FULL_KDEF_mood.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Ger8rw5NvxB"
      },
      "source": [
        "Import necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8tSJafuBpH7",
        "outputId": "ae8bc723-f0f7-426f-ee4e-b7bdeed8fe29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "EpII_g_Riddx",
        "outputId": "7b8950eb-ea60-4b73-871e-124de1f352df"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow\n",
        "!pip install keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIYicychBSzH"
      },
      "source": [
        "# Preprocessing\n",
        "Preparing Data For Training Fine-Tuned VGG-16 Model.\n",
        "This involves loading the dataset, performing image augmentation, and dividing the data into training and validation sets.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngf1i2xrDl4A"
      },
      "source": [
        "To make the task more efficient, we reorganised the dataset. Initially, the KDEF dataset was organised in folders by subject, but I would like it to be organised by emotion. The name of each file is meaningful.\n",
        "\n",
        "###KDEF\n",
        "The Karolinska Directed Emotional Faces; 4900 pictures on facial emotional expressions.\n",
        "\n",
        "\n",
        "**Session-Gender-Identity number**(01 - 35)\n",
        "\n",
        "      A = series one; B = series two\n",
        "      F = female; M = male\n",
        "\n",
        "      \n",
        "**Letter 5 & 6: Expression**\n",
        "\n",
        "      AF = afraid\n",
        "      AN = angry\n",
        "      DI = disgusted\n",
        "      HA = happy\n",
        "      NE = neutral\n",
        "      SA = sad\n",
        "      SU = surprised\n",
        "**Letter 7 & 8: Angle**\n",
        "\n",
        "      FL = full left profile\n",
        "      HL = half left profile\n",
        "      S = straight\n",
        "      HR = half right profile\n",
        "      FR = full right profile\n",
        "\n",
        "So in order to extract informations we need:\n",
        "\n",
        "        emozione = filename[4:6]\n",
        "        angle = filename[6:8]\n",
        "\n",
        "\"emozione\" will be the name of new folders and we used angle to filter front faces (minimal dataset).\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/drive/MyDrive/ColabNotebooks/AsItSounds/FacialExpRecognition/KDEF.zip -d KDEF"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LifvPkAxV9Xc",
        "outputId": "a7939d40-a1a8-4ef4-a51c-e22676ee6b1e",
        "collapsed": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "   creating: KDEF/KDEF/AF11/\n",
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            "   creating: KDEF/KDEF/AF14/\n",
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            "   creating: KDEF/KDEF/AF27/\n",
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            "   creating: KDEF/KDEF/AM07/\n",
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            "   creating: KDEF/KDEF/AM08/\n",
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            "   creating: KDEF/KDEF/AM09/\n",
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            "   creating: KDEF/KDEF/AM10/\n",
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            "   creating: KDEF/KDEF/AM11/\n",
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            "   creating: KDEF/KDEF/AM13/\n",
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            "   creating: KDEF/KDEF/AM14/\n",
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            "   creating: KDEF/KDEF/AM15/\n",
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            "   creating: KDEF/KDEF/AM16/\n",
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            "   creating: KDEF/KDEF/AM17/\n",
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            "   creating: KDEF/KDEF/AM18/\n",
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            "   creating: KDEF/KDEF/AM19/\n",
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            "   creating: KDEF/KDEF/AM20/\n",
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            "   creating: KDEF/KDEF/AM21/\n",
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            "   creating: KDEF/KDEF/AM22/\n",
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            "   creating: KDEF/KDEF/AM23/\n",
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            "   creating: KDEF/KDEF/AM24/\n",
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            "   creating: KDEF/KDEF/AM25/\n",
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            "   creating: KDEF/KDEF/AM26/\n",
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            "   creating: KDEF/KDEF/AM27/\n",
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            "   creating: KDEF/KDEF/AM28/\n",
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            "   creating: KDEF/KDEF/AM29/\n",
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            "   creating: KDEF/KDEF/AM30/\n",
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            "   creating: KDEF/KDEF/AM31/\n",
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            "   creating: KDEF/KDEF/AM32/\n",
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            "   creating: KDEF/KDEF/AM33/\n",
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            "   creating: KDEF/KDEF/AM34/\n",
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            "   creating: KDEF/KDEF/AM35/\n",
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            "   creating: KDEF/KDEF/BF01/\n",
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            "   creating: KDEF/KDEF/BF11/\n",
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            "   creating: KDEF/KDEF/BF12/\n",
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            "   creating: KDEF/KDEF/BF13/\n",
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            "   creating: KDEF/KDEF/BF14/\n",
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            "   creating: KDEF/KDEF/BF15/\n",
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            "   creating: KDEF/KDEF/BF16/\n",
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            "   creating: KDEF/KDEF/BF17/\n",
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            "   creating: KDEF/KDEF/BF21/\n",
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            "   creating: KDEF/KDEF/BF22/\n",
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            "   creating: KDEF/KDEF/BF23/\n",
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            "   creating: KDEF/KDEF/BF24/\n",
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            "   creating: KDEF/KDEF/BF25/\n",
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            "   creating: KDEF/KDEF/BF28/\n",
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            "   creating: KDEF/KDEF/BF29/\n",
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            "   creating: KDEF/KDEF/BF30/\n",
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            "   creating: KDEF/KDEF/BF31/\n",
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            "   creating: KDEF/KDEF/BF32/\n",
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            "   creating: KDEF/KDEF/BF33/\n",
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            "   creating: KDEF/KDEF/BM01/\n",
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            "   creating: KDEF/KDEF/BM02/\n",
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            "   creating: KDEF/KDEF/BM03/\n",
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            "   creating: KDEF/KDEF/BM07/\n",
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            "   creating: KDEF/KDEF/BM08/\n",
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            "   creating: KDEF/KDEF/BM09/\n",
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            "   creating: KDEF/KDEF/BM10/\n",
            "  inflating: KDEF/KDEF/BM10/BM10AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM11/\n",
            "  inflating: KDEF/KDEF/BM11/BM11AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM12/\n",
            "  inflating: KDEF/KDEF/BM12/BM12AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM13/\n",
            "  inflating: KDEF/KDEF/BM13/BM13AFFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM13/BM13SUS.JPG  \n",
            "   creating: KDEF/KDEF/BM14/\n",
            "  inflating: KDEF/KDEF/BM14/BM14AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM16/\n",
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            "   creating: KDEF/KDEF/BM17/\n",
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            "   creating: KDEF/KDEF/BM18/\n",
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            "   creating: KDEF/KDEF/BM19/\n",
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            "   creating: KDEF/KDEF/BM20/\n",
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            "   creating: KDEF/KDEF/BM21/\n",
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            "   creating: KDEF/KDEF/BM22/\n",
            "  inflating: KDEF/KDEF/BM22/BM22AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM23/\n",
            "  inflating: KDEF/KDEF/BM23/BM23AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM24/\n",
            "  inflating: KDEF/KDEF/BM24/BM24AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM25/\n",
            "  inflating: KDEF/KDEF/BM25/BM25AFFL.JPG  \n",
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            "   creating: KDEF/KDEF/BM26/\n",
            "  inflating: KDEF/KDEF/BM26/BM26AFFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM26/BM26SUS.JPG  \n",
            "   creating: KDEF/KDEF/BM27/\n",
            "  inflating: KDEF/KDEF/BM27/BM27AFFL.JPG  \n",
            "  inflating: KDEF/KDEF/BM27/BM27AFFR.JPG  \n",
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            "  inflating: KDEF/KDEF/BM27/BM27SUS.JPG  \n",
            "   creating: KDEF/KDEF/BM28/\n",
            "  inflating: KDEF/KDEF/BM28/BM28AFFL.JPG  \n",
            "  inflating: KDEF/KDEF/BM28/BM28AFFR.JPG  \n",
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            "   creating: KDEF/KDEF/BM29/\n",
            "  inflating: KDEF/KDEF/BM29/BM29AFFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM29/BM29SUS.JPG  \n",
            "   creating: KDEF/KDEF/BM30/\n",
            "  inflating: KDEF/KDEF/BM30/BM30AFFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM30/BM30DIS.JPG  \n",
            "  inflating: KDEF/KDEF/BM30/BM30HAFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM30/BM30HAS.JPG  \n",
            "  inflating: KDEF/KDEF/BM30/BM30NEFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM30/BM30NES.JPG  \n",
            "  inflating: KDEF/KDEF/BM30/BM30SAFL.JPG  \n",
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            "  inflating: KDEF/KDEF/BM30/BM30SAS.JPG  \n",
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            "  inflating: KDEF/KDEF/BM30/BM30SUHR.JPG  \n",
            "  inflating: KDEF/KDEF/BM30/BM30SUS.JPG  \n",
            "   creating: KDEF/KDEF/BM31/\n",
            "  inflating: KDEF/KDEF/BM31/BM31AFFL.JPG  \n",
            "  inflating: KDEF/KDEF/BM31/BM31AFFR.JPG  \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6dn3p1tA4It",
