diff --git "a/Malaria_CNN.ipynb" "b/Malaria_CNN.ipynb"
new file mode 100644--- /dev/null
+++ "b/Malaria_CNN.ipynb"
@@ -0,0 +1,2209 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "##  Verileri Kaggleden ApıKey ile çekme"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "-dGvJQTC0zON"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import os"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AAmNaVyw5XN9",
+        "outputId": "59e05c4a-1c87-4205-dc0e-eff1525a161a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4oDsA2Rj7HHd",
+        "outputId": "4b199d12-df35-4287-df5d-c605d21afcc9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "drive  sample_data\n"
+          ]
+        }
+      ],
+      "source": [
+        "!ls"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "9B92MxaJ7HKK"
+      },
+      "outputs": [],
+      "source": [
+        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN\"\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9kYhtSJI7HMx",
+        "outputId": "61c522ff-e591-4d28-fa4e-34e1353acbb0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN\n"
+          ]
+        }
+      ],
+      "source": [
+        "#changing the working directory\n",
+        "%cd \"/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN\"\n",
+        "#Check the present working directory using pwd command"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OXyNe5fc7HPT",
+        "outputId": "2784c774-a9e6-4659-fba3-3948cbd3aca0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pwd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8DerJWaP7HSb",
+        "outputId": "974bbb06-344c-4bdc-ed5e-de66cc6ad58c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.17 / client 1.6.14)\n",
+            "Dataset URL: https://www.kaggle.com/datasets/iarunava/cell-images-for-detecting-malaria\n",
+            "License(s): unknown\n",
+            "Downloading cell-images-for-detecting-malaria.zip to /content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN\n",
+            " 47% 319M/675M [00:05<00:06, 56.7MB/s]\n",
+            "User cancelled operation\n"
+          ]
+        }
+      ],
+      "source": [
+        "!kaggle datasets download -d iarunava/cell-images-for-detecting-malaria"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QOjMEgTG8oxZ",
+        "outputId": "200abf38-914e-490e-b1cf-239e80f4e285"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " cell_images   cell-images-for-detecting-malaria.zip  'Malaria CNN.ipynb'\n"
+          ]
+        }
+      ],
+      "source": [
+        "!ls"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "zw3l1N7h8u1t",
+        "outputId": "b1eb17d3-1395-4b51-be00-65fed0f4abb6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Archive:  cell-images-for-detecting-malaria.zip\n",
+            "  End-of-central-directory signature not found.  Either this file is not\n",
+            "  a zipfile, or it constitutes one disk of a multi-part archive.  In the\n",
+            "  latter case the central directory and zipfile comment will be found on\n",
+            "  the last disk(s) of this archive.\n",
+            "unzip:  cannot find zipfile directory in one of *.zip or\n",
+            "        *.zip.zip, and cannot find cell-images-for-detecting-malaria.zip.ZIP, period.\n",
+            "\n",
+            "No zipfiles found.\n"
+          ]
+        }
+      ],
+      "source": [
+        "#unzipping the zip files and deleting the zip files\n",
+        "!unzip \\*.zip  && rm *.zip"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Malaria Datası ve Taransfer Learning "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "JNq9G7Ki83Ta"
+      },
+      "outputs": [],
+      "source": [
+        "path='/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN/cell_images'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O2kS-llBA5fh",
+        "outputId": "c3dd29fc-4d0b-4e54-c94d-3f0eb1f97beb"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['Parasitized', 'Uninfected', 'cell_images']"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "os.listdir(path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_S9iJ73XCHs_"
+      },
+      "source": [
+        "# Malaria\n",
+        "\n",
+        "## Açıklama :📊 Malaria Veri Seti\n",
+        "Malaria veri seti, kan hücrelerinin mikroskopik görüntülerini içerir. Bu görüntüler, hücrelerin sağlıklı olup olmadığını veya Malaria gibi bir hastalığı gösterip göstermediğini belirlemek için kullanılır. Veri setiniz, bu görüntüleri çeşitli sınıflara ayırmak için kullanılacak.\n",
+        "\n",
+        "## 🚀 Ne Yapacağız?\n",
+        "Veri Kümesini Küçültme:\n",
+        "\n",
+        "14,000 görüntüden sadece 1,500'ünü seçeceğiz. Bu, eğitim sürecini hızlandıracak ve daha iyi performans elde etmenize yardımcı olacak.\n",
+        "Model Eğitimi:\n",
+        "\n",
+        "VGG19 modelini kullanarak eğitimi gerçekleştireceğiz. VGG19, önceden eğitilmiş ağırlıklarıyla güçlü bir görüntü sınıflandırma modelidir.\n",
+        "Modeli, bu küçük veri kümesi ile eğitecek ve Malaria'nın doğru sınıflandırılması için iyileştireceğiz.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "ofznJcWKA798"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "import cv2\n",
+        "import os\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "path='/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN/cell_images/cell_images'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "JRr_QClOCL0Z"
+      },
+      "outputs": [],
+      "source": [
+        "def create_dataset(folders, path, max_images_per_label=1250):\n",
+        "    data = {'imgpath': [], 'labels': []}\n",
+        "\n",
+        "    for folder in folders:\n",
+        "        folderpath = os.path.join(path, folder)\n",
+        "        files = os.listdir(folderpath)\n",
+        "\n",
+        "        # Her bir klasörden sadece max_images_per_label kadar görsel alıyoruz\n",
+        "        selected_files = files[:max_images_per_label]\n",
+        "\n",
+        "        for file in selected_files:\n",
+        "            filepath = os.path.join(folderpath, file)\n",
+        "            data['imgpath'].append(filepath)\n",
+        "            data['labels'].append(folder)\n",
+        "\n",
+        "    dataset = pd.DataFrame(data)\n",
+        "    return dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MITq56EPCW52",
+        "outputId": "3bb23a81-f2a0-4ffa-cd9c-44d35f37610b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sınıflarımız:  ['Parasitized', 'Uninfected'] \n",
+            "Sınıf sayisi: 2\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "sınıf=os.listdir(path)  # Kolon adlarımız\n",
+        "num_classes = len(os.listdir(path))\n",
+        "print('Sınıflarımız: ',sınıf,'\\nSınıf sayisi:',num_classes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "JBubikq5CfUu",
+        "outputId": "adad6351-08f1-4037-9b7a-bc35f142afa9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2500,\n  \"fields\": [\n    {\n      \"column\": \"imgpath\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2500,\n        \"samples\": [\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Uninfected/C102P63ThinF_IMG_20150918_163054_cell_127.png\",\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Parasitized/C116P77ThinF_IMG_20150930_172112_cell_67.png\",\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Parasitized/C116P77ThinF_IMG_20150930_171954_cell_112.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Uninfected\",\n          \"Parasitized\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a118ad0b-25b0-4776-a5ba-5b5e95812f8b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>imgpath</th>\n",
+              "      <th>labels</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "    </tr>\n",
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+              "      <th>1</th>\n",
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+              "      <td>Parasitized</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "    </tr>\n",
