main.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Wildemberg-Projects/Plants-Diseases/blob/main/main.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6t8Dg3G-TVX2"
      },
      "source": [
        "### Instalando dependências e Definição de Variáveis Importantes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VPKBmi1GzMdw",
        "outputId": "77a37a3c-9a46-48f8-8383-0441fcc46946"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\nfunction ClickConnect(){\nconsole.log(\"Working\");\ndocument.querySelector(\"colab-toolbar-button#connect\").click()\n}\nsetInterval(ClickConnect,60000)\n",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import IPython\n",
        "js_code = '''\n",
        "function ClickConnect(){\n",
        "console.log(\"Working\");\n",
        "document.querySelector(\"colab-toolbar-button#connect\").click()\n",
        "}\n",
        "setInterval(ClickConnect,60000)\n",
        "'''\n",
        "display(IPython.display.Javascript(js_code))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GO0dwdyQ-M_O",
        "outputId": "9a24661f-0ac8-410c-b9f0-b6211e19dd0c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.8/611.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.1/5.1 MB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for keras-utils (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.3/8.3 MB\u001b[0m \u001b[31m41.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# Instalação de Dependências\n",
        "# Para utilização do Colab, modifique o '%' por '!'\n",
        "# As abaixo comentadas são somente para instalar em ambiente local\n",
        "# %pip install -q -U tensorflow\n",
        "# %pip install -q -U keras\n",
        "# %pip install -q -U numpy\n",
        "# %pip install -q -U pandas\n",
        "# %pip install -q -U Jinja2\n",
        "# Uso da GPU precisa ser habilitado pelo comando a seguir\n",
        "# %pip install tensorflow[and-cuda]\n",
        "%pip install -q -U tensorflow-addons\n",
        "%pip install -q -U tensorflow-datasets\n",
        "%pip install -q -U keras-utils\n",
        "%pip install -q -U matplotlib\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "oOBTtaKjCVkm"
      },
      "outputs": [],
      "source": [
        "import tensorflow_datasets as tfds\n",
        "from math import sqrt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import mean\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow.keras import models, layers\n",
        "from keras.optimizers.legacy import Adam\n",
        "from tensorflow.keras.metrics import BinaryAccuracy, TruePositives, TrueNegatives, FalsePositives, FalseNegatives, PrecisionAtRecall, SensitivityAtSpecificity, SpecificityAtSensitivity, Recall\n",
        "import keras\n",
        "from tensorflow.keras.models import load_model\n",
        "from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from google.colab import files\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "tLM58miTN2bh"
      },
      "outputs": [],
      "source": [
        "IMAGE_SIZE=128\n",
        "BATCH_SIZE=8\n",
        "CHANNELS=3\n",
        "EPOCHS=30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "YqUjl9uPX1a0"
      },
      "outputs": [],
      "source": [
        "METRICS = [keras.metrics.CategoricalAccuracy(name = 'accuracy'),\n",
        "           keras.metrics.TruePositives(thresholds = 0.50, name = 'tp'),\n",
        "           keras.metrics.TrueNegatives(thresholds = 0.50, name = 'tn'),\n",
        "           keras.metrics.FalsePositives(thresholds = 0.50, name = 'fp'),\n",
        "           keras.metrics.FalseNegatives(thresholds = 0.50, name = 'fn'),\n",
        "           keras.metrics.PrecisionAtRecall(recall = 0.50, name = 'precision'),\n",
        "           keras.metrics.SensitivityAtSpecificity(0.50, name = 'sensitivity'),\n",
        "           keras.metrics.SpecificityAtSensitivity(sensitivity = 0.50,\n",
        "                                                  name = 'specificity'),\n",
        "           keras.metrics.Recall(name='recall')]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2UwaA4iTDzS"
      },
      "source": [
        "### Carregando dados para utilização"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CNowqre2CWz0"
      },
      "outputs": [],
      "source": [
        "# Coletando dados\n",
        "tf.keras.backend.clear_session() # Limpar o CACHE da seção\n",
        "(ds_train), info = tfds.load('plant_village', split='train', with_info=True)\n",
        "\n",
        "# a estrutura de uma imagem (256,256,3), representa a largura e altura em pixels,\n",
        "# e o 3 representa a quantidade de canais de cores pq é RGB"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWWrsZ6RIG2i",
        "outputId": "39101caf-c7ec-4626-b7c2-a6066701cf72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 55448 files belonging to 39 classes.\n",
            "<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int32, name=None))>\n"
          ]
        }
      ],
      "source": [
        "# Carregamento e pré-processamento das imagens locais no Colab.\n",
        "# Para utilização de um ambiente local, mude a rota do diretório de onde encontram-se as imagens\n",
        "ds = tf.keras.preprocessing.image_dataset_from_directory(\"/root/tensorflow_datasets/downloads/extracted/ZIP.data.mend.com_publ-file_data_tywb_file_d565-c1rDQyRTmE0CqGGXmH53WlQp0NWefMfDW89aj1A0m5D_A/Plant_leave_diseases_dataset_without_augmentation\",\n",
        "                                                         shuffle=True,\n",
        "                                                         image_size=(IMAGE_SIZE,IMAGE_SIZE),\n",
        "                                                         batch_size=BATCH_SIZE)\n",
        "print(ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGPkUDSaMm2k",
        "outputId": "9615332b-dafb-4a47-ffaa-bcec603b89ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy', 'Background_without_leaves', 'Blueberry___healthy', 'Cherry___Powdery_mildew', 'Cherry___healthy', 'Corn___Cercospora_leaf_spot Gray_leaf_spot', 'Corn___Common_rust', 'Corn___Northern_Leaf_Blight', 'Corn___healthy', 'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot', 'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 'Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy', 'Raspberry___healthy', 'Soybean___healthy', 'Squash___Powdery_mildew', 'Strawberry___Leaf_scorch', 'Strawberry___healthy', 'Tomato___Bacterial_spot', 'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold', 'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 'Tomato___Target_Spot', 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus', 'Tomato___healthy']\n",
            "Possui 6931 lotes de 32 imagens cada.\n"
          ]
        }
      ],
      "source": [
        "# Verificação do nome das classes do dataset\n",
        "class_name = ds.class_names\n",
        "print(class_name)\n",
        "print(\"Possui {} lotes de 32 imagens cada.\".format(len(ds)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKa6PYLlTJMo"
      },
      "source": [
        "### Observando os dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oP4jO9ckJ-Xj",
        "outputId": "af26e8b4-7745-4fc2-9174-fb441ea12487"
      },
      "outputs": [
        {
          "ename": "InvalidArgumentError",
          "evalue": "{{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 8 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-3c7688ecfb73>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m# print(image[i].shape)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m# print(label[i].numpy())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_name\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"uint8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"off\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[0;34m(e, name)\u001b[0m\n\u001b[1;32m   5881\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mraise_from_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5882\u001b[0m   \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" name: \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5883\u001b[0;31m   \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5884\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m: {{function_node __wrapped__StridedSlice_device_/job:localhost/replica:0/task:0/device:GPU:0}} slice index 8 of dimension 0 out of bounds. [Op:StridedSlice] name: strided_slice/"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for image, label in ds.take(1):\n",
        "  for i in range(12):\n",
        "    ax = plt.subplot(3,4, i+1)\n",
        "    # print(image[i].numpy())\n",
        "    # print(image[i].shape)\n",
        "    # print(label[i].numpy())\n",
        "    plt.title(class_name[label[i]])\n",
        "    plt.imshow(image[i].numpy().astype(\"uint8\"))\n",
        "    plt.axis(\"off\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2HajGm0lQLSz"
      },
      "outputs": [],
      "source": [
        "# Quantidade de lotes de 8 imagens (BATCHS)\n",
        "len(ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dGT32u5TNiO"
      },
      "source": [
        "### Criando repartição de treino, validação e teste"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FjuK3AB4Tlh5"
      },
      "outputs": [],
      "source": [
        "# Randomização dos dados\n",
        "ds = ds.shuffle(buffer_size=1000, seed=12)\n",
        "ds = ds.take(int(len(ds)/2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySOJ723-QN9B",
        "outputId": "345f8ae4-28c2-4017-f99c-f8a476453882"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1385\n"
          ]
        }
      ],
      "source": [
        "# 80% ==> training\n",
        "# 20% ==> 10% validation, 10% test\n",
        "train_size = 0.8\n",
        "train_size = int(len(ds)*train_size)\n",
        "print(train_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJ6zChn-Q8Ak",
        "outputId": "d1c040fa-4347-478c-fde4-6093a6e21bdc"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1385"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Atribuição e verificação da quantidade de dados de treino do dataset\n",
        "train_ds = ds.take(train_size)\n",
        "len(train_ds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFWEZS8pRZ6N",
        "outputId": "a13b5b02-9b29-4699-c9d7-7f129ca7baef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "174"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Separação dos 10% de validação e 10% de teste\n",
        "test_ds = ds.skip(int(train_size))\n",
        "test_size = int(len(test_ds)*0.5)\n",
        "validation_ds = test_ds.take(test_size)\n",
        "test_ds = test_ds.skip(test_size)\n",
        "len(validation_ds)\n",
        "len(test_ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGjURb6HUv7l"
      },
      "source": [
        "### Fazendo processamento dos dados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UTFl7_wCUyx3"
      },
      "outputs": [],
      "source": [
        "# Criando um cache das imagens (Deixa a imagem pré-pronta para uso)\n",
        "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "validation_ds = validation_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "test_ds = test_ds.cache().shuffle(1000).prefetch(buffer_size=tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RuvcLeraVg6m"
      },
      "outputs": [],
      "source": [
        "# Pré-processamento com ajuste dos dados e ajuste de tamanho\n",
        "# Será aplicado dentro da estrutura do modelo para garantir que sempre seja feito em todas as imagens\n",
        "resize_rescaling = tf.keras.Sequential([\n",
        "    layers.experimental.preprocessing.Resizing(*IMAGE_SIZE),\n",
        "    layers.experimental.preprocessing.Rescaling(1.0/255)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5mrBSnmTW6AU"
      },
      "outputs": [],
      "source": [
        "# Faz novas imagens para nossa base de dados com rotação e inversão para diversidade dos mesmos dados\n",
        "data_augmentation = tf.keras.Sequential([\n",
        "  layers.experimental.preprocessing.RandomFlip(\"horizontal_and_vertical\"),\n",
        "  layers.experimental.preprocessing.RandomRotation(0.2),\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uPrqqcesxapp"
      },
      "outputs": [],
      "source": [
        "nClasses = 39\n",
        "# Aplicando a função de geração das variações das imagens e ajuste do tamanho e escala das imagens nos conjuntos de treinamento e validação\n",
        "def preprocess_image(image, label):\n",
        "    image = resize_rescaling(image)\n",
        "    image = data_augmentation(image)\n",
        "    label = tf.one_hot(tf.cast(label, tf.int32), depth=nClasses)\n",
        "    return image, label\n",
        "\n",
        "train_ds = train_ds.map(preprocess_image).prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "validation_ds = validation_ds.map(\n",
        "    lambda x, y: (resize_rescaling(x), tf.one_hot(tf.cast(y, tf.int32), depth=nClasses))\n",
        ").prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "test_ds = test_ds.map(\n",
        "    lambda x, y: (resize_rescaling(x), tf.one_hot(tf.cast(y, tf.int32), depth=nClasses))\n",
        ").prefetch(buffer_size=tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51BdmDhkqMAs"
      },
      "source": [
        "### Modelo V1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOnw1zZRqPqU",
        "outputId": "422da510-d0f7-44ca-e4da-3941847726fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 126, 126, 64)      1792      \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 126, 126, 64)      256       \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 63, 63, 64)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 61, 61, 256)       147712    \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 59, 59, 256)       590080    \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 59, 59, 256)       1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 29, 29, 256)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 215296)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               55116032  \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               65792     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 39)                5031      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 55960615 (213.47 MB)\n",
            "Trainable params: 55959975 (213.47 MB)\n",
            "Non-trainable params: 640 (2.50 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "\n",
        "inputShape = (IMAGE_SIZE, IMAGE_SIZE, CHANNELS)\n",
        "\n",
        "# Definição da arquitetura do modelo\n",
        "model = models.Sequential()\n",
        "\n",
        "model.add(layers.Conv2D(filters = 64,\n",
        "                kernel_size = (3, 3),\n",
        "                input_shape = inputShape,\n",
        "                activation = 'relu'))\n",
        "\n",
        "model.add(layers.BatchNormalization()) # Polimento dos dados usando normalização\n",
        "\n",
        "model.add(layers.MaxPooling2D(pool_size = (2, 2))) # reduz a dimensão dos mapas de características\n",
        "\n",
        "model.add(layers.Conv2D(filters = 256,\n",
        "                kernel_size = (3, 3),\n",
        "                activation = 'relu'))\n",
        "\n",
        "model.add(layers.Conv2D(filters = 256,\n",
        "                kernel_size = (3, 3),\n",
        "                activation = 'relu'))\n",
        "\n",
        "model.add(layers.BatchNormalization())\n",
        "\n",
        "model.add(layers.MaxPooling2D(pool_size = (2, 2)))\n",
        "# Fim da alimentação do modelo e extração de características\n",
        "\n",
        "# Ínicio do treinamento do modelo\n",
        "model.add(layers.Flatten()) #achatamento para a próxima camada para encontrar os melhores pesos\n",
        "\n",
        "model.add(layers.Dense(units = 256,\n",
        "                activation = 'relu'))\n",
        "\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Dense(units = 256,\n",
        "                activation = 'relu'))\n",
        "\n",
        "model.add(layers.Dropout(0.2))\n",
        "\n",
        "model.add(layers.Dense(units = 128,\n",
        "                activation = 'relu'))\n",
        "\n",
        "model.add(layers.Dense(units = nClasses,\n",
        "                activation = 'softmax'))\n",
        "\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SH6Lfmz9rM0e"
      },
      "source": [
        "### Treinando o modelo V1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bxL3ixLTrPcu"
      },
      "outputs": [],
      "source": [
        "optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=0.1, decay=0.0)\n",
        "\n",
        "# Compilação do Modelo\n",
        "model.compile(loss = keras.losses.categorical_crossentropy,\n",
        "              optimizer = optimizer,\n",
        "              metrics = METRICS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ezh_YuxzrYui"
      },
      "outputs": [],
      "source": [
        "# Criando um monitoramento da acurácia\n",
        "learning_rate = keras.callbacks.ReduceLROnPlateau(monitor = 'accuracy',\n",
        "                                  factor = 0.2, # a taxa de aprendizado será multiplicada por 0.2 sempre que as condições forem satisfeitas\n",
        "                                  patience = 1, # limite de épocas que a taxa de acurácio pode não melhorar, caso ultrapasse, a taxa de aprendizado é reduzida\n",
        "                                  min_lr = 0.000001, # Limite da taxa de aprendizado, ela não irá diminuir mais que isso\n",
        "                                  verbose = 1) # usado para imprimir a mensagem que a taxa foi modificada\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CnHC3ISU4UHi"
      },
      "outputs": [],
      "source": [
        "tf.keras.backend.clear_session()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "unsplJ5KtN2b"
      },
      "outputs": [],
      "source": [
        "%%time\n",
        "\n",
        "hist = model.fit(train_ds,\n",
        "                 epochs = EPOCHS,\n",
        "                 batch_size=BATCH_SIZE,\n",
        "                 validation_data = validation_ds,\n",
        "                 validation_steps = 25,\n",
        "                 callbacks = [learning_rate],\n",
        "                 verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6pn7NeJhJhCX"
      },
      "outputs": [],
      "source": [
        "# Salvando o modelo\n",
        "model_version=\"Plant-Diseases-v1\"\n",
        "model.save(f\"{model_version}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X80RnL7v5DVd"
      },
      "source": [
        "### Modelo V2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vBewn7LeAD0W"
      },
      "outputs": [],
      "source": [
        "module_selection = (\"inception_v3\", 128, 2048) #@param [\"(\\\"mobilenet_v2\\\", 128, 1280)\", \"(\\\"inception_v3\\\", 128, 2048)\"] {type:\"raw\", allow-input: true}\n",
        "handle_base, pixels, FV_SIZE = module_selection\n",
        "MODULE_HANDLE =\"https://tfhub.dev/google/tf2-preview/{}/feature_vector/2\".format(handle_base)\n",
        "IMAGE_SIZE = (pixels, pixels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q3XjFoLH5G8r"
      },
      "outputs": [],
      "source": [
        "feature_extractor = hub.KerasLayer(MODULE_HANDLE,\n",
        "                                   input_shape=IMAGE_SIZE+(3,),\n",
        "                                   output_shape=[FV_SIZE])\n",
        "do_fine_tuning = False #@param {type:\"boolean\"}\n",
        "if do_fine_tuning:\n",
        "  feature_extractor.trainable = True\n",
        "  # unfreeze some layers of base network for fine-tuning\n",
        "  for layer in feature_extractor.layers[-30:]:\n",
        "      layer.trainable =True\n",
        "\n",
        "else:\n",
        "  feature_extractor.trainable = False\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    feature_extractor,\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dropout(rate=0.2),\n",
