOiUdqk/TDZ9aMFAwdEQbW+KSd2btyT++ZXpG6\nVsiQgoYE2M/0H/xjxzLJ5rRSCKVSljt4go/46FNP5AA+pv7NUYDOMqrRiMnaGjIvMI3FdkvqpqZp\nGmzfIaIjhASUY4xIHDKCiDZp8/3QGg8xFHp7uran71KXBm1ydJaBVEihUDISAkMgkGo+tAjoGFE+\n9cD3WhB9A9YgZYZWARkEXec4Wq0QOjIhoDLN4tiSeRhpQ7AecSoJFGRSIXU2FMU5EAqpVWrBRypA\nD7iBDPDpPAInSP90/k76q38cn7Q2HpMNLTvLPOe1N97g9ddfZ1Ev6bue7Z0LECOHe/tkOgUoSiVK\nZ1JVaGNYLWu6don1llLkRB8J0dG3LfPlDJNnvPbaqygpuXL1Kj/0/HNceOoZ2NvlYPGQrXUH4gJU\nJQRLlmvW1ieUo5Ki0CzqJYYCO2RMYoxMqkjfaVyI9M6m1p62o46R+XyBlpJJkeH9iqNDzetvvElR\njXjqmU9gooLxOA1ivwSdMp9JvygJfU/fexarBesb2+TFlPmi59Zbt1mrpoyrHBt6lM4wuuD2nbsw\nW7KzGRiNK/IYEVUJVQaZQnUtMfZ0fUuIGqUk1aQE5RICCgq6iO0DSgbKquJzn73B0WzFwXHL7tGC\nO3fuYLKS//tX/x+ij2SmYt4tyYsx+wcLTF7SOk9fz/nMD3+KO3dep14t2dzaou87Dg9ayrKEEDG5\nSVk+IZFKDfVKeQo6mgbnHG3bopRiMpngHDRtSySisuR7N9c3WdUdRwczlNY4253KYd2wR0+nawgh\nsdaitaFpGlaLGX1bM55MqaqK7e0diqKgbVsODg8oixIhBFtbW9QHh1zYucTBwQEBOcCb5HOMMXhn\nuX37Lg9zjfOWUsMrN99ha32dZ248y9UrV4k+YMyYznpUlDRtT9O2lEVOVRaI4IGAVKlQXyAoipyf\n/9d+ir7/l/inv/ElXNewtbXB2mTMrVtv8cabN/nrf+2v8fLL3+bC1jpxb4+NUiOrnG9+/Xfg5h7V\n1jP8Cxc/Qbt8iVBtcbSas7j3Iv2912keThk/8S/i3Yxb3/h1fvLz0w+8Pv9YicW7mIn4qM/ve+1P\nugn+8zQR47s3pOFU3tXv/1RI8h6TjyQlIgwJ4wDSO0LXAWCbhm65YjU/ZjVfcLS/z9tv3KTKkrxD\nqaRl8t6jYmRkzNCzNjXNl1ITM5H+sYgUHB4dknlDVIpVu2Bne8LaZILOl0xG6+zu7wGRjc1LbCrJ\narVKbdZyw7yes/dwl8P9A955660U6CjNaDzi2o1nuH79KQRQjsdUVQXWsVosCd4zHk+QeTlU7Abc\noI8UQqReomf02ifb9snQRc4ApXeh1PfMxfBkGN50Wug5WPgAgPRR7BRQD5/9KLLNj2V/ykFijD/A\nESoxtBhLrczKQrKxsUZRGYLv8d6hDaAHBlYm8LtarYDEAFdVlfrHDixwWZYURXH6jxW6riPPc7Is\nY7lcnv6jghPm4uT+n7ZP+JN8X/ABaSCYjFnfEvcXOFuzagx9XKecblE99iTL/TvUs32kdcjesCkl\nWihKEfj/2HuzGNuy877vt9baaw9nruFW3XnoiU2ySWoiKWqCBkNGosBGACPwQ5DADwLy4CRIYgMB\nDCUIksCAgQR5sx8yPAhQgLxESGJFlmNbjiRAlCJK4ty8Pd6p6tZ05j2tKQ9rn3OrL7vJbkqUIjkb\nuKjqvnWq6p49rG993///+1u3xrvIgBV5HvntRN27NIFgPcEYvInFcRKizlkqRV7kBMAZR9vGKdwW\n95YIUpUiVcRmBi9jF8pYtNQgPLEHK7pis0NtElF4EN57w3WDm+9UpyVaE0gw1mNt5Dk7H2k6PmIi\nEEKRaEGR5/TygizLQCcUeU6W5TRtE2UhUkQJBPH+jKz4qA1WChIl4p9uI62kijKzrnOdIEjSDJUk\nGO9ZVRW2tUgkaZqRJgohwTpPIjzKR50lTsQwIGtBW6SPm/DWeUxrkOWKRBsS46jWa0amYaQUwYqt\nx2AzUZKio1L4EKkLSJRQ224zRIKQtT5uahSw1ZS/d7r1Ufbss9kUayOLcjqb0csyLqYXZFqjtObB\nO2+RqoRWQpZlWGO3np9lWZHnOYlWFEVKYMPP9zF4qm3xNsfrwNnZGXWzZu+VF/nYrduY+/dZnJ7y\n5OFD9q7vwmIKOzsgAr1Bj/6g4O4Ltzmfznh0fEaaZDhrkEJQpDmfe+0F7r/zNgbJdF5hbZxYmnqF\nkpCkmnlZky4lRVOzMxnx4OERr7/5Lq+99lqUQoQQHc9KRB209bEIEymrquT8Ys2rH78HLNB6QJYu\nOTo6Y7Y0HB5OuH1nLyItdc7p0xOOn15w8+Y1DvZ2GMqoFccYssmAsQhMzw3WOdbLFXmhkSqNRbpO\nYLRLPltg1ivIMgohKHoFV29NuHI+QwjJF7/4Ja4cXkMJTdNC6yIX/NqNu8zXJa9cvcajx0d8/f5X\n2Rn3uXt4lxACj548RiqFqVvarCJVSYesregV/Q7rGadRvV6P9XqFMTFobDabMh5PuHHjRrzH5+fU\ndcViNaWuDet6hlaaogvXinhQTdO0TKcz9vb2WK/XW4hACIYsSyE4ekUGRUbbNhS9HETAupY8Lzg7\nP8OYlrSNhJp+r0eiM5RO45TcW7xMSNMM5y39ZEBdV5TeQ9ny9Hf/AOF+jxvXb3B44xY2yZEikCTR\nQ9E0DcZahv08TkBMnEapRJFqjVSCPEv463/9F8iygv/q7/8DZos5qU65cniFr3zlK8xnU+r1gr3J\nkL29MaUxDJly/uD/5tEf/nNGP/+3GI6v8c0/+KecHz2gLR/TVCc0q5Ls6AG4hpd3DeED2xHfpUB2\n3YJ0ub/6kWqAjZ7ufc1Q7/uC7ddtDH9i+/8//LHpPH7o5TQoNmVunLpd1g7HVWfb/NwWz5ekFRuz\nTggI62N3wRoWTy8AmE8vuHh6wuLiAtcYgrNM0gKZ1NuFzjkXF19A6qQLqOgQSTqJmsgiivov1kuC\nVVy/dZvhZJ8rV64wHo1JEk2e93BvvAHAaDRiuVzwztERs8UFg8GAqqzo9/NoGOqMJm3bUq3WnJ6e\n8eSdR0ghuHP3Li++8gr7e1coVBqLIGMRMhoJhRSIELFYsXn3bPMUP8QFMXTFsdvgxp4/Ud927rpu\nIc/QglKI7fp/maQQf9ZHvDb+FIu2b5MPfHCD/CMfwdbfkySpbhqsixSJRAnyPEV14T6eAD4aFYUU\nW6e+UFEatU2C1BqtNXVdb7sReZ5jjIms1tmM/f396KbvyBeXdcaXk/i+V1nV+0kz3iPfeu77freg\nkiABEUjyPkF4libwcNmybhsqt8LpBfdehHF+CPQoyzlh2TAgkApB4iWr0NJKi5CgezlJmmLWDa5s\n6ZsE30DjAhLFpsEo8Z1JMo72XbDbMb6UG5xe/PtEykic8Bvmt0WpBK3iZkbI+PwRziFknPBsx3pC\nxg1qAOcE1rrIZzebn5USpAMZUCJaI6uqoTaOsoG68TSNx7uY3KeTOK5XKrKadZZ25rZI8BiNhgQR\nCN5SSzCmxbYOK6IsTYmYZqk7s1CqNVoKdNK56FE0bUrVVASivKJ1Ieruu1CQjSk80QnSx6CGGhDe\nxOdsKuN0zke+rux6GMZ6TNvivaXoQ1Ktaedz0rbtnity65fYTKJ8h/2TXUCVeu56il7MGCzk6KYv\nl/TGl+WEkWrx4Q5jWpRSlGVFkWesVytG/T7r9ZqqqSnygkCXSSCIpAoXsNaQD2JYSGsjDg4ZzY/W\nGERwSKFo5zMyU7B2axbLOZPxT3P31m1EG9GBozxlW/FXFRQpk0rRyzN29/a5dvMG+qv3scagRHz2\n/82f/TwkihtXDimGE7767iPeePNtHj58RH/Qx3VpkokUVHVAZ2Mu5i0H1+6Q9/qxOK5byDIQGqyF\nYoBdLUjGO0grUdrQmCl/9OX7OAdlabm4WFGWhnztaNqADYKip/EiIcn7zFZnfP31t6nuwJ3bBdgV\nEFjMp1w/vMnO7lXKcklZrlitSoZJilBJnPicnhCCwDmBboDgWa9rltbTNC3/8B/+I+ZLw+c//+P8\n4Zf+mPPTM4bDMc451pVlPNljva6wzrO3t0uSKKaLNf0848V7LzBbzBgNHJVtSVNNkmbsZilCJFHb\n7QUiOLLhAKXGaF1RVRVtG8My3nzzTV566SVSkbJqVzycPWTQH1A3JQfXblNXNXkng1IqoSgM63VJ\n0zTcunUrpoGmmouLi2hqBx49esR4PMY5S69XIEScSieJom1j06Nu14x3JiipkELiAvT7I1arxdYP\norMM7x1FIWlNS20sMsuwdcPbj56wcDF/IVGK8WTI7s6EJJE4X5JmmlR1r6tr0iSJxbiAVEl0Ghs7\n//nf+08hCP7n//1/oy4rqnXJ2fk5e/t7vHDvHlIqvvzVr7A6ecioN0Tka17/rV+hcTltLUiFoj8Q\nFCFnuDfitK1IfMPnP/kJesPBB9ydH9Kkt1mqPqqvf/vwEJf/6zt8/bPmHpfbIB95jd3IQT5UESUI\n8nLnM+BD1zlwtluRYnqd7HZgxjqsNQgR6KkePhhMa6jWa9bzGbPTU97+2tfIux2qKWswhjTRZELQ\nVg31as14T0WxfhCUbYtrLSpJCMZhO7NIOsjZv3qVw9s3ETpnvL/LzeNjkrzPtcMbTHYm9HpDRv0h\nQkaI+9WHDwEoipS2bXj5+Ijj48dcnJ3y7rtvUbSOxXzOel2TaE0iNYlQjMcTZsenEAIP7r/FP/vH\nv07eG3B4eMjdu3f5yZ/6KWZVHRnKSRLd212HxftofAlAY1pc8CRdDK4PEREk5TPs36YvvO0sX9YJ\nC8HGxb+dWHQXxwehxj7S5cH3RxcvLlXIEtFxUb+3I1HJ9/S72eCi1lOG6Dwvcrytqb2Pww7ZbeCE\npG1iB1CiqKqKK1eusLOzw/n5OUdHR1tjXtM0HB0dAbEDuLOzw+np6dZ8dvmcWGvfcz425+ky/eb9\nCufnz6FSz/wM30mq8WHfo0LMwUPjCkRvAGJC5R0nxvLNo5p//uSUX/3Sb/CJ6wNevX2Nm9f2+eHM\nMcESaGnNCpFLUqUgC5A2eGvwjSGUAdkUyCahSaLRSwi3XUCEjOP6xjTRONlJUqSM2ltUgpCCi+mU\n1XIZx7BJJBEUuSLPM4QUeBs3ITIQqQ68VyX0rJkQ6RcbGgOAFx6SeJ/5zmy2LlsaB01Io0cgAEKR\n5QV5MISgqNYLqsWK/Z2diJgynlYERK6RtUaIBAzo1pNaQeE0rXGMhgWT4YBhFqk50jX0pCSXCSo0\nMThKglCKtbXIWUXWdSmCcOS9nKJfRGSljBO2VoIyDoGKhbFN8Ubh03gNSyGoqxWNlpjKUbmSARks\nFtTTOTkVoRc67nrcdFhjMNbgG0+apiidoBP9HoJFohTWhRgT3umivYhQvU0OwOY6bJqauqw+1DUJ\ncHBwwKNHj6iqEp1qrkzGMZ3ROlY6oVyXNG1Lr9djPr2grmMASq/XI8lzqnXF+fk5e3t7+FYiVEAL\nRao0NngcjmW5IhsX/Nhrr/IzP/qjvPnG29y9cQ1mNfVxCVcq2NuP8biDAWpd8/GX7nFnZ8KLoxHv\nfuNtHjw+Y3dnl5s3D/jaO2dkUvH04RH9Xo/HoWE4GPHJT3yS4WhInuU8eOchy9WaYTHg+Pgpu7u7\n/NP/6/f5F7/5JWazGTduHPITX/gs8/k589kiohDrGLP9qU+9xsXFlNl8xaNHJyiZMR5OMLbl+Okp\neS/He8snP/Xxju5gqOqaT7z2cc7PLvjVX/tt+kXOi/duM59NmYxGTAYDfuzzP8JkMqY0ltOHc4ZL\nz3q14OOvvoLOxvzu7/w+Z6cXTC8u+MM//DJfPVoy7KXgKzyS2np+87d/h3svvsq1Oy9gW8POeMTr\nr7/OdDbjxo2bWOuZLc5oak9rDOuy5HQ6A2J41tXDqxgbJ8veG9bVAmM8k/EEb589F4fDAUVRILuA\nkyTRNE3DeDRmb3ePJEmoqgpxU/DkyePt9RRCTFts25YkUSyXC6qqotfrobXmypWD7tnzbO1cLlc0\njSFNNcWo3yVDKpJEkWcZVbUiTXM+9sonePDgIfP5jCzLkDJjOByyWq1YrZYURY+DK9epmoqmrhB9\nWCwiC9p2uNqz8xnH/TOyPEXgGI17jAZRZ9zrZdTO0pyfM8wzfKbJ0hQhLNY1JEnKv/PX/nUCCq0z\n/sa/++9x5/p1fvd3/x/6vYLjoyWf/uTP8fZb3+L2wQGtWZNqT1W1CFLq1nN48yZBBPbrp/zHf+dv\n85M//lcplx9cP3x4ovlf5qPrVG4Xep5hrTrpTexmCKIur4uLDT6QSkFZN0xPTzk5fsL05IRyNifU\nVcSuBJC+QRJQXYdDsemRGwAAIABJREFU4MjzBK0VUsYkPkFMkBOE+MAWsSskpCDRKVKm7F65gsoz\nbt25i9U5+1evMxyOyfOCPO1jfCzGX3jtU0CgqSuWyxmHOiEb9xjuTVi2K9LpGmtMBOBrjUo0rrGs\nZgt6eRE7gGlKsJa6XPPowQOW8zm9fp+P/eCnY9HqAs5b3KbzolQsfLtVW9Cl92kd2bpdVywIvk2m\ns43t7s4F4tmC47u/h2/vIH7Y4uj7Zg79gG+7Kf2/k+vw+Zd+25dekip8lENKRZoowKH0Rg0duujk\nWMRHrGCUT0gRA102Jj0pJXmeMxgMtt3jLYrwkhEvy7Jtpxjie/w8qeKDiuDnOeXv97WXX3OZt/xh\nI62fvz5kaJAu0CKwDTihcSEaurACh+KibPnK/SdY48n6E9zNHUIWcO2KxrcIq0l1QqIEtrXUlUe2\nAe8kZQAhI3WDIBAkW7yTVB3hoTuVSioSrUizmFLWGjB1S7WqcMaRpSm9IidNU5JE4p1Detl5YmX3\nnIrfy3emiA648J4iOb4PsYiWSsYO8ua+6iY78XnEs/tOySjz6P57Y/oUUtIaw2rVMKtqqtZTriua\npondfu8QNoa5JEqitIrBClJgaosikPQydKa78+qiFnTQjya8kCK8wHtL3dYgAyIRUTYgFEF1G04R\noOs4By9xVuJtjC+2eObLU0IKKg2I4PFB0htfoz1Y8+7Ru9yeKHK7QmtJc36Ka2qCF6RZH5EOMQiC\nUqRSILzDmxaCxQWiMnyD1RM2Bl91CahSPDP5fRSTnqkrRAjoDrsVvCfPNEZKkFBkOU3bblndhBDZ\n7d39YL2N6W3WRoa0UBTjMU1ZooWiCY7+sM/dF+/yiVdeQekEqxUX51P21pbT4xM+9sM/FI1tIsB8\nHa8F5yj6Pa7fuM7Hbl4nR+JVxpuPjpifLwmtQKM4+eZbzF1JCJbxZExepHgTwxjqsubG1ZsQBI8e\nPub6tWtcnJ/jnOXk9IR3HzwhVYHjo4fs7O1HKkPl+M3f+iJlVXeP0Iw8S9nLewifU/RrnLfkxYB3\n33lMr1cwGPdpm5ovf+VrrNYrZtM5O+NdlDrlyZN36feHVOs1v/p//AZ/7Rf+NYajAd98/VsgwZiG\n+S//L/zo5z7H1776Db7+jW8QnGdRB6wueHpWMhyOWK+XTGczfJDszmc455nNpsjgKXJNf9Bjvpjy\n8ME7KAX7V/apG8Nytdw+26uqwhrLnbs3KQY5ITienjxlPMpJkx5aZ2RZhnOOpmno9Xq0rWEy2WG5\nXDIcDri4uOD4+BgpJZPJGGsdWqecnZ3R7/dpmmgw9d6TJDkvvXSD9bpkMplwfn7Ogwfvsre3RwiB\noigYjyfs7ERt+yYsKha7BWenUyYTG9NYXWCxmFIUGVVVMhwOopdASOq6JssKmqbl7GyGUtC2lizX\njMe7VHXDcDCkqirmsylZlqCsin4mWWGMY11WjIYDdnbHXOmPkcFhjWe5nJPnGWmSAZZUZ52R2fDL\n/+i/RQnN3/47v4QTgv7OHvPK0QbN6rSk34vo0tu3blNXDW1juXfzDq99+jV2Dof8wl/9N3CuT6a/\nR4nFn9fxp61m/k5FRgDqzpAWNZ8d5kwKEqVI0thJ8MbStBXBe5SEXpIhE8Hpw0dMz055evSU9eKC\nYA2Z9GSpRLoYW+3VxltvCDZ2RXvDAp1HWoTwHr0NlRDR5BviIh+8RKgEh2D3ygGrpmYyHHG2WiOS\nBJnnqKKHzvtR1yc148kE7z2nJ0e0K4HMc4ZyQust/dGI3Mekqv5wgPDgWsesmbOYzRDjrghSCYPe\ngMZYqrri9OSUL/3Bl1i1DQdXD7l27VrHxZRYZyMbNXTxDt2q7J2lDVHbqJOE1scRJ+GZROX9xJLf\np3L2+35s5CHPpNYfLCkRzxd6z2mr3ftIDD7MIZQk3SSCdbICtoVF994Hj/WBPM1i8eHd1qDX7/fZ\n2dnhypUrPHjwgJ2dHbz3HB8fc35+DtBFgEcE36ZAulwobz5e7hQ///n369gU0M8X0qlvY2FpYiFl\nSPBCb1PzWi8JKqOqKt58cMxqXVFdH/LxF27y0s1Dsskh4mxBhiUPMcSmXbf0VQGDPqczj5EeZQ1S\nRCOctZtUyCidkEJFMpDwKJmghI5jzdbgrY9O9jyn3y8oijzqSL2LEdYixrQrKTvCxGURmcD7Z0AQ\nKTpeMpGJK0SkZzhhYh0MGBeoEovovATW+6g/Dp2crNMgi6QzrGWagKAxhtVqTVVZnIvfTycKaySt\ns0hvY4hAQhfjvGFmd3G9SkHnW9Bao2SGCQHRxmRB7xzGNEgFSau2iVzBSaztNo2dP2PTqOhmQgQ8\npm2gNSjnSXWCDBG9l6YZlYjpfqJpkUZEnTJswxO81jTG42xkFElnI0owWKwX+MTGZGQZ4r+rC+BR\nKgUrqJaGdq1QfLDp5/mjLku0iKmASZqh8z5N2xJUoCiGWGdZ1QofMtarMkZrE9NKvXdbJnaSKOrG\nkGRJ7GALCVqxN5xw84U7fPYLn+XKaMBytaJZrTkIKdV8zt54BGkCqYbzp3C4C70eMWipQkrBC/fu\nkqYZ/8Ov/S5FkVMuS3rZEOECCxNI8x4HB7s0bY1pWqqyZG9vj5c+8UkePTzi6tVDVss1eVpw+/Y9\nynKBNRVF0Sct+rz6yR8iYDk6ekSSZkyXC1QWO5mrdUNZV5yeX5AXBVnRo1yvWK9rBv2C5arEhcBg\n2I8ylaKHEJJVueSbb67I0j6udGhVIDPB//nPfpuf/Omf4SvfuM+qXDOajNGJ5n/6lf+V1WodN791\nw7SBLGswxnJxcUGSaEDhreHk9BgfoChybFODUNT1EiUVt29fZzabRT2tiBi/xkTG+mg0YTKZUBQF\nrrG01jLojdCppi4dq9UFN2/eJIQQDXXrNf1+PyYkpprZbL41yFVVxcOHDzu/SJ9r164ync4oimiy\nGwxiA2O9LpFSsF6vGY9H7O3t4ZzbmqyjjKPZPq9jgm2UYL788susVqsuaOyCB1rx8suvMOwkCXXd\nsljMY5iOTrddaSEERZHivaOqapSO65FONf3+IN6POqV2GwxdGwNFFktMY9B7+whh2RkNUComaCop\nKDKNFN3zxAdsU7KqHf/1L/0njCc9dnYm/K3/4JdwKmH/xm2m50ekqebHfvav8Pabb9AvevzE538i\nGghv3qDQQxrpkfKDJVH/nyyQ/6wPE7oYWx9AeBIEzoOWgkwl+BBoQ9QOJwS0iHGw87MLHr3xFrPp\nOYvpBcJZepmm0AkmgVTH4to5j2sNdd1grYsXSN5DKPBYpJSoLHJXQ4gLeO0i99c5j2kc1liKfMDa\neIKQqCRF6gytY057UvQjt1Vk6KwfdY46RyY6hhQpGPRrbty8waJ6wCTL2PEBUzdcXMxgHjtb5brs\nJCUapRMynRJCoDYNp6enHP/L3+bei3f49Guf5t4L9+j1+yRSYcKzRC9CTCR0zm2d+WmqY3Es4lK2\nIVrEvLT//9gcH0k7/36HFAjRnYsQgE2Ah0N0ATfBx7S6jQbWGEue5zjntu5piAXOZjRXVVUcl61W\nlGXJzs4OaZpuGcGbbvOm+L3M9AW2Ha/N53/Wh+zS+VRwMdCjU7V7v0HdOcrGUS4DVemp2imFaHiy\nrni8WPHpV++yl04oF6eAZSAK+kWPpYG1cbQJlDjyuunoNT6SLYSip3vU5Zosy8jzHnVbbzWvzoYY\niuE8w+GQPE1RKhrCjG0wjSGRSbd4eRKlkSIGE3nPlv/rQ4h7++0gRiBkQCaxAEyUgKDeE3OtZAMi\nyjCci3g3Jz1eyEjr0ZKQJEidkugcn6TkRY+REyjV0NTrmCwnAs6UmGBQwZEpjRA+Gn5kNChqHISI\nnEqzFN1N57yUuKaNxbaHmJwSCMIR6PTFm8JfQvyiWBBvJyEKlJYoLfGmxZY1KmtIhz2aqqGXKJTO\nsYiIpmssptu4FEks/HWiKZ3DdEmJrQykIpCqLm5cBGRM1Yn3WBK71huzX9SObzpRH/6JVtd1d/0F\nxv0hrbHIQuGcjsivRJNqR93U9PpFpAd4j+1MXAA6TdE6pWqa+N4mKUVRELzl7u3b7PfHNKslU2cJ\nbY9CKg5M4Iv3X+fFV1+G1sByQbd7jppg4UAp+oOc0XjAXjXh/PyMNI2F17uPHrM/2ePg8ID1esFw\nuMv6+DF12eA9zKYzymXJrZt3sNZz4/oNTk5OubF/hb1JgTE1T8/OubZ7nUE/J81zsrTHuloz3Cmp\nmprFakVvJGjbFuEFVROT8Fzb0uv1aTrk7Gp9zsX5jCxPccKRZpr5fMl0esHuzjUm410Ort5Cq4Sj\nx0/4J//kX3Dr1m3Ko4ecnM4ZTcYYUvo7BXVZInONNCWum5zNZnOyLCXT8X0W1sbp8Shn1B8TbLx/\n5/MLBoNd1qtVNDkHaNrYCc4Tjakrzs8dg8GALMsoywZnLYMiwRnDYDDk6dMT+v0+vV6PLMu2PpC2\ndR3RwjIYDDg/P2cTxhRCoGnitbBcLtnZ2aFp6th8E4KiKJjPL9jf3+fGjRs8evRoG7ISQqSOXL9+\nfVvcWmtwznN0dLQtmifjXRazFY8ePGQ0HrIz2eW11z7BF7/4e10zwJBlefRZdAx8pRIQcTq5kXtN\nJmOc96xWK6SMTYQkSUhUoGktpgm8+fZDlJJUB5Z7dw5QdISufoFU4KyNRmLfIrEEFKu1xzrLf//f\n/RcURcG//5/9A0Sq2N8fcbFa8YWf+WlefuklXrjzAgmKYvcKmBoZHFlRfOD9+a9EgfzdOsgk6Zad\nEF1ljuADrTFxdEggdCD92tTMq5rZxQUP3n4L5TzeGfpCIBNFjkcHQ5YnhG7UlkhP43zEygSHShVO\nOXSaIg0opUmDxLuANZFiIVpDIKZxllVNv/WUrSHJCnxQ7O7us7ezz2i8Q1700NkQ01qMWVPX3djT\nQSpTnPQ0PiCF5NqVA9onZzhn8a3BpbEbOOgPMFU0eBFi7KmxBkSUTugkZV1X6KLHg7feYb1ac3Fx\nzr0XX2BnZ4eQSJqm3TQpQcVkKZ0kICRN27ynUfye7ur3SwLxF+R4z/X5XiH+Rz4ulaSXtNbPuvUR\nLRaLxaTrNHsf6Pf7uK5A2PwBuLi4YDwec+XKFay1PHz4cNtJjr/us9S8y8Ehm797/vPN13yQFvn7\ndThSIlG3QxMGT4KLJnqlsCJ2VZESa8CsA195UiIflzyYLmmk4udePCRJHbQ1aXAopVibNVVV4fMc\nKxw6jTxcIEq1uh2j2HRkiQl4kXyw8Tp0koluaiSFiJIkawjeIpWOdAjrIKi48BCnT0miYzemrmlM\nAy7GcmdZ1vGoO/lLp/kNPuAlSCGxzmGdxfnYBEDFaZWwISqvjYmM3zQDlWBMNH6laUbdWFxn8PW2\njhpDPF7EiVkgRCyYLuj3ClTwWNviaku/P4jnXQnKxlA3hna9ilOMRDKcRP1lXhRdLLqKpsLGoVpD\nEFFbG9PYLN61EfuFRgqoyhUyLehlOfPFiv7eEI+mMhLv6d4TSZ4V9NMEazxCSZqypbUGY23EayYi\nbjA2RTFdgmEiEUohUFu/hLPRZO2JMpgPe9R1jWlNfF2esTg/Z3dvn2XT4omTt5DlpFnKcDxitVyz\nXK2ZTmeoVGOqmqzIKMs1O5MdlNTs7+9H4oVruXfnFq++8gqFDCzLFdVyyZ6VvPnwKaenR9y8ewPG\nfUgSGOyCsTG8Q3hYrcB6bty4wr/9d/8+UiZUlSXLCg4PD0mDx9QL7t+/z+PjI25cP+yu6ZiS1+8X\nPDl6xM5kl9OTE5yxXDvc5+DaEOeH6ETSVCuK/Bp1XeFMzXgw4O7tW1RNxeOTUx6fPGVdr9FS47yJ\nv5eIcoWj4yMm4wlSxSmGsXEzNb1YIBMQMlJ2QoDWNOxM9lBpQYrm9TfeYTDqo1LPk6enqETRLBta\nY2iqitVyFbueOol4tkAXxmG4OL+g1+vRrFKsqmnNilQrpEqYz2IXN01TpJTUVcnJ8VMCcHB4QE8n\nXJyfRYLDcBTlAtZRL9e89OKLvPnOO1hru7Cl2HSImyhB2y7JspT5fM50OmUymdDr9TCmRYgC7y+6\n4JQ5t27dZjabxYjrNGV3d5eqisa/Z91iyc7ODsa0nJ2dMhyO0DpOqpqmpW0b2taglKJpms70vyZO\nrALzxZxr1w558uSYui4RQqG1pm0Nw+GI/iBHlWIbPgJgSku/3ydLs0jXkIrVsiSESMmqSstyviTN\nVOdpcty8fsjepI91kApJCBZwMb0yADKNzURrWS2XhOD5b/7ef8iv/MZvc/eFF/n0Zz7DoJcjQ8Dr\niKjFNltGvWwtKs/e9/78zkEhcEmzFhFAz9/62wVu8/E5gw58cEdsY2p6/qWxi3Z5qX+/1773s83E\n8TnV43fdywfi7l8ikEkMwFB0KVXWxyIxeFzdsF6tWMynlIsFZVlGWL0x4GKwQJFn9HOFcJbF9ALv\nIhUgPuQ9aSpJi5Si6CHTlCTP0TkkQiFRtLXF1I5eNiJpGnyAtYtxr9YGHj56wu0XX8brBI1Ge4Vy\nEukSspCgZAJpLFSQHnpjtPW4tkQXfcxgxP1vTBnv7bBczFlUFU3TopIYI3rqT7fBKD7EDpgXkiRL\nSVINNdi2IYTA0yfHrOZLvvGNb7K3v8ePfOHzXDk8AOgWtMhsdj7EbsT2fIf31n/ivV3Tf1VK5e+0\nafsTFY0uUAlBmqQIZWl8hU4FSgqCiAWhcb5DK23u62iojLq1ZPuAPzs74+TkBCEEL730EkopLi4u\nOD093WqTL5vwLmuQ309n/EHvw59FkWzIO3NVJ18CwEdqDNFf4BNNuruD8BaH58S2pBouzhr++Nf+\nmCefu8uPvXSHV3eukc9OaOZTnLX0laTVDUp7+jIlQUZtMwIv2LJrITKoY6EMhDha9yZKBrx1OGeR\nXVxxIMbP60Rho50BGSIqzuNRMrLH29C+R6v93u79Robgo7kNtumXxlisdQQUIpFIHxnXoSucZ+sV\nvlcwnkxIlGZVl4zHE5LacPL0DK0Skn6P9bLF2HZLTgkiFs5pnuKsxcgOZyklqco6NJnGest8VeGM\n3eIbZbegR4xgTEWLnSiBcbFQ8GykXFE+1LY1VblGKIESGTQG2RhSK3h8PGUnj3HKU+eRSQ8pGgiS\nLFH08pSlXWHaFkFClmdIlyDwqMTjpafxDiGTZz4LqbrglgTvYrFg2kgniUL/D18gr1drBDDc26X1\nBpmnNLah6PeoTEOS5njTMp9N0VlK3db0Bz36gx4XZxfM25Z+v8d5dcEnP/EJMp2iVBJTH6VAIjg7\nOWH29DH37t1m9uQxd2SP+++8QfCO0WgApgSrI7wpWKgM5FncfATPzs6Q/b1dVut1nFhqjTOOYBpI\nCq5fndAfDNmbxEjqPE9jyqFOCAGKrOAHf/CH+dbr97n/+h9x8rjgpZdfpNASLRz9VHB17ypFf8DF\nxZTGGnb6fRg1vPXWG+RZSttYkkwhkwKjW5bzFR9/9VWWHWZSpwXWtLSmZV2tmM5m7O7vUpsmFnum\njd1nZ8mzAcYL1mVDU9dMp9OYNRCi8dW2ZqsJd94TTMtoMKIs10ivyLWOjQXrcCbgfYbxAWtakiTl\n4OphpFus1pH+Yw2j4TjKY0Jgf3e36/4LdnZ2wQeu9vs0dcMXPvs53nr7Ld54+y0OD68yHke5TtM0\nVFUVTZqJYm9vLzamQuDp06ddcNMmvdPRti1Xrx5u/81FUbC7u8vbb7/DaDSMFJoQIm7N2JgsCczn\niy0HfzAYxihqneCcZ3d3lyTpwkscrOuKydizt79DtkwpyxJrDSE42rZGNdEQW1U1RZHjrCNNo15+\nw9L33pPoBCE03geW9aorVgWLdUVdl0ihWK1LxsOcG1evICXd7yhBZNSmI/N00dnOOdIs49/6uR+N\n8fY7O2R5zrJcxkwHneCMRekkRpR/h6rjQ3GQLy/ll5WRz+PfPnDJ/6BiQDz/yWUT1nd5vXhWXAfA\ndOI6AdEMB1288zNtnrhUgINHhTh4dq5BColqBSJE/JkC0qAp5yXeOxazC+YXpzTlGhEcIwEhz3BS\n0tRlhzaKhIDGNqzbmiTVCCARCUmWonsKIRVF0Y+LkqKLpo1j7zQTIBMGwz6y1nEkbANWxFjX06dP\nuX77LnleUGQ9+kVBkeUxDS/NYsKTEyzKedT/rGvKVYk3JUmRELzj8aNHSAW94YBEJkzDOefLc9bl\nmizV1GXsAisVU8NsN2bCWqx3pErRGkvTWpq65uTslIePHuATwQ//0I+gEsV4b4ciLzA+3thSSbK8\nwLVt19X021MtiHrcv8gc7T/N408ssQgeTwY6wuitn5HqlKR7MAUvqQOkMmFulthg+fXe3+SFGzc4\nPj5muVxugz4+/elPc35+zvHxMbPZjDzPmUwmW7LFZZ3v5eQ9eIZp25otL2mq/zzOtZUFwXtsiKP7\neMSHx0bX2TpwQiJJEAIGWY/WNQSZIvqOX/+jdzmZLfmpl+7yV67ukBBoZw3KtRxqRektsujFDqVx\nXbcyFr1a53gfJRUhJAgkzkPbRA5xmsXYb+F9JB5LiVOQKtXJkzzOxPvGGU9QgSxLY0S9NzGERCYR\n09QRUJ6FiHQbmRC295+zHVoymh1I0hTtJevGUzYt1jla5wguZji4ruhrG0NwJrrQvafFslEGezxO\nCAIO7w0QAwTaxpGkmlynDIcFeZbRGwwo64aqOibRGb1RxMjlecZgHNFmOk3JewVeCpaLNbPZjCdP\nT1ktI06LzKIzQaIV3jsa26B0TiE1OgiEiSmpIQh8UDipY6eJBOM8QQmCj9emNYEky8iKHBMCIUTt\nsbc1rW3IsqTDzsWwnERFPr3B0zpHa1qsC6RCxc3nhzyKPCctCqyCRbkiLTJ6/QFJqtF1StNGDFye\nFazLkovplBdffJGz0zNGwz5NVSKAq9evcrC3j5SS4XDA3Zs3sdby9OgJwhrSRHJ+ckpaVnzr8bs8\nfPddXrh7m9EnXoXVHM5buLL/rCJwAdIB0pUMewW3b14nAK31HBwcYMqK+ekZWZZzeHWX8XjEjesH\nPHnylP5gyBe+8GN86Ut/AASCC3zxi7/Dx15+mb/xb/5HfOv11zl/+pQX7t3j8eNH9LWgn0G1mjIa\n5KhkSF3VhHGfa/0+T+YrlIgR6603gCfLEnq9HOssw8GAtmlJdUo7n5GnPV566YA3336DummQQrBe\nr7hy5YA8z2naNRcXc4ajHVIdDXZSKabzGVmaohE0TRu/tmnwzlEUObdv3+boyRMyvUmbjWP91WqF\nSqJMq8g9b37rm4x3dvHd+jfoDxgM+5jWRCKFkhjTUq0rDvau0B/2eePtt0hSzde/9jWCgFSnWGu6\nICbTpZsqFosFu7u75LmkLEsGgwG3bt3iyZMnFEXByclTrl69RtPU3L9/TJ7nFEVBXTecnMTGxnTq\nSNOMsoxBP2dnZ9y+fXvLtp/NInUjTTXr9bqLr45de2MMbduQ5zm9Xo/RaMKNG9e5/8a3uP+t+wil\nSFNF3ZTMl1NGo9F2DZhMJpTlmtls3klIUpz3DNMhZVlSFAXGGnSiOX56zLXeIdZbTs8veHp6zMHe\nDoPhmDxTCCw6CHyQpDo2Zzbrz0Ym5bsEU47eoAH6hy+TJ1lsAhC6jaziOyUi/KWRWGweSeojrr0C\nSLXu9G8CJSSJEGAcy+mM2fkZBE+9WmHrBhUCWikkjqaxBNeSKIHUKamW+OAwxiJTTZYXQOSd5lkf\nqRTBCbQqMNajCGiRYJwleIfSmlwLhjs9RBW7FcMkZVG1hNBw+mhG/fIrjAdDfGhQSpAIz6BIQScE\nbxFK0BDjAxpgVAwonWWxKGmaNU5olGhJBFSmZr08py7PEL5GyxJ0xMsFJEYGnPU03SLppcQlKdY4\nrPdkiSLTBT4E/viPvsLT41PSVHPvhRf4zGc+xWiyQ2MrnLVYEXmoroPuC8mWfKClJDjfncTNOOHS\nSfqgz/8CH88XiZsYbDYfv8d/pxBRS76J/RUqR6ERpGiZg0oYAkHCYnaO8SWrYsVsNkNKye7uLr1e\nj7quOTs7o21j52XDzAQYDKJJY9Op3HQtN/xk4D0EjA1fWQhBmqZxLL/tqj4z1G1oGZe7oZv35oOK\n6k3h/Z3oFh4IQhOkx1sBXcreRhMPIBKF8Q4bIjM4psWrGESRKnye8Hi5IrxzRluueKX4DHeHWTSw\n1Y6JSsitZalELGZFNEkG5/E+kClN60002BHDJLwFZ2KnWauULImT7lRrfGhRDkQ3BZNAEJIQZNTl\nqnh+jbNd5z9GfEsVyRiXjZObj5s4bONiip13PgbFJBqtNKINlM2aVbWmsWC9JRBoXKTQWBuoyiUB\nETtCxuBMjIv1PoCM5lsfwrZg14lGJ9HwrLVCJTrKGOYLyrqOhXmS0BMJNkRmdGOizjjIFmk1IQia\ntqFqHVXZRNRaiCnFWmmyNEckCu8DqUookhRhPOViwcH+Aa2NqY4Hd+5w+viPuNJLSLOctj7nvF7j\nncEhtmi3QKA1LcG34Bs8DplEKknUdnddmRCLv+Bc15WP8eTvCQ75Lkev12PZVoQg6fdiEZVmGSK+\nnWghmIxH9FLN7HzOzb2rDPoDgvVI55menlNkGQcHh1y7ehCjh53j9PSEg50ddgd98jTh62+8wc3b\nt8inM778lS9zsL/Pp37oBzrNsYhdJCmgqaE/iKEdwUGi+Lv/5f/IvVs3UDrlwaNHzM5PmeQ5UgW8\nb8nzjCxNePz4McZY0lrxh3/wRY6PT/jMD77GcjHnmt5DCMtsOmMyHlOkGikcP/tjn2Vd1SzWFQrD\n+dEJWdFHKo0Sni/8+Bf40tdf5+GjJ2S9ATM7o+xwh48fP8T7QJEVtK1hOp2xLtcIJOen5yAjom82\nW2JtvNY33dQrVw5wztIagwCKPCGEPhfn07gR7Z4pg8GQREja1nB6esJg0Eciuvs6Ekx6vQKpnvkr\nhJQsZlO0TqMhK1kOAAAgAElEQVR8y9ltWt1iFmONF8sFo+EOAOt17DT3+33Ozs5Yrpfs7u4yHo95\n+OhR183tI4Tk2rWr1HXDbDajriP7OM/zrflud3eXxWJBnmckiWa1WtPr9RiPx5yfnyNl3ISu1+ut\nGXCzEViv1zFEBLYa5V6vh7WGg4NDHj9+jPd+G2YyHMYu9HK54tHDJ+R5wbpccv3adS5mi0jv6eR3\nvvtZdR0bKypRsU7qgsxMd4/2+zE45eDKwRY9qbKCJ28/Ik00R8dPGQx6DId9RCJJsziNKtdrtNZb\n/GzbJblaY0i6JNjpo693+mXP3dd+HGT0kv2JOsh/UY7k0iZgQ+LZ1lrvK/V4Fhsdoeox0Q5nqKuG\n2ck5b7/+DVzTxK60hEIn5FqiFd3NVeJNjEp0gGkd1rVICTdv3ImOdWKkaZJk8VlkotHGum5nWFXU\ndWQLp3mOdZ7z6QVK52idMp7soPKWeW3YO7jCw8ePuPPKKwz29xju7iBlEpOAihytFM6sGY6HQKDI\nUx688zbDQZ+mXLC8mHO4s8d8+oD5fMnp0yesZxdoPEmR4auSIGMB6/Bxs6AEVgScAx8cVdWgEk2m\n48XfOt+V44KHD+ID6/633uX89Jwf/cLnmOzskucpVd1QGctg0MdIS1PHznJMcksQ4Tk00nOGLted\nvfcUkfELPtQY//nPPxwHWcAWq/XtHN7ni7LLRS7EBNP4w3gPpULwbJqxPZ411ePfi9jZ+6jscZVA\nqhyJ9KgEkAV4iWkUQqQIEiQab1vaMvJc92/t0zRROpNl2dZ4N5vNtjqtTbdhgwcCWK1WMf1RKay1\nW93rxpD3Qe/b5r3aFNiXz8Hmddv381IBvvmz+Z7vFybyfkcMsons0fjEe/93NZOK2FK2eDzzxBJS\nkE7SXnjy5CZzG/i9x5bTf/w1fuDWPj/36Zf5xAt9wsU79MWIZXKMQqJFoDVRl2rIUA6cFTgbE+EQ\nAu+i5DPVGf1BnyIH1UmxrPWYBtq2RYUaa8AHSSITiqKHV9H0m2W6c9jTbUB0px2MsotEyS6VMmr0\npFRYYyjLCp1qimKIsZLFuuV8vmKxaqhaQxsSVJ5hkxy0xgTPoiyxRuCR2LaJMqwuPMP4BissOstI\nexl9oRgOC0ZZTpFqhnnGICsI1qO1xlhPqguuXxvikOiQ0DQ1y3LBYl4iU4FqJdNlgzWWddVgnUTr\nPqNBH0vA2xFFMiJNe/gEQiLAWVSrSHVAO3B1S9kalmLFyq/5mBWsrEKbAMagcbEbDEgR47aFcwRn\ncLZF4JBKkaRpNHr6EIUq3mFaR101NI1ByIQsSylrQ920H+q6BLB1zeGgz97BFZarNV4LCuc43D/g\n6OiYYjJiuViyl/b53Bde4/z8nMFwwEPxhLOLCz77Qz9Arz9mPJkQrOXoyRH7kwmJgDe+9hUSKUiD\no1ks+crv/BZmXVKWcz7/2o9z74d/AAoNRoLTsLTQNLAsWTpP3hugR0Nu7+9zdnGBDIbDYZ/dezdZ\nLha88vI9losVbdvE6UzbcuP2dabTGXXVkirN6twwHlxlUERDaNUYLs5OsVXJyy/c5vTJO4zGY+4c\njDh6ekq+PyTvjzg6PWd2fsG//K3f5M4Lr3LrcIezswsWJ0esyipOYzpd8FLMI/s5Aa8T6rqhNDV7\nh3td8ZnQtBXLpeyKwZblcsFiscS5yG23R3Ga4gWMhgNcl3ZZlxVNXaPTNCJQTXweap0wGU9oTYur\nu8TctoUgSPM8spnbFhAMB0PefOM+k9EYL+PPGfSGWOuoqookS8mzjLOzM6aLOd573n77rci61inj\n8ZjlcsXt27d488230FozHA63Rr6yLDGmZbFYdsi1DCFkZ6TW22vtxo0b1HXN5SCnfhdK8+TJY6RU\nXL16FYA8zzk+PoobX9h2fSO6UbJarTk5OeHk5ASlFL/4i7/IL//yL0OIE/L2+JRev0fbNkiZ0u/1\nmE5nLJYLbt68SbkuKcuS3d1dNkFUm5j6s7MzXnzxRdq2jRSNVUM6ucqTueHN3/ljbL1gZ5jx8z/9\nU0zGguVqFWWBWRbpOEkSI9i7TYB1rqPnxEOrhKff+n0QFmNbbr728x94f/6lKJAvyyrg/Ztv7/f/\nom7ZI1yFzjJkgKouOT855fToKcvljF6adsuLwLmWVnQaPm+RKkq2WmOxpkWogJSCNM3o9Xpbv5V1\nEfEUXaWgkoRUOxrTsnFkO2up6wprHNlojFASoeSWQ6qThN5whEpSsl6PxlmEkiRpGhmW3b9yW1Ba\nR11XjEcDpmcnBOcoV3MSAovZBXVZEpxBK4mUmgRB01EJArGAEyFyUb3zeOERziLoku1kLJh95yRO\n85ymDZ0er+H+G/cJEl555WVeeeUVNug3pROEijpp2WXH1+0zcsL2XIlvLyIvF5/xdwx/bgY/8R12\nne85wiVSx59B91vKgJAtUsREMC9URIU5ETtCQuFdHFPqRCFVxmRnErVaXYGbJMlWU7xarUjTdGuu\n25Ar1uv1ezq+l7XEf1bGu+/nIYHUepwX4Dw4R+1jL1cqwYMkZXE05fH/y92bPFl23Xd+nzPc8Y05\nZ40AQRCcJGggm2ralpsR7o7otlrt/8ErO8Jhb+1whLW3/4NeORz2yqHwzl5IDluU2mpSbYmiSIDE\nUEChqrIqxzff6UxenPteVYEACLGptqQbQWai8uXLzPfuved7vr/vUP+IX7t7yD//2qtMUs+IS7rO\nYPsmN9tB62sWM0+eD1BaEmyHtevYjCYMg8GITIA0jmAdtgsELCoIUqHj+aMl0gs8Hh868C6aAIk1\nzNvzbLtx2L4vXm3j9tiVhrg+0i3q8VKkCzRtS1XXxCI7BV6Q6IyQxsxr0TPCo70xxgWurupYuJHI\nWG4kA8FGcbfQCiUcSmuU1jsGJxrc1G4jph10ISC1RBiFVilpkiO1jrIJokay8RbvQCiF1CmJjM2n\nPuRonSJEEgUeIWYEJx5KFUsGGuWpfE2LxxQJ2f4BSQIOi+uSmHe8vT6dI2ouAlrE6LTenxc3am67\nSY/SFRECMvTZxNYRZLyHib+GxOIfffNbXF1dMZ3sMbz9CsvFktPTU0bjKW/c/wKCyDCuVxvGxYCN\nWGBWaw6LIaE0ZDpjrDPCooKk4HB/imttzKtdLKgWc3zXcHN+xvVFTCVoqw1Ht08x1QZZb1BF0bcw\nGmyzpnKG8b1TrmcL9vOETEoORyMWVcV0MmA8KrGm5fzZM8bjMffufpH1esP9u/dpupYijRFi6d0U\nQobzjqxMWS8XPHz4ASo4RoOU2fUFx+MS0zbYImMyGvL04gpjW7742itkWcI//s5/yDvvPsA2G8ZF\nSrVacHV9w52797m5XvRGupTaNshe2x9w1Kbh4vKS4WBAlmUYa1kuV3SdYW9v2hdwDLDWROOajMzo\nYDjEdoa2L3txxpFlKZPJmLaqmW3WnBwdI4SgqioQIVZw74qQYuuhk5I0icYvYy3D4YQgFCJ4yjzn\n6OQu19fX1F3Da3duMxgMOL+4oDHdLkZt/+CAp0+fMpvdcHJyyo9//FafPOFZrVYAXFxcMBwOKcuy\nl0JEnJHn2XMArhO0djvw3DQNaZpgbUybqOv4tyZJ0rfxpTx9+hRrXZz6uni/t9ZgrdtF0eV53qcb\nLfj93/99jOk4ODjof69zJpMpxhjKwYDVaokQEmfdLoEjMr0dzjryUdxUbO8NW0JmOByyWi3J0ij/\nKAclq2XJ06tz/s8//EPefPNNbt++TZ7nMS9eKZTWcSPgPV3X0XVxw1oOBpRFiRaSLM/oujhtePaT\nP+D0K//kE6/PvxcAmSBeBlMvSJojCO61d+JFjNJnXeLRhGiKcR2b5YLL82dcXDzDmQ6RxYQLKTyC\ngLee1nfEwa3D+9hLjghonVIUCXlexJzTnu6PuaiCzliE8AR8dPqGEPOWE4mzHtsZOucpyyGOWAHc\nGRMd13mGShPKwYAQFJumJai4W0Wnu79Kp5okTemqivmNYToe89Zf/L94UzG7uGY0UHSbNSoEhllG\nNgbTNvimQ/XmrgBIH+h8tDkmIu7WJQKZ6B749xFMveMpeB8d1yogpWM2W/DDH75FVdWU5ZB79+9E\n3VUds6SlEGRJinWfHKr/8ffrkwBpgE/Xt3/e4xf9fvGZ/9k/N+zSOl74/LlJ7DO+9xc8skySpA6l\nPDoFh8S0USITQiB4R/ToOfI8IdGS+WLBaDxGakVrIvNYt83OxLFlG4BdkchsNnuJ6f0k/fHf6aNn\nCpWHgCRIRYfEC4FTsE4lm2bDs/fnPL665PXXTnilKHlFvoptVpjNErtpkFYgbUpTGWqzwgswoQMR\nSAtNmhfoRKJ0nMQE73vg63uTtOwHGYIgA9Z3dNaTePqSkQpENNk4a6iqNuYvS41SKcElMaNUSJSe\n4XygaTu6pmNv7zZWJqw2C9Z1R9PFyCQhJEoEgoJCSgqlkN6hE02WF0jbZ8VrReITdCJ7X1oE4kJ4\n0iSNY+OiZJCljIqcQZqDD1R1jVaxZMO1LUlSoJMUoSRDLVFZTLOo2wYh1jRth9COEDRSaESiUUHi\ndIIUyc7saENA6UCuFKmUaCHxrqNua5YsaQVYF2gVaDxCKZSSKOHAxbG9dfG+o4Tc1U5LEX0S3kYz\nkBSC4CQyOBTsypFikY363GU2AAejMYmH8XjMrZNTNkVMQZhf3TAYjxFSs6k7cgRpgFwqVHAYLyn2\nDghKY01Mjcg3Dfl0wF+99y6nB/vcvnXKW08fc352xuzygs1qhdCSX//1N3nt1S+QJJpNtWaQ6Cix\nWC5ZrNf4TPH48SNq2/G//h/fpzUd1xeX3P/Cq1S+oe3aWAcsNc5FGZ4i6qm11gwHg2huR+KtYrmZ\nU282dG2NcIKqrug2ApdLlheWAAzHI45ObqHSFBvAC8FoOGQyeJUH773HelPRmkCh4XAyoujbXEGQ\nJBpjOtqmJU1SsmHJfq6Zz+fM5/NYhiUlbWMJoSHLYk6zkILBYMh0OmW+WHJ5c8WmqhgWJR5IdRJb\nFK2jWm/QSjEYDPHBY9o4wp/uDTFWY7pul0TjvO/TIBKKoqCqKnRfwtS1HVVrWCwXO+/Dhw8fUpYl\n19fXjCZjZrM5OtFcXV31EzyPtXGdLIoCay0PHrzPyckpX/nKV/qkjsBwOADg5mbGkydP+oKTDd47\nfvM3v7HbDDjnGA4HzGYziqJgMBj0kizLarWi7eP0ptNJn8Gc9glHUZ7w5MkTtFaMx+N+ozHk/PwZ\np6e3+N3f/ec8ePABRVHwwQcf4r3n+voa7x3lYIiUktVy1acmWVbLVTTN2TilzPKMpGe9x5MxpjMU\nRZTQLFdLRsMReZ5z+849Hr7zAyb7z9BJjDW0SpEqhXeOclBirY165H798s7R1E2Uq8UFGYh68E87\nPhMg+4+Bhx1rB32WpniJ0fsZsLFllD7NevTXYppefo7wgnREEJDhhd9j+/EzaDsvn8eGpni6zYr5\nzYyPPnzIww8/YnUzZ1RkJJh408sT0ixB4nCuw9oudrjTMzwyejOUlCQ6QSdZn3UaQ+izLKf0gbYf\nuzjTkYsYpo0AIw2ubiMTLQXBBiwO17QM9g9IyzGLqmVvfz9q6nQAKZGJJr6NMrIAsteThZiNWGE4\nf3ZGJmPwvfaCRMacY5FoWiVYG0PVV0FnWcw9xng8EUwBMbEjCBKpsCFuDILbpoQInLMopXcMlrGe\nzXrNww/jxX96ekpeZLRNR2c78iwnJHHUk2d5f0P9vO/+L+/4+c/5chU2bKUUP0fWwSeD4/5C4ZcL\ni58fOoU0C0glUAk4C0Eqgk4IISGuWwETLHsjSZErNtUmtkS5WBhSliVSSc7OzhiPx+zv7/P48WOu\nr685PDzk9PSU+Xy+e69f1LhuP3689fDvmglTAEnvS0BIglAElWCUwElBpQIy03greNgYvvvBA75e\nHzEc5BTZiMFgj3p1Qds2ZCpDmAbvLV74WDSRxagwlaVICSKR4G2UK/UnuxQCVARsAo8gZjbrrc5c\nil2M2yex+UKImGzjY06y8yLmsCcp072SIDTLdcPVfMWm6eJP0BItFMp6rG04BKZIxKamSFLeefIR\nxsAgKZnNr9HCoaSiHJagHY1qGWUlRToiEQkCSSJT8rSgzPM4ijeGbSyTtY5cSQQZOiSkWU4+iK8J\nStCZLqYyKIdx2zVHRRZXJvFe6WOyhceDBy0ViZZIAp21rJqKuVlSGceyNjgHUnmmSqFVQEuJlB7T\n2ViSouKGT+sIspWCbZXArgyBgN2+zqH/vaREBEn4azDI9uwR0/EU7QObmxmqC0hhGQSFrFs21Qya\njkFZII3j9ZNjNqsljbOIJI3R/ULSdiZGYM2WnOqM1bNLrm1LW9c0zQazXLE/HDBfrcmQjMshKI3r\nDAwCFDm4mk1VIUTOw6ePSQc51/MZwTrGp8e01jJf35DqnLpuSNKCYlDiOoMSColkPBz1a0PMp66r\nmuAbCI7Tk0PaqkJ5h6kWzDYdTbWiGJW8/+FDDo+vuPuFL/H6G19mtVyz3Ky4e+uYr//KV1ks11zN\nNhyfnPLs8pqqNszmK84vr8myvC/jsdS1o9lsyAcFg8EQYzq0TpBCxAxj+Tz3PSfG7C2XK1xwDAZD\ngne7KLSmaaIMUAjqriGRCcE7Uq1RWsWJTngebWltlOt0bUsIkOXPz18p5fPEBp2ybmKrXrd0jIex\nIa9pWw6yDKkkSicIIXj48CHj8Yj1esObb77Jcrnkt3/7t7l79y5/+Zd/yfX1NUpJLi4u2dvbI0k0\no9GIJInteHfu3GGxWFAUBet1NOdZ62jbbmfUk1JyenpK27Z9RbWmbaOpz3vPrVu3UErtGOjJJALn\n8/NzhsMBi8WS4+NjqqriwYMPuHfvHt/97ndZLOasVmuOj48iy1/XpGnKpus4OzuLbYObzc7zEu9X\niq7rmM1mZFnK0dFxL/WLnhbn+9c7eHRecnZxzWJdc/vefUTX7TKXnXFMplPquma1XNKZjrZrEDpe\nt+v1hiRRkZwMv8SikI8v7S+QtT9zbL8mXjRf/YJHTKsQOzaYbRKCEHwepaaQcTTaGRuZkCxluV4z\nLEv8Ysblsws++ijutpvNmkRLCA7nDVIGsmxAnul4U++iRs07C0i0FkiRoBKJlBqtI4u81fuEEH/d\nLIsRI6azqCTWFZguoJTEe4VKFMpL1tWGIGK0UZYWHB4dkY32WH/0lNM7dwlakZcpQUgq01GkGkEG\nUlI1NaLa4I0hTRSPP/qQw70JtBV7d27z3tt/zv4g77NuO7AdmlhpOiiH0fHtPVXdsmo6YiiswwdI\nZIvt+psG4AQ7jRIhYNp298YrFasYqqrmJ2//lNu37/Clr74eR/lpiooajc8PnHp970uPfxF4ftq3\nbU+eTzl2LOinP+KF/3/xOT/zJ/7M7yhe/PxvDh/HBVxJkjQjCEFjPTYoPAkuJAQtEd7ENw8PwtNZ\nx6auesd2BFkSQdebQIQQu2rppmnw3u+KRZxzO7nFNgt5C5i3pomtLGP7vVrr3eM+bsR7EVx/Uvve\nv2ugHcFQjO6SKjZQOgmdcKR5jk5gXVd878EZm1XD6d5t7t/bJ5MjnLB4f0OapCTB44JHKoFOJSJL\nSTJNkkiECgjtCV0A4aMJzEcTnoywD9nf9wQgVT/NkSpq1fvXxfvQRz0JoC8EkZLQ/7tzkQ3P8pxi\nMOZ83bHcbKjbLubwphmJTghSYaSjaTqyvGQ0mTKeTBBBcKIzVsuKi0dPcG1FkSls20YvRoh18jIE\niqxkbzJlf1BSJgmZijXcznnyLGNdNzTNJsrR8oKFHWHaluODA24d7vHRowcs5y1NFRiUU7RqWVY1\nSipMXzUcVwQLiUblBegALrLLUkUdvxahl4JkZFkgCVGrmugOJTRoR/CWgI2pHiLKaIQChURLGW8f\nL1yvL27W44SQmCkuFN69rKn/+edXILcd02CpNiukyFFJSqHSXhGVkRfRVNhYg1ivyUK8hyRKYVEE\nIUhCNGNmRYE+OGIwn7Nc3XBeV5i6ZZjlSOeZFiml1OSvvAJPHjEajiDNgEBoI0ipNmuWizn/y//2\nlyhgMp5imia+rkKwd6BJeklWlmbIoBiMyn5TFtl3AWiVsLenaBrLfL4kmJh4Ytua9XLJ6dEBdVVT\nFFNu5WOGewfcunsPLzTpaEwRHI21HN++QzmuODz1PH78lCwvePrsCnhCWeSx1CvpU5eMpRCSel2T\nFBnORUlSlmYURWSDAdo2gqk09WyqWK/tvUMGImHTtUgdHx+2a4/rdmwqwGQ6wHlLonVfN25RWtE0\nLc55ZBdZ5kTHaQpAa7o+1ix6nhaLJTfzGV994w0efPCA23fv9N6lmEG/t7eH1skuf74oCp4+PeP4\n+IQ0TXs9see1115jsVhgrSNNBdPplPV6xdXVFVmW8vbbb5H2MoU8z1ksYgOe1smOWY5NqiXeh93P\nNqbbJWlYa9Fa8+zZM7TWkRnXiuPjY9brNdPplB/+8Ie8//77CCG4f/8VBoMB3/ve9zg5OWa1WpIk\nMcKxzAvmNzMGveEbYkrGaDRCiNj8B+zYZ60TVqsVWRqJx9FgwGR6zOz6nNV6zf/9x/8Pt06P+Y1f\n/1XGZTwXvXOkScJwNCJpU4Lv6Hopzmq1xBhLCJ589MkZyPD3RWLBCzesnxl994sEUYMnRby92S52\n21tj6JYrFvMZi9k1TVWB8ygEqdYUOkEnCalSfZZsX+OaJLgQd8pJf6NI0oREpyitsV2sX96KxGMk\nTGR52q4mBIc1LU3b9aUc/XMlKW3TkJcjVJqxf7DPYDim8YG9g33SPtNzMBj0ujjfs5mReZJa4RpD\nIKC0xHQdUgpG4zHBtCznczp7gfWeelNTZHEEZE0gEA1zPng64wnEBA6VxPFekjroRBwDhzii2E4U\ngo+GophmkPbmIJBKUDUNf/RH3+Xm5oKvfPWr3Lp9i0ynOGfRfQi50ipKYggvsWD0wGm3Rm3ZyJ6J\n/WVhzU+FXeGTvhoZ5J8Hzj8RzIfP+Fm/hENJ2acHlLTO03QNiAwbcjwJUqqYhkBDkFGCZM12fFcC\nsFmvo0M6jdmW278lz3OWyyXvvffejqHcZlpuj62Jbvv5J7Gbn/Qafbxl79MA8YtZy9uf8Td1BGS/\nCXcQohbfBXB4Oh+QQpNkA4L3PLqp0M0Nd2cZC19yPB7hXI5QA6QKZInEi4DONMkwRWQapyNLqbSL\niReyzwgXAaTvR/fRdOxFBMmqB75SqL4IJGYaR6nX8wQQ0aM6KQXWhdh26EErTVYMyMohbnGN8QGd\nJMTZmCbICG7wEIRCJTpmwkpBcJClKW3qEAgyncREM+PBBCSKhIBygtGg5GBvysFkTCYEygfAYaWi\nbhqMNYgkYbJ/gMxycp1ycrjXG5Ju2FTrPuO2oizzmK1smjip04oQbJybJRpVaoqDMSaTiA4KV5C4\nEOM7raHwCQM3hi7hsXYUZsmd0HE376CegTUoAcGkBJ2hkhSt+vKoHowK70lUzMsm+AiqncMHt0PM\nvUT9r3X89L13ePX2PbqqZlQOcN2GNM/wyBhLHEBKhe8MpXDoRKEsSGT8vZxFJilNCCRSQtuRK0Uo\nU5QYcrq3z+bmima1QsiETKf8s3/6T2G1AWOpmxbVdWSjnPOLCzZdw8HpIT95p+GDhw8ZT0a0XYcM\nMMgGFFmOt4JpOWa1WWIbx97eEbaz6FQRA1Ykw7LA4zBtS7AOGRxnTx6TKIltapaLBdUqRpYNyhGv\nvv4lWqeQqqRtLa23DKYHfPjuT/n2t/4B777/gDwtuLi8QVY1w2FJ2zYoHScUWmuSRHBTzVEKDg8O\nWW/WFCrDETfnw+EAqeKav1pFVnF/f5+9/QOarmM6nbKczXeZwrHmuM957w3CQkqSLI1vOPG6225O\nsywlSVNA0LUxucd0Fq0Srq6uALiezzk+vRVbHPts4sFgQFYUvPb660wmY86ePsPaWNZ0cnICwN27\ndzk/v8CYjiQZxDVTKdq2YTKZ7lpPN5sNjx8/RmvVyzFcT1RERno4HEZpyHSK1pr79+/x7Nk5q9WS\nm5sVbdugdcJwOCBNM7zPWffNgNY6lsslr7/+Og8fPmS9XnN1FT0Fi0VcF77xjd/k1Ve/hfee5XLJ\nbDbj8PCQDz98iLWG6XSPzWbDcDhkMV/0aRkHXF1dolQkLvOiwLqYjV5kKZuqioZjrfsmwXFkkrWm\nHE9pm5qfvPMei+Wcw8N97t27w6goEFLgezlYked9s7Gg6zxJmmJdNNrObpafen3+vQDIAXaJFPAy\nSBZCIFVkLrYjDohgLk00bbVheTPj+uKS1WxOV7fxxiMFiSrRPdvpnMMah+uB7G7kLmIsU1mWZHmG\nklE/0zRdv2sU6H4c6qynMw1t20IIu+QM7yIrZULA9cazvMiRKonNVkqhRUIudNQHC4nqDQmxtUns\n/qcSSdcErOtYrBc0bYNzlto1yK6jbTdcXj1DIKmbhjovWS42hKDJsgEISxACH2eNSKVJhMMq0EKC\nVvjgcH1c1Rad+uCfFxT0zH58eRRSCC4vr/nxj38c9UF5QXlnwHpT9zeUOE72RC2zd54gQjQGKYU3\nNuqWtd5p/SCy1F3TvqAnAp1Elj6E2DwW4BNz+3eGsn51C1sA618GZD7EjYdA/Mzi9xJT1IOZIPoa\nbfHC118Aec8Z5Off+3FNcv9UvxDwFyoBCjwFxlk6AyIZ4slwXpPqtEdbDUFWeOkjk5AkDMoSpTU3\nWjOfzVBCslwuWS6XvQEkjt5ubmJt6fY1SNPnuaBbgGxtHFs557DW7upHXwTJHwfMf5tkGE5CrQQy\ngHIhSoywaK+i5MFIhI+ud2UzMIHrJfzpZs77jeN4WHAySHh1knNYCpJGkkhBMUzJBzkhEbQ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Mmy7DzPe9awpzOfnGvqqq7u6uoZM0GIAEmBZAhAkKIsO3Th0D+QdGH/Ad/5wndy+NLh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\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c7/nkDC3DFC7SMYXzmLpEpz3OFp63v/2U7Z2avStX+YEf/Wku3txnf/+QzmhCenDA6ckp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\nrXPYZMYX/90f8uhDD3DqZMbl1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+OXf/5vcuV7L/Cdv/or/GadUZERBj4nT59n5f0f5d72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RqNKMvBNv5YaSu8scLSxw7HFEvrXEgK4CsulHjwxm0sZd9lhmu8yEjf9Uonml1DsrZi/ijo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\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omMseGVCMPr/jrfHiVpjTd8818aAW/rNRYs/ofWDUEoQgXPdURaxzOCd7agyabGU/UT6T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\nV+quQb6ru7qru7qru7qru7qru3ql/i+UCC51icMMHgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "b5Ej-HLGVrWZ"
      },
      "source": [
        "# The Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnKuVm1V3bAy",
        "colab_type": "text"
      },
      "source": [
        "Time to build a neural network!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXYA5LZq3Rp1",
        "colab_type": "text"
      },
      "source": [
        "## Create the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "wEgW4i18VrWZ"
      },
      "source": [
        "Sequential(...) - Builds a neural network in layers. For arguments, you pass in pre-defined layers in the order you want.  The first layer must define the shame of the input data.  In our case this  is \"input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)\" for the height, width, and 3 color channels of each image.\n",
        "\n",
        "Conv2D(filters, kernel_size, ...) - Creates a convolution layer. kernel_size is the height and width of the window used to study the previous layer.  filters is the number of unique windows used.\n",
        "\n",
        "MaxPooling2D() - Creates a pooling layer. Default size is 2x2.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F15-uwLPVrWa",
        "colab": {}
      },
      "source": [
