AlzheimerCNN_BrainSight.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLb6ItL3fLcE"
      },
      "source": [
        "# BrainSight\n",
        "\n",
        "We propose a neural network model intended to help in detecting different stages of Alzheimer's disease.\n",
        "\n",
        "The model was trained on a dataset of 6400 MRI immages divided into 4 classes according to the severity of disease : NonDemented, VeryMildDemented, MildDemented, ModerateDemented."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install split-folders\n",
        "!pip install tensorflow_addons"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQd7haUFm1zO",
        "outputId": "b83b09ea-7fd8-42f1-c673-2d458096d332"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: split-folders in /usr/local/lib/python3.9/dist-packages (0.5.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: tensorflow_addons in /usr/local/lib/python3.9/dist-packages (0.19.0)\n",
            "Requirement already satisfied: typeguard>=2.7 in /usr/local/lib/python3.9/dist-packages (from tensorflow_addons) (2.13.3)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from tensorflow_addons) (23.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "XoQa9SRNp2Mq"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import os\n",
        "import tensorflow as tf \n",
        "\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Circle, RegularPolygon\n",
        "from matplotlib.path import Path\n",
        "from matplotlib.projections.polar import PolarAxes\n",
        "from matplotlib.projections import register_projection\n",
        "from matplotlib.spines import Spine\n",
        "from matplotlib.transforms import Affine2D\n",
        "import plotly.io as pio\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.offline as pyo\n",
        "import plotly.offline as py\n",
        "import plotly.tools as tls\n",
        "\n",
        "import pathlib\n",
        "from pathlib import Path\n",
        "\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Flatten, Dense,Dropout\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "from tensorflow.keras.metrics import SparseCategoricalAccuracy\n",
        "import tensorflow_addons as tfa\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import SeparableConv2D, BatchNormalization, GlobalAveragePooling2D\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import splitfolders\n",
        "\n",
        "pio.templates.default = \"plotly_dark\"\n",
        "matplotlib.style.use('dark_background')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "N_LUP9ke9Cck"
      },
      "outputs": [],
      "source": [
        "# Importing dataset\n",
        "data_root_orig = tf.keras.utils.get_file(origin='https://brain-disease-detector.s3.eu-west-3.amazonaws.com/alzheimer_no_split_folders_renamed.zip',\n",
        "                                         fname='/content/alzheimer.zip',\n",
        "                                         cache_subdir=\"/content\",\n",
        "                                         archive_format='zip',\n",
        "                                         extract=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "lvKbluXvkf9Y"
      },
      "outputs": [],
      "source": [
        "class_names = os.listdir('/content/alzheimer_no_split_folders_renamed')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKiIpCM7EVnK",
        "outputId": "868ff1d9-5543-4118-c02e-894276105a97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['3_ModerateDemented', '0_NonDemented', '2_MildDemented', '1_VeryMildDemented']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "class_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ROcZfYObKW_3",
        "outputId": "7c79f03e-aba5-433a-a85f-efa4c24b6c68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Copying files: 6400 files [00:00, 7436.83 files/s]\n"
          ]
        }
      ],
      "source": [
        "# Splitting dataset into training, validation and test sets.\n",
        "splitfolders.ratio('/content/alzheimer_no_split_folders_renamed', output='/content/output', ratio=(0.64, 0.16, 0.20))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZnOKXkDK5Q0",
        "outputId": "17dcf7af-83fc-4fbc-affd-c8cbf81a6000"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  folder               class          file_name\n",
            "0    val  3_ModerateDemented  moderateDem46.jpg\n",
            "1    val  3_ModerateDemented  moderateDem36.jpg\n",
            "2    val  3_ModerateDemented         29 (2).jpg\n",
            "3    val  3_ModerateDemented   moderateDem5.jpg\n",
            "4    val  3_ModerateDemented  moderateDem38.jpg\n",
            "5    val  3_ModerateDemented  moderateDem47.jpg\n",
            "6    val  3_ModerateDemented         30 (2).jpg\n",
            "7    val  3_ModerateDemented  moderateDem22.jpg\n",
            "8    val  3_ModerateDemented   moderateDem1.jpg\n",
            "9    val  3_ModerateDemented  moderateDem27.jpg\n",
            "                                   folder               class  \\\n",
            "12790  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12791  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12792  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12793  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12794  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12795  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12796  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12797  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12798  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "12799  alzheimer_no_split_folders_renamed  1_VeryMildDemented   \n",
            "\n",
            "                 file_name  \n",
            "12790  verymildDem1783.jpg  \n",
            "12791           32 (4).jpg  \n",
            "12792  verymildDem1016.jpg  \n",
            "12793   verymildDem207.jpg  \n",
            "12794  verymildDem1713.jpg  \n",
            "12795   verymildDem835.jpg  \n",
            "12796   verymildDem647.jpg  \n",
            "12797          32 (69).jpg  \n",
            "12798  verymildDem1767.jpg  \n",
            "12799   verymildDem214.jpg  \n"
          ]
        }
      ],
      "source": [
        "image_dir_path = '.'\n",
        "paths = [path.parts[-3:] for path in\n",
        "         Path('/content').rglob('*.jpg')]\n",
        "df = pd.DataFrame(data=paths, columns=['folder', 'class', 'file_name'])\n",
        "print(df.head(10))\n",
        "print(df.tail(10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CwWhqcDqL1vY",
        "outputId": "ae7c2516-8fc6-4ddd-9df2-27af77816aab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "folder                              class             \n",
              "alzheimer_no_split_folders_renamed  0_NonDemented         3200\n",
              "                                    1_VeryMildDemented    2240\n",
              "                                    2_MildDemented         896\n",
              "                                    3_ModerateDemented      64\n",
              "test                                0_NonDemented          640\n",
              "                                    1_VeryMildDemented     449\n",
              "                                    2_MildDemented         180\n",
              "                                    3_ModerateDemented      14\n",
              "train                               0_NonDemented         2048\n",
              "                                    1_VeryMildDemented    1433\n",
              "                                    2_MildDemented         573\n",
              "                                    3_ModerateDemented      40\n",
              "val                                 0_NonDemented          512\n",
              "                                    1_VeryMildDemented     358\n",
              "                                    2_MildDemented         143\n",
              "                                    3_ModerateDemented      10\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.groupby(['folder', 'class']).size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SktJq5MsL68N",
        "outputId": "bc91787b-e8ac-4e89-9134-5d73d4d39682"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "0_NonDemented         0.500244\n",
              "1_VeryMildDemented    0.350024\n",
              "2_MildDemented        0.139961\n",
              "3_ModerateDemented    0.009770\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Checking whether the percentage of different classes it the same between train, validation and test sets\n",
        "df[df[\"folder\"]==\"train\"].groupby([\"class\"]).size()/len(df[df[\"folder\"]==\"train\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQVfB-K1L7pg",
        "outputId": "4d494bef-bfe6-4905-a610-430a119eb5f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "0_NonDemented         0.500489\n",
              "1_VeryMildDemented    0.349951\n",
              "2_MildDemented        0.139785\n",
              "3_ModerateDemented    0.009775\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df[df[\"folder\"]==\"val\"].groupby([\"class\"]).size()/len(df[df[\"folder\"]==\"val\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xr2C2OQoL5u7",
        "outputId": "375b2769-d107-49c3-aa18-bc72e4576d52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "class\n",
              "0_NonDemented         0.498831\n",
              "1_VeryMildDemented    0.349961\n",
              "2_MildDemented        0.140296\n",
              "3_ModerateDemented    0.010912\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df[df[\"folder\"]==\"test\"].groupby([\"class\"]).size()/len(df[df[\"folder\"]==\"test\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_yQJT4woDnyI",
        "outputId": "67886475-1088-48d4-c081-9feb37d11a06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4094 images belonging to 4 classes.\n",
            "Found 1023 images belonging to 4 classes.\n",
            "Found 1283 images belonging to 4 classes.\n"
          ]
        }
      ],
      "source": [
        "#Defining image dataset \n",
        "train_image_generator = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "val_image_generator = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "test_image_generator = ImageDataGenerator(rescale=1/255)\n",
        "\n",
        "\n",
        "train_dataset = train_image_generator.flow_from_directory(batch_size=8,\n",
        "                                                 directory='/content/output/train',\n",
        "                                                 shuffle=True,\n",
        "                                                 target_size=(176,208), \n",
        "                                                 class_mode='categorical')\n",
        "\n",
        "validation_dataset = val_image_generator.flow_from_directory(batch_size=8,\n",
        "                                                 directory='/content/output/val',\n",
        "                                                 shuffle=True,\n",
        "                                                 target_size=(176,208), \n",
        "                                                 class_mode='categorical')\n",
        "\n",
        "test_dataset = test_image_generator.flow_from_directory(batch_size=1300,\n",
        "                                                 directory='/content/output/test',\n",
        "                                                 shuffle=True,\n",
        "                                                 target_size=(176,208))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "57LO0Fj_Y9jZ"
      },
      "outputs": [],
      "source": [