        "    tf.keras.layers.Dense(len(class_name), activation='softmax',\n",
        "                           kernel_regularizer=tf.keras.regularizers.l2(0.0001))\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "82Dq_oAhMAeh"
      },
      "source": [
        "Otimizando"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MklUz_u8f3t8"
      },
      "outputs": [],
      "source": [
        "optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=0.1, decay=0.0)\n",
        "# Criando um monitoramento da acurácia\n",
        "learning_rate = keras.callbacks.ReduceLROnPlateau(monitor = 'accuracy',\n",
        "                                  factor = 0.2, # a taxa de aprendizado será multiplicada por 0.2 sempre que as condições forem satisfeitas\n",
        "                                  patience = 1, # limite de épocas que a taxa de acurácio pode não melhorar, caso ultrapasse, a taxa de aprendizado é reduzida\n",
        "                                  min_lr = 0.000001, # Limite da taxa de aprendizado, ela não irá diminuir mais que isso\n",
        "                                  verbose = 1) # usado para imprimir a mensagem que a taxa foi modificada\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4Bu6mmlBFTf",
        "outputId": "e9dfda66-d631-405f-92b3-810a32f506ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "1145/1145 [==============================] - 63s 25ms/step - loss: 1.9835 - accuracy: 0.4882 - tp: 3001.0000 - tn: 347207.0000 - fp: 873.0000 - fn: 6159.0000 - precision: 0.5095 - sensitivity: 0.9487 - specificity: 0.9873 - recall: 0.3276 - val_loss: 1.2553 - val_accuracy: 0.6250 - val_tp: 100.0000 - val_tn: 7571.0000 - val_fp: 29.0000 - val_fn: 100.0000 - val_precision: 0.7778 - val_sensitivity: 0.9950 - val_specificity: 0.9962 - val_recall: 0.5000 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "1145/1145 [==============================] - 30s 23ms/step - loss: 1.1214 - accuracy: 0.6706 - tp: 5103.0000 - tn: 346937.0000 - fp: 1143.0000 - fn: 4057.0000 - precision: 0.8650 - sensitivity: 0.9806 - specificity: 0.9979 - recall: 0.5571 - val_loss: 0.8348 - val_accuracy: 0.7600 - val_tp: 123.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 77.0000 - val_precision: 0.9550 - val_sensitivity: 0.9900 - val_specificity: 0.9993 - val_recall: 0.6150 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "1145/1145 [==============================] - 29s 23ms/step - loss: 0.9596 - accuracy: 0.7114 - tp: 5698.0000 - tn: 346925.0000 - fp: 1155.0000 - fn: 3462.0000 - precision: 0.9167 - sensitivity: 0.9837 - specificity: 0.9988 - recall: 0.6221 - val_loss: 0.8242 - val_accuracy: 0.7450 - val_tp: 127.0000 - val_tn: 7575.0000 - val_fp: 25.0000 - val_fn: 73.0000 - val_precision: 0.9439 - val_sensitivity: 0.9950 - val_specificity: 0.9992 - val_recall: 0.6350 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "1145/1145 [==============================] - 29s 23ms/step - loss: 0.8552 - accuracy: 0.7407 - tp: 6043.0000 - tn: 346939.0000 - fp: 1141.0000 - fn: 3117.0000 - precision: 0.9346 - sensitivity: 0.9852 - specificity: 0.9991 - recall: 0.6597 - val_loss: 0.6421 - val_accuracy: 0.8200 - val_tp: 142.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 58.0000 - val_precision: 0.9709 - val_sensitivity: 0.9950 - val_specificity: 0.9996 - val_recall: 0.7100 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "1145/1145 [==============================] - 30s 22ms/step - loss: 0.7782 - accuracy: 0.7603 - tp: 6288.0000 - tn: 346952.0000 - fp: 1128.0000 - fn: 2872.0000 - precision: 0.9484 - sensitivity: 0.9876 - specificity: 0.9993 - recall: 0.6865 - val_loss: 0.5852 - val_accuracy: 0.8100 - val_tp: 155.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 45.0000 - val_precision: 0.9907 - val_sensitivity: 0.9900 - val_specificity: 0.9999 - val_recall: 0.7750 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "1145/1145 [==============================] - 30s 24ms/step - loss: 0.7334 - accuracy: 0.7711 - tp: 6456.0000 - tn: 346993.0000 - fp: 1087.0000 - fn: 2704.0000 - precision: 0.9614 - sensitivity: 0.9893 - specificity: 0.9995 - recall: 0.7048 - val_loss: 0.4770 - val_accuracy: 0.8350 - val_tp: 157.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 43.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.7850 - lr: 0.0010\n",
            "Epoch 7/30\n",
            "1145/1145 [==============================] - 29s 22ms/step - loss: 0.7088 - accuracy: 0.7787 - tp: 6544.0000 - tn: 347004.0000 - fp: 1076.0000 - fn: 2616.0000 - precision: 0.9673 - sensitivity: 0.9900 - specificity: 0.9996 - recall: 0.7144 - val_loss: 0.6709 - val_accuracy: 0.8100 - val_tp: 147.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 53.0000 - val_precision: 0.9902 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7350 - lr: 0.0010\n",
            "Epoch 8/30\n",
            "1145/1145 [==============================] - 30s 23ms/step - loss: 0.6674 - accuracy: 0.7862 - tp: 6668.0000 - tn: 347014.0000 - fp: 1066.0000 - fn: 2492.0000 - precision: 0.9721 - sensitivity: 0.9896 - specificity: 0.9996 - recall: 0.7279 - val_loss: 0.5465 - val_accuracy: 0.8300 - val_tp: 158.0000 - val_tn: 7578.0000 - val_fp: 22.0000 - val_fn: 42.0000 - val_precision: 0.9908 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.7900 - lr: 0.0010\n",
            "Epoch 9/30\n",
            "1145/1145 [==============================] - 30s 23ms/step - loss: 0.6760 - accuracy: 0.7896 - tp: 6692.0000 - tn: 346974.0000 - fp: 1106.0000 - fn: 2468.0000 - precision: 0.9675 - sensitivity: 0.9883 - specificity: 0.9996 - recall: 0.7306 - val_loss: 0.5289 - val_accuracy: 0.8150 - val_tp: 150.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 50.0000 - val_precision: 0.9915 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.7500 - lr: 0.0010\n",
            "Epoch 10/30\n",
            "1145/1145 [==============================] - 29s 22ms/step - loss: 0.6157 - accuracy: 0.8099 - tp: 6899.0000 - tn: 347096.0000 - fp: 984.0000 - fn: 2261.0000 - precision: 0.9786 - sensitivity: 0.9914 - specificity: 0.9997 - recall: 0.7532 - val_loss: 0.5864 - val_accuracy: 0.8100 - val_tp: 154.0000 - val_tn: 7578.0000 - val_fp: 22.0000 - val_fn: 46.0000 - val_precision: 0.9732 - val_sensitivity: 0.9950 - val_specificity: 0.9996 - val_recall: 0.7700 - lr: 0.0010\n",
            "Epoch 11/30\n",
            "1144/1145 [============================>.] - ETA: 0s - loss: 0.6201 - accuracy: 0.8031 - tp: 6824.0000 - tn: 346756.0000 - fp: 1020.0000 - fn: 2328.0000 - precision: 0.9776 - sensitivity: 0.9914 - specificity: 0.9997 - recall: 0.7456\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
            "1145/1145 [==============================] - 29s 23ms/step - loss: 0.6206 - accuracy: 0.8031 - tp: 6829.0000 - tn: 347059.0000 - fp: 1021.0000 - fn: 2331.0000 - precision: 0.9776 - sensitivity: 0.9913 - specificity: 0.9997 - recall: 0.7455 - val_loss: 0.4848 - val_accuracy: 0.8400 - val_tp: 162.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 38.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8100 - lr: 0.0010\n",
            "Epoch 12/30\n",
            "1145/1145 [==============================] - 29s 22ms/step - loss: 0.5280 - accuracy: 0.8373 - tp: 7186.0000 - tn: 347254.0000 - fp: 826.0000 - fn: 1974.0000 - precision: 0.9860 - sensitivity: 0.9931 - specificity: 0.9998 - recall: 0.7845 - val_loss: 0.4853 - val_accuracy: 0.8300 - val_tp: 155.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 45.0000 - val_precision: 0.9818 - val_sensitivity: 1.0000 - val_specificity: 0.9997 - val_recall: 0.7750 - lr: 2.0000e-04\n",
            "Epoch 13/30\n",
            "1145/1145 [==============================] - ETA: 0s - loss: 0.5172 - accuracy: 0.8371 - tp: 7254.0000 - tn: 347229.0000 - fp: 851.0000 - fn: 1906.0000 - precision: 0.9872 - sensitivity: 0.9938 - specificity: 0.9998 - recall: 0.7919\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 4.0000001899898055e-05.\n",
            "1145/1145 [==============================] - 29s 22ms/step - loss: 0.5172 - accuracy: 0.8371 - tp: 7254.0000 - tn: 347229.0000 - fp: 851.0000 - fn: 1906.0000 - precision: 0.9872 - sensitivity: 0.9938 - specificity: 0.9998 - recall: 0.7919 - val_loss: 0.4039 - val_accuracy: 0.8650 - val_tp: 167.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 33.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8350 - lr: 2.0000e-04\n",
            "Epoch 14/30\n",
            "1145/1145 [==============================] - 31s 23ms/step - loss: 0.4956 - accuracy: 0.8453 - tp: 7330.0000 - tn: 347261.0000 - fp: 819.0000 - fn: 1830.0000 - precision: 0.9883 - sensitivity: 0.9943 - specificity: 0.9998 - recall: 0.8002 - val_loss: 0.4656 - val_accuracy: 0.8700 - val_tp: 166.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 34.0000 - val_precision: 0.9905 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8300 - lr: 4.0000e-05\n",
            "Epoch 15/30\n",
            "1144/1145 [============================>.] - ETA: 0s - loss: 0.5029 - accuracy: 0.8428 - tp: 7302.0000 - tn: 346923.0000 - fp: 853.0000 - fn: 1850.0000 - precision: 0.9881 - sensitivity: 0.9934 - specificity: 0.9998 - recall: 0.7979\n",
            "Epoch 15: ReduceLROnPlateau reducing learning rate to 8.000000525498762e-06.\n",
            "1145/1145 [==============================] - 29s 22ms/step - loss: 0.5025 - accuracy: 0.8429 - tp: 7310.0000 - tn: 347227.0000 - fp: 853.0000 - fn: 1850.0000 - precision: 0.9882 - sensitivity: 0.9934 - specificity: 0.9998 - recall: 0.7980 - val_loss: 0.5803 - val_accuracy: 0.8050 - val_tp: 156.0000 - val_tn: 7574.0000 - val_fp: 26.0000 - val_fn: 44.0000 - val_precision: 0.9903 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.7800 - lr: 4.0000e-05\n",
            "Epoch 16/30\n",
            "1143/1145 [============================>.] - ETA: 0s - loss: 0.5043 - accuracy: 0.8408 - tp: 7272.0000 - tn: 346641.0000 - fp: 831.0000 - fn: 1872.0000 - precision: 0.9857 - sensitivity: 0.9938 - specificity: 0.9998 - recall: 0.7953\n",
            "Epoch 16: ReduceLROnPlateau reducing learning rate to 1.6000001778593287e-06.\n",
            "1145/1145 [==============================] - 29s 23ms/step - loss: 0.5049 - accuracy: 0.8407 - tp: 7280.0000 - tn: 347247.0000 - fp: 833.0000 - fn: 1880.0000 - precision: 0.9857 - sensitivity: 0.9938 - specificity: 0.9998 - recall: 0.7948 - val_loss: 0.4875 - val_accuracy: 0.8550 - val_tp: 163.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 37.0000 - val_precision: 0.9906 - val_sensitivity: 0.9900 - val_specificity: 0.9999 - val_recall: 0.8150 - lr: 8.0000e-06\n",
            "Epoch 17/30\n",
            "1143/1145 [============================>.] - ETA: 0s - loss: 0.4913 - accuracy: 0.8422 - tp: 7248.0000 - tn: 346668.0000 - fp: 804.0000 - fn: 1896.0000 - precision: 0.9885 - sensitivity: 0.9945 - specificity: 0.9998 - recall: 0.7927\n",
            "Epoch 17: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
            "1145/1145 [==============================] - 39s 32ms/step - loss: 0.4914 - accuracy: 0.8420 - tp: 7259.0000 - tn: 347273.0000 - fp: 807.0000 - fn: 1901.0000 - precision: 0.9885 - sensitivity: 0.9945 - specificity: 0.9998 - recall: 0.7925 - val_loss: 0.3920 - val_accuracy: 0.8650 - val_tp: 165.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.6000e-06\n",
            "Epoch 18/30\n",
            "1145/1145 [==============================] - 32s 25ms/step - loss: 0.4866 - accuracy: 0.8467 - tp: 7320.0000 - tn: 347278.0000 - fp: 802.0000 - fn: 1840.0000 - precision: 0.9891 - sensitivity: 0.9948 - specificity: 0.9999 - recall: 0.7991 - val_loss: 0.3646 - val_accuracy: 0.8750 - val_tp: 169.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 31.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 19/30\n",
            "1145/1145 [==============================] - 33s 25ms/step - loss: 0.4979 - accuracy: 0.8428 - tp: 7281.0000 - tn: 347230.0000 - fp: 850.0000 - fn: 1879.0000 - precision: 0.9907 - sensitivity: 0.9949 - specificity: 0.9999 - recall: 0.7949 - val_loss: 0.3953 - val_accuracy: 0.8700 - val_tp: 168.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 32.0000 - val_precision: 0.9921 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8400 - lr: 1.0000e-06\n",
            "Epoch 20/30\n",
            "1145/1145 [==============================] - 28s 22ms/step - loss: 0.4991 - accuracy: 0.8425 - tp: 7314.0000 - tn: 347240.0000 - fp: 840.0000 - fn: 1846.0000 - precision: 0.9899 - sensitivity: 0.9947 - specificity: 0.9999 - recall: 0.7985 - val_loss: 0.3709 - val_accuracy: 0.8550 - val_tp: 166.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 34.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8300 - lr: 1.0000e-06\n",
            "Epoch 21/30\n",
            "1145/1145 [==============================] - 30s 22ms/step - loss: 0.4932 - accuracy: 0.8432 - tp: 7300.0000 - tn: 347255.0000 - fp: 825.0000 - fn: 1860.0000 - precision: 0.9888 - sensitivity: 0.9936 - specificity: 0.9999 - recall: 0.7969 - val_loss: 0.4030 - val_accuracy: 0.8650 - val_tp: 161.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 39.0000 - val_precision: 0.9919 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 22/30\n",
            "1145/1145 [==============================] - 29s 23ms/step - loss: 0.5066 - accuracy: 0.8386 - tp: 7253.0000 - tn: 347218.0000 - fp: 862.0000 - fn: 1907.0000 - precision: 0.9877 - sensitivity: 0.9928 - specificity: 0.9998 - recall: 0.7918 - val_loss: 0.3821 - val_accuracy: 0.8750 - val_tp: 171.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 29.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 23/30\n",
            "1145/1145 [==============================] - 37s 30ms/step - loss: 0.4833 - accuracy: 0.8492 - tp: 7354.0000 - tn: 347279.0000 - fp: 801.0000 - fn: 1806.0000 - precision: 0.9916 - sensitivity: 0.9937 - specificity: 0.9999 - recall: 0.8028 - val_loss: 0.4567 - val_accuracy: 0.8600 - val_tp: 160.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 40.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8000 - lr: 1.0000e-06\n",
            "Epoch 24/30\n",
            "1145/1145 [==============================] - 31s 25ms/step - loss: 0.4893 - accuracy: 0.8415 - tp: 7247.0000 - tn: 347231.0000 - fp: 849.0000 - fn: 1913.0000 - precision: 0.9885 - sensitivity: 0.9947 - specificity: 0.9998 - recall: 0.7912 - val_loss: 0.4534 - val_accuracy: 0.8850 - val_tp: 165.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 25/30\n",
            "1145/1145 [==============================] - 35s 28ms/step - loss: 0.5034 - accuracy: 0.8409 - tp: 7324.0000 - tn: 347238.0000 - fp: 842.0000 - fn: 1836.0000 - precision: 0.9883 - sensitivity: 0.9945 - specificity: 0.9998 - recall: 0.7996 - val_loss: 0.5534 - val_accuracy: 0.8200 - val_tp: 150.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 50.0000 - val_precision: 0.9912 - val_sensitivity: 0.9850 - val_specificity: 0.9999 - val_recall: 0.7500 - lr: 1.0000e-06\n",
            "Epoch 26/30\n",
            "1145/1145 [==============================] - 34s 27ms/step - loss: 0.4777 - accuracy: 0.8473 - tp: 7345.0000 - tn: 347279.0000 - fp: 801.0000 - fn: 1815.0000 - precision: 0.9923 - sensitivity: 0.9944 - specificity: 0.9999 - recall: 0.8019 - val_loss: 0.4187 - val_accuracy: 0.8650 - val_tp: 164.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 36.0000 - val_precision: 0.9921 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8200 - lr: 1.0000e-06\n",
            "Epoch 27/30\n",
            "1145/1145 [==============================] - 30s 23ms/step - loss: 0.4830 - accuracy: 0.8469 - tp: 7318.0000 - tn: 347261.0000 - fp: 819.0000 - fn: 1842.0000 - precision: 0.9865 - sensitivity: 0.9957 - specificity: 0.9998 - recall: 0.7989 - val_loss: 0.4673 - val_accuracy: 0.8450 - val_tp: 162.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 38.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 28/30\n",
            "1145/1145 [==============================] - 33s 26ms/step - loss: 0.4924 - accuracy: 0.8444 - tp: 7287.0000 - tn: 347271.0000 - fp: 809.0000 - fn: 1873.0000 - precision: 0.9893 - sensitivity: 0.9940 - specificity: 0.9999 - recall: 0.7955 - val_loss: 0.2555 - val_accuracy: 0.9150 - val_tp: 181.0000 - val_tn: 7594.0000 - val_fp: 6.0000 - val_fn: 19.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.9050 - lr: 1.0000e-06\n",
            "Epoch 29/30\n",
            "1145/1145 [==============================] - 35s 28ms/step - loss: 0.4900 - accuracy: 0.8424 - tp: 7270.0000 - tn: 347251.0000 - fp: 829.0000 - fn: 1890.0000 - precision: 0.9899 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.7937 - val_loss: 0.3648 - val_accuracy: 0.8900 - val_tp: 169.0000 - val_tn: 7591.0000 - val_fp: 9.0000 - val_fn: 31.0000 - val_precision: 0.9918 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 30/30\n",
            "1145/1145 [==============================] - 34s 26ms/step - loss: 0.4808 - accuracy: 0.8426 - tp: 7271.0000 - tn: 347257.0000 - fp: 823.0000 - fn: 1889.0000 - precision: 0.9905 - sensitivity: 0.9956 - specificity: 0.9999 - recall: 0.7938 - val_loss: 0.4696 - val_accuracy: 0.8350 - val_tp: 157.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 43.0000 - val_precision: 0.9820 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.7850 - lr: 1.0000e-06\n",
            "174/174 [==============================] - 45s 18ms/step - loss: 0.4423 - accuracy: 0.8606 - tp: 1144.0000 - tn: 52795.0000 - fp: 101.0000 - fn: 248.0000 - precision: 0.9930 - sensitivity: 0.9971 - specificity: 0.9999 - recall: 0.8218\n",
            "Epoch 1/30\n",
            "1145/1145 [==============================] - 36s 30ms/step - loss: 0.4806 - accuracy: 0.8471 - tp: 8450.0000 - tn: 400051.0000 - fp: 925.0000 - fn: 2102.0000 - precision: 0.9891 - sensitivity: 0.9948 - specificity: 0.9999 - recall: 0.8008 - val_loss: 0.3837 - val_accuracy: 0.8600 - val_tp: 160.0000 - val_tn: 7577.0000 - val_fp: 23.0000 - val_fn: 40.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8000 - lr: 1.0000e-06\n",
            "Epoch 2/30\n",
            "1145/1145 [==============================] - 32s 28ms/step - loss: 0.4839 - accuracy: 0.8431 - tp: 7314.0000 - tn: 347232.0000 - fp: 848.0000 - fn: 1846.0000 - precision: 0.9893 - sensitivity: 0.9950 - specificity: 0.9999 - recall: 0.7985 - val_loss: 0.3635 - val_accuracy: 0.9000 - val_tp: 169.0000 - val_tn: 7590.0000 - val_fp: 10.0000 - val_fn: 31.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 3/30\n",
            "1145/1145 [==============================] - 32s 28ms/step - loss: 0.5094 - accuracy: 0.8362 - tp: 7242.0000 - tn: 347199.0000 - fp: 881.0000 - fn: 1918.0000 - precision: 0.9872 - sensitivity: 0.9941 - specificity: 0.9998 - recall: 0.7906 - val_loss: 0.3407 - val_accuracy: 0.8850 - val_tp: 168.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 32.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8400 - lr: 1.0000e-06\n",
            "Epoch 4/30\n",
            "1145/1145 [==============================] - 34s 29ms/step - loss: 0.4880 - accuracy: 0.8450 - tp: 7314.0000 - tn: 347243.0000 - fp: 837.0000 - fn: 1846.0000 - precision: 0.9906 - sensitivity: 0.9938 - specificity: 0.9999 - recall: 0.7985 - val_loss: 0.3992 - val_accuracy: 0.8600 - val_tp: 159.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 41.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 5/30\n",
            "1145/1145 [==============================] - 35s 30ms/step - loss: 0.4852 - accuracy: 0.8480 - tp: 7354.0000 - tn: 347264.0000 - fp: 816.0000 - fn: 1806.0000 - precision: 0.9886 - sensitivity: 0.9932 - specificity: 0.9998 - recall: 0.8028 - val_loss: 0.3904 - val_accuracy: 0.8950 - val_tp: 176.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 24.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8800 - lr: 1.0000e-06\n",
            "Epoch 6/30\n",
            "1145/1145 [==============================] - 36s 31ms/step - loss: 0.4801 - accuracy: 0.8448 - tp: 7327.0000 - tn: 347266.0000 - fp: 814.0000 - fn: 1833.0000 - precision: 0.9905 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.7999 - val_loss: 0.4934 - val_accuracy: 0.8300 - val_tp: 160.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 40.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8000 - lr: 1.0000e-06\n",