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+              "      <th>4</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "    </tr>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-89de27d3-042f-470a-a1b5-0609d19991cc button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                             imgpath       labels\n",
+              "0  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized\n",
+              "1  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized\n",
+              "2  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized\n",
+              "3  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized\n",
+              "4  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "folders1 = ['Parasitized', 'Uninfected']\n",
+        "df=create_dataset(folders1,path)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "x1rHTDstCtQp"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "encoder = LabelEncoder()\n",
+        "\n",
+        "# Etiketleri sayısal değerlere dönüştür\n",
+        "df['encode_label'] = encoder.fit_transform(df['labels'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "8mhpIRDwC6qy",
+        "outputId": "ea81f88c-774b-41d9-c45d-9d66cc1b299a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2500,\n  \"fields\": [\n    {\n      \"column\": \"imgpath\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2500,\n        \"samples\": [\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Uninfected/C102P63ThinF_IMG_20150918_163054_cell_127.png\",\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Parasitized/C116P77ThinF_IMG_20150930_172112_cell_67.png\",\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Parasitized/C116P77ThinF_IMG_20150930_171954_cell_112.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Uninfected\",\n          \"Parasitized\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"encode_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a0c5339b-92a9-4373-97e9-cce47e84ea2e\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>imgpath</th>\n",
+              "      <th>labels</th>\n",
+              "      <th>encode_label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Parasitized</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0c5339b-92a9-4373-97e9-cce47e84ea2e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a0c5339b-92a9-4373-97e9-cce47e84ea2e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a0c5339b-92a9-4373-97e9-cce47e84ea2e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-0cdf65c3-6e5a-45c3-8486-a333516cb0f5\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0cdf65c3-6e5a-45c3-8486-a333516cb0f5')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-0cdf65c3-6e5a-45c3-8486-a333516cb0f5 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                             imgpath       labels  \\\n",
+              "0  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized   \n",
+              "1  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized   \n",
+              "2  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized   \n",
+              "3  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized   \n",
+              "4  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Parasitized   \n",
+              "\n",
+              "   encode_label  \n",
+              "0             0  \n",
+              "1             0  \n",
+              "2             0  \n",
+              "3             0  \n",
+              "4             0  "
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zdrQr5hrDL-f"
+      },
+      "source": [
+        "## Yerlerini değiştirelim\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "54a8f22qC8A7",
+        "outputId": "d308a739-4ebe-423a-e475-85bc43cc3400"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 2500,\n  \"fields\": [\n    {\n      \"column\": \"imgpath\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2500,\n        \"samples\": [\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Parasitized/C107P68ThinF_IMG_20150924_222233_cell_161.png\",\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Uninfected/C110P71ThinF_IMG_20150930_105559_cell_192.png\",\n          \"/content/drive/MyDrive/S\\u0131tma Hastal\\u0131g\\u0306\\u0131 veri Ve Ipnd dosyas\\u0131 CNN/cell_images/cell_images/Parasitized/C108P69ThinF_IMG_20150924_223834_cell_220.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Parasitized\",\n          \"Uninfected\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"encode_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-75e651fc-a6ed-4fc6-a638-aea197ec0fcc\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>imgpath</th>\n",
+              "      <th>labels</th>\n",
+              "      <th>encode_label</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Uninfected</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Uninfected</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Uninfected</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Uninfected</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>/content/drive/MyDrive/Sıtma Hastalığı veri V...</td>\n",
+              "      <td>Uninfected</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75e651fc-a6ed-4fc6-a638-aea197ec0fcc')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-75e651fc-a6ed-4fc6-a638-aea197ec0fcc button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-75e651fc-a6ed-4fc6-a638-aea197ec0fcc');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-396b3bc7-a5be-41ac-9fe9-766d3ed658aa\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-396b3bc7-a5be-41ac-9fe9-766d3ed658aa')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-396b3bc7-a5be-41ac-9fe9-766d3ed658aa button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "                                             imgpath      labels  encode_label\n",
+              "0  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Uninfected             1\n",
+              "1  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Uninfected             1\n",
+              "2  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Uninfected             1\n",
+              "3  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Uninfected             1\n",
+              "4  /content/drive/MyDrive/Sıtma Hastalığı veri V...  Uninfected             1"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df = df.sample(frac=1).reset_index(drop=True)\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cipN3gLmDRrz",
+        "outputId": "e0936af4-427e-457f-c4b0-e5ec8a107683"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 2500 entries, 0 to 2499\n",
+            "Data columns (total 3 columns):\n",
+            " #   Column        Non-Null Count  Dtype \n",
+            "---  ------        --------------  ----- \n",
+            " 0   imgpath       2500 non-null   object\n",
+            " 1   labels        2500 non-null   object\n",
+            " 2   encode_label  2500 non-null   int64 \n",
+            "dtypes: int64(1), object(2)\n",
+            "memory usage: 58.7+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()\n",
+        "## Her Birinden 7000 tane almış olduk"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-kXCndBkDfpS"
+      },
+      "source": [
+        "# Görselleştirme"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "KzfpxzNZDUpR",
+        "outputId": "7e37e2b3-265b-4719-e19e-77a80e0a9042"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import cv2\n",
+        "import matplotlib.pyplot as plt\n",
+        "for index, row in df.sample(4).iterrows():\n",
+        "    # Resmi yükle\n",
+        "    img = cv2.imread(row['imgpath'])\n",
+        "\n",
+        "    # OpenCV resmi RGB formatına dönüştür\n",
+        "    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
+        "\n",
+        "    # Resmi matplotlib ile göster\n",
+        "    plt.figure(figsize=(6, 6))\n",
+        "    plt.imshow(img_rgb)\n",
+        "    plt.title(row['labels'])\n",
+        "    plt.axis('off')\n",
+        "    plt.show()"
+      ]
+    },