        "train_images, train_labels = train_dataset.next()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cne5JvgjZKJE",
        "outputId": "e36b6bc9-b847-4aa4-a3d2-8ae621f94ca3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [1. 0. 0. 0.]]\n"
          ]
        }
      ],
      "source": [
        "print(train_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "TlGASKbEY8PO"
      },
      "outputs": [],
      "source": [
        "validation_images, validation_labels = validation_dataset.next()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnYgRtKbZNts",
        "outputId": "1bbac44d-f2ad-44c5-a4ce-5a79798cb5b9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]]\n"
          ]
        }
      ],
      "source": [
        "print(validation_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "tDr2avfPY42V"
      },
      "outputs": [],
      "source": [
        "test_images, test_labels = test_dataset.next()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ogBpwRjTZP8A",
        "outputId": "0a9d41fe-ff2f-4bba-bd77-2120ce3fd612"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 1. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " ...\n",
            " [0. 0. 1. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [1. 0. 0. 0.]]\n"
          ]
        }
      ],
      "source": [
        "print(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvm6rvoufQwP",
        "outputId": "d16488d9-09a5-4ac1-c463-b4cb0feef921"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0_NonDemented': 0,\n",
              " '1_VeryMildDemented': 1,\n",
              " '2_MildDemented': 2,\n",
              " '3_ModerateDemented': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "train_dataset.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSGJohfmFT26",
        "outputId": "733d4421-ffdf-42b0-8f80-a0e808a72a38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0_NonDemented': 0,\n",
              " '1_VeryMildDemented': 1,\n",
              " '2_MildDemented': 2,\n",
              " '3_ModerateDemented': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "test_dataset.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "NHTalQu5ohbN"
      },
      "outputs": [],
      "source": [
        "class_indices = {v:k for k,v in train_dataset.class_indices.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "F1VH3Ai2Fd1g",
        "outputId": "59041ea6-a7fa-43a3-9f17-4a3bdb92a0ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  1_VeryMildDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  1_VeryMildDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  1_VeryMildDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  1_VeryMildDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class :  2_MildDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualising several images of the dataset\n",
        "imgs, targets = next(iter(train_dataset))\n",
        "\n",
        "for i, (img, target) in enumerate(zip(imgs, targets)):\n",
        "  print('Class : ', class_indices[np.argmax(target)])\n",
        "  plt.imshow(img)\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model building"
      ],
      "metadata": {
        "id": "m0eYPnwanrMC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_UCLNUgr56Z",
        "outputId": "ed01e815-e713-497a-b7b6-a3bf9608e6d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 176, 208, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 88, 104, 16)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 88, 104, 16)       0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 88, 104, 32)       4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 44, 52, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 44, 52, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 44, 52, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 22, 26, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 22, 26, 64)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 36608)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               4685952   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,718,052\n",
            "Trainable params: 4,718,052\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "\n",
        "model = tf.keras.Sequential([\n",
        "\n",
        "     Conv2D(filters = 16, kernel_size = (3,3), strides = 1, padding = \"same\",\n",
        "            activation = \"relu\", input_shape = (176, 208, 3)), # the input shape (height, width, channels)\n",
        "     MaxPool2D(pool_size=2, # the size of the pooling window\n",
        "               strides=2), # the movement of the pooling on the input\n",
        "     Dropout(0.1),\n",
        "     Conv2D(filters = 32, kernel_size = (3,3), strides = 1, padding = \"same\",\n",
        "            activation = \"relu\"),\n",
        "     MaxPool2D(2,2),\n",
        "     Dropout(0.1),\n",
        "     Conv2D(filters = 64, kernel_size = (3,3), strides = 1, padding = \"same\",\n",
        "            activation = \"relu\"),\n",
        "     MaxPool2D(2,2),\n",
        "     Dropout(0.1),\n",
        "     tf.keras.layers.Flatten(), # this layer turns multi-dimensional images into flat objects\n",
        "     tf.keras.layers.Dense(128, activation=\"relu\"),\n",
        "     Dropout(0.1),\n",
        "     tf.keras.layers.Dense(64, activation=\"relu\"),\n",
        "     tf.keras.layers.Dense(4, activation=\"softmax\")\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "Yu0wK6rOGSFH"
      },
      "outputs": [],
      "source": [
        "# Defining metrics and optimization\n",
        "METRICS = [\n",
        "    tf.keras.metrics.CategoricalAccuracy(name='acc'),\n",
        "    tf.keras.metrics.AUC(name='auc'),\n",
        "    tfa.metrics.F1Score(num_classes=4)\n",
        "    ]\n",
        "\n",
        "callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
        "                                            patience=3,\n",
        "                                            restore_best_weights=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "YqGhm5ruZZ6K"
      },
      "outputs": [],
      "source": [
        "initial_learning_rate = 0.0005\n",
        "\n",
        "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate,\n",
        "    decay_steps=1000,\n",
        "    decay_rate=0.96,\n",
        "    staircase=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9ucehyOZe2k",
        "outputId": "465d4836-26e8-4403-cda3-42ea8d8d39ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 176, 208, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 88, 104, 16)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 88, 104, 16)       0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 88, 104, 32)       4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 44, 52, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 44, 52, 32)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 44, 52, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 22, 26, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 22, 26, 64)        0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 36608)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               4685952   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 4)                 260       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,718,052\n",
            "Trainable params: 4,718,052\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Compiling the model\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
        "              loss = tf.keras.losses.CategoricalCrossentropy(),\n",
        "              metrics = METRICS)\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model training"
      ],
      "metadata": {
        "id": "MMDpyOJRoixP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mUAV4OpBZoHN",
        "outputId": "834e6884-2d13-40f6-a206-6934378d311b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.9/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
            "Instructions for updating:\n",
            "Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "512/512 [==============================] - 12s 13ms/step - loss: 0.9895 - acc: 0.5264 - auc: 0.8133 - f1_score: 0.2631 - val_loss: 0.8869 - val_acc: 0.5846 - val_auc: 0.8517 - val_f1_score: 0.3119\n",
            "Epoch 2/50\n",
            "512/512 [==============================] - 7s 13ms/step - loss: 0.8265 - acc: 0.6282 - auc: 0.8740 - f1_score: 0.3965 - val_loss: 0.7161 - val_acc: 0.6931 - val_auc: 0.9124 - val_f1_score: 0.5019\n",
            "Epoch 3/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.5849 - acc: 0.7601 - auc: 0.9390 - f1_score: 0.5801 - val_loss: 0.4272 - val_acc: 0.8348 - val_auc: 0.9700 - val_f1_score: 0.8429\n",
            "Epoch 4/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.3350 - acc: 0.8666 - auc: 0.9797 - f1_score: 0.8236 - val_loss: 0.2876 - val_acc: 0.9032 - val_auc: 0.9856 - val_f1_score: 0.8816\n",
            "Epoch 5/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.1979 - acc: 0.9282 - auc: 0.9924 - f1_score: 0.9167 - val_loss: 0.2110 - val_acc: 0.9238 - val_auc: 0.9905 - val_f1_score: 0.9312\n",
            "Epoch 6/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.1220 - acc: 0.9587 - auc: 0.9964 - f1_score: 0.9600 - val_loss: 0.1452 - val_acc: 0.9443 - val_auc: 0.9959 - val_f1_score: 0.9447\n",
            "Epoch 7/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0777 - acc: 0.9734 - auc: 0.9988 - f1_score: 0.9705 - val_loss: 0.1089 - val_acc: 0.9619 - val_auc: 0.9973 - val_f1_score: 0.9721\n",
            "Epoch 8/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0664 - acc: 0.9770 - auc: 0.9987 - f1_score: 0.9788 - val_loss: 0.0969 - val_acc: 0.9668 - val_auc: 0.9954 - val_f1_score: 0.9766\n",
            "Epoch 9/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0459 - acc: 0.9831 - auc: 0.9994 - f1_score: 0.9865 - val_loss: 0.0778 - val_acc: 0.9726 - val_auc: 0.9982 - val_f1_score: 0.9779\n",
            "Epoch 10/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0550 - acc: 0.9802 - auc: 0.9993 - f1_score: 0.9773 - val_loss: 0.0880 - val_acc: 0.9717 - val_auc: 0.9969 - val_f1_score: 0.9776\n",
            "Epoch 11/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0392 - acc: 0.9866 - auc: 0.9995 - f1_score: 0.9853 - val_loss: 0.0882 - val_acc: 0.9638 - val_auc: 0.9981 - val_f1_score: 0.9719\n",
            "Epoch 12/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0455 - acc: 0.9834 - auc: 0.9993 - f1_score: 0.9831 - val_loss: 0.1254 - val_acc: 0.9580 - val_auc: 0.9950 - val_f1_score: 0.9525\n",
            "Epoch 13/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0260 - acc: 0.9912 - auc: 0.9999 - f1_score: 0.9923 - val_loss: 0.0634 - val_acc: 0.9785 - val_auc: 0.9978 - val_f1_score: 0.9838\n",
            "Epoch 14/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0201 - acc: 0.9936 - auc: 0.9999 - f1_score: 0.9927 - val_loss: 0.0684 - val_acc: 0.9775 - val_auc: 0.9969 - val_f1_score: 0.9806\n",
            "Epoch 15/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0392 - acc: 0.9893 - auc: 0.9994 - f1_score: 0.9886 - val_loss: 0.0531 - val_acc: 0.9853 - val_auc: 0.9984 - val_f1_score: 0.9868\n",
            "Epoch 16/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0239 - acc: 0.9934 - auc: 0.9994 - f1_score: 0.9956 - val_loss: 0.0816 - val_acc: 0.9804 - val_auc: 0.9968 - val_f1_score: 0.9844\n",
            "Epoch 17/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0124 - acc: 0.9961 - auc: 1.0000 - f1_score: 0.9937 - val_loss: 0.1074 - val_acc: 0.9795 - val_auc: 0.9944 - val_f1_score: 0.9791\n",
            "Epoch 18/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0317 - acc: 0.9895 - auc: 0.9993 - f1_score: 0.9906 - val_loss: 0.1104 - val_acc: 0.9736 - val_auc: 0.9957 - val_f1_score: 0.9777\n",