        "outputId": "456f4f53-6e58-46bb-9e1b-91bc4ff7f23f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3134 validated image filenames belonging to 7 classes.\n",
            "Found 784 validated image filenames belonging to 7 classes.\n",
            "Found 980 validated image filenames belonging to 7 classes.\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.layers import Flatten, Dense, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "\n",
        "# Define the path to the dataset directory\n",
        "dataset_path = \"/content/KDEF/KDEF\"\n",
        "\n",
        "# Create a list of possible emotions\n",
        "emozioni = [\"AF\", \"AN\", \"DI\", \"HA\", \"NE\", \"SA\", \"SU\"]\n",
        "\n",
        "# Navigate through each patient in the dataset directory\n",
        "for paziente in os.listdir(dataset_path):\n",
        "    paziente_path = os.path.join(dataset_path, paziente)\n",
        "\n",
        "    # Go through every patient file\n",
        "    for filename in os.listdir(paziente_path):\n",
        "        # Extract emotion and angle from the file name\n",
        "        emozione = filename[4:6]\n",
        "        angle = filename[6:8]\n",
        "\n",
        "        # Check whether the emotion is among those defined\n",
        "        if emozione in emozioni:\n",
        "            # Create the emotion directory if it does not already exist\n",
        "            emozione_path = os.path.join(dataset_path, emozione)\n",
        "            if not os.path.exists(emozione_path):\n",
        "                os.makedirs(emozione_path)\n",
        "\n",
        "            # Move the file to the directory corresponding to the emotion\n",
        "            shutil.move(os.path.join(paziente_path, filename), os.path.join(emozione_path, filename))\n",
        "\n",
        "# Define dataset and saved model paths\n",
        "DATASET_DIR = \"/content/KDEF/KDEF\"\n",
        "SAVED_MODEL_PATH = \"/content/drive/MyDrive/Colab Notebooks/AsItSounds/Saved Models/KDEF/full_kdef_VGG16_finetuned.h5\"\n",
        "\n",
        "# Define training parameters\n",
        "BATCH_SIZE = 64\n",
        "IMG_SIZE = (224, 224) # Dimensions of input images\n",
        "N_EPOCHS = 50\n",
        "LEARNING_RATE = 0.00001\n",
        "\n",
        "# Delete unnecessary folders\n",
        "for nome_cartella in os.listdir(dataset_path):\n",
        "    if nome_cartella not in emozioni:\n",
        "        cartella_da_eliminare = os.path.join(dataset_path, nome_cartella)\n",
        "        shutil.rmtree(cartella_da_eliminare)  # Delete the folder and its contents\n",
        "\n",
        "##### Stratified split into train, validation and test ####\n",
        "image_paths = []\n",
        "labels = []\n",
        "for emozione in emozioni:\n",
        "    emozione_path = os.path.join(dataset_path, emozione)\n",
        "    for filename in os.listdir(emozione_path):\n",
        "        image_paths.append(os.path.join(emozione_path, filename))\n",
        "        labels.append(emozione)\n",
        "\n",
        "# Split dataset into train set and test set (80%-20%)\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    image_paths, labels, test_size=0.2, stratify=labels, random_state=42\n",
        ")\n",
        "\n",
        "# Split of the train set into train and validation (80%-20% of the original train)\n",
        "X_train, X_val, y_train, y_val = train_test_split(\n",
        "    X_train, y_train, test_size=0.2, stratify=y_train, random_state=42\n",
        ")\n",
        "\n",
        "# Creation of data generators for augmentation and preprocessing\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rotation_range=20, # Randomly rotates images by up to 20 degrees\n",
        "    width_shift_range=0.2, # Randomly shifts images horizontally by up to 20% of the width\n",
        "    height_shift_range=0.2, # Randomly shifts images vertically by up to 20% of the height\n",
        "    horizontal_flip=True, # Randomly flips images horizontally\n",
        "    rescale=1./255, # Rescales pixel values to be between 0 and 1\n",
        "    zoom_range=0.1, # Randomly zooms in or out on images by up to 10%\n",
        ")\n",
        "\n",
        "val_datagen = ImageDataGenerator(\n",
        "    rescale=1.0 / 255 # Only rescaling for the test set\n",
        ")\n",
        "\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "    rescale=1.0 / 255  # Only rescaling for the test set\n",
        ")\n",
        "\n",
        "train_generator = train_datagen.flow_from_dataframe(\n",
        "    pd.DataFrame({\"filename\": X_train, \"class\": y_train}),\n",
        "    x_col=\"filename\",\n",
        "    y_col=\"class\",\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode=\"categorical\",\n",
        "    shuffle=True,\n",
        ")\n",
        "\n",
        "validation_generator = val_datagen.flow_from_dataframe(\n",
        "    pd.DataFrame({\"filename\": X_val, \"class\": y_val}),\n",
        "    x_col=\"filename\",\n",
        "    y_col=\"class\",\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode=\"categorical\",\n",
        "    shuffle=False,\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_dataframe(\n",
        "    pd.DataFrame({\"filename\": X_test, \"class\": y_test}),\n",
        "    x_col=\"filename\",\n",
        "    y_col=\"class\",\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode=\"categorical\",\n",
        "    shuffle=False,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ay3d2ACBaJX"
      },
      "source": [
        "# VGG 16\n",
        "\n",
        "Fine Tuning VGG-16 Pre-Trained Model.\n",
        "\n",
        "This section involves loading the pre-trained VGG-16 model, freezing the initial layers to preserve pre-learned features, adding custom layers for the specific task, and compiling the model with an appropriate optimizer, loss function, and metrics.\n",
        "\n",
        "*Why have we added these particular layers?*\n",
        "\n",
        "1. **Increased Learning Capacity**: The dense layers with numerous neuron (4096) increase the model's ability to learn complex representations from data. This is particularly useful when working with classification tasks that require fine discrimination between classes as in the case of the facial emotion recognition task.\n",
        "\n",
        "2.  **Overfitting Prevention**: Dropout layers reduce the risk of overfitting,thus improving the generalisation of the model to unseen data. This is crucial when adding capacity to the model to prevent it from learning the training data completely by memory.\n",
        "\n",
        "3. **Specific Fitting**: Freezing the first layers of the VGG-16 model allows it to take advantage of pre-trained generic features, while fine-tuning the last layers and adding new dense layers allows the model to better fit the specific features of the new dataset.\n",
        "\n",
        "4. **Specific output**: The final layer with the softmax activation feature and the number of neurons equal to the number of classes in the dataset allows the model to produce output that can be interpreted as probabilities for each class, facilitating accurate image classification.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "OH1Cnw0ZBZ0M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b00ee16d-6ad7-4875-9fb3-d78604116480"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_2 False\n",
            "1 block1_conv1 False\n",
            "2 block1_conv2 False\n",
            "3 block1_pool False\n",
            "4 block2_conv1 False\n",
            "5 block2_conv2 False\n",
            "6 block2_pool False\n",
            "7 block3_conv1 False\n",
            "8 block3_conv2 False\n",
            "9 block3_conv3 False\n",
            "10 block3_pool False\n",
            "11 block4_conv1 False\n",
            "12 block4_conv2 False\n",
            "13 block4_conv3 False\n",
            "14 block4_pool True\n",
            "15 block5_conv1 True\n",
            "16 block5_conv2 True\n",
            "17 block5_conv3 True\n",
            "18 block5_pool True\n",
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14714688 (56.13 MB)\n",
            "Trainable params: 7079424 (27.01 MB)\n",
            "Non-trainable params: 7635264 (29.13 MB)\n",
            "_________________________________________________________________\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4096)              102764544 \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 7)                 28679     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 134289223 (512.27 MB)\n",
            "Trainable params: 126653959 (483.15 MB)\n",
            "Non-trainable params: 7635264 (29.13 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.activations import relu, softmax\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "\n",
        "\n",
        "# Load the pre-trained VGG-16 model\n",
        "vgg_model = VGG16(weights=\"imagenet\", include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Freeze layers for transfer learning\n",
        "# This prevents the weights of these layers from being updated during training,\n",
        "# which helps to preserve the pre-learned features.\n",
        "for layer in vgg_model.layers[:-5]:  # Fine-tune the last few blocks\n",
        "    layer.trainable = False # Freezes all layers except the last five block\n",
        "\n",
        "# Adding flatten and dense layers to vgg-16 in order final model\n",
        "output = vgg_model.output\n",
        "output = Flatten()(output) # Flattens the output from the VGG-16 model\n",
        "output = Dense(4096, activation='relu')(output)  # Increased neurons\n",
        "output = Dropout(0.5)(output) # Adds a dropout layer to prevent overfitting\n",
        "output = Dense(4096, activation='relu')(output)  # Increased neurons\n",
        "output = Dropout(0.5)(output)\n",
        "# Adds the final output layer with a softmax activation\n",
        "output = Dense(len(train_generator.class_indices), activation='softmax')(output)\n",
        "final_model = Model(inputs=vgg_model.inputs, outputs=output) # Creates the final model\n",
        "\n",
        "# Initializing the Adam optimizer\n",
        "opt = Adam(learning_rate=LEARNING_RATE)\n",
        "\n",
        "# Compiling the model\n",
        "final_model.compile(\n",
        "    optimizer=opt,\n",
        "    loss=categorical_crossentropy, # Uses categorical crossentropy as the loss function\n",
        "    metrics=[\"accuracy\"] # Uses accuracy as the evaluation met\n",
        ")\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    # Make sure we have frozen the correct layers\n",
        "    for i, layer in enumerate(vgg_model.layers):\n",
        "        tf.print(i, layer.name, layer.trainable)\n",
        "\n",
        "    # Displaying vgg-16 model summary\n",
        "    vgg_model.summary()\n",
        "\n",
        "    # Displaying final model summary\n",
        "    final_model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJxzfIfDBdtB"
      },
      "source": [
        "# Training\n",
        "\n",
        "This section covers the training process of the fine-tuned model. This includes defining a learning rate scheduler, implementing early stopping and model checkpointing, and training the model using the training and validation data generators."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "TgMRYC3UBghZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "208fbf1b-b61a-40d9-cd5c-d46ce6675055"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 4096)              102764544 \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 7)                 28679     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 134289223 (512.27 MB)\n",