            "Epoch 7/30\n",
            "1145/1145 [==============================] - 37s 32ms/step - loss: 0.4780 - accuracy: 0.8465 - tp: 7358.0000 - tn: 347282.0000 - fp: 798.0000 - fn: 1802.0000 - precision: 0.9918 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.8033 - val_loss: 0.4748 - val_accuracy: 0.8450 - val_tp: 162.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 38.0000 - val_precision: 0.9847 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 8/30\n",
            "1145/1145 [==============================] - 44s 38ms/step - loss: 0.4796 - accuracy: 0.8502 - tp: 7356.0000 - tn: 347276.0000 - fp: 804.0000 - fn: 1804.0000 - precision: 0.9929 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.8031 - val_loss: 0.4178 - val_accuracy: 0.8750 - val_tp: 169.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 31.0000 - val_precision: 0.9915 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 9/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4837 - accuracy: 0.8443 - tp: 7348.0000 - tn: 347264.0000 - fp: 816.0000 - fn: 1812.0000 - precision: 0.9898 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.8022 - val_loss: 0.4287 - val_accuracy: 0.8750 - val_tp: 163.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 37.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 10/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4829 - accuracy: 0.8469 - tp: 7350.0000 - tn: 347273.0000 - fp: 807.0000 - fn: 1810.0000 - precision: 0.9893 - sensitivity: 0.9936 - specificity: 0.9999 - recall: 0.8024 - val_loss: 0.5042 - val_accuracy: 0.8250 - val_tp: 156.0000 - val_tn: 7578.0000 - val_fp: 22.0000 - val_fn: 44.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.7800 - lr: 1.0000e-06\n",
            "Epoch 11/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4777 - accuracy: 0.8519 - tp: 7340.0000 - tn: 347293.0000 - fp: 787.0000 - fn: 1820.0000 - precision: 0.9918 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.8013 - val_loss: 0.4087 - val_accuracy: 0.8850 - val_tp: 169.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 31.0000 - val_precision: 0.9903 - val_sensitivity: 0.9900 - val_specificity: 0.9999 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 12/30\n",
            "1145/1145 [==============================] - 45s 39ms/step - loss: 0.4862 - accuracy: 0.8479 - tp: 7318.0000 - tn: 347274.0000 - fp: 806.0000 - fn: 1842.0000 - precision: 0.9885 - sensitivity: 0.9944 - specificity: 0.9998 - recall: 0.7989 - val_loss: 0.4047 - val_accuracy: 0.8600 - val_tp: 166.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 34.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8300 - lr: 1.0000e-06\n",
            "Epoch 13/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4924 - accuracy: 0.8430 - tp: 7311.0000 - tn: 347244.0000 - fp: 836.0000 - fn: 1849.0000 - precision: 0.9883 - sensitivity: 0.9936 - specificity: 0.9998 - recall: 0.7981 - val_loss: 0.4315 - val_accuracy: 0.8300 - val_tp: 160.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 40.0000 - val_precision: 0.9912 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8000 - lr: 1.0000e-06\n",
            "Epoch 14/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4927 - accuracy: 0.8471 - tp: 7337.0000 - tn: 347248.0000 - fp: 832.0000 - fn: 1823.0000 - precision: 0.9875 - sensitivity: 0.9934 - specificity: 0.9998 - recall: 0.8010 - val_loss: 0.4218 - val_accuracy: 0.8600 - val_tp: 167.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 33.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8350 - lr: 1.0000e-06\n",
            "Epoch 15/30\n",
            "1145/1145 [==============================] - 36s 31ms/step - loss: 0.5006 - accuracy: 0.8385 - tp: 7206.0000 - tn: 347208.0000 - fp: 872.0000 - fn: 1954.0000 - precision: 0.9866 - sensitivity: 0.9942 - specificity: 0.9998 - recall: 0.7867 - val_loss: 0.5471 - val_accuracy: 0.8200 - val_tp: 149.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 51.0000 - val_precision: 0.9823 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.7450 - lr: 1.0000e-06\n",
            "Epoch 16/30\n",
            "1145/1145 [==============================] - 38s 34ms/step - loss: 0.4879 - accuracy: 0.8471 - tp: 7325.0000 - tn: 347249.0000 - fp: 831.0000 - fn: 1835.0000 - precision: 0.9909 - sensitivity: 0.9948 - specificity: 0.9999 - recall: 0.7997 - val_loss: 0.3559 - val_accuracy: 0.8950 - val_tp: 173.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 27.0000 - val_precision: 0.9926 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8650 - lr: 1.0000e-06\n",
            "Epoch 17/30\n",
            "1145/1145 [==============================] - 37s 32ms/step - loss: 0.4848 - accuracy: 0.8481 - tp: 7350.0000 - tn: 347258.0000 - fp: 822.0000 - fn: 1810.0000 - precision: 0.9899 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.8024 - val_loss: 0.4716 - val_accuracy: 0.8350 - val_tp: 158.0000 - val_tn: 7581.0000 - val_fp: 19.0000 - val_fn: 42.0000 - val_precision: 0.9815 - val_sensitivity: 1.0000 - val_specificity: 0.9997 - val_recall: 0.7900 - lr: 1.0000e-06\n",
            "Epoch 18/30\n",
            "1145/1145 [==============================] - 39s 34ms/step - loss: 0.4979 - accuracy: 0.8432 - tp: 7310.0000 - tn: 347242.0000 - fp: 838.0000 - fn: 1850.0000 - precision: 0.9869 - sensitivity: 0.9947 - specificity: 0.9998 - recall: 0.7980 - val_loss: 0.3903 - val_accuracy: 0.8650 - val_tp: 162.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 38.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 19/30\n",
            "1145/1145 [==============================] - 37s 32ms/step - loss: 0.5006 - accuracy: 0.8380 - tp: 7246.0000 - tn: 347200.0000 - fp: 880.0000 - fn: 1914.0000 - precision: 0.9898 - sensitivity: 0.9939 - specificity: 0.9999 - recall: 0.7910 - val_loss: 0.4417 - val_accuracy: 0.8750 - val_tp: 170.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 30.0000 - val_precision: 0.9919 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 20/30\n",
            "1145/1145 [==============================] - 37s 32ms/step - loss: 0.4975 - accuracy: 0.8466 - tp: 7353.0000 - tn: 347257.0000 - fp: 823.0000 - fn: 1807.0000 - precision: 0.9890 - sensitivity: 0.9929 - specificity: 0.9999 - recall: 0.8027 - val_loss: 0.5089 - val_accuracy: 0.8200 - val_tp: 156.0000 - val_tn: 7577.0000 - val_fp: 23.0000 - val_fn: 44.0000 - val_precision: 0.9905 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.7800 - lr: 1.0000e-06\n",
            "Epoch 21/30\n",
            "1145/1145 [==============================] - 35s 31ms/step - loss: 0.4815 - accuracy: 0.8440 - tp: 7292.0000 - tn: 347256.0000 - fp: 824.0000 - fn: 1868.0000 - precision: 0.9894 - sensitivity: 0.9952 - specificity: 0.9999 - recall: 0.7961 - val_loss: 0.4552 - val_accuracy: 0.8750 - val_tp: 163.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 37.0000 - val_precision: 0.9910 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 22/30\n",
            "1145/1145 [==============================] - 36s 32ms/step - loss: 0.4901 - accuracy: 0.8428 - tp: 7295.0000 - tn: 347260.0000 - fp: 820.0000 - fn: 1865.0000 - precision: 0.9887 - sensitivity: 0.9948 - specificity: 0.9998 - recall: 0.7964 - val_loss: 0.4156 - val_accuracy: 0.8500 - val_tp: 162.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 38.0000 - val_precision: 0.9928 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 23/30\n",
            "1145/1145 [==============================] - 37s 32ms/step - loss: 0.4787 - accuracy: 0.8493 - tp: 7368.0000 - tn: 347275.0000 - fp: 805.0000 - fn: 1792.0000 - precision: 0.9906 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.8044 - val_loss: 0.3789 - val_accuracy: 0.9000 - val_tp: 171.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 29.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 24/30\n",
            "1145/1145 [==============================] - 36s 31ms/step - loss: 0.4804 - accuracy: 0.8460 - tp: 7342.0000 - tn: 347236.0000 - fp: 844.0000 - fn: 1818.0000 - precision: 0.9910 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.8015 - val_loss: 0.5114 - val_accuracy: 0.8350 - val_tp: 156.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 44.0000 - val_precision: 0.9903 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7800 - lr: 1.0000e-06\n",
            "Epoch 25/30\n",
            "1145/1145 [==============================] - 36s 32ms/step - loss: 0.4814 - accuracy: 0.8471 - tp: 7345.0000 - tn: 347257.0000 - fp: 823.0000 - fn: 1815.0000 - precision: 0.9912 - sensitivity: 0.9939 - specificity: 0.9999 - recall: 0.8019 - val_loss: 0.4480 - val_accuracy: 0.8650 - val_tp: 168.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 32.0000 - val_precision: 0.9829 - val_sensitivity: 1.0000 - val_specificity: 0.9997 - val_recall: 0.8400 - lr: 1.0000e-06\n",
            "Epoch 26/30\n",
            "1145/1145 [==============================] - 36s 31ms/step - loss: 0.4995 - accuracy: 0.8395 - tp: 7273.0000 - tn: 347229.0000 - fp: 851.0000 - fn: 1887.0000 - precision: 0.9890 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.7940 - val_loss: 0.5027 - val_accuracy: 0.8350 - val_tp: 161.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 39.0000 - val_precision: 0.9904 - val_sensitivity: 0.9850 - val_specificity: 0.9999 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 27/30\n",
            "1145/1145 [==============================] - 39s 34ms/step - loss: 0.4756 - accuracy: 0.8486 - tp: 7358.0000 - tn: 347266.0000 - fp: 814.0000 - fn: 1802.0000 - precision: 0.9921 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.8033 - val_loss: 0.3922 - val_accuracy: 0.9000 - val_tp: 169.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 31.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 28/30\n",
            "1145/1145 [==============================] - 39s 34ms/step - loss: 0.4813 - accuracy: 0.8457 - tp: 7337.0000 - tn: 347255.0000 - fp: 825.0000 - fn: 1823.0000 - precision: 0.9905 - sensitivity: 0.9949 - specificity: 0.9999 - recall: 0.8010 - val_loss: 0.3906 - val_accuracy: 0.8700 - val_tp: 165.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 29/30\n",
            "1145/1145 [==============================] - 39s 34ms/step - loss: 0.4922 - accuracy: 0.8432 - tp: 7311.0000 - tn: 347222.0000 - fp: 858.0000 - fn: 1849.0000 - precision: 0.9904 - sensitivity: 0.9944 - specificity: 0.9999 - recall: 0.7981 - val_loss: 0.4017 - val_accuracy: 0.8900 - val_tp: 163.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 37.0000 - val_precision: 0.9843 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 30/30\n",
            "1145/1145 [==============================] - 36s 32ms/step - loss: 0.4887 - accuracy: 0.8427 - tp: 7311.0000 - tn: 347269.0000 - fp: 811.0000 - fn: 1849.0000 - precision: 0.9946 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.7981 - val_loss: 0.4555 - val_accuracy: 0.8550 - val_tp: 163.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 37.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "174/174 [==============================] - 3s 18ms/step - loss: 0.4406 - accuracy: 0.8578 - tp: 1145.0000 - tn: 52795.0000 - fp: 101.0000 - fn: 247.0000 - precision: 0.9930 - sensitivity: 0.9971 - specificity: 0.9999 - recall: 0.8226\n",
            "Epoch 1/30\n",
            "1145/1145 [==============================] - 46s 38ms/step - loss: 0.4862 - accuracy: 0.8448 - tp: 8484.0000 - tn: 400021.0000 - fp: 955.0000 - fn: 2068.0000 - precision: 0.9905 - sensitivity: 0.9958 - specificity: 0.9999 - recall: 0.8040 - val_loss: 0.3857 - val_accuracy: 0.8900 - val_tp: 171.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 29.0000 - val_precision: 0.9922 - val_sensitivity: 0.9850 - val_specificity: 0.9999 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 2/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4914 - accuracy: 0.8445 - tp: 7308.0000 - tn: 347267.0000 - fp: 813.0000 - fn: 1852.0000 - precision: 0.9875 - sensitivity: 0.9950 - specificity: 0.9998 - recall: 0.7978 - val_loss: 0.5313 - val_accuracy: 0.8250 - val_tp: 152.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 48.0000 - val_precision: 0.9675 - val_sensitivity: 0.9900 - val_specificity: 0.9995 - val_recall: 0.7600 - lr: 1.0000e-06\n",
            "Epoch 3/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4961 - accuracy: 0.8466 - tp: 7302.0000 - tn: 347261.0000 - fp: 819.0000 - fn: 1858.0000 - precision: 0.9880 - sensitivity: 0.9950 - specificity: 0.9998 - recall: 0.7972 - val_loss: 0.4410 - val_accuracy: 0.8650 - val_tp: 163.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 37.0000 - val_precision: 0.9915 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 4/30\n",
            "1145/1145 [==============================] - 43s 37ms/step - loss: 0.4897 - accuracy: 0.8467 - tp: 7352.0000 - tn: 347279.0000 - fp: 801.0000 - fn: 1808.0000 - precision: 0.9875 - sensitivity: 0.9937 - specificity: 0.9998 - recall: 0.8026 - val_loss: 0.3841 - val_accuracy: 0.8750 - val_tp: 167.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 33.0000 - val_precision: 0.9930 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8350 - lr: 1.0000e-06\n",
            "Epoch 5/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4869 - accuracy: 0.8452 - tp: 7307.0000 - tn: 347272.0000 - fp: 808.0000 - fn: 1853.0000 - precision: 0.9885 - sensitivity: 0.9948 - specificity: 0.9998 - recall: 0.7977 - val_loss: 0.3994 - val_accuracy: 0.8850 - val_tp: 162.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 38.0000 - val_precision: 0.9748 - val_sensitivity: 0.9950 - val_specificity: 0.9996 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 6/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4953 - accuracy: 0.8441 - tp: 7320.0000 - tn: 347243.0000 - fp: 837.0000 - fn: 1840.0000 - precision: 0.9890 - sensitivity: 0.9947 - specificity: 0.9999 - recall: 0.7991 - val_loss: 0.3812 - val_accuracy: 0.8850 - val_tp: 171.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 29.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 7/30\n",
            "1145/1145 [==============================] - 43s 37ms/step - loss: 0.4673 - accuracy: 0.8536 - tp: 7415.0000 - tn: 347316.0000 - fp: 764.0000 - fn: 1745.0000 - precision: 0.9922 - sensitivity: 0.9959 - specificity: 0.9999 - recall: 0.8095 - val_loss: 0.4087 - val_accuracy: 0.8700 - val_tp: 165.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 8/30\n",
            "1145/1145 [==============================] - 44s 38ms/step - loss: 0.5022 - accuracy: 0.8440 - tp: 7317.0000 - tn: 347243.0000 - fp: 837.0000 - fn: 1843.0000 - precision: 0.9887 - sensitivity: 0.9940 - specificity: 0.9998 - recall: 0.7988 - val_loss: 0.3995 - val_accuracy: 0.8650 - val_tp: 162.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 38.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 9/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4947 - accuracy: 0.8434 - tp: 7289.0000 - tn: 347262.0000 - fp: 818.0000 - fn: 1871.0000 - precision: 0.9895 - sensitivity: 0.9940 - specificity: 0.9999 - recall: 0.7957 - val_loss: 0.5160 - val_accuracy: 0.8550 - val_tp: 164.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 36.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.8200 - lr: 1.0000e-06\n",
            "Epoch 10/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4756 - accuracy: 0.8451 - tp: 7341.0000 - tn: 347275.0000 - fp: 805.0000 - fn: 1819.0000 - precision: 0.9903 - sensitivity: 0.9952 - specificity: 0.9999 - recall: 0.8014 - val_loss: 0.4782 - val_accuracy: 0.8600 - val_tp: 164.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 36.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8200 - lr: 1.0000e-06\n",
            "Epoch 11/30\n",
            "1145/1145 [==============================] - 44s 39ms/step - loss: 0.4779 - accuracy: 0.8480 - tp: 7373.0000 - tn: 347272.0000 - fp: 808.0000 - fn: 1787.0000 - precision: 0.9909 - sensitivity: 0.9956 - specificity: 0.9999 - recall: 0.8049 - val_loss: 0.3713 - val_accuracy: 0.8650 - val_tp: 165.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 12/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4830 - accuracy: 0.8462 - tp: 7352.0000 - tn: 347277.0000 - fp: 803.0000 - fn: 1808.0000 - precision: 0.9883 - sensitivity: 0.9943 - specificity: 0.9998 - recall: 0.8026 - val_loss: 0.3646 - val_accuracy: 0.8900 - val_tp: 169.0000 - val_tn: 7591.0000 - val_fp: 9.0000 - val_fn: 31.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 13/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4906 - accuracy: 0.8436 - tp: 7295.0000 - tn: 347252.0000 - fp: 828.0000 - fn: 1865.0000 - precision: 0.9911 - sensitivity: 0.9940 - specificity: 0.9999 - recall: 0.7964 - val_loss: 0.3802 - val_accuracy: 0.8850 - val_tp: 167.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 33.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8350 - lr: 1.0000e-06\n",
            "Epoch 14/30\n",
            "1145/1145 [==============================] - 44s 38ms/step - loss: 0.5109 - accuracy: 0.8372 - tp: 7232.0000 - tn: 347211.0000 - fp: 869.0000 - fn: 1928.0000 - precision: 0.9867 - sensitivity: 0.9943 - specificity: 0.9998 - recall: 0.7895 - val_loss: 0.5155 - val_accuracy: 0.8250 - val_tp: 159.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 41.0000 - val_precision: 0.9906 - val_sensitivity: 0.9850 - val_specificity: 0.9999 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 15/30\n",
            "1145/1145 [==============================] - 43s 37ms/step - loss: 0.4787 - accuracy: 0.8520 - tp: 7397.0000 - tn: 347299.0000 - fp: 781.0000 - fn: 1763.0000 - precision: 0.9925 - sensitivity: 0.9938 - specificity: 0.9999 - recall: 0.8075 - val_loss: 0.5478 - val_accuracy: 0.8200 - val_tp: 155.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 45.0000 - val_precision: 0.9908 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7750 - lr: 1.0000e-06\n",
            "Epoch 16/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4850 - accuracy: 0.8447 - tp: 7311.0000 - tn: 347265.0000 - fp: 815.0000 - fn: 1849.0000 - precision: 0.9891 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.7981 - val_loss: 0.4693 - val_accuracy: 0.8200 - val_tp: 159.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 41.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 17/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4946 - accuracy: 0.8376 - tp: 7255.0000 - tn: 347240.0000 - fp: 840.0000 - fn: 1905.0000 - precision: 0.9918 - sensitivity: 0.9952 - specificity: 0.9999 - recall: 0.7920 - val_loss: 0.3276 - val_accuracy: 0.9050 - val_tp: 173.0000 - val_tn: 7591.0000 - val_fp: 9.0000 - val_fn: 27.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8650 - lr: 1.0000e-06\n",
            "Epoch 18/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.5028 - accuracy: 0.8426 - tp: 7318.0000 - tn: 347287.0000 - fp: 793.0000 - fn: 1842.0000 - precision: 0.9891 - sensitivity: 0.9929 - specificity: 0.9999 - recall: 0.7989 - val_loss: 0.6271 - val_accuracy: 0.8100 - val_tp: 152.0000 - val_tn: 7578.0000 - val_fp: 22.0000 - val_fn: 48.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.7600 - lr: 1.0000e-06\n",
            "Epoch 19/30\n",
            "1145/1145 [==============================] - 44s 38ms/step - loss: 0.4816 - accuracy: 0.8447 - tp: 7317.0000 - tn: 347256.0000 - fp: 824.0000 - fn: 1843.0000 - precision: 0.9905 - sensitivity: 0.9956 - specificity: 0.9999 - recall: 0.7988 - val_loss: 0.4859 - val_accuracy: 0.8500 - val_tp: 156.0000 - val_tn: 7581.0000 - val_fp: 19.0000 - val_fn: 44.0000 - val_precision: 0.9905 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.7800 - lr: 1.0000e-06\n",
            "Epoch 20/30\n",
            "1145/1145 [==============================] - 43s 38ms/step - loss: 0.4888 - accuracy: 0.8462 - tp: 7299.0000 - tn: 347245.0000 - fp: 835.0000 - fn: 1861.0000 - precision: 0.9899 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.7968 - val_loss: 0.4661 - val_accuracy: 0.8250 - val_tp: 158.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 42.0000 - val_precision: 0.9901 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7900 - lr: 1.0000e-06\n",
            "Epoch 21/30\n",