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+              "                            \n",
+              "var gd = document.getElementById('56f7506f-0ad4-4d93-b481-54c5e6d611bd');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
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+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "import matplotlib.pyplot as plt\n",
+        "import plotly.express as px\n",
+        "\n",
+        "sayi = df['labels'].value_counts() #hurma türe göre sayisi\n",
+        "\n",
+        "# Plotly ile hurma dağılımını görselleştirme\n",
+        "fig=px.bar(x=sayi.index,  #6 indexi var\n",
+        "           y=sayi.values, # sayisi\n",
+        "           color=sayi.index, # rekleri yine sayisina göre\n",
+        "           labels={'x': 'Çeşitler','y': 'Toplam Sayı'}, #label yazdırma\n",
+        "           title='Sıtma Çeşitleri Grafiği', #başlık\n",
+        "           template='plotly_dark') # arkapılan\n",
+        "\n",
+        "fig.show()\n",
+        "\n",
+        "\n",
+        "fig = px.pie(names=sayi.index,\n",
+        "             values=sayi.values,\n",
+        "             title='Sıtma Dağılımı',\n",
+        "             labels={'names': 'Sıtma',\n",
+        "                     'values': 'Toplam Sayı'},\n",
+        "             template='plotly_white')\n",
+        "fig.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AIMAalG8D0co"
+      },
+      "source": [
+        "<img src='https://www.mdpi.com/agriculture/agriculture-11-00707/article_deploy/html/images/agriculture-11-00707-g004.png'>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3o1dKQrIEKPh",
+        "outputId": "7a2c3b47-dda5-472b-8ffb-80e20552e3ca"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['imgpath', 'labels', 'encode_label'], dtype='object')"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GwP4Kpe2EFeV",
+        "outputId": "637e2d46-ba06-4ba0-d1d3-855aecab46e1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Görsel boyutları: 118 x 127\n"
+          ]
+        }
+      ],
+      "source": [
+        "from PIL import Image\n",
+        "\n",
+        "# Görselin yolu\n",
+        "img_path=df['imgpath'][86]\n",
+        "# Görseli aç\n",
+        "img = Image.open(img_path)\n",
+        "\n",
+        "# Görsel boyutlarını öğren\n",
+        "width, height = img.size\n",
+        "\n",
+        "print(f'Görsel boyutları: {width} x {height}')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TW_hkd4ID8RG"
+      },
+      "source": [
+        "# Normalizasyon"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "6dfo2I0MDtet"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "x=[]\n",
+        "for img in df['imgpath']:\n",
+        "    try:\n",
+        "        img = cv2.imread(img)\n",
+        "        img = cv2.resize(img, (128, 128))# 128 pixle boyutunda\n",
+        "        img = img / 255.0  # normalize etmee\n",
+        "        x.append(img)      # img\n",
+        "\n",
+        "    except:\n",
+        "        print(f\"Error loading image: {img}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "P88rIjXIDyX1",
+        "outputId": "2a775794-1759-4b09-aa1b-e87005e506f7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "((1875, 128, 128, 3), (625, 128, 128, 3))"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "x=np.array(x)\n",
+        "y=df['encode_label']\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)\n",
+        "\n",
+        "x_train.shape,x_test.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "BLPntCmDDyVW"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow.keras import layers, models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5FH94BewDyQ7",
+        "outputId": "786c8b0e-03ee-4c37-a4f9-5bdb95ec5250"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d (Conv2D)             (None, 126, 126, 32)      896       \n",
+            "                                                                 \n",
+            " max_pooling2d (MaxPooling2  (None, 63, 63, 32)        0         \n",
+            " D)                                                              \n",
+            "                                                                 \n",
+            " dropout (Dropout)           (None, 63, 63, 32)        0         \n",
+            "                                                                 \n",
+            " conv2d_1 (Conv2D)           (None, 61, 61, 64)        18496     \n",
+            "                                                                 \n",
+            " max_pooling2d_1 (MaxPoolin  (None, 30, 30, 64)        0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_1 (Dropout)         (None, 30, 30, 64)        0         \n",
+            "                                                                 \n",
+            " conv2d_2 (Conv2D)           (None, 28, 28, 128)       73856     \n",
+            "                                                                 \n",
+            " max_pooling2d_2 (MaxPoolin  (None, 14, 14, 128)       0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_2 (Dropout)         (None, 14, 14, 128)       0         \n",
+            "                                                                 \n",
+            " conv2d_3 (Conv2D)           (None, 12, 12, 128)       147584    \n",
+            "                                                                 \n",
+            " max_pooling2d_3 (MaxPoolin  (None, 6, 6, 128)         0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_3 (Dropout)         (None, 6, 6, 128)         0         \n",
+            "                                                                 \n",
+            " conv2d_4 (Conv2D)           (None, 4, 4, 256)         295168    \n",
+            "                                                                 \n",
+            " max_pooling2d_4 (MaxPoolin  (None, 2, 2, 256)         0         \n",
+            " g2D)                                                            \n",
+            "                                                                 \n",
+            " dropout_4 (Dropout)         (None, 2, 2, 256)         0         \n",
+            "                                                                 \n",
+            " flatten (Flatten)           (None, 1024)              0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 512)               524800    \n",
+            "                                                                 \n",
+            " dropout_5 (Dropout)         (None, 512)               0         \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 2)                 1026      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 1061826 (4.05 MB)\n",
+            "Trainable params: 1061826 (4.05 MB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = models.Sequential()\n",
+        "\n",
+        "# Evrişimsel katmanlar(Conv) ve havuzlama(pooling) katmanları\n",
+        "\n",
+        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)))\n",
+        "model.add(layers.MaxPooling2D((2, 2)))\n",
+        "model.add(layers.Dropout(0.25))   # overfiting yapasın diye bazı değerleri atıyoruz dropout ile\n",
+        "\n",
+        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
+        "model.add(layers.MaxPooling2D((2, 2)))\n",
+        "model.add(layers.Dropout(0.25))\n",
+        "\n",
+        "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
+        "model.add(layers.MaxPooling2D((2, 2)))\n",
+        "model.add(layers.Dropout(0.25))\n",
+        "\n",
+        "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
+        "model.add(layers.MaxPooling2D((2, 2)))\n",
+        "model.add(layers.Dropout(0.25))\n",
+        "\n",
+        "model.add(layers.Conv2D(256, (3, 3), activation='relu'))\n",
+        "model.add(layers.MaxPooling2D((2, 2)))\n",
+        "model.add(layers.Dropout(0.25))\n",
+        "\n",
+        "# Düzleştirme ve tam bağlı katmanlar\n",
+        "model.add(layers.Flatten())  # düzleştirme\n",
+        "\n",
+        "model.add(layers.Dense(512, activation='relu'))\n",