            "Epoch 19/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0172 - acc: 0.9941 - auc: 0.9999 - f1_score: 0.9824 - val_loss: 0.0778 - val_acc: 0.9844 - val_auc: 0.9965 - val_f1_score: 0.9868\n",
            "Epoch 20/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0130 - acc: 0.9951 - auc: 0.9998 - f1_score: 0.9919 - val_loss: 0.0896 - val_acc: 0.9775 - val_auc: 0.9971 - val_f1_score: 0.9698\n",
            "Epoch 21/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0272 - acc: 0.9900 - auc: 0.9995 - f1_score: 0.9924 - val_loss: 0.0722 - val_acc: 0.9853 - val_auc: 0.9976 - val_f1_score: 0.9879\n",
            "Epoch 22/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0171 - acc: 0.9932 - auc: 0.9999 - f1_score: 0.9940 - val_loss: 0.1311 - val_acc: 0.9707 - val_auc: 0.9938 - val_f1_score: 0.9741\n",
            "Epoch 23/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0200 - acc: 0.9936 - auc: 0.9996 - f1_score: 0.9943 - val_loss: 0.0745 - val_acc: 0.9804 - val_auc: 0.9968 - val_f1_score: 0.9851\n",
            "Epoch 24/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0164 - acc: 0.9936 - auc: 0.9998 - f1_score: 0.9891 - val_loss: 0.0804 - val_acc: 0.9795 - val_auc: 0.9959 - val_f1_score: 0.9838\n",
            "Epoch 25/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0143 - acc: 0.9941 - auc: 1.0000 - f1_score: 0.9916 - val_loss: 0.0762 - val_acc: 0.9814 - val_auc: 0.9960 - val_f1_score: 0.9838\n",
            "Epoch 26/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0101 - acc: 0.9963 - auc: 0.9998 - f1_score: 0.9876 - val_loss: 0.0719 - val_acc: 0.9814 - val_auc: 0.9970 - val_f1_score: 0.9843\n",
            "Epoch 27/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0207 - acc: 0.9934 - auc: 0.9995 - f1_score: 0.9883 - val_loss: 0.0768 - val_acc: 0.9765 - val_auc: 0.9968 - val_f1_score: 0.9697\n",
            "Epoch 28/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0067 - acc: 0.9983 - auc: 1.0000 - f1_score: 0.9957 - val_loss: 0.0834 - val_acc: 0.9834 - val_auc: 0.9964 - val_f1_score: 0.9870\n",
            "Epoch 29/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0184 - acc: 0.9936 - auc: 0.9998 - f1_score: 0.9917 - val_loss: 0.1407 - val_acc: 0.9707 - val_auc: 0.9938 - val_f1_score: 0.9640\n",
            "Epoch 30/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0130 - acc: 0.9958 - auc: 0.9998 - f1_score: 0.9965 - val_loss: 0.0735 - val_acc: 0.9883 - val_auc: 0.9950 - val_f1_score: 0.9892\n",
            "Epoch 31/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0061 - acc: 0.9985 - auc: 1.0000 - f1_score: 0.9988 - val_loss: 0.0983 - val_acc: 0.9824 - val_auc: 0.9946 - val_f1_score: 0.9723\n",
            "Epoch 32/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0173 - acc: 0.9941 - auc: 0.9998 - f1_score: 0.9951 - val_loss: 0.0761 - val_acc: 0.9853 - val_auc: 0.9967 - val_f1_score: 0.9746\n",
            "Epoch 33/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0150 - acc: 0.9929 - auc: 1.0000 - f1_score: 0.9948 - val_loss: 0.0919 - val_acc: 0.9804 - val_auc: 0.9952 - val_f1_score: 0.9832\n",
            "Epoch 34/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0099 - acc: 0.9968 - auc: 1.0000 - f1_score: 0.9975 - val_loss: 0.0676 - val_acc: 0.9873 - val_auc: 0.9952 - val_f1_score: 0.9886\n",
            "Epoch 35/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0065 - acc: 0.9978 - auc: 1.0000 - f1_score: 0.9978 - val_loss: 0.0732 - val_acc: 0.9853 - val_auc: 0.9963 - val_f1_score: 0.9881\n",
            "Epoch 36/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0021 - acc: 0.9993 - auc: 1.0000 - f1_score: 0.9994 - val_loss: 0.0947 - val_acc: 0.9824 - val_auc: 0.9965 - val_f1_score: 0.9861\n",
            "Epoch 37/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0084 - acc: 0.9973 - auc: 0.9999 - f1_score: 0.9946 - val_loss: 0.0947 - val_acc: 0.9892 - val_auc: 0.9950 - val_f1_score: 0.9903\n",
            "Epoch 38/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0076 - acc: 0.9978 - auc: 0.9998 - f1_score: 0.9981 - val_loss: 0.0975 - val_acc: 0.9834 - val_auc: 0.9959 - val_f1_score: 0.9856\n",
            "Epoch 39/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0057 - acc: 0.9978 - auc: 1.0000 - f1_score: 0.9977 - val_loss: 0.0748 - val_acc: 0.9873 - val_auc: 0.9972 - val_f1_score: 0.9885\n",
            "Epoch 40/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0097 - acc: 0.9980 - auc: 0.9998 - f1_score: 0.9952 - val_loss: 0.1065 - val_acc: 0.9795 - val_auc: 0.9950 - val_f1_score: 0.9832\n",
            "Epoch 41/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0048 - acc: 0.9985 - auc: 1.0000 - f1_score: 0.9960 - val_loss: 0.0936 - val_acc: 0.9863 - val_auc: 0.9953 - val_f1_score: 0.9885\n",
            "Epoch 42/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0041 - acc: 0.9985 - auc: 1.0000 - f1_score: 0.9958 - val_loss: 0.1021 - val_acc: 0.9834 - val_auc: 0.9946 - val_f1_score: 0.9862\n",
            "Epoch 43/50\n",
            "512/512 [==============================] - 6s 11ms/step - loss: 0.0055 - acc: 0.9983 - auc: 0.9998 - f1_score: 0.9988 - val_loss: 0.0779 - val_acc: 0.9892 - val_auc: 0.9966 - val_f1_score: 0.9903\n",
            "Epoch 44/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0088 - acc: 0.9973 - auc: 0.9997 - f1_score: 0.9885 - val_loss: 0.0857 - val_acc: 0.9785 - val_auc: 0.9972 - val_f1_score: 0.9812\n",
            "Epoch 45/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0046 - acc: 0.9980 - auc: 1.0000 - f1_score: 0.9979 - val_loss: 0.0950 - val_acc: 0.9873 - val_auc: 0.9960 - val_f1_score: 0.9885\n",
            "Epoch 46/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0024 - acc: 0.9990 - auc: 1.0000 - f1_score: 0.9991 - val_loss: 0.1185 - val_acc: 0.9834 - val_auc: 0.9965 - val_f1_score: 0.9720\n",
            "Epoch 47/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0056 - acc: 0.9983 - auc: 0.9998 - f1_score: 0.9987 - val_loss: 0.1286 - val_acc: 0.9717 - val_auc: 0.9955 - val_f1_score: 0.9642\n",
            "Epoch 48/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0050 - acc: 0.9983 - auc: 1.0000 - f1_score: 0.9982 - val_loss: 0.1349 - val_acc: 0.9785 - val_auc: 0.9945 - val_f1_score: 0.9838\n",
            "Epoch 49/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0088 - acc: 0.9983 - auc: 0.9997 - f1_score: 0.9983 - val_loss: 0.0886 - val_acc: 0.9853 - val_auc: 0.9959 - val_f1_score: 0.9879\n",
            "Epoch 50/50\n",
            "512/512 [==============================] - 6s 12ms/step - loss: 0.0046 - acc: 0.9985 - auc: 1.0000 - f1_score: 0.9990 - val_loss: 0.1262 - val_acc: 0.9844 - val_auc: 0.9946 - val_f1_score: 0.9873\n"
          ]
        }
      ],
      "source": [
        "\n",
        "EPOCHS = 50\n",
        "\n",
        "history = model.fit(train_dataset, validation_data=validation_dataset,shuffle=True, epochs=EPOCHS)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Performance evaluation"
      ],
      "metadata": {
        "id": "pX_50ZSHoLrF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "hNNo3-s65kxK",
        "outputId": "8a1fcd9f-4f9b-4007-c784-3988dc1e1f09"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize = (30, 5))\n",
        "ax = ax.ravel()\n",
        "\n",
        "for i, metric in enumerate([\"acc\", \"auc\", \"loss\"]):\n",
        "    ax[i].plot(history.history[metric])\n",
        "    ax[i].plot(history.history[\"val_\" + metric])\n",
        "    ax[i].set_title(\"Model {}\".format(metric))\n",
        "    ax[i].set_xlabel(\"Epochs\")\n",
        "    ax[i].set_ylabel(metric)\n",
        "    ax[i].legend([\"train\", \"val\"])\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "rIxHFpcO6prY",
        "outputId": "9833a2fb-256c-421b-8605-bcfb309c4dfa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x360 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize = (30, 5))\n",
        "ax = ax.ravel()\n",
        "\n",
        "for i, metric in enumerate([\"f1_score\"]):\n",
        "    ax[i].plot(history.history[metric])\n",
        "    ax[i].plot(history.history[\"val_\" + metric])\n",
        "    ax[i].set_title(\"Model {}\".format(metric))\n",
        "    ax[i].set_xlabel(\"Epochs\")\n",
        "    ax[i].set_ylabel(metric)\n",
        "    ax[i].legend([\"train\", \"val\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ljLu5QXzFgnL",
        "outputId": "2b4e4d52-4764-4ba9-d15a-7b15d37c2a90"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values([0, 1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "train_dataset.class_indices.values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUokONjKFukW",
        "outputId": "eb5c86b2-ef53-4e69-c97d-bb503f9d74ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "type(train_dataset.class_indices.values())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPj-o13esVxb",
        "outputId": "37bf1107-ea58-4e74-84bf-f2d34c71df1d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0_NonDemented': 0,\n",
              " '1_VeryMildDemented': 1,\n",
              " '2_MildDemented': 2,\n",
              " '3_ModerateDemented': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# test dataset class_indices\n",
        "test_dataset.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crgzypxAsGMR",
        "outputId": "07b9828e-52b5-43d8-e802-55e06d9cfadc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "128/128 [==============================] - 1s 9ms/step - loss: 0.1262 - acc: 0.9844 - auc: 0.9946 - f1_score: 0.9873\n"
          ]
        }
      ],
      "source": [
        "# Evaluation on validation dataset\n",
        "_ = model.evaluate(validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXhqkPoRim-X",
        "outputId": "6a1f5a34-4d9c-4d05-b4b5-6b89087cd1ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step - loss: 0.1469 - acc: 0.9766 - auc: 0.9930 - f1_score: 0.9798\n"
          ]
        }
      ],
      "source": [
        "# Evaluation on test dataset\n",
        "_ = model.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "Ljj83kI_sVxa"
      },
      "outputs": [],
      "source": [
        "test_images, test_labels = test_dataset.next()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BK0i0oZb689",
        "outputId": "589e5342-da97-41da-ae96-bf3ff39f6331"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['0_NonDemented', '1_VeryMildDemented', '2_MildDemented', '3_ModerateDemented'])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "test_dataset.class_indices.keys()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XBoa-wlYsVxb",
        "outputId": "9ca559cc-a4c2-468c-8a05-d0e9f7237355"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., 0.],\n",
              "       [1., 0., 0., 0.],\n",
              "       [1., 0., 0., 0.],\n",
              "       ...,\n",
              "       [1., 0., 0., 0.],\n",
              "       [0., 1., 0., 0.],\n",
              "       [1., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "test_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BoxXEv1rmAWS",
        "outputId": "ca849f58-6768-4e4f-e1ea-4b64cb74c20b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1283"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "len(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srOAxMd0Cv7U",
        "outputId": "0275c1ac-96f7-4d66-ba22-ee26653a0fb8",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 2s 2s/step\n"
          ]
        }
      ],
      "source": [
        "#Predicting the test data\n",
        "predicted_labels = model.predict(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Czpy_ypwsVxb",
        "outputId": "bf27ea9c-2b50-420a-c02a-8025236029ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0000000e+00, 3.1603481e-08, 1.7769372e-13, 3.3775760e-17],\n",