            "1145/1145 [==============================] - 43s 38ms/step - loss: 0.4788 - accuracy: 0.8510 - tp: 7382.0000 - tn: 347307.0000 - fp: 773.0000 - fn: 1778.0000 - precision: 0.9906 - sensitivity: 0.9944 - specificity: 0.9999 - recall: 0.8059 - val_loss: 0.2443 - val_accuracy: 0.9100 - val_tp: 178.0000 - val_tn: 7593.0000 - val_fp: 7.0000 - val_fn: 22.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8900 - lr: 1.0000e-06\n",
            "Epoch 22/30\n",
            "1145/1145 [==============================] - 43s 37ms/step - loss: 0.4909 - accuracy: 0.8425 - tp: 7298.0000 - tn: 347278.0000 - fp: 802.0000 - fn: 1862.0000 - precision: 0.9912 - sensitivity: 0.9939 - specificity: 0.9999 - recall: 0.7967 - val_loss: 0.3104 - val_accuracy: 0.8950 - val_tp: 176.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 24.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8800 - lr: 1.0000e-06\n",
            "Epoch 23/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4894 - accuracy: 0.8468 - tp: 7359.0000 - tn: 347277.0000 - fp: 803.0000 - fn: 1801.0000 - precision: 0.9916 - sensitivity: 0.9937 - specificity: 0.9999 - recall: 0.8034 - val_loss: 0.4177 - val_accuracy: 0.8600 - val_tp: 161.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 39.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 24/30\n",
            "1145/1145 [==============================] - 43s 38ms/step - loss: 0.4889 - accuracy: 0.8469 - tp: 7349.0000 - tn: 347255.0000 - fp: 825.0000 - fn: 1811.0000 - precision: 0.9901 - sensitivity: 0.9939 - specificity: 0.9999 - recall: 0.8023 - val_loss: 0.4167 - val_accuracy: 0.8550 - val_tp: 166.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 34.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.8300 - lr: 1.0000e-06\n",
            "Epoch 25/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4920 - accuracy: 0.8402 - tp: 7256.0000 - tn: 347237.0000 - fp: 843.0000 - fn: 1904.0000 - precision: 0.9903 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.7921 - val_loss: 0.3387 - val_accuracy: 0.8900 - val_tp: 170.0000 - val_tn: 7591.0000 - val_fp: 9.0000 - val_fn: 30.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 26/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4802 - accuracy: 0.8488 - tp: 7310.0000 - tn: 347278.0000 - fp: 802.0000 - fn: 1850.0000 - precision: 0.9907 - sensitivity: 0.9963 - specificity: 0.9999 - recall: 0.7980 - val_loss: 0.4655 - val_accuracy: 0.8550 - val_tp: 164.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 36.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8200 - lr: 1.0000e-06\n",
            "Epoch 27/30\n",
            "1145/1145 [==============================] - 37s 32ms/step - loss: 0.4869 - accuracy: 0.8421 - tp: 7320.0000 - tn: 347253.0000 - fp: 827.0000 - fn: 1840.0000 - precision: 0.9912 - sensitivity: 0.9947 - specificity: 0.9999 - recall: 0.7991 - val_loss: 0.4566 - val_accuracy: 0.8700 - val_tp: 167.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 33.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8350 - lr: 1.0000e-06\n",
            "Epoch 28/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4808 - accuracy: 0.8420 - tp: 7344.0000 - tn: 347264.0000 - fp: 816.0000 - fn: 1816.0000 - precision: 0.9925 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.8017 - val_loss: 0.4740 - val_accuracy: 0.8500 - val_tp: 163.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 37.0000 - val_precision: 0.9846 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 29/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4700 - accuracy: 0.8528 - tp: 7369.0000 - tn: 347304.0000 - fp: 776.0000 - fn: 1791.0000 - precision: 0.9940 - sensitivity: 0.9939 - specificity: 0.9999 - recall: 0.8045 - val_loss: 0.3703 - val_accuracy: 0.9050 - val_tp: 172.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 28.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8600 - lr: 1.0000e-06\n",
            "Epoch 30/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4836 - accuracy: 0.8480 - tp: 7297.0000 - tn: 347263.0000 - fp: 817.0000 - fn: 1863.0000 - precision: 0.9882 - sensitivity: 0.9945 - specificity: 0.9998 - recall: 0.7966 - val_loss: 0.3210 - val_accuracy: 0.9050 - val_tp: 177.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 23.0000 - val_precision: 0.9912 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8850 - lr: 1.0000e-06\n",
            "174/174 [==============================] - 3s 16ms/step - loss: 0.4394 - accuracy: 0.8592 - tp: 1146.0000 - tn: 52796.0000 - fp: 100.0000 - fn: 246.0000 - precision: 0.9924 - sensitivity: 0.9971 - specificity: 0.9999 - recall: 0.8233\n",
            "Epoch 1/30\n",
            "1145/1145 [==============================] - 54s 45ms/step - loss: 0.4653 - accuracy: 0.8524 - tp: 8531.0000 - tn: 400103.0000 - fp: 873.0000 - fn: 2021.0000 - precision: 0.9921 - sensitivity: 0.9957 - specificity: 0.9999 - recall: 0.8085 - val_loss: 0.4331 - val_accuracy: 0.8350 - val_tp: 159.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 41.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 2/30\n",
            "1145/1145 [==============================] - 55s 48ms/step - loss: 0.4860 - accuracy: 0.8460 - tp: 7340.0000 - tn: 347265.0000 - fp: 815.0000 - fn: 1820.0000 - precision: 0.9906 - sensitivity: 0.9943 - specificity: 0.9999 - recall: 0.8013 - val_loss: 0.3194 - val_accuracy: 0.8950 - val_tp: 166.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 34.0000 - val_precision: 0.9930 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8300 - lr: 1.0000e-06\n",
            "Epoch 3/30\n",
            "1145/1145 [==============================] - 50s 44ms/step - loss: 0.4998 - accuracy: 0.8395 - tp: 7277.0000 - tn: 347226.0000 - fp: 854.0000 - fn: 1883.0000 - precision: 0.9863 - sensitivity: 0.9940 - specificity: 0.9998 - recall: 0.7944 - val_loss: 0.3358 - val_accuracy: 0.9050 - val_tp: 173.0000 - val_tn: 7592.0000 - val_fp: 8.0000 - val_fn: 27.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8650 - lr: 1.0000e-06\n",
            "Epoch 4/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4743 - accuracy: 0.8532 - tp: 7348.0000 - tn: 347296.0000 - fp: 784.0000 - fn: 1812.0000 - precision: 0.9927 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.8022 - val_loss: 0.3485 - val_accuracy: 0.9000 - val_tp: 171.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 29.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 5/30\n",
            "1145/1145 [==============================] - 50s 44ms/step - loss: 0.4703 - accuracy: 0.8515 - tp: 7387.0000 - tn: 347269.0000 - fp: 811.0000 - fn: 1773.0000 - precision: 0.9891 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.8064 - val_loss: 0.3800 - val_accuracy: 0.8650 - val_tp: 165.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 35.0000 - val_precision: 0.9916 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 6/30\n",
            "1145/1145 [==============================] - 51s 44ms/step - loss: 0.4879 - accuracy: 0.8464 - tp: 7329.0000 - tn: 347251.0000 - fp: 829.0000 - fn: 1831.0000 - precision: 0.9906 - sensitivity: 0.9951 - specificity: 0.9999 - recall: 0.8001 - val_loss: 0.5497 - val_accuracy: 0.8300 - val_tp: 154.0000 - val_tn: 7581.0000 - val_fp: 19.0000 - val_fn: 46.0000 - val_precision: 0.9808 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.7700 - lr: 1.0000e-06\n",
            "Epoch 7/30\n",
            "1145/1145 [==============================] - 52s 45ms/step - loss: 0.4855 - accuracy: 0.8430 - tp: 7318.0000 - tn: 347248.0000 - fp: 832.0000 - fn: 1842.0000 - precision: 0.9922 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.7989 - val_loss: 0.5303 - val_accuracy: 0.8100 - val_tp: 151.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 49.0000 - val_precision: 0.9907 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7550 - lr: 1.0000e-06\n",
            "Epoch 8/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4843 - accuracy: 0.8471 - tp: 7321.0000 - tn: 347263.0000 - fp: 817.0000 - fn: 1839.0000 - precision: 0.9904 - sensitivity: 0.9951 - specificity: 0.9999 - recall: 0.7992 - val_loss: 0.4368 - val_accuracy: 0.8550 - val_tp: 161.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 39.0000 - val_precision: 0.9910 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 9/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4703 - accuracy: 0.8485 - tp: 7343.0000 - tn: 347300.0000 - fp: 780.0000 - fn: 1817.0000 - precision: 0.9940 - sensitivity: 0.9949 - specificity: 0.9999 - recall: 0.8016 - val_loss: 0.4617 - val_accuracy: 0.8550 - val_tp: 160.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 40.0000 - val_precision: 0.9916 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8000 - lr: 1.0000e-06\n",
            "Epoch 10/30\n",
            "1145/1145 [==============================] - 50s 44ms/step - loss: 0.4907 - accuracy: 0.8419 - tp: 7331.0000 - tn: 347207.0000 - fp: 873.0000 - fn: 1829.0000 - precision: 0.9876 - sensitivity: 0.9955 - specificity: 0.9998 - recall: 0.8003 - val_loss: 0.3301 - val_accuracy: 0.9050 - val_tp: 174.0000 - val_tn: 7593.0000 - val_fp: 7.0000 - val_fn: 26.0000 - val_precision: 0.9925 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8700 - lr: 1.0000e-06\n",
            "Epoch 11/30\n",
            "1145/1145 [==============================] - 50s 44ms/step - loss: 0.4824 - accuracy: 0.8450 - tp: 7304.0000 - tn: 347261.0000 - fp: 819.0000 - fn: 1856.0000 - precision: 0.9920 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.7974 - val_loss: 0.3732 - val_accuracy: 0.8700 - val_tp: 168.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 32.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8400 - lr: 1.0000e-06\n",
            "Epoch 12/30\n",
            "1145/1145 [==============================] - 40s 35ms/step - loss: 0.4846 - accuracy: 0.8442 - tp: 7322.0000 - tn: 347248.0000 - fp: 832.0000 - fn: 1838.0000 - precision: 0.9904 - sensitivity: 0.9957 - specificity: 0.9999 - recall: 0.7993 - val_loss: 0.3036 - val_accuracy: 0.8800 - val_tp: 170.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 30.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 13/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4971 - accuracy: 0.8412 - tp: 7291.0000 - tn: 347247.0000 - fp: 833.0000 - fn: 1869.0000 - precision: 0.9897 - sensitivity: 0.9950 - specificity: 0.9999 - recall: 0.7960 - val_loss: 0.4437 - val_accuracy: 0.8550 - val_tp: 157.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 43.0000 - val_precision: 0.9907 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7850 - lr: 1.0000e-06\n",
            "Epoch 14/30\n",
            "1145/1145 [==============================] - 41s 36ms/step - loss: 0.4924 - accuracy: 0.8447 - tp: 7317.0000 - tn: 347246.0000 - fp: 834.0000 - fn: 1843.0000 - precision: 0.9920 - sensitivity: 0.9948 - specificity: 0.9999 - recall: 0.7988 - val_loss: 0.4502 - val_accuracy: 0.8400 - val_tp: 159.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 41.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 15/30\n",
            "1145/1145 [==============================] - 42s 36ms/step - loss: 0.4850 - accuracy: 0.8459 - tp: 7325.0000 - tn: 347288.0000 - fp: 792.0000 - fn: 1835.0000 - precision: 0.9895 - sensitivity: 0.9951 - specificity: 0.9999 - recall: 0.7997 - val_loss: 0.4687 - val_accuracy: 0.8650 - val_tp: 161.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 39.0000 - val_precision: 0.9718 - val_sensitivity: 0.9950 - val_specificity: 0.9996 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 16/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4840 - accuracy: 0.8449 - tp: 7325.0000 - tn: 347276.0000 - fp: 804.0000 - fn: 1835.0000 - precision: 0.9897 - sensitivity: 0.9943 - specificity: 0.9999 - recall: 0.7997 - val_loss: 0.3984 - val_accuracy: 0.8500 - val_tp: 165.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 17/30\n",
            "1145/1145 [==============================] - 41s 35ms/step - loss: 0.4837 - accuracy: 0.8460 - tp: 7318.0000 - tn: 347235.0000 - fp: 845.0000 - fn: 1842.0000 - precision: 0.9929 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.7989 - val_loss: 0.4745 - val_accuracy: 0.8550 - val_tp: 165.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 35.0000 - val_precision: 0.9848 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 18/30\n",
            "1145/1145 [==============================] - 41s 35ms/step - loss: 0.4886 - accuracy: 0.8430 - tp: 7272.0000 - tn: 347240.0000 - fp: 840.0000 - fn: 1888.0000 - precision: 0.9893 - sensitivity: 0.9938 - specificity: 0.9999 - recall: 0.7939 - val_loss: 0.3510 - val_accuracy: 0.8850 - val_tp: 170.0000 - val_tn: 7590.0000 - val_fp: 10.0000 - val_fn: 30.0000 - val_precision: 0.9932 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 19/30\n",
            "1145/1145 [==============================] - 50s 44ms/step - loss: 0.4807 - accuracy: 0.8460 - tp: 7363.0000 - tn: 347283.0000 - fp: 797.0000 - fn: 1797.0000 - precision: 0.9929 - sensitivity: 0.9956 - specificity: 0.9999 - recall: 0.8038 - val_loss: 0.4377 - val_accuracy: 0.8700 - val_tp: 165.0000 - val_tn: 7581.0000 - val_fp: 19.0000 - val_fn: 35.0000 - val_precision: 0.9917 - val_sensitivity: 0.9900 - val_specificity: 0.9999 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 20/30\n",
            "1145/1145 [==============================] - 42s 36ms/step - loss: 0.4917 - accuracy: 0.8460 - tp: 7288.0000 - tn: 347230.0000 - fp: 850.0000 - fn: 1872.0000 - precision: 0.9894 - sensitivity: 0.9938 - specificity: 0.9999 - recall: 0.7956 - val_loss: 0.4839 - val_accuracy: 0.8600 - val_tp: 161.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 39.0000 - val_precision: 0.9823 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 21/30\n",
            "1145/1145 [==============================] - 50s 43ms/step - loss: 0.4933 - accuracy: 0.8415 - tp: 7265.0000 - tn: 347244.0000 - fp: 836.0000 - fn: 1895.0000 - precision: 0.9891 - sensitivity: 0.9949 - specificity: 0.9999 - recall: 0.7931 - val_loss: 0.5083 - val_accuracy: 0.8300 - val_tp: 158.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 42.0000 - val_precision: 0.9906 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7900 - lr: 1.0000e-06\n",
            "Epoch 22/30\n",
            "1145/1145 [==============================] - 41s 35ms/step - loss: 0.4723 - accuracy: 0.8508 - tp: 7350.0000 - tn: 347295.0000 - fp: 785.0000 - fn: 1810.0000 - precision: 0.9918 - sensitivity: 0.9948 - specificity: 0.9999 - recall: 0.8024 - val_loss: 0.3533 - val_accuracy: 0.8750 - val_tp: 171.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 29.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 23/30\n",
            "1145/1145 [==============================] - 42s 37ms/step - loss: 0.4798 - accuracy: 0.8465 - tp: 7352.0000 - tn: 347266.0000 - fp: 814.0000 - fn: 1808.0000 - precision: 0.9910 - sensitivity: 0.9938 - specificity: 0.9999 - recall: 0.8026 - val_loss: 0.4494 - val_accuracy: 0.8450 - val_tp: 159.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 41.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 24/30\n",
            "1145/1145 [==============================] - 49s 43ms/step - loss: 0.4843 - accuracy: 0.8476 - tp: 7346.0000 - tn: 347295.0000 - fp: 785.0000 - fn: 1814.0000 - precision: 0.9901 - sensitivity: 0.9947 - specificity: 0.9999 - recall: 0.8020 - val_loss: 0.3966 - val_accuracy: 0.8700 - val_tp: 170.0000 - val_tn: 7579.0000 - val_fp: 21.0000 - val_fn: 30.0000 - val_precision: 0.9907 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 25/30\n",
            "1145/1145 [==============================] - 51s 45ms/step - loss: 0.4782 - accuracy: 0.8479 - tp: 7357.0000 - tn: 347275.0000 - fp: 805.0000 - fn: 1803.0000 - precision: 0.9913 - sensitivity: 0.9952 - specificity: 0.9999 - recall: 0.8032 - val_loss: 0.3816 - val_accuracy: 0.8850 - val_tp: 170.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 30.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 26/30\n",
            "1145/1145 [==============================] - 61s 53ms/step - loss: 0.4887 - accuracy: 0.8451 - tp: 7317.0000 - tn: 347262.0000 - fp: 818.0000 - fn: 1843.0000 - precision: 0.9914 - sensitivity: 0.9947 - specificity: 0.9999 - recall: 0.7988 - val_loss: 0.4030 - val_accuracy: 0.8600 - val_tp: 165.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 27/30\n",
            "1145/1145 [==============================] - 56s 49ms/step - loss: 0.4812 - accuracy: 0.8462 - tp: 7318.0000 - tn: 347306.0000 - fp: 774.0000 - fn: 1842.0000 - precision: 0.9906 - sensitivity: 0.9963 - specificity: 0.9999 - recall: 0.7989 - val_loss: 0.3892 - val_accuracy: 0.8650 - val_tp: 163.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 37.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 28/30\n",
            "1145/1145 [==============================] - 58s 50ms/step - loss: 0.4844 - accuracy: 0.8485 - tp: 7361.0000 - tn: 347281.0000 - fp: 799.0000 - fn: 1799.0000 - precision: 0.9889 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.8036 - val_loss: 0.4753 - val_accuracy: 0.8600 - val_tp: 161.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 39.0000 - val_precision: 0.9903 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8050 - lr: 1.0000e-06\n",
            "Epoch 29/30\n",
            "1145/1145 [==============================] - 54s 47ms/step - loss: 0.4684 - accuracy: 0.8473 - tp: 7339.0000 - tn: 347267.0000 - fp: 813.0000 - fn: 1821.0000 - precision: 0.9931 - sensitivity: 0.9950 - specificity: 0.9999 - recall: 0.8012 - val_loss: 0.3522 - val_accuracy: 0.9050 - val_tp: 171.0000 - val_tn: 7589.0000 - val_fp: 11.0000 - val_fn: 29.0000 - val_precision: 0.9922 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 30/30\n",
            "1145/1145 [==============================] - 51s 44ms/step - loss: 0.4977 - accuracy: 0.8440 - tp: 7306.0000 - tn: 347242.0000 - fp: 838.0000 - fn: 1854.0000 - precision: 0.9907 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.7976 - val_loss: 0.3913 - val_accuracy: 0.8600 - val_tp: 162.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 38.0000 - val_precision: 0.9925 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "174/174 [==============================] - 4s 20ms/step - loss: 0.4380 - accuracy: 0.8599 - tp: 1147.0000 - tn: 52795.0000 - fp: 101.0000 - fn: 245.0000 - precision: 0.9924 - sensitivity: 0.9971 - specificity: 0.9999 - recall: 0.8240\n",
            "Epoch 1/30\n",
            "1145/1145 [==============================] - 64s 54ms/step - loss: 0.4894 - accuracy: 0.8459 - tp: 8489.0000 - tn: 400031.0000 - fp: 945.0000 - fn: 2063.0000 - precision: 0.9904 - sensitivity: 0.9944 - specificity: 0.9999 - recall: 0.8045 - val_loss: 0.4845 - val_accuracy: 0.8300 - val_tp: 156.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 44.0000 - val_precision: 1.0000 - val_sensitivity: 0.9850 - val_specificity: 1.0000 - val_recall: 0.7800 - lr: 1.0000e-06\n",
            "Epoch 2/30\n",
            "1145/1145 [==============================] - 52s 45ms/step - loss: 0.4765 - accuracy: 0.8486 - tp: 7369.0000 - tn: 347268.0000 - fp: 812.0000 - fn: 1791.0000 - precision: 0.9911 - sensitivity: 0.9955 - specificity: 0.9999 - recall: 0.8045 - val_loss: 0.3685 - val_accuracy: 0.8750 - val_tp: 170.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 30.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 3/30\n",
            "1145/1145 [==============================] - 49s 43ms/step - loss: 0.4968 - accuracy: 0.8443 - tp: 7340.0000 - tn: 347240.0000 - fp: 840.0000 - fn: 1820.0000 - precision: 0.9901 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.8013 - val_loss: 0.4866 - val_accuracy: 0.8600 - val_tp: 162.0000 - val_tn: 7578.0000 - val_fp: 22.0000 - val_fn: 38.0000 - val_precision: 0.9911 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 4/30\n",
            "1145/1145 [==============================] - 51s 45ms/step - loss: 0.4606 - accuracy: 0.8525 - tp: 7381.0000 - tn: 347312.0000 - fp: 768.0000 - fn: 1779.0000 - precision: 0.9936 - sensitivity: 0.9961 - specificity: 0.9999 - recall: 0.8058 - val_loss: 0.4031 - val_accuracy: 0.8800 - val_tp: 168.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 32.0000 - val_precision: 0.9917 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8400 - lr: 1.0000e-06\n",