+        "model.add(layers.Dropout(0.5))  # Yüksek dropout oranı, tam bağlı katmanlarda aşırı öğrenmeyi azaltabilir\n",
+        "model.add(layers.Dense(2, activation='softmax'))  # Çıkış katmanı: 2 sonucumuz var  sınıf için # çeşit sayımıza göre\n",
+        "\n",
+        "# Modeli derleme\n",
+        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
+        "\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5EfjjQuSDyN6",
+        "outputId": "845a8279-c864-4567-eb7e-9dfa1af3c1e6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/30\n",
+            "13/13 [==============================] - 11s 299ms/step - loss: 0.7357 - accuracy: 0.4917 - val_loss: 0.6930 - val_accuracy: 0.5056\n",
+            "Epoch 2/30\n",
+            "13/13 [==============================] - 1s 106ms/step - loss: 0.6930 - accuracy: 0.5083 - val_loss: 0.6929 - val_accuracy: 0.5056\n",
+            "Epoch 3/30\n",
+            "13/13 [==============================] - 1s 114ms/step - loss: 0.6902 - accuracy: 0.5237 - val_loss: 0.6933 - val_accuracy: 0.5056\n",
+            "Epoch 4/30\n",
+            "13/13 [==============================] - 1s 114ms/step - loss: 0.6873 - accuracy: 0.5419 - val_loss: 0.6929 - val_accuracy: 0.5056\n",
+            "Epoch 5/30\n",
+            "13/13 [==============================] - 1s 108ms/step - loss: 0.6786 - accuracy: 0.5781 - val_loss: 0.6771 - val_accuracy: 0.6336\n",
+            "Epoch 6/30\n",
+            "13/13 [==============================] - 1s 109ms/step - loss: 0.6817 - accuracy: 0.5397 - val_loss: 0.6712 - val_accuracy: 0.5376\n",
+            "Epoch 7/30\n",
+            "13/13 [==============================] - 1s 108ms/step - loss: 0.6320 - accuracy: 0.6368 - val_loss: 0.6216 - val_accuracy: 0.6016\n",
+            "Epoch 8/30\n",
+            "13/13 [==============================] - 2s 126ms/step - loss: 0.5978 - accuracy: 0.6768 - val_loss: 0.6362 - val_accuracy: 0.5120\n",
+            "Epoch 9/30\n",
+            "13/13 [==============================] - 1s 114ms/step - loss: 0.5853 - accuracy: 0.7376 - val_loss: 0.5124 - val_accuracy: 0.8336\n",
+            "Epoch 10/30\n",
+            "13/13 [==============================] - 2s 129ms/step - loss: 0.4635 - accuracy: 0.8165 - val_loss: 0.4270 - val_accuracy: 0.8944\n",
+            "Epoch 11/30\n",
+            "13/13 [==============================] - 1s 108ms/step - loss: 0.3520 - accuracy: 0.8752 - val_loss: 0.3427 - val_accuracy: 0.9200\n",
+            "Epoch 12/30\n",
+            "13/13 [==============================] - 1s 105ms/step - loss: 0.2555 - accuracy: 0.9072 - val_loss: 0.2322 - val_accuracy: 0.9184\n",
+            "Epoch 13/30\n",
+            "13/13 [==============================] - 1s 105ms/step - loss: 0.2208 - accuracy: 0.9173 - val_loss: 0.2324 - val_accuracy: 0.9168\n",
+            "Epoch 14/30\n",
+            "13/13 [==============================] - 1s 105ms/step - loss: 0.2074 - accuracy: 0.9189 - val_loss: 0.2185 - val_accuracy: 0.9232\n",
+            "Epoch 15/30\n",
+            "13/13 [==============================] - 1s 107ms/step - loss: 0.1920 - accuracy: 0.9269 - val_loss: 0.1966 - val_accuracy: 0.9344\n",
+            "Epoch 16/30\n",
+            "13/13 [==============================] - 1s 107ms/step - loss: 0.1760 - accuracy: 0.9323 - val_loss: 0.2042 - val_accuracy: 0.9280\n",
+            "Epoch 17/30\n",
+            "13/13 [==============================] - 1s 107ms/step - loss: 0.1625 - accuracy: 0.9285 - val_loss: 0.1993 - val_accuracy: 0.9408\n",
+            "Epoch 18/30\n",
+            "13/13 [==============================] - 1s 114ms/step - loss: 0.1640 - accuracy: 0.9397 - val_loss: 0.1916 - val_accuracy: 0.9312\n",
+            "Epoch 19/30\n",
+            "13/13 [==============================] - 1s 112ms/step - loss: 0.1642 - accuracy: 0.9387 - val_loss: 0.1898 - val_accuracy: 0.9328\n",
+            "Epoch 20/30\n",
+            "13/13 [==============================] - 1s 115ms/step - loss: 0.1579 - accuracy: 0.9339 - val_loss: 0.1862 - val_accuracy: 0.9392\n",
+            "Epoch 21/30\n",
+            "13/13 [==============================] - 1s 103ms/step - loss: 0.1563 - accuracy: 0.9403 - val_loss: 0.1986 - val_accuracy: 0.9456\n",
+            "Epoch 22/30\n",
+            "13/13 [==============================] - 1s 105ms/step - loss: 0.1624 - accuracy: 0.9376 - val_loss: 0.1721 - val_accuracy: 0.9536\n",
+            "Epoch 23/30\n",
+            "13/13 [==============================] - 1s 106ms/step - loss: 0.1677 - accuracy: 0.9387 - val_loss: 0.1812 - val_accuracy: 0.9328\n",
+            "Epoch 24/30\n",
+            "13/13 [==============================] - 1s 107ms/step - loss: 0.1546 - accuracy: 0.9408 - val_loss: 0.1867 - val_accuracy: 0.9472\n",
+            "Epoch 25/30\n",
+            "13/13 [==============================] - 1s 105ms/step - loss: 0.1434 - accuracy: 0.9456 - val_loss: 0.1725 - val_accuracy: 0.9488\n",
+            "Epoch 26/30\n",
+            "13/13 [==============================] - 1s 106ms/step - loss: 0.1511 - accuracy: 0.9419 - val_loss: 0.1920 - val_accuracy: 0.9440\n",
+            "Epoch 27/30\n",
+            "13/13 [==============================] - 1s 106ms/step - loss: 0.1395 - accuracy: 0.9461 - val_loss: 0.1714 - val_accuracy: 0.9472\n",
+            "Epoch 28/30\n",
+            "13/13 [==============================] - 1s 111ms/step - loss: 0.1352 - accuracy: 0.9477 - val_loss: 0.2167 - val_accuracy: 0.9472\n",
+            "Epoch 29/30\n",
+            "13/13 [==============================] - 1s 113ms/step - loss: 0.1276 - accuracy: 0.9541 - val_loss: 0.2246 - val_accuracy: 0.9552\n",
+            "Epoch 30/30\n",
+            "13/13 [==============================] - 1s 112ms/step - loss: 0.1259 - accuracy: 0.9579 - val_loss: 0.1939 - val_accuracy: 0.9536\n"
+          ]
+        }
+      ],
+      "source": [
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "history = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=30, batch_size=150)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 881
+        },
+        "id": "c17D0ZGUEnkK",
+        "outputId": "fbcf45f4-645f-4d34-fa90-023423ca1ed2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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HD/LLL7/wyiuvMHHiRK677jpCQ0NZvnw5999/P/fddx/79+93PScwMJD333+fLVu28NprrzF9+nReffXVs9aUlpbGZZddBsDChQuJjIx0PVZYWEivXr2YPXs2mzZt4t577+Wuu+5yTQa69dZbcTgcFTYGzcjIYPbs2fzhD39wHdu5cyeffvop33zzDXPmzGHt2rWV7qheGzwtedUyYWFheHh4nJKy09PTK3zwlcnPz+eTTz7hmWeeOeN5Pj4++Pj4nHetZ3VsLyx6FWx2wGb+tNkq+d1W4fd4m50PWx9lVUoW+z7/gnWxwfh7gb8n+HmBn6eBt80JzlJwOsp+llb+Ozawe5o3Dy+we5z43f7b3z3B46T73gHQ7RYIalHzn5WI1A0lBfC8Rf+f/9tB8K58mMG56NixIxs2bADMYLJx40b27NnjarWfMWMGXbp0YeXKlfTp04c33niDe+65h5EjRwIwYcIEfvzxR/Ly8ipc19/fn3//+994e3tXuzYvLy+efvpp1++tW7dm6dKlfPrpp9x2222u402bNuX111/HbrfToUMHXnzxRQoKCvjb3/4GmP+x/sILL7Bo0SJuv/12AJ544gnX8+Pi4hg/fjyffPIJf/3rX89Y00MPPUSbNm1c41lPFh0dzfjx412/jx07lh9++IFPP/2Uvn370qRJE+68807ee+891zjY//znP8TGxjJgwADX8woLC5kxYwbR0dEAvPHGG1x77bW8/PLLZ/3b7m6Whhxvb2969erF/PnzXU1XTqeT+fPn8+CDD57xuZ999hlFRUX8/ve/r4VKqyDvMKx+v1pP7Qf08wScwF73lXTOfn4eLh4L/R4CnwALCxERqRrDMFx7AW7dupWYmJgKwxI6d+5MSEgIW7dupU+fPiQnJ5/SqtC3b19++umnCse6det2XgGn3NSpU3n33XdJSUnh+PHjFBcXnzL7qEuXLtjtJzpWIiIiKoyR8fDwoFmzZhVmHc+cOZPXX3+dXbt2kZeXR2lpaZX2c7ruuuuYNWsWb731Fn/6058qPOZwOHj++ef59NNPOXDgAMXFxRQVFVUIQ6NGjaJPnz4cOHCA6Oho3n//fdfA8HKxsbGugAOQmJiI0+kkOTm5cYUcgHHjxjFixAh69+5N3759mTx5Mvn5+a6UPXz4cKKjo5k0aVKF573zzjsMGTKEZs2aWVH2qYJawOWPm02whhMo+3na36nwe35xCdsO5ZBbbJBXDLnFBjnFcNwBpYYHTuyUYseBB6V44MB+4qdh/vTysNGsiQehvnZCm9gI9rER4mMnyMdGkLeNQG+DAC/wxPmbFqFSOLQe9q+EX140w9oVj0PPu8zWHxFpmLz8zBYVq17bDbZu3eoaU+JOlU1msdlsp6xUf6ZxMJ988gnjx4/n5ZdfJjExkcDAQF566SWWL19e4TwvL69TXqeyY+UzyZYuXcqwYcN4+umnGThwIMHBwXzyySe8/PLLZ31fd911FzfccAN/+MMfMAyDcePGuR576aWXeO2115g8eTLdunXD39+fhx9+uMLA6549exIfH8+MGTO4+uqr2bx5M7Nnzz7r61rF8pAzdOhQDh8+zIQJE0hLS6NHjx7MmTPHNRg5JSWlQsIFSE5OZtGiRfz4449WlFy54Gi47MzNhGfiD/Sq5HhBcSkZOUWk5xSSkVtk3lz3C12P5RSWmi1BJcBZJpAFN/EiMsiXiGBfIgJ9iAz2JaqrL0ldlxO+/Hk4tge+eQiWvwVX/x3aJZ35giJSP9lsbukysspPP/3Exo0bXS0SnTp1IjU1ldTUVFdrzpYtW8jKyqJz584AdOjQgZUrVzJ8+HDXdVauXFml12vevDmHDp1Y8mPHjh0UFBSc9vzFixdz8cUXV2g52rVrV9Xf4GksWbKEVq1a8fjjj7uO7du3r8rPHzFiBHa7nZEjR+J0Ol1dVIsXL2bw4MGuHhKn08n27dtdn125P/7xj0yePJkDBw6QlJR0yoSelJQUDh48SIsWZlfosmXLXF1xtc3ykAPw4IMPnrZ7asGCBacc69ChQ6PZ98nP25O4ME/iws78D1FhicMMPLmFpGUXkp5TfisiLaeQjJxC0nIKKSxxkn28hOzjJSSn51a4xt/wp1PzfzK+1a9cdug9PDO2wH9uhrZXwNXPQkSXmnyrIiKnVVRURFpaGg6Hg/T0dObMmcOkSZO47rrrXIElKSmJbt26MWzYMCZPnkxpaSmjR4/msssuo3fv3oA5zmTUqFH07duXSy65hI8//pj169fTtm3bs9ZwxRVXMGXKFBITE3E4HDzyyCOntLicrH379syYMYMffviB1q1b8+GHH7Jy5crzbnlq3749KSkpfPLJJ/Tp04fZs2fz5ZdfntM17rrrLux2OyNGjMAwDP7yl7/Qvn17Pv/8c5YsWUJoaCivvPIK6enpp4ScO++8k/HjxzN9+nRmzJhxyrV9fX0ZMWIE//znP8nJyeH//u//uO2222q9qwrqSMiR8+fr5UFsMz9im52+CdgwDHIKS10BKC3bbBFKyy5kZ0YeK/ceZevhIu453JdgOvPXJl8zlDl47voJY9ol2Hr+3uySC6z9L6qING5z5swhKioKT09PQkNDiY+P5/XXX3e1SoDZpfPVV18xduxYLr30Uux2O4MGDeKNN95wXWfYsGHs3r2bcePGUVhYyG233cbIkSPPuJ1QuZdffpmRI0fSv39/WrRowWuvvcbq1atPe/59993H2rVrGTp0KDabjTvuuIPRo0fz/fffn9dnccMNN/CnP/2JBx98kKKiIq699lqefPJJnnrqqXO6zrBhw7Db7dx11104nU6eeOIJdu/ezcCBA/Hz8+Pee+9lyJAhFaZ/AwQHB3PzzTcze/bsSqeCt2vXjptuuolrrrmGo0ePct111/Gvf/3rPN5x9dmMxtIkUiYnJ4fg4GCys7OrNEirMck+XsKC5Azmbc1gwbYMcotKibWl84jnx1zrYf4DUOLRhOKEsfgPePi8mrmLSs2Wp0PZhWQfL+GSdmE08db4H5GaUlhYyJ49e2jdujW+vr5Wl1OnXHXVVURGRvLhhx9aXUq9ceWVV9KlSxdef/11t173TN/T6vz9VkuOuAQ38WJwj2gG94imuNTJij1Hmbc1nee3xPFu9jqe8PqInuzEa8mLHFn2DuvbjyX28j/QNiKowsj6whIHadmFHMou5FD2cQ5lF7p+T8s5Tlp2IZl5FVcQvSG+Ba/f0bO237KINDIFBQVMmzaNgQMH4uHhwccff8y8efOYO3eu1aXVC8eOHWPBggUsWLDAstaZc6GWHDkrwzDYeiiXuZvTKFz/OXfmvEuM/TAAW5yteMfvHo5GJJKWU0Ra9nGOFVRt1U1vTztRwb6kHC3AMOB/DyTSq1XTmnwrIo2WWnJMx48f5/rrr2ft2rUUFhbSoUMHnnjiCW666SarS6sX4uLiOHbsGE8++WSFNXXcxd0tOQo5cs4OHTnGwR9fp+P2t/A38gFY5byAlc4OrHO2Zb2zLdlezYkKaUJUsC+RQWU/g31P+tmEUD8vbDYbj3y+gZmrUomPCeHLBy7GbredpQIROVcKOVIfqLtKLBfVLJSoOyZC/oMU/zQJzzXv0du+nd727a5zjIBIbC16QfSFEF320ze40uv9eeAFfLvhIOtTs/hq/QFu7Nmytt6KiIg0YAo5Un3+zfC+/p9wyVjYsxAOrIb9qyFjC7a8NEiebd7KhV1QFnjKQk9EV/D0ITzQl9GXt+OlH5J5cU4yg7pEaRCySA1pZI33Us+4+/upkCPnL7QVhA6HC8sW1yrOh0MbzNBTfsvaB5nbzdv6sl1tPbwhshu07Ms9iQ/z3+VNOJB1nLd/2c1DSe2tez8iDVD5ei4FBQU0adLE4mpEKle+urKHh3v+Q1djcqR25GfCgTUVg8/xoycej03k217v8ODH62ji5cHP4wcQGaxxAyLudOjQIbKysggPD8fPz6/CrEgRqzmdTg4ePIiXlxexsbGnfD818LgKFHLqCMMwd27fvxK+/j8oPY5x6wfc+ks4q/Yd46YLo3nlth5WVynSoBiGQVpaGllZWVaXIlIpu91O69atK90cVSGnChRy6qCfJ8HCFyAklg2D53LDW+YKol+N6Ud8TIi1tYk0QA6H44wbS4pYxdvb+5T9KstpdpXUT/3+D9bMgKwUuu//Lzf1vIIv1h7g799u4bP7E9WkLuJmHh4ebhvzIFKXVR6XRGqTtz8kPWXe//VlHukfShMvD1btO8bsjYfO+FQREZHTUciRuqHbrebU8uI8Ila+xH2XtQHghe+3UVjisLg4ERGpjxRypG6w22HQC+b9tf/h/g4FRAX7sv/Ycd5dvMfa2kREpF5SyJG6I6YvdL0FMPCd9zh/HXgBAFN/2klGbqG1tYmISL2jkCN1S9JT4OkL+xYx2Hct8TEh5Bc7eOXH7Wd9qoiIyMkUcqRuCYmBi8cCYJ/7JBN/Z47Nmbkqlc0Hs62sTERE6hmFHKl7+j0MAZFwbC8XHvqM6+NbYBjw92+3aN8dERGpMoUcqXt8AiBponn/l5d47NJm+HjaWbb7KD9uSbe2NhERqTcUcqRu6n47RPWAohxarHmFP/ZvDcCk77ZSXOq0tjYREakXFHKkbrLbYdAk8/6aDxjTuYjmgT7sPVLAjKV7LS1NRETqB4UcqbtaXQydh4DhxO+nJ/nLVeaU8tfm7+BIXpG1tYmISJ2nkCN121VPg4cP7FnIzYGb6NIiiNzCUibP22F1ZSIiUscp5EjdFhoHiWMA8Jj7BBN+1w6Aj5bvY3t6roWFiYhIXaeQI3Vf/3HgHw5Hd5GQ+QWDukTiNODZ2VutrkxEROowhRyp+3wC4conzfsL/sHjlzfH28POL9sP83NyRvWvaxiQuRNKtGWEiEhDpJAj9UOPYRDZDYqyiVn/GiP7xQHw7LdbKHGc45Ryw4Ad82D65TClF/z4uPvrFRERyynkSP1g94CBZVPKV73H2G6lNPP3ZtfhfP67PKXq19nzK7w7CD66GQ6uNY9tm20GHxERaVAUcqT+aN0fOl0PhoOABRP4U1J7AF6dt5207LN0OaWuhBmD4YPrIHWZuQnoRaPB7gm5hyDrHIKSiIjUCwo5Ur9c9Qx4eMOun7gjdBudo4LIKijhvg9XUVjiOPX8Q+vhv0PhnSTYvQDsXtDnj/B/a83FBiO7m+elrqjVtyEiIjVPIUfql6Zt4KIHAHNK+bQ7uhPq58X6/dk88r8NJzbwzNgGnw6Hty6F7XPA5gE9fw9jV8O1L0NQC/O82IvMn6nLLXgzIiJSkxRypP7pPx78wuDIDmL3fMK/hvXC027jq3UH+WjOAvjiXvjXRbDlK8AG3W6FMStg8FQIbVXxWjF9zZ+py2r7XYiISA1TyJH6xzcIrnjCvP/z8yRG2XjxqqY87zmd25fdBBtmAgZ0vA4eWAI3/xvC2lV+rZgE82f6ZijS4oIiIg2Jp9UFiFTLhcNh5b8hfRPMuIGbDieDZzEAvxg9iL3leeK69Tv7dYJaQHAsZKfAgdXQZkDN1i0iIrVGLTlSP9k9YODz5v20jeAoxtnqEiY2e4XhRX/lru+LOJZfXLVrlXdZpWhcjohIQ6KQI/VXm8vM8Tntr4bhX2G/+1se/sNdxDb1I/XocR74aHXVFgos77LS4GMRkQZFIUfqtyufhGGfmd1MNhuh/t78e0RvAnw8Wbb7KE9/s/ns14gtCzn7V4KzkmnoIiJSLynkSINzQUQgk4f2wGaD/yxL4cNl+878hPAu4OUPRTlweFvtFCkiIjVOIUcapKTOEfxlYAcAnv56M0t2ZZ7+ZA9PaNnbvK8uKxGRBkMhRxqsBy5ry5AeLSh1Goz+aA0pRwpOf3L5uBwNPhYRaTAUcqTBstlsvHBzd+JbBpNVUMIfZ6wkt7Ck8pM1+FhEpMFRyJEGzdfLg7eH9yY80Ift6Xn8aeY6HM5Kdhxv2RuwwbE9kJdR63WKiIj7KeRIgxcR5Mvbw3vj7Wln3tYM/vlj8qknNQmB8E7mfbXmiIg0CJaHnKlTpxIXF4evry8JCQmsWHHm3aCzsrIYM2YMUVFR+Pj4cMEFF/Ddd9/VUrVSX/WICeGlW8wdx99csIuv1h049STXPlYKOSIiDYGlIWfmzJmMGzeOiRMnsmbNGuLj4xk4cCAZGZV3FxQXF3PVVVexd+9ePv/8c5KTk5k+fTrR0dG1XLnUR4N7RPPAgLYA/PXzDaxPzap4Qkz5juRnDtoiIlI/2AzDqGSAQu1ISEigT58+TJkyBQCn00lMTAxjx47l0UcfPeX8adOm8dJLL7Ft2za8vLyq9Zo5OTkEBweTnZ1NUFDQedUv9Y/TaXDvh6uYtzWD8EAfvhl7CRFBvuaDR3bBGxeChzc8mgpevtYWKyIiLtX5+21ZS05xcTGrV68mKSnpRDF2O0lJSSxdurTS53z99dckJiYyZswYIiIi6Nq1K88//zwOx+lXqS0qKiInJ6fCTRovu93Gq0N7cEFEABm5Rdw7YxWFJWXfn6ZtwC8MHMVwaL21hYqIyHmzLORkZmbicDiIiIiocDwiIoK0tLRKn7N7924+//xzHA4H3333HU8++SQvv/wyzz777GlfZ9KkSQQHB7tuMTExbn0fUv8E+nrx7+F9CPHzYv3+bG6YsoiZK1MoLHVCbHmX1TJrixQRkfNm+cDjc+F0OgkPD+ftt9+mV69eDB06lMcff5xp06ad9jmPPfYY2dnZrltqamotVix1VWwzP94c1osAH0+2p+fxyP820u+Fn/jleBvzBI3LERGp9zyteuGwsDA8PDxIT0+vcDw9PZ3IyMhKnxMVFYWXlxceHh6uY506dSItLY3i4mK8vb1PeY6Pjw8+Pj7uLV4ahMS2zVj8yBV8sjKFD5bs5WB2IZO3N+VSH8jdsZjUA9l0jg62ukwREakmy1pyvL296dWrF/Pnz3cdczqdzJ8/n8TExEqf069fP3bu3InT6XQd2759O1FRUZUGHJGzCfbz4r7L2vLLXy9nyp098WzZgyLDk0DHMR6Y8jl3vL2MuVvScVa2gKCIiNRplnZXjRs3junTp/PBBx+wdetWHnjgAfLz8xk5ciQAw4cP57HHHnOd/8ADD3D06FEeeughtm/fzuzZs3n++ecZM2aMVW9BGghPDzvXdW/Bp2MupyTCXE+nj8cOlu4+wqgZq7ji5QV8sGQv+UWlFlcqIiJVZVl3FcDQoUM5fPgwEyZMIC0tjR49ejBnzhzXYOSUlBTs9hM5LCYmhh9++IE//elPdO/enejoaB566CEeeeQRq96CNEAB7fpBxhqe7plPM782fLw8hb1HCpj49Wb++WMyd/SNZcTFcUSHNLG6VBEROQNL18mxgtbJkbPa+g3M/D2Ed4bRS8kvKuV/a/bz3uK97MnMB8DDbmNQ10juu7QN3VuGWFuviEgjUK/WyRGps8p3JM/YCsez8PfxZHhiHPPHXcY7I3pzcdtmOJwGszcc4pZpS9mZkWdtvSIiUimFHJHfCgiH0NaAAQdWuQ7b7Tau7BTBf0ddxPcP9adzVBDFpU4WJGvXchGRukghR6Qy5a05KZVv1tkpKojr41sAsGrvsdqqSkREzoFCjkhlYstCzhl2JO8TFwrAqn1HaWRD20RE6gWFHJHKlLfk7F8FjsqnjXeNDsbbw05mXjH7jhTUYnEiIlIVCjkilWneEXyCoCQfMjZXeoqvlwfdWporIq/apy4rEZG6RiFHpDJ2D2jZx7x/hn2sepd1Wa3ed7Q2qhIRkXOgkCNyOq7Bx6ffkbx3q6YArNTgYxGROkchR+R0YvqaP8/QktOrldmSszMjj6yC4tqoSkREqkghR+R0WvYGmx2yUyDnYKWnNPX3pm1zfwBWa1yOiEidopAjcjo+gRDRxbx/hqnk6rISEambFHJEziTmIvPnmbqsNPhYRKROUsgROZOYsy8K2LtsXM76/dkUlTpqoyoREakChRyRMykffHxoPRRXvuBf6zB/mvl7U1zqZNOB7FosTkREzkQhR+RMQmIhMAqcpXBwbaWn2Gw21ywr7WMlIlJ3KOSInInNdtJU8jN0Wbn2sVLIERGpKxRyRM6mKuNy4swZVqv3HdNmnSIidYRCjsjZuGZYLYfTBJiuLYLx8bRzNL+Y3Zn5tViciIicjkKOyNlEdgNPXzh+DDJ3VHqKt6ed+JYhAKzWuBwRkTpBIUfkbDy9ocWF5v0qjcvRejkiInWBQo5IVcRWZVyOZliJiNQlCjkiVVGFwccXxpohZ3dmPkfyimqjKhEROQOFHJGqaFk2jTxzOxRU3h0V4udN+/AAQJt1iojUBQo5IlXh3wyatTfv71952tPKp5JrvRwREesp5IhUVXmXVcqy057S27XysQYfi4hYTSFHpKpcg49PvyN5+eDjjQeyKSzRZp0iIlZSyBGpqvKWnAOrwVFS6SmxTf1oHuhDicNgw35t1ikiYiWFHJGqatYefEOg9Dikbaj0FJvNdqLLSuvliIhYSiFHpKrs9pOmkp++y6p8R3KtfCwiYi2FHJFzUb4j+RkGH/cp36wz5RhOpzbrFBGxikKOyLmIPftmnZ1bBNHEy4OsghJ2Hc6rxeJERORkCjki56LFhWDzgNxDkL2/0lO8POzExwQDWi9HRMRKCjki58LbD6K6m/fPsMVDeZeV9rESEbGOQo7IuYo5qcvqNHpphpWIiOUUckTOVfng4zNt1tkqFJsN9h0p4HCuNusUEbGCQo7IuSqfRp62CYoqH1gc5OtFh4hAAFarNUdExBIKOSLnKjgagmPAcJirH59G+RYPKzUuR0TEEgo5ItXh6rI6wz5WrbQjuYiIlRRyRKrDNfj49IsClg8+3nwgm+PF2qxTRKS2KeSIVIerJWclOJ2VntIytAmRQb6UOg3WpWbVXm0iIgIo5IhUT0RX8PKHomw4vLXSU2w2G73KxuVo8LGISO1TyBGpDg9PaHWxeT/5u9OedmJHco3LERGpbQo5ItXVebD5c/NXpz3FtVnnPm3WKSJS2xRyRKqr47XmPlbpG+HIrspPiQzEz9uD3MJStmfk1nKBIiKNW50IOVOnTiUuLg5fX18SEhJYseL003Lff/99bDZbhZuvr28tVitSxq8ptLnMvL9lVqWneHrY6RkbAmgfKxGR2mZ5yJk5cybjxo1j4sSJrFmzhvj4eAYOHEhGRsZpnxMUFMShQ4dct3379tVixSIncXVZzTrtKeXr5azWuBwRkVplech55ZVXGDVqFCNHjqRz585MmzYNPz8/3n333dM+x2azERkZ6bpFRETUYsUiJ+l4vdlllbYBju6u9JQTKx9rhpWISG2yNOQUFxezevVqkpKSXMfsdjtJSUksXbr0tM/Ly8ujVatWxMTEMHjwYDZv3nzac4uKisjJyalwE3Eb/2bQur95f0vlA5B7xoZit8H+Y8dJzymsxeJERBo3S0NOZmYmDofjlJaYiIgI0tLSKn1Ohw4dePfdd/nqq6/4z3/+g9Pp5OKLL2b//v2Vnj9p0iSCg4Ndt5iYGLe/D2nkyrusThNyAnw86RgZBGhcjohIbbK8u+pcJSYmMnz4cHr06MFll13GF198QfPmzXnrrbcqPf+xxx4jOzvbdUtNTa3liqXB63g92OxwcC0c21vpKX3UZSUiUussDTlhYWF4eHiQnp5e4Xh6ejqRkZFVuoaXlxc9e/Zk586dlT7u4+NDUFBQhZuIWwU0h1b9zPtbvq70lF5xGnwsIlLbLA053t7e9OrVi/nz57uOOZ1O5s+fT2JiYpWu4XA42LhxI1FRUTVVpsjZdRli/jzNVPLylY+3HMohv6i0dmoSEWnkLO+uGjduHNOnT+eDDz5g69atPPDAA+Tn5zNy5EgAhg8fzmOPPeY6/5lnnuHHH39k9+7drFmzht///vfs27ePP/7xj1a9BRGzywobHFgNWSmnPNwipAnRIU1waLNOEZFa42l1AUOHDuXw4cNMmDCBtLQ0evTowZw5c1yDkVNSUrDbT2SxY8eOMWrUKNLS0ggNDaVXr14sWbKEzp07W/UWRCAwwuyy2rfI7LK6+MFTTunVKpQDWcdZtfcY/dqFWVCkiEjjYjMMo1FtqJOTk0NwcDDZ2dkanyPutWI6fDceWvaBP8475eEZS/cy4avN9G8fxof3JFhQoIhI/VWdv9+Wd1eJNBidyrqs9q+E7FOXNChf+XhtShYObdYpIlLjFHJE3CUwEmLLBsxXMsuqQ2QggT6e5BWVsi1Ni1KKiNQ0hRwRdzrDwoAedhs9yjbr1FRyEZGap5Aj4k6dbzB/pi6DnIOnPFzeZaWVj0VEap5Cjog7BbWAmIvM+5V0WZWvfLxKKx+LiNQ4hRwRdztDl1WP2BA87DYOZhdyMOt4LRcmItK4KOSIuFt5l1XKUsituNGsn7cnnaPKNuvUuBwRkRqlkCPibsEtzbVyMGDrN6c83FtdViIitUIhR6QmdB5i/tw865SHNPhYRKR2KOSI1ITyLqt9iyE3vcJD5S0529JyyC0sqe3KREQaDYUckZoQEgvRvQADtlXssooI8iWmaROchrn6sYiI1AyFHJGaUpUuKw0+FhGpMQo5IjXl5C6rvMMVHurVSoOPRURqmkKOSE0JjYMWPcFwntJlldDabMlZk3KMolKHBcWJiDR8CjkiNek0CwO2Cw8gLMCbwhIn61OzLShMRKThU8gRqUnl43L2/Ar5R1yHbTYbCW2aAbB015FKnigiIudLIUekJjVtDVHxYDhO6bJKLAs5y3Yr5IiI1ASFHJGadpouq4vKQs7qlGMUlmhcjoiIuynkiNS08i6r3Quh4MRsqrbN/Wke6ENxqVPr5YiI1ACFHJGa1qwtRHQr67Ka7Tpss9lcXVZL1WUlIuJ21Qo5qamp7N+/3/X7ihUrePjhh3n77bfdVphIg9KlvMtqVoXDiW01LkdEpKZUK+Tceeed/PzzzwCkpaVx1VVXsWLFCh5//HGeeeYZtxYo0iC4uqwWVOiyKm/JWZeSpXE5IiJuVq2Qs2nTJvr27QvAp59+SteuXVmyZAkfffQR77//vjvrE2kYwtpDeBdwlkLy967DrZr5ERnkS7HDyWpt8SAi4lbVCjklJSX4+PgAMG/ePG64wVy+vmPHjhw6dMh91Yk0JF2GmD9P6rKy2WyuLiutlyMi4l7VCjldunRh2rRp/Prrr8ydO5dBgwYBcPDgQZo1a+bWAkUajPKp5Lt+huNZrsNaL0dEpGZUK+T84x//4K233mLAgAHccccdxMfHA/D111+7urFE5Dead4DmncBZUqHLqrwlZ/3+LAqKS62qTkSkwfGszpMGDBhAZmYmOTk5hIaGuo7fe++9+Pn5ua04kQan82BYuNVcGLDHHQC0DG1CdEgTDmQdZ9XeY1x6QXOLixQRaRiq1ZJz/PhxioqKXAFn3759TJ48meTkZMLDw91aoEiDUj4uZ9d8KDQ35rTZbK7Vj7VejoiI+1Qr5AwePJgZM2YAkJWVRUJCAi+//DJDhgzhzTffdGuBIg1KeCcI6wCOYkie4zqs9XJERNyvWiFnzZo19O/fH4DPP/+ciIgI9u3bx4wZM3j99dfdWqBIg1PJXlYXtWkKwIb92eQVaVyOiIg7VCvkFBQUEBgYCMCPP/7ITTfdhN1u56KLLmLfvn1uLVCkwSnvsto5DwpzAGgZ6kdM0yY4nAYr9x49/XNFRKTKqhVy2rVrx6xZs0hNTeWHH37g6quvBiAjI4OgoCC3FijS4IR3hmbtwFEEO350HXZNJdd6OSIiblGtkDNhwgTGjx9PXFwcffv2JTExETBbdXr27OnWAkUaHJvtxDYPm790Hda4HBER96pWyLnllltISUlh1apV/PDDD67jV155Ja+++qrbihNpsE7usirKBSCxTRgAGw9kk1NYYlFhIiINR7VCDkBkZCQ9e/bk4MGDrh3J+/btS8eOHd1WnEiDFdEVmraF0kLYbv6HQmSwL63D/HEasHKPxuWIiJyvaoUcp9PJM888Q3BwMK1ataJVq1aEhITw97//HafT6e4aRRoemw263GjeP6nLqnyWlfaxEhE5f9UKOY8//jhTpkzhhRdeYO3ataxdu5bnn3+eN954gyeffNLdNYo0TOUhZ8dcV5eVFgUUEXGfam3r8MEHH/Dvf//btfs4QPfu3YmOjmb06NE899xzbitQpMGK6GLOsjqy01wYsPutrhlWWw7lkF1QQrCfl8VFiojUX9VqyTl69GilY286duzI0aMaSyBSJSd3WW2ZBUB4kC9tm/tjGLB8j1pzRETOR7VCTnx8PFOmTDnl+JQpU+jevft5FyXSaJRPJd8x17UwoLqsRETco1rdVS+++CLXXnst8+bNc62Rs3TpUlJTU/nuu+/cWqBIgxbRBZq1hyM7YPsc6H4biW2b8dHyFA0+FhE5T9VqybnsssvYvn07N954I1lZWWRlZXHTTTexefNmPvzwQ3fXKNJwVZhlNQs40ZKzLS2XY/nFFhUmIlL/2QzDMNx1sfXr13PhhRficDjcdUm3y8nJITg4mOzsbG1BIXVD+mZ482Lw8Ia/7ALfIK5+dSHb0/OY9vsLGdQ1yuoKRUQsV52/39VeDFBE3CS8M4RdAI5iSP4eOGlcjrqsRESqrU6EnKlTpxIXF4evry8JCQmsWLGiSs/75JNPsNlsDBkypGYLFKlJlcyyStTgYxGR82Z5yJk5cybjxo1j4sSJrFmzhvj4eAYOHEhGRsYZn7d3717Gjx9P//79a6lSkRpUPstq5zwozCahLORsT88jM6/IurpEROqxc5pdddNNN53x8aysrHMu4JVXXmHUqFGMHDkSgGnTpjF79mzeffddHn300Uqf43A4GDZsGE8//TS//vprtV5XpE4J7wRhHSAzGZK/p2n87XSMDGRbWi7Ldx/l2u4alyMicq7OqSUnODj4jLdWrVoxfPjwKl+vuLiY1atXk5SUdKIgu52kpCSWLl162uc988wzhIeHc88995z1NYqKisjJyalwE6lzbLYTO5OXzbJKbFveZZVpTU0iIvXcObXkvPfee2598czMTBwOBxERERWOR0REsG3btkqfs2jRIt555x3WrVtXpdeYNGkSTz/99PmWKlLzutwIC/8Bu+ZDYTYXtWnGe4v3avCxiEg1WT4m51zk5uZy1113MX36dMLCwqr0nMcee4zs7GzXLTU1tYarFKmm8E7QvKNrltVFrZths8Guw/lk5BZaXZ2ISL1TrRWP3SUsLAwPDw/S09MrHE9PTycyMvKU83ft2sXevXu5/vrrXcecTicAnp6eJCcn07Zt2wrP8fHxwcfHpwaqF6kBnYfAwhdg85cEx99O56ggNh/MYdnuo9wQ38Lq6kRE6hVLW3K8vb3p1asX8+fPdx1zOp3Mnz/ftV3EyTp27MjGjRtZt26d63bDDTdw+eWXs27dOmJiYmqzfBH3Kx+Xs3M+HM86MZVcXVYiIufM0pYcgHHjxjFixAh69+5N3759mTx5Mvn5+a7ZVsOHDyc6OppJkybh6+tL165dKzw/JCQE4JTjIvVSeCdo3gkObzW7rNpcwb8X7WGZ1ssRETlnloecoUOHcvjwYSZMmEBaWho9evRgzpw5rsHIKSkp2O31auiQyPnpMgQWbIXNX9L35luw22BPZj5p2YVEBvtaXZ2ISL3h1r2r6gPtXSV1XsY2+FcC2L3gLzu54Z1NbNifzeShPRjSM9rq6kRELKG9q0QagvCOZpeVswSSv9O4HBGRalLIEamLyvey2vzlic06NS5HROScKOSI1EXls6x2/UyfSDsedhspRws4kHXc0rJEROoThRyRuqh5BwjvDM4SAvb8QLfoYACWqctKRKTKFHJE6qqTuqxO7GOlkCMiUlUKOSJ1Vech5s/dP9O/pbnagwYfi4hUnUKOSF3V/AII7wLOUnodX4yn3caBrOOkHi2wujIRkXpBIUekLivrsvJJ/pr4mBBAXVYiIlWlkCNSl5XPstq9gMtjzC4rDT4WEakahRyRuiysPUR0BWcpAz1XA2ZLTiNbqFxEpFoUckTqurLWnDYZP+LlYeNQdiH7jmhcjojI2SjkiNR1nc1xOR57f6F/tAeAdiUXEakChRyRui6sHUR0A2cptwdtADT4WESkKhRyROqDsi6rvgW/AOZ6ORqXIyJyZgo5IvVB2VTy4EOLCffMJyO3iN2Z+RYXJSJStynkiNQHzdpCZDdshoN7mm0CNC5HRORsFHJE6ouy1pxBtuWAtngQETkbhRyR+qJsL6vY7JWEksOy3Uc1LkdE5AwUckTqi2ZtIbI7NsPBtV6rycwrYmdGntVViYjUWQo5IvVJ2SyroX7m6se/7si0sBgRkbpNIUekPinrsupSvJ6m5PBzcoa19YiI1GEKOSL1SbO2EBWP3XAw0GMly3cfJb+o1OqqRETqJIUckfqmrDXnJp9VFDucLN6pLisRkcoo5IjUN2Xjcno5N6rLSkTkDBRyROqbpm3MLiucXOmxhp+3HdZUchGRSijkiNRH7ZIAuMRjK2k5hWw5lGNxQSIidY9Cjkh91PpSAPp7bQUMft6mLisRkd9SyBGpj2ISwMObpo5M4mxp/KSQIyJyCoUckfrIqwm07APAxfYtrE3N4mh+scVFiYjULQo5IvVVWZfVQL/tGAb8sv2wxQWJiNQtCjki9VVcfwB6swkw1GUlIvIbnlYXICLV1LI3eDbBv+QY7W0HWLjdm1KHE08P/beLiAioJUek/vL0gdgEAK7w3Ub28RLWpmZZW5OISB2ikCNSn5WNy7nGfweAuqxERE6ikCNSn8WZIadT0XpsOLVejojISRRyROqzFj3AOwDvkhy62FPYlpbLgazjVlclIlInKOSI1GceXtDqYgBuaboHQK05IiJlFHJE6ruyqeSXeW8DFHJERMop5IjUd2WDj2Nz1+KBg8W7MikscVhclIiI9RRyROq7yG7gG4xHSR4DAg9SWOJk2e4jVlclImI5hRyR+s7u4eqyurXZbkBdViIioJAj0jCUb/FgbAbgp+QMDMOwsiIREcsp5Ig0BK3NkNPs6Br8PZ2kHj3OrsN5FhclImIthRyRhqB5J/ALw1ZSwB0tzN3ItfqxiDR2dSLkTJ06lbi4OHx9fUlISGDFihWnPfeLL76gd+/ehISE4O/vT48ePfjwww9rsVqROshuh7hLALgmUFs8iIhAHQg5M2fOZNy4cUycOJE1a9YQHx/PwIEDycio/B/opk2b8vjjj7N06VI2bNjAyJEjGTlyJD/88EMtVy5Sx5RNJe9cuA6AVXuPkVNYYmFBIiLWsjzkvPLKK4waNYqRI0fSuXNnpk2bhp+fH++++26l5w8YMIAbb7yRTp060bZtWx566CG6d+/OokWLarlykTqmLOT4pq2mU5gnpU6DRTsyLS5KRMQ6loac4uJiVq9eTVJSkuuY3W4nKSmJpUuXnvX5hmEwf/58kpOTufTSSys9p6ioiJycnAo3kQapWTsIjAJHEcOi0wF1WYlI42ZpyMnMzMThcBAREVHheEREBGlpaad9XnZ2NgEBAXh7e3PttdfyxhtvcNVVV1V67qRJkwgODnbdYmJi3PoeROoMm+3EFg9e5hYPC5IzcDo1lVxEGifLu6uqIzAwkHXr1rFy5Uqee+45xo0bx4IFCyo997HHHiM7O9t1S01Nrd1iRWpT2VTy6KyVBPh4kplXzMYD2RYXJSJiDU8rXzwsLAwPDw/S09MrHE9PTycyMvK0z7Pb7bRr1w6AHj16sHXrViZNmsSAAQNOOdfHxwcfHx+31i1SZ5WNy7EfXENSW39mbcnmp20ZxMeEWFuXiIgFLG3J8fb2plevXsyfP991zOl0Mn/+fBITE6t8HafTSVFRUU2UKFK/hMZBcCw4S7kxbD8APydrXI6INE6Wd1eNGzeO6dOn88EHH7B161YeeOAB8vPzGTlyJADDhw/nsccec50/adIk5s6dy+7du9m6dSsvv/wyH374Ib///e+tegsidUtZa05vYyMAG/Znk5FbaGVFIiKWsLS7CmDo0KEcPnyYCRMmkJaWRo8ePZgzZ45rMHJKSgp2+4kslp+fz+jRo9m/fz9NmjShY8eO/Oc//2Ho0KFWvQWRuqV1f1j3H/wPLKF7y9+xYX82C5IPc1tvDboXkcbFZjSyXfxycnIIDg4mOzuboKAgq8sRcb/sA/BqZ7DZmdp3Pi8tPMQ13SL517BeVlcmIlJt1fn7bXl3lYi4WXA0NG0LhpNrgnYD8Ov2TEocTosLExGpXQo5Ig1R2VTyuJzVhAV4k1tUysq9Ry0uSkSkdinkiDREZYOPbXt/5bILwgH4Wasfi0gjo5Aj0hCVrXxM+kYGtvYCtMWDiDQ+CjkiDVFAODTvBMAlXtvwtNvYdTiflCMFFhcmIlJ7FHJEGqqycTl+B5bQOy4UgJ+2pZ/pGSIiDYpCjkhDVTYuhz2/cEVHc1zOT8mHLSxIRKR2KeSINFSt+gE2yEzmqljz0LLdRygoLrW0LBGR2qKQI9JQ+TWFyK4AxOWuJaZpE4pLnSzZecTiwkREaodCjkhD1voyAGx7fuGKDuVdVpplJSKNg0KOSENWPpV8768M6HhivZxGtpuLiDRSCjkiDVmri8HmAUd3kxh2HF8vO4eyC9mWlmt1ZSIiNU4hR6Qh8w2CFj3Mu6lL6Nc2DNDCgCLSOCjkiDR0J00lv7yjtngQkcZDIUekoTtpXM7lHZoDsCblGMfyiy0sSkSk5inkiDR0sReB3QuyU4k20ukYGYjTgF92aGFAEWnYFHJEGjpvf2jZ27yvLisRaUQUckQag5O6rK4sCznfbUxjbcoxC4sSEalZCjkijcFJg497xYZwdecIih1O7v/PajJyCq2tTUSkhijkiDQGLfuAhw/kpWM7spNXhvbggogA0nOKuO8/qykqdVhdoYiI2ynkiDQGXr4Qm2De37OQAB9Ppg/vTXATL9amZPHEl5u0CrKINDgKOSKNRVxZl9XeXwFo1cyfKXf2xG6Dz1bv54Mle62rTUSkBijkiDQWrcsHHy8CpxOA/u2b87drOgHw99lbWbIz06rqRETcTiFHpLFocSF4+UPBEcjY4jp8zyWtualnNA6nwej/riH1aIGFRYqIuI9Cjkhj4eltLgwIri4rAJvNxvM3dSO+ZTBZBSWMmrGK/KJSi4oUEXEfhRyRxuSkqeQn8/Xy4K27etM80IdtabmM/2y9BiKLSL2nkCPSmLjG5SwGZ8Vp45HBvkz7/YV4edj4flMaU37aaUGBIiLuo5Aj0phExoNPMBRlQ9qGUx7u1aopzw7pCsDLc7czd0t6bVcoIuI2CjkijYmHJ7S62Lz/my6rckP7xDIisRUAf5q5jh3pubVVnYiIW3laXYCI1LLW/WH797D8Ldi3BAwnGIb5E/PnRMPg1qAs8gqLyX/LTmmLQDxtnDjHqwn0GAZdbzGDk4hIHWQzGtnowpycHIKDg8nOziYoKMjqckRq3+FkmJoAuOH/+k3bQP/x0H2owo6I1Kjq/P1WyBFpjPb8Akd3g80O2MyfNjvYbCcds5GadZyXf9xJoQOu6hzBzb1izXMytsLSqXD8qHm90Djo/2eIvwM8vCx8YyLSUCnkVIFCjsi5mb3hEGP+uwaAyUN7MKRntPlAUR6segcWvw4FZSslB8dC/3FmV5ant0UVi0hDVJ2/3xp4LCJndG33KB68vB0Aj/xvAxv2Z5kP+ARAv4fg4Q1w9XPgHw7ZKfDtw/B6T1j5bygtsqxuEamGgqOw6QvY8Kk5Vq+eU0uOiJyV02kwasYq5m/LICrYl68e7Ed4oG/Fk0qOw+r3YdFkyEszjwW2gEv+BBcON3dCF5G6xemAg+tg5zzzdmBV2QQD4OKxcPWzlpZ3MnVXVYFCjkj15BSWcOPUxew6nE+vVqF8eE9f/LwrGWxcUghrP4RfX4Hcg+axgEi45GHodbc5M0tErJObDrvmw875sOunE2PryjVtC0d3mfeTnjb/v1sHKORUgUKOSPXtPpzH4KmLyS0spam/NyMvjmN4YhzBfpUMNi4tKgs7r0LOfvOYf7jZxdV7JHj7127xIo2VowRSl59orUnbWPFxnyBoMwDaJUG7KyG4JSx5A358wnz8hjfM1liLKeRUgUKOyPlZtvsIf/l8PalHjwPg7+3