              "       [9.9981648e-01, 1.8352225e-04, 7.3662054e-10, 3.0419578e-10],\n",
              "       [1.0000000e+00, 3.5720278e-09, 3.9915687e-18, 4.8986334e-20],\n",
              "       ...,\n",
              "       [9.9999809e-01, 1.8647337e-06, 3.1731015e-10, 7.1331897e-14],\n",
              "       [2.1524608e-10, 1.0000000e+00, 1.0909061e-09, 4.1864387e-15],\n",
              "       [1.0000000e+00, 1.0857192e-12, 3.2425144e-16, 9.2630116e-21]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "predicted_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "INCw23QesVxb"
      },
      "outputs": [],
      "source": [
        "predicted_labels = np.argmax(predicted_labels, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2znrh12zsVxb",
        "outputId": "a82cffa8-96e5-441c-fbfb-efb6a3e12987"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "predicted_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "2Lqh7BAksVxc"
      },
      "outputs": [],
      "source": [
        "test_labels = np.argmax(test_labels, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xyjuNGGrsVxc",
        "outputId": "c9d693d3-43cc-467c-d48b-451a2fdf9dc5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "test_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "Iyt16Xj4GOeq"
      },
      "outputs": [],
      "source": [
        "target_names = [k + ' : ' + str(v) for k,v in test_dataset.class_indices.items()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DWWcE3zJGYCV",
        "outputId": "8a7f307a-1fd4-49c4-e90a-3aff89299567"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0_NonDemented : 0',\n",
              " '1_VeryMildDemented : 1',\n",
              " '2_MildDemented : 2',\n",
              " '3_ModerateDemented : 3']"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "target_names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKWznB1DsVxc",
        "outputId": "b09845ec-73b9-45d0-a1d0-df407e5ac739"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        precision    recall  f1-score   support\n",
            "\n",
            "     0_NonDemented : 0       0.97      1.00      0.98       640\n",
            "1_VeryMildDemented : 1       0.98      0.96      0.97       449\n",
            "    2_MildDemented : 2       0.99      0.94      0.97       180\n",
            "3_ModerateDemented : 3       1.00      1.00      1.00        14\n",
            "\n",
            "              accuracy                           0.98      1283\n",
            "             macro avg       0.98      0.98      0.98      1283\n",
            "          weighted avg       0.98      0.98      0.98      1283\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(test_labels, predicted_labels, target_names = target_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "id": "JcIOjm6PsVxc",
        "outputId": "9d402853-de6b-4bd2-b782-ec44d76f3f2e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3]),\n",
              " [Text(0, 0, '0_NonDemented : 0'),\n",
              "  Text(1, 0, '1_VeryMildDemented : 1'),\n",
              "  Text(2, 0, '2_MildDemented : 2'),\n",
              "  Text(3, 0, '3_ModerateDemented : 3')])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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ToObNm3PhwoXHlk2aNIl69epZKCIRERGRl6dENAX67TdNUi0iIiJirWw6ERURERGxBbZaEVWvdhERERGxCFVERURERKyYNV0JKampIioiIiIiFqGKqIiIiIi1s82CqBJREREREWunpnkRERERkSSkiqiIiIiIlVNFVEREREQkCakiKiIiImLlbLUiqkRURERExIoZDLabiKppXkREREQsQhVREREREWtnmwVRVURFRERExDJUERURERGxarZ7rXkloiIiIiJWzlYTUTXNi4iIiIhFqCIqIiIiYs00fZOIiIiISNJSRVRERETE2tlmQVQVURERERGxDFVERURERKycrfYRVSIqIiIiYsUM2G4iqqZ5EREREbEIVURFRERErJrtXllJFVERERERsQgloiIiIiLW7P8ntE/q24soVKgQZcqUwd3dHU9PTwBu3ryJt7c3xYoVw9vbm1u3bgFgMpno378/Li4uuLm5ERgYmOj+lYiKiIiIWDtDMtxe0M6dOwkKCiIgIAAAPz8/6tSpQ0hICHXq1MHPzw+AjRs3EhISQkhICHPmzKFXr16J7luJqIiIiIi8sNWrV+Pj4wOAj48Pq1atMi/v3LkzBoOBSpUqERkZSVhY2HP3pURURERExMolR9N8REQEnp6e5tucOXOeety6detSvnx58+Ph4eHkzZsXgDx58hAeHg5AaGgo+fPnN2/r7OxMaGjoc5+XRs2LiIiIvIFy5sxpbm5/lj/++AMnJyeuXbuGt7c3JUuWfOzxl+lv+jSqiIqIiIhYOUsNVnJycgIgV65cNG/eHH9/f3Lnzm1ucg8LCyNXrlzmdS9fvmze9sqVK+btn0WJqIiIiIgVM5D0SeiLJKJ3797lzp075r+3bNnCW2+9RdOmTZk/fz4A8+fPp1mzZgA0bdqUBQsWYDKZOHDgAJkzZzY34T+LmuZFRERE5Anh4eE0b94cgLi4ONq3b0/9+vXx8vKidevWzJ07l4IFC7J06VIAGjZsyIYNG3BxcSF9+vT89NNPiR5DiaiIiIiINXvJ6ZaSSpEiRTh69OgTy7Nnz8727dufWG4wGJg5c+ZLHUNN8yIiIiJiEaqIioiIiFg5XWteRERERCQJqSIqIiIiYuVstSKqRFRERETEytlqIqqmeRERERGxCFVERURERKycKqIiIiIiIklIFVF5bTxK5uem/3RLhyEvodqkXZYOQV7S7iE1LR2CvAJ7O9usdkkSsdCE9q+DElERERERK5ZwrXlbpKZ5EREREbEIVURFRERErJwqoiIiIiIiSUgVURERERErZ6MFUVVERURERMQyVBEVERERsXK22kdUiaiIiIiINTOoaV5EREREJEmpIioiIiJixQzYbtO8KqIiIiIiYhGqiIqIiIhYORstiCoRFREREbF2dna2mYmqaV5ERERELEIVURERERFrpumbRERERESSliqiIiIiIlbMgMFmp29SIioiIiJi5Ww0D1XTvIiIiIhYhiqiIiIiIlbOVpvmVREVEREREYtQRVRERETEyqkiKiIiIiKShFQRFREREbFmNjyhvRJREREREStmQE3zIiIiIiJJShVREREREStnowVRVURFRERExDJUERURERGxcrbaR1SJqIiIiIiVs9E8VE3zIiIiImIZqoiKiIiIWDODwWab5lURFRERERGLUEVURERExIo9mtDe0lEkDyWiIiIiIlZOTfMiIiIiIklIFVERERERK2ejBVFVREVERETEMlQRFREREbFy6iMqIiIiIpKEVBEVERERsWYG2+0jqkRURERExIo9mkfUNjNRNc2LiIiIiEWoIioiIiJi5Wy0IKqKqIiIiIhYhiqiIiIiIlbOVvuIKhEVERERsWoGm01E1TQvIiIiIhahiqiIiIiINbPheURVERURERERi1AiKiIiImLFEia0T+rbizAajXh4eNC4cWMALly4QMWKFXFxcaFNmzY8fPgQgAcPHtCmTRtcXFyoWLEiFy9efKH9q2le5BXdfxBL4x5f8+BhHHHGeJrWcWd490aWDkv+n50BfvD1JOLOA4YuP86wBiUomdcRgMs37/H5+mBiYo208cpP47J5McabiLwXy8QNpwm//cDC0cs/fbdoB7+s3o/BYKBU0bxMH9WRtGlSWToseY5t+04x/MvlGOPj6dTsbQb61rV0SPKKvvnmG0qVKsXt27cBGDp0KAMHDqRt27b07NmTuXPn0qtXL+bOnUvWrFk5e/YsixcvZujQoSxZsiTR/asiKvKK0qR2YNW3/fn91+HsWTiM7ftPc+j4BUuHJf+vlWd+/rp+z3x/2vaz+P54CN8fDxF++wEtyzsB8Gf4HbrNC8D3x0PsOnON3rWKWipkeYqwa5F8v2Q32+YN5o9FI4iPN/Hb1sOWDkuew2iMZ/AXS1n2TW8OLB3Jii2HCT4fZumwUjyDIelvibly5Qrr16+nW7duAJhMJnbs2MF7770HgI+PD6tWrQJg9erV+Pj4APDee++xfft2TCZTosd4bYloly5dyJUrF2+99dZz15s/fz7t2rV7bNn169fJmTMnDx78tyrFvHnzMBgMbNu2zbxs1apVGAwGli9fDkC3bt04deoUAIUKFeL69etP7GfMmDFMmTIFAF9fXwoXLkzZsmUpXrw4nTt35sqVK/8pzv9i3rx5XL169aW2uXjxYqLn5d9mzJiBi4sLBoPhqa/Rm8BgMJAxfRoAYuOMxMUZbXZ6jZQmZ6Y0VC6anbXH/vdeuPfQaP47jYMdCZ+PRy5F8iAuHoCTV2+TM1Pa1xqrJC7OGM/9B7HExRm5d/8heXJktnRI8hyHT16kSP4cFHLOQepUDrTwLseG3ccsHVaKZ4mm+QEDBvDFF19gZ/coXbxx4wZZsmTBweFRg7qzszOhoaEAhIaGkj9/fgAcHBzInDkzN27cSPQYry0R9fX1ZdOmTYmu17x5c7Zu3cq9e/+rZCxfvpwmTZqQJk2aRLePi4t77uNlypRh8eLF5vuLFi2ibNmy5vs//PADrq6uiR7nnyZPnszRo0c5c+YMHh4e1K5d29xn4nV7lUT0VVSpUoVt27ZRsGDBZD+WNTMa46newY8S9YZTs0JJPN8qZOmQBOhfx4Xvdp7l3z/GhzcsyZp+VSiYPT3LDz/5g7GxW14Onk/8g1Nen7y5stCnQx3cm31K6UYjccyYjlqVSlk6LHmOsIgonHJnNd/PlzsrYRFRFoxIniUiIgJPT0/zbc6cOebH1q1bR65cuShfvnyyxvDaEtHq1auTLVu2RNdzdHSkRo0arF271rxs8eLFtGvXjoiICFq2bImXlxdeXl7s3bsXeFSh7NSpE1WqVKFTp05Ur16doKAg8/ZVq1bl6NGjAFSrVg1/f39iY2OJjo7m7NmzuLu7m9etWbMmAQEBT8Q1YcIEihcvTtWqVTlz5sxTYzcYDAwcOJA8efKwceNGALZs2ULlypUpV64crVq1Ijo6GnhUbR0+fDju7u54enoSGBhIvXr1KFq0KLNmzTLvc/LkyXh5eeHm5sbo0aOBRxXMUqVK8cEHH1C6dGnq1q1LTEwMy5cvJyAggA4dOuDu7k5MTAyHDx+mRo0alC9fnnr16hEW9qh55PDhw5QtW5ayZcsyc+bMRM/Lv3l4eFCoUKFE15szZ475P7gtVk7t7e3Ys3AYJ9Z9RuCpvzh1Lvl/BMjzvV00O5H3YjkTHv3EYxM3BPPujL38deMedUrleuyxuqVzUzJPJn49eOl1hSovIPL2PTbuOcbh38ZwYv147sU8YOnGQ5YOS+S1S46m+Zw5cxIQEGC+de/e3Xy8vXv3smbNGgoVKkTbtm3ZsWMHH374IZGRkeai35UrV3ByetTNycnJicuXLwOPioJRUVFkz5490edllX1E27VrZ65aXr16lT///JPatWvz4YcfMnDgQA4dOsSKFSvMfRYATp06xbZt21i0aBFdu3Zl3rx5APz555/cv3/fXPU0GAy88847bN68mdWrV9O0adNE4zl8+DCLFy8mKCiIDRs2cOjQ8z8Ey5UrR3BwMNevX2f8+PFs27aNwMBAPD09mTp1qnm9AgUKEBQURLVq1fD19WX58uUcOHDAnHBu2bKFkJAQ/P39CQoK4vDhw+zZsweAkJAQ+vTpw8mTJ8mSJQsrVqzgvffew9PTk4ULFxIUFISDgwP9+vVj+fLlHD58mC5duvDJJ58A8P777zN9+nRzgv4s/0zSX0X37t3N/8Fz5Mjxn/ZlzTJnSk/V8sXYvv+0pUN545VxzkwVl+ws61WJMU1dKV8wK6Ma/6+CFm+CbafDqVEip3mZZ8GsdK5ckKErjhNrTLxPk7w+uw+doWC+7OTImolUDvY0rlWWQ8fPWzoseY68OTMTGn7LfP9q+C3y5lR3ipRm4sSJXLlyhYsXL7J48WJq167NwoULqVWrlrk74/z582nWrBkATZs2Zf78+cCjluzatWu/UPO/VY6ab9SoEb179+b27dssXbqUli1bYm9vz7Zt28z9NwFu375trjA2bdqUdOnSAdCqVSs+++wzJk+ezI8//oivr+9j+2/bti3Tpk0jKiqKL7/8ks8///y58fz+++80b96c9OnTm4/1PAmdcw8cOMCpU6eoUqUKAA8fPqRy5crm9RL2U6ZMGaKjo8mUKROZMmUiTZo0REZGsmXLFrZs2YKHhwcA0dHRhISEUKBAAQoXLmxOEsuXL//UaRLOnDnDiRMn8Pb2Bh5NwZA3b14iIyOJjIykevXqAHTq1Mlcwf23f1aW5XHXb90hlYM9mTOlJ+b+Q3YdDObDzt6WDuuNN3v3eWbvfpSoeBTIQtsK+fls3WmcsqQjNDIGgKouObh041H3n2K5MzK4fgk+WnqUyHuxFotbns45d1YCTlzk3v2HpEuTij2H/sS9VAFLhyXPUc61IOcuRfBX6HXy5srCyq2BfP+Zr6XDStEMBrCzkjEIkyZNom3btowcORIPDw+6du0KQNeuXenUqRMuLi5ky5btsW6Qz2OViWi6dOmoX78+v/32G4sXLzZXEePj4zlw4ABp0z45mCBDhgzmv9OnT4+3tzerV69m6dKlHD78+AjLChUqcPz4cdKnT0/x4sWTPP4jR45Qp04dTCYT3t7eLFq06KnrJfR5tbOze6z/q52dHXFxcZhMJoYPH06PHj0e2+7ixYuPrW9vb09MTMwT+zeZTJQuXZr9+/c/tjwyMvJVn5r8Q/j12/Qe+wvG+Hji4028+44H9aq93KAveT0MwCeNS5EhtT0GA5y9Fs2UzX8C0KdWUdKltuezd0sDEH77AcNWHLdgtPJP5d8qRJPa7tTuPAkHe3vKFHem87tvWzoseQ4HB3u+GNKalv1nYjSa6NC0EqWK5rV0WCmeJfPQmjVrUrNmTQCKFCmCv7//E+ukTZuWZcuWvfS+rTIRhUfN88OGDeP27dvmKmLdunWZPn06gwcPBh5V657VdNytWzeaNGlCtWrVyJo16xOP+/n5PTWhfZrq1avj6+vL8OHDiYuLY+3atU8kh/Ao8Zs+fTphYWHUr1+fqKgo+vTpw9mzZ3FxceHu3buEhoa+cPJbr149Ro0aRYcOHciYMSOhoaGkSvX8ufMyZcrEnTt3AChRogQRERHs37+fypUrExsby59//knp0qXJkiULf/zxB1WrVmXhwoUvFI88rnQxJ3b/MtTSYchzHLkUyZFLkQD0/iXwqesMWPz87iliecO6N2KY5uhNUepWKU3dKqUtHYakAK+tj2i7du2oXLkyZ86cwdnZmblz5z53fW9vb65evUqbNm3MfQymTZtGQEAAbm5uuLq6Pjao59/Kly+Po6Mj77///lMfb9CgAbVq1Xqh2MuVK0ebNm0oW7YsDRo0wMvL67HHBw8ebJ6+6dChQ+zcuZPUqVOTM2dO5s2bR7t27XBzc6Ny5coEBwe/0DHhUeLdvn17KleuTJkyZXjvvffMSeaz+Pr60rNnT9zd3TEajSxfvpyhQ4dStmxZ3N3d2bdvHwA//fQTffr0wd3d/bnzfD0r0Z82bRrOzs5cuXIFNze3x/rrioiISNKy1JWVkpvB9CKzjaZAV69epWbNmgQHB5vnvxLLio838cCY+HpiPapN2mXpEOQl7R5S09IhyCuwt7OOpEBeTGr7R1dve13+vBZNv+Unk3y/m3tXTPJ9viybzNAWLFhAxYoVmTBhgpJQERERSfHsDEl/swYW7SPap08f81ygCT788MNnNqe/qM6dO9O5c+f/tA8RERERa2DAeprSk5pFE9FXmUhdRERERGyD1Y6aFxERERHAYNnpm5KTOlCKiIiIiEWoIioiIiJi5QzYZklUFVERERERsQhVREVERESsnLVMt5TUlIiKiIiIWDED2Oz0TWqaFxERERGLUEVURERExMrZaEFUFVERERERsQxVREVERESsnJ2NlkSViIqIiIhYM11ZSUREREQkaakiKiIiImLFNH2TiIiIiEgSU0VURERExMrZaEFUiaiIiIiIdTPY7Kh5Nc2LiIiIiEWoIioiIiJi5WyzHqqKqIiIiIhYiCqiIiIiIlZM0zeJiIiIiCQxVURFRERErJkB7GyzIPrsRLRfv37PLQNPmzYtWQISERERkcfZatP8MxNRT0/P1xmHiIiIiLxhnpmI+vj4PHb/3r17pE+fPtkDEhEREZHH2WhBNPHBSvv378fV1ZWSJUsCcPToUXr37p3sgYmIiIiIbUs0ER0wYACbN28me/bsAJQtW5Y9e/Yke2AiIiIi8r/pm5L6Zg1eaNR8/vz5H7tvb2+fLMGIiIiIyJPeuFHzCfLnz8++ffswGAzExsbyzTffUKpUqdcRm4iIiIjYsESb5mfNmsXMmTMJDQ0lX758BAUFMXPmzNcRm4iIiIgY3uCm+Rw5crBw4cLXEYuIiIiIvEESrYieP3+eJk2akDNnTnLlykWzZs04f/7864hNRERE5I1nSKabNUg0EW3fvj2tW7cmLCyMq1ev0qpVK9q1a/c6YhMRERERG5ZoInrv3j06deqEg4MDDg4OdOzYkfv377+O2EREREQEA3aGpL9Zg2f2Eb158yYADRo0wM/Pj7Zt22IwGFiyZAkNGzZ8bQGKiIiIvOmsJG9Mcs9MRMuXL4/BYMBkMgEwe/Zs82MGg4GJEycmf3QiIiIiYrOemYheuHDhdcYhIiIiIs9gLdMtJbUXurLSiRMnOHXq1GN9Qzt37pxsQYmIiIiI7Us0ER07diy7du3i1KlTNGzYkI0bN1K1alUloiIiIiKvwaNrzVs6iuSR6Kj55cuXs337dvLkycNPP/3E0aNHiYqKeh2xiYiIiIgBmx01n2gimi5dOuzs7HBwcOD27dvkypWLy5cvv47YRERERMSGJdo07+npSWRkJB988AHly5cnY8aMVK5c+XXEJiIiIiLYbtN8oonot99+C0DPnj2pX78+t2/fxs3NLdkDExERERHb9sxENDAw8JkbBQYGUq5cuWQJSGyYwWCzv+hs1R/Dalk6BHlJB8/dtHQI8goqFs1m6RDEyr1x0zd99NFHz9zIYDCwY8eOZAlIRERERP7HwAsM6kmhnpmI7ty583XGISIiIiJvmBea0F5ERERELMdWm+ZttdIrIiIiIlZOFVERERERa2YAO9ssiCZeETWZTPzyyy+MGzcOgEuXLuHv75/sgYmIiIiIbUs0Ee3duzf79+9n0aJFAGTKlIk+ffoke2AiIiIi8v+j5g1Jf7MGiTbNHzx4kMDAQDw8PADImjUrDx8+TPbARERERATA8OYOVkqVKhVGo9H8AkRERGBnpzFOIiIiIvLfJJpR9u/fn+bNm3Pt2jU++eQTqlatyogRI15HbCIiIiLCG9w036FDB8qXL8/27dsxmUysWrWKUqVKvY7YRERERMRC7t+/T/Xq1Xnw4AFxcXG89957jB07lgsXLtC2bVtu3LhB+fLl+fnnn0mdOjUPHjygc+fOHD58mOzZs7NkyRIKFSr03GMkWhG9dOkS6dOnp0mTJjRt2pQMGTJw6dKlpHqOIiIiIvIcBsBgSPpbYtKkScOOHTs4evQoQUFBbNq0iQMHDjB06FAGDhzI2bNnyZo1K3PnzgVg7ty5ZM2albNnzzJw4ECGDh2a6DESTUQbNWpE48aNadSoEXXq1KFIkSI0aNAg8ehFRERE5L8zgJ3BkOS3RA9rMJAxY0YAYmNjiY2NxWAwsGPHDt577z0AfHx8WLVqFQCrV6/Gx8cHgPfee8/cmv48iSaix48f59ixYxw/fpyQkBD8/f2pXLlyosGLiIiIiPWKiIjA09PTfJszZ84T6xiNRtzd3cmVKxfe3t4ULVqULFmy4ODwqHens7MzoaGhAISGhpI/f34AHBwcyJw5Mzdu3HhuDC99ZaVy5cpx8ODBl91MRERERF5RcsxXlDNnTgICAp67jr29PUFBQURGRtK8eXOCg4OTNIZEE9GpU6ea/46PjycwMJB8+fIlaRAiIiIiYr2yZMlCrVq12L9/P5GRkcTFxeHg4MCVK1dwcnICwMnJicuXL+Ps7ExcXBxRUVFkz579uftNNMG+c+eO+fbgwQMaNWrE6tWrk+ZZiYiIiMhzWWqwUkREBJGRkQDExMSwdetWSpUqRa1atVi+fDkA8+fPp1mzZgA0bdqU+fPnA7B8+XJq166d6ET8z62IGo1G7ty5w5QpUxKPVkRERESSxYsMLkpqYWFh+Pj4YDQaiY+Pp3Xr1jRu3BhXV1fatm3LyJEj8fDwoGvXrgB07dqVTp064eLiQrZs2Vi8eHGix3hmIppQct27d2/SPSMRERERSRHc3Nw4cuTIE8uLFCmCv7//E8vTpk3LsmXLXuoYz0xEK1SoQGBgIO7u7jRt2pRWrVqRIUMG8+MtWrR4qQOJiIiIyKux0UvNJz5Y6f79+2TPnp0dO3ZgMBgwmUwYDAYloiIiIiLynzwzEb127RpTp07lrbfeMiegCRLreCoiIiIiScOA9VwbPqk9MxE1Go1ER0c/dUZ8JaIiIiIi8l89MxHNmzcvn3766euMRURERET+zWCZUfOvwzMT0cSuDSoiIiIir4eN5qHPntB++/btrzMOEREREXnDPLMimi1bttcZh4iIiIg8hS0PVkr0Ep8iIiIiIskh0XlERURERMSyDNhmSVSJqIiIiIiVU9O8iIiIiEgSUkVURERExIppsJKIiIiISBJTRVRERETEmhkMNnt5dSWiIiIiIlZOTfMiIiIiIklIFVERERERK2ejLfOqiIqIiIiIZagiKiIiImLFHk3fZJslUVVERURERMQiVBEVERERsXK2OmpeiaiIiIiINTNosJKIiIiISJJSRVRERETEihkAO2yzJKqKqIiIiIhYhCqiIiIiIlbOVvuIKhEVERERsXK2OmpeTfMiIiIiYhGqiIqIiIhYMV1ZSUREREQkiakiKiIiImLlbLQgqkRU5L/Ytu8Uw79cjjE+nk7N3magb11LhyTPceXvW/Qas4CIm3cwAD7Nq9CzXS1LhyXA1Fm/cTDwT7I4ZmD2lL4AfP71Uq6EXQcg+u59MmZIy7eTegOweNUeNu8MxM7OQC/fhniWLWax2OVJ+myUF6VEVOQVGY3xDP5iKb/N6Eu+3Fmo7TOZBtXLULJIXkuHJs/g4GDH+AEtKFsyP3fu3qdW50nUrFhS58wKeNfwoEm9ikyZudK8bMSA1ua/5/y8iQzp0wDw15Vr7N53nNlT+nLz1h2Gj5/HD19/iL2deptZA302JgOD+oharcuXL1OrVi1cXV0pXbo033zzzXPX9/X1JX369Ny5c8e8bMCAARgMBq5ff/TL++233wbg4sWLvPXWW0/dT82aNQkICACgUKFClClThjJlyuDq6srIkSO5f/9+Ujy9V/L555+/9Dbz5s2jb9++L7z+y77utujwyYsUyZ+DQs45SJ3KgRbe5diw+5ilw5LnyJMjM2VL5gcgU4a0FC+Uh7CISMsGJQCUKVWITBnSPfUxk8nEnv0nqPm2GwD7A4Kp8XYZUqdyIE+urOTNk40zZ6+8znDlOfTZmPQMPGqaT+qbNUjxiaiDgwNffvklp06d4sCBA8ycOZNTp049dxsXFxdWr14NQHx8PDt27MDJycn8+L59+146jp07d3L8+HH8/f05f/48PXr0eOl9JJVXSURf1qu87rYmLCIKp9xZzffz5c5KWESUBSOSl3Hp6g2OnblC+dKFLB2KJOJE8F9kzZIRp7zZAbhx8zY5s2c2P54jW2Zu3LzzrM3lNdNno7yMFJ+I5s2bl3LlygGQKVMmSpUqRWho6HO3adu2LUuWLAFg165dVKlSBQeH//VSyJgx4xPbxMTE0LZtW0qVKkXz5s2JiYl56r4zZszIrFmzWLVqFTdv3gRg8uTJeHl54ebmxujRo4FH1daSJUvi6+tL8eLF6dChA9u2baNKlSoUK1YMf39/AO7evUuXLl2oUKECHh4e5gR63rx5tGjRgvr161OsWDGGDBkCwLBhw4iJicHd3Z0OHToA8Msvv1ChQgXc3d3p0aMHRqMRgJ9++onixYtToUIF9u7d+wKv9v+86Os+Z84cPD098fT05HpExEsdQyS5RN97QOehPzBxUEscMz69CifWY9fe49R8u4ylwxCxKLtkuFkDa4kjSVy8eJEjR45QsWLF565XvHhxIiIiuHXrFosWLaJt27aJ7vu7774jffr0nD59mrFjx3L48OFnruvo6EjhwoUJCQlhy5YthISE4O/vT1BQEIcPH2bPnj0AnD17lo8++ojg4GCCg4P59ddf+eOPP5gyZYq5qjlhwgRq166Nv78/O3fuZPDgwdy9exeAoKAglixZwvHjx1myZAmXL1/Gz8+PdOnSERQUxMKFCzl9+jRLlixh7969BAUFYW9vz8KFCwkLC2P06NHs3buXP/7445nVzICAALp16/bc1+Z5r3v37t0JCAggICCAHDlzJvo6pyR5c2YmNPyW+f7V8FvkzZn5OVuINYiNM+Iz9Hta1fekSW13S4cjiTAajew9dIrqlf/XTSp7Nkcibvyvwnb9ZhTZs2WyRHjyFPpslJdhM4lodHQ0LVu25Ouvv8bR0THR9Vu0aMHixYs5ePAg1apVS3T9PXv20LFjRwDc3Nxwc3N77vomkwmALVu2sGXLFjw8PChXrhzBwcGEhIQAULhwYcqUKYOdnR2lS5emTp06GAwGypQpw8WLF83b+/n54e7uTs2aNbl//z6XLl0CoE6dOmTOnJm0adPi6urKX3/99UQc27dv5/Dhw3h5eeHu7s727ds5f/48Bw8epGbNmuTMmZPUqVPTpk2bpz4PT09Pfvjhh2c+z5d93W1JOdeCnLsUwV+h13kYG8fKrYE0qP78/xdiWSaTiX6fLaR4oTz06VDH0uHICzhy/Dz58+V4rCm+UvmS7N53nIexcfx97RZX/75JCRdnC0Yp/6TPxuRgwGBI+ps1sIlR87GxsbRs2ZIOHTrQokWLF9qmTZs2lC9fHh8fH+ySeKTlnTt3uHjxIsWLF8dkMjF8+PAn+oxevHiRNGnSmO/b2dmZ79vZ2REXFwc8+uJcsWIFJUqUeGz7gwcPPra9vb29eZt/MplM+Pj4MHHixMeWr1q16j89R3i1192WODjY88WQ1rTsPxOj0USHppUoVVSjQq3ZgaPnWbLBH1eXfFRr/+g9MapPU+pWKW3hyGTitGUcO3WB23fu0bH3FDq+V4v6tcuza99x8yClBIXy56J65bfo8dF07Ozt6PN+I42YtyL6bEwe1pE2Jr0Un4iaTCa6du1KqVKlGDRo0AtvV7BgQSZMmMA777zzQutXr16dX3/9ldq1a3PixAmOHXv6CMDo6Gh69+7Nu+++S9asWalXrx6jRo2iQ4cOZMyYkdDQUFKlSvXCcdarV4/p06czffp0DAYDR44cwcPD47nbpEqVitjYWFKlSkWdOnVo1qwZAwcOJFeuXNy8eZM7d+5QsWJFPvzwQ27cuIGjoyPLli2jbNmyLxzXq77utqZuldJKYlKQyu5FuXVohqXDkKcY3r/VU5d/3PvpP3LbNa9Bu+Y1kjMk+Q/02SgvKsX/hNy7dy8///wzO3bswN3dHXd3dzZs2PBC2/bo0YOiRYu+0Lq9evUiOjqaUqVK8emnn1K+fPnHHq9VqxZvvfUWFSpUoECBAsyePRuAunXr0r59eypXrkyZMmV47733Hps6KjGjRo0iNjYWNzc3SpcuzahRoxLdpnv37ri5udGhQwdcXV0ZP348devWxc3NDW9vb8LCwsibNy9jxoyhcuXKVKlShVKlSj11X8/qI/pfXncRERF5cQnXmk/qmzUwmBI6M4oks3gTPDRaOgoR23bw3E1LhyCvoGLRbJYOQV5Canuwe4153I27D9kYfC3J99uxvOX7Vqf4pnkRERERW2cd9cukZ7OJaJ8+fZ6YG/PDDz/k/ffft1BEIiIiIq/GSlrSk5zNJqIzZ860dAgiIiIi8hw2m4iKiIiI2AQDVjPvZ1JL8aPmRURERCRlUkVURERExIoZsN3Koa0+LxERERGxcqqIioiIiFg5W+0jqkRURERExMrZZhqqpnkRERERsRBVREVERESsnK02zasiKiIiIiIWoYqoiIiIiBWz5emblIiKiIiIWDWDmuZFRERERJKSKqIiIiIiVs4266GqiIqIiIjIU1y+fJlatWrh6upK6dKl+eabbwC4efMm3t7eFCtWDG9vb27dugWAyWSif//+uLi44ObmRmBgYKLHUCIqIiIiYsUMgMGQ9LfEODg48OWXX3Lq1CkOHDjAzJkzOXXqFH5+ftSpU4eQkBDq1KmDn58fABs3biQkJISQkBDmzJlDr169Ej2GElERERERK2eHIclvicmbNy/lypUDIFOmTJQqVYrQ0FBWr16Nj48PAD4+PqxatQqA1atX07lzZwwGA5UqVSIyMpKwsLBEnpeIiIiIyHNcvHiRI0eOULFiRcLDw8mbNy8AefLkITw8HIDQ0FDy589v3sbZ2ZnQ0NDn7leJqIiIiIg1S4ZmeYMBIiIi8PT0NN/mzJnz1MNHR0fTsmVLvv76axwdHR8PzfDfppbSqHkRERGRN1DOnDkJCAh47jqxsbG0bNmSDh060KJFCwBy585NWFgYefPmJSwsjFy5cgHg5OTE5cuXzdteuXIFJyen5+5fFVERERERK2dIhn+JMZlMdO3alVKlSjFo0CDz8qZNmzJ//nwA5s+fT7NmzczLFyxYgMlk4sCBA2TOnNnchP8sqoiKiIiIyBP27t3Lzz//TJkyZXB3dwfg888/Z9iwYbRu3Zq5c+dSsGBBli5dCkDDhg3ZsGEDLi4upE+fnp9++inRYxhMJpMpOZ+ESIJ4Ezw0WjoKEdt28NxNS4cgr6Bi0WyWDkFeQmp7sHuNM8xHxcSy9/ytJN9vw9K5knyfL0sVUREREREr9yLTLaVE6iMqIiIiIhahiqiIiIiIlfsPMyRZNVVERURERMQiVBEVERERsXK2WhFVIioiIiJixQzwQvN+pkRqmhcRERERi1BFVERERMTKvc55S18nVURFRERExCJUERURERGxai92bfiUSBVREREREbEIVURFRERErJlB0zeJiIiIiAVo+iYRERERkSSmiqiIiIiIldP0TSIiIiIiSUgVURERERErZ6t9RJWIioiIiFg5Wx01r6Z5EREREbEIVURFRERErJjh/2+2SBVREREREbEIVURFRERErJydjXYSVSIqImJDKhbNZukQ5BXEGeMtHYK8hFR2r/+am7aZhqppXkREREQsRBVREREREWtnoyVRVURFRERExCJUERURERGxcrZ6ZSVVREVERETEIlQRFREREbFihtc/SP+1USIqIiIiYuVsNA9V07yIiIiIWIYqoiIiIiLWzkZLoqqIioiIiIhFqCIqIiIiYtUMNjt9kxJREREREStnq6Pm1TQvIiIiIhahiqiIiIiIlbPRgqgqoiIiIiJiGaqIioiIiFg7Gy2JKhEVERERsWIGsNlR82qaFxERERGLUEVURERExMpp+iYRERERkSSkiqiIiIiIlbPRgqgqoiIiIiJiGaqIioiIiFgzAzZbElUiKiIiImLlNH2TiIiIiEgSUkVURERExMpp+iYRERERkSSkiqiIiIiIFbPhsUpKREVERESsno1momqaFxERERGLUEVURERExMpp+iYRERERkSSkiqiIiIiIldP0TSIiIiIiSUgVURERERErZ6MFUSWiIiIiIlbPRjNRNc2LiIiIiEUoERURERGxcoZk+JeYLl26kCtXLt566y3zsps3b+Lt7U2xYsXw9vbm1q1bAJhMJvr374+Liwtubm4EBga+0PNSIioiIiIiT/D19WXTpk2PLfPz86NOnTqEhIRQp04d/Pz8ANi4cSMhISGEhIQwZ84cevXq9ULHUCIqIiIiYsUMhuS5JaZ69epky5btsWWrV6/Gx8cHAB8fH1atWmVe3rlzZwwGA5UqVSIyMpKwsLBEj6FEVERERMTKGZLhFhERgaenp/k2Z86cROMIDw8nb968AOTJk4fw8HAAQkNDyZ8/v3k9Z2dnQkNDE92fRs2LiIiIvIFy5sxJQEDAK29vMBgw/MeZ9lURFREREbF2yVESfQW5c+c2N7mHhYWRK1cuAJycnLh8+bJ5vStXruDk5JTo/pSIioiIiMgLadq0KfPnzwdg/vz5NGvWzLx8wYIFmEwmDhw4QObMmc1N+M+jpnkRERERq/Zi0y0ltXbt2rFr1y6uX7+Os7MzY8eOZdiwYbRu3Zq5c+dSsGBBli5dCkDDhg3ZsGEDLi4upE+fnp9++umFjmEwmUym5HwSIgniTfDQaOkoRESsT5wx3tIhyEtIl8qAvd3rSwzvx8Zz5daDJN+vS650Sb7Pl6WmeRERERGxCDXNi4iIiFg5G73UvCqiIiIiImIZqoiKiIiIWDsbLYmqIiryH2zbdwqvluMo13wMX83bYulw5AXonKU8OmfWr//4hZRqMIJq7Sc+8di3C3eQs1J/bkRGWyAysXZKREVekdEYz+AvlrLsm94cWDqSFVsOE3w+8evqiuXonKU8OmcpQ9tGFVn8Va8nloeG32KnfzDOebJaICrb8Wj++aT/Zw2SJRG9f/8+FSpUoGzZspQuXZrRo0c/d/2aNWtSoEAB/jmT1LvvvkvGjBlf6ri+vr4sX778lWL+t127drFv375E15s3bx45c+bEw8ODYsWKUa9evRfaLrm8aNz/VqhQIa5fv/7C68+aNYsyZcrg7u5O1apVOXXq1EsfM6U7fPIiRfLnoJBzDlKncqCFdzk27D5m6bDkOXTOUh6ds5ThbQ8Xsjqmf2L5yK9XMrpvM6tJelIygyHpb9YgWRLRNGnSsGPHDo4ePUpQUBCbNm3iwIEDz90mS5Ys7N27F4DIyEjz5aOSU1xc3DMfe5mErk2bNhw5coSQkBCGDRtGixYtOH36dFKF+VJeNRF9We3bt+f48eMEBQUxZMgQBg0alOzHtDZhEVE45f7fr/x8ubMSFhFlwYgkMTpnKY/OWcq1cc8x8ubMwlvFEr/Mo7y5kiURNRgM5mpmbGwssbGxGBJJvdu2bcvixYsBWLlyJS1atDA/ZjKZGDx4MG+99RZlypRhyZIl5uV9+/alRIkSvPPOO1y7ds28zeHDh6lRowbly5enXr165sS2Zs2aDBgwAE9PT7755hvWrl1LxYoV8fDw4J133iE8PJyLFy8ya9YsvvrqK9zd3fn999+JiIigZcuWeHl54eXlZU6a/61WrVp0796dOXPmAHDu3Dnq169P+fLlqVatGsHBwcCj6m2vXr2oVKkSRYoUYdeuXXTp0oVSpUrh6+tr3t+WLVuoXLky5cqVo1WrVkRHP+pjU6hQIUaPHk25cuUoU6YMwcHBLxX3jRs3qFu3LqVLl6Zbt2687HUNHB0dzX/fvXv3med3zpw5eHp64unpyfWIiJc6hoiIpEz37j/k63lbGda9oaVDsRlWcqn5JJdsfUSNRiPu7u7kypULb29vKlas+Nz169Spw549ezAajSxevJg2bdqYH1u5ciVBQUEcPXqUbdu2MXjwYMLCwvjtt984c+YMp06dYsGCBeZKYGxsLP369WP58uUcPnyYLl268Mknn5j39/DhQwICAvjoo4+oWrUqBw4c4MiRI7Rt25YvvviCQoUK0bNnTwYOHEhQUBDVqlXjww8/ZODAgRw6dIgVK1bQrVu3Zz6XcuXKmRPO7t27M336dA4fPsyUKVPo3bu3eb1bt26xf/9+vvrqK5o2bcrAgQM5efKkudJ4/fp1xo8fz7Zt2wgMDMTT05OpU6eat8+RIweBgYH06tWLKVOmvFTcY8eOpWrVqpw8eZLmzZtz6dKlpz6Xbt26ERAQ8NTHZs6cSdGiRRkyZAjTpk176jrdu3cnICCAgIAAcuTM+czXLCXKmzMzoeG3zPevht8ib87MFoxIEqNzlvLonKVMF69c51LYDWp2nES5d8dwNSKSOj6TCb9x29KhiZVJtumb7O3tCQoKIjIykubNm3PixAneeuut565ftWpVFi9eTExMDIUKFTI/9scff9CuXTvs7e3JnTs3NWrU4NChQ+zZs8e8PF++fNSuXRuAM2fOcOLECby9vYFHSXHevHnN+/tnknvlyhXatGlDWFgYDx8+pHDhwk+Nb9u2bY/1g7x9+7a5OvlvCdXF6Oho9u3bR6tWrcyPPXjwv0t0NWnSBIPBQJkyZcidOzdlypQBoHTp0ly8eJErV65w6tQpqlSpAjxKoCtXrmzePqFqXL58eVauXPlSce/Zs8e8TaNGjcia9ekdyX/44YenLgfo06cPffr04ddff2X8+PHMnz//mevaonKuBTl3KYK/Qq+TN1cWVm4N5PvPfC0dljyHzlnKo3OWMrm65OP0xs/N98u9O4at8z4me5aXG/sh/8+aSphJLNnnEc2SJQu1atVi06ZNz01E4VHzfPPmzRkzZsx/OqbJZKJ06dLs37//qY9nyJDB/He/fv0YNGgQTZs2ZdeuXc88dnx8PAcOHCBt2rSJHv/IkSOUKlWK+Ph4smTJQlBQ0FPXS5MmDQB2dnbmvxPux8XFYW9vj7e3N4sWLXru9vb29s/s7/oycb+qtm3b0qvXk6MlbZ2Dgz1fDGlNy/4zMRpNdGhaiVJF8ya+oViMzlnKo3OWMnQfNY+9gWe5GRmNW5NRDPmgIR2bVk58Q3lhtjrgK1ma5iMiIoiMjAQgJiaGrVu3UrJkyUS3q1atGsOHD6ddu3ZPLF+yZAlGo5GIiAj27NlDhQoVqF69unl5WFgYO3fuBKBEiRJERESYE9HY2FhOnjz51GNGRUXh5PSoI/U/K3qZMmXizp075vt169Zl+vTp5vvPSi53797NnDlz+OCDD3B0dKRw4cIsW7YMeJQgHz16NNHXIUGlSpXYu3cvZ8+eBR71xfzzzz+fu82Lxl29enV+/fVXADZu3MitW7d4GSEhIea/169fT7FixV5qe1tRt0ppAlaM5siqMXzcpb6lw5EXoHOW8uicWb85n/lycv14wvZ+zbG1nz2RhAauGqNqqDxVsiSiYWFh1KpVCzc3N7y8vPD29qZx48aJbmcwGPj444/JkSPHY8ubN2+Om5sbZcuWpXbt2nzxxRfkyZOH5s2bU6xYMVxdXencubO52Tp16tQsX76coUOHUrZsWdzd3Z85knzMmDG0atWK8uXLP3bcJk2a8Ntvv5kH/UybNo2AgADc3NxwdXVl1qxZ5nWXLFmCu7s7xYsX5/PPP2fFihWUKlUKgIULFzJ37lzzVFarV69+4dcxZ86czJs3j3bt2uHm5kblypXNfU+f5UXjHj16NHv27KF06dKsXLmSAgUKPHV/z+ojOmPGDEqXLo27uztTp05945rlRUREXidbnb7JYHrZ4dIiryjeBA+Nlo5CRMT6xBnjLR2CvIR0qQzY272+TO5BXDzht2OTfL8FsqVJfKVkpmvNi4iIiFgxGx6r9HoT0ebNm3PhwoXHlk2aNIl69eq9zjBEREREUhRraUpPamqal9dGTfMiIk+npvmU5XU3zT+Mi+fanaRvmnfOqqZ5EREREUmUbZZEk+3KSiIiIiIiz6OKqIiIiIiVs9U+oqqIioiIiIhFqCIqIiIiYuVstCCqRFRERETE2qlpXkREREQkCakiKiIiImLFHl1ZyTZLoqqIioiIiIhFqCIqIiIiYs1s+GLzSkRFRERErJyN5qFqmhcRERERy1BFVERERMTKafomEREREZEkpIqoiIiIiFUzaPomEREREZGkpIqoiIiIiLWzzYKoElERERERa2ejeaia5kVERETEMlQRFREREbFiBjR9k4iIiIhIklJFVERERMTK2er0TUpERURERKycmuZFRERERJKQElERERERsQgloiIiIiJiEeojKiIiImLNDLbbR1SJqIiIiIiVs9VR82qaFxERERGLUEVURERExIrpykoiIiIiIklMFVERERERK2ejBVFVREVERETEMlQRFREREbF2NloSVSIqIiIiYuU0fZOIiIiISBJSRVRERETEymn6JhERERGRJKSKqIiIiIiVs9GCqBJREREREatno5momuZFRERExCKUiIqIiIhYMUMy/XsRmzZtokSJEri4uODn55f0z81kMpmSfK8iTxFvgodGS0chImJ94ozxlg5BXkK6VAbs7V5fW3l8PDxIhu/PdKme/7jRaKR48eJs3boVZ2dnvLy8WLRoEa6urkkWgyqiIiIiItbM8Gj6pqS+Jcbf3x8XFxeKFClC6tSpadu2LatXr07Sp6bBSvLa2BkgrQ3+j4uIiCBnzpyWDkNegs5ZymPz58zBNutCNn/eXpPk+v68fPkyzZs3N9/v3r073bt3N98PDQ0lf/785vvOzs4cPHgwSWOwzf/5Iq9RgwYNLB2CvCSds5RH5yxl0nmzbvnz5ycgIMB8+2cS+rooERURERGRJzg5OXH58mXz/StXruDk5JSkx1AiKiIiIiJP8PLyIiQkhAsXLvDw4UMWL15M06ZNk/QYNthjT+T1skRThvw3Omcpj85ZyqTzlrI5ODgwY8YM6tWrh9FopEuXLpQuXTpJj6Hpm0RERETEItQ0LyIiIiIWoURURERERCxCiaiIiIiIWIQSUREREXmjabiM5SgRFUmBLl68yC+//MKhQ4eIioqydDgiNuvWrVucO3dOiYoNunfvHkbjowu4GwwGnWML0ah5kRTmzz//pEaNGrRs2ZJjx47x9ttvU716dRo2bGjp0OQ/CA4O5ty5czRq1MjSocj/O3XqFF26dCFdunQULFiQwYMHJ/nUNWIZZ86coVevXpQqVQqTycS3335r6ZDeWKqIiqQw+/fvp1evXsyYMYPvvvuOIkWK8Ntvv7F+/XpLhyav6OLFi1SsWJEmTZqwfPlyS4cjwIULF2jfvj2DBg1i586dpE6dWsmKjfjzzz9p1aoV7733Hr169eLq1atcu3bN/Hh8fLwFo3vzKBEVSWHi4uLYtGkTcXFxlC5dmkaNGuHl5cXOnTv5+++/LR2evAJ/f3+++eYbduzYweDBg1myZAmgfmuWdPnyZdq0aUPr1q0BGD9+PH/++Sd37tzReUnBYmNjWbNmDR999BG9e/cmf/78nD9/noULFzJ27FgA7OzsdI5fIyWiIilM165dKVu2LOPGjSMuLg4nJyeqV6/OyZMnCQ4OtnR48goaN26Mt7c3NWvWZNasWQwbNozFixdjMBiARz8+5PWqVKkSH3zwAQAPHz7EZDJx7do1bt++jcFgIDo6WslKCpQqVSq6dOmCj48P8fHxDB8+nJIlS+Ll5cUff/xhvnxlwntPkp8SUZEUJOGLr0OHDkRFRTF+/HhiY2MpWbIkFStWJDAw0MIRyqtInz49Tk5OANSrV4/vvvuO4cOHs2PHDo4ePcqoUaOIjY21cJRvltSpU5MjRw4A7O3tyZ07N7lz58bJyYkDBw4wfPhwIiMjLRukvJJs2bIBYDQa6dSpE0uXLqVq1aqsXLmS1KlTc/v2bQtH+GZRIiqSgiT8Sq9YsSItW7YkIiICb29vFixYwLfffounp6eFI5T/Kj4+nvr16/Pbb7/RrFkzatSowdtvv02qVKksHdoby97eHoB8+fLxzTff0LNnT9555x2yZs1q4cjkv0iVKhWVK1c23/f39yc8PFw/+l4zjZoXsXLx8fHY2dk9cd9kMmEymZg6dSp2dnaUKlWKBg0aWDBSSUqXLl3C1dWVJUuWaCT9a2Iymcw/9v75t9FoxGQy4eXlxcWLF1mxYgW1a9d+bB1JuYxGI7///jv9+/fHz89PM5C8ZkpERVIAk8nEgQMHHvv1/qz19MVo/f59nv5932g0smjRInLnzo23t7e5S4bOrWVNmzaNwoUL06RJE0uHIi8hsc/Fy5cvM2rUKFq3bq0k1AKUiIpYoX9/cO7cuZPTp0/Tu3dvC0YlSclkMtGlSxdmzZpFmjRpnnjcaDSam4T1A+P1mTJlCkWKFKFFixZPPBYXF4eDg4PORwpkMpmYMWMGbdq0IVeuXE88HhkZSZYsWV5/YIKDpQMQkf+5efMmjo6OT3zZValShVq1alk4Ovmv/tnNwmAwUL9+fXMS+u/kJiEJTVhXktbDhw9JnTr1Y8tMJhPt27cnX758jy1P+FHg4PDoK1PnI+UxGo3kyJHjiSQ04X2nJNRyNFhJxErcvXuXyZMnM27cOGJjYzEYDOaJlf/5hXny5El27NhhqTDlJf31118sWLAA4LG+vQBt2rQxr6fGqdfn5s2bfPLJJ/j7+5uXJSQk/0xCL1y4ADz+o0Cs24ULFzh48OBjy+Lj43FwcKBdu3bmZffv3wf0o8IaKBEVsRKpUqWiRo0a3L59m6+//pq4uDjs7Oweu8pHfHw8Fy5cIH369BaMVF5GaGgoo0eP5scffwSe/OIzmUycO3eOBg0amAfFSPKKiIggffr0/Pzzzxw9ehR48rzcu3ePOXPmcPLkSUuEKK/o999/p379+uzfvx949P7652BPgLNnzzJp0iTu3r1riRDlX9RHVMQKJDTZbt26lUWLFhEcHEzr1q3p06cPqVKlemIEryo0KUPCeVuxYgUjR45k1KhRtG/f/rHHEhw7dgw3NzdLhfrGWbVqFWvXrsXOzo4BAwY8cQ15k8lEdHQ0mTJlslCE8jL+OaBv/PjxLFq0iNmzZ1O1atUn3muxsbGcOnWKsmXLWipc+QdVREWsgJ2dHX/88QcffPABLVq0oFq1aoSEhPDll18SFxf3WDO9ktCUw2AwsGbNGlatWkWpUqUYN24cc+bMMT9mMpnM57VMmTKWDPWNsn79ej799FOyZ8/OpUuXmDlzJkeOHDE/npC4KAlNOQwGAwaDgQ0bNnDu3Dny5s1LgwYN2L17t/m9Bo9+9KdKlUpJqBVRIipiYQkfkCEhIXTo0IHGjRszevRovL292b17N1999RWxsbFPNC+J9bt27RojRoygR48ezJ8/n2+//ZbvvvuOn3/+GXj05fnPwUuS/B4+fMhvv/3G1KlT+eKLL/Dz8yNv3rzMmTOHEydOADoXKVVoaCiDBw/G19eXbdu28eWXX9K2bVv27NljPqf6HLU+OiMiFvLvuSELFSrE2rVrCQoKIn369Lz77rsYDAaOHj3K5cuXLRmqvKJUqVJRqFAhypcvT6ZMmahZsyYtW7Zk8ODBzJs3z9LhvZFSp07NgwcPWLt2LQAeHh54eHiwZ88epk2bxp07dywcobyqXLlyUb58eZycnDAajXTv3p1OnTrxzjvvcODAAUuHJ8+gRFTEAhKa/nbt2sWnn37KypUryZQpEx988AGzZs1i7969BAcHEx0dzfDhwylSpIilQ5YXkPDjIjQ0lIcPH5I1a1by5s1L69atgUfVmBIlStC8eXOd09ck4ZwcOXKEPXv2cOXKFT766COMRiM//PAD8OhHoKurK4MGDVJzfAqScG6vX7/O9evXzZfBXbRokbkLU4MGDahUqRJGo9FiccrzabCSiIVs3LiRIUOG8PHHHzNnzhwaNGhA8+bN2bVrFz/++CMZM2akf//+tGzZ0tKhykvYuHEjY8eOpXbt2vz999988803+Pr6cv36ddq0acOUKVNYvHgxFSpU0MTor8nq1auZMGEC1apV4+zZs7Ru3Zq0adMyY8YMMmTIwOnTp/n66691xaQUaN26dXz++efkz5+ffPnyMXjwYFq2bEn58uXJnDkzq1evZu7cuVSsWFHvNyulRFTkNUoYHR8fH8+wYcPo2bMnYWFh9O3bl40bN5InTx4AoqKiiI+PJ2vWrPrwTEGCgoLw9fVlyZIlLFu2jA0bNrBr1y5Sp07NtGnTsLe3p2jRotSvX9/Sodq8hPfNtWvX8PX1ZdmyZSxdupTvvvuOHTt2kC5dOmJiYjh69ChZsmShdOnSeq+lMAEBAfTt25fly5ezZs0apkyZwvnz5wkPD2fr1q1cvXoVDw8PvL29LR2qPIeurCTyGty5c4eYmBhy5crF0aNHKV26NNmzZ6dbt27cu3ePtWvXkidPHtatW4fBYKBRo0bmbfXFaL3i4+PNo3Xh0bn68MMPuXLlCmvWrGHx4sWkTp2aoKAg+vbtax4ooYQn+dy/f5+0adNiMBi4efMm2bJlw8nJiWnTprF+/Xp+/fVXMmbMyI4dOyhTpgxVqlQxb6tzYt0SLrGaIE2aNHz88cfs3buXBQsWsH37dgBu3LhBx44dzevp/Wbd1EdU5DU4f/48TZo0Ydq0afTp04dz586Z57fz8fHB2dmZAwcO8NFHH2my+hTi/v377Nixg7i4ONauXcvPP/9M+vTpGT58ON27d2f37t0UKVKEXbt2MWnSJCIiIszb6ksxeZhMJhYtWsT06dM5fPgw3bt3586dO+TIkYMff/yRL7/8EhcXF3bu3En//v25fv26pUOWF/TgwQP279/PmTNn2LlzJ7/++qv5YhFfffUV27dvp3DhwuzcuZMhQ4Zw9epV87Z6v1k3VURFXoOyZctSuXJlBg4cyOzZsylRogTZsmWjVatWbNmyhVWrVvH3338zZcoUXVM+hUibNi379+9n3LhxXLt2jRkzZlCsWDEmTZrErFmz2Lp1KyaTidGjRzN27Fhy585t6ZBtnsFg4P333ydr1qwYDAb27t1L1qxZ8fb2JjY2lvHjx1O1alXmzZvH5MmTKVWqlKVDlhcUFxfHX3/9hZ+fH8ePH2flypV4enoSEBDA3LlzOX78OGfOnGHy5Mn4+fk9dqlWsW7qIyqSjP7ZJLRx40b27NnD999/z4YNG6hQoQIAt27d4tatW5hMJooWLapmpBQg4RxdunSJRo0a4eTkxPLly0mXLh1Go5F169Yxd+5c8uXLR/PmzWnYsKHO62uQcNWxXr16sXbtWnx9fRk/fjwAV65cYd++fdy9excXFxeqVaumc5LCHD16lCZNmlCxYkWGDBmCl5cXAJMmTSIsLIyoqCjatWtH3bp1dW5TECWiIsns4MGDhISEULduXXLlysV3333HJ598QmBgIPfv32fZsmWMGjXK0mHKC0r4gouMjCRLliyEhYUxefJkrl+/zqeffoqLi4t5UNq/t5HkkfD6XrhwgQwZMpArVy6MRiPFixenYcOGTJ8+nWPHjmFnZ8dbb71l6XDlJSSc2+joaDJmzMilS5dYvXo158+f55133qFRo0bExMRgNBpJly6drjyXAqmPqEgy2rlzJ507d2bZsmU0atSInTt30qtXL8aNG0ft2rVp06aNLjWXwiRcRrBevXqMGDGCixcvMnXqVBwdHZk4cSKzZ8+mUKFCnDt37omLFkjySDgnTZo0oUaNGkycOBF7e3sOHTrEb7/9Rvv27WndujU3btywdKjykhIuk9uwYUMGDhzIyZMn6dGjBzlz5mTHjh188sknvPPOO0RERCgJTaFUERVJJsHBwXz88cd89tlneHh48OWXX7J371769u1L7dq1OXPmDHZ2dhQrVkwVsxQkMDAQPz8/3nvvPU6dOsWNGzeoV68ejRs3ZurUqZw/fx5vb2+aNWtm6VDfGMeOHWPEiBFMnz6de/fu8cknn1CpUiWGDRvG7du3WbBgAR4eHo+NkJeU4dy5cwwZMoQWLVpgMpmYN28evXr1omnTpixdupQtW7bQokULvd9SMA1WEkkGsbGx7Nu3jzNnzrB582Y8PDz46KOPsLe3Z9KkScTHx/POO++Y11cSmjKEhobSpk0b3nvvPVq3bk1ERAQrV65ky5Yt3L9/n0GDBpmnmNGPi9fj5s2bzJ8/n7Nnz5I1a1YKFy7M6NGj+eyzzxg3bhwDBw6kb9++lg5TXsGpU6do3rw5HTp0oEOHDsTExJA9e3amTp1KTEwMHTt2pG3bttjb2+v9loKpaV4kiSQ0Lty6dYvY2Fi6dOnCJ598wrlz51i0aBEAAwYMwNvbm6xZs1oyVHlFTk5OdOnShR9++IHAwEBy5sxJ69atcXFxYceOHYSFhZnnOdSXYvL5Z0NetmzZ6NChA+XKlWPSpEncuHEDDw8PRowYgb+//2PTZon1++e5dXV1pW7duixfvpybN2+SLl066tSpQ79+/fj++++5cuWKuS+23m8pl5rmRZLQ6tWrmT59OqlSpaJGjRq0a9eOHTt2cPDgQSpXroyPj4+lQ5SXkFBlOXjwIKdPn8bd3R0XFxd++eUXZs+ezdy5cylXrhw3b94kOjqaAgUKWDpkm5dwTtatW8fvv/9OfHw8H330EcHBwaxfv560adPy4YcfkiNHDu7cuaNrx6cgCef2999/JyQkhCJFilCzZk369evHiRMnWLlyJVmzZuXBgwfcvn2bnDlzWjpkSQKqiIokkUOHDjF58mSWLl1KxYoVWbJkCfnz5+fdd9/Fw8ODXbt2PTbJslg/g8HA+vXr6dKlC2FhYXTq1IkVK1bQs2dPPvjgA9q0aUNAQADZsmVTEvqaGAwGdu7cyaeffkrXrl357bffGD9+PFWqVKFp06bcuHGDKVOmEBsbS4YMGSwdrryEhIFJgwYN4s6dO4waNYrZs2czffp0ihcvTt26dbl16xZp0qRREmpD1EdUJIk8ePCAbt26sWHDBjZv3syKFSuws7MjOjqabt260ahRI02ynMJERkaybNkytm3bRkhICIsXLzZfJ75nz57ExsZy//59C0f55tm9ezdffPEFly9fJmfOnAwePJhUqVJRrVo1UqdOTaZMmUiVKpWlw5SXdP/+fX777Te2bNnCjh07ePjwoflyx7Nnz6Zbt26cOXOGSpUqWThSSUpKREVe0b87x6dJk4affvoJo9HI0qVLyZ8/P+vXr2fy5MmsXLkSZ2dnC0YrryJLliw4OzszbNgwzpw5w+rVq8mdOzfr1q2jSJEifPjhh5YO8Y2UN29evv/+e65evcovv/xCwYIF+fHHH4mIiGDo0KGWDk9eUXx8PAAjRozg5MmT/Prrrzg7O7Nu3ToKFizIDz/8YOEIJTmoaV7kFSX0Uxs+fDjTpk2jfPnyeHt7kzFjRkJCQli6dClDhw7l448/Jlu2bJYOV15AQpf5q1evcv78eQDy58/PyZMnGT16NIUKFeLAgQMMGjSIqKgoS4b6xjlw4AB79uzh3LlzeHt7c/ToUbp06ULBggU5fPgwX331FW5ubpYOU15Cwvvt/PnzREREkD59eurXr8+WLVsYOHAgRYsWZc+ePQwaNIiYmBgLRyvJRYOVRF7RpUuXaNy4MV26dOHo0aPcu3ePxYsXM2PGDEJCQrh16xYdO3akXr16mlokBVm1ahWjR4/GZDLh4+ODr68vn332GeHh4djZ2REUFISfnx9NmjSxdKhvjH379tGkSRPatWvHoUOHmDZtGgCfffYZadKk4dq1awwePJimTZvqvZbCbNy4kSFDhpA+fXp69+5NqVKlCAgIYM6cOdSoUYMtW7YwZcoUcxO92B4loiKv4NSpU5w9e5Zbt27h4+PD/fv36d27N3fv3uXnn38mderUxMTEkC5dOkuHKi8gIXmJj49n8ODB+Pj4kCVLFurWrUu/fv3w9fXlwoUL/PXXXzg7O1O2bFklPMks4fWNiIjg0KFDZMyYkerVq7Ns2TLGjh3LDz/8QKVKlbhx4wbR0dEULFhQ5ySFSDhP9+/fp2/fvgwcOJCwsDB++eUXqlSpQr169YiMjCQyMpJMmTLh4eGhc2vD1EdU5AUlfBBu376dXr16kT59epycnKhYsSIlS5bku+++o0uXLjRq1IjNmzeTOnVqS4csLyjhEpGbNm3i8uXLpEmThgIFCrBmzRpatmzJtWvXGDt27GPXKdeXYvJKmLFg4sSJ3L17lw4dOuDl5UWrVq0wGAy0a9eOL7/8khYtWpA9e3bzNmL9Ero1HTt2jKtXr1KoUCFKly5NXFwcixcv5s6dO7Rv3/6xrhY6t7ZLfURFXpDBYGDv3r38+OOPrFu3jjVr1pAnTx5Wr15NSEgIadKk4ccff2Tq1KnY2dnpuscpQEKDUHBwMJ999hnZs2fH3t6e7777jpCQEIoXL87y5ctZtGgRISEhFo72zeLv78/ChQv57LPPaNiwIefOnWPfvn08fPiQ9957jy+++EJT+KRQR44cYfjw4aRLl47bt2/Tvn17AOrXr0+rVq0ICAjgwYMHFo5SXhc1zYu8hNGjRzN+/HhOnjxJyZIlOXLkCDNnzsTZ2Zl27dpRokQJS4coL+ngwYMMHjyYLl264Ovry8GDB1m/fj13796le/fulChRgnv37pE+fXpLh/pGMJlMREVFUbt2bZydnVmzZg0mk4nx48cTFhZG06ZNqVWrFmnSpDGvr2pZynH8+HGmTp2Kq6srgwcPJjo6ml69ehETE8Py5csBuH79Ojly5LBwpPK6qCIq8hwJv9MiIyOBR4nohx9+iK+vL+Hh4Xh4eNCrVy8uXLhgvrSjpCzFixfnwYMH/PbbbwBUrFiRJk2aYGdnx8yZM7l375456ZHkk/BeMxgMZMmShYkTJxIQEMDs2bMxGAyMGjWKbNmysXz5cqKjo83bKQlNWeLi4oiKiiIoKIizZ8+SMWNGvvvuO0wmk3kAoGYZebOoIiqSiHXr1rFgwQKcnJyoWrUqderUYdKkSezfv59ff/2VfPny6VKCKUhCBe3QoUPExMRQsGBBcufOTZ06dfDy8uLrr78GHlVKs2bNSvHixS0b8Bsg4Zzs3r2bP/74Azc3N7y8vAgJCaF///706dOHbt26AXD27FlcXFwsHLG8qH++3wwGA46OjmTPnp2BAwdStmxZmjdvTpEiRYiOjubcuXOULVvW0iHLa6aKqMhz+Pv7M2LECCZPnsypU6dYtmwZadOm5bPPPqNcuXK0aNGChw8fqtk2BUm4jGDv3r3ZvXs3nTt35vDhw6xbtw5/f3+6d+8OPKqMKgl9PQwGA1u2bKFnz54ULlyYQYMGMX/+fKpVq8bXX3/NpEmTmDVrFoCS0BTGYDCwceNGunTpQkBAABUrViQ0NJQBAwZw8uRJFi1axLlz58iYMaOS0DeUElGR5zhz5gwDBgzg0qVLREZG4ufnR9q0abl27RpTpkxh/vz5pE6dWgOTUoj4+Hhu3rzJt99+y7Zt28ibNy+xsbEULlyYrFmzsmHDBgIDAzl16pSlQ32jREdHs3HjRpYvX07x4sXJmDEjnTp1AqBq1arMnj37sRkLJGWIj48nPDycyZMns3btWnLlykWRIkXImTMn5cqVY8CAAZw6dUrdK95wapoX+X+XL1/m8OHDREZGUqRIEapXr86BAwcYMmQIkZGRbNy4EScnJ1asWMHOnTv58ssv1XcwBYiNjX3suuPR0dH06dMHDw8PVqxYwY8//kixYsXYuHEjVatWJX369Pphkcxu3bpFTEwM169fN0/RM336dDZs2MCNGzdYsWIF+fPnZ+XKlWTIkIF69epZOGJ5UUajkfj4+Mfec0OHDqVEiRLMnTuXn376ieLFi7NixQpq165N6tSpyZAhgwUjFkvT6AoRHk3f06pVKypWrMjdu3fZtGkTY8aMoUaNGhQvXhxnZ2euXLnC5cuXGTduHOPHj1cSmgIEBweb56GMjIxk4MCB1KxZkyJFijBx4kQ2bdpEsWLF2LVrF0OHDmXx4sW4urpaOmybdurUKXr37o2joyOXLl0ib968zJo1C2dnZ2JiYhgwYAD58+fn8OHDjBgxgpkzZ1o6ZHlBwcHBTJo0idDQUJycnChfvjx9+/Y1V0WjoqLIlCkT/v7+TJw4keLFi1OmTBlLhy0WpoqovPGCg4Pp0qULPXr0wMfHB3h0XeuPPvoIHx8fqlWrxurVq9mxYwcZM2bEx8eHZs2aadoYK3fmzBnatGlD165dqVixIuvXryckJAQ3NzfKly/P7t27+f3332nevDlz5sxh0qRJumxnMjtz5gydOnWiX79+tGzZEnt7ezp06EDatGkZNmwYGzdu5NixY/z999/cuHGDMWPG0LRpU0uHLS8g4f3WrVs3PDw8OHLkCPv27cPJyYkxY8bQpk0bHBwcqFixIkuXLmXMmDE0a9bM0mGLFVAiKm+0mJgYKlWqRKVKlZg9ezbx8fHEx8fj4ODAvn37aNSoEevXr+ftt9/mwYMH3L9/n8yZMysJtXIXL16kUaNG9O/fnx49epiXL1u2jI0bN9KwYUMaN27MvHnzyJw5M05OTlSvXl3nNRmFhYVRoUIFPv/8czp16kRcXJx5yrPWrVsTHx/P8uXLuXHjBn/99ReOjo64uLjonKQAf/31F9WrV2fChAl07NgReNQ/9NChQ3z99ddUrVqV3r178/XXX5MzZ07y589PjRo1dG4F0GAlecOlS5eOcePGsWvXLlatWmW+IpLRaOTtt9+mXbt2/P777wCkSZOGzJkzA5q70NodOnSIXLly4eHhQUxMjHl5q1atKFasGDNmzCBt2rT07NmTdu3aUb16dUDnNTnlzp2bAgUK4O/vD4CDgwNxcXEALF26lODgYPbs2UP27NkpV66ceXS8zon1O3nyJK6urtjZ/S+lsLOzw9PTk/r167N3714MBgMDBw6kY8eO1KhRA9C5lUeUiMoby2QyYTKZaNasGRMnTmTgwIGsWrUKg8Fgnlzb0dFRlxFMQa5du8bGjRtp1aoVrVu35rvvvmP79u2PJaOtW7fG3t6ee/fuWTDSN8eVK1eYPHkydnZ27N69m5MnT5q7wDg4OHD//n0AihUrpvdaClWzZk0++OAD9uzZw/Tp083L7e3tKVeuHMHBwdy6dcuCEYo1UyIqb6SEJqFr164RExNDixYtmDZtGgMHDmTlypXmpvlVq1Zp8EoKER8fz5o1a1iyZAlr166lV69eeHp6snLlSnbs2GG+Gk9ISAiOjo7mHyKSvBKuWjVp0iTs7e3ZsmULly9fxtfXF5PJRNq0afH39+fq1aukTp3a0uHKS0h4/6RPn56GDRtSv359jh8/zowZM8zrhIWFUbRoUdKmTWupMMXKadS8vJEMBgPr1q3Dz88PgAkTJtCkSRPs7e0ZMGAAJ06cYP369UyePJlKlSpZOFpJjMlkws7Ojm7dunH9+nU2bNhA2rRp6dOnD99++y3Lly8nZ86c3L59m48//hg/Pz9NGfMaxMXFUbRoUQYPHsyECRP4+++/+eqrr9iyZQv16tXjww8/pF27dvTu3ZsxY8ZQtGhRS4csLyjhx/z27du5evUq9vb2tG/fHpPJxObNm1m0aBFFihTho48+YuLEiaRLl87SIYu1Mom8gQ4fPmyqW7eu6ejRo6avvvrK1LRpU9Pq1atNJpPJtG7dOlOePHlMa9euNZlMJlN8fLwlQ5WXsGXLFlOtWrVMXl5epnr16pnWrFljMplMppkzZ5oaN25sKlSokGndunUmk0nnNbnFxcWZTKZH76datWqZRo8ebcqdO7dp5MiRJpPJZIqNjTVVrVrVlCFDBtP69etNJpPOSUqzadMmU5kyZUybN2822dvbm2bPnm26e/euacWKFaaWLVuasmTJonMridKoeXnjhIWFMWLECMLDw9mwYQMAP/zwA+vWraNTp060bNmSyMhIsmTJolGdVu7hw4fm5tyrV6/y7rvv8vPPP1OoUCFmzpzJ2bNnad68Od7e3syfP58iRYpQrVo1nddkFBoaSq5cuUiVKhW3b9+mQ4cOdOzYkTZt2hASEsIHH3yAh4cHX331FUajkePHj+Pu7m7psOUlmEwmYmNjef/99xkxYgR///03w4cPN1+IwGg0sm7dOrJnz07VqlUtHa5YOfURlTfCP39vZcqUiTp16nD37l1mz54NQLdu3ahbty5z587l2rVrZMmSBdCoTmt269YtatWqxdGjR4FHfURNJhPx8fGkSZOGbt26ERUVxejRo1m1apV5TljQeU1Ofn5+1K5dm9jYWBwdHSlVqpT5SkrFihXj888/Z9q0aUycOBE7OztzEqqaiHV78OABly9fBh796IuNjaVYsWIsWrSIcePG8fPPP5M/f34WLFjAnj17aNasmTkJ1bmV51EiKjYvofr1+++/8+uvv7J+/Xpat25Njx49OHr0KHPnzgWgd+/ezJ49m1y5clk4YknMw4cPyZo1K++88w4+Pj4cP34cZ2dnGjRowJo1azh//jyOjo60aNGCvHnzUrJkSUuHbPNCQ0MBmDx5Mq6urjRr1oy4uDg8PT3Zu3cvISEhAGTPnp0GDRpQq1atx34Q6MeB9TKZTAQFBbF+/XrGjx9P+/btgUfT302ePJnvv/+eEiVKcOTIESZPnvzY5T1B51aeT03zYtOMRiP29vZs376d7t2706dPH2bPnk2nTp1o06YNgYGBrF27lurVq9O9e3c12aYAERER+Pn5MWHCBNKmTcsXX3zB/PnzWbFiBdHR0SxdupQTJ05Qs2ZNfvzxR7799ltq165t6bBtVsJ7pl27dnzyySe89dZbAHTu3Jl79+6xbNkyJk+eTGBgIHFxcZw4cYLp06fj7e1t4cjlZdy4cYM+ffqwadMmPv30UwYNGgRAz549OXbsGG+99RYBAQG6Gpa8NCWiYpOuXr1K+vTpyZIlCzExMfTs2ZN33nmHTp06ce3aNXr27EnZsmUZPXo0CxcupGzZsuYvULFeCVfjuXjxIkajkQcPHuDq6sqkSZP49ddfWb58OdmyZWPz5s2cPXuWKlWqUKdOHUuHbdNu3LhB9uzZAfjzzz8ZOXIkS5cuxWQy0alTJx4+fMjixYsJCwvj9OnTZMmSBU9PTwtHLS/qnz/OlyxZwpYtWyhQoACVKlWiXr16AGzdupXs2bObu1roB728DCWiYnOMRiPjxo1j3759LFmyhGzZsjFmzBgyZcpEz549yZAhA5cuXaJt27bs2LEDBwcH86UGxXqFhIQwaNAgPv30U7y8vBg+fDgBAQFMnz6dkiVL4ufnx5IlS5g3bx5ly5bVl+Fr8Pfff1O8eHFGjRrF4MGDMZlMFC1alMqVK7Nw4UJMJhPvv/8+oaGhrF+/XvOEpjAJ76HAwEDSp09P+vTpyZ07NxMnTiQ6Opq2bduSNm1azpw5Q8uWLS0drqRQ6iMqNsfe3p7u3bvj7u5Ot27duH37NpUqVeLgwYOcPHmShw8fEhUVhYODAw8ePFASmkIEBgayefNmVq5cSWBgIBMnTqRSpUoMHz6c06dPM2zYMFq0aEGrVq24c+eOBki8BhEREaRLl46pU6cybdo0DAYD58+f59SpU7Rp0waDwcC8efPImzeveVCZpBwJ8y37+vqydOlSevXqRWBgIEOGDMHR0ZHp06dTvXp1MmXKZOlQJQVTRVRsxqVLlwgNDaVy5coA3Llzh9GjRxMaGsqCBQuYP38+O3bsIDY2lkuXLjFixAiaN29u4ajlRQUHB+Pr60vu3Llxd3enefPmuLu7M2LECEJCQhgzZgylS5fm/PnzFClSxNLhvjF++OEHNm/eTGBgIO+//z4jR44EwMvLizx58rB27VoLRyiv6s8//6Rjx46sXLmSVatW8f3335M5c2bGjh1LrVq1OHPmDPfu3cPDw8PSoUoKpkRUbMLt27epU6cOR44coXv37qRLl46BAwcSFRXFmjVrOHHiBD/99BNRUVH89ddfpEmThjJlyqj51sqFh4cDkCtXLgwGAwsXLmTbtm2kS5cOe3t7unXrRtmyZfnoo48ICQlh0aJFpE+fXuc0GZ05c4Zjx47h7e1NlixZCAkJYfPmzVStWpXOnTvTsmVLRo8eDUCZMmVYsGAB7u7uOicpzPHjxwkPD6dAgQKEhYUxYMAAli9fzrx581i+fDkTJkygRYsW5vX1WSqvSk3zYhMcHR3p0aMHb731FqVLl+batWt89tln9OjRg3Tp0vHnn3/StWtXUqdOjaenJ2XKlAE0rYg1u3HjBmXKlKFZs2YMGzaMBw8e4OHhgZeXF8OGDSNLliz88MMPHDlyhC+//JLJkyeTIUMGndNkFBMTQ79+/