            "Epoch 5/30\n",
            "1145/1145 [==============================] - 53s 47ms/step - loss: 0.4834 - accuracy: 0.8490 - tp: 7326.0000 - tn: 347267.0000 - fp: 813.0000 - fn: 1834.0000 - precision: 0.9908 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.7998 - val_loss: 0.4671 - val_accuracy: 0.8600 - val_tp: 164.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 36.0000 - val_precision: 0.9918 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.8200 - lr: 1.0000e-06\n",
            "Epoch 6/30\n",
            "1145/1145 [==============================] - 53s 46ms/step - loss: 0.4694 - accuracy: 0.8491 - tp: 7368.0000 - tn: 347284.0000 - fp: 796.0000 - fn: 1792.0000 - precision: 0.9899 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.8044 - val_loss: 0.3387 - val_accuracy: 0.8900 - val_tp: 171.0000 - val_tn: 7590.0000 - val_fp: 10.0000 - val_fn: 29.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8550 - lr: 1.0000e-06\n",
            "Epoch 7/30\n",
            "1145/1145 [==============================] - 58s 51ms/step - loss: 0.4852 - accuracy: 0.8429 - tp: 7308.0000 - tn: 347268.0000 - fp: 812.0000 - fn: 1852.0000 - precision: 0.9915 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.7978 - val_loss: 0.3923 - val_accuracy: 0.9000 - val_tp: 169.0000 - val_tn: 7593.0000 - val_fp: 7.0000 - val_fn: 31.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 8/30\n",
            "1145/1145 [==============================] - 56s 49ms/step - loss: 0.4722 - accuracy: 0.8513 - tp: 7359.0000 - tn: 347290.0000 - fp: 790.0000 - fn: 1801.0000 - precision: 0.9906 - sensitivity: 0.9952 - specificity: 0.9999 - recall: 0.8034 - val_loss: 0.4249 - val_accuracy: 0.8550 - val_tp: 163.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 37.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 9/30\n",
            "1145/1145 [==============================] - 56s 49ms/step - loss: 0.4899 - accuracy: 0.8442 - tp: 7299.0000 - tn: 347279.0000 - fp: 801.0000 - fn: 1861.0000 - precision: 0.9894 - sensitivity: 0.9934 - specificity: 0.9999 - recall: 0.7968 - val_loss: 0.3902 - val_accuracy: 0.8900 - val_tp: 170.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 30.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8500 - lr: 1.0000e-06\n",
            "Epoch 10/30\n",
            "1145/1145 [==============================] - 53s 47ms/step - loss: 0.4780 - accuracy: 0.8468 - tp: 7345.0000 - tn: 347276.0000 - fp: 804.0000 - fn: 1815.0000 - precision: 0.9908 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.8019 - val_loss: 0.4644 - val_accuracy: 0.8550 - val_tp: 157.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 43.0000 - val_precision: 0.9904 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.7850 - lr: 1.0000e-06\n",
            "Epoch 11/30\n",
            "1145/1145 [==============================] - 50s 44ms/step - loss: 0.4726 - accuracy: 0.8496 - tp: 7358.0000 - tn: 347296.0000 - fp: 784.0000 - fn: 1802.0000 - precision: 0.9891 - sensitivity: 0.9954 - specificity: 0.9999 - recall: 0.8033 - val_loss: 0.3681 - val_accuracy: 0.8650 - val_tp: 165.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 35.0000 - val_precision: 0.9907 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 12/30\n",
            "1145/1145 [==============================] - 55s 48ms/step - loss: 0.4712 - accuracy: 0.8538 - tp: 7414.0000 - tn: 347313.0000 - fp: 767.0000 - fn: 1746.0000 - precision: 0.9901 - sensitivity: 0.9950 - specificity: 0.9999 - recall: 0.8094 - val_loss: 0.4883 - val_accuracy: 0.8600 - val_tp: 156.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 44.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7800 - lr: 1.0000e-06\n",
            "Epoch 13/30\n",
            "1145/1145 [==============================] - 54s 48ms/step - loss: 0.4851 - accuracy: 0.8454 - tp: 7382.0000 - tn: 347280.0000 - fp: 800.0000 - fn: 1778.0000 - precision: 0.9899 - sensitivity: 0.9949 - specificity: 0.9999 - recall: 0.8059 - val_loss: 0.3132 - val_accuracy: 0.9250 - val_tp: 174.0000 - val_tn: 7590.0000 - val_fp: 10.0000 - val_fn: 26.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8700 - lr: 1.0000e-06\n",
            "Epoch 14/30\n",
            "1145/1145 [==============================] - 55s 48ms/step - loss: 0.4802 - accuracy: 0.8471 - tp: 7353.0000 - tn: 347270.0000 - fp: 810.0000 - fn: 1807.0000 - precision: 0.9922 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.8027 - val_loss: 0.4337 - val_accuracy: 0.8600 - val_tp: 159.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 41.0000 - val_precision: 0.9841 - val_sensitivity: 0.9950 - val_specificity: 0.9997 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 15/30\n",
            "1145/1145 [==============================] - 54s 47ms/step - loss: 0.4850 - accuracy: 0.8475 - tp: 7316.0000 - tn: 347300.0000 - fp: 780.0000 - fn: 1844.0000 - precision: 0.9914 - sensitivity: 0.9949 - specificity: 0.9999 - recall: 0.7987 - val_loss: 0.4276 - val_accuracy: 0.8550 - val_tp: 158.0000 - val_tn: 7583.0000 - val_fp: 17.0000 - val_fn: 42.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7900 - lr: 1.0000e-06\n",
            "Epoch 16/30\n",
            "1145/1145 [==============================] - 54s 47ms/step - loss: 0.4774 - accuracy: 0.8473 - tp: 7314.0000 - tn: 347268.0000 - fp: 812.0000 - fn: 1846.0000 - precision: 0.9919 - sensitivity: 0.9950 - specificity: 0.9999 - recall: 0.7985 - val_loss: 0.4682 - val_accuracy: 0.8600 - val_tp: 165.0000 - val_tn: 7585.0000 - val_fp: 15.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 0.9900 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 17/30\n",
            "1145/1145 [==============================] - 58s 51ms/step - loss: 0.4904 - accuracy: 0.8524 - tp: 7355.0000 - tn: 347292.0000 - fp: 788.0000 - fn: 1805.0000 - precision: 0.9887 - sensitivity: 0.9922 - specificity: 0.9998 - recall: 0.8029 - val_loss: 0.4685 - val_accuracy: 0.8450 - val_tp: 157.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 43.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7850 - lr: 1.0000e-06\n",
            "Epoch 18/30\n",
            "1145/1145 [==============================] - 54s 47ms/step - loss: 0.4768 - accuracy: 0.8463 - tp: 7349.0000 - tn: 347294.0000 - fp: 786.0000 - fn: 1811.0000 - precision: 0.9908 - sensitivity: 0.9931 - specificity: 0.9999 - recall: 0.8023 - val_loss: 0.3375 - val_accuracy: 0.9050 - val_tp: 169.0000 - val_tn: 7590.0000 - val_fp: 10.0000 - val_fn: 31.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8450 - lr: 1.0000e-06\n",
            "Epoch 19/30\n",
            "1145/1145 [==============================] - 45s 40ms/step - loss: 0.4816 - accuracy: 0.8463 - tp: 7357.0000 - tn: 347285.0000 - fp: 795.0000 - fn: 1803.0000 - precision: 0.9899 - sensitivity: 0.9945 - specificity: 0.9999 - recall: 0.8032 - val_loss: 0.4691 - val_accuracy: 0.8450 - val_tp: 162.0000 - val_tn: 7581.0000 - val_fp: 19.0000 - val_fn: 38.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 20/30\n",
            "1145/1145 [==============================] - 54s 47ms/step - loss: 0.4904 - accuracy: 0.8393 - tp: 7288.0000 - tn: 347218.0000 - fp: 862.0000 - fn: 1872.0000 - precision: 0.9918 - sensitivity: 0.9947 - specificity: 0.9999 - recall: 0.7956 - val_loss: 0.5405 - val_accuracy: 0.8100 - val_tp: 153.0000 - val_tn: 7577.0000 - val_fp: 23.0000 - val_fn: 47.0000 - val_precision: 0.9806 - val_sensitivity: 1.0000 - val_specificity: 0.9997 - val_recall: 0.7650 - lr: 1.0000e-06\n",
            "Epoch 21/30\n",
            "1145/1145 [==============================] - 55s 48ms/step - loss: 0.4900 - accuracy: 0.8409 - tp: 7300.0000 - tn: 347235.0000 - fp: 845.0000 - fn: 1860.0000 - precision: 0.9908 - sensitivity: 0.9944 - specificity: 0.9999 - recall: 0.7969 - val_loss: 0.3553 - val_accuracy: 0.9050 - val_tp: 172.0000 - val_tn: 7591.0000 - val_fp: 9.0000 - val_fn: 28.0000 - val_precision: 1.0000 - val_sensitivity: 0.9950 - val_specificity: 1.0000 - val_recall: 0.8600 - lr: 1.0000e-06\n",
            "Epoch 22/30\n",
            "1145/1145 [==============================] - 54s 47ms/step - loss: 0.4754 - accuracy: 0.8447 - tp: 7329.0000 - tn: 347266.0000 - fp: 814.0000 - fn: 1831.0000 - precision: 0.9879 - sensitivity: 0.9950 - specificity: 0.9998 - recall: 0.8001 - val_loss: 0.4584 - val_accuracy: 0.8500 - val_tp: 163.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 37.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8150 - lr: 1.0000e-06\n",
            "Epoch 23/30\n",
            "1145/1145 [==============================] - 52s 46ms/step - loss: 0.4850 - accuracy: 0.8451 - tp: 7321.0000 - tn: 347263.0000 - fp: 817.0000 - fn: 1839.0000 - precision: 0.9897 - sensitivity: 0.9940 - specificity: 0.9999 - recall: 0.7992 - val_loss: 0.3839 - val_accuracy: 0.9000 - val_tp: 174.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 26.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8700 - lr: 1.0000e-06\n",
            "Epoch 24/30\n",
            "1145/1145 [==============================] - 47s 41ms/step - loss: 0.4909 - accuracy: 0.8471 - tp: 7382.0000 - tn: 347303.0000 - fp: 777.0000 - fn: 1778.0000 - precision: 0.9904 - sensitivity: 0.9928 - specificity: 0.9999 - recall: 0.8059 - val_loss: 0.4195 - val_accuracy: 0.8750 - val_tp: 165.0000 - val_tn: 7587.0000 - val_fp: 13.0000 - val_fn: 35.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8250 - lr: 1.0000e-06\n",
            "Epoch 25/30\n",
            "1145/1145 [==============================] - 55s 48ms/step - loss: 0.4764 - accuracy: 0.8502 - tp: 7395.0000 - tn: 347291.0000 - fp: 789.0000 - fn: 1765.0000 - precision: 0.9904 - sensitivity: 0.9942 - specificity: 0.9999 - recall: 0.8073 - val_loss: 0.5040 - val_accuracy: 0.8250 - val_tp: 157.0000 - val_tn: 7582.0000 - val_fp: 18.0000 - val_fn: 43.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.7850 - lr: 1.0000e-06\n",
            "Epoch 26/30\n",
            "1145/1145 [==============================] - 47s 41ms/step - loss: 0.4811 - accuracy: 0.8459 - tp: 7365.0000 - tn: 347225.0000 - fp: 855.0000 - fn: 1795.0000 - precision: 0.9904 - sensitivity: 0.9950 - specificity: 0.9999 - recall: 0.8040 - val_loss: 0.4330 - val_accuracy: 0.8250 - val_tp: 159.0000 - val_tn: 7580.0000 - val_fp: 20.0000 - val_fn: 41.0000 - val_precision: 0.9908 - val_sensitivity: 0.9950 - val_specificity: 0.9999 - val_recall: 0.7950 - lr: 1.0000e-06\n",
            "Epoch 27/30\n",
            "1145/1145 [==============================] - 46s 40ms/step - loss: 0.4730 - accuracy: 0.8464 - tp: 7357.0000 - tn: 347282.0000 - fp: 798.0000 - fn: 1803.0000 - precision: 0.9913 - sensitivity: 0.9962 - specificity: 0.9999 - recall: 0.8032 - val_loss: 0.4265 - val_accuracy: 0.8500 - val_tp: 162.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 38.0000 - val_precision: 1.0000 - val_sensitivity: 1.0000 - val_specificity: 1.0000 - val_recall: 0.8100 - lr: 1.0000e-06\n",
            "Epoch 28/30\n",
            "1145/1145 [==============================] - 56s 49ms/step - loss: 0.4910 - accuracy: 0.8463 - tp: 7341.0000 - tn: 347276.0000 - fp: 804.0000 - fn: 1819.0000 - precision: 0.9898 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.8014 - val_loss: 0.4402 - val_accuracy: 0.8550 - val_tp: 166.0000 - val_tn: 7586.0000 - val_fp: 14.0000 - val_fn: 34.0000 - val_precision: 0.9856 - val_sensitivity: 0.9900 - val_specificity: 0.9997 - val_recall: 0.8300 - lr: 1.0000e-06\n",
            "Epoch 29/30\n",
            "1145/1145 [==============================] - 48s 42ms/step - loss: 0.4804 - accuracy: 0.8481 - tp: 7316.0000 - tn: 347251.0000 - fp: 829.0000 - fn: 1844.0000 - precision: 0.9914 - sensitivity: 0.9948 - specificity: 0.9999 - recall: 0.7987 - val_loss: 0.4452 - val_accuracy: 0.8650 - val_tp: 167.0000 - val_tn: 7584.0000 - val_fp: 16.0000 - val_fn: 33.0000 - val_precision: 0.9931 - val_sensitivity: 0.9900 - val_specificity: 0.9999 - val_recall: 0.8350 - lr: 1.0000e-06\n",
            "Epoch 30/30\n",
            "1145/1145 [==============================] - 52s 45ms/step - loss: 0.4979 - accuracy: 0.8404 - tp: 7270.0000 - tn: 347250.0000 - fp: 830.0000 - fn: 1890.0000 - precision: 0.9894 - sensitivity: 0.9941 - specificity: 0.9999 - recall: 0.7937 - val_loss: 0.3908 - val_accuracy: 0.8750 - val_tp: 167.0000 - val_tn: 7588.0000 - val_fp: 12.0000 - val_fn: 33.0000 - val_precision: 0.9912 - val_sensitivity: 1.0000 - val_specificity: 0.9999 - val_recall: 0.8350 - lr: 1.0000e-06\n",
            "174/174 [==============================] - 3s 17ms/step - loss: 0.4362 - accuracy: 0.8599 - tp: 1146.0000 - tn: 52797.0000 - fp: 99.0000 - fn: 246.0000 - precision: 0.9924 - sensitivity: 0.9971 - specificity: 0.9999 - recall: 0.8233\n",
            "Mean scores: [4.39297301e-01 8.59482753e-01 1.14560000e+03 5.27956000e+04\n",
            " 1.00400000e+02 2.46400000e+02 9.92637932e-01 9.97126460e-01\n",
            " 9.99894130e-01 8.22988510e-01]\n",
            "Std scores: [2.10258524e-03 9.74459527e-04 1.01980390e+00 8.00000000e-01\n",
            " 8.00000000e-01 1.01980390e+00 2.81465321e-04 0.00000000e+00\n",
            " 9.25646124e-06 7.32623497e-04]\n"
          ]
        }
      ],
      "source": [
        "# Número de imagens\n",
        "num_images = len(train_ds)\n",
        "\n",
        "# Tamanho do lote\n",
        "BATCH_SIZE = 8\n",
        "\n",
        "# Número de épocas\n",
        "EPOCHS = 30\n",
        "\n",
        "# Calculando o número de grupos\n",
        "n_groups = num_images // (BATCH_SIZE * EPOCHS)\n",
        "\n",
        "# Lista para armazenar os resultados de cada grupo\n",
        "scores = []\n",
        "\n",
        "# Obtendo os grupos para cada exemplo de treinamento\n",
        "train_groups = np.arange(n_groups)\n",
        "\n",
        "# Função para gerar os grupos de treinamento e validação\n",
        "def group_generator(train_ds, train_groups):\n",
        "    for group in train_groups:\n",
        "        # Calculando os índices para este grupo\n",
        "        start_index = group * BATCH_SIZE * EPOCHS\n",
        "        end_index = min(start_index + BATCH_SIZE * EPOCHS, num_images)\n",
        "        val_index = slice(start_index, end_index)\n",
        "        train_index = np.concatenate([np.arange(start_index), np.arange(end_index, num_images)])\n",
        "\n",
        "        # Dividindo o conjunto de treinamento em subconjuntos de treino e validação para este grupo\n",
        "        train_fold = train_ds.enumerate().filter(lambda i, _: i < start_index or i >= end_index).map(lambda _, data: data)\n",
        "        val_fold = train_ds.enumerate().filter(lambda i, _: start_index <= i < end_index).map(lambda _, data: data)\n",
        "\n",
        "        yield train_fold, val_fold\n",
        "\n",
        "# Iterando sobre cada grupo\n",
        "for train_fold, val_fold in group_generator(train_ds, train_groups):\n",
        "    # Limpando a sessão do TensorFlow\n",
        "    tf.keras.backend.clear_session()\n",
        "\n",
        "    # Compilação do modelo\n",
        "    model.compile(loss=keras.losses.categorical_crossentropy,\n",
        "                  optimizer=optimizer,\n",
        "                  metrics=METRICS)\n",
        "\n",
        "    # Treinamento do modelo para este grupo\n",
        "    hist = model.fit(train_fold,\n",
        "                     epochs=EPOCHS,\n",
        "                     batch_size=BATCH_SIZE,\n",
        "                     validation_data=val_fold,\n",
        "                     validation_steps=25,\n",
        "                     callbacks=[learning_rate],\n",
        "                     verbose=1)\n",
        "\n",
        "    # Avaliação do modelo para este grupo\n",
        "    scores.append(model.evaluate(test_ds))\n",
        "\n",
        "# Calculando a média e o desvio padrão dos resultados dos grupos\n",
        "scores = np.array(scores)\n",
        "mean_score = np.mean(scores, axis=0)\n",
        "std_score = np.std(scores, axis=0)\n",
        "\n",
        "# Exibindo os resultados médios e desvios padrão\n",
        "print(\"Mean scores:\", mean_score)\n",
        "print(\"Std scores:\", std_score)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TLKGlbzsvgEo"
      },
      "outputs": [],
      "source": [
        "# Salvando o modelo\n",
        "model_version=\"Plant-Diseases-v2\"\n",
        "model.save(f\"{model_version}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ktlX0IcF3pQe"
      },
      "source": [
        "### Métricas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KyJmbtEr2891"
      },
      "outputs": [],
      "source": [
        "# Métricas retornadas durante o treinamento\n",
        "acc = hist.history['accuracy']\n",
        "loss =  hist.history['loss']\n",
        "fp = hist.history['fp']\n",
        "fpv = hist.history['val_fp']\n",
        "fn = hist.history['fn']\n",
        "fnv = hist.history['val_fn']\n",
        "tp = hist.history['tp']\n",
        "tpv = hist.history['val_tp']\n",
        "tn = hist.history['tn']\n",
        "tnv = hist.history['val_tn']\n",
        "pre = hist.history['precision']\n",
        "rec = hist.history['recall']\n",
        "lr = hist.history['lr']\n",
        "# Métricas retornadas pela última época de processamento\n",
        "# fb = hist.history['FBetaScore'][-1]\n",
        "FP = hist.history['fp'][-1]\n",
        "FN = hist.history['fn'][-1]\n",
        "TP = hist.history['tp'][-1]\n",
        "TN = hist.history['tn'][-1]\n",
        "LOSS = hist.history['loss'][-1]\n",
        "LOSSV = hist.history['val_loss'][-1]\n",
        "ACC = hist.history['accuracy'][-1]\n",
        "ACCV = hist.history['val_accuracy'][-1]\n",
        "PRE = hist.history['precision'][-1]\n",
        "PREV = hist.history['val_precision'][-1]\n",
        "REC = hist.history['recall'][-1]\n",
        "RECV = hist.history['val_recall'][-1]\n",
        "LR = hist.history['lr'][-1]\n",
        "# Média das métricas retornadas nas últimas 10 épocas de processameto\n",
        "accU10 = mean(acc[-10])\n",
        "tpU10 = mean(tp[-10])\n",
        "fpU10 = mean(fp[-10])\n",
        "tnU10 = mean(tn[-10])\n",
        "fnU10 = mean(fn[-10])\n",
        "# Métricas calculadas como média para todas as épocas de processamento\n",
        "acc_media_total = round(mean(acc[:-1]),2)\n",
        "media_tp_total = round(mean(tp[:-1]),2)\n",
        "media_fp_total = round(mean(fp[:-1]),2)\n",
        "media_tn_total = round(mean(tn[:-1]),2)\n",
        "media_fn_total = round(mean(fn[:-1]),2)\n",
        "# Métricas processadas após o final do treinamento\n",
        "TPR = TP /(TP + FN) # Sensitivity, hit rate, recall, or true positive rate\n",
        "TNR = TN /(TN + FP) # Specificity or true negative rate\n",
        "PPV = TP /(TP + FP) # Precision or positive predictive value\n",
        "NPV = TN /(TN + FN) # Negative predictive value\n",
        "FPR = FP /(FP + TN) # Fall out or false positive rate\n",
        "FNR = FN /(TP + FN) # False negative rate\n",
        "FDR = FP /(TP + FP) # False omission rate / False discovery rate\n",
        "\n",
        "OACC = (TP + TN) / (TP + FP + FN + TN) # Overall accuracy\n",
        "ACCCM = (TP + TN) / (TP + TN + FP + FN) # Confusion matrix accuracy\n",
        "FM = (2 * PRE * REC) / (PRE + REC) # F-measure\n",
        "F1S = 2*((PRE * REC) / (PRE + REC)) # F1-score\n",
        "F1S2 = 2 * TP / (2 * TP + FP + FN) # F1-score alternative method\n",
        "\n",
        "MCC = (TP * TN - FP * FN) / (sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))) # Matthews Correlation Coeficient\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6hzufgau3EO8",
        "outputId": "af9f3a92-296e-4d4d-8d4f-1bebd06b98fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Verdadeiros Positivos (Todas as épocas de processamento): \n",
            "[11041.0, 14422.0, 15301.0, 15869.0, 16236.0, 16483.0, 16606.0, 16872.0, 16853.0, 17651.0, 17847.0, 17964.0, 18069.0, 18138.0, 18097.0, 18286.0, 18348.0, 18314.0, 18478.0, 18352.0, 18432.0, 18413.0, 18488.0, 18468.0, 18448.0, 18417.0, 18432.0, 18427.0, 18395.0, 18395.0]\n",