        "\n",
        "model = Sequential([\n",
        "    # Feature Learning\n",
        "    Conv2D(16, 7, padding='same', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.15),\n",
        "    Conv2D(16, (5,5), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.1),\n",
        "    MaxPooling2D(pool_size=(4, 4)),    \n",
        "    Conv2D(32, (2,2), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.1),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    BatchNormalization(),\n",
        "    Dropout(0.1),\n",
        "    Conv2D(64, (5,5), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.2),\n",
        "    Dropout(0.1),\n",
        "    Conv2D(96, (3,3), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.5),\n",
        "    Dropout(0.1),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    BatchNormalization(),\n",
        "    Conv2D(128, (4,4), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.1),\n",
        "    MaxPooling2D(pool_size=(2, 2)),\n",
        "    Conv2D(128, (2,2), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.1),\n",
        "    Dropout(0.2),\n",
        "    Conv2D(256, (3,3), padding='same'),\n",
        "    tf.keras.layers.LeakyReLU(alpha=0.1),\n",
        "    Dropout(0.2),\n",
        "\n",
        "    # Classification\n",
        "    Flatten(),\n",
        "    Dense(class_counts[0], activation='softmax')\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PI5cdkMQVrWc"
      },
      "source": [
        "Complie the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6Mg7_TXOVrWd",
        "outputId": "83c953d0-5540-42ed-daeb-56eec3c940b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "\n",
        "model.compile(otimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy', 'top_k_categorical_accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_8 (Conv2D)            (None, 256, 256, 16)      2368      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_8 (LeakyReLU)    (None, 256, 256, 16)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 256, 256, 16)      6416      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_9 (LeakyReLU)    (None, 256, 256, 16)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 64, 64, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 64, 64, 32)        2080      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_10 (LeakyReLU)   (None, 64, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 32, 32, 64)        51264     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_11 (LeakyReLU)   (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 32, 32, 96)        55392     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_12 (LeakyReLU)   (None, 32, 32, 96)        0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 32, 32, 96)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 96)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 96)        384       \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 16, 16, 128)       196736    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_13 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 8, 8, 128)         65664     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_14 (LeakyReLU)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_15 (LeakyReLU)   (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 16384)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 120)               1966200   \n",
            "=================================================================\n",
            "Total params: 2,641,800\n",
            "Trainable params: 2,641,544\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "N06iqE8VVrWj"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "oub9RtoFVrWk"
      },
      "source": [
        "Train the model with the test data set and use the validation set to check."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KSF2HqhDVrWk",
        "outputId": "19091819-749d-40ca-b8c3-a9e9eb9f2a53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        }
      },
      "source": [
        "history = model.fit_generator(\n",
        "    train_data_gen,\n",
        "    steps_per_epoch=file_counts[1] // BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_data=val_data_gen,\n",
        "    validation_steps=file_counts[2] // BATCH_SIZE\n",
        ")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "236/237 [============================>.] - ETA: 1s - loss: 4.7720 - accuracy: 0.0283 - top_k_categorical_accuracy: 0.1118Epoch 1/50\n",
            "237/237 [==============================] - 251s 1s/step - loss: 4.7711 - accuracy: 0.0284 - top_k_categorical_accuracy: 0.1119 - val_loss: 4.8724 - val_accuracy: 0.0185 - val_top_k_categorical_accuracy: 0.0971\n",
            "Epoch 2/50\n",
            "236/237 [============================>.] - ETA: 0s - loss: 4.2840 - accuracy: 0.0560 - top_k_categorical_accuracy: 0.2060Epoch 1/50\n",
            "237/237 [==============================] - 248s 1s/step - loss: 4.2829 - accuracy: 0.0562 - top_k_categorical_accuracy: 0.2062 - val_loss: 4.3450 - val_accuracy: 0.0688 - val_top_k_categorical_accuracy: 0.2357\n",
            "Epoch 3/50\n",
            "236/237 [============================>.] - ETA: 0s - loss: 4.0279 - accuracy: 0.0862 - top_k_categorical_accuracy: 0.2840Epoch 1/50\n",
            "237/237 [==============================] - 244s 1s/step - loss: 4.0272 - accuracy: 0.0863 - top_k_categorical_accuracy: 0.2842 - val_loss: 4.1784 - val_accuracy: 0.0698 - val_top_k_categorical_accuracy: 0.2625\n",
            "Epoch 4/50\n",
            " 81/237 [=========>....................] - ETA: 2:28 - loss: 3.8425 - accuracy: 0.1053 - top_k_categorical_accuracy: 0.3308"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-64-9ab0d1fc55f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_data_gen\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile_counts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m )\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m         steps_name='steps_per_epoch')\n\u001b[0m\u001b[1;32m   1336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m   def evaluate_generator(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[0;34m(model, data, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch, mode, batch_size, steps_name, **kwargs)\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0mstep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtarget_steps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m       \u001b[0mbatch_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_next_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mbatch_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_dataset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_generator.py\u001b[0m in \u001b[0;36m_get_next_batch\u001b[0;34m(generator)\u001b[0m\n\u001b[1;32m    362\u001b[0m   \u001b[0;34m\"\"\"Retrieves the next batch of input data.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    363\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 364\u001b[0;31m     \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    365\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    873\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m       \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 875\u001b[0;31m         \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    876\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 635\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5_YKQuU1S4A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CwKv3ysBlzyN",
        "colab_type": "code",