WjTpg3Dhg1jwoQJFC5cmD179nDy5Ek2bdrEypUrzVXR48eP4+HhoXOSQiTUpfz9/enduzdFixalePHinDhxggEDBlC0aFGcnZ2pWbMm+fLle2xbnWN5VeocJylefHy8+Trj9+7dY8eOHQwfPpyiRYvyxx9/sGfPHuLj41m9ejUff/wxZcuWtXTI8gJMJhM9evTg9OnTbN68GaPRiLOzM3PmzMFoNDJmzBgGDRrE999/z6RJkyhRooSlQ7Z56dKlo2/fvmTIkMGcgH7++ecUK1aMGTNm0L59e5YsWUKTJk3w9fWlSJEi2Nmp3pFSGAwG/P39+eGHH2jfvj2FCxcGIDo6mmXLlgGP5oldvHixZj6QJKNEVFI8Ozs7czLav39/Hj58yKeffsqwYcNo0qQJTZo04f79+5w7d47SpUtbOlx5QTly5MDHx4ft27dTuHBhsmbNiqenJz/99BMBAQHEx8czYcIErly5osESr0FC02vTpk25d+8eu3btomvXrsTGxhIVFUXmzJk5d+4cJUuW5OjRo6RPn97SIcsrCA8PZ+/evWTIkIG7d++SIUMGhg4dyoMHDwgODmbq1KlKQiVJqY+opFj/7pOUkIwCfPXVV2zbto3BgwdTuXJl0qRJ88ztxDoFBARw7tw53nnnHX7++WdOnz5Nv379KFCgAFevXqVo0aJPXMFFktc/3zs///wzO3fupE6dOrRr147o6GgyZcqEwWDQeywFSThXJ0+eJHPmzGTNmpXg4GAGDRpE//79qV+/PhkyZHhsXZ1fSUpqM5EUx2g0Av/rk5TwWyqhMgowcOBAqlevzoQJE7h3795j2+sDNGW4efMmI0eOJCYmhtatW1OiRAlmzJhBYGAgJUuWVBJqAQlJCECnTp1455132Lp1KwsWLCAuLk5JSgqTcK42bNhAp06dmDdvHrVr16ZgwYIMHDiQWbNmsXr1au7fvw/877NT51eSkhJRSVFOnz7NiBEjGDduHCdOnODevXuPfTn+MxkdOnQoc+bMIWvWrJYMWV5Qwjk0mUyYTCbq1q1Lp06d2LdvH/ny5aNp06YULlyYX375hZs3b2qe0GSW8D76t3++39q3b0/t2rXZunXrE8mKWK+HDx8Cj87VhQsXGDt2LGvWrCFr1qwYDAYMBgPvvvsuffv25dtvvyUyMtKyAYtNU9O8pBhXr16lfPnyjBw5kiNHjpApUyYyZ87MwIEDyZw582OVmPj4ePMHqio0Kcfu3bvZvn07JUqUoGXLlqxfv56ffvqJdevWAXDu3Dns7e0pVKiQZQO1cWfOnOHXX3/F3t6ejh07UqhQoScGHf3zfXX16tUnRlGLdQoPD2fu3LnUr1+fcuXKER4ezvTp0/H09GTSpEksWLCAYsWKsXXrVry9vbl+/To5cuSwdNhiw1QRlRQjIiKCBg0a0KdPH2bPnk3jxo25e/cuU6dOJTo6+rFkM+FLMioqiiVLlpgrAGK9duzYwaxZs0ibNi2HDx+mbt265MiRg9OnT7NkyRIAihYtqiQ0mQUHB9OiRQuyZ8/O0aNH+eKLL4iNjX1ivYQfefHx8eTLl++ZFVSxLnFxcZw5c4b169dz7NgxcuXKxa5du3j//ffZvHkzxYoV4/fff2f8+PFcuHBBSagkOyWikmJkz56dnTt3sn79euzt7alduzYNGzbk7t27HD9+HHiUgBqNRuzt7YmKiqJ+/foUKFBA17i2csHBwYwfP57x48czYsQIpk6dSrdu3Th69CgPHz5k9+7dlg7xjXD//n1GjhxJt27d6N+/P0uWLCE4OJh58+Y9df2EAYJRUVHMmDHjif7YYl2MRiNOTk60aNGCP/74gylTpnDlyhWmTZtGlSpVGDlyJD///DP9+vVj0KBB5umbRJKTElFJMZydnfnss89YsmQJe/fuxWAwULVqVYxGIytWrDCvZ29vT2RkJC1atGDy5Mm8/fbbFoxaEnPjxg1mzJhBeHg4Dx48MC/v3Lkz/fv3Z8uWLWzdulXJ6GuQNm1aRowYgY+PD0ajEQcHBxo0aPDYeUnwzx98jRo1onz58pqyycrZ29uzadMmxo8fT4MGDQgKCuK7777D3t6eOXPmAI+6WXzxxRc0a9ZM/bDltVAiKilK06ZNKV++PN9//z2bN28mVapU1K9fn1u3bnH//n0MBgN3796lefPmjBkzhqpVq1o6ZHmKf37BZc+enZ49e+Lp6cmqVav466+/zI/FxcVRqlQpGjduzN9//22JUN84ZcuWJVu2bNjb2wOP5nMNCwsD4OjRoxw6dMichCb84PPz86NKlSqWDFsSYTKZePjwIYsXL6ZPnz4MGDCAtWvX8vfffzN9+nTi4+OZNm0aQ4cOpW7duoAGnsnroURUUhRHR0d8fX2pWbMmffv2pV+/fnTs2JF3332XtGnTAvDgwQO++eYbqlWrZuFo5WkSpvlZu3YtvXv35v333ydDhgz07t2b69evs2zZMi5cuACAg4MDoaGhnD9/Hjc3NwtH/mZISEATfiw8fPiQNGnScOzYMVq1akV8fDz29vbExMTQqVMnRo8erR98KYDBYCB16tQUK1aM48ePc+vWLQoWLMjQoUP57bffmD17NlFRUZYOU95AGjUvKdbZs2e5dOkSjo6OeHp6mgdOJHyRinU5d+4cadKkwdnZma1btzJy5Eh++eUXPvjgAxwdHVmzZg07duxg6dKl5nkM06ZNS3x8PPfu3SNjxoyWfgpvpO3bt/Pxxx+TLl06hg8fTpMmTQCIiooiPDyc4sWLWzhCeZaEQZtnzpzh+vXrlCpVikOHDrFlyxaqV69O/fr1uXr1Kj169GDSpEl4eHhYOmR5A+kSn5Jiubi44OLiYr5vMBiUhFqxefPmsXv3bnbt2sWZM2f46quvOHXqFLGxsXzzzTcA1KpVi/Tp0+Po6EjatGkxmUzY2dkpCbUgR0dHTp8+zW+//UaDBg3MP/gyZ85M5syZLR2ePEPCQLJ169YxcOBA3NzciIiIYMCAAWTJkoVNmzbxzTffEB4ezhdffKEkVCxGiaiIvBZdunTh77//5syZMxiNRsaNG4fRaOTnn3+mcOHCLFu2jBMnTjBmzBhdwcWKeHh4cPr0aQoXLmyusOkHn/W6ffs2jo6O2NnZce3aNb799lsWLVqEp6cn8+fPZ+fOnfj4+ODq6kpwcDBp0qShdOnSlg5b3mDqIyoiyc5kMlGgQAHu3bvHjz/+yAcffMDFixepVKkSRYoUYc+ePXz66ae8/fbbSj6tjIODg6bxSSEuXbpE0aJF+f7774FHA81SpUplHgDo4+ODo6Mjfn5+pEuXjnLlyikJFYtTIioiySI8PJwJEyaYL8Nqb2/Pl19+yZ49ezh37hxr1qxh69at+Pj4MHLkSKZMmUK9evUsHbY8h34kWLcLFy5w7949+vbty8yZM7Gzs6N69eqcOXOGY8eOAdC4cWNy5MhBXFychaMVeUSDlUQkWWzZsoXZs2dz7tw5BgwYgKurKxUqVGDUqFHkzJmT/v37c+PGDdKkScPt27fJly+fLscq8h/9+OOPHD9+nAULFjB+/Hg6d+7MsGHDiIiIIGvWrOzatQs/Pz+aNWtm6VBFACWiIpLM5s+fT0hICKtWrWLw4MHcvXuXr7/+mvXr11OsWDFLhyeSop07d46//vqL2rVrA7Bp0yb+/PNPGjVqxNtvv82ECRPo1q0b+/bt48SJE5QtW5aKFSvqR59YDQ1WEpFkkTBq18fHB5PJRIMGDfjqq68oXLgwZ8+eZeHChYwaNUoDX0Re0Z07d2jRogXBwcF8+umn5MuXj/fff5+ZM2dSqFAh9u3bR5UqVbh58yZDhgx57CpzSkLFWigRFZFkYWf3vy7oJpOJKlWqULJkSSIiIrh8+TLVqlVTEiryH2TKlIl+/foxc+ZMDAYDGzdu5MSJE1SrVo1ff/2VxYsXm+cMfe+99yhYsKDec2J11DQvIkniVZr61Dwo8moSWhwAZsyYwfbt2/n00085cuQIISEh7N+/nwULFlCgQAGio6M1F69YLSWiIvKf/PXXX9y/f58SJUo89uX4LAkfOQaDQYmoyH/wz/fb5MmT+eOPPxgyZAhVqlTh2rVr5MyZU+8zsXqavklEXtmZM2fw8vKiXr16+Pv7Y2dnR3x8/DPXNxqNj30h6stR5OX8s3b0z/fb4MGDqVGjBhMnTmTnzp3kyJFDSaikCEpEReSVGI1GfvzxR/r27cvEiRP54IMPnpuMGo1G7O3tiYqKYvr06dy7d88CUYukTAnzfv47qfzn+23QoEFUq1aNzz//nKioqKeuL2Jt1DQvIq/sxo0b3L17lwIFCvDdd98xa9Ys5syZQ8WKFYH/9QGNi4vDwcGBqKgoGjVqxKRJk6hSpYqFoxdJGYKDg/n222/JkSMHrVq1olSpUk+s889m+gsXLuhqWJJiKBEVkSQza9YsvvvuO9auXYvBYODo0aPUr18fBwcHIiMjadmyJWPHjqVq1aqWDlUkRTh9+jQdOnSga9eu7Nmzh7Rp0zJ//nzz4/9seo+Pj8dgMKhJXlIUNc2LSJLp2bMnI0eOpEKFCri6upIhQwYcHByIiYmhU6dOjB49WkmoyAt68OABAwcOpEuXLvTp04dvv/2Wu3fvsnjxYs6dO0dMTMxjyWZC8hkVFcWSJUt4+PChBaMXeTGqiIpIktq3bx8NGzbkl19+oXHjxgBERUURHh5O8eLFLRydSMpy/fp187Xhy5UrR7FixciWLRsZMmSgSpUqtGrVCpPJRHx8vLkPdv369fnyyy8fm8BexFopERWRJGEymYiNjaVfv340bNiQZs2aPfYFKSL/zeLFi2nbti0An332GeHh4cyYMcNcCVX3F0mJlIiKSJK6f/8+adOmfWy+UBF5dU/r77l582YWL17Mt99+S7p06bh79y6NGzdm3LhxVKtWzUKRirw8JaIiIiIpyPbt2xk4cCB+fn40bNgQgJs3b3LlyhXc3NwsHJ3Iy1EiKiIikgIYjUZCQkLo2rUrQ4cOpWnTpur+IimeElEREZEUwmg0cu3aNfLmzaspmsQmKBEVEREREYvQPKIiIiIiYhFKREVERETEIpSIioiIiIhFKBEVEREREYtQIioi8h/Y29vj7u7OW2+9RatWrbh3794r78vX15fly5cD0K1bN06dOvXMdXft2sW+ffte+hiFChXi+vXrL7z8nzJmzPhSxxozZgxTpkx5qW1E5M2iRFRE5D9Ily4dQUFBnDhxgtSpUzNr1qzHHo+Li3ul/f7www+4uro+8/FXTURFRKyJElERkSRSrVo1zp49y65du6hWrRpNmzbF1dUVo9HI4MGD8fLyws3NjdmzZwOPLt3Yt29fSpQowTvvvMO1a9fM+6pZsyYBAQEAbNq0iXLlylG2bFnq1KnDxYsXmTVrFl999RXu7u78/vvvRERE0LJlS7y8vPDy8mLv3r0A3Lhxg7p161K6dGm6devGi8zY9+6771K+fHlKly7NnDlzHnts4MCBlC5dmjp16hAREQHAuXPnqF+/PuXLl6datWoEBwcnyespIrbPwdIBiIjYgri4ODZu3Ej9+vUBCAwM5MSJExQuXJg5c+aQOXNmDh06xIMHD6hSpQp169blyJEjnDlzhlOnThEeHo6rqytdunR5bL8RERF88MEH7Nmzh8KFC3Pz5k2yZctGz549yZgxIx9//DEA7du3Z+DAgVStWpVLly5Rr149Tp8+zdixY6latSqffvop69evZ+7cuYk+lx9//JFs2bIRExODl5cXLVu2JHv27Ny9exdPT0+++uorxo0bx9ixY5kxYwbdu3dn1qxZFCtWjIMHD9K7d2927NiR9C+yiNgcJaIiIv9BTEwM7u7uwKOKaNeuXdm3bx8VKlSgcOHCAGzZsoVjx46Z+39GRUUREhLCnj17aNeuHfb29uTLl4/atWs/sf8DBw5QvXp1876yZcv21Di2bdv2WJ/S27dvEx0dzZ49e1i5ciUAjRo1ImvWrIk+p2nTpvHbb78BcPnyZUJCQsiePTt2dna0adMGgI4dO9KiRQuio6PZt28frVq1Mm//4MGDRI8hIgJKREVE/pOEPqL/liFDBvPfJpOJ6dOnU69evcfW2bBhQ5LF8X/t2zFrIkEYxvHHTjYoSWEbSGHhrrhgpRAkVbSx0A9gJfgVrOJ3iIUgWLoQBINVGlGxUcTGTmws0ykiAUGzd8Vx2yTk7riEhbv/rxtmZhne6mFmX9d1NZ1OFQwG/+o7o9FI/X5fk8lEhmHo5uZGh8Ph3bWBQECu6+r8/PzdGgDAr/CPKAB8sWw2q0ajoePxKElarVZ6eXlRJpPRw8ODXl9f9fz8rOFw+GZvKpXSeDzWer2WJG02G0lSKBTSfr/31t3e3qper3vjn8Ewk8nIcRxJ0tPTk7bb7Ydn3e12uri4kGEYWi6Xmk6n3pzrut6truM4ur6+Vjgc1tXVlTqdjqQfoXuxWPxRfQD8vwiiAPDFyuWyTNNUMplUPB5XpVLR6XRSoVBQNBqVaZoqlUpKp9Nv9kYiETWbTRWLRdm27T2N5/N5PT4+es1K9/f3ms/nSiQSMk3T696v1Woaj8eyLEvdbleXl5cfnjWXy+l0OikWi6larSqVSnlzZ2dnms1misfjGgwGuru7kyS12221Wi3Zti3LstTr9T6rdAD+cYFvv9NCCQAAAHwybkQBAADgC4IoAAAAfEEQBQAAgC8IogAAAPAFQRQAAAC+IIgCAADAFwRRAAAA+OI73smJbuq+Zb4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm = confusion_matrix(predicted_labels, test_labels)\n",
        "cmp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=target_names)\n",
        "plt.rcParams[\"figure.autolayout\"] = True\n",
        "plt.rcParams.update({'text.color': \"black\",\n",
        "                     'axes.labelcolor': \"black\", 'xtick.color': \"black\", 'ytick.color': \"black\"})\n",
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "fig.patch.set_facecolor('white')\n",
        "cmp.plot(ax=ax, cmap=plt.cm.Blues)\n",
        "plt.xticks(rotation = 45)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o5nk-6IiCv7V",
        "outputId": "1adfebee-865a-454a-d8c2-bef483515c26",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['alzheimer_cnn_dropout_layers_lazy_adam.h5']"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "#Saving the model for future use\n",
        "custom_cnn_model_dir = './models/' + \"alzheimer_cnn_dropout_layers_lazy_adam.h5\"\n",
        "model.save(custom_cnn_model_dir, save_format='h5')\n",
        "os.listdir('./models')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Xb0KSA7SuQi"
      },
      "source": [
        "### Grad-Cam visualisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "h1xc5P3fSnwj"
      },
      "outputs": [],
      "source": [
        "class_indices = validation_dataset.class_indices\n",
        "indices = {v:k for k,v in class_indices.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "dbbGy5A6SEsY"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "Ezeqe5SpQr8Y"
      },
      "outputs": [],
      "source": [
        "filenames = validation_dataset.filenames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJnnrolmSdup",
        "outputId": "48f75d03-fcf5-4b12-ec04-34ca4dc8272d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "128/128 [==============================] - 1s 9ms/step\n"
          ]
        }
      ],
      "source": [
        "y_val = validation_dataset.classes\n",
        "y_pred = model.predict(validation_dataset)\n",
        "y_pred = np.argmax(y_pred,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "XdoEjD3aR69v",
        "outputId": "d97bd025-cf31-4af2-fabc-bd94b84eefce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    filename         actual           predicted   Same\n",
              "0  0_NonDemented/26 (69).jpg  0_NonDemented  1_VeryMildDemented  False\n",
              "1  0_NonDemented/26 (82).jpg  0_NonDemented       0_NonDemented   True\n",
              "2  0_NonDemented/26 (99).jpg  0_NonDemented  1_VeryMildDemented  False\n",
              "3  0_NonDemented/27 (14).jpg  0_NonDemented      2_MildDemented  False\n",
              "4  0_NonDemented/27 (21).jpg  0_NonDemented       0_NonDemented   True\n",
              "5  0_NonDemented/27 (22).jpg  0_NonDemented      2_MildDemented  False\n",
              "6  0_NonDemented/27 (29).jpg  0_NonDemented       0_NonDemented   True\n",
              "7  0_NonDemented/27 (30).jpg  0_NonDemented  1_VeryMildDemented  False\n",
              "8  0_NonDemented/27 (36).jpg  0_NonDemented       0_NonDemented   True\n",
              "9  0_NonDemented/27 (46).jpg  0_NonDemented       0_NonDemented   True"
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-239c7120-aa0e-48b7-b84f-d580505a1ba8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-239c7120-aa0e-48b7-b84f-d580505a1ba8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "val_df = pd.DataFrame()\n",
        "val_df['filename'] = filenames\n",
        "val_df['actual'] = y_val\n",
        "val_df['predicted'] = y_pred\n",
        "val_df['actual'] = val_df['actual'].apply(lambda x: indices[x])\n",
        "val_df['predicted'] = val_df['predicted'].apply(lambda x: indices[x])\n",
        "val_df.loc[val_df['actual']==val_df['predicted'],'Same'] = True\n",
        "val_df.loc[val_df['actual']!=val_df['predicted'],'Same'] = False\n",
        "val_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "KTLsN9ehStH7"
      },
      "outputs": [],
      "source": [
        "val_df = val_df.sample(frac=1).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "Ma4b7AR6TJ8G"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "Mykwzot3S4XE"
      },
      "outputs": [],
      "source": [
        "def readImage(path):\n",
        "    img = load_img(path,color_mode='rgb',target_size=(img_size,img_size))\n",
        "    img = img_to_array(img)\n",
        "    img = img/255.\n",
        "    \n",
        "    return img\n",
        "\n",
        "def display_images(temp_df):\n",
        "    temp_df = temp_df.reset_index(drop=True)\n",
        "    plt.figure(figsize = (20 , 20))\n",
        "    n = 0\n",
        "    for i in range(15):\n",
        "        n+=1\n",
        "        plt.subplot(5 , 5, n)\n",
        "        plt.subplots_adjust(hspace = 0.5 , wspace = 0.3)\n",
        "        image = readImage(f\"/content/output/val/{temp_df.filename[i]}\")\n",
        "        plt.imshow(image)\n",
        "        plt.title(f'A: {temp_df.actual[i]} P: {temp_df.predicted[i]}')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_size = 32"