            "Falsos Positivos (Todas as épocas de processamento): \n",
            "[2857.0, 3065.0, 2985.0, 2895.0, 2808.0, 2762.0, 2722.0, 2588.0, 2694.0, 2245.0, 2165.0, 2120.0, 2114.0, 2073.0, 2063.0, 1976.0, 1895.0, 1906.0, 1844.0, 1916.0, 1888.0, 1934.0, 1868.0, 1867.0, 1883.0, 1871.0, 1874.0, 1901.0, 1909.0, 1906.0]\n",
            "Verdadeiros Negativos (Todas as épocas de processamento): \n",
            "[839831.0, 839623.0, 839703.0, 839793.0, 839880.0, 839926.0, 839966.0, 840100.0, 839994.0, 840443.0, 840523.0, 840568.0, 840574.0, 840615.0, 840625.0, 840712.0, 840793.0, 840782.0, 840844.0, 840772.0, 840800.0, 840754.0, 840820.0, 840821.0, 840805.0, 840817.0, 840814.0, 840787.0, 840779.0, 840782.0]\n",
            "Falsos Negativos (Todas as épocas de processamento): \n",
            "[11135.0, 7754.0, 6875.0, 6307.0, 5940.0, 5693.0, 5570.0, 5304.0, 5323.0, 4525.0, 4329.0, 4212.0, 4107.0, 4038.0, 4079.0, 3890.0, 3828.0, 3862.0, 3698.0, 3824.0, 3744.0, 3763.0, 3688.0, 3708.0, 3728.0, 3759.0, 3744.0, 3749.0, 3781.0, 3781.0]\n"
          ]
        }
      ],
      "source": [
        "print(f'Verdadeiros Positivos (Todas as épocas de processamento): \\n{tp}')\n",
        "print(f'Falsos Positivos (Todas as épocas de processamento): \\n{fp}')\n",
        "print(f'Verdadeiros Negativos (Todas as épocas de processamento): \\n{tn}')\n",
        "print(f'Falsos Negativos (Todas as épocas de processamento): \\n{fn}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sxWtOwGW3Fnb",
        "outputId": "2c5968dd-984d-4c65-dfa2-2b5b58402d43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão (Todas as Épocas de Processamento)\n",
            "[[11041.0, 14422.0, 15301.0, 15869.0, 16236.0, 16483.0, 16606.0, 16872.0, 16853.0, 17651.0, 17847.0, 17964.0, 18069.0, 18138.0, 18097.0, 18286.0, 18348.0, 18314.0, 18478.0, 18352.0, 18432.0, 18413.0, 18488.0, 18468.0, 18448.0, 18417.0, 18432.0, 18427.0, 18395.0, 18395.0]] [[2857.0, 3065.0, 2985.0, 2895.0, 2808.0, 2762.0, 2722.0, 2588.0, 2694.0, 2245.0, 2165.0, 2120.0, 2114.0, 2073.0, 2063.0, 1976.0, 1895.0, 1906.0, 1844.0, 1916.0, 1888.0, 1934.0, 1868.0, 1867.0, 1883.0, 1871.0, 1874.0, 1901.0, 1909.0, 1906.0]]\n",
            "[[11135.0, 7754.0, 6875.0, 6307.0, 5940.0, 5693.0, 5570.0, 5304.0, 5323.0, 4525.0, 4329.0, 4212.0, 4107.0, 4038.0, 4079.0, 3890.0, 3828.0, 3862.0, 3698.0, 3824.0, 3744.0, 3763.0, 3688.0, 3708.0, 3728.0, 3759.0, 3744.0, 3749.0, 3781.0, 3781.0]] [[839831.0, 839623.0, 839703.0, 839793.0, 839880.0, 839926.0, 839966.0, 840100.0, 839994.0, 840443.0, 840523.0, 840568.0, 840574.0, 840615.0, 840625.0, 840712.0, 840793.0, 840782.0, 840844.0, 840772.0, 840800.0, 840754.0, 840820.0, 840821.0, 840805.0, 840817.0, 840814.0, 840787.0, 840779.0, 840782.0]]\n",
            "Acurácia da Matriz de Confusão: 81.0%\n"
          ]
        }
      ],
      "source": [
        "print(\"Matriz de Confusão (Todas as Épocas de Processamento)\")\n",
        "print(f\"[{tp}] [{fp}]\")\n",
        "print(f\"[{fn}] [{tn}]\")\n",
        "\n",
        "print(f'Acurácia da Matriz de Confusão: {round(acc_media_total, 2)*100-2}%')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mJ3v_X6j3HAc",
        "outputId": "546b689d-c7c7-4132-d3a4-1894b82b0b32"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Verdadeiros Positivos (Média de todas as épocas de processamento): \n",
            "17418.86\n",
            "Falsos Positivos (Média de todas as épocas de processamento): \n",
            "2230.62\n",
            "Verdadeiros Negativos (Média de todas as épocas de processamento): \n",
            "840457.38\n",
            "Falsos Negativos (Média de todas as épocas de processamento): \n",
            "4757.14\n"
          ]
        }
      ],
      "source": [
        "print(f'Verdadeiros Positivos (Média de todas as épocas de processamento): \\n{media_tp_total}')\n",
        "print(f'Falsos Positivos (Média de todas as épocas de processamento): \\n{media_fp_total}')\n",
        "print(f'Verdadeiros Negativos (Média de todas as épocas de processamento): \\n{media_tn_total}')\n",
        "print(f'Falsos Negativos (Média de todas as épocas de processamento): \\n{media_fn_total}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C685JKbV3IQY",
        "outputId": "c2b82dda-2d7d-4b11-9e2a-a1d54a3fa49b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão (Média de todas as épocas de Processamento)\n",
            "[17418.86] [2230.62]\n",
            "[4757.14] [840457.38]\n",
            "Acurácia da Matriz de Confusão: 81.0%\n"
          ]
        }
      ],
      "source": [
        "print(\"Matriz de Confusão (Média de todas as épocas de Processamento)\")\n",
        "print(f\"[{media_tp_total}] [{media_fp_total}]\")\n",
        "print(f\"[{media_fn_total}] [{media_tn_total}]\")\n",
        "\n",
        "print(f'Acurácia da Matriz de Confusão: {round(acc_media_total, 2)*100-2}%')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riA9b09x3JWz",
        "outputId": "cf81f38e-e5f2-4c3b-8ffe-1e0898e53130"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'np' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-68-2002f66d93f5>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Categorias Reais'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Categorias Preditas'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtick_marks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassNames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick_marks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassNames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick_marks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassNames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = [[media_tn_total, media_fp_total],[media_fn_total, media_tp_total]]\n",
        "\n",
        "plt.clf()\n",
        "plt.imshow(data, cmap = plt.cm.Blues_r)\n",
        "classNames = ['Negativos','Positivos']\n",
        "plt.title('Matriz de Confusão (Média de todas as épocas)', fontsize=16)\n",
        "plt.ylabel('Categorias Reais', fontsize=14)\n",
        "plt.xlabel('Categorias Preditas', fontsize=14)\n",
        "tick_marks = np.arange(len(classNames))\n",
        "plt.xticks(tick_marks, classNames)\n",
        "plt.yticks(tick_marks, classNames, rotation=90)\n",
        "s = [['TN','FP'], ['FN', 'TP']]\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4YnEp0Ce3LxE",
        "outputId": "3e2cd858-60b3-4941-d132-3aefd892403e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Verdadeiros Positivos (Média das últimas 10 épocas de processameto): \n",
            "18432.0\n",
            "Falsos Positivos (Média das últimas 10 épocas de processameto): \n",
            "1888.0\n",
            "Verdadeiros Negativos (Média das últimas 10 épocas de processameto): \n",
            "840800.0\n",
            "Falsos Negativos (Média das últimas 10 épocas de processameto): \n",
            "3744.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Verdadeiros Positivos (Média das últimas 10 épocas de processameto): \\n{tpU10}')\n",
        "print(f'Falsos Positivos (Média das últimas 10 épocas de processameto): \\n{fpU10}')\n",
        "print(f'Verdadeiros Negativos (Média das últimas 10 épocas de processameto): \\n{tnU10}')\n",
        "print(f'Falsos Negativos (Média das últimas 10 épocas de processameto): \\n{fnU10}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ugiMjiw3Mu9",
        "outputId": "befd25b8-379b-49c5-c95d-54f75fcda1aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão (Média das últimas 10 épocas de Processamento)\n",
            "[18432.0] [1888.0]\n",
            "[3744.0] [840800.0]\n",
            "Acurácia da Matriz de Confusão: 84.0%\n"
          ]
        }
      ],
      "source": [
        "print(\"Matriz de Confusão (Média das últimas 10 épocas de Processamento)\")\n",
        "print(f\"[{tpU10}] [{fpU10}]\")\n",
        "print(f\"[{fnU10}] [{tnU10}]\")\n",
        "\n",
        "print(f'Acurácia da Matriz de Confusão: {round(accU10, 2)*100-2}%')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-27J1Qom3N0P",
        "outputId": "e863753c-80eb-4f64-afc7-58bf9121fb13"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'np' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-71-f348068ed595>\u001b[0m in \u001b[0;36m<cell line: 9>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Categorias Reais'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Categorias Preditas'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mtick_marks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassNames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick_marks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassNames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myticks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtick_marks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclassNames\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m90\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
          ]
        },
        {
          "data": {
            "image/png": 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r165FXk/+5avqtFp5kyP5qboXk3khng9wPXLkiFJZWlqa2LNnT5EkIDc3V0ybNk0AED169CiyrfK01cu0bdtWABB79+5VufzFL/EffvihyPv933//CQCiffv2QghRbHKUkJAgLC0thUwmEytWrFA6BZuSkiI6d+4sAIjZs2crrTdp0iTpx879+/el8uzsbNG7d2/pPSlLcrR58+Yif0wKCwvF8uXLBQDRuHFjUVhYWEyrFfX9999Lie3ly5el8ry8PDF27FgpxhdjyczMFLt27SpyAYrisdC9e3elZfLB/N26dStyDBUUFIioqKgSL2hR9LqTIyFebUC2/PMp/zwpvgfffvutACBq1aolqlevLtavX6+07scffywACF9f3yKvt2/fPvHgwYMi5SdOnBCmpqZCV1dX3Lt3Typ/9uyZMDQ0FNWqVVN5uvnOnTuvdBFHaZMjCwsLAUDs3LlT5fK0tDSpvS5evFiq137y5In0I3H69OlKx0p2drYYNGiQACBCQ0OV1lP8+9e7d2+l4RHx8fHSD9MXT/H9+eefAlB9sclPP/0kAAhdXd0i8ct/5Ds5OYnY2FilZYWFheLgwYNKCVx8fLw4ePBgkaEd2dnZ0g+ocePGKS2TJ8CjR48u8nnPzc0VBw8eVNmGhYWFwtzcXAAQ8fHxKusU55WSIyGEGDJkiAAgoqKihBDPExsAomPHjkIIUWJyVBL5l/vkyZOVysuSHFlYWBTJqsuyHbmYmBhRrVo1oa2tLXbv3l3qfZg3b54AIFq1aqVy+UcffaTyj7j8IPXw8BB5eXlF1isoKJB+kV64cKFUscgTsVWrVpU6frm7d+8KLS0tIZPJxL///ltk+b1796TtK/6SV0yOVLVbQkKCAJ73Hr34x6MikqPu3bsLAOLs2bMv2ePSsbe3F9ra2krJa3nb6mXkv/hu3bqlcvmLX+IZGRnCyMhI1KlTR/oymTp1qgAg1q1bJ4QoPjn69NNPBVD8FW/37t0Turq6wtraWtr2kydPpB8Cf/75Z5F1EhISpP1+XVeryXspLl26VOp16tWrJ4DnYyFf9PTpU6lHp6yx2NvbCy0tLaVjYdGiRSp/GJWHpiZHXl5eRf5Y5eXlSclC//79i2wzJSVFAM977l783JdEnoQuX75cKktOThYARNOmTUu9nbIobXKkq6srAIgDBw6oXJ6bmyt9J504caJUry0fr9uzZ0+Vyx8/fixq1KghdHR0RFpamlQuf18NDQ1FQkJCkfX27NkjgOe92oq9R/JxUWFhYSpfr2fPngKAGDVqlFSWlJQk9PT0BABx5syZUu1XSbKzs4WOjo6wtrZWKpf36Jbnij/598SuXbvKtN4rT5IwYsQIbNiwAWvWrEGHDh2kcQ7ycQ8vk5WVhT///BPnzp1DSkqKNL+OfDxHeccVAICvr+8rXwFz9+5d9OjRA1lZWVi5ciUCAgJKva78PP2QIUNULg8JCcE333xTpHzv3r0AgKCgIJXzWGhpaaF9+/a4ePEiTpw4gSZNmpQ6pvI4evQoCgsL4enpqXKiuZo1a8LPzw+7du3C4cOH0bp1a6XlOjo68Pf3L7Kera0tqlevjkePHiE1NbXcV3GUlpeXF/bt24exY8di9uzZ6NChAwwMDF663r///ovIyEjcvn0b2dnZKCwsBPB87EpBQQFu3LghzUz8qm2lSnZ2NrKzswEAlpaWpdpXU1NTBAUF4X//+x+ioqLQvn17rF+/HiYmJiqvLlUkP/4GDhyocnnNmjVRv359XL58GdevX0eDBg1w9uxZPH78GFZWVsW+1127dsXu3btLFb+iGzduICIiAjdu3MDjx49RUFAA4PmcMsDz7whXV9eXbuf+/fu4ceMGAODdd98tstzAwAADBgzAt99+W+w2SjoWCgsLlY6Fli1bAgAWLVoES0tL9OzZExYWFmXYc83XrVu3ImP7dHR04OzsjLS0NHTv3r3IOpaWlrCwsEBaWprKz31qair27t2Lixcv4tGjR9K4rOvXrwNQ/ptgbW0NJycnnD9/HhMnTsTIkSNLdSy8CV72OZSPX923bx/++eefIuN7u3btqvI7tWfPnrC0tERqairOnj2L1q1bIz8/H8ePHweAYuf4GjlyJP744w8cPnxYKjt8+DByc3PRvHnzMs/UfeLECfz999+Ii4vDkydPIIQAAOjp6eHhw4d49OgRqlevDuD5d/eKFSswdepUCCHQtWtXlWO7VJF/Z8q/L0rrlZOjTp06wdnZGb/99huWLVuG9evXw9TUFP369Xvpunv27EFoaGiJg4szMzPLHdurTvD46NEjdOvWDYmJiZg6dSrGjBlTpvXv3bsHAEUul5crrvzWrVsAgBkzZmDGjBklvsbDhw9LFYu1tTXi4+ORnJxcqvqK5APui4sXgDRj8ouD8wHAzs6u2CkTTE1N8ejRIzx79qzMcZXV5MmTcezYMRw8eBD+/v7Q1dWFu7s72rdvj+DgYOmPmVx2djaGDh0qDbYsjuIx+qptpUpGRob0fxMTk1KtAzz/gfK///0Pa9aswZMnT/DgwQO89957L52eQX78tWvX7qWv8fDhQzRo0EA61kv6zJXUJqoUFBRg/Pjx+OGHH6QvTlVK+x0hj9HKyqrYL9biYizPsdCxY0d8+umn+OqrrxASEgKZTIb69eujTZs26N27NwICAl56YYSmK+4Sf3n7FrfcxMQEaWlpRT73q1evxieffCL9GFDlxfd7/fr16NevH5YsWYIlS5bAwsIC3t7eeOeddzB06FBYWVmVZZfKRb4/xcWdlZUl/d/U1LRU25R/DocOHYqhQ4eWWFfV34GSPm9OTk5ITU2VPhOpqanSe1Hceqq+t+7evQsAaNiwYYnxKUpOTkZQUBCOHTtWYr3MzEwpORo6dCgOHDiADRs2ICgoCNra2nB1dUXbtm3Rr18/dO7cudjtyNu7rBfCvHJyJL+6ITw8HCEhIUhMTMTo0aOLXOn0ovv372PgwIF4+vQppkyZgiFDhsDJyQnVqlWDlpYW/vrrL/j5+ZX4pfgyL4uhJDk5OQgMDMSVK1cwZMgQfPnll+XeVlnJf422bdv2pbdpaNy4cam22bx5c8THx6tlzqnK/gMgb78XGRkZ4cCBA/jnn38QERGBEydO4MSJEzhz5gyWLFmCcePGYfny5VL9adOmYceOHWjYsCEWLFiAli1bwsrKCnp6egCA1q1bIzo6+pWO0dIwNzeX/v/48eNSf7l26NABdevWxe+//47ExEQApevRlbeffGbwkpS2J6s8vvnmG6xatQq2trZYsmQJWrduDRsbG6m3b/Dgwdi0aVOFtz9Q/mNhwYIFGDNmDPbs2YNjx47h+PHjWLt2LdauXYuWLVvi8OHDr23uqOKO+4r0ss92WT77MTExeP/996GtrY2FCxciICAAtWvXhpGREWQymTRf0Itt3K5dO9y5cwd79+7FkSNHcOLECezfvx9//vknwsPDsWPHDnTp0qVc+1daTk5OSEtLK/aqzPj4eADP/146OjqWapvy99Pf3/+l8xqVdpsvqozPzovee+89HDt2DD4+Ppg9ezbc3d1RvXp16Qe0vb09EhISlGLT0tLCr7/+is8++wx79+7F8ePHcfz4caxcuVI6o7Njxw6VV+DKf1zKE63Sei1zjw8fPhyzZ8/Gnj17AJTuC3jPnj14+vQp+vTpg4ULFxZZLu9CVQchBEJCQnD06FF06tQJa9asKdctPmrWrImrV68We2ltceUODg4AgN69e2PSpEllfl1VevfujZ07d2L//v1ISkoq0yRiNWvWBPD/f8moIl8mr1uR5H+QHj9+rHK5/NdMcVq2bCn1EuXn52Pnzp0YNmwYVqxYgX79+qFTp04AIE0nsWXLFpWnyFQdoxXRVkZGRjA2NkZ2djZSU1NLnRzJf7jMmDEDBw8eRKNGjeDj4/PS9RwcHHD9+nV8+umnaNGiRaleS74vJV1GXppLzBXJ2/+HH35Ar169iiwv63eEPMaUlBRkZWWp7D0qLsbyHAtyTk5OmDBhgnTLl3/++Qfvvvsu/vnnHyxatEi6pL4kLzvm8/LySpxa4k2wbds2CCEwYcIElbd9KKmNDQ0N0a9fP+mMxcOHDzF9+nT8+OOPGDFixEu/E16Vp6cnzp49W+zl7vLy+vXrl/p0kIODA65evYqRI0eW6kzMi27fvl3sMvlxXqtWLQDPf+To6+sjJycHt27dUnmMq/rekvcMXr16tVQxZWdnY9++fdDS0sK+ffuUfvjJl8t/yKni6uoKV1dXTJ48GUIIHDp0CIMHD8aePXuwfv16hIaGFllHfmaqrBNnvpaf9LVr10bv3r1haWmJVq1awdvb+6XrpKWlAVCd8QohsHHjRpXryb8kiptn5XWYMmUKtmzZgiZNmmDHjh3Sa5ZVhw4dAAAbNmxQuXz9+vUqy7t16wbg/39ZvA7ynrnc3FyMHTv2pb8yY2JipLkp2rdvDy0tLcTGxuLff/8tUjchIQEREREAICUWFUkxAVF1Dzj5ufrS0NHRQb9+/eDn5wfg+WzQciUdo/v370dKSkqR8opqK09PTwDP5wYri+HDh8Pa2hqWlpZ4//33S7WO/PhTNddYcZo3b45q1aohJSUFf/31V5HlSUlJKstLUlL7X7p0Sem9Ko1atWqhTp06AKDy+yUnJwfbtm0rcyzFHQvFadmyJcaNGwcApd4Ha2tr6OnpIS0tTeWp8f3795f5O7EyvkvLoqQ2fvbsGX7//fdSb8va2hqLFi0CAMTFxZVrbrGy6NOnDwBg9+7dKk+tyY+3vn37lnqb5fkcKvrrr79UHiv79u1DamoqTExMpHFCOjo6aNu2LYDn846pIh9PrPi91blzZ+jp6SEmJqZU94/LyMhAQUEBTE1NiyRGAPDrr7+W+m+eTCZDly5dpDmkVH2WCgsLceXKFQAo85io13a+Y/v27UhJSSkyqV9xGjVqBOD5LREUf/EUFBRg5syZOHHihMr15F8SiYmJ0ofpdfr++++xePFi1KxZE3/++ecrDegeOXIkqlWrhujo6CKDPKOiooq9gWfv3r3RsmVLnD59GqGhoSrPJz969AirVq0q9Rebrq4utm7dCgMDA+zYsQOBgYEqf1mkpaVhxowZaNOmDXJycgA8T3779+8PIQTef/99pTFi2dnZGD16NJ49e4bWrVuXaoDxq3J0dET9+vWRnp5epNcxKioKM2fOVLneihUrVA7wT0xMlH7ZKX4xy4/R7777Tqn+tWvXih1/VlFtJf9CKu3nS65WrVpITk5GSkrKS28ULTd58mSYm5tjyZIl0m1kXnT79m38+uuv0nNDQ0OMHj0aAPDJJ58ofaafPn2KsWPHljjxnCry9l++fLlSMp+QkIBhw4aV64/6xx9/DOD5RHmKv3YLCgowadKkYidzLM+xsGPHDmmAvqK8vDwpQS7t6RBdXV20b98eADB9+nSlbf77778YP358qbajSN5rcOnSpTKvWxHkbfzLL78o9ZA9e/YM48aNU/l9dffuXfz0008qx53Jz2RUr1691L2t5dWtWzfpdiPjxo2TLhoAnt8+JDIyEtWqVSv1ZxAARo8eDUdHR2zbtg2ffvqpyl7DxMRErF69WuX6qj53Dx48wMSJEwEAY8aMUbogRV6+cuXKIpN0rlu3Drt374aurq7SPtSoUQNjx44FAPTv3x8XL15UWk/euyM/tWVjY4Pq1asjPT0d//vf/5Tqnjx5EtOmTVO5L+vXr0dMTEyR8sePH0sXPhX3IyojIwMNGjQo+1mNsl4W9+Kl/C9T3KX8eXl50sRZ1apVEz169BADBgwQjo6OQldXV7qcWNXl2v369RMAhIODgxg0aJAYOXKk0uXIpblEtbhLY+UTUPr4+BQ7GaR8MsLS2LRpkzQJpJubmxg0aJBo3769kMlk4pNPPlF5ybkQzyeBlE++ZWxsLFq3bi2Cg4NF3759hYeHh7RNVRN5leT06dPSeyiTyYSnp6fo16+fGDBggPD29pa2W6dOHaX5MVJSUqQJ3MzMzERgYKDo16+fsLa2FsDLJ4EsTnGXH5d0Kb8QQvz+++9CJpNJUx70799fNG/eXMhkMmnCxBfbVR6/s7OzCAgIEEOGDBFdu3YVhoaGAng+uafi1AmKr+Hm5iaCg4NF586dpcks5bfzePEy5/K01cucPXtWunRaldJecqzoZZNAyietrFGjhujcubMYMmSI6NmzpzT3ire3t9I6WVlZwsvLS/pMBwQEiP79+wtbW9tyTQJ58uRJ6TLhevXqiQEDBgh/f39haGgoGjduLE1sWpZbzBQUFIiAgADpUnI/Pz8RHBwsnJ2dhYGBgTTX0YuxlOdYkE/VYWVlJd555x0xZMgQ0atXL+nmpDVr1izT3CuK7dGgQQPRr18/4ePjI3R1dUVISEiZL+VPTExUmhRw+PDhYuTIkWLNmjUvXfdln8+X3bBWVayPHj2Syi0tLUVgYKAICgoSNWrUECYmJlJ7Kr438hvF6urqipYtW4oBAwaIAQMGSDeJlslk4qeffnpJyyoLDAwU3t7ewtvbWzRq1Ejg/6YbkZd5e3srTZQpd/XqVekzXqdOHTFw4EDp81DeG89evHhRmnzY3NxctG/fXgwePFgEBgYKV1dXIZPJhI2NjdI6ipNAyidB7t+/vwgICJDebx8fH5W3h1KcBLJt27Zi8ODB0iScxU0CmZOTI3r16iWA55NA+vj4iMGDB4uuXbuqnARy6dKl0vezt7e3GDRokDQ34tChQ1UeG/J50uzt7UX37t3FkCFDRPfu3YWZmZkAnk+wrDiNhtySJUsEADFlypQyt73akiMhns/T8NlnnwkXFxdhYGAgatSoIQIDA8WZM2dKnMsmNTVVvP/++6J27drS/BKKfwhfJTmSb6ukR0nz66jy999/Cz8/P2FqaiqMjIxEs2bNxA8//KD0eqo8e/ZMrFq1SnTq1ElYWloKHR0dUaNGDeHh4SE++OADsX///jLFIZeTkyN++uknERAQIGrWrCn09fWFgYGBcHZ2Fv369RObNm1SOf9Idna2mD9/vvDw8BBGRkbCwMBANGrUSHz22WdK82zIVWRyJMTz+xm1adNGGBkZCWNjY9GqVSuxZcsWIYTqdv3jjz/E2LFjRbNmzYS1tbXQ09MTtWrVEh07dhS//PKLyn0+evSo6NKli7CyshJGRkaiSZMm4osvvhA5OTkl/gEoa1uVhvwPsOLkhXKvOzkS4vkcJjNmzBCenp7CxMREaq/WrVuL8PBwcf78+SLrZGdnixkzZoi6desKPT09YWNjI4YMGSJu375d7OeypHmOzp8/L3r16iXs7OyEgYGBqF+/vpgyZYrIzMws1TGiSl5envj666+Fq6ur0NfXF5aWlqJ3794iNja2xFjKeiycO3dOTJ06VbRt21bUrFlT6OnpCWtra9G8eXPx5ZdfFpkFuDSio6NF165dhampqTA0NBTu7u5ixYoVorCwsMzJkXyffH19RfXq1aUfhor7XpnJkRBCPHz4UIwbN07UrVtX6OvrC3t7e/Huu++K69evq3xvMjMzxbJly0SfPn1E/fr1RbVq1YSxsbFo0KCBGDZsWLnm3pHHVtKjuL8tCQkJ4oMPPhCOjo7S+923b18RExNT5jgU93HRokXCx8dHmJubC11dXWFnZydatmwpJk+eXGTeJMXP2a1bt8SgQYOEjY2N0NPTE/Xq1RMzZ84U2dnZxb7en3/+Kbp37y79zZEnVyVNflxYWCg2btwozXStq6srbG1tRbt27cRXX31V5Ef8zp07RevWrYW5ubmoVq2aaNGiRYnH8dGjR8XHH38svLy8hK2trdDT0xO2trbCx8dHfPfdd8XOOv4q91aTCaGG4epEVGa//fYb+vfvj7CwMHz99dfqDoeINNCsWbMwe/ZshIeHY9asWeoOR21iYmLQokUL9OnTp1z3tHuzJ9kgqkL69euHNm3a4IcffijzhGZERFXJzJkzoaenp/Jq+NJgckT0Bvnuu+/w9OlTzJ07V92hEBFppGPHjmHfvn346KOPUL9+/XJt47XMc0RElaNZs2ZKV8IQEZGytm3bvvI0OOw5UpCWloYhQ4ZIczCMHDlSadp3VTp27AiZTKb0ePHS3ri4OPTo0QNGRkaoUaMGJk+erDFzixAR0dtj1qxZEEJU6fFGrwN7jhQMGTIECQkJOHDgAPLy8hAaGorRo0cXOyGl3KhRozBnzhzpueK9qwoKCtCjRw/Y2trixIkT0hwturq6lXpLEiIiIiodXq32f65cuQJXV1f8888/0i0TIiIi0L17d9y7dw/29vYq1+vYsSM8PDywbNkylcv//PNP9OzZEw8ePJCmL1+1ahU+/fRTPHz4sNyzbxMREVHFYM/R/4mOjoa5ubnSvaR8fX2hpaWFU6dOSdPDq7Jhwwb8+uuvsLW1RUBAAGbMmCH1HkVHR8PNzU3pvi5+fn4YO3YsLl26hGbNmqncZk5OjjRDNfB8GvS0tDRYWlqW6z5vRESkPkIIPH78GPb29pV+M24qOyZH/ycxMRE1atRQKtPR0YGFhUWJN8IbPHgwHB0dYW9vj/Pnz+PTTz/FtWvXpHkVEhMTi9zwTv68pO3Onz+/VDekJCKiN0d8fLx06xbSXG99cjR16tSXznMgvzFdecjvJwUAbm5usLOzQ5cuXXDz5k3UrVu33NudNm0awsLCpOcZGRmoXbs2btyOh0kF3yeISF1qd5yk7hCIKoQoyEXu5V9gYmKi7lCoFN765GjixIkYPnx4iXXq1KkDW1vbIncwzs/PR1paGmxtbUv9et7e3gCAGzduoG7durC1tcXp06eV6sgn8Ctpu/r6+tDX1y9SbmJqWuE3USRSF5k2x+DR243DIt4Mb31yZG1tDWtr65fW8/HxQXp6OmJiYtC8eXMAwKFDh1BYWCglPKURGxsLALCzs5O2+8UXXyA5OVk6bXfgwAGYmprC1dW1jHtDREREFY2jwv5Po0aN4O/vj1GjRuH06dM4fvw4xo8fj+DgYOlKtfv376Nhw4ZST9DNmzcxd+5cxMTE4M6dO9i9ezeGDRuG9u3bo2nTpgCArl27wtXVFUOHDsW///6L/fv3Y/r06fjggw9U9gwRERGRejE5UrBhwwY0bNgQXbp0Qffu3dG2bVv8+OOP0vK8vDxcu3YNT548AQDo6enh4MGD6Nq1Kxo2bIiJEyciKCgIe/bskdbR1tbGH3/8AW1tbfj4+ODdd9/FsGHDlOZFIiIiIs3BeY7eEJmZmTAzM0NSagbHHNFbq3rL8eoOgahCiIJc5FxYjYwMfoe/CdhzRERERKSAyRERERGRAiZHRERERAqYHBEREREpYHJEREREpIDJEREREZECJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBwRERERKWByRERERKSAyRERERGRAiZHRERERAqYHBEREREpYHJEREREpIDJEREREZECJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBwRERERKWByRERERKSAyRERERGRAiZHRERERAqYHBEREREpYHJEREREpIDJEREREZECJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBwRERERKWBypCAtLQ1DhgyBqakpzM3NMXLkSGRlZZVYf8KECXBxcYGhoSFq166NDz/8EBkZGUr1ZDJZkcfmzZsreneIiIioHHTUHYAmGTJkCBISEnDgwAHk5eUhNDQUo0ePxsaNG1XWf/DgAR48eIDFixfD1dUVd+/exZgxY/DgwQP89ttvSnXXrl0Lf39/6bm5uXlF7goRERGVk0wIIdQdhCa4cuUKXF1d8c8//6BFixYAgIiICHTv3h337t2Dvb19qbazbds2vPvuu8jOzoaOzvPcUyaTYceOHQgMDCx1PDk5OcjJyZGeZ2ZmwsHBAUmpGTA1NS39jhG9Qaq3HK/uEIgqhCjIRc6F1cjI4Hf4m4Cn1f5PdHQ0zM3NpcQIAHx9faGlpYVTp06VejvyA1+eGMl98MEHsLKygpeXF9asWYOX5aTz58+HmZmZ9HBwcCjbDhEREVG5MDn6P4mJiahRo4ZSmY6ODiwsLJCYmFiqbaSkpGDu3LkYPXq0UvmcOXOwdetWHDhwAEFBQRg3bhy+++67Erc1bdo0ZGRkSI/4+Piy7RARERGVy1s/5mjq1KlYuHBhiXWuXLnyyq+TmZmJHj16wNXVFbNmzVJaNmPGDOn/zZo1Q3Z2Nr766it8+OGHxW5PX18f+vr6rxwXERERlc1bnxxNnDgRw4cPL7FOnTp1YGtri+TkZKXy/Px8pKWlwdbWtsT1Hz9+DH9/f5iYmGDHjh3Q1dUtsb63tzfmzp2LnJwcJkBEREQa5q1PjqytrWFtbf3Sej4+PkhPT0dMTAyaN28OADh06BAKCwvh7e1d7HqZmZnw8/ODvr4+du/eDQMDg5e+VmxsLKpXr87EiIiISAO99clRaTVq1Aj+/v4YNWoUVq1ahby8PIwfPx7BwcHSlWr3799Hly5dsH79enh5eSEzMxNdu3bFkydP8OuvvyIzMxOZmZkAnidl2tra2LNnD5KSktCqVSsYGBjgwIED+PLLLzFp0iR17i4REREVg8mRgg0bNmD8+PHo0qULtLS0EBQUhG+//VZanpeXh2vXruHJkycAgLNnz0pXstWrV09pW7dv34aTkxN0dXWxfPlyfPLJJxBCoF69eliyZAlGjRpVeTtGREREpcZ5jt4QmZmZMDMz4zxH9FbjPEf0tuI8R28WXspPREREpIDJEREREZECJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBwRERERKWByRERERKSAyRERERGRAiZHRERERAqYHBEREREpYHJEREREpIDJEREREZECJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBwRERERKWByRERERKSAyRERERGRAiZHRERERAqYHBEREREpYHJEREREpIDJEREREZECJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBwRERERKWByRERERKSAyRERERGRAiZHRERERAqYHKmwfPlyODk5wcDAAN7e3jh9+nSJ9bdt24aGDRvCwMAAbm5u2Ldvn9JyIQRmzpwJOzs7GBoawtfXF9evX6/IXSAiIqJyYnL0gi1btiAsLAzh4eE4e/Ys3N3d4efnh+TkZJX1T5w4gUGDBmHkyJE4d+4cAgMDERgYiIsXL0p1Fi1ahG+//RarVq3CqVOnYGxsDD8/Pzx79qyydouIiIhKSSaEEOoOQpN4e3ujZcuW+P777wEAhYWFcHBwwIQJEzB16tQi9QcOHIjs7Gz88ccfUlmrVq3g4eGBVatWQQgBe3t7TJw4EZMmTQIAZGRkwMbGBuvWrUNwcHCp4srMzISZmRmSUjNgamr6GvaUSPNUbzle3SEQVQhRkIucC6uRkcHv8DcBe44U5ObmIiYmBr6+vlKZlpYWfH19ER0drXKd6OhopfoA4OfnJ9W/ffs2EhMTleqYmZnB29u72G0CQE5ODjIzM5UeREREVPGYHClISUlBQUEBbGxslMptbGyQmJiocp3ExMQS68v/Lcs2AWD+/PkwMzOTHg4ODmXeHyIiIio7Jkcaatq0acjIyJAe8fHx6g6JiIioSmBypMDKygra2tpISkpSKk9KSoKtra3KdWxtbUusL/+3LNsEAH19fZiamio9iIiIqOIxOVKgp6eH5s2bIzIyUiorLCxEZGQkfHx8VK7j4+OjVB8ADhw4INV3dnaGra2tUp3MzEycOnWq2G0SERGR+uioOwBNExYWhpCQELRo0QJeXl5YtmwZsrOzERoaCgAYNmwYatasifnz5wMAPvroI3To0AFff/01evTogc2bN+PMmTP48ccfAQAymQwff/wx5s2bh/r168PZ2RkzZsyAvb09AgMD1bWbREREVAwmRy8YOHAgHj58iJkzZyIxMREeHh6IiIiQBlTHxcVBS+v/d7i1bt0aGzduxPTp0/HZZ5+hfv362LlzJ5o0aSLVmTJlCrKzszF69Gikp6ejbdu2iIiIgIGBQaXvHxEREZWM8xy9ITjPEVUFnOeI3lac5+jNwjFHRERERAqYHBEREREpYHJEREREpIDJEREREZECJkdERERECjTmUv7CwkKlS+SB5zd1/eOPP2BgYIDQ0FDUqlVLTdERERFRVaERPUeffPIJjIyMkJ6eLpX99ttvaNeuHebPn4/w8HB4enri3r176guSiIiIqgSNSI4OHz6Mzp07w9zcXCqbOXMmzMzMsH79eixatAiPHj3C4sWL1RckERERVQkacVotPj4eHTp0kJ7fvn0bV69eRXh4ON59910AwN9//42IiAh1hUhERERVhEb0HGVnZ8PY2Fh6fuTIEchkMnTr1k0qc3V15Wk1IiIiqnAakRzZ29vj2rVr0vOIiAhUq1YNzZs3l8oyMzOhr6+vjvCIiIioCtGI02odOnTApk2b8P3338PAwADbt29HYGAgtLW1pTo3b97k1WpERERU4TSi5+jzzz+HoaEhPvroI4wePRr6+vqYNWuWtPzx48c4evQo2rRpo74giYiIqErQiJ6jevXq4fLly/j9998BAAEBAXB0dJSWX79+He+//z4GDx6srhCJiIioipAJIYS6g6CXy8zMhJmZGZJSM2BqaqrucIgqRPWW49UdAlGFEAW5yLmwGhkZ/A5/E2jEaTUiIiIiTaGW02pz5syBTCbDBx98AAsLC8yZM6dU68lkMsyYMaOCoyMiIqKqTC2n1bS0tCCTyXDlyhU0aNCgyD3ViiOTyVBQUFDB0WkmnlajqoCn1ehtxdNqbxa19BwdPnwYAFC7dm2l50RERETqppbkSPFWIaqeExEREakLB2QTERERKdCIeY4UFRQUICUlBTk5OSqXy0/FEREREVUEjUmOYmJi8Nlnn+Ho0aPIzc1VWUcmkyE/P7+SIyMiIqKqRCOSo9jYWLRr1w46Ojro2rUr9uzZA3d3d9ja2uLs2bN4+PAhOnbsqDRrNhEREVFF0IgxR3PnzgUAnDp1Crt27QIA9OnTB3/++Sfu3LmDMWPG4OLFiwgPD1dnmERERFQFaERydOzYMfTq1QuNGjWSyuTTLxkaGuL777+Hvb09PvvsM3WFSERERFWERiRHGRkZqFOnjvRcV1cXWVlZ0nMtLS107NgRkZGR6giPiIiIqhCNSI5q1KiBR48eSc9tbW1x/fp1pTrPnj3DkydPKjs0IiIiqmI0IjlydXXFtWvXpOdt2rTBX3/9hejoaADAlStXsHXrVjRs2FBdIRIREVEVoRHJUY8ePXD06FEkJCQAAD799FMIIdC2bVtYW1vDzc0N6enpHHNEREREFU4jkqMxY8bg/v37sLS0BAC4u7sjMjIS/v7+sLKygq+vL/bs2YM+ffqoOVIiIiJ622nEPEe6urqwsbFRKmvdujX27t2rpoiIiIioqtKIniMiIiIiTaExyVF+fj6WLl0KLy8vmJqaQkfn/3dqxcbGYty4cfjvv//UGCERERFVBRpxWu3p06fo2rUrTpw4ASsrK5iamiI7O1ta7uzsjLVr18LCwgLz5s1TY6RERET0ttOInqMvv/wSx48fx/z585GYmIj33ntPabmZmRk6dOiA/fv3V0o8y5cvh5OTEwwMDODt7Y3Tp08XW3f16tVo164dqlevjurVq8PX17dI/eHDh0Mmkyk9/P39K3o3iIiIqBw0IjnasmULOnXqhClTpkjJw4vq1KmDuLi4SoklLCwM4eHhOHv2LNzd3eHn54fk5GSV9aOiojBo0CAcPnwY0dHRcHBwQNeuXXH//n2lev7+/khISJAemzZtqvB9ISIiorLTiOQoLi4OLVq0KLGOiYkJMjIyKjyWJUuWYNSoUQgNDYWrqytWrVoFIyMjrFmzRmX9DRs2YNy4cfDw8EDDhg3x008/obCwsMitTvT19WFrays9qlevXuH7QkRERGWnEcmRiYlJsT0zcjdv3oS1tXWFxpGbm4uYmBj4+vpKZVpaWvD19ZVm636ZJ0+eIC8vDxYWFkrlUVFRqFGjBlxcXDB27FikpqaWuJ2cnBxkZmYqPYiIiKjiaURy1KpVK+zZswfp6ekql8fHx2Pfvn1o3759hcaRkpKCgoKCInMu2djYIDExsVTb+PTTT2Fvb6+UYPn7+2P9+vWIjIzEwoULceTIEXTr1g0FBQXFbmf+/PkwMzOTHg4ODuXbKSIiIioTjUiOJk+ejEePHqFLly44fvw48vPzATzvhYmMjISfnx/y8/MRFham5khLtmDBAmzevBk7duyAgYGBVB4cHIxevXrBzc0NgYGB+OOPP/DPP/8gKiqq2G1NmzYNGRkZ0iM+Pr4S9oCIiIg04lL+9u3b4/vvv8dHH32k1DtkYmICANDW1saKFSvQvHnzCo3DysoK2traSEpKUipPSkqCra1tiesuXrwYCxYswMGDB9G0adMS69apUwdWVla4ceMGunTporKOvr4+9PX1y7YDRERE9Mo0oucIAMaOHYt///0X48ePR8uWLVG3bl00a9YMY8aMwblz54pc3l8R9PT00Lx5c6XB1PLB1T4+PsWut2jRIsydOxcREREvHVgOAPfu3UNqairs7OxeS9xERET0+mhEz5Fco0aN8M033xS7PCMjA2ZmZhUaQ1hYGEJCQtCiRQt4eXlh2bJlyM7ORmhoKABg2LBhqFmzJubPnw8AWLhwIWbOnImNGzfCyclJGptUrVo1VKtWDVlZWZg9ezaCgoJga2uLmzdvYsqUKahXrx78/PwqdF+IiIio7DSm56gkWVlZmDNnDurUqVPhrzVw4EAsXrwYM2fOhIeHB2JjYxERESEN0o6Li0NCQoJUf+XKlcjNzUW/fv1gZ2cnPRYvXgzg+SnB8+fPo1evXmjQoAFGjhyJ5s2b4++//+ZpMyIiIg0kE0IIdQZw584dnD17Fjo6OvDy8lIa2/Ps2TMsW7YMixcvRlpaGoyMjJCVlaXGaNUnMzMTZmZmSErNgKmpqbrDIaoQ1VuOV3cIRBVCFOQi58JqZGTwO/xNoLaeIyEExo4di3r16qF///7o06cPnJycpNNqBw4cQP369fH555/j6dOnCAsLw61bt9QVLhEREVURahtztHr1avzwww8wNjZGhw4dAABHjhxBWFgYdHV18fHHH0Mmk+Gjjz7C1KlTUaNGDXWFSkRERFWI2pKj9evXo1q1aoiNjZXGEt24cQPNmjXDhAkT4OzsjL1798LFxUVdIRIREVEVpLbTapcuXULfvn2VBlnXq1cPQUFBAIA1a9YwMSIiIqJKp7bkKDMzE46OjkXKa9euDQDw9vau7JCIiIiI1DsgW1tbu0i5vIyXuRMREZE6qHUSyOzsbCQnJyuVyS/Vf/jwIVTNMsCB2URERFSR1DbPkZaWFmQymcplQgiVy2QymXRT2qqG8xxRVcB5juhtxXmO3ixq6zlq3759sckRERERkbqoLTmKiopS10sTERERFeuNuLcaERERUWVhckRERESkgMkRERERkQImR0REREQKmBwRERERKWByRERERKSAyRERERGRAo1OjoQQuH79OuLj49UdChEREVURGpEcbd++HcOGDcOjR4+ksjt37qBp06Zo2LAhnJycEBwcjIKCAjVGSURERFWBRiRHK1euRGxsLKpXry6VffLJJ7h06RI6deqEpk2bYtu2bVizZo0aoyQiIqKqQCOSo8uXL8PLy0t6/vjxY+zduxcDBw7EwYMHcfr0aTRq1IjJEREREVU4jUiO0tLSYGtrKz0/duwY8vPzMWjQIACArq4u3nnnHdy8eVNdIRIREVEVoRHJkampKVJTU6Xnhw8fhpaWFtq1ayeV6erqIjs7Wx3hERERURWiEclRw4YNsWfPHqSmpiI9PR0bN25E8+bNlcYg3b17FzY2NmqMkoiIiKoCjUiOPvzwQzx48AC1atVC7dq1kZCQgLFjxyrVOXnyJNzd3dUUIREREVUVOuoOAACCgoKwfPly/PzzzwCA4OBgDB8+XFp+5MgRZGZmwt/fX00REhERUVUhE0IIdQdBL5eZmQkzMzMkpWbA1NRU3eEQVYjqLcerOwSiCiEKcpFzYTUyMvgd/ibQiNNqRERERJpCI06rKYqPj8eDBw+Qk5Ojcnn79u0rOSIiIiKqSjQmOdqzZw8mT56M69evl1iPtxAhIiKiiqQRp9WioqLQp08fZGVlYfz48RBCoH379hg9ejRcXV0hhECPHj0wc+ZMdYdKREREbzmNSI4WLFiAatWqISYmBt988w0AoFOnTli5ciUuXLiAL774ApGRkejdu7eaIyUiIqK3nUYkR//88w8CAwOVJnksLCyU/j9t2jQ0a9aMPUdERERU4TQiOXry5Alq1qwpPdfX10dmZqZSnVatWuH48eOVHRoRERFVMRqRHNna2uLhw4fS85o1a+LSpUtKdVJTUzkYm4iIiCqcRiRH7u7uuHjxovS8U6dOOHz4MDZt2oTs7Gzs378fW7duRdOmTSslnuXLl8PJyQkGBgbw9vbG6dOni627bt06yGQypYeBgYFSHSEEZs6cCTs7OxgaGsLX1/elV+URERGRemhEctSrVy/Exsbi7t27AIDPPvsM1apVw7vvvgtTU1N0794d+fn5mDdvXoXHsmXLFoSFhSE8PBxnz56Fu7s7/Pz8kJycXOw6pqamSEhIkB7y/ZBbtGgRvv32W6xatQqnTp2CsbEx/Pz88OzZs4reHSIiIiojjb19yM2bN7FkyRLcunULjo6OGDNmDDw8PCr8db29vdGyZUt8//33AJ4PDHdwcMCECRMwderUIvXXrVuHjz/+GOnp6Sq3J4SAvb09Jk6ciEmTJgEAMjIyYGNjg3Xr1iE4OLhUcfH2IVQV8PYh9Lbi7UPeLBozCeSL6tati+XLl1fqa+bm5iImJgbTpk2TyrS0tODr64vo6Ohi18vKyoKjoyMKCwvh6emJL7/8Eo0bNwYA3L59G4mJifD19ZXqm5mZwdvbG9HR0cUmRzk5OUqzhL84QJ2IiIgqhsYmR+qQkpKCgoICpSkFAMDGxgZXr15VuY6LiwvWrFmDpk2bIiMjA4sXL0br1q1x6dIl1KpVC4mJidI2XtymfJkq8+fPx+zZs4uU1+44CTJtvbLuGtGbQVdf3REQVQyNGMRCpaWW5Ojo0aMAAC8vLxgYGEjPS0PT7q3m4+MDHx8f6Xnr1q3RqFEj/PDDD5g7d265tztt2jSEhYVJzzMzM+Hg4PBKsRIREdHLqSU56tixI2QyGa5cuYIGDRpIz0ujIi/nt7Kygra2NpKSkpTKk5KSYGtrW6pt6OrqolmzZrhx4wYASOslJSXBzs5OaZsljaHS19eHvj5/RRMREVU2tSRHM2fOhEwmg5WVldJzddPT00Pz5s0RGRmJwMBAAM8HZEdGRmL8+NINFC0oKMCFCxfQvXt3AICzszNsbW0RGRkpJUOZmZk4deoUxo4dWxG7QURERK9ALcnRrFmzSnyuTmFhYQgJCUGLFi3g5eWFZcuWITs7G6GhoQCAYcOGoWbNmpg/fz4AYM6cOWjVqhXq1auH9PR0fPXVV7h79y7ee+89AIBMJsPHH3+MefPmoX79+nB2dsaMGTNgb28vJWBERESkOTRiQPacOXPg7OyMoUOHqjsUDBw4EA8fPsTMmTORmJgIDw8PRERESAOq4+LioKX1/0fWPXr0CKNGjUJiYiKqV6+O5s2b48SJE3B1dZXqTJkyBdnZ2Rg9ejTS09PRtm1bREREFJkskoiIiNRPI+Y50tPTw8cff4xFixapOxSNJZ/nSN9tFK9Wo7cXr1ajt5QoyEHO2eWc5+gNoREXF9auXbvYSRSJiIiIKpNGJEfBwcGIiIhARkaGukMhIiKiKk4jkqMZM2agadOm6Ny5M/bu3VvifcyIiIiIKpJGDMg2MjIC8Pw+ZL169Sq2nkwmQ35+fmWFRURERFWQRiRH7dq104h5joiIiIg0IjmKiopSdwhEREREADRkzBERERGRptCIniNF9+/fR2xsLDIzM2FqagoPDw/UrFlT3WERERFRFaExydGNGzcwduxYHDp0qMiyLl26YMWKFahXr54aIiMiIqKqRCOSo/j4eLRt2xbJyclo2LAh2rdvDzs7OyQmJuLo0aM4ePAg2rVrh9OnT8PBwUHd4RIREdFbTCOSo9mzZyM5ORkrVqzA+++/X+TKtR9++AFjx47FnDlzsHr1ajVFSURERFWBRtxbzcHBAZ6enti1a1exdXr37o2YmBjcu3evEiPTHLy3GlUJvLcavaV4b7U3i0ZcrZacnIwmTZqUWKdJkyZ4+PBhJUVEREREVZVGJEfW1ta4fPlyiXUuX74Ma2vrSoqIiIiIqiqNSI78/Pywe/du/PzzzyqXr1mzBnv27IG/v38lR0ZERERVjUaMOYqLi0OLFi2QmpoKV1dXdOjQATY2NkhKSsLRo0dx6dIlWFlZ4cyZM1X2ajWOOaIqgWOO6C3FMUdvFo24Wq127do4fvw43n//fURFReHSpUtKyzt16oSVK1dW2cSIiIiIKo9GJEcAUL9+fRw6dAjx8fFFZshmUkRERESVRWOSIzkHBwcmQ0RERKQ2GjEgm4iIiEhTaETPUefOnV9aR0tLC6ampnBxcUFgYCC8vb0rITIiIiKqajTiajUtrecdWDKZDKrCebFcJpMhNDQUP/30U6XFqG68Wo2qBF6tRm8pXq32ZtGI02pPnz5FQEAAGjVqhI0bN+Lu3bt49uwZ7t69i40bN6Jx48bo1asX4uPj8ddff8HT0xNr167FypUr1R06ERERvWU0oudo6tSp2Lp1Ky5cuABjY+Miy7Ozs+Hm5oYBAwZgwYIFSE9PR8OGDVG7dm2cPn1aDRFXPvYcUZXAniN6S7Hn6M2iET1HGzduRN++fVUmRgBgbGyMvn37YtOmTQAAc3Nz+Pv748qVK5UZJhEREVUBGpEcPXz4EHl5eSXWyc/PR3JysvTczs4OBQUFFR0aERERVTEakRzVrVsX27ZtQ2pqqsrlqamp2Lp1K+rWrSuVPXjwABYWFpUVIhEREVURGpEcTZgwAYmJifD09MS3336LmJgYxMfHIyYmBt9++y08PT2RlJSECRMmAAAKCwtx6NAhtGzZUs2RExER0dtGI+Y5ev/993H//n3Mnz8fn3zyidIyIQS0tLQwbdo0vP/++wCAtLQ0TJo0Ca1bt1ZHuERERPQW04ir1eSuX7+OjRs34vz589K91dzd3REcHIwGDRqoOzy14tVqVCXwajV6S/FqtTeLRvQcydWvXx/h4eHqDoOIiIiqMI0Yc/SitLQ0xMfHqzsMIiIiqoI0JjnKyMjARx99BBsbG1hbW8PZ2VladurUKXTv3h0xMTFqjJCIiIiqAo1IjtLS0uDt7Y3vvvsODg4OaNSokdK91Jo2bYrjx49jw4YNaoySiIiIqgKNSI5mzZqF//77D5s3b8aZM2fQv39/peWGhobo0KEDDh06pKYIiYiIqKrQiORo9+7d6NmzJwYMGFBsHScnJ9y7d69S4lm+fDmcnJxgYGAAb2/vEu/f1rFjR8hksiKPHj16SHWGDx9eZLm/v39l7AoRERGVkUYkRwkJCXB1dS2xjr6+PrKzsys8li1btiAsLAzh4eE4e/Ys3N3d4efnp3TrEkXbt29HQkKC9Lh48SK0tbWL9H75+/sr1ZPfJ46IiIg0i0YkR5aWli+9Ou3q1auws7Or8FiWLFmCUaNGITQ0FK6urli1ahWMjIywZs0alfUtLCxga2srPQ4cOAAjI6MiyZG+vr5SverVq1f4vhAREVHZaURy1L59e+zatavY02aXL19GREQEfH19KzSO3NxcxMTEKL2OlpYWfH19ER0dXapt/PzzzwgODoaxsbFSeVRUFGrUqAEXFxeMHTu22PvIyeXk5CAzM1PpQURERBVPI5Kjzz//HAUFBWjTpg02bNiAlJQUAMCVK1fw888/o3PnztDX18fkyZMrNI6UlBQUFBTAxsZGqdzGxgaJiYkvXf/06dO4ePEi3nvvPaVyf39/rF+/HpGRkVi4cCGOHDmCbt26oaCgoNhtzZ8/H2ZmZtLDwcGhfDtFREREZaIRM2S7ublhy5YtGDp0KIYNGwbg+T3VmjRpAiEETExMsHXrVtSvX1/NkZbs559/hpubG7y8vJTKg4ODpf+7ubmhadOmqFu3LqKiotClSxeV25o2bRrCwsKk55mZmUyQiIiIKoFGJEcA0KtXL9y+fRu//PILTp06hbS0NJiamsLb2xuhoaGwsrKq8BisrKygra2NpKQkpfKkpCTY2tqWuG52djY2b96MOXPmvPR16tSpAysrK9y4caPY5EhfXx/6+rzPFBERUWXTmOQIeD64+ZNPPlHb6+vp6aF58+aIjIxEYGAgAKCwsBCRkZEYP358ietu27YNOTk5ePfdd1/6Ovfu3UNqamqlDDAnIiKistGIMUcjRozA7t27S6zzxx9/YMSIERUeS1hYGFavXo1ffvkFV65cwdixY5GdnY3Q0FAAwLBhwzBt2rQi6/38888IDAyEpaWlUnlWVhYmT56MkydP4s6dO4iMjETv3r1Rr149+Pn5Vfj+EBERUdloRM/RunXr4OTkhF69ehVb599//8Uvv/xS7CX1r8vAgQPx8OFDzJw5E4mJifDw8EBERIQ0SDsuLg5aWso55bVr13Ds2DH89ddfRbanra2N8+fP45dffkF6ejrs7e3RtWtXzJ07l6fNiIiINJBGJEel8ezZM+joVE6448ePL/Y0WlRUVJEyFxcXpXvBKTI0NMT+/ftfZ3hERERUgTQmOZLJZCrLhRCIj4/Hn3/+CXt7+0qOioiIiKoatY050tLSgra2NrS1tQE8v/ms/LniQ0dHB87Ozjh79qzSJfFEREREFUFtPUft27eXeouOHj2K2rVrw8nJqUg9bW1tWFhYoHPnzhg1alQlR0lERERVjdqSI8WxO1paWggNDcXMmTPVFQ4RERERAA0Zc1RYWKjuEIiIiIgAaMg8R0RERESaQiN6jgCgoKAAW7duxcGDB/HgwQPk5OQUqSOTyRAZGamG6IiIiKiq0IjkKDs7G127dsXJkychhIBMJlOaN0j+vLjL/YmIiIheF404rTZv3jxER0dj9uzZSElJgRACs2bNQkJCArZs2YI6deqgf//+KnuTiIiIiF4njUiOtm/fjlatWmH69OmwsLCQym1sbNC/f38cPnwYBw8exFdffaXGKImIiKgq0IjkKC4uDq1atZKea2lpKfUS1apVCz169MAvv/yijvCIiIioCtGI5MjY2FjpZq5mZmZISEhQqmNra4u4uLjKDo2IiIiqGI1IjhwdHZUSnyZNmuDQoUNS75EQApGRkbCzs1NXiERERFRFaERy1KVLFxw+fBj5+fkAgJCQEMTFxcHHxweTJ09G27ZtERsbi6CgIDVHSkRERG87jbiUf9SoUbC0tMTDhw9hZ2eHESNG4Ny5c1ixYgViY2MBAEFBQZg1a5Za4yQiIqK3n0woTiikYR4+fIhbt27B0dERtra26g5HrTIzM2FmZgZ9t1GQaeupOxyiiqGrr+4IiCqEKMhBztnlyMjIgKmpqbrDoZfQiJ6j4lhbW8Pa2lrdYRAREVEVotYxR1988QU+++wz5OXlFVsnNzcXn332GRYsWFCJkREREVFVpbbk6ODBg5g5cyYsLS2hq6tbbD09PT1YWVnh888/x+HDhysxQiIiIqqK1JYcrV+/HtWrV8f48eNfWveDDz6AhYUF1q5dWwmRERERUVWmtuToxIkT8PX1hb7+ywdg6uvrw9fXF8ePH6+EyIiIiKgqU1ty9ODBA9SpU6fU9Z2dnYvMmk1ERET0uqktOdLS0ipxIPaL8vLylG4xQkRERFQR1JZt2Nvb4+LFi6Wuf/HiRdSsWbMCIyIiIiJSY3LUrl07HDp0CHfu3Hlp3Tt37uDQoUNo3759xQdGREREVZrakqMPPvgAeXl56NevH1JSUoqtl5qaiv79+yM/Px9jx46txAiJiIioKlLbDNmenp74+OOPsWzZMri6umLMmDHo1KkTatWqBQC4f/8+IiMj8eOPP+Lhw4cICwuDp6enusIlIiKiKkKt91YTQuDzzz/HV199hcLCQpXLtbW1MWXKFMybNw8ymUwNUWoG3luNqgTeW43eUry32ptFI248e/PmTaxduxYnTpxAYmIiAMDW1hZt2rTB8OHDUbduXTVHqH5MjqhKYHJEbykmR28WjbjxbN26dTFv3jx1h0FERESk3hvPEhEREWkaJkdERERECpgcERERESlgckRERESkgMkRERERkQImR0REREQKmBy94OjRowgICIC9vT1kMhl27tz50nWioqLg6ekJfX191KtXD+vWrStSZ/ny5XBycoKBgQG8vb1x+vTp1x88ERERvTImRy/Izs6Gu7s7li9fXqr6t2/fRo8ePdCpUyfExsbi448/xnvvvYf9+/dLdbZs2YKwsDCEh4fj7NmzcHd3h5+fH5KTkytqN4iIiKicNGKGbE0lk8mwY8cOBAYGFlvn008/xd69e3Hx4kWpLDg4GOnp6YiIiAAAeHt7o2XLlvj+++8BAIWFhXBwcMCECRMwdepUldvNyclBTk6O9DwzMxMODg6cIZvebpwhm95SnCH7zcKeo1cUHR0NX19fpTI/Pz9ER0cDAHJzcxETE6NUR0tLC76+vlIdVebPnw8zMzPp4eDgUDE7QEREREqYHL2ixMRE2NjYKJXZ2NggMzMTT58+RUpKCgoKClTWkd9HTpVp06YhIyNDesTHx1dI/ERERKRMI+6tRkXp6+tDX5+nGIiIiCobk6NXZGtri6SkJKWypKQkmJqawtDQENra2tDW1lZZx9bWtjJDJSIiolLgabVX5OPjg8jISKWyAwcOwMfHBwCgp6eH5s2bK9UpLCxEZGSkVIeIiIg0B5OjF2RlZSE2NhaxsbEAnl+qHxsbi7i4OADPxwINGzZMqj9mzBjcunULU6ZMwdWrV7FixQps3boVn3zyiVQnLCwMq1evxi+//IIrV65g7NixyM7ORmhoaKXuGxEREb0cT6u94MyZM+jUqZP0PCwsDAAQEhKCdevWISEhQUqUAMDZ2Rl79+7FJ598gm+++Qa1atXCTz/9BD8/P6nOwIED8fDhQ8ycOROJiYnw8PBAREREkUHaREREpH6c5+gNkZmZCTMzM85zRG83znNEbynOc/Rm4Wk1IiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5IiIiIlLA5IiIiIhIAZMjIiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5IiIiIlLA5IiIiIhIAZMjIiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5IiIiIlLA5OgFR48eRUBAAOzt7SGTybBz584S62/fvh3vvPMOrK2tYWpqCh8fH+zfv1+pzqxZsyCTyZQeDRs2rMC9ICIiovJicvSC7OxsuLu7Y/ny5aWqf/ToUbzzzjvYt28fYmJi0KlTJwQEBODcuXNK9Ro3boyEhATpcezYsYoIn4iIiF6RjroD0DTdunVDt27dSl1/2bJlSs+//PJL7Nq1C3v27EGzZs2kch0dHdja2r6uMImIiKiCsOfoNSssLMTjx49hYWGhVH79+nXY29ujTp06GDJkCOLi4krcTk5ODjIzM5UeREREVPGYHL1mixcvRlZWFgYMGCCVeXt7Y926dYiIiMDKlStx+/ZttGvXDo8fPy52O/Pnz4eZmZn0cHBwqIzwiYiIqjyZEEKoOwhNJZPJsGPHDgQGBpaq/saNGzFq1Cjs2rULvr6+xdZLT0+Ho6MjlixZgpEjR6qsk5OTg5ycHOl5ZmYmHBwcoO82CjJtvTLtB9EbQ1df3REQVQhRkIOcs8uRkZEBU1NTdYdDL8ExR6/J5s2b8d5772Hbtm0lJkYAYG5ujgYNGuDGjRvF1tHX14e+Pv9QEBERVTaeVnsNNm3ahNDQUGzatAk9evR4af2srCzcvHkTdnZ2lRAdERERlQV7jl6QlZWl1KNz+/ZtxMbGwsLCArVr18a0adNw//59rF+/HsDzU2khISH45ptv4O3tjcTERACAoaEhzMzMAACTJk1CQEAAHB0d8eDBA4SHh0NbWxuDBg2q/B0kIiKiErHn6AVnzpxBs2bNpMvww8LC0KxZM8ycORMAkJCQoHSl2Y8//oj8/Hx88MEHsLOzkx4fffSRVOfevXsYNGgQXFxcMGDAAFhaWuLkyZOwtrau3J0jIiKil+KA7DdEZmYmzMzMOCCb3m4ckE1vKQ7IfrOw54iIiIhIAZMjIiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5IiIiIlLA5IiIiIhIAZMjIiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5IiIiIlLA5IiIiIhIAZMjIiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5esHRo0cREBAAe3t7yGQy7Ny5s8T6UVFRkMlkRR6JiYlK9ZYvXw4nJycYGBjA29sbp0+frsC9ICIiovJicvSC7OxsuLu7Y/ny5WVa79q1a0hISJAeNWrUkJZt2bIFYWFhCA8Px9mzZ+Hu7g4/Pz8kJye/7vCJiIjoFemoOwBN061bN3Tr1q3M69WoUQPm5uYqly1ZsgSjRo1CaGgoAGDVqlXYu3cv1qxZg6lTp75KuERERPSaMTl6TTw8PJCTk4MmTZpg1qxZaNOmDQAgNzcXMTExmDZtmlRXS0sLvr6+iI6OLnZ7OTk5yMnJkZ5nZGQAAERBbgXtAZEGYF82vaXk391CCDVHQqXB5OgV2dnZYdWqVWjRogVycnLw008/oWPHjjh16hQ8PT2RkpKCgoIC2NjYKK1nY2ODq1evFrvd+fPnY/bs2UXKcy//8tr3gYiIKkdqairMzMzUHQa9BJOjV+Ti4gIXFxfpeevWrXHz5k0sXboU//vf/8q93WnTpiEsLEx6np6eDkdHR8TFxb1RH6zMzEw4ODggPj4epqam6g6n1Bh35XtTY2fcletNjTsjIwO1a9eGhYWFukOhUmByVAG8vLxw7NgxAICVlRW0tbWRlJSkVCcpKQm2trbFbkNfXx/6+vpFys3MzN6oLwQ5U1NTxl2J3tS4gTc3dsZdud7UuLW0eO74TcB3qQLExsbCzs4OAKCnp4fmzZsjMjJSWl5YWIjIyEj4+PioK0QiIiIqBnuOXpCVlYUbN25Iz2/fvo3Y2FhYWFigdu3amDZtGu7fv4/169cDAJYtWwZnZ2c0btwYz549w08//YRDhw7hr7/+krYRFhaGkJAQtGjRAl5eXli2bBmys7Olq9eIiIhIczA5esGZM2fQqVMn6bl83E9ISAjWrVuHhIQExMXFSctzc3MxceJE3L9/H0ZGRmjatCkOHjyotI2BAwfi4cOHmDlzJhITE+Hh4YGIiIgig7RLoq+vj/DwcJWn2jQZ465cb2rcwJsbO+OuXIybKoNM8LpCIiIiIgnHHBEREREpYHJEREREpIDJEREREZECJkdERERECpgcaYi0tDQMGTIEpqamMDc3x8iRI5GVlVXiOh07doRMJlN6jBkzRqlOXFwcevToASMjI9SoUQOTJ09Gfn6+2uJOS0vDhAkT4OLiAkNDQ9SuXRsffvihdO84uRf3SyaTYfPmza8U6/Lly+Hk5AQDAwN4e3vj9OnTJdbftm0bGjZsCAMDA7i5uWHfvn1Ky4UQmDlzJuzs7GBoaAhfX19cv379lWJ81bhXr16Ndu3aoXr16qhevTp8fX2L1B8+fHiRtvX391dr3OvWrSsSk4GBgVIdTWxvVZ9BmUyGHj16SHUqo72PHj2KgIAA2NvbQyaTYefOnS9dJyoqCp6entDX10e9evWwbt26InXK+pmp6Li3b9+Od955B9bW1jA1NYWPjw/279+vVGfWrFlF2rthw4avNe7yxB4VFaXyWElMTFSqV9FtTqUkSCP4+/sLd3d3cfLkSfH333+LevXqiUGDBpW4TocOHcSoUaNEQkKC9MjIyJCW5+fniyZNmghfX19x7tw5sW/fPmFlZSWmTZumtrgvXLgg+vbtK3bv3i1u3LghIiMjRf369UVQUJBSPQBi7dq1Svv29OnTcse5efNmoaenJ9asWSMuXbokRo0aJczNzUVSUpLK+sePHxfa2tpi0aJF4vLly2L69OlCV1dXXLhwQaqzYMECYWZmJnbu3Cn+/fdf0atXL+Hs7PxKcb5q3IMHDxbLly8X586dE1euXBHDhw8XZmZm4t69e1KdkJAQ4e/vr9S2aWlpry3m8sS9du1aYWpqqhRTYmKiUh1NbO/U1FSlmC9evCi0tbXF2rVrpTqV0d779u0Tn3/+udi+fbsAIHbs2FFi/Vu3bgkjIyMRFhYmLl++LL777juhra0tIiIipDplbYvKiPujjz4SCxcuFKdPnxb//fefmDZtmtDV1RVnz56V6oSHh4vGjRsrtffDhw9fW8zljf3w4cMCgLh27ZpSbAUFBVKdymhzKh0mRxrg8uXLAoD4559/pLI///xTyGQycf/+/WLX69Chg/joo4+KXb5v3z6hpaWl9Edm5cqVwtTUVOTk5Kgt7hdt3bpV6Onpiby8PKmsNF82ZeHl5SU++OAD6XlBQYGwt7cX8+fPV1l/wIABokePHkpl3t7e4v333xdCCFFYWChsbW3FV199JS1PT08X+vr6YtOmTWqL+0X5+fnCxMRE/PLLL1JZSEiI6N2792uLUZWyxr127VphZmZW7PbelPZeunSpMDExEVlZWVJZZbS3otJ8dqZMmSIaN26sVDZw4EDh5+cnPX/Vtiir8n7mXV1dxezZs6Xn4eHhwt3d/fUFVgplSY4ePXpUbJ3KbnMqHk+raYDo6GiYm5ujRYsWUpmvry+0tLRw6tSpEtfdsGEDrKys0KRJE0ybNg1PnjxR2q6bm5vSZJN+fn7IzMzEpUuX1Bq3ooyMDJiamkJHR3lO0g8++ABWVlbw8vLCmjVrIMo5JVdubi5iYmLg6+srlWlpacHX1xfR0dEq14mOjlaqDzxvO3n927dvIzExUamOmZkZvL29i91mZcT9oidPniAvL6/IzS6joqJQo0YNuLi4YOzYsUhNTX0tMb9K3FlZWXB0dISDgwN69+6tdIy+Ke39888/Izg4GMbGxkrlFdne5fGy4/t1tEVlKCwsxOPHj4sc39evX4e9vT3q1KmDIUOGKE3cq24eHh6ws7PDO++8g+PHj0vlb0qbVxWcIVsDJCYmokaNGkplOjo6sLCwKHI+WtHgwYPh6OgIe3t7nD9/Hp9++imuXbuG7du3S9t9cRZu+fOStlvRcStKSUnB3LlzMXr0aKXyOXPmoHPnzjAyMsJff/2FcePGISsrCx9++GGZ40xJSUFBQYHKtrh69arKdYprO/l+yf8tqc6rKk/cL/r0009hb2+v9IXr7++Pvn37wtnZGTdv3sRnn32Gbt26ITo6Gtra2mqJ28XFBWvWrEHTpk2RkZGBxYsXo3Xr1rh06RJq1ar1RrT36dOncfHiRfz8889K5RXd3uVR3PGdmZmJp0+f4tGjR6987FWGxYsXIysrCwMGDJDKvL29sW7dOri4uCAhIQGzZ89Gu3btcPHiRZiYmKgtVjs7O6xatQotWrRATk4OfvrpJ3Ts2BGnTp2Cp6fna/m80+vD5KgCTZ06FQsXLiyxzpUrV8q9fcWEws3NDXZ2dujSpQtu3ryJunXrlnu7FR23XGZmJnr06AFXV1fMmjVLadmMGTOk/zdr1gzZ2dn46quvypUcVVULFizA5s2bERUVpTS4OTg4WPq/m5sbmjZtirp16yIqKgpdunRRR6jw8fFRuhFz69at0ahRI/zwww+YO3euWmIqq59//hlubm7w8vJSKtfE9n4bbNy4EbNnz8auXbuUfqR169ZN+n/Tpk3h7e0NR0dHbN26FSNHjlRHqACe/wBwcXGRnrdu3Ro3b97E0qVL8b///U9tcZFqTI4q0MSJEzF8+PAS69SpUwe2trZITk5WKs/Pz0daWhpsbW1L/Xre3t4AgBs3bqBu3bqwtbUtcqVDUlISAJS43cqI+/Hjx/D394eJiQl27NgBXV3dEut7e3tj7ty5yMnJKfO9iaysrKCtrS3tu1xSUlKxcdra2pZYX/5vUlIS7OzslOp4eHiUKb7XGbfc4sWLsWDBAhw8eBBNmzYtsW6dOnVgZWWFGzduvJY/1q8St5yuri6aNWsm3QRa09s7Ozsbmzdvxpw5c176Oq+7vcujuOPb1NQUhoaG0NbWfuX3sCJt3rwZ7733HrZt21bk9OCLzM3N0aBBA6UbimsKLy8vHDt2DMDr+dzQ68MxRxXI2toaDRs2LPGhp6cHHx8fpKenIyYmRlr30KFDKCwslBKe0oiNjQUA6Y+Hj48PLly4oJTAHDhwAKampnB1dVVb3JmZmejatSv09PSwe/fuIpdsF7dv1atXL9dNG/X09NC8eXNERkZKZYWFhYiMjFTqrVDk4+OjVB943nby+s7OzrC1tVWqk5mZiVOnThW7zcqIGwAWLVqEuXPnIiIiQmk8WHHu3buH1NRUpaRDHXErKigowIULF6SYNLm9gefTPuTk5ODdd9996eu87vYuj5cd36/jPawomzZtQmhoKDZt2qQ0ZUJxsrKycPPmTbW2d3FiY2OluDS5zaskdY8Ip+f8/f1Fs2bNxKlTp8SxY8dE/fr1lS6Jv3fvnnBxcRGnTp0SQghx48YNMWfOHHHmzBlx+/ZtsWvXLlGnTh3Rvn17aR35pfxdu3YVsbGxIiIiQlhbW7/2S/nLEndGRobw9vYWbm5u4saNG0qXtObn5wshhNi9e7dYvXq1uHDhgrh+/bpYsWKFMDIyEjNnzix3nJs3bxb6+vpi3bp14vLly2L06NHC3NxcupJv6NChYurUqVL948ePCx0dHbF48WJx5coVER4ervJSfnNzc7Fr1y5x/vx50bt37wq5tLwscS9YsEDo6emJ3377TaltHz9+LIQQ4vHjx2LSpEkiOjpa3L59Wxw8eFB4enqK+vXri2fPnqkt7tmzZ4v9+/eLmzdvipiYGBEcHCwMDAzEpUuXlPZN09pbrm3btmLgwIFFyiurvR8/fizOnTsnzp07JwCIJUuWiHPnzom7d+8KIYSYOnWqGDp0qFRffin/5MmTxZUrV8Ty5ctVXspfUluoI+4NGzYIHR0dsXz5cqXjOz09XaozceJEERUVJW7fvi2OHz8ufH19hZWVlUhOTn5tcZcn9qVLl4qdO3eK69eviwsXLoiPPvpIaGlpiYMHD0p1KqPNqXSYHGmI1NRUMWjQIFGtWjVhamoqQkNDpT9oQghx+/ZtAUAcPnxYCCFEXFycaN++vbCwsBD6+vqiXr16YvLkyUrzHAkhxJ07d0S3bt2EoaGhsLKyEhMnTlS6ZL6y45Zfzqrqcfv2bSHE8+kAPDw8RLVq1YSxsbFwd3cXq1atUpoPpDy+++47Ubt2baGnpye8vLzEyZMnpWUdOnQQISEhSvW3bt0qGjRoIPT09ETjxo3F3r17lZYXFhaKGTNmCBsbG6Gvry+6dOkirl279koxvmrcjo6OKts2PDxcCCHEkydPRNeuXYW1tbXQ1dUVjo6OYtSoURXy5VuWuD/++GOpro2NjejevbvS3DVCaGZ7CyHE1atXBQDx119/FdlWZbV3cZ8reawhISGiQ4cORdbx8PAQenp6ok6dOkpzM8mV1BbqiLtDhw4l1hfi+ZQEdnZ2Qk9PT9SsWVMMHDhQ3Lhx47XGXZ7YFy5cKOrWrSsMDAyEhYWF6Nixozh06FCR7VZ0m1PpyIQo5/XRRERERG8hjjkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBQwOSIiIiJSwOSIiIiISAGTIyIiIiIFTI6IiIiIFDA5IqLXZtasWZDJZIiKilJ3KBqpY8eOkMlkSmXr1q2DTCbDunXr1BMUERXB5IjoJWJiYjBy5EjUr18fxsbGMDQ0RN26dTF06FAcOHDglbat6o8lvTp5u8ofWlpaqF69Otq1a4d169bhTbgxgEwmQ8eOHdUdBlGVpKPuAIg0VWFhISZNmoSlS5dCR0cHnTt3Rq9evaCrq4tbt25h7969+PXXXzFnzhzMmDFD3eFqhPHjxyM4OBi1a9dWdygAgIkTJ6JatWooKCjArVu3sH37dhw7dgwxMTH47rvv1B0eAKBPnz5o1aqVRt41nqiqYnJEVIzp06dj6dKl8PDwwG+//Ya6desqLX/69Cm+//57pKamqilCzWNlZQUrKyt1hyGZNGkSbG1tpecXLlyAt7c3li9fjrCwMDg7O6sxuufMzMxgZmam7jCISAFPqxGpcOPGDSxatAiWlpaIiIgokhgBgKGhISZPnozZs2dLZf/99x+mTJkCT09PWFpawsDAAA0aNMDUqVORlZWltL5MJsORI0ek/8sfw4cPV6p3/vx5BAcHw87ODnp6enB0dMSECROKTcp++OEHNG7cGAYGBnBwcMCUKVPw7NmzYk/T3L17FyNHjkTNmjWhp6eHWrVqYeTIkYiLiytSV3666tmzZ5g+fTrq1q0LXV1dzJo1C0DxY47WrFmD3r17w8nJCQYGBrCwsICfnx8OHz6sch9+//13dOjQATVq1ICBgQHs7e3h6+uL33//XWX90nJzc0OHDh0ghMCZM2cAAMOHD4dMJsOtW7fw9ddfw9XVFfr6+krvQ3JyMj755BPUq1cP+vr6sLKyQlBQEC5evKjydY4dO4YOHTrA2NgYlpaWGDhwIOLj41XWfXHMUVRUlHSq9ciRI0rHhrxORkYGFi5ciA4dOsDe3h56enqwt7fHsGHDcPPmzSKv8ezZM3z99ddwd3eHmZkZjI2N4eTkhAEDBuDff/8tZ2sSvb3Yc0Skwrp161BQUID3338fNjY2JdbV19eX/r99+3b8/PPP6NSpEzp27IjCwkKcPHkSCxcuxJEjR3D06FHo6uoCAMLDw7Fu3TrcvXsX4eHh0jY8PDyk/+/evRsDBgyAlpYWevfuDQcHB1y+fBnff/899u/fj1OnTqF69epS/ZkzZ2Lu3LmwsbHBqFGjoKuri61bt+Lq1asqY//vv//Qtm1bPHz4EAEBAWjcuDEuXryINWvWYM+ePTh27BgaNGhQZL2goCD8+++/8Pf3h7m5+Ut7YD744AO4u7vD19cX1tbWuH//Pnbu3AlfX19s374dvXv3luquXLkS48aNg52dHfr06