        "outputId": "edbe7f0d-3a42-4430-fbd1-4a4fab6ba7b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "score = model.evaluate(val_data_gen)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3853/3853 [==============================] - 18s 5ms/step - loss: 1.5492 - accuracy: 0.5816 - top_k_categorical_accuracy: 0.8653\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nWRUQlLlz7l",
        "colab_type": "code",
        "outputId": "b8d09c18-baa8-4f0e-e2c0-38a7bc574806",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "print(f1_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-074f3b1b0b62>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'f1_score' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LZPYT-EmVrWo"
      },
      "source": [
        "Visualize the results after training the network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "K6oA77ADVrWp",
        "outputId": "41024855-6f55-445f-8642-c79917b09de2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        }
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "top_k_acc = history.history['top_k_categorical_accuracy']\n",
        "top_k_val_acc = history.history['val_top_k_categorical_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(EPOCHS)\n",
        "\n",
        "plt.figure(figsize=(12, 12))\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot(epochs_range, top_k_acc, label='Top k Training Accuracy')\n",
        "plt.plot(epochs_range, top_k_val_acc, label='Top K Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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V4xS9V0l1A79emcOcUelckz3C6uGIjmhVJuG2bhxC9CCpGRa299yXhxkzMJkz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\nhRBCCCFEoyXFsBBCCCGEaLT+HwBPPMZIlSyMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x864 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MVbHfkiYtVO",
        "colab_type": "text"
      },
      "source": [
        "Code to save the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcwakU3JZAiV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save(MODEL_SAVE_FILE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENTwhmD6ZRUn",
        "colab_type": "text"
      },
      "source": [
        "Code to load a saved model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0MtteNLZT-X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = load_model(MODEL_SAVE_FILE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUAyyjQQZkGH",
        "colab_type": "text"
      },
      "source": [
        "Have the model classify a picture from the web"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1GtEWtwZqC0",
        "colab_type": "code",
        "outputId": "69289d18-e16b-42d2-b991-f5375bc07da7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "img_data = requests.get(\"https://vetstreet-brightspot.s3.amazonaws.com/ea/df8540a75911e0a0d50050568d634f/file/Irish-Terrier-2-645mk062411.jpg\").content\n",
        "with open(TMP_IMAGE_FILE, 'wb') as handler:\n",
        "    handler.write(img_data)\n",
        "\n",
        "image = load_img(TMP_IMAGE_FILE, color_mode='rgb', target_size=(IMG_HEIGHT, IMG_WIDTH))\n",
        "x = img_to_array(image)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "x /= 255\n",
        "\n",
        "probabilities = model.predict(x)\n",
        "prediction = probabilities.argmax(axis=-1)\n",
        "\n",
        "sorted_probabilities = sorted(probabilities[0], reverse=True)\n",
        "print(\"Classifications:\")\n",
        "for prob in sorted_probabilities:\n",
        "  index = np.where(probabilities[0] == prob)[0][0]\n",
        "  print(\"%s - %.2f%% probability\" % (classes[index], probabilities[0][index] * 100))\n",
        "  if prob < .01:\n",
        "      break\n",
        "\n",
        "#print(\"Probabilities: \" + probabilities)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classifications:\n",
            "n02087394-Rhodesian_ridgeback - 27.90% probability\n",
            "n02115913-dhole - 17.82% probability\n",
            "n02108089-boxer - 11.69% probability\n",
            "n02091244-Ibizan_hound - 8.14% probability\n",
            "n02093991-Irish_terrier - 8.01% probability\n",
            "n02107312-miniature_pinscher - 6.12% probability\n",
            "n02107142-Doberman - 4.71% probability\n",
            "n02112706-Brabancon_griffon - 3.29% probability\n",
            "n02090379-redbone - 3.21% probability\n",
            "n02110806-basenji - 2.99% probability\n",
            "n02093754-Border_terrier - 1.82% probability\n",
            "n02105412-kelpie - 1.45% probability\n",
            "n02113978-Mexican_hairless - 1.19% probability\n",
            "n02093428-American_Staffordshire_terrier - 0.25% probability\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YU8rVJdV6U54",
        "colab_type": "code",
        "outputId": "b663b184-dd1c-4406-d9aa-de8bac3daec4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "image_generator = ImageDataGenerator(rescale=1./255)\n",
        "val_data_gen = image_generator.flow_from_directory(batch_size=1, shuffle=False, directory=DATA_DIRS[2], target_size=(IMG_HEIGHT, IMG_WIDTH))\n",
        "\n",
        "classes = [\"Unknown\"] * class_counts[1]\n",
        "for name, index in val_data_gen.class_indices.items():\n",
        "  classes[index] = name\n",
        "\n",
        "Y_pred = model.predict_generator(val_data_gen, file_counts[2])\n",
        "y_pred = np.argmax(Y_pred, axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3849 images belonging to 120 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qguut6D1Hgpe",
        "colab_type": "code",
        "outputId": "6ed583c7-a68a-4bc5-cb4e-e73db1c05c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        }
      },
      "source": [
        "for i in range(5):\n",
        "  print(f\"classes[y_pred[{i}]] = {classes[y_pred[i]]}\")\n",
        "  sorted_probabilities = sorted(Y_pred[i, :], reverse=True)\n",
        "  for prob in sorted_probabilities:\n",
        "    index = np.where(Y_pred[i] == prob)[0][0]\n",
        "    print(\"%s - %.2f%% probability\" % (classes[index], Y_pred[0][index] * 100))\n",
        "    if prob < .01:\n",
        "      break"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classes[y_pred[0]] = n02090379-redbone\n",
            "n02090379-redbone - 70.29% probability\n",
            "n02087394-Rhodesian_ridgeback - 26.40% probability\n",
            "n02100583-vizsla - 3.01% probability\n",
            "n02088466-bloodhound - 0.14% probability\n",
            "classes[y_pred[1]] = n02111889-Samoyed\n",
            "n02111889-Samoyed - 0.00% probability\n",
            "n02111500-Great_Pyrenees - 0.00% probability\n",
            "n02109961-Eskimo_dog - 0.00% probability\n",
            "n02108915-French_bulldog - 0.00% probability\n",
            "classes[y_pred[2]] = n02096051-Airedale\n",
            "n02096051-Airedale - 0.00% probability\n",
            "n02093991-Irish_terrier - 0.00% probability\n",
            "n02093754-Border_terrier - 0.00% probability\n",
            "n02095570-Lakeland_terrier - 0.00% probability\n",
            "n02094114-Norfolk_terrier - 0.00% probability\n",
            "n02093428-American_Staffordshire_terrier - 0.00% probability\n",
            "classes[y_pred[3]] = n02110958-pug\n",
            "n02110958-pug - 0.00% probability\n",
            "n02113624-toy_poodle - 0.00% probability\n",
            "n02116738-African_hunting_dog - 0.00% probability\n",
            "n02111129-Leonberg - 0.00% probability\n",
            "n02109047-Great_Dane - 0.00% probability\n",
            "n02113799-standard_poodle - 0.00% probability\n",
            "n02106662-German_shepherd - 0.00% probability\n",
            "n02111277-Newfoundland - 0.00% probability\n",
            "n02111500-Great_Pyrenees - 0.00% probability\n",