      ],
      "metadata": {
        "id": "XYXGvJu0_L2B"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "2hT0K_eJTAOs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "555ebf4b-6c4d-4f2d-bd60-08ff1b9e4c12"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 15 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "display_images(val_df[val_df['Same']==True])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7_4LJudjj9Q",
        "outputId": "214dbf02-ca83-4805-c52f-557c5f3ff782"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.convolutional.conv2d.Conv2D at 0x7fcc180792b0>,\n",
              " <keras.layers.pooling.max_pooling2d.MaxPooling2D at 0x7fcc182079a0>,\n",
              " <keras.layers.regularization.dropout.Dropout at 0x7fcc18207e50>,\n",
              " <keras.layers.convolutional.conv2d.Conv2D at 0x7fcc176717c0>,\n",
              " <keras.layers.pooling.max_pooling2d.MaxPooling2D at 0x7fcc17664340>,\n",
              " <keras.layers.regularization.dropout.Dropout at 0x7fcc176711f0>,\n",
              " <keras.layers.convolutional.conv2d.Conv2D at 0x7fcc18309580>,\n",
              " <keras.layers.pooling.max_pooling2d.MaxPooling2D at 0x7fcc17fd0610>,\n",
              " <keras.layers.regularization.dropout.Dropout at 0x7fcc182acbb0>,\n",
              " <keras.layers.reshaping.flatten.Flatten at 0x7fcc17402130>,\n",
              " <keras.layers.core.dense.Dense at 0x7fcc17402520>,\n",
              " <keras.layers.regularization.dropout.Dropout at 0x7fcc174025e0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fcc17402b80>,\n",
              " <keras.layers.core.dense.Dense at 0x7fcc17402fa0>]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "model.layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFXrY0E9j6lR",
        "outputId": "5c4a7a6d-5825-49c6-cb77-66fe9dd38626"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.convolutional.conv2d.Conv2D at 0x7fcc18309580>"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "model.layers[-8]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50-E-nL6hk8a",
        "outputId": "64c7cd31-5c04-46a6-f4ae-f935e80bf0c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 4) dtype=float32 (created by layer 'dense_2')>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "model.output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SoRHrJG8jPea",
        "outputId": "0c1fe4b6-a210-44a7-93ff-fcb63313c8ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.core.dense.Dense at 0x7fcc17402520>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "model.layers[-4]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "GH3ZWZ-1gckH"
      },
      "outputs": [],
      "source": [
        "def make_gradcam_heatmap(img_array, model, pred_index=None):\n",
        "    \n",
        "    grad_model = tf.keras.Model(inputs=model.inputs, outputs=[model.layers[-8].output, model.output])\n",
        "\n",
        "    with tf.GradientTape() as tape:\n",
        "        last_conv_layer_output, preds = grad_model(img_array)\n",
        "        if pred_index is None:\n",
        "            pred_index = tf.argmax(preds[0])\n",
        "        class_channel = preds[:, pred_index]\n",
        "\n",
        "    grads = tape.gradient(class_channel, last_conv_layer_output)\n",
        "\n",
        "    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n",
        "\n",
        "    last_conv_layer_output = last_conv_layer_output[0]\n",
        "    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]\n",
        "    heatmap = tf.squeeze(heatmap)\n",
        "\n",
        "    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n",
        "    return heatmap.numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "id": "a1qGh9SjhUSi",
        "outputId": "514f1297-24b3-44d3-a5fb-39c1a30eaffe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/IPython/core/pylabtools.py:128: UserWarning:\n",
            "\n",
            "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 340.364x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "heatmaps = []\n",
        "\n",
        "for img in imgs:\n",
        "  heatmap = make_gradcam_heatmap(\n",
        "      tf.expand_dims(img, axis=0), model\n",
        "  )\n",
        "  heatmaps.append(heatmap)\n",
        "\n",
        "# Display heatmap\n",
        "plt.matshow(heatmaps[0])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "oN4pPpPZllkz"
      },
      "outputs": [],
      "source": [
        "imgs, labels = validation_dataset.next()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28IpqwHqlqqT",
        "outputId": "ed88d643-1f31-4aa0-ca34-547485234f2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 101ms/step\n"
          ]
        }
      ],
      "source": [
        "pred_labels = model.predict(imgs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77ZhZWekn7bf",
        "outputId": "aefb70de-0af5-4a2e-aba7-8ea0f30eafa5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_items([('0_NonDemented', 0), ('1_VeryMildDemented', 1), ('2_MildDemented', 2), ('3_ModerateDemented', 3)])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "validation_dataset.class_indices.items()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DcgQ509-0z22",
        "outputId": "ff348b5e-b6c8-45a4-a0fd-e13ace4f7bd3"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'0_NonDemented': 0,\n",
              " '1_VeryMildDemented': 1,\n",
              " '2_MildDemented': 2,\n",
              " '3_ModerateDemented': 3}"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "from matplotlib import cm\n",
        "from IPython.display import Image\n",
        "\n",
        "for img, pred_label, true_label, heatmap in zip(imgs, pred_labels, labels, heatmaps):\n",
        "  # We rescale heatmap to a range 0-255\n",
        "  heatmap = np.uint8(255 * heatmap)\n",
        "\n",
        "  # We use jet colormap to colorize heatmap\n",
        "  jet = cm.get_cmap(\"jet\")\n",
        "\n",
        "  # We use RGB values of the colormap\n",
        "  jet_colors = jet(np.arange(256))[:, :3]\n",
        "  jet_heatmap = jet_colors[heatmap]\n",
        "\n",
        "  # We create an image with RGB colorized heatmap\n",
        "  jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)\n",
        "  jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
        "  jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)\n",
        "\n",
        "  # Superimpose the heatmap on original image\n",
        "  superimposed_img = jet_heatmap * 0.003 + img\n",
        "  superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)\n",
        "\n",
        "  # Save the superimposed image\n",
        "  save_path = \"saved_img.jpg\"\n",
        "  superimposed_img.save(save_path)\n",
        "\n",
        "  # Display Grad CAM\n",
        "  pred_file_path = np.argmax(validation_dataset.labels == pred_label)\n",
        "  pred_label_name = Path(validation_dataset.filepaths[pred_file_path]).parent.name\n",
        "\n",
        "  true_file_path = np.argmax(validation_dataset.labels == tf.argmax(true_label))\n",
        "  true_label_name = Path(validation_dataset.filepaths[true_file_path]).parent.name\n",
        "\n",
        "  print(\"Predicted label:\",pred_label_name)\n",
        "  print(\"True label:\", true_label_name)\n",
        "\n",
        "  display(Image(save_path))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7zRbBvNjxzVG",
        "outputId": "b110648e-ece0-4a3f-9703-4a6184cd296a"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 1_VeryMildDemented\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-0c4d70603574>:30: DeprecationWarning:\n",
            "\n",
            "elementwise comparison failed; this will raise an error in the future.\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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yYyPSm24JtFvnUdHdnmd9I8dvIk+cYKoMcgkVz7V6R4ptrfULcPboFxJ2HXivPbqLynK+lZVIx5XbcunV5nyvcr0UUVym4UUUUASRtjvW5pd5cBViiBGwgcd8k/8A16wUPzCtmxvfsk8fyrtdl3E9v85rtoy5ochnJ2Z2tjp08uHmlJDknG7ArZtNAgWUPwzDnCiufjvprxlW0XeuR8xFdlpQezZZLkFTjADd6UVNKzRFSTi/dV7mdeaNLfuf3nlpGOAO9UYLyGxZllRgc4BxzXVteWcLGRy4Xvg8Vy/iLV7O7YJbx42/mamFSftJX+H8jNOrz2cdDRWZJ7Xcp460yCfyJww5OelVNGLPbeWwIyDgGrkUIMq7h0Pas6jV9DqRsBTMAAvDdjUosoLV1kmYqeoAFV2ufs4VnAzngVcv76KaBHZRwvaoqSqKm/Z7nLiakoNKPUia/g1ST/SXVIkP+rAPzD/Irl7y4jutUlazBhVGIIA4PSs0+JBbaoFCnCN0I69as2uqxahcMEVULNyRXl06WOoUYJR11uTJ04OdS1trG1p915jmOUc8YbHWtGQhVx1Pasq4nCSqEx8verdlfCX5HVTjoe9exNy6nRRbcE5blGZ2a4OQBWhBemxsiR97GQKjubN2n3IcButZurvHtMYdg64xinRSs0wqw548pc/tm6uo2jEG8kYPSqEOhIQ8k2Ey2fWs611O8V8RxqR0J9a7jw9YDWrSRrkrEUOdo4zRKdKnFy6dTOnTjSTaRkSx/aYY0tsExjHPesbUdQvVhMMlm42jGQMiuk1US6fIY7FAyAdxmuen1y8t7aY3FoGVerAHvwP51rGXMryiYwlLWbjY4pdVeznuI5EwpOQvpmufu5fOlLDuatalKXfJGCetZtZVXBN2WrOilFP3+rCiiiuc3CiiigB0YzIo966eLRFu4VZZMcVy6ffH1rtNAZVjIaQfiampVnSpSnB6o5cTzJxcWdFY6RFosFnJ9vwZEViuemQK7600W11oRyfayzIAQMH+tcvaQWF/Yo8ke54CRx/FwD/WtfRvGENpL9kS12jpuFZ0MVPGUVOl8a3MqtKpGUXuy5qfhdJU2mQgL2HSuTuPD0cdwUC4I/i212z6x5zbwMj0rN1DW2kVl+yYJBGQK7qntoxvGPNJjg6+0v8Ahjn7OwmtrmNRMXQ56VuFRs4UbgOuKytHM63DNIpKnOM1txDc2PX9KqVNppPqdNPmUfedzAuDLLON+4Y6A1NcXNvFAFmn2uRgLnmusufCpvoP9HuVjYrkttziuej8KwWU5knma5mzkseAKKcoTbhTeqOWcvbO0XY5Gx8I32oXxuBGxjZ8gkgcVrN4D1bSlknEDlSdwZSDj8jXYQLIGGwEKO1dppl1E1q3muuAv3WrkzOdajS9pB38huEpSVOUtGeFy3yW0my5Z0b1INX9PP2wCS3cMPUGvQdR8N6P4nScsFt5EyQy1xlj4fGiXE6RStKpbAyMVeFxkcVTd1qtzpjCrTfJURp7cRAknIFclqNzELqTy1Jct3rrRkoc+lYraTvui2QQTniumnKEYalSjzblbRYZ2nLuFEfeujW2uXTZBMIie6nmmQ2nlJgLj8KlMklv8yoWPpU0mr6oJ3toc3r9te2EwVbyd3PPyk1kXHiHydOnsbiF2lkVSXbsAf8A61dNPqV7dXQQ2qYGRlj/APWrgvF06LfsVUAqPLIznkE1dSEX7st2ck5NxVKW7OXv5xNKSBjmqdOc7mJ96bXLNpyujshHlVgoooqSgooooAKtW93JFkByM+9VaKuEuVilFNamxYazd2F4lxHIzYzlSeGB7Gu70vWLW8n4iQnIy69jXmMbDOTWtpuqyW0gVNoViAcnGPeu1pOHuaEbM9di1K1tgA0sfB6ZzV7+07WcAKY2P1Ga88htb24c5fBHYDNdFo+jyWz+bcSlm4wOlZU5X62HKKep0GVbooBzT0YLxkbvrWbqeqrZREooLnpVfRtSuL6YrJHkYyDilzylOydx6JHWw6pJbwFd2cjFZk14DlmPQ84rN1ySdLMmEH/axxXPQ6hcW5bzEcA4+9zkVrG796OhmoQg9OpsavrMsDr9nvXiR+D+8xj86q2Wqywys9zfysoHOZSQTWVcrDfP54cIEbIUnvkUyG1t78iOW5wM9McZrLlnJWsTShGDet2dTZ+JIGnMayyKGP8AerUVxMu9WyD0Jqlpngy2kTz/ALSp/iwq1Y2LaJ5Q5VeKSUYL3d+pcasZtpPYfu2ggYqIMkR3SAKoPLHisgeIYvtJhaLA3YyW/wDrVvTWUOoac4SYAsM4zRGLavLYcpxjuTLqFsQBG8bn+6rbjVHUNTZbbfHHkkjHFcmbK4tLhnSU4U9CcZ/GtF/EEcGnSG4VpHBHAHP+cVu2k7oxlKMN9znNZ8USQLKnmsJyCFVeqnHBI/EVwk9xJM5aSRnYnJZjkk+tWNUvHvb2WeTAd25AGAB6VQrKvVd+VG8YrcKKKK5CwooooAKKKKACiiigAp6timUVUZOLuDR0nh3Xm0x3ildvJblfRT/9f/PWuxs/EETTsRdpImBwrA15YHxSrIyHKsVPscVpJwdmtBanpGoakb2bbEd5yMBRnFdfoMi2Vvl4vmK4yRXkOk67JYyxmSJZIgfmP8R/HPWuyi8U219GIIJyJmH3WGKtwaV4dSZRUtJHZvd2by7biRAhPILCrWsaRpF1bq9pfRJ3Klq86RZWYyPIG9s1Vnmv4J2ZNxiPOM0pwqqUZN6IyqUlNpp2aL+rQQQF1j2sFH3lbvWNpt0VkKswGTxnimz6zvDRzLt4x901W0hoJb52L8jkA9K6HJ046alrV2O80O8vIrlUWUmNuorXvJtsJZs9RXP2+ow2qZRCzepxUn9vQSNsuZFRCeABk5+lZ8zqatWQScaacmIumx6jG8isFZBxS6dcOkkarPyG2sM1lal4is9NOLdpHDj+EAD9TXG3WvXUrHynaIE5+Q4P50Tqab6GEOecuaL909M1e7giyPtSFuqxjGTyf0965u8uPIgZpnRiw6KMDpXD/a5i5dpHZickk5JNSTX0kygMx4rmnBSkmpWXVDqUJT0k9CGdw8rkdzUVB5NFKUnJ3Z1pWVgoooqQCiiigAooooAKKKKACiiigAooooAUEjpRuPqaSimm0BIsmCDVxL5VxlM1n0Vqq0rWZEqaluasmopIuBGBWe8gZs4qLNFP27StFWFClGGw/fTlmZD8pxUVFT7aZfKiSSZ5MbmJqOiiolJyd2NJLYKKKKkAooooAKKKKACiiigD/9k=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 2_MildDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted label: 0_NonDemented\n",
            "True label: 0_NonDemented\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": []
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3.9.15 ('openbrain')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.15 (main, Nov 24 2022, 14:39:17) [MSC v.1916 64 bit (AMD64)]"
    },
    "vscode": {
      "interpreter": {
        "hash": "171c0e0f92bd3bf1cf036f42c9519fbec7ecaa9214615e6b543c2155a0a0499e"
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  "nbformat": 4,
  "nbformat_minor": 0
}