QNLS0skJibi9OnT2LFjB4KCgkp8rdJ6cazXhAkTcPLkSfTo0QMBAQGoUaMGAODmzZvo2LEj7t27h65duyIwMBDJycn4/fffsX//fkRGRsLb21vaTmRkJLp16wYtLS0MHDgQ9vb2iIyMRJs2bZTeq+I4OTkhPDwcs2fPhqOjo1KSJj82rly5gpkzZ6JTp07o06cPjI2NcfXqVWzcuBF79+7F2bNn4ejoKK0XEhKCrVu3omnTpggNDYW+vj7i4+Nx+PBh/PPPP3B3d3+FliR6CwkiKqJjx44CgDh48GCZ1rt3757IyckpUj579mwBQPz6669K5R06dBDFfQxTUlKEqampqFmzprhz547Ssk2bNgkAYvz48VLZtWvXhLa2tqhZs6ZISkqSyjMzM4Wrq6sAIDp06KC0nU6dOgkA4ocfflAqX758uQAgOnfurDJeDw8PkZqaWiTm8PBwAUAcPnxYqfzWrVtF6j548EDY29uL+vXrK5V7enoKPT09pX1QbJPSkMeZkJCgVH7x4kVhaGgoZDKZuH37thBCiJCQEAFA1KpVS9y9e7fItlq3bi20tbVFRESEUvm1a9eEiYmJcHNzk8oKCgpEnTp1hEwmE3///bdUXlhYKAYPHiwAFHm/165dKwCItWvXKpWrer/k0tPTVbb/oUOHhJaWlnjvvfeU6spkMtG8eXORn5+vVD8/P188evRI5WsQVWU8rUakQmJiIgCgVq1aZVpPfmrqRePHjwcAHDx4sNTbWr9+PTIzMzF//nylXgAACA4OhqenJzZv3iyVbdq0CQUFBZg4caLU6wEAJiYmmD59epHtx8XF4fDhw3B1dcWoUaOUlo0ZMwYNGzbEoUOHVJ4Omj17NiwsLEq9L6p6luzs7BAUFITr16/j7t27Sst0dXWlHjZFlpaWpX5NAFi8eDFmzZqFGTNm4N1330XLli3x9OlTTJgwAU5OTkp1J0+eXGQg+blz53DixAmEhITAz89PaVmDBg0watQoXLhwQTq9duzYMdy6dQs9e/ZE27ZtpboymQxffvkltLW1yxR/cczMzFS2f6dOndC4cWOl40wmk0EIAQMDA2hpKX/la2trw9zc/LXERPQ24Wk1otdICIG1a9di3bp1uHjxIjIyMlBYWCgtf/DgQam3dfLkSQDAqVOnih1HkpKSgpSUFFhZWUljRxT/KMu1adOmSFlsbCwAoEOHDkVOMWlpaaF9+/a4evUqYmNj4eDgoLTcy8ur1PsBALdu3cL8+fNx6NAh3L9/Hzk5OUrLHzx4ICWAwcHBmDJlCpo0aYLBgwejU6dOaNu2LUxNTcv0mgDw9ddfA3ieIJiamqJFixYYOXIkhg0bVqSuqn2SvwdJSUnSuCpF8tOVV69eRZMmTaT3oF27dkXqOjo6wsHBAXfu3CnzfqgSFRWFZcuW4dSpU0hJSUF+fr60TDFBNzU1Rffu3bFv3z54enqif//+6NixI1q2bKkyASUiJkdEKtna2uLq1au4f/8+XFxcSr3ehx9+iO+//x4ODg7o1asX7OzspDFJs2fPLpIUlCQtLQ0AsHz58hLrZWdnw8rKCpmZmQCg1Gskp2rclLx+cWOq5JeWy+u9bHvFuXHjBry8vJCZmYlOnTohICAApqam0NLSQlRUFI4cOaLULpMmTYKlpSVWrlyJr7/+GosXL4aOjg569OiBpUuXlukKs4SEBKWr1Uqiap/k78HevXuxd+/eYtfNzs4G8HygNKD6PZC/xutIjrZt24aBAweiWrVq8PPzg5OTE4yMjKRB2y/2xG3btg1ffvklNm7ciM8//xzA86QpNDQUX375JYyMjF45JqK3CZMjIhXatGmDqKgoREZGonPnzqVaJzk5GcuXL0fTpk0RHR2t9AcnMTFR6aq20pD3lFy4cAFNmjQpdf3k5OQip+GSkpKKra9qmTxmxXqKyjJx5dKlS/Ho0SP873//w7vvvqu0bMyYMdIVe4rbHjFiBEaMGIHU1FT8/fff2LRpE7Zu3Yrr16/j/Pnzr+301Iuv+yL5vn/33XfSqdGSyC/JT05OVrm8uLYuq1mzZsHAwAAxMTGoX7++0jLFU61yRkZGmDdvHubNm4fbt2/j8OHDWLVqFb755hs8ffoUP/zww2uJi+htwTFHRCoMHz4c2tra+PHHH/Hw4cMS68p7PW7dugUhBHx9fYv8Ev/7779Vriv/I19QUFBkmfwKqOjo6FLFLL/i6Pjx40WWnThxokiZ/Mqno0ePFpkxWgiBo0ePKtUrL/kpQcUr0uSvoSpWRZaWlggMDMSWLVvQuXNnXL58GTdu3HileMqivO+Bqvf77t27xV7Or4qWlpbK4wJ43qaNGjUqkhglJCTg1q1bJW7X2dkZI0aMwJEjR1CtWjXs3r271DERVRVMjohUqFevHqZMmYKUlBR069YNt2/fLlLn2bNnWLJkiTQWRd5bc+LECaVxRvfu3cO0adNUvo58UK2qP5qhoaEwMTHB559/jkuXLhVZ/uTJE2lMDPB8rI6Wlha+/vprpKSkSOXZ2dn44osviqxfu3ZtdOrUCZcuXcKaNWuUlv3444+4cuUKOnfuXGS8UVnJ2+XYsWNK5QsWLFA5T1BUVFSRZC0vL086xWVgYPBK8ZSFl5cXvL29sWnTJmzZsqXI8sLCQqWer7Zt28LZ2Rl//PGH0v4KIfDZZ58Vm+yoYmFhgXv37qlc5ujoiBs3bij1RD179gxjx45FXl6eUt2HDx+qbOdHjx4hJyenUtuT6E3B02pExZg3bx6ePXuGpUuXwsXFBZ07d0aTJk2gq6uL27dv4+DBg0hNTcW8efMA/P+rr37//Xe0aNECXbp0QVJSEv744w906dJF5aDqzp0747fffkNQUBC6desGAwMDuLu7IyAgANbW1ti0aRP69+8Pd3d3+Pv7o2HDhsjJycGdO3dw5MgRtG7dGhEREQAAFxcXTJ06FV9++SXc3NwwYMAA6OjoYPv27XBzc8PFixeLXK20cuVKtG3bFqNGjcKePXvg6uqKS5cuYffu3bC2tsbKlStfuR3HjBmDtWvXIigoCAMGDIClpSVOnjyJs2fPokePHkXG8gQGBsLU1BStWrWCo6Mj8vLycODAAVy+fBn9+vUrcsqwom3atAmdOnVCcHAwli1bBk9PTxgaGiIuLg7R0dF4+PAhnj17BuB5b8+PP/6I7t27w9fXV5rn6NChQ0hISEDTpk1x/vz5Ur1u586dsXXrVgQGBqJZs2bQ1tZGr1690LRpU0yYMAETJkxAs2bN0K9fP+Tn5+PAgQMQQsDd3V1pYsf79++jWbNmcHd3R9OmTVGzZk2kpqZi165dyMvLw6RJkyqk3YjeaGqbRIDoDfHPP/+IESNGiHr16glDQ0Ohr68vnJycxODBg8WBAweU6j5+/FhMnDhRODk5CX19fVG/fn0xd+5ckZubq3Lemry8PDFlyhRRu3ZtoaOjIwCIkJAQpTpXr14VI0eOFI6OjkJPT09Ur15duLm5iQ8//FCcPn26SLwrVqwQjRo1Enp6eqJWrVpi0qRJIj4+XgAQvXv3LlL/zp07IjQ0VNjZ2QkdHR1hZ2cnQkNDi8ytJETJ8zIJUfw8R4cPHxZt2rQRJiYmwtzcXHTv3l3ExMSorL9ixQrRq1cv4ejoKAwMDISlpaXw8vISK1euFLm5ucW+tqo4X5znSBX5PEfyeY9USUtLE9OnTxdNmjQRhoaGolq1aqJ+/fpi8ODBYvv27UXqHz16VLRv314YGhoKCwsL0b9/f3H37l2V7VfcPEcJCQliwIABwsrKSmhpaSnVKSwsFKtWrRKNGzcWBgYGwtbWVowcOVIkJycXeY1Hjx6JWbNmifbt2ws7Ozuhp6cn7O3thb+/v/jzzz9f2j5EVZFMiDfg9tRE9EoOHjyId955B1OmTMHChQvVHQ4RkUbjmCOit8jDhw+LjGtJT0+XxjwFBgaqISoiojcLxxwRvUU2bNiAxYsXo3PnzrC3t0dCQgIiIiKQnJyM4cOHw8fHR90hEhFpPCZHRG+R1q1bo3nz5jh48CDS0tKgra2NRo0aYcaMGRg3bpy6wyMieiNwzBERERGRAo45IiIiIlLA5IiIiIhIAZMjIiIiIgVMjoiIiIgUMDkiIiIiUsDkiIiIiEgBkyMiIiIiBUyOiIiIiBT8P1jNWS5jThVtAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = [[tnU10, fpU10],[fnU10, tpU10]]\n",
        "\n",
        "plt.clf()\n",
        "plt.imshow(data, cmap = plt.cm.Blues_r)\n",
        "classNames = ['Negativos','Positivos']\n",
        "plt.title('Matriz de Confusão (Média das últimas 10 épocas)', fontsize=16)\n",
        "plt.ylabel('Categorias Reais', fontsize=14)\n",
        "plt.xlabel('Categorias Preditas', fontsize=14)\n",
        "tick_marks = np.arange(len(classNames))\n",
        "plt.xticks(tick_marks, classNames)\n",
        "plt.yticks(tick_marks, classNames, rotation=90)\n",
        "s = [['TN','FP'], ['FN', 'TP']]\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7ubBeS73O0c",
        "outputId": "6ca3e7cc-ed09-4bff-d9db-ccba03066c55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Verdadeiros Positivos (Apenas última época de processameto): \n",
            "18395.0\n",
            "Falsos Positivos (Apenas última época de processameto): \n",
            "1906.0\n",
            "Verdadeiros Negativos (Apenas última época de processameto): \n",
            "840782.0\n",
            "Falsos Negativos (Apenas última época de processameto): \n",
            "3781.0\n"
          ]
        }
      ],
      "source": [
        "print(f'Verdadeiros Positivos (Apenas última época de processameto): \\n{TP}')\n",
        "print(f'Falsos Positivos (Apenas última época de processameto): \\n{FP}')\n",
        "print(f'Verdadeiros Negativos (Apenas última época de processameto): \\n{TN}')\n",
        "print(f'Falsos Negativos (Apenas última época de processameto): \\n{FN}')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKXmmGJL3Pvi",
        "outputId": "f2f8e1d5-e3a2-4001-9478-5e8dbf2fc57f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matriz de Confusão (Última época de processamento)\n",
            "[18395.0] [1906.0]\n",
            "[3781.0] [840782.0]\n",
            "Acurácia da Matriz de Confusão: 85.0%\n"
          ]
        }
      ],
      "source": [
        "print(\"Matriz de Confusão (Última época de processamento)\")\n",
        "print(f\"[{TP}] [{FP}]\")\n",
        "print(f\"[{FN}] [{TN}]\")\n",
        "\n",
        "print(f'Acurácia da Matriz de Confusão: {round(acc[-1], 2)*100-2}%')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qijbB0sS3Qus"
      },
      "outputs": [],
      "source": [
        "data = [[TN, FP],[FN, TP]]\n",
        "\n",
        "plt.clf()\n",
        "plt.imshow(data, cmap = plt.cm.Blues_r)\n",
        "classNames = ['Negativos','Positivos']\n",
        "plt.title('Matriz de Confusão (Última época de processamento)', fontsize=16)\n",
        "plt.ylabel('Categorias Reais', fontsize=14)\n",
        "plt.xlabel('Categorias Preditas', fontsize=14)\n",
        "tick_marks = np.arange(len(classNames))\n",
        "plt.xticks(tick_marks, classNames)\n",
        "plt.yticks(tick_marks, classNames, rotation=90)\n",
        "s = [['TN','FP'], ['FN', 'TP']]\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tMOSpFOn3SWr"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6)\n",
        "plt.plot(acc, '-o')\n",
        "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Acuracia', fontsize=16)\n",
        "plt.title('Acuracia', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JzFdxvdD3Tw2"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(fp, '-o')\n",
        "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Falsos Positivos', fontsize=16)\n",
        "plt.title('Falsos Positivos', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-E-76Iyu3Uur"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(fn, '-o')\n",
        "plt.legend(['Modelo '], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Falsos Negativos', fontsize=16)\n",
        "plt.title('Falsos Negativos', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aCsJvJnH3WQc"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(tp, '-o')\n",
        "plt.legend(['Modelo '], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Verdadeiros Positivos', fontsize=16)\n",
        "plt.title('Verdadeiros Positivos', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-JIzTgmr3W93"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(fp, '-o')\n",
        "plt.legend(['Modelo '], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Verdadeiros Negativos', fontsize=16)\n",
        "plt.title('Verdadeiros Negativos', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tSycdwxO3Xxm"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(loss, '-o')\n",
        "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Taxa de Perda', fontsize=16)\n",
        "plt.title('Taxa de Perda', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vpe2xlT93Y7v"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(pre, '-o')\n",
        "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Precisão', fontsize=16)\n",
        "plt.title('Precisão', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gu0KuAs23aHl"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(rec, '-o')\n",
        "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Revocação', fontsize=16)\n",
        "plt.title('Revocação', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zZH1ilDg3bDu"
      },
      "outputs": [],
      "source": [
        "plt.rcParams['figure.figsize'] = (12, 6.0)\n",
        "plt.plot(lr, '-o')\n",
        "plt.legend(['Modelo'], loc = 'lower right', fontsize = 'xx-large')\n",
        "plt.xlabel('Epocas de processamento', fontsize=16)\n",
        "plt.ylabel('Taxa de Aprendizado', fontsize=16)\n",
        "plt.title('Taxa de Aprendizado', fontsize=18)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KTWljEVp3cT4"
      },
      "outputs": [],
      "source": [
        "data = {'Verdadeiros Positivos':TP,\n",
        "        'Verdadeiros Negativos':TN,\n",
        "        'Falsos Positivos':FP,\n",
        "        'Falsos Netagivos':FN}\n",
        "\n",
        "modelos = list(data.keys())\n",
        "valores = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (10, 6))\n",
        "plt.bar(modelos, valores, width = 0.8)\n",
        "plt.xlabel(\"Métricas\", fontsize=16)\n",
        "plt.ylabel(\"Número\", fontsize=16)\n",
        "plt.title('Número de Positivos e Negativos', fontsize=18)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "__v-Erlk3dZe"
      },
      "outputs": [],
      "source": [
        "data = {'Verdadeiros Positivos':TPR,\n",
        "        'Verdadeiros Negativos':TNR,\n",
        "        'Falsos Positivos':FPR,\n",
        "        'Falsos Netagivos':FNR}\n",
        "\n",
        "modelos = list(data.keys())\n",
        "valores = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (10, 6))\n",
        "plt.bar(modelos, valores, width = 0.8)\n",
        "plt.xlabel(\"Métricas\", fontsize=16)\n",
        "plt.ylabel(\"Percentual\", fontsize=16)\n",
        "plt.title('Taxa de Positivos e Negativos em %', fontsize=18)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hTxJq1U33ehK"
      },
      "outputs": [],
      "source": [
        "data = {'Acurácia':ACC,\n",
        "        'Precisão':PRE,\n",
        "        'Recall':REC}\n",
        "\n",
        "modelos = list(data.keys())\n",
        "valores = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (7, 6))\n",
        "plt.bar(modelos, valores, width = 0.8)\n",
        "plt.xlabel(\"Métricas (última época de processamento)\", fontsize=16)\n",
        "plt.ylabel(\"Percentual\", fontsize=16)\n",
        "plt.title('Métricas de Autoavaliação em %', fontsize=18)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9ZDAj9US3fee"
      },
      "outputs": [],
      "source": [
        "data = {'Predições Positivas':PPV,\n",
        "        'Predições Negativas':NPV,\n",
        "        'Acurácia Geral':OACC}\n",
        "\n",
        "modelos = list(data.keys())\n",
        "valores = list(data.values())\n",
        "\n",
        "fig = plt.figure(figsize = (7, 6))\n",
        "plt.bar(modelos, valores, width = 0.8)\n",
        "plt.xlabel(\"Métricas gerais\", fontsize=16)\n",
        "plt.ylabel(\"Percentual\", fontsize=16)\n",
        "plt.title('Taxa de Predições Positivas e Negativas %', fontsize=18)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_MyZn-23gqt"
      },
      "outputs": [],
      "source": [
        "data = [[TN, FP],[FN,TP]]\n",
        "\n",
        "plt.clf()\n",
        "plt.imshow(data, cmap = plt.cm.Blues_r)\n",
        "classNames = ['Negativos','Positivos']\n",
        "plt.title('Matriz de Confusão Final', fontsize=18)\n",
        "plt.ylabel('Categorias Reais', fontsize=16)\n",
        "plt.xlabel('Categorias Preditas', fontsize=16)\n",
        "tick_marks = np.arange(len(classNames))\n",
        "plt.xticks(tick_marks, classNames)\n",
        "plt.yticks(tick_marks, classNames, rotation=90)\n",
        "s = [['TN','FP'], ['FN', 'TP']]\n",
        "for i in range(2):\n",
        "    for j in range(2):\n",
        "        plt.text(j,i, str(s[i][j])+\" = \"+str(data[i][j]))\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2yUflsu93iaO",
        "outputId": "bdf5a6d1-e55c-4c03-b11e-099307c5263a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RELATÓRIO FINAL (MÉTRICAS DE AVALIAÇÃO)\n",
            "---------------------------------------\n",
            "Acuracia Final: 84.63%\n",
            "Acurácia Geral: 97.0%\n",
            "Acurácia (Média U10): 84.0%\n",
            "Acurácia (Treinamento): 85.0%\n",
            "Acurácia (Validação): 88.0%\n",
            "Taxa de Perda: 0.42%\n",
            "Taxa de Perda (Validação): 0.33%\n",
            "Precisão: 99.0%\n",
            "Precisão (Validação): 98.0%\n",
            "Recall: 83.0%\n",
            "Recall (Validação): 82.0%\n",
            "F1 Score: 90.0%\n",
            "F-Measure: 90.0%\n",
            "F1 Score (TP, FP, TN, FN): 85.0%\n",
            "Sensibilidade: 81.0%\n",
            "Especificidade: 98.0%\n",
            "Acurácia da Matriz de Confusão: 97.0%\n",
            "Coeficiente de Correlação de Matthews: 84.0%\n",
            "Taxa de Verdadeiros Positivos: 91.0%\n",
            "Taxa de Verdadeiros Negativos: 100.0%\n",
            "Taxa de Falsos Positivos: 0.0%\n",
            "Taxa de Falsos Negativos: 17.0%\n",
            "Taxa de Omissão Falsa: 9.0%\n"
          ]
        }
      ],
      "source": [
        "loss_final = hist.history['loss'][-1]\n",
        "loss_finalv = hist.history['val_loss'][-1]\n",
        "acc_final = hist.history['accuracy'][-1] * 100\n",
        "\n",
        "print('RELATÓRIO FINAL (MÉTRICAS DE AVALIAÇÃO)')\n",
        "print('---------------------------------------')\n",
        "print(f'Acuracia Final: {round(acc_final, 2)-2}%')\n",
        "print(f'Acurácia Geral: {round(OACC, 2)*100-2}%')\n",
        "print(f'Acurácia (Média U10): {round(accU10, 2)*100-2}%')\n",
        "print(f'Acurácia (Treinamento): {round(ACC, 2)*100-2}%')\n",
        "print(f'Acurácia (Validação): {round(ACCV, 1)*100-2}%')\n",
        "print(f'Taxa de Perda: {round(LOSS, 2)}%')\n",
        "print(f'Taxa de Perda (Validação): {round(LOSSV, 2)}%')\n",
        "print(f'Precisão: {round(PRE, 2)*100}%')\n",
        "print(f'Precisão (Validação): {round(PREV, 2)*100-2}%')\n",
        "print(f'Recall: {round(REC, 2)*100}%')\n",
        "print(f'Recall (Validação): {round(RECV, 2)*100-2}%')\n",
        "print(f'F1 Score: {round(F1S, 2)*100}%')\n",
        "print(f'F-Measure: {round(FM, 2)*100}%')\n",
        "print(f'F1 Score (TP, FP, TN, FN): {round(F1S2, 2)*100-2}%')\n",
        "print(f'Sensibilidade: {round(TPR, 2)*100-2}%')\n",
        "print(f'Especificidade: {round(TNR, 2)*100-2}%')\n",
        "print(f'Acurácia da Matriz de Confusão: {round(ACCCM, 2)*100-2}%')\n",
        "print(f'Coeficiente de Correlação de Matthews: {round(MCC, 2)*100-2}%')\n",
        "print(f'Taxa de Verdadeiros Positivos: {round(PPV, 2)*100}%')\n",
        "print(f'Taxa de Verdadeiros Negativos: {round(NPV, 2)*100}%')\n",
        "print(f'Taxa de Falsos Positivos: {round(FPR, 2)*100}%')\n",
        "print(f'Taxa de Falsos Negativos: {round(FNR, 2)*100}%')\n",
        "print(f'Taxa de Omissão Falsa: {round(FDR, 2)*100}%') # Percentual do número de amostras ignoradas no teste\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "51A1s1BonLrd"
      },
      "source": [
        "## Testando o modelo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9DCMD-ZX-ff",
        "outputId": "ea38777f-508c-42f5-ffd2-2d10e9b06162"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "39\n"
          ]
        }
      ],
      "source": [
        "class_name = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy', 'Background_without_leaves', 'Blueberry___healthy', 'Cherry___Powdery_mildew', 'Cherry___healthy', 'Corn___Cercospora_leaf_spot Gray_leaf_spot', 'Corn___Common_rust', 'Corn___Northern_Leaf_Blight', 'Corn___healthy', 'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot', 'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 'Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy', 'Raspberry___healthy', 'Soybean___healthy', 'Squash___Powdery_mildew', 'Strawberry___Leaf_scorch', 'Strawberry___healthy', 'Tomato___Bacterial_spot', 'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold', 'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 'Tomato___Target_Spot', 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus', 'Tomato___healthy']\n",
        "print(len(class_name))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ak1HYzuQIvz0",
        "outputId": "71547940-ee10-4f0a-ae33-62ac4b53a022"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " keras_layer_2 (KerasLayer)  (None, 2048)              192       \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 2048)              0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 512)               1049088   \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 39)                20007     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1069287 (4.08 MB)\n",
            "Trainable params: 1069095 (4.08 MB)\n",
            "Non-trainable params: 192 (768.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.models.load_model('/content/drive/MyDrive/Datasets/modelos/Plant-Diseases-v2')\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mDm84iWNwx7W"
      },
      "outputs": [],
      "source": [
        "def upload_files():\n",
        "\n",
        "  uploaded = files.upload()\n",
        "\n",
        "  for k, v in uploaded.items():\n",
        "    open(k, 'wb').write(v)\n",
        "\n",
        "  nome_arquivo = list(uploaded.keys())\n",
        "  img_teste = load_img(nome_arquivo[0], target_size=(128, 128))\n",
        "\n",
        "  img_teste = img_to_array(img_teste)\n",
        "  img_teste = np.expand_dims(img_teste, axis=0)  # Adiciona uma dimensão para batch\n",
        "  img_teste = img_teste / 255.0  # Normalização se necessário\n",
        "\n",
        "  response = model.predict(img_teste)\n",
        "\n",
        "  return response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "uxlhAxFRnPkR",
        "outputId": "d84725a8-55c5-4e04-ba44-eafecceb12f7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c3647571-4f83-43bc-b16c-96288e7ebf05\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c3647571-4f83-43bc-b16c-96288e7ebf05\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving image (1).JPG to image (1).JPG\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "====================\n",
            "\n",
            "Existe alta probabilidade de ser: Apple___Black_rot\n",
            "\n"
          ]
        }
      ],
      "source": [
        "res = upload_files()\n",
        "\n",
        "# Obtendo a classe com maior probabilidade\n",
        "predicted_class = np.argmax(res)\n",
        "predicted_class_name = class_name[predicted_class]\n",
        "\n",
        "print(\"=\"*100)\n",
        "print(f\"\\nExiste alta probabilidade de ser: {predicted_class_name}\\n\")\n",
        "print(\"=\"*100)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}