            "Trainable params: 126653959 (483.15 MB)\n",
            "Non-trainable params: 7635264 (29.13 MB)\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "48/48 [==============================] - ETA: 0s - loss: 2.1799 - accuracy: 0.1427"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48/48 [==============================] - 87s 2s/step - loss: 2.1799 - accuracy: 0.1427 - val_loss: 1.9404 - val_accuracy: 0.1797 - lr: 1.0000e-05\n",
            "Epoch 2/50\n",
            "48/48 [==============================] - 87s 2s/step - loss: 2.0554 - accuracy: 0.1664 - val_loss: 1.8802 - val_accuracy: 0.2240 - lr: 1.0000e-05\n",
            "Epoch 3/50\n",
            "48/48 [==============================] - 97s 2s/step - loss: 1.9654 - accuracy: 0.1870 - val_loss: 1.8257 - val_accuracy: 0.2305 - lr: 1.0000e-05\n",
            "Epoch 4/50\n",
            "48/48 [==============================] - 87s 2s/step - loss: 1.8706 - accuracy: 0.2296 - val_loss: 1.7140 - val_accuracy: 0.3307 - lr: 1.0000e-05\n",
            "Epoch 5/50\n",
            "48/48 [==============================] - 94s 2s/step - loss: 1.7709 - accuracy: 0.2818 - val_loss: 1.6249 - val_accuracy: 0.3359 - lr: 1.0000e-05\n",
            "Epoch 6/50\n",
            "48/48 [==============================] - 86s 2s/step - loss: 1.6525 - accuracy: 0.3300 - val_loss: 1.5371 - val_accuracy: 0.3867 - lr: 1.0000e-05\n",
            "Epoch 7/50\n",
            "48/48 [==============================] - 91s 2s/step - loss: 1.5504 - accuracy: 0.3811 - val_loss: 1.3990 - val_accuracy: 0.4414 - lr: 1.0000e-05\n",
            "Epoch 8/50\n",
            "48/48 [==============================] - 92s 2s/step - loss: 1.4686 - accuracy: 0.4202 - val_loss: 1.2466 - val_accuracy: 0.5312 - lr: 1.0000e-05\n",
            "Epoch 9/50\n",
            "48/48 [==============================] - 99s 2s/step - loss: 1.3828 - accuracy: 0.4547 - val_loss: 1.2199 - val_accuracy: 0.5378 - lr: 1.0000e-05\n",
            "Epoch 10/50\n",
            "48/48 [==============================] - 89s 2s/step - loss: 1.2855 - accuracy: 0.4919 - val_loss: 1.1062 - val_accuracy: 0.5755 - lr: 1.0000e-05\n",
            "Epoch 11/50\n",
            "48/48 [==============================] - 95s 2s/step - loss: 1.2291 - accuracy: 0.5140 - val_loss: 1.0313 - val_accuracy: 0.6172 - lr: 9.0484e-06\n",
            "Epoch 12/50\n",
            "48/48 [==============================] - 82s 2s/step - loss: 1.1756 - accuracy: 0.5388 - val_loss: 0.9903 - val_accuracy: 0.6263 - lr: 8.1873e-06\n",
            "Epoch 13/50\n",
            "48/48 [==============================] - 71s 1s/step - loss: 1.1257 - accuracy: 0.5541 - val_loss: 1.0198 - val_accuracy: 0.6198 - lr: 7.4082e-06\n",
            "Epoch 14/50\n",
            "48/48 [==============================] - 86s 2s/step - loss: 1.0827 - accuracy: 0.5831 - val_loss: 0.9350 - val_accuracy: 0.6549 - lr: 6.7032e-06\n",
            "Epoch 15/50\n",
            "48/48 [==============================] - 67s 1s/step - loss: 1.0469 - accuracy: 0.6052 - val_loss: 0.9365 - val_accuracy: 0.6510 - lr: 6.0653e-06\n",
            "Epoch 16/50\n",
            "48/48 [==============================] - 83s 2s/step - loss: 1.0303 - accuracy: 0.6046 - val_loss: 0.9297 - val_accuracy: 0.6432 - lr: 5.4881e-06\n",
            "Epoch 17/50\n",
            "48/48 [==============================] - 80s 2s/step - loss: 1.0031 - accuracy: 0.6235 - val_loss: 0.8998 - val_accuracy: 0.6693 - lr: 4.9659e-06\n",
            "Epoch 18/50\n",
            "48/48 [==============================] - 89s 2s/step - loss: 0.9725 - accuracy: 0.6313 - val_loss: 0.8489 - val_accuracy: 0.6888 - lr: 4.4933e-06\n",
            "Epoch 19/50\n",
            "48/48 [==============================] - 63s 1s/step - loss: 0.9458 - accuracy: 0.6371 - val_loss: 0.8899 - val_accuracy: 0.6654 - lr: 4.0657e-06\n",
            "Epoch 20/50\n",
            "48/48 [==============================] - 67s 1s/step - loss: 0.9188 - accuracy: 0.6505 - val_loss: 0.8632 - val_accuracy: 0.6888 - lr: 3.6788e-06\n",
            "Epoch 21/50\n",
            "48/48 [==============================] - 83s 2s/step - loss: 0.9083 - accuracy: 0.6505 - val_loss: 0.8309 - val_accuracy: 0.6940 - lr: 3.3287e-06\n",
            "Epoch 22/50\n",
            "48/48 [==============================] - 89s 2s/step - loss: 0.9176 - accuracy: 0.6489 - val_loss: 0.7956 - val_accuracy: 0.7188 - lr: 3.0119e-06\n",
            "Epoch 23/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.9039 - accuracy: 0.6541 - val_loss: 0.8051 - val_accuracy: 0.7227 - lr: 2.7253e-06\n",
            "Epoch 24/50\n",
            "48/48 [==============================] - 60s 1s/step - loss: 0.8828 - accuracy: 0.6622 - val_loss: 0.8128 - val_accuracy: 0.7083 - lr: 2.4660e-06\n",
            "Epoch 25/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.8795 - accuracy: 0.6678 - val_loss: 0.8159 - val_accuracy: 0.7031 - lr: 2.2313e-06\n",
            "Epoch 26/50\n",
            "48/48 [==============================] - 90s 2s/step - loss: 0.8573 - accuracy: 0.6678 - val_loss: 0.7913 - val_accuracy: 0.7135 - lr: 2.0190e-06\n",
            "Epoch 27/50\n",
            "48/48 [==============================] - 91s 2s/step - loss: 0.8600 - accuracy: 0.6821 - val_loss: 0.7890 - val_accuracy: 0.7161 - lr: 1.8268e-06\n",
            "Epoch 28/50\n",
            "48/48 [==============================] - 91s 2s/step - loss: 0.8530 - accuracy: 0.6749 - val_loss: 0.7844 - val_accuracy: 0.7253 - lr: 1.6530e-06\n",
            "Epoch 29/50\n",
            "48/48 [==============================] - 61s 1s/step - loss: 0.8486 - accuracy: 0.6743 - val_loss: 0.7903 - val_accuracy: 0.7188 - lr: 1.4957e-06\n",
            "Epoch 30/50\n",
            "48/48 [==============================] - 58s 1s/step - loss: 0.8375 - accuracy: 0.6873 - val_loss: 0.7861 - val_accuracy: 0.7227 - lr: 1.3534e-06\n",
            "Epoch 31/50\n",
            "48/48 [==============================] - 94s 2s/step - loss: 0.8374 - accuracy: 0.6798 - val_loss: 0.7710 - val_accuracy: 0.7266 - lr: 1.2246e-06\n",
            "Epoch 32/50\n",
            "48/48 [==============================] - 81s 2s/step - loss: 0.8219 - accuracy: 0.6906 - val_loss: 0.7699 - val_accuracy: 0.7370 - lr: 1.1080e-06\n",
            "Epoch 33/50\n",
            "48/48 [==============================] - 64s 1s/step - loss: 0.8152 - accuracy: 0.6919 - val_loss: 0.7745 - val_accuracy: 0.7227 - lr: 1.0026e-06\n",
            "Epoch 34/50\n",
            "48/48 [==============================] - 64s 1s/step - loss: 0.8177 - accuracy: 0.7000 - val_loss: 0.7700 - val_accuracy: 0.7279 - lr: 9.0718e-07\n",
            "Epoch 35/50\n",
            "48/48 [==============================] - 60s 1s/step - loss: 0.8075 - accuracy: 0.6997 - val_loss: 0.7786 - val_accuracy: 0.7253 - lr: 8.2085e-07\n",
            "Epoch 36/50\n",
            "48/48 [==============================] - 61s 1s/step - loss: 0.8214 - accuracy: 0.6909 - val_loss: 0.7815 - val_accuracy: 0.7214 - lr: 7.4273e-07\n",
            "Epoch 37/50\n",
            "48/48 [==============================] - 87s 2s/step - loss: 0.8016 - accuracy: 0.6886 - val_loss: 0.7592 - val_accuracy: 0.7344 - lr: 6.7205e-07\n",
            "Epoch 38/50\n",
            "48/48 [==============================] - 63s 1s/step - loss: 0.8053 - accuracy: 0.6945 - val_loss: 0.7766 - val_accuracy: 0.7188 - lr: 6.0810e-07\n",
            "Epoch 39/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.8159 - accuracy: 0.6997 - val_loss: 0.7649 - val_accuracy: 0.7305 - lr: 5.5023e-07\n",
            "Epoch 40/50\n",
            "48/48 [==============================] - 61s 1s/step - loss: 0.8082 - accuracy: 0.6948 - val_loss: 0.7626 - val_accuracy: 0.7318 - lr: 4.9787e-07\n",
            "Epoch 41/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.8126 - accuracy: 0.6906 - val_loss: 0.7670 - val_accuracy: 0.7279 - lr: 4.5049e-07\n",
            "Epoch 42/50\n",
            "48/48 [==============================] - 60s 1s/step - loss: 0.7938 - accuracy: 0.6987 - val_loss: 0.7665 - val_accuracy: 0.7253 - lr: 4.0762e-07\n",
            "Epoch 43/50\n",
            "48/48 [==============================] - 62s 1s/step - loss: 0.8100 - accuracy: 0.6870 - val_loss: 0.7632 - val_accuracy: 0.7292 - lr: 3.6883e-07\n",
            "Epoch 44/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.8161 - accuracy: 0.6928 - val_loss: 0.7649 - val_accuracy: 0.7279 - lr: 3.3373e-07\n",
            "Epoch 45/50\n",
            "48/48 [==============================] - 94s 2s/step - loss: 0.8028 - accuracy: 0.6928 - val_loss: 0.7527 - val_accuracy: 0.7331 - lr: 3.0197e-07\n",
            "Epoch 46/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.7975 - accuracy: 0.6951 - val_loss: 0.7568 - val_accuracy: 0.7305 - lr: 2.7324e-07\n",
            "Epoch 47/50\n",
            "48/48 [==============================] - 61s 1s/step - loss: 0.7937 - accuracy: 0.7007 - val_loss: 0.7535 - val_accuracy: 0.7396 - lr: 2.4723e-07\n",
            "Epoch 48/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.7946 - accuracy: 0.7016 - val_loss: 0.7572 - val_accuracy: 0.7344 - lr: 2.2371e-07\n",
            "Epoch 49/50\n",
            "48/48 [==============================] - 59s 1s/step - loss: 0.7939 - accuracy: 0.7010 - val_loss: 0.7588 - val_accuracy: 0.7318 - lr: 2.0242e-07\n",
            "Epoch 50/50\n",
            "48/48 [==============================] - 61s 1s/step - loss: 0.8012 - accuracy: 0.6951 - val_loss: 0.7552 - val_accuracy: 0.7266 - lr: 1.8316e-07\n"
          ]
        }
      ],
      "source": [
        "from math import ceil\n",
        "from tensorflow.keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint\n",
        "\n",
        "# Define learning rate scheduler\n",
        "# This function adjusts the learning rate during training based on the current epoch.\n",
        "def lr_scheduler(epoch, lr):\n",
        "    if epoch < 10: # Keep the learning rate constant for the first 10 epochs\n",
        "        return lr\n",
        "    else: # Reduce the learning rate exponentially after 10 epochs\n",
        "        return lr * tf.math.exp(-0.1)\n",
        "\n",
        "# Implement Early Stopping and Model Checkpoint\n",
        "# Early stopping helps prevent overfitting by stopping the training process if the model's\n",
        "# performance on the validation set stops improving\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)  # Stops training if validation loss doesn't improve for 10 epochs\n",
        "model_checkpoint = ModelCheckpoint(SAVED_MODEL_PATH, monitor='val_loss', save_best_only=True) # Saves the model with the lowest validation loss\n",
        "lr_schedule = LearningRateScheduler(lr_scheduler)\n",
        "\n",
        "# Displaying final model summary\n",
        "final_model.summary()\n",
        "\n",
        "# Train Section\n",
        "history = final_model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=ceil(train_generator.samples // BATCH_SIZE), # Calculates the number of steps per epoch\n",
        "    epochs=N_EPOCHS,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=ceil(validation_generator.samples // BATCH_SIZE),\n",
        "    callbacks=[early_stopping, model_checkpoint, lr_schedule],\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# Saving the trained model\n",
        "path = \"/content/drive/MyDrive/Colab Notebooks/AsItSounds/Saved Models/KDEF\"\n",
        "final_model.save(filepath=path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e1bLiPVZdOAF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "09124856-23f3-4409-c644-d31a8afa9d83"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Estrai l'accuracy dal training history\n",
        "train_accuracy = history.history['accuracy']\n",
        "val_accuracy = history.history['val_accuracy']\n",
        "\n",
        "# Plotta l'accuracy\n",
        "epochs = range(1, len(train_accuracy) + 1)\n",
        "plt.plot(epochs, train_accuracy, 'b', label='Training Accuracy')\n",
        "plt.plot(epochs, val_accuracy, 'r', label='Validation Accuracy')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merge train and validation set\n",
        "X_train_val = X_train + X_val\n",
        "y_train_val = y_train + y_val\n",
        "\n",
        "# Create a generator for train+validation\n",
        "train_val_generator = train_datagen.flow_from_dataframe(\n",
        "    pd.DataFrame({\"filename\": X_train_val, \"class\": y_train_val}),\n",
        "    x_col=\"filename\",\n",
        "    y_col=\"class\",\n",
        "    target_size=IMG_SIZE,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode=\"categorical\",\n",
        "    shuffle=True,\n",
        ")\n",
        "\n",
        "# Train the model on train+validation\n",
        "history = final_model.fit(\n",
        "    train_val_generator,\n",
        "    steps_per_epoch=ceil(len(X_train_val) // BATCH_SIZE),\n",
        "    epochs=10,\n",
        "    callbacks=[early_stopping, model_checkpoint, lr_schedule],\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# Final evaluation on the test set\n",
        "test_loss, test_acc = final_model.evaluate(test_generator)\n",
        "print(\"Loss sul test set:\", test_loss)\n",
        "print(\"Accuratezza sul test set:\", test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOUcSZl-eu0N",
        "outputId": "00e45720-7a41-41dc-c4dd-0f48a0df5bcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 3918 validated image filenames belonging to 7 classes.\n",
            "Epoch 1/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8209 - accuracy: 0.6946"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 69s 1s/step - loss: 0.8209 - accuracy: 0.6946 - lr: 1.1109e-07\n",
            "Epoch 2/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8132 - accuracy: 0.6951"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 69s 1s/step - loss: 0.8132 - accuracy: 0.6951 - lr: 1.1109e-07\n",
            "Epoch 3/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8102 - accuracy: 0.6845"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 66s 1s/step - loss: 0.8102 - accuracy: 0.6845 - lr: 1.1109e-07\n",
            "Epoch 4/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8300 - accuracy: 0.6814"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 69s 1s/step - loss: 0.8300 - accuracy: 0.6814 - lr: 1.1109e-07\n",
            "Epoch 5/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8158 - accuracy: 0.6873"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 67s 1s/step - loss: 0.8158 - accuracy: 0.6873 - lr: 1.1109e-07\n",
            "Epoch 6/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8181 - accuracy: 0.6959"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 68s 1s/step - loss: 0.8181 - accuracy: 0.6959 - lr: 1.1109e-07\n",
            "Epoch 7/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8107 - accuracy: 0.7011"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 69s 1s/step - loss: 0.8107 - accuracy: 0.7011 - lr: 1.1109e-07\n",
            "Epoch 8/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8214 - accuracy: 0.6868"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 69s 1s/step - loss: 0.8214 - accuracy: 0.6868 - lr: 1.1109e-07\n",
            "Epoch 9/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8089 - accuracy: 0.6972"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r61/61 [==============================] - 68s 1s/step - loss: 0.8089 - accuracy: 0.6972 - lr: 1.1109e-07\n",
            "Epoch 10/10\n",
            "61/61 [==============================] - ETA: 0s - loss: 0.8099 - accuracy: 0.6928"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
            "WARNING:tensorflow:Can save best model only with val_loss available, skipping.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "61/61 [==============================] - 67s 1s/step - loss: 0.8099 - accuracy: 0.6928 - lr: 1.1109e-07\n",
            "16/16 [==============================] - 12s 742ms/step - loss: 0.6821 - accuracy: 0.7551\n",
            "Loss sul test set: 0.6820552349090576\n",
            "Accuratezza sul test set: 0.7551020383834839\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Count the number of images per label\n",
        "unique_labels, counts = np.unique(y_train, return_counts=True)\n",
        "  # unique_labels: This variable stores an array containing the unique labels present in y_train.\n",
        "  # counts: This variable stores an array containing the count of each unique label, i.e. how many times each label appears in y_train.\n",
        "  # return_counts=True: This argument tells the function np.unique to return both unique labels and their counts.\n",
        "\n",
        "# For each pair of label and count, prints a line indicating how many images are associated with that particular label\n",
        "for label, count in zip(unique_labels, counts):\n",
        "    print(f\"Etichetta {label}: {count} immagini\")\n",
        "\n",
        "# Histogram\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(unique_labels, counts)\n",
        "plt.xlabel(\"Etichette\")\n",
        "plt.ylabel(\"Numero di immagini\")\n",
        "plt.title(\"Distribuzione delle immagini per etichetta (Istogramma)\")\n",
        "plt.xticks(unique_labels)\n",
        "plt.show()\n",
        "\n",
        "# Pie chart\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(counts, labels=unique_labels, autopct=\"%1.1f%%\", startangle=90)\n",
        "plt.title(\"Distribuzione delle immagini per etichetta (Grafico a torta)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "p13WRSqWhR6X",
        "outputId": "c8d98d23-f401-4a9d-c9fe-bdaaf41b9e6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Etichetta AF: 448 immagini\n",
            "Etichetta AN: 448 immagini\n",
            "Etichetta DI: 448 immagini\n",
            "Etichetta HA: 448 immagini\n",
            "Etichetta NE: 448 immagini\n",
            "Etichetta SA: 447 immagini\n",
            "Etichetta SU: 447 immagini\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#Same as before but for the whole dataset\n",
        "y_tot = y_train + y_val + y_test\n",
        "\n",
        "unique_labels, counts = np.unique(y_tot, return_counts=True)\n",
        "\n",
        "for label, count in zip(unique_labels, counts):\n",
        "    print(f\"Etichetta {label}: {count} immagini\")\n",
        "\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.bar(unique_labels, counts)\n",
        "plt.xlabel(\"Etichette\")\n",
        "plt.ylabel(\"Numero di immagini\")\n",
        "plt.title(\"Distribuzione delle immagini per etichetta (Istogramma)\")\n",
        "plt.xticks(unique_labels)\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(counts, labels=unique_labels, autopct=\"%1.1f%%\", startangle=90)\n",
        "plt.title(\"Distribuzione delle immagini per etichetta (Grafico a torta)\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "FhIOkmb-zXXy",
        "outputId": "e035498a-9555-445c-fc24-1233754f03e4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Etichetta AF: 700 immagini\n",
            "Etichetta AN: 700 immagini\n",
            "Etichetta DI: 700 immagini\n",
            "Etichetta HA: 700 immagini\n",
            "Etichetta NE: 700 immagini\n",
            "Etichetta SA: 699 immagini\n",
            "Etichetta SU: 699 immagini\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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3v/+93TzN740ffvgBdrsdM2fObPXzj46ORmpqapuff3ffm5WVlVixYgVmzpyJurq6ltutqKjAhRdeiKysLBQUFPT48Xz//fcIDw9v93k9cXqoq6++GiEhIS1fNz+3zc9nf2VsptfrWz4PbDYbKioqYDAYMGTIkFavnfZUVlZCCNEqPwDU1tYCOPoZcqKkpCRERES0/Pv555/b5GnvZ3f867v5eTj77LPR0NCAAwcOdO/BHqc7P6PffvsNarUa//znP1t9/4EHHoAQAr///nun93F8ZqPRiPLyckyYMAFCCKSlpfU4M9Dzz/Ge/p7q6euh+WdfXl7em4fjlXhI18Ga54XqyNVXX42PPvoIt912Gx566CGcf/75uPLKK3HVVVe1ehN1Ji4urkcDgVNTU1t9rSgKUlJS+nTafW1tLa688krExcVh/vz5nc41mJubC+Bo4TieTqdDUlJSy/ebDRgwoM3thYSEYNeuXS1fZ2VlYf/+/YiIiGj3PpsHUHeW58TnRavVIikpqdVlfbmfE2VlZQEAbrrppg63qampafNLrCs+Pj5t8gUFBbX7PAYFBaGqqqrNbcTHx7fZDkCrw9sdXX/RokV49tlnkZ6eDrPZ3HL58fedm5sLlUrV5lBySkpKVw+vw4zA0ddFe4+nu058DSQnJ0OlUrW8N7KysiCEaLNds+MPYQHdf28ePHgQQgg8/vjjePzxx9vdprS0FHFxcd14FH86dOgQhgwZ0q0TxU58Pptfd83PZ39lbGa32/HGG2/g3XffRXZ2Nmw2W8v3OhsecjwhRKuvm/+wPXHYCXD0j+ympibs3LkT//rXv9p8v6Of3d69e/HYY49hxYoVLYWyWU1NTbdyHq87P6Pc3FzExsa2OeGk+dDqiZ+ZJzpy5AieeOIJ/Pzzz23eH73JfPx9dvdzvKe/p3r6emj+2TtrnltPwMLnQPn5+aipqen0l5ivry/WrFmDlStX4tdff8Uff/yBb775Bueddx6WLFnSrbMTezrurjs6etPYbLZ2M82aNQuFhYXYsmWLw8dPdPQcHP/hbrfbcdJJJ+HVV19td9sTi0pvOfJ+mvfevfzyyzjllFPa3aa9PRNd6ej56s7z2JvbOP76a9euxfTp03HOOefg3XffRUxMDLRaLT755BN8+eWX3YnfbT15PL114vvAbrdDURT8/vvv7d7/iT+v7r43m18L//rXv3DhhRe2u01PynBvdPV89nfG559/Ho8//jhuueUW/Oc//0FoaChUKhXuvffeLvd0h4aGQlGUNmUmJSWlw0l5J06cCAAdFq32fnbV1dWYOHEiAgMD8cwzzyA5ORk+Pj7YsWMHHnzwwV7vke9PNpsNU6ZMQWVlJR588EEMHToU/v7+KCgowKxZs5yWuae/p3r6emj+2YeHhzskrzdg4XOgzz77DAA6/HBsplKpcP755+P888/Hq6++iueffx6PPvooVq5cicmTJzv8L5bmPUvNhBA4ePBgq0HFISEhqK6ubnPd3NzcNnu9XnjhBSxcuBA//PADhg4d2uX9JyQkAAAyMjJa3ZbFYkF2djYmT57ck4cD4OiemJ07d+L888/v8fPVnCcrK6vl0CwANDU1ITs7GyeffLJD7qe9zAAQGBjYq8fsir7//nv4+Phg8eLF0Ov1LZd/8sknrbZLSEiA3W5HdnZ2q71lBw8edFrW9mRlZbXa63jw4EHY7XYMGjQIAFoG0CcmJmLw4MEOu9/m94FWq+3ytdCT111ycjI2b96MpqamNnsfZWXs6HsLFizAueeei7lz57a6vLq6ustf4hqNBsnJycjOzm51ub+/PyZNmoTVq1ejoKCg13sfm61atQoVFRX44YcfcM4557RcfuL99kR3fkYJCQlYtmwZ6urqWu3laz6E3PwZ1p7du3cjMzMTn376aauTq5YuXdqtfB39vBz1Oe6o10N2djZUKpVD35eejmP4HGTFihX4z3/+g8TERFx33XUdbldZWdnmsua9Pc2Hw/z9/QGg3QLWG/Pnz281rnDBggUoKipqOeMVOPohtGnTJlgslpbLFi1ahLy8vFa3tWzZMjz22GN49NFHcfnll3fr/idPngydToc333yz1d6YuXPnoqampkdnajabOXMmCgoK8OGHH7b5XmNjI4xGY4fXHTt2LCIiIvDee++1erzz5s1r85z35X5ONGbMGCQnJ+N///tfu4ecysrKun1brkKtVkNRlFaHX3Jyctqcvdz8R9C7777b6vK33nqr3zN25p133mn1dXOe5vfGlVdeCbVajaeffrrNnkQhBCoqKnp1v5GRkZg0aRLef/99FBUVtfn+8a+FnnwezJgxA+Xl5Xj77bfbfK+ne0IdldHf37/dy9VqdZtM3333XbfHBZ5xxhnYtm1bm8ufeOIJ2Gw2XH/99e2+z3ryPDTvBT3+OhaLpc3ruCe68zO6+OKLYbPZ2mzz2muvQVGUVp/d3ckshMAbb7zRrXwd/Swd9TnuqNfD9u3bMWLEiDbjrqlj3MPXC7///jsOHDgAq9WKkpISrFixAkuXLkVCQgJ+/vnnTid+feaZZ7BmzRpMmzYNCQkJKC0txbvvvosBAwbgrLPOAnC0fAUHB+O9995DQEAA/P39MX78+Dbjn7orNDQUZ511Fm6++WaUlJTg9ddfR0pKSqupY2677TYsWLAAU6dOxcyZM3Ho0CF8/vnnraZxAIBrr70WERERSE1Nxeeff97qe1OmTGl3GoqIiAg8/PDDePrppzF16lRMnz4dGRkZePfdd3Haaae1OkGju2644QZ8++23+Nvf/oaVK1fizDPPhM1mw4EDB/Dtt99i8eLFrSbHPp5Wq8Wzzz6LO+64A+eddx6uvvpqZGdn45NPPmmzN7Mv93MilUqFjz76CBdddBFGjBiBm2++GXFxcSgoKMDKlSsRGBiIX375pcfPhUzTpk3Dq6++iqlTp+Kvf/0rSktL8c477yAlJaXVmMsxY8ZgxowZeP3111FRUdEyLUtmZiYAeeNwsrOzMX36dEydOhUbN27E559/jr/+9a8te3mTk5Px7LPP4uGHH0ZOTg4uv/xyBAQEIDs7Gz/++CNuv/32dseDdcc777yDs846CyeddBJmz56NpKQklJSUYOPGjcjPz8fOnTsBHP2DUK1W48UXX0RNTQ30ej3OO++8dscK33jjjZg/fz7uv/9+bNmyBWeffTaMRiOWLVuGu+66C5dddpnTM44ZMwZz5szBs88+i5SUFERGRuK8887DJZdcgmeeeQY333wzJkyYgN27d+OLL75o8x7syGWXXYbPPvsMmZmZrfbynH322Xj77bdx9913IzU1Fddddx2GDh0Ki8WCzMxMfPHFF9DpdIiOju7yPiZMmICQkBDcdNNN+Oc//wlFUfDZZ5/1aRhBd35Gl156Kc4991w8+uijyMnJwcknn4wlS5bgp59+wr333tvmc/l4Q4cORXJyMv71r3+hoKAAgYGB+P7777s91rWzn6UjPscd8XpoamrC6tWr25wERl1wyrnAHuLESSl1Op2Ijo4WU6ZMEW+88UarqU+anTgty/Lly8Vll10