            "n02108915-French_bulldog - 0.00% probability\n",
            "n02109961-Eskimo_dog - 0.00% probability\n",
            "n02108422-bull_mastiff - 0.00% probability\n",
            "n02113712-miniature_poodle - 0.00% probability\n",
            "n02099712-Labrador_retriever - 0.00% probability\n",
            "classes[y_pred[4]] = n02110627-affenpinscher\n",
            "n02110627-affenpinscher - 0.00% probability\n",
            "n02097298-Scotch_terrier - 0.00% probability\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vgoHTd5U9Aon",
        "colab_type": "code",
        "outputId": "57cec358-a6be-4d01-bbca-f95c57fe5221",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        }
      },
      "source": [
        "print(f\"Y_pred = {Y_pred.shape}\")\n",
        "print(f\"Y_pred = {len(y_pred)}\")\n",
        "print(f\"y_pred = {y_pred}\")\n",
        "print(f\"val_data_gen.classes = {val_data_gen.classes}\")\n",
        "print(f\"val_data_gen.classes = {len(val_data_gen.classes)}\")\n",
        "equal_count = np.equal(y_pred, val_data_gen.classes)\n",
        "print(f\"equal_count = {equal_count.sum()}\")\n",
        "print(classes)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Y_pred = (3849, 120)\n",
            "Y_pred = 3849\n",
            "y_pred = [ 45   0   0 ... 119 119  95]\n",
            "val_data_gen.classes = [  0   0   0 ... 119 119 119]\n",
            "val_data_gen.classes = 3849\n",
            "equal_count = 2284\n",
            "['n02085620-Chihuahua', 'n02085782-Japanese_spaniel', 'n02085936-Maltese_dog', 'n02086079-Pekinese', 'n02086240-Shih-Tzu', 'n02086646-Blenheim_spaniel', 'n02086910-papillon', 'n02087046-toy_terrier', 'n02087394-Rhodesian_ridgeback', 'n02088094-Afghan_hound', 'n02088238-basset', 'n02088364-beagle', 'n02088466-bloodhound', 'n02088632-bluetick', 'n02089078-black-and-tan_coonhound', 'n02089867-Walker_hound', 'n02089973-English_foxhound', 'n02090379-redbone', 'n02090622-borzoi', 'n02090721-Irish_wolfhound', 'n02091032-Italian_greyhound', 'n02091134-whippet', 'n02091244-Ibizan_hound', 'n02091467-Norwegian_elkhound', 'n02091635-otterhound', 'n02091831-Saluki', 'n02092002-Scottish_deerhound', 'n02092339-Weimaraner', 'n02093256-Staffordshire_bullterrier', 'n02093428-American_Staffordshire_terrier', 'n02093647-Bedlington_terrier', 'n02093754-Border_terrier', 'n02093859-Kerry_blue_terrier', 'n02093991-Irish_terrier', 'n02094114-Norfolk_terrier', 'n02094258-Norwich_terrier', 'n02094433-Yorkshire_terrier', 'n02095314-wire-haired_fox_terrier', 'n02095570-Lakeland_terrier', 'n02095889-Sealyham_terrier', 'n02096051-Airedale', 'n02096177-cairn', 'n02096294-Australian_terrier', 'n02096437-Dandie_Dinmont', 'n02096585-Boston_bull', 'n02097047-miniature_schnauzer', 'n02097130-giant_schnauzer', 'n02097209-standard_schnauzer', 'n02097298-Scotch_terrier', 'n02097474-Tibetan_terrier', 'n02097658-silky_terrier', 'n02098105-soft-coated_wheaten_terrier', 'n02098286-West_Highland_white_terrier', 'n02098413-Lhasa', 'n02099267-flat-coated_retriever', 'n02099429-curly-coated_retriever', 'n02099601-golden_retriever', 'n02099712-Labrador_retriever', 'n02099849-Chesapeake_Bay_retriever', 'n02100236-German_short-haired_pointer', 'n02100583-vizsla', 'n02100735-English_setter', 'n02100877-Irish_setter', 'n02101006-Gordon_setter', 'n02101388-Brittany_spaniel', 'n02101556-clumber', 'n02102040-English_springer', 'n02102177-Welsh_springer_spaniel', 'n02102318-cocker_spaniel', 'n02102480-Sussex_spaniel', 'n02102973-Irish_water_spaniel', 'n02104029-kuvasz', 'n02104365-schipperke', 'n02105056-groenendael', 'n02105162-malinois', 'n02105251-briard', 'n02105412-kelpie', 'n02105505-komondor', 'n02105641-Old_English_sheepdog', 'n02105855-Shetland_sheepdog', 'n02106030-collie', 'n02106166-Border_collie', 'n02106382-Bouvier_des_Flandres', 'n02106550-Rottweiler', 'n02106662-German_shepherd', 'n02107142-Doberman', 'n02107312-miniature_pinscher', 'n02107574-Greater_Swiss_Mountain_dog', 'n02107683-Bernese_mountain_dog', 'n02107908-Appenzeller', 'n02108000-EntleBucher', 'n02108089-boxer', 'n02108422-bull_mastiff', 'n02108551-Tibetan_mastiff', 'n02108915-French_bulldog', 'n02109047-Great_Dane', 'n02109525-Saint_Bernard', 'n02109961-Eskimo_dog', 'n02110063-malamute', 'n02110185-Siberian_husky', 'n02110627-affenpinscher', 'n02110806-basenji', 'n02110958-pug', 'n02111129-Leonberg', 'n02111277-Newfoundland', 'n02111500-Great_Pyrenees', 'n02111889-Samoyed', 'n02112018-Pomeranian', 'n02112137-chow', 'n02112350-keeshond', 'n02112706-Brabancon_griffon', 'n02113023-Pembroke', 'n02113186-Cardigan', 'n02113624-toy_poodle', 'n02113712-miniature_poodle', 'n02113799-standard_poodle', 'n02113978-Mexican_hairless', 'n02115641-dingo', 'n02115913-dhole', 'n02116738-African_hunting_dog']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6A8pTzn8csc",
        "colab_type": "code",
        "outputId": "0d7d80eb-82e7-41f4-f86b-721bc3fa9b47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print('Confusion Matrix')\n",
        "arr = np.asarray(confusion_matrix(val_data_gen.classes, y_pred))\n",
        "np.savetxt(\"confusion.csv\", arr, delimiter=\",\")\n",
        "print('Classification Report')\n",
        "print(classification_report(val_data_gen.classes, y_pred))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.58      0.61        33\n",
            "           1       1.00      0.47      0.64        32\n",
            "           2       0.79      0.64      0.71        42\n",
            "           3       0.89      0.36      0.52        22\n",
            "           4       0.70      0.47      0.57        40\n",
            "           5       0.93      0.38      0.54        34\n",
            "           6       0.88      0.70      0.78        30\n",
            "           7       1.00      0.40      0.57        30\n",
            "           8       0.21      0.91      0.34        22\n",
            "           9       0.88      0.53      0.66        40\n",
            "          10       0.68      0.61      0.64        28\n",
            "          11       1.00      0.24      0.38        42\n",
            "          12       0.89      0.40      0.55        43\n",
            "          13       0.76      0.61      0.68        31\n",
            "          14       0.75      0.18      0.29        34\n",
            "          15       0.82      0.36      0.50        25\n",
            "          16       0.69      0.36      0.47        25\n",
            "          17       0.45      0.54      0.49        24\n",
            "          18       0.29      0.70      0.41        20\n",
            "          19       0.40      0.52      0.45        33\n",
            "          20       0.49      0.76      0.60        37\n",
            "          21       0.55      0.49      0.52        35\n",
            "          22       0.66      0.58      0.62        36\n",
            "          23       1.00      0.70      0.83        37\n",
            "          24       0.84      0.57      0.68        28\n",
            "          25       0.48      0.58      0.53        36\n",
            "          26       0.69      0.72      0.70        46\n",
            "          27       0.96      0.83      0.89        30\n",
            "          28       0.46      0.60      0.52        30\n",
            "          29       0.41      0.42      0.42        26\n",
            "          30       0.59      0.93      0.72        29\n",
            "          31       0.61      0.65      0.62        31\n",
            "          32       0.92      0.60      0.73        20\n",
            "          33       0.52      0.86      0.65        28\n",
            "          34       0.62      0.50      0.55        32\n",
            "          35       0.46      0.55      0.50        33\n",
            "          36       0.69      0.73      0.71        30\n",
            "          37       0.51      0.72      0.60        36\n",
            "          38       0.43      0.64      0.52        39\n",
            "          39       0.70      0.82      0.75        28\n",
            "          40       0.86      0.61      0.71        41\n",
            "          41       0.47      0.68      0.56        40\n",
            "          42       1.00      0.12      0.22        40\n",
            "          43       0.79      0.53      0.63        36\n",
            "          44       1.00      0.21      0.35        33\n",
            "          45       0.50      0.30      0.37        30\n",
            "          46       0.37      0.93      0.53        28\n",
            "          47       0.91      0.32      0.48        31\n",
            "          48       0.70      0.64      0.67        25\n",
            "          49       0.60      0.68      0.63        37\n",
            "          50       0.83      0.61      0.70        33\n",
            "          51       0.70      0.76      0.73        41\n",
            "          52       0.66      0.94      0.77        31\n",
            "          53       0.62      0.62      0.62        26\n",
            "          54       0.69      0.64      0.67        28\n",
            "          55       0.56      0.71      0.63        28\n",
            "          56       0.46      0.68      0.55        25\n",
            "          57       0.38      0.88      0.53        33\n",
            "          58       0.59      0.54      0.56        37\n",
            "          59       0.54      0.65      0.59        23\n",
            "          60       1.00      0.50      0.67        30\n",
            "          61       0.63      0.59      0.61        29\n",
            "          62       0.88      0.33      0.48        21\n",
            "          63       0.42      0.88      0.57        16\n",
            "          64       0.83      0.52      0.64        29\n",
            "          65       1.00      0.57      0.73        28\n",
            "          66       0.91      0.64      0.75        33\n",
            "          67       0.67      0.30      0.41        20\n",
            "          68       0.52      0.42      0.47        33\n",
            "          69       1.00      0.70      0.82        20\n",
            "          70       1.00      0.53      0.70        15\n",
            "          71       0.65      0.71      0.68        21\n",
            "          72       0.81      0.65      0.72        20\n",
            "          73       1.00      0.43      0.60        30\n",
            "          74       0.88      0.52      0.65        29\n",
            "          75       0.71      0.52      0.60        29\n",
            "          76       0.71      0.46      0.56        37\n",
            "          77       0.67      0.83      0.74        12\n",
            "          78       0.82      0.84      0.83        38\n",
            "          79       0.93      0.42      0.58        31\n",
            "          80       0.50      0.62      0.55        34\n",
            "          81       0.55      0.57      0.56        30\n",
            "          82       0.85      0.57      0.69        40\n",
            "          83       0.77      0.70      0.73        33\n",
            "          84       0.70      0.67      0.68        21\n",
            "          85       0.53      0.67      0.59        36\n",
            "          86       0.77      0.66      0.71        41\n",
            "          87       0.55      0.72      0.62        32\n",
            "          88       0.95      0.37      0.53        49\n",
            "          89       0.45      0.68      0.54        28\n",
            "          90       0.81      0.50      0.62        44\n",
            "          91       0.57      0.52      0.54        31\n",
            "          92       0.58      0.45      0.51        31\n",
            "          93       0.55      0.41      0.47        41\n",
            "          94       0.36      0.63      0.46        38\n",
            "          95       0.19      0.69      0.30        32\n",
            "          96       0.94      0.49      0.64        35\n",
            "          97       0.40      0.67      0.50        45\n",
            "          98       0.79      0.32      0.46        34\n",
            "          99       0.65      0.51      0.57        43\n",
            "         100       0.73      0.71      0.72        34\n",
            "         101       0.64      0.60      0.62        35\n",
            "         102       0.65      0.72      0.68        43\n",
            "         103       0.75      0.82      0.78        44\n",
            "         104       0.41      0.79      0.54        33\n",
            "         105       0.38      0.85      0.53        48\n",
            "         106       0.62      0.75      0.68        32\n",
            "         107       0.94      0.74      0.83        42\n",
            "         108       0.62      0.74      0.68        35\n",
            "         109       0.95      0.71      0.82        28\n",
            "         110       0.94      0.57      0.71        30\n",
            "         111       0.89      0.52      0.65        31\n",
            "         112       0.75      0.35      0.48        34\n",
            "         113       0.38      0.67      0.49        27\n",
            "         114       0.58      0.58      0.58        36\n",
            "         115       0.29      0.67      0.41        30\n",
            "         116       0.88      0.61      0.72        36\n",
            "         117       0.48      0.61      0.54        36\n",
            "         118       0.77      0.74      0.75        27\n",
            "         119       0.70      0.88      0.78        26\n",
            "\n",
            "    accuracy                           0.59      3849\n",
            "   macro avg       0.69      0.60      0.60      3849\n",
            "weighted avg       0.69      0.59      0.60      3849\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2G5mAlAVPR3",
        "colab_type": "code",
        "outputId": "e2ed4992-5f93-43da-a60d-04daea36e980",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "TP = np.count_nonzero(y_pred * val_data_gen.classes)\n",
        "TN = np.count_nonzero((y_pred - 1) * (val_data_gen.classes - 1))\n",
        "FP = np.count_nonzero(y_pred * (val_data_gen.classes - 1))\n",
        "FN = np.count_nonzero((y_pred - 1) * val_data_gen.classes)\n",
        "\n",
        "precision = TP / (TP + FP)\n",
        "recall = TP / (TP + FN)\n",
        "f1 = 2 * precision * recall / (precision + recall)\n",
        "\n",
        "print (f\"precision = {precision}\")\n",
        "print (f\"recall = {recall}\")\n",
        "print (f\"f1 = {f1}\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "precision = 0.5011851461680273\n",
            "recall = 0.5003286446693834\n",
            "f1 = 0.5007565291757121\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbnDVagi5yFf",
        "colab_type": "code",
        "outputId": "dc0df3ca-337d-49c9-e9a5-ea332b2b2256",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print (predictions.shape)\n",
        "#confusion = tf.confusion_matrix(labels=y_, predictions=predictions, num_classes=120)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3755, 120)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itJRj8Yj6sx_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}