mYmNjhU6nE7GxseLaa68VmZmZra73008/ieHDhwuNRtNqKo7mCS3b09G0LF999ZV4+OGHRWRkpPD19RXTpk1r99T8V155RcTFxQm9Xi/OPPNMsW3btja3efzjP/FfVxMvv/3222Lo0KFCq9WKqKgoceedd3Y4YeeJbrrpJpGQkNDqMovFIl588UUxYsQIodfrRUhIiBgzZox4+umnRU1NTbvP0fHeffddkZiYKPR6vRg7dmyHEy939366O/FyWlqauPLKK0VYWJjQ6/UiISFBzJw5UyxfvrzTvJ1NvHyijp7H5gmMm3U01VBHU0y0d39z585tmUB26NCh4pNPPml3wnGj0Sj+/ve/i9DQUGEwGMTll18uMjIyBADxwgsvtMnU3sTL7T3O9l7z3Z2WZd++feKqq64SAQEBIiQkRPzjH/9od+Ll77//Xpx11lnC399f+Pv7i6FDh4q///3vIiMjo1WWjt6bHTl06JC48cYbRXR0tNBqtSIuLk5ccsklYsGCBa22+/DDD0VSUpJQq9WtHl97r9eGhgbx6KOPisTERKHVakV0dLS46qqrxKFDh4QQnS+thhOmyHFExuLiYjFt2jQREBDQatojk8kkHnjgARETEyN8fX3FmWeeKTZu3Njt5Q3NZrMIDw8X//nPf9r9flpamrjxxhtFfHy80Ol0wt/fX4waNUo88MADbaY16exnt379enH66ae3TAL/f//3f2Lx4sVtXmfdnZZFiK5/RkIIUVdXJ+677z4RGxsrtFqtSE1N7fbEy/v27ROTJ08WBoNBhIeHi9mzZ7dMWdOdJTs7+lkK0bfPcSEc83r4/fffBdB2GTvqnCKEA0c8ExH1QHp6Ok499VR8/vnnnQ6FcLTmCWTLyso46NuN/ec//8Enn3yCrKwsLufoRS6//HIoioIff/xRdhS3wjF8ROQUjY2NbS57/fXXoVKpWg2IJ+qu++67D/X19fj6669lRyEn2b9/PxYtWoT//Oc/sqO4HY7hIyKneOmll7B9+3ace+650Gg0+P333/H777/j9ttvd9g0OuRdDAZDj+bCJPc3bNgwWK1W2THcEgsfETnFhAkTsHTpUvznP/9BfX094uPj8dRTT7WsqUlERP2HY/iIiIiIPBzH8BERERF5OBY+IiIiIg/HwkdERETk4Vj4iIiIiDwcCx8RERGRh2PhIyIiIvJwLHxEREREHo6Fj4iIiMjDsfAREREReTgWPiIiIiIPx8JHRERE5OFY+IiIiIg8HAsfERERkYdj4SMiIiLycCx8RERERB6OhY+IiIjIw7HwEREREXk4Fj4iIiIiD8fCR0REROThWPiIiIiIPBwLHxEREZGHY+EjIiIi8nAsfEREREQejoWPiIiIyMOx8BERERF5OBY+IiIiIg/HwkdERETk4Vj4iIiIiDwcCx8RERGRh2PhIyIiIvJwLHxEREREHo6Fj4iIiMjDsfAREREReTgWPiIiIiIPx8JHRERE5OFY+IiIiIg8HAsfERERkYdj4SMiIiLycCx8RERERB6OhY+IiIjIw7HwEREREXk4Fj4iIiIiD8fCR0REROThWPiIiIiIPBwLHxEREZGHY+EjIiIi8nAsfEREREQejoWPiIiIyMOx8BERERF5OBY+IiIiIg/HwkdERETk4Vj4iIiIiDwcCx8RERGRh2PhIyIiIvJwLHxEREREHo6Fj4iIiMjDsfAREREReTgWPiIiIiIPx8JHRERE5OFY+IiIiIg8HAsfERERkYdj4SOvsHHjRqjVakybNq3V5Tk5OVAUpc2/66+/XlJSIuqLsrIy3HnnnYiPj4der0d0dDQuvPBCrF+/HgCgKAoWLlzY5nqzZs3C5Zdf7tywRE6kkR2AyBnmzp2Lu+++G3PnzkVhYSFiY2NbfX/ZsmUYMWJEy9e+vr7OjkhEDjBjxgxYLBZ8+umnSEpKQklJCZYvX46KigrZ0YikYuEjj1dfX49vvvkG27ZtQ3FxMebNm4dHHnmk1TZhYWGIjo6WlJCIHKG6uhpr167FqlWrMHHiRABAQkICxo0bJzkZkXwsfOTxvv32WwwdOhRDhgzB9ddfj3vvvRcPP/wwFEWRHY0coMlmR53JijpT07H/WlFvtsLUZINdiKP/7IBNCAghYBfApdAe/fkrABQFUClQjv2/olVB0auh8tFA5aOG0vxfNUfAuDqDwQCDwYCFCxfi9NNPh16vlx2JyGWw8JHHmzt3bsuYvKlTp6KmpgarV6/GpEmTWraZMGECVKo/f6GvXbsWp556qrOjej2L1Y7iGhMKaxpRVNOIwmoTyurMqDU1of64Mldv/rPgma32Ht/P2QjseTiNcrQE6o+VQP2fZVDlo4EqQAd1sB6aIB3UQXqog/RQNCyJzqTRaDBv3jzMnj0b7733HkaPHo2JEyfimmuuwahRo2THI5KKhY88WkZGBrZs2YIff/wRwNFfCFdffTXmzp3bqvB98803GDZsWMvXAwcOdHZUj2e12VFSZ0ZRdSMKa0woqm5EUY0JhdWNKK41obDahAqjGULITtoBq4C9vgn2+qbuba8AKn9tS/nTBB/9rzr4WCEM1kMdoIei5p5mR5oxYwamTZuGtWvXYtOmTfj999/x0ksv4aOPPsKsWbNkxyOShoWPPNrcuXNhtVpbnaQhhIBer8fbb7/dctnAgQORkpIiI6LHsdkFssuNyCqpQ0ZJHbJK6pFRUoecciOsdldtc/1AoKUgNhXUt7+NWoEmzBfaKD9oo/ygifI/+t9wXygqFsHe8vHxwZQpUzBlyhQ8/vjjuO222/Dkk09i1qxZCAgIQE1NTZvrVFdXIygoSEJaIudg4SOPZbVaMX/+fLzyyiu44IILWn3v8ssvx1dffYWpU6dKSuf+hBA4UtmAzJJ6ZJbUIbOkDhnFdThcboSlF4dZvZJNwFraAGtpAxp3H3e5RoE23A+aaL9jZfBoEVSH+nDsaS8MHz68ZSqWIUOGYPv27bjppptavm+z2bBz507cdtttkhIS9T8WPvJYixYtQlVVFW699dY2f7nPmDEDc+fOZeHrgexyI7blVGLHkSrsKajFwdJ6NDbZZMfyTFaBpmIjmoqNaDzuYkWrgibSD7o4A3QJgdAlBEIbzimEmlVUVOAvf/kLbrnlFowaNQoBAQHYtm0bXnrpJVx22WUAgPvvvx+33norhg4diilTpsBoNOKtt95CVVUVCx95NBY+8lhz587F5MmT2z1MM2PGDLz00kuora2VkMz1ma027M6vwbbcKmzPrcKO3CpUGC2yY3k90WRHU0E9mgrqYdxSDABQGbTQxQdCnxBwtAQOCPDak0UMBgPGjx+P1157DYcOHUJTUxMGDhyI2bNnt0zFdO2110IIgVdffRUPPfQQ/Pz8MGbMGKxZswZRUVGSHwFR/1GEcNkh0kTkJGV1ZmzPrcT2YwVvT0EtLDbPPSy7rjdn6boLtdKyB1B/bC+gOkAnOxURScbCR+SFqhssWJ1ZhjWZ5diWW4ncigbZkZzKowtfO9ShPtAPCoTP4BD4DA6Byk8rOxIRORkLH5EXEEJgb2EtVh4oxcqMUuzMr4HNm86YPYG3Fb5WVIBu4LHyNzQU2lh/nghC5AVY+Ig8VK2pCWszy7EyoxSrM8tQVmeWHclleHXhO4EqQAufwaHwGXJs758Ph3YTeSIWPiIPsr+oFiszSrHqQBl2HKnyrnnveoCFrwMqBbr4APgMDYXP4BDoYg2yExGRg7DwEbm5tCNV+HlnIf7YU4yiGpPsOG6Bha971EE6+I4Mh98pkdANDJAdh4j6gIWPyA0dLK3DT+mF+HlnodedcOEILHw9pwnzge/JEfA7JRLaSD/ZcYioh1j4iNxEQXUjfj5W8vYXcf7AvmDh6xttjD/8TomA78kR0AT7yI5DRN3AwkfkwiqNFvy662jJ25ZbBb5bHYOFz0EUQJcQCL+TI+B7UjjUBs73R+SqWPiIXIypyYbf9xRhYVoh1h8s54kX/YCFrx+oFOhTguF3SgT8TgqHolXLTkREx2HhI3IRh8vq8fmmI1iwPQ+1JqvsOB6Nha9/Kb4a+I+Jgv/pMVzrl8hFsPARSWSzCyzdV4LPN+Vi/aFyHrJ1EhY+J1EAfXIwDKfHwGd4GBQVJ3gmkoWFj0iC0joTvtqch6+3HuFUKhKw8DmfOkgH/3Ex8B8XzbV9iSRg4SNyok2HK/DZplws2VuMJhvferKw8EmkVuA7Igz+42PgkxwsOw2R12DhI+pn9WYrftiRj8835SKzpF52HAILn6vQRPrBcHoM/EZHckk3on7GwkfUT0prTfhoXTa+3HwE9WaehOFKWPhci6JXw398DALOioM6kId7ifoDCx+Rg2WXG/H+6kP4Ia0AFqtddhxqBwufi9Io8B8dhYBzBkDDs3uJHIqFj8hB9hTUYM6qQ/h9TxE4dZ5rY+FzcQrgOzIcAZMGQhdnkJ2GyCOw8BH1UdqRKryxPAurMspkR6FuYuFzHz5DQhA4OQG6gQGyoxC5NRY+ol7anluJ15dlYW1Wuewo1EMsfO5HnxqMwMkJ0CfwZ0fUGyx8RD20+XAF3liehQ2HKmRHoV5i4XNf+pRgBJ4fD31ikOwoRG6FhY+om/YV1uK/v+/nHj0PwMLn/vSDQxB8cSK00f6yoxC5BRY+oi4U15jw8uIM/JiWz5MxPAQLn4dQAL/RUQi6IAHqIL3sNEQujYWPqAP1ZiveW3UIH607DFMTp1fxJCx8nkXRqmA4Ow4BEwdCpVfLjkPkklj4iE5gtdnx1dY8vLEsE+X1FtlxqB+w8HkmlUGLwMkJ8D8tGopakR2HyKWw8BEdZ+m+Erzw+34cKjPKjkL9iIXPs2kifBF0USJ8h4fJjkLkMlj4iADsyq/Gc7/ux+bsStlRyAlY+LyDLjEIwdMSoRvAOfyIWPjIqxVUN+LF3w/gl12F4DvBe7DweREF8B0VgaCLEqEJ5okd5L1Y+Mgr2ewCn6zPxqtLM9FgscmOQ07Gwud9FJ0agRckwDAhFoqK4/vI+7DwkdfZnV+Dh3/chT0FtbKjkCQsfN5LG2dAyJWpXKOXvA4LH3kNo9mKV5Zk4tONObBxQj2vxsLn5VSAYUIcAi9IgErHaVzIO7DwkVdYtq8ET/y0B4U1JtlRyAWw8BEAqIP1CL4sGb7DeDYveT4WPvJoJbUmPPXzXvy+p1h2FHIhLHx0PN+RYQiengx1IE/qIM/FwkceyW4X+GJzLl76IwN1ZqvsOORiWPjoRIpejaCpg+A/PoYndZBHYuEjj3OguBYP/7AbaUeqZUchF8XCRx3RxQcg5MpUaKP9ZUchcigWPvIYNrvAOysP4q0VWWiy8WVNHWPho06pFQROjkfAxIHc20ceg4WPPEJeZQPu+yYd23KrZEchN8DCR92hSwxE6NVDoAn2kR2FqM9Y+Mjt/ZiWjycW7uVYPeo2Fj7qLsVHjZDLU+B3SqTsKER9wsJHbqvW1ITHF+7BT+mFsqOQm2Hho57yPSUCIZenQOWjkR2FqFdY+MgtbcmuxH3fpKOgulF2FHJDLHzUG+pgPUKvGQL9oCDZUYh6jIWP3IrVZsdryzIxZ9UhcLEM6i0WPuo1FRAwcSACJydAUfOEDnIfLHzkNnLKjbjnm3TszKuWHYXcHAsf9ZV2gAGh1wyFNtxXdhSibmHhI7fwzdYjePqXfWiw2GRHIQ/AwkeOoOhUCL4kGf7jomVHIeoSCx+5tEaLDQ9+vws/7+SJGeQ4LHzkSL6nRCB0RioUrVp2FKIOsfCRy8qrbMDs+dtwoLhOdhTyMCx85GjaGH+E3TAcmlDO2UeuiYWPXNKazDL88+s0VDc0yY5CHoiFj/qDyk+D0GuHwic1RHYUojZY+MjlvLf6EF5enAEbT8OlfsLCR/1GBQRdOAgBEwfKTkLUCgsfuYwGixX/XrALv+4qkh2FPBwLH/U331HhCLlqMFQ6jusj18Apw8kl5FYYccdn2zlej4g8QuOuclhLG46O6wvj1C0kH/fwkXSrMkpxz9fpqGnkeD1yDu7hI2dRfDUIu2YIfIaEyo5CXo6Fj6R6Z+VBvLIkg6tmkFOx8JFTKUDgBQkIPDdedhLyYix8JEWDxYoHvt2J3/cUy45CXoiFj2TwHRmGkJlDOK6PpGDhI6erqDfjlnlbsTO/RnYU8lIsfCSLLj4AYTeNgNpfKzsKeRmV7ADkXY5UNOCq9zay7BGRV7IcqUPZnJ2wVppkRyEvw8JHTrM7vwZXzlmP7HKj7ChERNJYyxtROicdloJ62VHIi7DwkVOszizDNR9sRHm9RXYUIiLp7HVNKPtgF0xZVbKjkJdg4aN+9/32fNz26VYYLTbZUYiIXIYw21A+by+MO0pkRyEvwImXqV+9s/IgXl6cITsGEZFrsglUfZcJW60FgZO4HBv1HxY+6hd2u8BTv+zF/I25sqMQEbk2AdT+kQNbjRnBlyZDUSmyE5EHYuEjhzNbbbj363TOsUdE1APGjUWw11oQes1QKFqOuCLH4iuKHKqmsQk3zN3CskdE1AuNeytQNnc37I1W2VHIw7DwkcNUN1jw1w83YUt2pewoRERuy5JTi7KPWPrIsVj4yCGqGyy47qPN2FtYKzsKEZHbayqoP1r6GppkRyEPwcJHfcayR0TkeE0F9Sibu4eljxyChY/6hGWPiKj/sPSRo7DwUa/VNDSx7BER9TOWPnIEFj7qlZqGJlw3dxPLHhGRE7D0UV+x8FGPNZe9PQUse0REzsITOagvWPioR1j2iIjkaSo0svRRr7DwUbex7BERycfSR73BwkfdUtPIskdE5CpaSp+JkzNT97DwUZfMVhtmz9/GskdE5EKaCo0o/3QfhNUuOwq5ARY+6pTdLnDv1+lcLo2IyAVZsmtQ+U0GhF3IjkIujoWPOvX0L3vx+55i2TGIiKgDjbvLUbPosOwY5OJY+KhD7646iE835sqOQUREXajfUIi61XmyY5ALY+Gjdv2wIx8vL86QHYOIiLqp5o8cGNNKZccgF8XCR22szizDg9/vguCQECIi9yGAqgWZMGVWyU5CLoiFj1rZnV+Duz7fjiYb2x4RkduxCVR8vh+WgnrZScjFsPBRiyMVDbh53lYYLTbZUYiIqJeExYbyT/bAWtEoOwq5EBY+AgBU1Jtx0ydbUF5vlh2FiIj6yF7fhPKP98BWb5EdhVwECx+hwWLFLZ9uQ3a5UXYUIiJyEGuFCeXz9sLOozYEFj6vJ4TAfd+kY2detewoRETkYE359aj6LhOCZ+F5PRY+L/f6siws3lsiOwYREfWTxt3lqFvBOfq8HQufF/tjTzHeXJElOwYREfWz2mW5aNxbITsGScTC56UyiuvwwLfpnGuPiMgbCKDy2ww0lXCstrdi4fNC1Q0WzJ6/jdOvEBF5EWG2ofzTfbA3NMmOQhKw8Hkbuw07lnyJI5UNspMQEZGT2S1WbP7iGwi7XXYUcjIWPm+z4j84L/0eLEv9Hv5qvuGJiLxGpAZLCuZhw7Kvse6bz2SnISdj4fMm+xcB614HAKTkfY/Nsa9gRADHcxAReTpTnAULtr2MysoCAMCWnxYga+tGyanImVj4vEXFIWDhnQD+PEvDUJaGn3WP4rqYQnm5iIio/2gUFIYfwU/rXoPNdtyqG0Lgj3deQ2Vhgbxs5FQsfN7AYgS+uR4w17b5ltpYimdrHsZbKdslBCMiov6iGDRIs6/C2q1ftft9S2MDfn7lOTSZTE5ORjKw8HmDn/8JlO7r8NuKvQmX5r+CFSnfwV/DM3eJiNxepAZL8j9B5qFNnW5WkX8Ei99/00mhSCYWPk+3/VNgz4JubZqU/yO2Rv8PowLr+zkUERH1l8bYY+P1qro3XCdjwxrsWbm0n1ORbCx8nqziEPDHwz26il/5TizUPIKbYjmug4jIrWgUFITl4uf1J4zX64YV8z5AdXFRPwUjV8DC56lsVuD724Cmnp+Fq2oox1PVD2NOypZ+CEZERI6mGDTYYV+Jddu+7tX1m0yN+O2t/8Fu47AeT8XC56lWPQ8U7uj11RW7FRflv47VKV8jQGN1YDAiInKoSA3+yPsYWYc29+lmig5mYOOCLx0UilwNC58nyt0ArHvNITeVkP8ztkS/hNFBHNdHRORqGo/Nr1dd7ZjDsZsXfof8A3sdclvkWlj4PI2pBvjhDkA4bhUN3/I9WKB6GLfG5TnsNomIqA+ax+udOL9eHwm7Hb+//QrMDZyU39Ow8HmaXx8Aao44/GZVjRV4rPIRfJjS+Sn+RETUv5QADXbYVvR6vF5XastKseyjd/vltkkeFj5PsutbYPd3/XbzirBhSv6bWJvyBYK1Tf12P0RE1IFIDf448jGyDvfvSXUH1q/G/rUr+/U+yLlY+DxF9RHg13855a4G5v+KjZEvYWxQnVPuj4iIgMY4s0PH63Vl+cfvoaa0xCn3Rf1PEUKIrjcjl2a3A/OmAUc2OPdufUPxsuHfmJOX4NT79TamvD2o3fw9LCWHYKuvRMQVj8Jv8Bntblux+G3Up/+BkPNmI/C0yzq8zbq031CX9husNUc/zLXh8QiecC18k8e2bFO5/EMY9yyHovVB8MSbYBhxbsv3jAfWwbhnOSKvetJBj9K51iFQdoQe2ZSXjvc3f41dJRkora/Ah1c8h6mDz25324cX/w+fp/+MJ8/7B247bWaHtzk/bSE+S1uI/JpiAMDg8ETcO+EmnJt8ess2Ty9/G9/t+R1+Wh88PPEOXDHigpbvLTqwEt/vWYxPrnrBQY+SOqRRkB+YjfXbv3H6XccNHY6ZT/4XKpXa6fdNjsU9fJ5g3StOL3sAoGqsxP+VP4aPU9c7/b69ibCYoI1MQuiUv3W6XUPmBpgLM6A2hHZ5m+qAMIRMvAkxN72OmJteh0/CySj94VlYynKP3tbBzTDuX43Imf9ByKSbUfnHW7A11AAA7GYjqtfMR+gFd/b9wVG3NFpMGBaZjGen3Nfpdr9nrsGOwn2IMoR3eZsxARF4eOId+O2mD/HrTR9iQsJo3PrDI8goywYALD24Hj/tX4YvZr6CRybdiX//8RIqG6oBALXmery05kM8e0HneajvlAANtluXSyl7AFBwYB82//itlPsmx2Lhc3fFu4FV8v7CVoQN5+W9gw3J8xGm47i+/uCbPBYh59wAv8ETOtzGWleOyqXvI/ySfwEqTZe36ZcyHr7Jp0EbGgdtaBxCzrkRKp0PzIUZAICmijz4DDwJ+phU+A+fCEXn17I3sGrlJwg49WJoAiMd8wCpS+cmn47/O2c2Lhp8TofbFNWV4Ymlb+DNSx6HthuvgSkpZ+K85DOQGDoQSaED8eA5s+Gn80Va4dEpOQ5W5OL0gafg5JihuHz4ZATo/HGk5uihxOdXzsENp16OuMAoxzxAal+UBn8cmYuD2Vulxtj0/TcoP5IjNQP1HQufO7PbgV/uBezyJ0aOLfgD68P/izNCamRH8TpC2FG+6FUEjr8SuoieH14XdhuM+1bD3mSCPm4oAEAXkQhL8UHYTPUwFx+EsJqhCYmFKX8vLCWHEDDmUkc/DOoDu7Dj3kXP4m/jr8GQiMQeX99mt+GnfcvR2GTC6LiRAIBhESnYVZyBalMddhVnwGQ1Y1DIAGzJ34XdJVm4ZcwMRz8MOk5DnAkLtr6I6upi2VFgt1mx9MN3wBFg7q3rPwPJdW2bCxRsk52ihU/lAXzh8zBeHfh/eDtvkOw4XqN20wIoKjUCxkzv0fUsZTko/uxfEFYLFJ0vIq94FLrweACAb9IY+I+YhOJP74Oi0SF82n1QafWoXPwuwqbdd3QM4I5FUPsGIvTCf/SqaJLjvLvpS6hVatwy5qoeXW9/2SFc/tldMFst8Nf54sMrnsXg8EEAgElJ43DliCm45NPb4aPR4dVpj8BP64NHFr+CV6c9gs/SFuKTHT8g1DcIL1z4714VTWqHRkF+4GGsX+dah1ELM/dj17LfcfKUi2VHoV5i4XNXtUXA8mdkp2hDZarGA+bHMC71DtyY1f6gcnIcc/FB1G7/GTE3vQFFUXp0XW1oHGJufhN2cwMaMtah/NfXEPXXF1pKX/BZ1yH4rOtatq9e9yV8Bp0CRaVGzcZvEHvLO2g8uAUVv76KmFlvOPRxUfftKs7Ax9sX4LebPurxayA5NB5/3DwXdWYjfstYhft+fR7f/fWtltJ3/1m34P6zbmnZ/rV1n+CsQWOhVWnw5sbPsPSWeVh2cAPu+/U5/DbrI0c+LK+kBGiwtW4xDm13nT/kj7f2y0+RPPZ0GEK6HidMroeHdN3V7/8GzLWyU7RLEXackzcHm5LnIYLj+vqVOW8v7MYaFMy5GbkvTUfuS9Nhqy1F1cq5yJ9zS6fXVdRaaENioY9OQcjEWdBFJqJu28/tbttUkQfjvpUIPvt6mI7shs+AkVD7BcFv6NmwlByC3dzQHw+PumFL3k6UG6tw+py/YNBL52LQS+civ7YY/1n5Ls6Y0/FZugCgU2uRGDIAo6KH4KGJd2B4ZAo+3tb+XJ4HK3Lxw74l+PfZt2LjkTSMH3AywvyCcenQc7G7JBP1fA30iYhS4/fcj3Ao2zXLHgCYG4xY+cn7smNQL3EPnzvK+B3Y/4vsFF2KLliCdWE5uNV8H9ZVBsmO45H8R54Ln0Ent7qs9Nsn4D/iPBhOmtyj2xJCQNjaFnQhBCoWv4OQ826DSucLCDtE87jR5v86cCk/6pkZIy/EWYPGtrrs+m//hRkjLsDMk3p2+M0u7DB38Bp4aPH/8MR5/4C/zg82YUfTsZ99839twtbLR0ANcSb8tvEd2Gzyx2N3JXPzehzavgXJY8bJjkI9xMLnbsz1Tptg2RH0VZn4TP8g3oj/P7x+JEl2HLdktzTCWvXnRKvWmhJYSg5D5WuAJjASat8T5pRTaaD2D4E2bEDLRSVfPwLf1DMQeOxki6rV8+CbNBaawAjYLY0w7lsF85HdCJrZdphA/c7FUPsGwi9lPABAHzcM1eu+hLngABoPb4c2LB4qH0M/PHJqZrQ0IKeqoOXrvJoi7C3JQrBvIOICoxDi2/oPKq1Kgwj/UCSHxbdcds3X92Jq6tmYdexkixdWv49JSeMRFxiFeksDftq3DBuPpOPzmf9rc/9f7VyEMN9gTEk5EwAwNm4kXlv3CXYU7MXKw5sxOGwQgnwC+uOhezaNgrzAQ9iwrv9WSOoPyz+eg/gRo6D18ZEdhXqAhc/drHwOqM2XnaJHFHMt7il9HONSZ+O6gxMhRM/GGXk7S3EWSr56pOXrqhVHx0r5jzwf4dO6Nw9aU1Ux9I1/DgGwGWtQvuhV2IyVUOn9oYsYhMiZz8A38dRW17MZq1Cz8VtEX/9yy2X62CEIHHcFShc8DZVfULczUO/tKs7AzK/uafn6mRVvAwCuGjkVr017pKOrtZJbVYjKxj/Poi83VuG+Rc+j1FiBAL0/hkUk4/OZ/8M5iae1ul6ZsRJvbfwMP17/59qqp8YOx+3jrsZNCx5EuF8wXu1mBvqTEqDBlro/cHj7dtlReqyuvAzrv/0Mk26cLTsK9QBX2nAnhWnAh+cDbnzopDT2fEwvuAHFZp3sKOTF3G2lDfIsIkqNxfvnoqbGfZctU1QqXPfcq4hKSpEdhbqJJ224C7sN+OUety57ABBZuBxrQp7FpNAq2VGIiJzOGGvC91tfcuuyBwDCbseSD96C3e7ev5O8CQufu9j8PlC0U3YKh9BVH8Qn1gfxr4SDsqMQETmHVoUjIYewaP0bbnFyRneUZh/Cjt/aP7OfXA8LnzuoyT86ds+DKJZ6/L3kSXyTugKKwlEFROS5lEANtjYtwcYdC2RHcbgN336B2vIy2TGoG1j43MHSJwFLvewUDqdAYHzeR9ia+CFifCyy4xAROZyIUuP37A9xONv9Ts7ojiazCeu+ni87BnUDC5+rK9gO7Pledop+FV64CmuCnsH5YZWyoxAROYwxthELtryImtpS2VH61f51q1BymEN0XB0Ln6tb8jgAzz/kqa05jI+aHsQjgzJlRyEi6hutCkdCDmLR+je946QGIbD6849lp6AusPC5sgO/AbnrZadwGsVixO3FT+H71KVQK1y5gYjcjxKowVbLEmzc4dlHZk6Ut3cXDm3fIjsGdYKFz1XZrMCyJ2WnkGJM3ifYMugDDPAxy45CRNRtIlqNX7M/wOEczxyv15U1X3ziHXs03RQLn6vaMQ8o997Dm2FFa7Ay6GlMjaiQHYWIqEvG2EYs2Pwi6mq994zVyoI87F6+WHYM6gALnysy1wGrXpCdQjptTQ7mmB7CE4n7ZUchImqfVoXc4CzvGa/XhQ3ffQlLY4PsGNQOFj5XtO51wOi9fyUeT2ky4pai/2Bh6h/Qqjz/5BUich9KoAZbLH9gU9oPsqO4jIaaamz92bvGL7oLFj5XU1sIbHxHdgqXc0refGyJfxfxvibZUYiIWsbrZeekyY7icrb9uhB1leWyY9AJWPhczYrnAGuj7BQuKaR4PVYEPIVLIvhBQkTy1Mc2eP14vc5YzWas//pz2THoBCx8rqR4D7DzS9kpXJqm9gjeanwQzyTukx2FiLzNsfF6v65/i+P1urBvzQqU5hyWHYOOw8LnSpY+DgjOP9cVxdqIG4uexS+pv3JcHxE5hRKkwRbz7xyv101C2LHmi09kx6DjsPC5itwNwKEVslO4lZPyvsC2gW8hyY/j+oio/4hoNX499D6yc9NlR3ErubvSUJjJWRZcBQufq1jzP9kJ3FJQySYs8X8cl0V59lqVRCRHfazx6Hi9Oo4d7o1NP3wjOwIdw8LnCgp2AIeWy07htjR1BXjd+BCeT9otOwoReQqtCjlBGfh1/dscr9cH2WnbUJJ9SHYMAgufa1j7iuwEbk+xmvDXwv/it9RfoFdxHCQR9Z4SpMFm02/YnL5QdhSPsPlH7uVzBSx8spXsAw78KjuFxxie9xW2DnwTqf6c2oaIes5+bLxezpGdsqN4jKwtG1GRf0R2DK/HwifbulcB8ExTRwos2YI/fB/HjKgS2VGIyI3UxRrxPcfrOZ4Q2Pzjt7JTeD0WPpkqDwN7eIp/f1DXF+J/9Q/hpWT+lU5EnVO0KmQHZeA3jtfrNwc2rEF1cZHsGF6NhU+mda8Bgh8u/UWxmTGz4EUsTl0IXzWfZyJqSwnSYqPpN2zheL1+Jex2bPnpO9kxvBoLnyw1BcDOr2Wn8ApD8r7Flrg3MNTQIDsKEbkQe7QKvx6cg1yO13OKfWtWoLacy9HJwsIny4Y3AZtFdgqvEVC6Db/qH8PVMcWyoxCRC6iLrceCTS+irr5CdhSvYbNase0XDmOShYVPhvoyYPunslN4HbWxGC/UPoRXk9NkRyEiSY6O1zuA39a/A8GlLJ1u94olMFZXyY7hlVj4ZNj4NmDltCEyKDYLrix4GctSv4e/mh/2RN5ECdJiY+Ov2JL+k+woXstqMWPboh9lx/BKLHzOZqoFts6VncLrpeR9j82xr2BEgFF2FCJyAnvMsfF6ebtkR/F6u5b9AYuJOz2cjYXP2XZ+DVjqZKcgAIayNPysexTXxRTKjkJE/aguth4LNnK8nquwNDZg/9qVsmN4HRY+Z9vGvXuuRG0sxbM1D+OtlO2yoxCRgyk6FQ4H7uN4PReUvuQ32RG8DgufM2WvBcoOyE5BJ1DsTbg0/xWsSPkO/hrO10fkCZRgLTYYf8HWnb/IjkLtKD+Sg/z9e2TH8CosfM609SPZCagTSfk/Ymv0/zAqsF52FCLqA3uMCouy3sGRfBYKV8a9fM7FwucsdcXAgV9lp6Au+JXvxELNI7gptkB2FCLqhebxevX1nPrD1WVt3sApWpyIhc9ZdswH7E2yU1A3qBrK8VT1w5iTskV2FCLqJo7Xcz92mxW7ly+WHcNrsPA5g90GbJ8nOwX1gGK34qL817E65WsEaKyy4xBRJzhez33tXP4H7HaOnXYGFj5nyPgNqOUhQneUkP8ztkS/hNFBHNdH5Io4Xs+91VeU49C2zbJjeAUWPmfgyRpuzbd8DxaoHsatcXmyoxBRMwWojanleD0PwJM3nIOFr7+VHwQOr5adgvpI1ViBxyofwYcpm2RHIfJ6ik6FQ4a9+H3DHI7X8wBH9uxEZWG+7Bgej4Wvv22bC0DITkEOoAgbpuS/ibUpXyBYyxNwiGRoHq+3bdci2VHIUYTATu7l63csfP3J0gCkfyE7BTnYwPxfsTHyJYwN4hJ5RM5kj1Hhl0yO1/NEe9csR5PZJDuGR2Ph60/7fwZMNbJTUD/wrdiLb1UP486BubKjEHk+BaiJqcV3G1+A0cjxep7IbDTiIE/e6FcsfP1p93eyE1A/UjVW4v/KH8PHqetlRyHyWIpOhYOGPfhjwxxAcHiMJzuwbpXsCB6Nha+/GMuBw6tkp6B+pggbzst7BxuS5yNMx3F9RI6kBGuxvv5nbN/FVYq8Qc7ONDTWc6hMf2Hh6y97fwTsnLDXW8QW/IH14f/FGSE8hE/kCLYYBb9kvoO8gr2yo5CT2G1WZG5cJzuGx2Lh6y+7F8hOQE7mU3kAX4iH8Y+BObKjELmvY+P1Fmx8keP1vNB+HtbtNyx8/aE6D8jj4FNvpDJV44HyxzA/da3sKERuR9FzvJ63K8jYh9ryMtkxPBILX3/YswCce897KcKOc/LmYFPyPERwXB9RtyghWqyr+4nj9bydEDiwnosV9AcWvv6w+3vZCcgFRBcswbqw53BWKMf1EXXGFqPg5wNvIb9gn+wo5AJY+PoHC5+jlR4ASnbLTkEuQl+Vic9sD+Le+MOyoxC5HgWoianBgo0voqGBfxjRUWW52ajIPyI7hsdh4XO0PTxZg1pTzLW4p/RxfJm6CorCQ/1EwNHxelmG3fhjw3scr0dt7F/HvXyOxsLnaDw7l9qhQGBC3gfYnPgxovUW2XGIpFJCtFhbtxA7dnH9VGrfgQ0sfI7GwudI+duBqmzZKciFRRYux5qQZzExlNNNkHdqHq9XULBfdhRyYTUlxSjMPCA7hkdh4XMkLqVG3aCrPoh5tofwr4SDsqMQOY8CVMdUc7wedRv38jkWC58j7ftJdgJyE4q5Dn8veRLfpK7guD7yeIpejUy/nVi84X2O16NuO7SN89k6EgufoxTvBuoKZacgN6JAYHzeR9ia+CFifDiujzyTEqLF2tofkLbnD9lRyM3UlpWiPC9XdgyPwcLnKFlLZCcgNxVeuAprgp7B+WGVsqMQOZQtVsHPB95EQSHHYlHvZKdtkx3BY7DwOUrWMtkJyI1paw7jo6YH8cigTNlRiPquebzehhfR0FArOw25MRY+x2Hhc4TGaiB/i+wU5OYUixG3Fz+F71OXQq3YZcch6hVFr0amP8frkWMUZOyHuaFBdgyPwMLnCIdXAnar7BTkIcbkfYItgz7AAB+z7ChEPaKEarG29nuk7eZ4PXIMu82K3F07ZMfwCCx8jpC1VHYC8jBhRWuwMuhpTI2okB2FqFtsscDP+99EQWGG7CjkYQ7zsK5DsPD1lRDAQY7fI8fT1uRgjukhPJ7IAe/kwhSgKroKCza8xPF61C9ydu6A4PCAPmPh66uinUB9iewU5KGUJiNuLXoGC1P/gFbFDzxyLYpejQy/dCzZ+AHH61G/MVZVojT7kOwYbs8lC9/GjRuhVqsxbdq0Vpfn5ORAURRERkairq6u1fdOOeUUPPXUU05MeQwP55ITnJI3H1vi30W8r0l2FCIAf47XS9+zWHYU8gIyztYtKyvDnXfeifj4eOj1ekRHR+PCCy/E+vXrW23XUWdxNS5Z+ObOnYu7774ba9asQWFh28mM6+rq8L///U9CsnZw/j1ykpDi9VgR8BQujiiXHYW8nC0WWLiP4/XIeQ6nO7/wzZgxA2lpafj000+RmZmJn3/+GZMmTUJFReux1V11FlehkR3gRPX19fjmm2+wbds2FBcXY968eXjkkUdabXP33Xfj1Vdfxd///ndERkZKSgqgoRIo4GBSch5N7RG8o30InyXejyeyh8uOQ95GAaqiK7Fkw0c8hEtOVZyVica6WvgGBDrl/qqrq7F27VqsWrUKEydOBAAkJCRg3LhxrbbrTmdxFS63h+/bb7/F0KFDMWTIEFx//fX4+OOP2wzWvPbaa5GSkoJnnnlGUspjDq0ABOdLI+dSmhpwY9Gz+CX1V47rI6dRfNTI8E3Dkg0fsuyR0wlhR076dqfdn8FggMFgwMKFC2E2dzxFVnc6i6twucI3d+5cXH/99QCAqVOnoqamBqtXr261jaIoeOGFF/DBBx/g0CGJAzl5di5JdFLeF9g28C0k+XFcH/UvJVSLNdULkL6XQ1hIntzd6U67L41Gg3nz5uHTTz9FcHAwzjzzTDzyyCPYtWtXq+2601lchUsVvoyMDGzZsgXXXnstgKNP+NVXX425c+e22fbCCy/EWWedhccff9zZMf+Us77rbYj6UVDJJizxfxyXRZXKjkIeynpsvF5hEZf9I7nyD+x16v3NmDEDhYWF+PnnnzF16lSsWrUKo0ePxrx58wD0rLO4ApcqfHPnzoXVakVsbCw0Gg00Gg3mzJmD77//HjU1NW22f+GFF/DNN98gLS3N+WFr8oGaI86/X6ITaOoK8LrxITyftFt2FPIkClAZXYnv178IUyPn1yP5akqKUVfp3JPWfHx8MGXKFDz++OPYsGEDZs2ahSeffBJAzzuLbC5T+KxWK+bPn49XXnkF6enpLf927tyJ2NhYfPXVV22uM27cOFx55ZV46KGHnB84d6Pz75OoA4rVhL8W/he/pf4CvYrjSqlvFB81DvimYenGD2VHIWqlYL9z9/KdaPjw4TAajb3qLLK5zFm6ixYtQlVVFW699VYEBQW1+t6MGTMwd+5cTJ06tc31nnvuOYwYMQIajZMfyhEWPnI9w/O+wtaBhzCj/A5kGX1lxyE3pIRqsbrgGxQVZ8mOQtRG/oF9GHrmxH6/n4qKCvzlL3/BLbfcglGjRiEgIADbtm3DSy+9hMsuu6xbneVvf/tbv+fsCZfZwzd37lxMnjy5zRMHHH3ytm3bhtratocVBg8ejFtuuQUmk5MHrrPwkYsKLNmCP3wfx4worgBDPWONBRbufZ1lj1xWwf49Trkfg8GA8ePH47XXXsM555yDkSNH4vHHH8fs2bPx9ttvd6uznHiCh2yKcNXzh11ZQyXwUhIAPnXkuoRaj++i78X/HTpZdhSXsw7OmcvLbShARVQFlm38SHYSos4pCu766Ev4GgJkJ3E7LrOHz63kbwXLHrk6xWbGzIIXsTh1IXzVNtlxyEUdHa+3g2WP3IMQKMo6IDuFW2Lh6428LbITEHXbkLxvsSXuDQw1NMiOQi5GCdViVdW32LmXa4KT+yjKZOHrDRa+3sjfKjsBUY8ElG7Dr/rHcHVMsewo5CKax+sVFx+UHYWoRwqzuIZzb7Dw9ZTdDhRKmPePqI/UxmK8UPsQXk3m69erKUBFdPnR+fVM9bLTEPVY8cFMCDunn+opFr6eKs8AzJyElNyTYrPgyoKXsSz1e/ir+YHpbRRfNfb7bMeyja65EgBRd1gaG1BRkCc7htth4espHs4lD5CS9z02x76CEQFG2VHISZQwLVZVfotd+7gGOLm/Ih7W7TEWvp5i4SMPYShLw8+6R3FdTKHsKNTPmmIFftzzGsfrkccoOsjC11MsfD1VtFN2AiKHURtL8WzNw3gzZZvsKNQfVEBFVBl+WP8SzCbuzSXPUZabLTuC22Hh6wm7HSjLlJ2CyKEUexOm57+KFSnfwV/D+fo8heKrxn79Nizb9LHsKEQOV5GfB64b0TMsfD1RlQ1YG2WnIOoXSfk/Ymv0/zAqkGduujslTItVFd9g177lsqMQ9YsmUyNqy0plx3ArLHw9UcbJHsmz+ZXvxELNI7gptkB2FOqllvF6JYdkRyHqV+V5ubIjuBUWvp4o3S87AVG/UzWU46nqhzEnhSvKuBUVUM7xeuRFWPh6hoWvJ1j4yEsodisuyn8dq1O+RoDGKjsOdUHxVWOfbiuWc7weeZEKFr4eYeHrCR7SJS+TkP8ztkS/hNFBHNfnqpQwLVaWf4Pd+1fIjkLkVNzD1zMsfN1ltwHlWbJTEDmdb/keLFA9jFvjOLO9q2mKtePH3a+hpJTj9cj7VBbmw27nzALdxcLXXZWHAZtZdgoiKVSNFXis8hF8kLJJdhQCjo3XK8UP61+G2czxeuSdbE1NqCrixPHdpZEdwG2U7pOdgEgqRdhwQf6bWJtyCJfmzkR1k1Z2JK+k+KqxR2zEnk0rZUdxG4fKKrDqwGEUVNWg1mTGrDPHYGRcdLvbLti2G5sOH8H0U4bjnMGJHd7mhoO52HgoF5XGo1N1RQcZMHl4KobFRLZs83P6PmzNyYdOrca0UUMxOiGu5Xs784qwLScft559moMepXeqyD+CsLiBsmO4Be7h665Sjt/rrTW5Vlz6VQNiX6mD8nQtFh5o6nDbvy1qhPJ0LV7f1Pne1DlbLRg1px6B/61F4H9rccZcI37Pan279y82IfTFWgx8rQ5f7Gr9ve/2NuHSrxp6/6C82MD8X7Ex8iWMDaqTHcX7hGuxovwr7NnPstcTFqsNscGBuGL0yE63251fjCOV1Qj01Xd5m0F+Prh41FDcO+Us3DvlTKREhmHe+m0orjn6vthbWIK0I4W4/ZxxuOTkofh22y4YzRYAQKOlCb/vzsCVXeShrpUf4Ti+7mLh664ynqHbW0aLwMlRKrxzsU+n2/24vwmb8m2IDVC6vM0BgQpemKzH9tv9se12f5w3SI3Lvm7E3tKj4zl+yWjCl7ubsOQGf7w02Qe3/dKI8gY7AKDGJPDoCnOXeahjvhV78a3qYdwx4IjsKF6jKdaOH3e+itJSLinVU8NiInHRSUNw0oD29+oBQE2DCQvT9uKv40+BWun6V+OI2CgMi4lERIA/IgIMuOikodBpNMitqAIAlNbWIzkiFANDg3FqfBx8NBpUGI/+kfnrrgOYkByPEH9fxzxAL8YzdbuPha+7OCVLr12UqsWz5/ngimEdHwIsqLXj7t9N+OJKX2i78aq8dIgWF6dqkRqmxuAwNZ473wcGHbAp/2jh219ux6RBaoyNVePak7QI1CvIrjq6DM//LTXhzrFaxAfx5d8XqsZKPFTxKD5OXS87imdTAWVRJfhh/cuwWLhXuj/YhcCXW9IxaUgSooMCen59u0DakUJYrDYkhIUAAGKDA5FXVYMGSxPyK2vQZLMj3OCP7LJK5FfV4KzUjg8XU/fxTN3u4xi+7rBZgQqeBddf7ELghh8b8e8JOoyIVPf4+ja7wHf7rDA2AWcMPHr9k6PU+GC7BVWNAoer7GhsEkgJVWHdESt2FNvw7jTu3XMERdhwXt472JB8CJfmXYsKC8f1OZLiq8Zu+wbs3bRKdhSPtvLAIagVBWelDurR9Yqqa/HWig2w2uzQadSYdeaYlsI4JDoCo+Pj8MayddCq1bhm3MnQqdX4fsceXHPaydhwKBfrD+bAX6fDVWNP6lXRJKC6pAh2uw0qVc9/d3gbFr7uqC0A7B2PO6O+eXGdBRoV8M/xuh5db3eJDWfMNcJkBQw64MerfTE84uib/sIUDa4fpcVpH9bDV6vg08t94a8D7vzVhHmX+WLOtia8tcWCcD8FH1zi06uiSX+KLfgD68NzcHPjPdhYFSQ7jmcI12JF7hc8hNvP8itrsC4rB/dOOQuK0vVwkuNFBBhw/5SzYWqyYld+Eb7eshN3Tjq9pbxdOHIwLhw5uGX7JXszkRoZDpVKwfL9B/HABWdjX1EpvtqSjvumnO3Qx+Ut7DYbjFVVCAgLlx3F5bHwdUct1xXtL9sLbXhjswU77vDv8YftkHAV0v9mQI1JYMG+Jty00ITVs1Qtpe+pST54atKfe/KeXmXG5EQNtGrg2TVm7L7TH4syrbhxYSO2325w6OPyRj6VB/CFz8N4deD/4e28QbLjuDVLnB2/bnmVh3Cd4HB5JepNZjy36M+Jq+1C4Jed+7A2MxuPXnJeh9fVqFUID/AHAAwIDUJeZTXWZeXgqrEntdm2tLYeO3ILcN+Us7ElOw+J4aEw+Ohx8sAYfLt1F0xNVvho+Su5N+oqylj4uoGvru6o5Tw//WXtEStKjQLxr/25koNNAA8sMeP1TRbk3NvxYQ6dWkFK6NGSOCZWja2FNryxyYL3L207EPpAuQ2f725C2h3++DjNgnMS1IjwV2HmCC1u+dmEOrNAgL5nhZPaUpmq8YD5MYxLvQM3ZnGPRY+pFJSFF2HFuk9lJ/EaYxLikBrVuix8uGYzxiQMwGmDBvTotuwArHZ7m8uFEFiwfTcuPWU49FoNhBCwi6Pb2e2iZRvqnbqKctkR3AILX3fU5MtO4LFuGKXF5KTWL8MLP2/ADaO0uPmUno0HswvA3M6k60II3LHIhFcv0MOgU2CzA03HPpOb/2vjZ63DKMKOc/LmYFPyIVyadx3KOK6vWxQ/NXZb12Pv5tWyo3gcc5MV5fV/TlBdWd+Agqoa+Ol0CPH3hb++9XAStaJCgI8ekYF/7vl/b9UmjIyLbhnn99uuAxgSE4EQP1+Ym6xIO1KIw6UVmH3OuDb3v/lwHvz1OoyIjQIADAoPxZJ9WcitqMKBojJEBRrgq+P7pLfqystkR3ALLHzdwT18fVJvEThY+edfvdlVdqQX2xDqqyA+SIUwv9bba1VAtEHBkPA/x9WdP9+IK4Zq8Y9xRz+YH15mwkWpGsQHqVBnFvhydxNW5diw+Pq282d9tKMJEX4KLh1y9AP1zHgNnlptxqZ8K37PsmJ4hArBPty752jRBUuwLiwHt5rvw7pKjuvrjBKuxfKcz1FWliM7ikfKq6rBe6v+XCXm551HZ10YO2gArhl3crduo6K+oWUePQCoN5vx9eadqDWZ4aPVIDYoALPPGYfB0RGtrldnMmP5/oP4x/kTWi6LDwvGOYOTMHftVhj0Olwz7pQ+PDqqrWDh6w5FcD9y176+DjiwSHYKt7Uqx4pzP207Fummk7WYd3nbw6+DXq/DvafrcO/p+laXzTpF2zIm79afGrE824qieoEgvYJRUSo8eKYeU5Jb/w1TUm/H+I+M2HCrP2ID/pyG5ZnVZryx2YJI/6MndIyL40kb/UXoA/FG0P/h9SNJsqO0WIdA2RFaWOLs+HXz27A0NcqOQuSWUk47A5f961HZMVweC193vD8RKEqXnYLIbQko2DhwNq47OBFCyN+b6hKFT6WgNLwIKzdzvB5RX0QlpeL6/74mO4bL48yz3cFDukR9okBgQt4H2Jz4MaL1lq6v4OEUPzV2azaw7BE5QB0P6XYLC19XrBbAyBcTkSNEFi7HmpBnMTG0SnYUaZRwLZaXfIl9GWtkRyHyCA21NbBZOVduV1j4ulJbAIBHvYkcRVd9EPNsD+FfCQdlR3E6S5wNP6S/grLyHNlRiDyHEKirqJCdwuWx8HWFh3OJHE4x1+HvJU/im9QVUBQv+INKpaA0sgg/rvsfT84g6gd15aWyI7g8Fr6ucJUNon6hQGB83kfYmvghYnw8d1yf4qc5Nl5vvuwoRB6Lky93jYWvKyx8RP0qvHAV1gQ9g/PDKmVHcbxwDZaXfMHxekT9jIWvayx8Xaktkp2AyONpaw7jo6YH8VBCpuwoDmOOs+HH9Fc5Xo/ICRpqa2RHcHksfF0x8UVE5AyKxYg7Sp7G96lLoVbarkfqNtQKSiIKsZDj9Yicxtxg7HojL8fC1xVznewERF5DgcCYvE+wZdAHGOBjlh2nxxQ/DXap1mPVls9kRyHyKpaGtqs5UWssfF0x18pOQOR1worWYGXQ05ga4UZTLURosKzkM+zPXCs7CZHXMTey8HWFha8rLHxEUmhrcjDH9BAeTzwgO0qXzHFW/JD2CsrLj8iOQuSVLDyk2yUWvq7wkC6RNEqTEbcWPYOFqX9Aq3LB+frUCoojCrFw3StoajLJTkPktcw8pNslFr6usPARSXdK3nxsiX8X8b6uU6oUfw12qdZhNcfrEUnHkza6xsLXFRY+IpcQUrweKwKewsURLjDfVoQGS4vmY3/mOtlJiAiApZFnxHeFha8zVgtgdZ09CkTeTlN7BO+YHsLTifukZWger1dRkSctAxG1ZrWYYbNaZcdwaSx8neHePSKXozQ14KaiZ/FL6q/OHdenVlAcUcDxekQuiod1O8fC1xmeoUvksk7K+wLbBr6FJL/+L1+KvwY7lbVYveXzfr8vIuodzsXXORa+znAPH5FLCyrZhCX+j+OyqNL+u5Nj4/UOZK3vv/sgoj7jXHydY+HrDAsfkcvT1BXgdeNDeC5pj8Nv2xRnxfc7XuZ4PSI3wLn4OsfC1xke0iVyC4rVhOsKn8dvqb9Ar3LAOrxqBUXh+fhp3SuwWi19vz0i6neci69zLHyd4cLnRG5leN5X2DrwTaT69/69q/hrkI41WLP1CwcmI6L+ZrfbZEdwaSx8nXLBmf2JqFOBJVvwh+/jmBFV0vMrR2iwtPBTZBzc4PhgREQSsfARkcdR1xfif/UP4cWkXd2+jimu6eh4vcr8fkxGRCQHCx8ReSTFZsbVhS9gcepC+Ko7OdSjVlAUnoef1r3K8XpE7kzwqFxnWPiIyKMNyfsWW+LewFBD2wHdikGDNKzGmq1fSkhGROQ8LHxE5PECSrfhV/1juDqm+M8LIzVYUjAPmQc3ygtGRA7DHXyd08gOQETkDGpjMV4wPYTxyffAZJqAXze9xkO4ROQ1WPg6pcgOQER9ZFVpkBE9FOmhcUjXqRBddACa2lMRPfQqKChCQ3UuqoqOwG7jwutE7o27+DrDwkdEHqXWNwg7Y4cjPSAU6TBjtzEPjdZawFgLGIHP1w1Bk34TNmvOBRAN4FT4htoQEFYLna4MlsZ8VBcdhqmeE68Tkedg4SMit5YXNghpkclI8/VBelM1DtXnQ9gLgJqCNttObkiEbute6LAX4TOnoLz06F49m1WN6pIQACEABgPa8xCW2AA/QyWErRD1lTmoLingICEiFyb4/uwUCx8RuY0mtQ57Y4cjPSQa6RogvaEIFeYqwJQFmLq+/g3b/Fr+P7F8LcpxRofbGqv9YKz2AzAAwDgERlsREFINtaYUprojqCrKRpOJq/EQkXtg4SMil1XlH4a02GFINwQh3d6AfcZ8mG2VQF1lj29rgmkgfDf8ORGzYeUXCJlxDqrKm7p1fYtJg4qicADhAIZD7ScQMsAIH78K2CwFqC3PRl15L1b3ICJyAhY+InIJAgqyI1OQFpGANL0OOy2VyDEWAtYjQHXfb//W9OBWh2QVIZBcuxnbMLqXgRXUVhhQW2EAkABgAoIGNMEQVAmVqhiNNUdQVZQDW1P3CiUR9REP6XaKhY+IpDBpfbE7dgTSgyOQrrJjZ0MBaiy1QEMm0HaO5D45xRKNgDVtl1kLWDYPQZePR02lY0qZ2aiF2RgFIArAydAF2REUWgedTxmaTAWoKT6Ehtpqh9wXEbWmqDi1cGdY+Dqj8ZGdgMhjlAVGIy1mMNL8ApFur8OB+jxY7aVAbWm/3/ffdkcDtrZr5Cp2G5JN6diBEf1yv8KmQnVZEIAgACmAeiJCE0zwC6yEIopgrDo6JYyw2/vl/om8idaHv7M7w8LXGX2A7AREbsmuqJAVNRRp4QOQrtMg3VyGgoYSwJIDOHmu4yFN4QhdubPD7wcu+QiGaW+hvsY58/A11PqgoTYWQCyAMfAPtyEgrAZabSnMxnxUFx+G2VjvlCxEnkTn4ys7gktj4esMCx9Rtxj1AdgZOww7gyKQpliwqz4PRms9YDwAGOVm+8f+gUBTcYffV1ktSBH7kY5UJ6b6k7VJjariUAChAIYCOoHwqAb4GipgtxaivjwHNWVFHJ9E1AWdr1/XG3kxFr7O+ATKTkDkkgpC4pEelYw0Pz/stNYiqz4fNlEM1HRcrGSItwUjesXuLuffD1n8PvwueB0NdfJX21CgoL7KH/VV/gDiAZx+dEqY0CqoVCVHp4QpzIbVYpYdlcil6P1Y+DrDwtcZPQsfkVWlwYGYYUgLjUO6VkF6YwlKTeWA+RDg4p3jnqwkiMYtXW6nmBuRojmEXUhwQqqes5g0qCiMABABYCS0BoGwsHro/cphNRegtvQw6ivLZcckkop7+DrHwtcZFj7yQjW+wceWJgtBGkzYW5+HRlsNUF8jO1qPRNr9Eb90b7dX1wxd8h58Jr0Mk1H+Xr6uCKGgpjwAQACARABnIXigBYagSigoRkNNLqoKc7k+MHkVFr7OsfB1RqMD1HrA5uK7MYj6ICc8CemRSUj30SO9qQqH6wsg7PlATduzWt3JfYeHQNR3vXevmcpYixSfPOwxxvRjqv5jqtfBVB+No+sDnwKfUBuCwuqg1ZXC0piPmuLDaKzj+sDkmTRaHdQaVprO8Nnpik8gYCyTnYLIISxqPfbGDkdaSDTSNQI7GwpRaa4GGjMBD1olLMjug5SlGd3eu9csfOkc6M58DpZGW7/kcia7VY2qkmAAwQAGA5rzEDaoEX4BFRC2ItRX5qCmpABCcEoYcn86jt/rEgtfV/QBLHzktioMEUiPGYp0QxDS7Ebsr8+DxV4B1FXIjtav7s0bBlG9tcfXU9VWICWgBPsaw/shlXzGGl8Yawbg6PrAp8EQZUNAaDU06hKYjXmoKsqGpdHBs14TOYHOl1OydIWFryscx0duQkDBoajBSAuPR7peh3RzOY40FAHWXIcsTeYu/OxajFya3eO9e80ilr8H7dgn0GT2/D1fTWY1KovCAIQBGA6Vj0BknBE+fuWwNRWitozrA5N74Pi9rrHwdYVz8ZGLatT5HV2aLCgCaSordhrzUddUDzRkOHxpMndyd9FIiLKe791rpq4oQnJIJQ4UBzsulNs4fn3gQQAmICjOgoDgaiiqYjTWHkFVYQ5sTU6ePZuoC3oWvi6x8HXFJ0h2AiIAQElQLNKiU5HuF4A0Wy0y6/NhFSVALffANNMJNcYuy+v13r1mkSs/QNaoh2Br8vy9fF0xN+hgbogEEAlg1NH1gcPqoNMfnRKmuuQwGqorZcckL8cxfF1j4esK9/CRBDZFjczoIUgLO7Y0makERY1lgCXb6UuTuZO/lY6EKNze59vRlOQiKbwOWUX+DkjlWYRNherS5vWBkwHlHITEm2AIrARQDGNVDqqL82C3uf+JL+Q+eEi3ayx8XeEYPnKCep9A7IwdhvTAcKQpZuyuz0eDiyxN5i4UAZy9orTPe/eaxaybi0OD74HdxiXNutJY54PGuub1gUfDN8yGoNAaaHRlsDTkoaqI6wNT//IxGGRHcHksfF0xRMpOQB4oLzQe6VEpSPf1RXpTDQ4a82EXRUBNkexobuu2ypMgctIcdnuavAwknt2IQ4U+DrtNb2FrUqOypHl94CHH1gduhJ9/Bey2QtSVZ3N9YHKogFDPPLPekVj4uhI0QHYCcnNNKi32xw5HWkgMdmoVpDcWocxUCZgOAibZ6TzH5NWOXwkkdtOnODzoDnCqur45uj6wH+qr/AAMBDAeATFNCAyphlpVClP90fWBm8x8Q1DvGMJY+LrCwteVwDjZCcjN1PiFID12ONICgpEmGrGvPg8mWxVQXyU7mse6rnoYlIzdDr9d7eFdSDjdipxCflQ6WlOjFhWNzesDj4DaXyBkYP3RKWEsBagpzUZ9JedApe4JYOHrEj/FuhLEwkedy45IRnpEItJ89Ei3VCLHWAhhywOq82RH8xqXruu/5Q8H7PgcOTGz4LDBgdQ+oaC2IgC1FX+uDxwywAz/4KqW9YGri3Jhs3J9YGqLh3S7xsLXlcA4AAr4aU8AYNb4YE/scKQHRyFdY0e6sRDVlhqPW5rMnVxRNxiq3fv67fZ1B7YifvSNOFKo6rf7oPY1GvVoNP65PrA+xI6g0Fpo9WVoasxHdfFhNNY5/lA+uRlFgSE0THYKl8fC1xWNHvAP5/JqXqrcEIn02CFI8w9Cur0e++vz0GQvB+rKZUejY67a2P/3MWDPtzgSek3/3xF1ym5Voao0GEfXB04FNOcidJAJ/gEVgP3o+sDVxflcH9jL+AcFQ61hnekKn6HuCIxj4fMCdkWFg5GDkR4RjzSdBunmcuQ3FANN3rU0mTu5wJgE7fb+27vXzGfXWsT99RoUFPb7XVEPNdT4oKEmDkAcgLEwRFoREFoDjbYU5vqjU8JwfWDPZuDh3G5h4euOoAFAUbrsFORgDXoDdsUOQ3pgBNJVVuxqXpqMc9+5jeu3Om/KlISMn1AQcJnT7o96p8miQWVx8/rAw6DyEYiINcLHvwL2pqNTwtSWFcuOSQ4UEMbDud3BwtcdPFPXIxQHD0BadArSfA1It9Ugsz4fNi5N5rbONA2EzybHn5nbEZ/tSxB9zRUoLubhQveioK7SgLpKA4AEAGcgKK4JhuAqqFUlaKzNRSXXB3ZrAWERsiO4BRa+7uCZum7HqtIgI3oo0kPjkK5TIc1UgpLGcsB8GOi/EzrJiW7dEez0iXsH5f6BYv0FTr1Pcjxzg/a49YFPgi7QjsCweuh9ymA1F6Cm5DCMXB/YbfCEje5h4esO7uFzebW+QdgZOxzpAaFIhxm7jXlotNYCxloenvVAp1iiYVi3y+n367fxJ0RcfRHKSrhOrCcRdhVqygIBBOL49YH9g6qgiCIYq3JRXXyE6wO7KM7B1z0sfN3B1TZcTl7YIKRFJiPN1wfpTdU4VJ8PYS8AagpkRyMnuHN3FGDLl3LficUrUaacI+W+yXmOrg8cAyAGzesDB4bWQqsrhaXh6JQwpvo62TEJLHzdxcLXHdzDJ1WTWoe9scORHhKNdA2Q3lCECnMVYMri0mReaEhTOEJWOn/vXjPD6m8Q9pfzUFHGCYC9ia1JjaqSEAAhAIZAaM9DeGIjfAMqIayFqK/IQXVpAdcHloCFr3tY+LojIAZQaQA7P+Cdoco/DGmxw5BuCEK6vQH7jPkw2yqBOo6pIeDu/QOBJrlnWSZVbkAFxknNQHIpUFBf7Yf6aj8AAwCMQ2C0FQEh1VCrS2A6NiVMk4l/lfYntUaDgHCetNEdLHzdodYAoUlAeabsJB5HQEF2ZArSIhKQptdh57GlyWA9wrnvqI0EazCilu+Wvu6NYcV8BF95JqormiQnIVdiMWlQURQOIBzACKj9jq0P7FsOq6UQtWWHUV/BOV0dKSR2AFQqtewYboGFr7sih7HwOYBJ64vdsSOQHhyBdJUdOxsKUGOpBRoyAc6NSl24JysJwrRFdgwoQiDZuA3bcbLsKOTKhILa8gDUonl94DMRPMACw7H1gRtrclFVlMP1gfsgbEC87Ahug4WvuyKGAfhJdgq3UxYYjbSYwUjzC0S6vQ4H6vNgtZcCtaWyo5GbibT7Y+CyvdL37jULXPoxAqe/i9oq7uWj7jMZdTAZowBEATgZ+mA7AsPqoNOXocl0bH3g2mrJKd1HOAtft7HwdVfkMNkJXJ5dUSEraijSwgcgXadBurkMBQ0lgCUH4Jym1Ef3HR4CUS9/714zxWZFctMupIGfDdR7dpsK1aVBAIIApADqSQgb1Ai/gEoIe/OUMHkQdk743Z6wgSx83cXC112Rw2UncDlGfQB2xg7DzqAIpCkW7KrPg9HKpcnI8YLsPkhZmuEye/eaBS35EP5T34SxlofkyHGMNb4wHrc+sH+EDYGhNdBoS2A25qOq8BDXBz4mbECC7Ahug4Wvu0KTALUesHnvMg0FIfFIj0pGmp8fdlprkVWfD5soBmq4LiX1r/uODIeodp29e81UFjNSlEzsRJLsKOTBrBY1KotDAYQCGAbFZzIiYhvg618BW1MB6ityUFNaJDum06m1WgRHR8uO4TZY+LpLrQHCU4GSPbKTOIVVpcGBmGFIC41DulZBemMJSk3lgPkQlyYjp/KzazFy6SG46gGtkMXvwXfyq2is514+cg4FCuoq/VFX6Q8gHsAZCIxtQkDIsfWB646gqjAbVotnj6UJ5Rm6PcLC1xORwzy28NX4Bh9bmiwEaTBhb30eGm01QH2N7Gjk5e4pGgl7+VbZMTqkMhmRosvBbnBFHpLH0qhFReOf6wNrA+wIC6uH3qf86PrApYdhrKqQHdOheIZuz7Dw9UTEUNkJHCYnPAnpkUlI99EjvakKh+sLIOz5QI2c5aqI2qMTaoxeludyY/dOFLZkDvTnvABzA9daJdfQen3gJABnIyTeDENgJYBiGKtzUV2U69brA4cP5Pi9nmDh6wk3PXHDotZjb+xwpIVEI10jsLOhEJXmaqAxE2iUnY6oY3eVjIQo3C47RpdU9dVI8S/E3oYo2VGIOtRYpz9ufeBTj1sfuAyWhjy3Wx+Ye/h6hoWvJ9xkapYKQwTSY4Yi3RCENLsR++vzYLFXAHWetTufPJsaCs5cUeLye/eahS97D7rxz8Bict89JuRdWq8PPBjQno+wxAb4GSohbIWor8xBdYnrrg/MKVl6hoWvJ0IGAVo/oMl1TocXUHAoajDSwuORrtch3VyOIw1FgDWXS5ORW7u1fAREbrrsGN2mripFcmAZ9ptCZUch6jVjtR+M7a0PrCmFqe4Iqoqy0WSSf2hIo9MjOJJn6PYEC19PKAoQMQQoTJMWoVHnd3RpsqAIpKms2GnMR11TPdCQwaXJyKNMXuV+JwxFrngPWaMfg9XiqucUE/VM6/WBh0PtJxA8wAhfv3LYLAWoLctGXYXzV04KjRsARaVy+v26Mxa+noo+yamFryQoFmnRqUj3C0CarRaZ9fmwihKgtsRpGYic7frq4UDWLtkxekxdXoCk0GpkFgfKjkLUP4SCugoD6ioMAAYBOBNBA5pgCKqESlWMxpojR9cHburfJQejk1P79fY9EQtfT8WNBXbM75ebtilqZEYPQVrYsaXJTCUoaiwDLNlcmoy8yiVrTbIj9Fr0mg9xaPi/YLO65rgnIkczG7UwH7c+sC7IjqDQOuh8ytBkKkBN8SE0OHh94JiUIQ69PW/AwtdTA05z2E3V+wRiZ+wwpAeGI00xY3d9Phq4NBl5uSvrBkO1Z5/sGL2mKTyMxIlGHCzykx2FSAphU6G67Pj1gSciNMEEv8BKKPYi1Ffl9Hl94JhUFr6eYuHrqYihgD4QMNf2+Kp5ofFIj0pBuq8v0ptqcNCYD7soAmq8b0kcoo7M2Cg7Qd/FbvgEh5P/Abude/mIAKCh1gcNtbEAYgGMgX+4DQFhNdBqS2E25qG66DDMDd3b06H380do3MB+zeuJWPh6SqUCYk8Fsld3ulmTSov9scORFhKDnVoF6Y1FKDNVAqaDgPserSLqVxc2JEG73X337jXT5O7DoDPNOFyokx2FyCVZm9SoalkfeCigFwiPaYSvfzns1kLUl+egprSw3etGJadCURSn5vUELHy9MeC0NoWvxi8E6bHDkRYQjDTRiH31eTDZqoD6KkkhidzPdVt8ZEdwmLgt85E98DZXncKMyKUoUFBf6Yf6yngcXR/4dATGWBEQWgWVquTolDCF2bBazIjl4dxeYeHrjYHjkB2RjPSIRKT56JFuqUSOsRDClgdU58lOR+SWzjINhM+m3bJjOIz2YBrix1mRW8iPWaLesJg0qCiMABABYCS0BoGwsHoMGD5KdjS3xE+iXqhJOB2XGawQXJqMyGFu2RHssjP699bA9K+RG3U93Ga5ECIXJoSCmooARCUNkB3FLXHWwl4I0gchOThZdgwijzHGEgPD2p2yYzicbt9GDIhh2yNylLBYf/j4a2XHcEssfL00JmqM7AhEHuOOXZFAH6ZocGXx+36QHYHIY8QkB8uO4LZY+HppdORo2RGIPMLwpggEr/S8vXvNfNJXIDaGZxQSOUJMapDsCG6Lha+XRkex8BE5wt/3DQCsVtkx+lXCoUWyIxB5hNiUYNkR3BYLXy9F+0cjzhAnOwaRWxtkDUbkcvdbM7enfLf8hqgotewYRG4tINQHhhDPmbrJ2Vj4+oDj+Ij65p7MRAizWXYMpxiUv0R2BCK3FjckWHYEt8bC1wdnxJ4hOwKR24q2GTBgmfuvqtFd/ut/QHgkZ8Ii6q34EWGyI7g1Fr4+OCv2LKgUPoVEvXFv9mAIY/fWzvQUSWVrZEcgckuKSkH88FDZMdwa20ofBPsEY2T4SNkxiNxOiN0XyUsOyI7hdP6rvkRoOPfyEfVUdFIg9H6cf68vWPj66Oy4s2VHIHI79x4ZBlFTKzuG0ylCIKlms+wYRG6Hh3P7joWvj84ewMJH1BMGocOIpQdlx5AmYPmnCArlngqinkhg4eszFr4+Gh46HOG+4bJjELmNuwtGwF5eKTuGNIrdhmRTmuwYRG7DL0iHiPgA2THcHgtfHymKgjNjz5Qdg8gt6IQao5cdkR1DusAlc2EI4l4+ou7g4VzHYOFzAB7WJeqev5eMhCgqkR1DOpXVghS790xJQ9QXPJzrGCx8DjAhdgI0Cs+8I+qMGgomLC+WHcNlhCx5H/6B/Nwg6oxKpWAgp2NxCBY+BwjQBeDkyJNlxyByabPLR0IcKZAdw2Uo5kakqA/JjkHk0qKSAqH35R9GjsDC5yCcnoWoc+etqpIdweWELHkPPv78ZUbUkYSRPJzrKCx8DsJxfEQdu7FqBJCVIzuGy1EZa5Hiw5NYiDrCwuc4LHwOMjhkMKL9o2XHIHJJF69tkB3BZYUvmQO9r1p2DCKXYwjRI3wAp2NxFBY+Bzo//nzZEYhczlW1Q6DamyU7hstS1VUiOYAnsxCdKGVMpOwIHoWFz4EuSrxIdgQil3PlBrvsCC4vctl70Or5cUx0vNTTomRH8Cj8hHGgkyNOxgDDANkxiFzGRcZkaNL2y47h8lSVxUgO9t7VR4hOFBTpi8iEQNkxPAoLn4NxLx/Rn/66RSc7gtuIXPUBNFp+JBMB3LvXH/jp4mCXJF0iOwKRSzjbFA/95j2yY7gNTUkuksJrZccgcgmDWfgcjoXPwZKCkzAkZIjsGETS3bI9CBBCdgy3Er3uY6jUiuwYRFKFDzQgJNpfdgyPw8LXD3hYl7zdWHMs/NftlB3D7WjyMpAY2Sg7BpFUPJzbP1j4+sHFiRdDAf9KJ+91+64IwM6zc3sjdvOnUPjJTN5KAVLHsvD1B36s9IMYQwxOjTxVdgwiKYY3RSB4Fffu9Zb28C4kRDfJjkEkRUxyEAJCfWTH8EgsfP3k4sSLZUcgkuLv+wYAVqvsGG5twPYvwIME5I24d6//sPD1kwsHXQiNiouik3dJsoYgcvku2THcni5jK+JjeEicvItKpXB1jX7ERtJPgn2CcUbMGVhbsFZ2FCKn+WdmIoR5i+wYHmHg7m9xJOwa2TEc7mDhLizb+Q2OlGehtqECsy94GicnntXutl+teQ3r9y/CjDPuwrmjZnR4m2v3/oy1+35GZV0JACA6JAEXjbkBI+LHt2zz/YZ3sTlzCXQaH1w2/jacljq55Xs7Dq3Glswl+NtFzznoUVJvDBgaAt8Azt3ZX1j4+tHFSRez8ElizDCi/LdyNOY2wlptRfzd8Qgc0/6s7QXzClC1qgrR10Yj/MLwDm+zYkUFKldUoqn86PgqfZwekZdFImDUn4t7F31VhOp11VD0CqKvikbwhOCW79VsqUH1+mok3JfgmAfpYmJsAYhbugeciMUx9LvXIu7aa1BQJDuJY5mtjYgLS8YZQy/Ch0ue7HC7ndnrkFO6H0F+YV3eZrB/OC4bPxsRQXEQQmBz5hJ8sPgJPDTjfcSEDsLunA3YdnAF/j7tRZTVFOCLVS9j2IDTYPANQqO5Hr9snYu7p73syIdJvTB4HA/n9ice0u1H58efjwBtQNcbksPZzXb4xPsg9obYTrer3V6LxkON0AR3/bePNkSL6L9EI/mpZCQ/lQzDMAOOvHEEpgLT0dtKq0XNxhoM+tcgRM+MRsEnBbDWHR3LZmuwoeT7EsTcGNP3B+ei7juUCtHQIDuGR0nIWCg7gsONiB+PS8fd0uFePQCoNpbhu/VvYdZ5j0DdjaExJw2agBHx4xEZNABRwQMxfdyt0Gt9kV26DwBQXH0EqbEnIyFiCMamnAcfnR8q6o426YWbP8DZw6cjNIBlQya9vwbJPJzbr1j4+pGvxhfTU6bLjuGVAkYFIGpGVId79QCgqaoJhZ8XYsDfBkDpxmS3gacGIuDkAOij9dBH6xF1VRRUPio0HDxacsxFZvgP9Ydvoi+CTw+GylcFS5kFAFD8bTFCzwuFLswzD1eE2f2QuPSA7Bgex2fHUsREe9fHtF3YMX/FCzj/5JmICR3U8+vbbdh2cAUsTSYkRg0HAMSFJeNIWSYazHU4UpaJJqsFEUFxOFS0G3nlBzFp5BUOfhTUU8POiIFGq5Ydw6PxkG4/u3rI1fhy/5cQPNDlUoRdIP+DfIRfFA6fuJ5PASDsAjVbamA32+GX4gcA8Bnog6pVVbAZbbCUWSAsAvooPYyZRphyTYi9sfO9je7sntyhELUcu9cfEnJ/R5H+QtkxnGZp+tdQqdSYNPLKHl2voOIwXll4N6w2C/RaX8y+8GnEhAwCAAwfeBpOS52Ml364C1qNHjec+yB0Gh98ve4N3DDp/7B23y9YvedHGHyCcO059/eqaFIfKMCIc+Jkp/B4LHz9LDEoEeNixmFz0WbZUeg45b+VAyogbErX44OOZ8oz4fCzh2FvskOlVyH+7viWwhhwUgAazmjAoacPQdEpGDB7ABS9gsL5hRhw2wBUrqhExbIKaAwaxN4c26ui6YoMQodhSw/yT5p+4rfxZ0RefTFKS2yyo/S7I2WZWLX7Bzw44z0oSs/mpYkKHoiHr/oAjRYj0g6vwWcrX8Q9019tKX3Txt6EaWNvatn+t23zMTRuNNQqNf7Y8Tke+ctH2JO7CfNXvoAHZ7znyIdFXRg4LBTBkX6yY3g87zpWIMk1QzzvTDt31pjTiIolFRhw24Ae/1LRxeiQ/Ewykp9IRuh5ocj/KL9lDB8ARF0RhcEvDUbqs6kIHBOI8kXlMAw3QFErKPu5DEmPJCFkYgjyP8h39MOS5p/5IyAqKmXH8GiDilbIjuAUh4p2o76xGk98cS3++cEU/PODKaisL8EPm97DE1/8tdPratRaRATFIT5iMC4bfxviwpKxavcP7W5bXHUEW7OW4ZLTbkZW4U6kxIxCgG8wRidPRF55FkwWjkV1ppHcu+cU3MPnBOcOPBdRflEoaSiRHYVw9Axea50VGQ9k/HmhHSj+uhgVSyow5JUhHV5XpVFBH6UHAPgO8kVjdiMqllYgblbbDyxzoRnVG6uR/HQyqtdWw2+IHzSBGgSNC0LB3ALYGm1Q+7r3mBW9UOPUZbncu9fPDGu+RdhfzkdFmWdPaH3a4MkYMmB0q8ve+fVBjBs8BacPmdqj2xLCDqut7YolQgh8vfY1XHnG36DX+sIu7LDZj51cZT+6F9UuOAeisxhC9Bg0quPZEchxWPicQK1S46rBV+Gd9HdkRyEAwWcGwzDC0OqynP/lIHhCMELODunZjQlANLWtO0IIFHxagOhroqH2UUPYBYTt6HbCemx7D/id8veSkyCKt8mO4RWSKtejAuO73tDFmZsaUVZT0PJ1RV0x8ssPwk8fgNCAKBh8glptr1ZpEOgbiqjggS2XvfnLv3By4lmYOPJyAMBPmz/CiIHjEBIQCZOlAdsOrkBW4U7cNe2FNve/4cBvMPgE4aRBEwAASdEj8dv2+cgu2Yd9R7YgOiQBfnpDm+tR/xhxdixUKi4r4wwsfE5y1eCr8P6u92G1e/Zf6K7CZrLBUmJp+dpSbkFjbiPUBjV0YTpoDK1f+opagSZIA32MvuWy7BezETgmEGGTj47zK/6uGAGjAqAN1cJusqN6UzWMB4wY9MCgNvdftboKmgANAk89epawX6ofSheWouFgA+p210Efq4fa37337qmh4IzlRdy75ySGFZ8h+MqzUF3h3uvs5pZl4M1fHmj5+oeNcwAA4wdfgBvOfbBbt1FeW4h6U03L1/WNVZi/8gXUNlTCR+ePuLAk3DXtBQwbMLbV9WobKrF4xxe4//I3Wy4bFDkU54+6CnN+fwQBviHdzkB9p1IrGHam557M5moUIQQ/r53kX6v/hcU5i2XH8Ar1++uR82JOm8uDzwzGgNkD2lye8UAGwi4IazXxcsYDGQg+KxhRVxydnyt/bj6M+4yw1lih8lXBZ6APIi6OgGFk670B1horDj1zCEmPJUEbom25vPSnUlQsqYAmUIO42XHwS3LvQcp3lo3CuR/tkB3Dq9RceDu2m0+WHYPIIVLGROLC2SNlx/AaLHxOtK14G25efLPsGER9pgjgmwUDgIM5sqN4FaHWYPv0d1Fb5d57+YgA4PL7TkXckB4Oo6Fe41m6TjQ2eixSglNkxyDqsxurR7DsSaDYrEhu2iU7BlGfhcT4s+w5GQufk1095GrZEYj67KI1RtkRvFbQkg/hH8jh1+TeRp7DsXvOxsLnZJcmXwp/rb/sGES99pfaIVDtOyg7htdSWcxIUTK63pDIRel8NRh6uueuK+6qWPiczF/rj5mDZ8qOQdRrV2zwgPlk3FzI4vfhZ+BePnJPJ02Mg86Xr19nY+GT4MYRN0Kv1ne9IZGLudiYDE3aftkxvJ7KZESyLkd2DKIe0+hUOHnywK43JIdj4ZMg3DccV6b2bGFwIldw7Wad7Ah0TNiSOfDxc++5HMn7jDgrDr4Gfo7IwMInyS0jb4FWpe16QyIXMakxAfrNu2XHoGNU9dVI9iuUHYOo21QaBadMiZcdw2ux8EkS7R+N6cnTZccg6rabtgfIjkAniFj6LnQ+3MtH7mHo6TEwhHA4kywsfBLdOvJWqBV+WJPrG2eOg/96zv/malQ15UgOKpMdg6hLikrB6Au5d08mFj6JBgYOxNTEqbJjEHVp9s4wwM6zc11R5PL3oNHxo5xcW+rYSARFuPdyku6OnxKSzT5pNhQosmMQdWiEJRJBq7l3z1WpywuQHFotOwZRxxRg9NQE2Sm8HgufZMnByTg//nzZMYg69Pd9sYDVKjsGdSJ69QdQa/iHI7mmxFHhCIs1yI7h9Vj4XMDsUbNlRyBqV5I1BBEreGauq1MXZSMpksvdkWsae/Eg2REILHwuYXjYcJwVd5bsGERt/DMjEcJslh2DuiFm3VyoVNzLR65l4PBQRCYEyo5BYOFzGXeMukN2BKJWYmwBiFu2R3YM6ibNkQMYFG2SHYOolbEXceyeq2DhcxGnRJ6CCbETZMcganHfoVSIhgbZMagH4jbPh8KdfOQiEkaGITY1RHYMOoaFz4XcP+Z+qBT+SEi+MLsfEpcekB2Dekh7KB3xMTzBhuRTVArOuDJZdgw6DtuFCxkSOgSXJl0qOwYR7s0dClFbKzsG9cLA9K/BmZ5ItqFnRPPMXBfDwudi7j71bviofWTHIC8WYNdj6JIs2TGol3T7NmJgjJAdg7yYRqfC+EuTZMegE7DwuZgo/yjcMPwG2THIi91TMByiskp2DOqDgfu+lx2BvNgpk+PhH8w1c10NC58LuvWkWxHqEyo7BnkhvVDj5KW5smNQH/mkr0RsDI/rkvP5Bupw6gVcM9cVsfC5IH+tP+46+S7ZMcgL3V18EkRJqewY5AAJhxbJjkBeaNwlidD5aGTHoHaw8LmoGYNnIDEoUXYM8iIaocL45YWyY5CD+G75DVHR/Ign5wmJ9sPwM2Nkx6AO8NPARWlUGtw3+j7ZMciL3F4+EiKPhc+TJOYtkR2BvMjplydDpWatcFX8ybiwc+PPxWnRp8mOQV5AEcCkFRWyY5CD+a3/EeGRPLxG/S8mJQhJp0TIjkGdYOFzcQ+MfQAKJ9WifnZT1XDgME/W8ERJpatlRyBPpwBnzkiVnYK6wMLn4kaEjcBFiRfJjkEe7qI1XELNU/mv/gqh4dzLR/0nZUwkohIDZcegLrDwuYF7R98LX42v7BjkoWbWDoWy/6DsGNRPFCGQVLNJdgzyUFofNffuuQkWPjcQY4jB30/5u+wY5KEuX2+THYH6WcDy+QgK1cqOQR5o/PQkGEI4ybI7YOFzE9cPux7DQofJjkEeZlp9CjTp+2XHoH6m2G1IbkyTHYM8TGRCAEZNGiA7BnUTC5+bUKvUeHLCk1AratlRyINcs5lju7xF4NK5CAjmXj5yDEWlYNJ1Q6GoeFKhu2DhcyMjwkbg2qHXyo5BHuLcxkHQb9kjOwY5icpqQbJtr+wY5CFGnTcAEfEBsmNQD7DwuZm7T70b0f7RsmOQB7hpm0F2BHKykMXvwz+Qe3Wpbwyheoy/NEl2DOohFj4346f1w6PjH5Udg9zcOHMc/Dbskh2DnEyxmJCs4hnZ1DcTrxkCrZ7Di9wNC58bmjRwEqYkTJEdg9zY7TvDALtddgySIHTp+/D1514+6p2kUyMwaFS47BjUCyx8buqhcQ/BoOUhOeq5kyyRCFzNvXveSmWsRYrPEdkxyA3pfNQ45+rBsmNQL7HwualIv0jcM/oe2THIDd21NxawWmXHIInClsyB3peH5Khnxl+WDP9gzrnnrlj43NjMITNxcsTJsmOQG0ltCkP4Cu7d83aqukokG4plxyA3EjkoECdNjJMdg/qAhc+NqRQVnjzjSWhUHI9D3fOPjHgIi0V2DHIBkcvfg1bPXwHUNZVawbnXD+Gce26O73Y3lxqSijtG3SE7BrmBOFsgYpdx3j06SlVZjOTgCtkxyA2Mn56E8AGcc8/dsfB5gNknzcapkafKjkEu7t6DKRCNjbJjkAuJWvkBNFr+GqCOxaYG49Qp8bJjkAPwne4B1Co1/nv2f3nWLnUo3O6PQUu5Zi61pi49gqSwWtkxyEXp/TSYfPNwHsr1ECx8HiLOEIdHxj8iOwa5qHtzhkDU1cmOQS4oet1HUKn5C53amnjtEASE+siOQQ7CwudBLk2+FBcnXiw7BrmYALseQ5ZmyY5BLkqTn4XESB7qp9YGj49C6mlRsmOQA7HweZjHTn8Msf6xsmOQC7m3YAREZZXsGH2yraEBd+XnYeLBgxiecQDLOtlb+VRxMYZnHMD8yspOb/Prqipcnp2N07IycVpWJq7NzcGa+vpW27xYWoLTszJx3qGD+KW2ptX3/qirxV35eb1/UC4kdtMnUPjbgI4JCPPBxGuGyI5BDsa3uIcJ0AXgv2f/F2qFk6oS4CM0GLU0W3aMPmuw2zFE74PHozrf47Csrg47TY2I1HQ9VVGUVoP7IiLwXcIgfJcwCOP9/PGPgnxkmc0AgJX1dVhUW4uPBg7EAxGReKK4GFXHJqyus9nwRlkZHouK7vuDcwHa7D0YFN0kOwa5AEUBJt88HDpfTvflaVj4PNDoqNG4ZeQtsmOQC7i7eCRESZnsGH12jsGAeyIiMDmg46khSpqa8FxpCV6KiUV3flWdawjARIMBg3Q6DNLpcG9EBPxUKuw6dibzYbMF4/z8MNLHF9MCA2FQqZDfdLQU/a+sDNcEhyBWq3XEw3MJcdu/gMKhfF5v9IUJiE0Jlh2D+gELn4e665S7cFL4SbJjkEQaocK4ZYWyYziFXQg8VFyEW0JDkarv+dJPNiHwW20tGoXAyb6+AIAhPnrsMZlQY7Nhr8kEkxCI1+mwvaEB+80mXB8S4uiHIZUuYysGxthlxyCJIhMCcNqlibJjUD9h4fNQGpUGL5z9Avw0frKjkCR3lI2EyPeOwvdRZSXUAK4P7lkJyzSbMCYzA6dkZuDpkmK8GRuHlGOF8Sx/Ay4NDMTM3Bw8UlSE/0bHwFelwjMlJXgyKhpfV1fj4sOHcV1ubsthYHc3cPe3siOQJBq9GlNuGQG1mrXAU/En68HiA+Px0LiHZMcgCRQBTFxZLjuGU+w1mfBZVSWej4mB0sNjkoN0evwwKBFfJwzC1cHBeKS4CAePK2//CI/A4qRk/JSYiMkBAfiwogJn+PtBA+C9inJ8Hh+PGcFBeLjIM4q1fvdaxMXITkEynHVVCoKjuIPAk7HwebgrUq/ARYMukh2DnOzmypHA4SOyYzjF9oYGVNpsOP/QIZyUcQAnZRxAodWKl8pKMfnQwU6vq1MUJOh0GOHjg/sjIjFEr8dnVe2f0XzYbMYvtTW4OzwCWxobMNbPD6EaDaYGBGKf2Qyj3dYfD8/pEjIWyo5ATjZ4XBRGnB0nOwb1M56G4wWemvAUsqqzcLC6819+5DkuXOM9kyxPDwrCGf7+rS6bnZ+H6YGBuCIoqEe3JQA0ibbj2IQQeKqkGA9GRsJfpYJdAFYhAPz5X5voXX5X47NjKWKuuRJFxRzP5w3C4vwx6fqhsmOQE3APnxfw0/rhtUmvcek1L3FNzVAoBw7JjuFQRrsd+00m7DeZAAAFTU3YbzKhsKkJwWo1UvX6Vv80AMLVGiTq/jyB4+a8I/jiuL13r5aVYltDAwqaLMg0m/BqWSm2NDTgksC2JXFBTQ1C1Rqcazh6lvCpvr7Y3NCAnY2N+LSqEsk6HQLVnjMVUkLOb7IjkBPofDWYesdJ0Oo857VLHeMePi8xKGgQnj3rWdy38j4IeMiuCGrXZes9bz61vaZGzMr7c5LjF8tKAQCXBwbi+ZjuTTSeZ7GgymZt+brSZsNDRYUos9kQoFJhsF6PDwcMxIQT9haWW614v6IcXyYktFw2ytcXs0JC8bf8PIRpNHg+2rMGvvlt+gWRV09DaYlnHKamdijA5FnDEBzJcXveQhFC8Le/F3l9++uYu2eu7BjUT6bXp+L6t/bLjkEeoP7smdiinig7BvWT0VMTcMblybJjkBPxkK6XufvUu3F6zOmyY1A/mbnp/9u79+goywOP4793bpkkk4Qk5AbknnAL1yAiKCjIRSHcvOOl2gp7bLuK63bbrbt7POu2Xs661qJUUEOrWKXFbXXrnaoUvICAAREQiwgWJIZLCEkIuczM/oFQI3JJmJln8s73c857ksxMZn5wMnl/ed7nfV4OzSA0fCt/r/QMDgLZUW7/NJ03rch0DEQYhS/GOB1OPXDhA+rl62U6CkJs3OECedZ8ZDoGbKTowNumIyDEkjPiNfHmMlkOLqsSayh8MSglLkXzxs1jUWab+c66xNM/COgA35tPKzWdUT67cMc5Nfn7A+VNtM8lAXHmKHwxqjS1VPeMvkeW+CvPDkY091TCOx+ajgGbsYJBFTWsNR0DoWBJ47/bX+k9WK0hVlH4YtjFeRfr+0O+bzoGQmDO+nSJ868QBsl//rWSUxkR6uqGTylU0ZAM0zFgEIUvxt0y6BZNyJ9gOgbOwsCWTCWvYHQP4WH521TcssF0DJyFoiEZGj6lwHQMGEbhi3GWZeln5/9MA9IHmI6CTvrBRz2ktrbTPxDopJRlT8iXwly+riirMFkTvte/w9eZhv1Q+KAEd4Lmj5+vvKQ801HQQaWt6er+FqN7CC9HS7NKtNV0DHRQt6wEVfxwsFxcSQOi8OErad40LZiwQGneNNNR0AG3fpynYEuL6RiIAd1eW6iEJEb5uoqEZI+m3jpYXh/zL3EUhQ/H5Sbl6lfjf8VyLV1Er7YU5byx0XQMxAjHkUaVuD8zHQNnwO11quIfByu5e7zpKIgiFD60U5Zepl9c9Au5HPwlH+1u/7RYwaYjpmMghqS9/qi8CRwejGYOp6VL/2GgMvKSIv7aN910kyzL0n333dfu9ueff/74HMLly5fLsqxv3aqrqyOeOZZQ+Aw49qawLEtut1tZWVmaMGGCFi1apEAgcPxxBQUFeuihhyKeb1TPUbp71N2s0RfFMv0+5S/bbDoGYoyjoU7FCbtNx8DJWNK4G/oqt7+5qTler1f333+/amtrT/m4rVu3as+ePe22zMzMCKWMTRQ+Qy655BLt2bNHO3bs0CuvvKKxY8dq7ty5qqioUFsUnHE5tXiq5pbPNR0DJzF3R28F6xtMx0AMylj2qDxeRvmi0XnTi9TnvByjGcaPH6/s7Gzde++9p3xcZmamsrOz220OB5UknPjfNSQuLk7Z2dnq2bOnysvLdeedd+qFF17QK6+8ot/85jem40mSbh54s67rd53pGPiGlKBXvZd9YjoGYpSjbp+KU2pMx8A3DLyol4ZdUmA6hpxOp+655x49/PDD2rVrl+k4+BoKXxQZN26cBg8erD/84Q+moxz34+E/1sT8iaZj4Gvmft5PwdqDpmMghmW+sVAuD7uPaFE0NEOjryo1HeO4mTNnasiQIbrrrrtO+phevXrJ5/Md38rKyiKYMDYxMz/K9O3bVx9+GD3rqjksh+4dfa9qm2u1pnqN6Tgxzxt0aeCfPxMXUYNJzn27VZx2UFurk01HiXk5JSlHF1Z2RNec6/vvv1/jxo3Tj370o2+9f+XKlUpK+vuJJW43y8eEG3+iRZlgMBh1K6J7nB79cuwv1S+tn+koMe/WLwYoWLPPdAxA2X95TE5XdP2uijWZ+Uma8oNBcrmjb07lmDFjNGnSJP30pz/91vsLCwtVUlJyfMvPz49wwthD4YsyW7ZsUWFhoekYJ0jyJOnxiY9T+gxyBR06980vTMcAJEnOPZ+pMKPRdIyYlZmfpGlzhyguIXpHxu677z796U9/0nvvvWc6CkThiypvvvmmNm7cqMsvv9x0lG+VEpdC6TPolr0DFNxF4UP06PFOpRxORvkiLSMv+sueJA0cOFDXXXed5s2bd8J9NTU1qq6ubre1trYaSBk7KHyGNDc3q7q6Wrt379YHH3yge+65R9OnT1dFRYW+853vmI53UpQ+M6ygNOYtDuUiurg+/1gFWSz+HUkZeUmafnv0l71j7r777nbryx7Tp08f5eTktNvWrVtnIGHs4KQNQ1599VXl5OTI5XIpNTVVgwcP1rx583TjjTdG/VpEx0rfnNfnaMuBLabjxITv1Q6Qtq83HQM4Qc/VT+mz/DkKnrhPR4hF+8jety0pVlBQoObm5uNfX3TRRQoGOe3MBCvI/zw6qa65jtIXIb/7Q56srdtNxwC+1afX/lI7v2D8IJyOlT1vYnSWPUS/6B5KQlQ7NtLXP72/6Si2NquuH2UPUS236llxJcbwoewhFCh8OCspcSl6bMJjlL4wmvZ2i+kIwCl5tqxSbg4Hi8Khe66PsoeQoPDhrFH6wmdafamcH241HQM4rbxNz5mOYDvdc32afvtQyh5CgsKHkODwbnhctZq3KLqGuA3L1SOH47qhQtlDqLE3Qcgke5L1+MTHVZ5ZbjqKLYw/XCjPmk2mYwBnLH/b/5mOYAs9+3TTzDvKKXsIKQofQirZk6zHJj6m8XnjTUfp8m5Ym2A6AtAh8WteVVY2u5WzUXJOpqbeOkSeeM56RmjxzkTIxTnj9D8X/Y9m9Z1lOkqXNepIruLf/dB0DKDDCv/2uukIXdagcb008eYyOV3smhF6/FQhLByWQ3eOuFNzy+fKYr2GDrt5fTeJJTLRBSW880dlZDI61SGWNPKyYo2+qrcsi9+XCA8KH8Jq9sDZ+vkFP5fLwQ7gTA1pyVbSCkb30HUV1iw3HaHLcDgtjb+pv8on5puOApuj8CHsphZP1fxx85XgYk7ambhlY5bk95uOAXSab/mzSsvgj7zTccc5VfHDweozItt0FMQACh8iYlTPUfr1Jb9WujfddJSoVtqWrrS3GN1D11d0cJXpCFEtPtmjGXcMVW7/NNNRECMofIiY/un99fTkp1WQXGA6StS6bUue1NpqOgZw1pLeeEopaSwr8m1SMuJ1+b8MU2Z+sukoiCEUPkRUr6ReeurSpzQoY5DpKFEnz99N2W9sNB0DCAkr4FdJ0wemY0SdzPwkXf7jYUrJiDcdBTGGwoeIS/WmqnJiJWv1fcPcvxYp2HTEdAwgZJKWLVJSN0b5jik5J1Mz/rlc8Uke01EQgyh8MMLr8urBix7UbUNvk8PixzDT71PenzebjgGElKOtRSV+rhZjOSyNvKxYk2YPkNvjNB0HMYo9LYyxLEtzBs3R/IvnK9kT23NZbt/RW8H6BtMxgJDr9tpCJSbH7hm7cYkuTf3HwSy7AuMofDDugp4XaMmUJSpNLTUdxYiUgFelyz4xHQMIC6vliIod20zHMCK9p09X/utwzsRFVKDwISrkJufq6Uuf1iUFl5iOEnG3/62/grUHTccAwibttUcVnxhbo3wl52Tq8p9wcgaiB4UPUSPBnaD/vvC/dcewO+S0YmOeS0LArQHLtpuOAYSVo6lBJd6dpmNEhOWwNHIm8/UQfSh8iDrfHfBdPTr+UXWL62Y6StjdumeAgnv3mY4BhF366wsUF2/vAnR8vt4k5ush+lD4EJVG9hipJRVL1Detr+koYeMJOnXOn/9mOgYQEY76Ayr2VZuOETbM10O0o/AhavX09dTiSxeroqjCdJSwuKVmgIJf2HcHCHxTxhuPyh1nv91O6fAs5ush6tnvnQdb8bq8unf0vbpr5F2Kd9nnl6kVlEa/WWM6BhBRzgNfqrjbftMxQsbtderiG/tp4s1lzNdD1KPwoUu4ovcVWjp1qQakDzAdJSRmHxio4A4O5yL2ZL31mFzurr/rySpM1tX/Nlx9R+aYjgKcka7/rkPMyE/O1+LJizVn4Jwuf3WO8X+pMx0BMMJZ87mKunfdn3/LYemcyQW67EflSslIMB0HOGNde6+JmONyuHRb+W1aNGmReiT2MB2nU6492FfWVpZiQezKXvGEHE7LdIwOS0rzasYdQzViWpEcTnaf6Fr4iUWXNCxrmJ6b9pymFE0xHaXDpr7dajoCYJRr9zYVZR02HaNDSodn6er/OFc9SrqZjgJ0ihUMBoOmQwBn4+XtL+tnq36m+tZ601FOa3pDqa57eIvpGIBxrQVleqfohwoEonsX5PE6NWZWH/UZkW06CnBWGOFDlze5aLKem/achmUNMx3ltK56j7ccIEnuHZuUn91sOsYp5RSn6Op/P5eyB1tg7wNb6OHroUWTFmlu+Vy5HNF5zc7xhwvlXrvJdAwgavRc+1tZUTiVz+G0dO7UQs3453Ild7fPclCIbRQ+2IbDcmj2wNlaMmWJBnUfZDrOCW5Yw44D+DrPJ2uVl+M3HaOdnOIUXf1v52r4lEI5HFHYRoFOYg4fbCkQDOh3W3+neR/MU0Nrg+k4Ov9IruY+tEPi7Qa00zzgAr3TfZbpGPLEuzRyZrHKRveQFY3DjsBZovDB1moO1+i+9+/Tsp3LjOaofG+gkpZXGc0ARKtPZs3Xrj3mXr+4PFOjry5VYkqcuRBAmFH4EBOW/225fr7656pujPy1a4e0ZOvOh/ZI/ug6dAVEiyPl4/Vu8syIv64vLU4XXtNHBYO6R/y1gUij8CFmHG49rEfWP6JntjwjfzBy5WvhusFKfX1dxF4P6Iq2zHpUe/YEIvJalsPSoLG9NGJakdxxXAMXsYHCh5izef9m/ed7/6nN+zeH/bX6tHbXf/1yv9TKYsvAqRweMVWr4i8J++t0z/Vp7PV9lZmfHPbXAqIJhQ8xyR/w65mPn9EjVY/ocFv4Vvx/5MOhynxpTdieH7CTTVcv0Jdfhmf03RXn1LkVhRp8cS5n3yImUfgQ06obq/Xg2gf16o5XFVRo3wr5bd30wMMNCh45EtLnBeyqcfSVWu28KLRPakl9RmTrvOlF8qV6Q/vcQBdC4QMkfbTvIz2w9gGt+zJ0c+0e3FKuXs+/H7LnA2LBh1ct1L6atpA8V88+qTr/8hJl5CWF5PmArozCB3zNW5+/pV988At9VvfZWT1PZiBR8+e3KdjQGKJkQGyoH3eD1gTOO6vnSM1J1KjLilUwkLNvgWMofMA3tAXa9L+f/K9+teFXOnDkQKee495t5Speyuge0FFBy9L6yxaodn/HR/nikz06t6JQ/S/owTw94BsofMBJNLY2atFHi7R482I1tTWd8felBLx6YqFTwYN1YUwH2NehibO1tmXoGT/e5XFoyPg8DZ2YJ483Oq+lDZhG4QNOo+ZwjR6pekQvfPqCAsHTrxN2185ylT3D6B7QWUGHU+umP6pDtadezsiypD7nZWvEtGL5UrlKBnAqFD7gDH1S+4keXPeg3tn9zkkfkxBw66nKBAX27Y9gMsB+aqf8UFWN/U96f26/VI28rEQZuZyQAZwJCh/QQWuq12jhhoVaXb36hPv+dfdQlT/FunvA2Qp44rT20nlqqGs/ly+3f5qGTy5QTkk3M8GALorCB3RSVU2VFmxYoHe/eFeS5Ak69dunUhX8IvLX6wXsaP+0f9KGQyWSpLyyNA2fUqjsohTDqYCuicIHnKWNezdqwYcLNHRdnc5fxDVzgVAJehO0+weVGjShSFmFXAoNOBsUPiBEGj/erNqFj6v+tdelQGQuAg/YktOp5EkTlT5njrz9+plOA9gChQ8IsZadO7X/iUrVvfCCgi0tpuMAXYbl8Shlxgyl3/w9efLzTccBbIXCB4RJa02NDjz5pA4u+Z0CjVxxAzgZh8+nblddpbQbb5Q7K9N0HMCWKHxAmPkbGlT3x+dVu2SJWj791HQcIGrE9e6t1GtnKWXqVDkSE03HAWyNwgdEUOPq91X77LOqf+MNqfXUi8oCtuR2K3nCBKVeO0sJ55xjOg0QMyh8gAFte/eqdulSHfz9UrVVs4wL7M+Vk6PUq65UtyuvlKt7d9NxgJhD4QMMCvr9anjrLdU+86wa33tP4u0IO7EsJY48T91mzVLSuHGynE7TiYCYReEDokTLzp2qfXaJ6v74R/nr6kzHATrNkZyslBnTlXrNLMUVFZqOA0AUPiDqBI4cUf1rr6nuTy+qcdUqqa3t9N8EmOZ2yzdqlJIrKpQ0/mI54uNNJwLwNRQ+IIq1HTigQ6++qkMvvaymDz7gkC+ii2UpYdiwoyVv0kS5UlNNJwJwEhQ+oIto/eILHXr5ZdW99LKat2wxHQcxLK5fP6VUTFHy5Mly5+SYjgPgDFD4gC6oeft2HXrxJR166SW17NxpOg5igDsvT8lTJiulokJxxcWm4wDoIAof0MU1fbRJh156SYdeeYUlXhBSrh45Srp4vFIqpih+8GDTcQCcBQofYCNHPv5YDStWqmHFX9S0fgMnfKBjXC4llJfLd+EY+caMUVxpqelEAEKEwgfYlL++Xo3vvKuGFSvUuHKl2vbuNR0JUciZ0V2+0UcLXuIF58vp85mOBCAMKHxADAgGg2resuXo6N/KlWpav17y+03HggkOh+IHDZLvwjFKHDNG3v79ZVmW6VQAwozCB8Qgf12dGt99Vw0rVurwmjVq3bXLdCSEkSc/X/Hl5UocNVKJF1zA8ilADKLwAVDb3r06XFWlpqr1aqqq0pHNmxVsaTEdC51geTzylpUpvnyoEsrLFT90qFxpaaZjATCMwgfgBIGWFh35aJOaqqrUtL5Kh6vWy79vn+lY+BbOtDTFDx2qhPKhih9aLu+AMjk8HtOxAEQZCh+AM9Ly+edqqqrS4aoqHdn4kZq3b1ewqcl0rJjiSEiQp6RE3j59jpc8T0GB6VgAugAKH4BOCQYCat21S83btqn5r9vU/Ne/qnnbNrVs387h4LNkeTzyFBUprrT0q61EcaW95e7ZgxMsAHQKhQ9ASAX9frV8/vnfC+CxQrhjh9TaajpedHG55MnLO1rqSkqOfuxdKk9+viyn03Q6ADZC4QMQEcG2NrVWf6m2L6vVuqf6+MfW6j1qq/5SrdXV8u/fL9nlV5JlyZmeLnd2ttw5OXLlZMudnSN3TrZc2dly9+ghV/fuFDsAEUHhAxA1gi0taq2pUVv110rhlzUKHKqTv6FRgYaG45u/sVGB+noFm5sjks3yeuVI8snpS5LD55MzySeHL+mr23xypqYdLXM5OUcLXlYWJ08AiBoUPgBdWrC1Vf6GBgUa/14I/Q0NCh4+rKA/IAUDCgYCUiD498+DwaMjaw6nLKdDshxHPzqcsjxuORK/KnRJX5U7n0+W2236nwoAnUbhAwAAsDmH6QAAAAAILwofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwOQofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwOQofgJh10003acaMGSfcvnz5clmWpYMHD7a7vW/fvoqLi1N1dXVkAgJAiFD4AOAMvP3222pqatIVV1yhJ5980nQcAOgQCh8AnIHKykpde+21uuGGG7Ro0SLTcQCgQ1ymAwBAtKuvr9fSpUu1evVq9e3bV3V1dVq5cqVGjx5tOhoAnBEKH4CY9uKLL8rn87W7ze/3t/t6yZIlKi0tVVlZmSTpmmuuUWVlJYUPQJfBIV0AMW3s2LFav359u+2JJ55o95hFixbp+uuvP/719ddfr6VLl6q+vj7ScQGgUxjhAxDTEhMTVVJS0u62Xbt2Hf988+bNWrVqld5//3395Cc/OX673+/XkiVLNGfOnIhlBYDOYoQPAE6hsrJSY8aM0YYNG9qNAt5xxx2qrKw0HQ8AzgiFDwBOorW1VYsXL9asWbM0YMCAdtvs2bO1evVqbdq0yXRMADgtCh8AnMSKFSu0f/9+zZw584T7+vXrp379+jHKB6BLsILBYNB0CAAAAIQPI3wAAAA2R+EDAACwOQofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwOQofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwOQofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwOQofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwOQofAACAzVH4AAAAbI7CBwAAYHMUPgAAAJuj8AEAANgchQ8AAMDmKHwAAAA2R+EDAACwuf8Hv804gCiZjfcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}