BsItacc8lrYkIqqRLqrQY1n1ktuxkp5jHgmPg1g+gZa9arFykEclKhZ1zzdaa3Quh+DeLekb1KAs1SdCyd+WzIuc9BYteNWdc3jYDOl1fG5WflkJOFSjkiJy/UoeT2RsP8eaCXWxLM//x9Pawc3OvaO67tC1xYZW00jhKYP0nsPBFM+x4eMOgF6D3H8xp6SLiHtu+g0/vAmfpiWN+zaDtlWaoaXsFBDQ/+3UMA775P1gzAzx84Pf/O7H/nQUUcqpAIUfEfQzD4OfkDP718y5W7TsGgN0G13SL4oEBbenSIvjUJxVmw6zRsO1b8/fut8N1r4K3Xy1WLtJAFWbDlL7m4P+oHtDpOjPcRPUAezUmVDtK4bMR5v9fvQPh7m+hRQ83F101CjlVoJAjUjNW7DnKmwt28nPyYdexAR2a88Blbenbuim2k1trDMPs85/3FBgOCO8CQz+EZm1rv3CRhuS7v8CKt83Bww8scc+sxpJC+OgW2Psr+IXBH36AsHbnf91zpJBTBQo5IjVry8Ec3ly4i9kbDuIs+9elV6tQRg9oyxUdwyuGnb2L4LORkJ9hDn4c8qb5X54icu72r4Z/XwkYMPwrczCxuxTmwAfXwaH15qKf9/wAQS3cd/0qUMipAoUckdqxNzOft37Zzf9W76fYYa670TEykAcGtOXablF4epQ1neccgs9HQspS8/d+D8EVE7QXlsi5cJTC9AHmzKnuQ+Gmt93/GnmH4b1BcGQnNO8EI78Dv6buf53TUMipAoUckdqVkVPIO4v28J9l+8gvdgAQ18yPvw7qyO+6RpotO44Ss+tq6RTzSXH94ZZ3ISDcusJF6pMlU+DHx8E3BB5cVbWBxdWRlQLvDDTXwGrZx2wxqqXlIOrttg5Tp04lLi4OX19fEhISWLFixWnPnT59Ov379yc0NJTQ0FCSkpLOeL6IWCs8yJfHrunEkkevZPzVF9DU35u9RwoY/dEabvzXElbsOWpOXx34HNz6PngHmH3/0/pDyjL3FWIYkLnTXN21uMB91xWxWlYq/Py8ef+qZ2ou4ACExMJdX5hhav9K+HS4uUxEHWV5S87MmTMZPnw406ZNIyEhgcmTJ/PZZ5+RnJxMePip/xU3bNgw+vXrx8UXX4yvry//+Mc/+PLLL9m8eTPR0dFnfT215IhYK6+olOm/7Gb6r7spKGvZuapzBI8M6kC78EA4vN2c/np4G9g9zWXlE+6v3jTz/EzYvQB2/wy7FpxYlBAbNG0DEZ3NQc/lP5u2BruHu96qSO34+E5Ing0xF8HI76s3i+pcpa6EGTdASQF0vQVuml7jr1svu6sSEhLo06cPU6aYzdROp5OYmBjGjh3Lo48+etbnOxwOQkNDmTJlCsOHn31FRoUckbohI6eQyfN3MHNlKg6ngd0GQ/vE8qek9oT7lJrrc2z6n3lylxvNVVd9As980ZJCSF0Gu342g82h9RUf9/A2W4p+u4x9Oc8m0LwDRHSB8M4nwk9AuNbykbpp67cwc5j5HwT3L4LwTrX32jvmwcdDzfV4+t4Lv3uxRv9/Up2/35aO7CsuLmb16tU89thjrmN2u52kpCSWLl1apWsUFBRQUlJC06aVD34qKiqiqOjETsg5OTnnV7SIuEV4kC/P39iNP/RrzYtztvHjlnQ+XpHCrLUHGHVpG+697i0CYhLgh7/B5i8hfYs5zbx5hxMXMQxI31zWUvMz7FsCpcd/80JdoO3l5i32YnPvrLwMyNhsXjNji3mNw9vM5x5aZ95O5tesLPR0hVYXQ+tLoUlIDX9CdUhehtk94eltdSVysqJc+P6v5v2L/692Aw5A+yS48S343x/Naet+YTDgkdqt4Swsbck5ePAg0dHRLFmyhMTERNfxv/71ryxcuJDly5ef9RqjR4/mhx9+YPPmzfj6nroewFNPPcXTTz99ynG15IjULSv3HuX577ayNiULgLAAbx5KuoA7og7h+flIc6Cjlz9c85K5zHx5sMnPqHihgEgz0LS53JxCGxhRtQKcDji656TwU/bz6G7gN/9M2uwQ3dtcObbtFRDdq+HNBjMM8zNe+i9ze4DmneCuLyEoyurKpNycv8GyqRDSCkYvs25BzRXT4bvx5v1r/gl9R9XIy9S77qrzDTkvvPACL774IgsWLKB79+6VnlNZS05MTIxCjkgdZBgGczal8Y8529h7xBwc3CbMnycGNOfyzY9i2/PLqU/y8oNW/U4Em/BO7m0yLy4wW3kytpjdX7sXQOb2iuf4BJmtO20vN0NP0zbue/3aVlIIGz+FZW+a7/lkIa3M2TRNW1tTm5xwcB1MvxwMJwz7n9mqYqWfJ8HCFwAb3Pxv6HaL21+i3oWc4uJi/Pz8+PzzzxkyZIjr+IgRI8jKyuKrr7467XP/+c9/8uyzzzJv3jx69+5d5dfUmByRuq/E4eTjFSm8Nm8HR/LNmRt9YoN4LfJ7Wuz4GEJbmWGizeUQ0xc8fWq3wOz9ZivSrp/M1o7jxyo+Hhp3opUnrn/96NrKy4CV/4aV70BBpnnMyx96DoNON8DXY+HYHgiMgrtmQXhHS8tt1JwOc9G/g2uhy01w63tWV2S2/H33F1g5HexecOcn5j5ZblTvQg6YA4/79u3LG2+8AZgDj2NjY3nwwQdPO/D4xRdf5LnnnuOHH37goosuOqfXU8gRqT9yC0t4u2wmVmGJuaDgwC4RPPq7TrSubBNQKzgdZgvPrp/M4JO6rOLGiDYPszur7RVmIPNrCr7B5hgXnyDru7nSNsGyf8HGz8BRNhU4OMYcSHrh8BMBLTcNZgyBw1uhSVNzGnGLnlZVXfdlH4BNn5sB0d0tX8vfMsfi+ATBgyshMNK9168upxO++KM5YcAvDB7e4NY1dOplyJk5cyYjRozgrbfeom/fvkyePJlPP/2Ubdu2ERERwfDhw4mOjmbSpEkA/OMf/2DChAn897//pV+/fq7rBAQEEBAQcNbXU8gRqX/Scwp5de52Pl2VitMALw8bwxPj+L8r2hPs52V1eRUV5cLexWWh5yc4suPM53sHmIHHN/jErclvfi8PRUFREBJnBqXz6ZJzOmHHj+Z4jpO7AFv2gYtGm3+YKwtfBUfhPzfDwTXmZo13zoS4fqee15gZBmz8HL77s7lZpncgXD/Zfd03OQfNDTiLc+Hal6HPH91zXXcpLYZZ90Pf+yA2wa2XrpchB2DKlCm89NJLpKWl0aNHD15//XUSEswPZ8CAAcTFxfH+++8DEBcXx759+065xsSJE3nqqafO+loKOSL11/b0XCZ9t9W1CWionxcPJ13AnQmxeHnUibVNT5WVanZp7ZwPh5PNP3yF2VCSX/1regeYi7KFxJrjZEJbnbgfEnv67rHifFj3X1g+zVyaH8yWps43wEVjIKbP2V+7KBf+ezvsWwSevjD0P9D+quq/l4ak4CjMHmfOBgTwCYaibPN+z9+bU6zPt2Xj0+Gw5Stz4Ps9c2tnTZw6ot6GnNqkkCNS/y3cfphnv93Cjow8ANqFB/D4tZ24vEM92gbCUWJueliYVRZ8sk4EoJNvx8uOHz8GOQcg99DZr+0bfFLoKQtBOQdg9Qfm64D5B7jXcPO/uENizq32kuPw2d2wfY65PstN06HrTed2jZp0PAvSNpjdiLlpZm3RvWr2NXfMg6/GQF6a+Zlc9og5rXvRK7DwRcCAsAvM7Uoiu1XvNbb/AP+9zQym9y2s/nXqKYWcKlDIEWkYSh1OPl6Zyis/JnOsoASASy9ozhPXduKCiLMsGliflRRCdipk7YNj+8y9hE6+Xz5o+HRCW8NFD0CPO8++uOKZOErgy/vM8Rc2O1z/mjmGp7blHylb22h92W0dHNt76nkXDIIBj7p/HFFxPsydYA7aBjPI3PgWRF944pw9v8IXo8yA6uFjbmHS54/n1uVYnA9TL4LsFLh4rLkSeCOjkFMFCjkiDUv28RKm/LSD95fspcRh4GG3cWffWP50lblPVqNTlGeGoGP7zPCTlXLij36PO80/9u7ausLpMLtnVr9v/j7weUgc455rVyY37aQws96cRu3aquM3QlpBVLzZqrJlljnVGqDDNWbYiYo//3r2r4Iv7oWju8zfE+6HpKfMBSd/K/8IfDXabP0C6HiduYp3VXfxnjsBFr9mDgofs7zWNsWsSxRyqkAhR6Rh2puZz/PfbeXHLekABPp68tCV7RmeGIe3Z+MZt1DrDMP8A7zkdfP3yx41Q8T5rlVkGOb6RNu+NfdJOrQO8tIrP7dZOzO0lN8iu1cMD5k74ZcXzRlk5WGn43VmndXp8nGUmF1Qv74MhgOComHwVHOdpLO9p+XT4McnwVkCQS3NNWVaJZ75eWmb4K1Lzde64xPo8Ltzr7kBUMipAoUckYZtya5Mnv12K1sOmVu4xDXz47FrOnF15whs2n+qZhgG/PpP+KmsC+Wi0Warzrl+3oZhrv2y9RvY+vWJwdHlbHYI6/CbQNMNfKv4b3nmDlj4D3P2U/kq1p2uhwGPmfuVVcXhZLP1pnzrj263mqtwNwmt2vPBfI+f/8FcTdtmN1+//58rb2FzOuHdq80dvztdbw70bqQUcqpAIUek4XM4DT5fncpLP2wnM89c8TyxTTOeuK4TXVoEW1xdA1a+fguYs4muf/3sXWNOB6QuLws235hdbeU8fE4sqtiihxlE3NFNczjZDDubvsAVdjoPNluhIjqfpk6nuT/TvIlQWmhO6b/uFeh6c/VqKMqF2eNhwyfm73H94aa3IahFxfNWvmN2CXoHwJgVEBxdvddrABRyqkAhR6TxyCsq5c0FO5n+6x6KS53YbHDLhS0Zc3k74urKYoINzbr/mrOMDCd0HmLOvPrtxp6OEtj7K2z5GrbNrrj/mJe/OSW98w3Q/urzGxx9NhlbYcEL5pgdAGzQZYgZdk5e0Tl7P8waDXsWmr+3vRIGTzk1kFTHuo9h9p/NJQWaNIUbp8EFA83HctNhSh9zGvqgF8wB442YQk4VKOSIND6pRwv4x5xtfLvBnH5tt8H18S0YPaAdHSIb8Ewsq2z52uyOcZaYS/vf9qHZLbPrJ7O1Jvm7E1PZwZzy3uEaszum7RWVD9ytSembzbCz9euyAzazheayR8wBzrP/bAYNzyZw9d/PfWbU2WTuhM/vhrSN5u8XjTYHMM8aba6aHNUDRv3kvgHj9ZRCThUo5Ig0XmtSjvHG/B2uxQQBru4cwZjL2xEfE2JdYQ3RzvnwyTAoPW5uWJqXAcV5Jx73b24O/u10vbm5qUcdWLk6baMZdrZ9e+pj0b3MqeFh7WvmtUuLzAHcy6eZvzdrZ45JstnNgKMtNBRyqkIhR0Q2HcjmXwt28v2mNMr/BezfPowxl7cjoXVTDVB2l5Rl8NFtJ1b9DWpphprON0BMQt1tmTi03gw7yd+ZC+9d9og5MLg29hnb9p051bx809eE++F3/6j5160HFHKqQCFHRMrtzMjlXwt28dW6gzic5j+FvVuFMuaKdgy4oLnCjjscTjbH3bS5DFpceM7dPAXFpfh4euBht+B/i4xt5jo7Ye1q93WzD8B3482Wr6EfVX32WAOnkFMFCjki8lupRwuYtnAXn63aT7HDXEela3QQYwa0Y2CXSOxW/IFtZAzDIC2nkM0HcthyKIctB82fKUcLaB7ow8h+cQxLaEVwkzrQrSWWUMipAoUcETmd9JxCpv+ym4+Wp3C8xAGY+2KNHtCW6+NbVHsT0FKHk7yiUgpLnDgNA6dhYBjmsjAGBk6Dk44ZGJi/O53m4+X/Ssc28yPIt/7/kS91ONl1OJ8th7JdYWbLwRzX9hyn4+/twZ0JsfzhktZEBdfy4GSxnEJOFSjkiMjZHM0v5r3Fe3h/yV5yC0sBaBnahPsubUN0aBNyC0vJLSwlr6iUvLKf5rES81jZ8dwi81hhidMtddls0D48gJ4xoVzYKoQLY0Np2zygTrc0FRSXVggymw/mkJyeS3HpqZ+Jh91G+/AAOkcF0blFEJ2jgmgXEcCiHZm8tXA3yem5AHjabQzuEc29l7bR7LhGRCGnChRyRKSqcgpL+HDpPt5dtIcj+cXnfT0Puw0Pmw1s5jR2u82GjbKfNrDZbCeO/+b3UqfB4dyiU64Z6OtJjxgz8FzYKpQeLUMI9rOmtcfhNNiZkce61GOsS81iXWo229NzXeOdThbg41khzHRuEUS78AB8vSofjGwYBgu2H+athbtYtvuo6/gVHcO579I29NWA8QZPIacKFHJE5FwdL3bwycoUvlhzADD/QAf6ehLg60mgj/kzwMeLQN+y4z5lN19PAsuO+/t4nvceWodzi1ibcoy1qVms2XeMDfuzXd1qJ2sXHsCFsSH0jA3lwthQ2ofXTGtPWnZhWZjJYl3qMTbuzya/+NR6IoN86dLiRKDp0iKYlqFNql3T2pRjvP3LbuZsPjE7rkdMCPdf1oarOkdaM0hZapxCThUo5IhIQ1HqcLItLZe1KcdYk5LFmpRj7DtScMp5gT6edIoKItjPi8CygBbo6+X6GVAWzoJOOh7g44m/t6criOQXlbJhfzbr92exLsUMNmk5hae8lr+3B91bhhAfE0KPmBB6xoYQEeRbI+9/T2Y+03/dzeer97u6v1qH+TOqfxtuujD6tK1CUj8p5FSBQo6INGSZeUWsKws8a1KOsT618taeqrDZcIWdjNxCftvrZLdBh8ggesQE0yMmhB4xobQLD6j1lpTDuUXMWLqXGUv3kX3cHLwcFmDOyPp9QivLuu/EvRRyqkAhR0Qak1KHk+T0XHZm5FUYIF0+eLrC70Ul5oDpwlJKKxlH0yLY19VC0yMmhG4tg/HzroUF8qoov6iUT1am8s6vuzmYbbYy+Xl7MKRnNHf2jaVrtDZnrc8UcqpAIUdE5MwMw6CwxEluYQk5ZbPHooJ9a6zbyd1KHE6+3XCQtxbuZltarut495bB3NE3luvjWxDgU3fCmVSNQk4VKOSIiDQOhmGwdPcRPl6Ryg+b0lwLPfp7ezC4Blp3nE6DLYdy+HVHJr/uOMy2tFwSWjflrotakdi2mWZ/nSeFnCpQyBERaXyO5BXxxZoDfLwihd2Z+a7j59u6k5FT6Ao1i3ZmkplX+VIDbZr7MyyhFbdc2FJjhKpJIacKFHJERBovwzBYtvso/12RUq3WncISByv3HuWX7Yf5dUdmhe6w8usktm1G//bN6RAZyOwNh/hy7QHyisxFJX297NwQ34K7LoqjW0uNEToXCjlVoJAjIiJQtdYdf28PktNz+XV7Jr/sOMyKPUcpOmm1ZpsNukUHc2n75vRvH0bP2NBT1kPKKypl1toD/GfZvgqhKL5lML+/qBXXx7fQdPcqUMipAoUcERE5WXnrzscrUpjzm9Ydfx9PMn6z0nRkkC/924dx6QXN6dcujKb+3lV+nTUpx/hw6T6+23jidYKbeHFLr5YMS4ilTfMA9765BkQhpwoUckRE5HSO5hfzv9X7K7Tu+HrZSWjdjEsvaM6l7cNoFx5w3oOIj+QV8emq/fx3xT5Sjx53Hb+kXRi/v6gVSZ3C8azmhrANlUJOFSjkiIjI2ZitLlkUlTq4MDa0xrqTHE6DX7Yf5j/L9vFTcoZrm4rIIF+uj4+ifUQgbZv70yYsgNAqthg1VAo5VaCQIyIidVHq0QI+XpHCzJWplW4IG+rnRZvmAbQJ8zd/NvenbXN/Ypv6n/e+aPWBQk4VKOSIiEhdVlTq4IfN6azcc5TdmXnsPpzPoexT9wkr52G3ERPa5JQA1L2OrUh9vhRyqkAhR0RE6puC4lJ2H85nd2Y+uw/nld3PY8/h/Ep3fgfw8rDRq1Uo/ds355J2YXSNDq7XO7Qr5FSBQo6IiDQUhmGQkVvErvLgUxZ+tqfluvbvKhfi58XFZWv4XNIujJimfhZVXT0KOVWgkCMiIg2dYRjsPVLAoh3mooVLdx0ht2xBwnJxzfy4pH0Yl7RrTmLbZgQ3qdsrMSvkVIFCjoiINDalDifr92eb20/syGRtahaOk3aa97DbiG8ZzCVlixr2iAnBq45NYVfIqQKFHBERaexyC0tYtvuoq6Xn5BWfwVzJOcDHkyBfLwJ8PAn09STA15NAXy8CfT0JLDsW+JvHg8oeD27iRYife6e8V+fvd8MZdi0iIiJVEujrxVWdI7iqcwQAB7KOuwLP4p2ZHCsoIbewlNzC0rNcqXJdo4P4dmx/d5ZcLQo5IiIijVx0SBOG9ollaJ9YnE6DI/nF5BaeCDp5RSXklN8vLD3xWNGJc3ILS8grMu8H+daN8T0KOSIiIuJit9toHuhD80Cfal/D6awbI2Hq1qgiERERqffsdWQ9HoUcERERaZAUckRERKRBUsgRERGRBkkhR0RERBokhRwRERFpkBRyREREpEFSyBEREZEGSSFHREREGiTLQ87UqVOJi4vD19eXhIQEVqxYcdpzN2/ezM0330xcXBw2m43JkyfXXqEiIiJSr1gacmbOnMm4ceOYOHEia9asIT4+noEDB5KRkVHp+QUFBbRp04YXXniByMjIWq5WRERE6hObYRiWbTCRkJBAnz59mDJlCgBOp5OYmBjGjh3Lo48+esbnxsXF8fDDD/Pwww+f8byioiKKiopcv+fk5BATE3NOW7WLiIiItXJycggODj6nv9+WteQUFxezevVqkpKSThRjt5OUlMTSpUvd9jqTJk0iODjYdYuJiXHbtUVERKTusizkZGZm4nA4iIiIqHA8IiKCtLQ0t73OY489RnZ2tuuWmprqtmuLiIhI3eVpdQE1zcfHBx+fE9vFl/fO5eTkWFWSiIiInKPyv9vnMsrGspATFhaGh4cH6enpFY6np6fX6KDi3NxcAHVbiYiI1EO5ubkEBwdX6VzLQo63tze9evVi/vz5DBkyBDAHHs+fP58HH3ywxl63RYsWpKamEhgYiM1mc+u1ywc1p6amalDzOdDndu70mVWPPrfq0edWPfrczt2ZPjPDMMjNzaVFixZVvp6l3VXjxo1jxIgR9O7dm759+zJ58mTy8/MZOXIkAMOHDyc6OppJkyYB5mDlLVu2uO4fOHCAdevWERAQQLt27ar0mna7nZYtW9bMGyoTFBSkL3Q16HM7d/rMqkefW/Xoc6sefW7n7nSfWVVbcMpZGnKGDh3K4cOHmTBhAmlpafTo0YM5c+a4BiOnpKRgt58YG33w4EF69uzp+v2f//wn//znP7nssstYsGBBbZcvIiIidZjlA48ffPDB03ZP/Ta4xMXFndOAIxEREWm8LN/WoSHx8fFh4sSJFWZzydnpczt3+syqR59b9ehzqx59bufO3Z+ZpSsei4iIiNQUteSIiIhIg6SQIyIiIg2SQo6IiIg0SAo5IiIi0iAp5LjJ1KlTiYuLw9fXl4SEBFasWGF1SXXaU089hc1mq3Dr2LGj1WXVOb/88gvXX389LVq0wGazMWvWrAqPG4bBhAkTiIqKokmTJiQlJbFjxw5riq1Dzva53X333ad8/wYNGmRNsXXEpEmT6NOnD4GBgYSHhzNkyBCSk5MrnFNYWMiYMWNo1qwZAQEB3HzzzadszdPYVOVzGzBgwCnft/vvv9+iiuuGN998k+7du7sW/UtMTOT77793Pe6u75pCjhvMnDmTcePGMXHiRNasWUN8fDwDBw4kIyPD6tLqtC5dunDo0CHXbdGiRVaXVOfk5+cTHx/P1KlTK338xRdf5PXXX2fatGksX74cf39/Bg4cSGFhYS1XWrec7XMDGDRoUIXv38cff1yLFdY9CxcuZMyYMSxbtoy5c+dSUlLC1VdfTX5+vuucP/3pT3zzzTd89tlnLFy4kIMHD3LTTTdZWLX1qvK5AYwaNarC9+3FF1+0qOK6oWXLlrzwwgusXr2aVatWccUVVzB48GA2b94MuPG7Zsh569u3rzFmzBjX7w6Hw2jRooUxadIkC6uq2yZOnGjEx8dbXUa9Ahhffvml63en02lERkYaL730kutYVlaW4ePjY3z88ccWVFg3/fZzMwzDGDFihDF48GBL6qkvMjIyDMBYuHChYRjmd8vLy8v47LPPXOds3brVAIylS5daVWad89vPzTAM47LLLjMeeugh64qqJ0JDQ41///vfbv2uqSXnPBUXF7N69WqSkpJcx+x2O0lJSSxdutTCyuq+HTt20KJFC9q0acOwYcNISUmxuqR6Zc+ePaSlpVX47gUHB5OQkKDvXhUsWLCA8PBwOnTowAMPPMCRI0esLqlOyc7OBqBp06YArF69mpKSkgrft44dOxIbG6vv20l++7mV++ijjwgLC6Nr16489thjFBQUWFFeneRwOPjkk0/Iz88nMTHRrd81y7d1qO8yMzNxOByu/bbKRUREsG3bNouqqvsSEhJ4//336dChA4cOHeLpp5+mf//+bNq0icDAQKvLqxfS0tIAKv3ulT8mlRs0aBA33XQTrVu3ZteuXfztb3/jd7/7HUuXLsXDw8Pq8izndDp5+OGH6devH127dgXM75u3tzchISEVztX37YTKPjeAO++8k1atWtGiRQs2bNjAI488QnJyMl988YWF1Vpv48aNJCYmUlhYSEBAAF9++SWdO3dm3bp1bvuuKeSIJX73u9+57nfv3p2EhARatWrFp59+yj333GNhZdIY3H777a773bp1o3v37rRt25YFCxZw5ZVXWlhZ3TBmzBg2bdqkcXLn6HSf27333uu6361bN6KiorjyyivZtWsXbdu2re0y64wOHTqwbt06srOz+fzzzxkxYgQLFy5062uou+o8hYWF4eHhccqo7/T0dCIjIy2qqv4JCQnhggsuYOfOnVaXUm+Uf7/03Tt/bdq0ISwsTN8/zE2Tv/32W37++WdatmzpOh4ZGUlxcTFZWVkVztf3zXS6z60yCQkJAI3+++bt7U27du3o1asXkyZNIj4+ntdee82t3zWFnPPk7e1Nr169mD9/vuuY0+lk/vz5JCYmWlhZ/ZKXl8euXbuIioqyupR6o3Xr1kRGRlb47uXk5LB8+XJ9987R/v37OXLkSKP+/hmGwYMPPsiXX37JTz/9ROvWrSs83qtXL7y8vCp835KTk0lJSWnU37ezfW6VWbduHUCj/r5Vxul0UlRU5N7vmnvHRjdOn3zyieHj42O8//77xpYtW4x7773XCAkJMdLS0qwurc7685//bCxYsMDYs2ePsXjxYiMpKckICwszMjIyrC6tTsnNzTXWrl1rrF271gCMV155xVi7dq2xb98+wzAM44UXXjBCQkKMr776ytiwYYMxePBgo3Xr1sbx48ctrtxaZ/rccnNzjfHjxxtLly419uzZY8ybN8+48MILjfbt2xuFhYVWl26ZBx54wAgODjYWLFhgHDp0yHUrKChwnXP//fcbsbGxxk8//WSsWrXKSExMNBITEy2s2npn+9x27txpPPPMM8aqVauMPXv2GF999ZXRpk0b49JLL7W4cms9+uijxsKFC409e/YYGzZsMB599FHDZrMZP/74o2EY7vuuKeS4yRtvvGHExsYa3t7eRt++fY1ly5ZZXVKdNnToUCMqKsrw9vY2oqOjjaFDhxo7d+60uqw65+effzaAU24jRowwDMOcRv7kk08aERERho+Pj3HllVcaycnJ1hZdB5zpcysoKDCuvvpqo3nz5oaXl5fRqlUrY9SoUY3+P0oq+7wA47333nOdc/z4cWP06NFGaGio4efnZ9x4443GoUOHrCu6Djjb55aSkmJceumlRtOmTQ0fHx+jXbt2xl/+8hcjOzvb2sIt9oc//MFo1aqV4e3tbTRv3ty48sorXQHHMNz3XbMZhmFUs2VJREREpM7SmBwRERFpkBRyREREpEFSyBEREZEGSSFHREREGiSFHBEREWmQFHJERESkQVLIERERkQZJIUdEREQaJIUcEWn0bDYbs2bNsroMEXEzhRwRsdTdd9+NzWY75TZo0CCrSxORes7T6gJERAYNGsR7771X4ZiPj49F1YhIQ6GWHBGxnI+PD5GRkRVuoaGhgNmV9Oabb/K73/2OJk2a0KZNGz7//PMKz9+4cSNXXHEFTZo0oVmzZtx7773k5eVVOOfdd9+lS5cu+Pj4EBUVxYMPPljh8czMTG688Ub8/Pxo3749X3/9dc2+aRGpcQo5IlLnPfnkk9x8882sX7+eYcOGcfvtt7N161YA8vPzGThwIKGhoaxcuZLPPvuMefPmVQgxb775JmPGjOHee+9l48aNfP3117Rr167Cazz99NPcdtttbNiwgWuuuYZhw4Zx9OjRWn2fIuJm7ts4XUTk3I0YMcLw8PAw/P39K9yee+45wzAMAzDuv//+Cs9JSEgwHnjgAcMwDOPtt982QkNDjby8PNfjs2fPNux2u5GWlmYYhmG0aNHCePzxx09bA2A88cQTrt/z8vIMwPj+++/d9j5FpPZpTI6IWO7yyy/nzTffrHCsadOmrvuJiYkVHktMTGTdunUAbN26lfj4ePz9/V2P9+vXD6fTSXJyMjabjYMHD3LllVeesYbu3bu77vv7+xMUFERGRkZ135KI1AEKOSJiOX9//1O6j9ylSZMmVTrPy8urwu82mw2n01kTJYlILdGYHBGp85YtW3bK7506dQKgU6dOrF+/nvz8fNfjixcvxm6306FDBwIDA4mLi2P+/Pm1WrOIWE8tOSJiuaKiItLS0ioc8/T0JCwsDIDPPvuM3r17c8kll/DRRx+xYsUK3nnnHQCGDRvGxIkTGTFiBE899RSHDx9m7Nix3HXXXURERADw1FNPcf/99xMeHs7vfvc7cnNzWbx4MWPHjq3dNyoitUohR0QsN2fOHKKioioc69ChA9u2bQPMmU+ffPIJo0ePJioqio8//pjOnTsD4Ofnxw8//MBDDz1Enz598PPz4+abb+aVV15xXWvEiBEUFhby6quvMn78eMLCwrjllltq7w2KiCVshmEYVhchInI6NpuNL7/8kiFDhlhdiojUMxqTIyIiIg2SQo6IiIg0SBqTIyJ1mnrURaS61JIjIiIiDZJCjoiIiDRICjkiIiLSICnkiIiISIOkkCMiIiINkkKOiIiINEgKOSIiItIgKeSIiIhIg/T/t91GK+Q5EC8AAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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EmrmD68yem21fm9/b/GDIFPN/8KGx1c/P20f++i84tv4L2mUvxdc4vjptseHPj46+LHAMZk/kCLp360GCTz4D0mcxosBsP98I4rmK3/GefTR2js9SSowINHt7YkPpWfk1LNCH1xfv5IPlGa7i2tG9Ypg+JoXeCbX8e3J0Dyx/3azbOXHfJ/8wcyXhatsNnPj1V6/7hzbsvW2JDMPze1tJi6Qkpw6U5Ig0U4e2mhs3bpprfm+xwcCr4ax7ICKpyqmZeSUs23WYn3ceYdnOw+zMKQIggFJGWDdyrnU15/muIdo4XPUecf3NlX0ri4QrBlzD1r53sjHPj61ZBaRnFZKemU9W/q+Ghmowslt77jwvhcEdI+v285UWQv7+yo0gI1UwK+ImbesgIi3P0d3mejTrZlcO61ig72Vwzv3QvqvrtB2HCvl45T6+XH+Q3YerrmBrsUCvuDBSu7QjtfMIUju3IzLIFzLXVxbqfg37fjEXxgNzrZgL/oJPh6H0Bnr/KqTc4rLKpfuPL+OfnllAfkkFgztGcNfYFEZ0jXLv5/QPgegUd98dEWkA9eSIiHfkH4DFz8Kqd8FROeun54VmchPbBzCTjf+uPcBHq/azdm+u61KrBXonhHFa5/akdmnP8OR2p17srfAQ7FgAfsGQcoHbeyQZhkFhaQUh/j7NZxaSSBuinhwRaf6KcuDHF8x9oCpKzGNdR8GoByFxCOV2Bz9syuLjVftYsDnbtUidzWrhrB7RXDIosX4zg0KiYcCV9Q7bYrGctBhZRJofJTki0rgMAw5vPz4bas9Px1cJ7pgGox6C5JFsPJDHx//dxGdr95NTeLx4uGdcKL8d0oGLByYQExrgpR9CRFoiJTki4nkVZZDx0/GNIY/srPp6wmA45wEOxZ7OvLUH+GjuYrZkHp951D7Yj/EDE7lsSCJ9EsKbOHgRaS2U5Ii0RCvfNlfn9Q89xRTkyq8B4W7XoLit8BBs/9ZMarYvrDpd2uYHyadDj3GUJI9mQVYQHy/Zxw9bF2KvXGjGz2bl3F4xXDa4A2elROPrzi7WIiI1UJIj0tJs+RL+ezvuLShngcCIWpKgyNqTI9/AWls0HA4K966lYvNX+O34hqBDa7CcEFOBTzvWB6WyzHcYP9r7cnCfD0e3lnOsfEuVdgYkRfDbwYlcNCCBiCCtZCsinqMkR6QlObgOPr4OMGDAVeY2ASfdHuBoZY9K5d5Hx47CkR11v59PYJVkqMgWzoajNg4eKSTVsYp4y5Eqp693JLPQMZgF9kGsL+mMUejsjamofJjiwwOYMCiRywZ3oFtMSEPfFRGRGinJEWkpCjLNJe7Li8y9kC7+W90WlKsoq0xwTpEMnbhn0rEj5rTuimPmAnb5+wEIBlKd7VrgmOHHUvqz3HcYm0JScYQkEBHky4AgP84O8iUiyI/I4MqvQX5EVh4LC9A0bBFpfEpyRFqC8mPwwVVmshHVA373Tt1XzPXxM7dDqGlLhNoYBkZpPmvTdzF36Xp2ZuwlgkIiLYUMbO9gaIcgQlLOIrD7WYwKCmFU/X4qEZFGpSRHpLlzOODTm+DAKnM7gKtmm/U1jXY7g4Vbsnn1hx2s3HMUaIfV0o7f9E/g8rO6aLaTiLQYSnJEmrtFM839nKy+cMV7VbY68KRyu4P/rj3Aaz/sYGtWIWDOePrt0A7ccEYXkqOCG+W+IiKNRUmOSHO27j+w+Bnz+UWzzGnYHnaszM5/ftnL64t3sj/3GAAh/j5cc1pHrh3ZmZgwLcAnIi2TkhyR5ipjGcybaj4feRsM+j+PNp9XXM67S3fz1k+7OVJkrjAcFeLHlJGd+b/TOrm/bYKISDOjJEekOTq6B2ZfDfYyc9PKcx/xWNMOh8Gs77byxo+7KCozt1foEBnIjWd24XdDkwjwbeRFA0VEmoiSHJHmpiTfnCpenANx/eHS18HqudV/31yyixcXbgfMfaFuPrsrv+kXj49WGBaRVkZJjkhzYq+Aj/4A2ZsgJM6cSeXnuYLfHYcKefbrdADuv6An15/RRevViEir5fX/ur388sskJycTEBBAamoqy5cvr/Xc8vJyHnvsMbp27UpAQAADBgxg/vz5TRitSCP75kFz/yefQLjqAwhP9FjTdofBXR+upbTCwRndo5TgiEir59UkZ86cOUyfPp2HH36YVatWMWDAAMaOHUt2dnaN5z/44IP8/e9/529/+xubNm3ipptu4pJLLmH16tVNHLlII1jxBix71Xx+yWuQONijzf/jfztZnZFLqL8Pf76svxIcEWn1LIZhuLPLn0elpqYybNgwXnrpJQAcDgdJSUnceuut3HvvvdXOT0hI4IEHHmDq1KmuY5dddhmBgYG89957dbpnfn4+4eHh5OXlERYW5pkfRKShdnwP710Ghh1GPQRn3uXR5rdmFXDhiz9SZnfwzGX9uXxYkkfbFxFpbPX5/e21npyysjJWrlzJ6NGjjwdjtTJ69GiWLl1a4zWlpaUEBFRdsyMwMJAff/yx1vuUlpaSn59f5SHSrBzaCv+ZZCY4/a+EM+70aPPldgd3/mctZXYH56RE87uhHTzavohIc+W1JCcnJwe73U5sbNX9dGJjY8nMzKzxmrFjx/L888+zbds2HA4H3377LZ988gkHDx6s9T4zZ84kPDzc9UhK0v9gpRkpPgLvXw6leZB0Glz8Inh4GOm1RTtYvz+PsAAfntYwlYi0IV4vPHbHX//6V7p3707Pnj3x8/Nj2rRpTJkyBetJptfed9995OXluR579+5twohFTqKiDOb8HxzdBRGd4Mp/g4+/R2+x6UA+Ly7cBsCj4/sQq9WLRaQN8VqSExUVhc1mIysrq8rxrKws4uLiarwmOjqauXPnUlRUxJ49e9iyZQshISF06dKl1vv4+/sTFhZW5SHidYYBn98Be5aAfxhcPQeCozx6i7IKB3d+uJZyu8F5vWOZMNBzM7VERFoCryU5fn5+DBkyhAULFriOORwOFixYQFpa2kmvDQgIIDExkYqKCj7++GPGjx/f2OGKeNaOBbDmPbBY4bdvQUwvj9/ipYXb2Hwwn8ggX568pJ+GqUSkzfHqYoDTp09n0qRJDB06lOHDhzNr1iyKioqYMmUKABMnTiQxMZGZM2cCsGzZMvbv38/AgQPZv38/jzzyCA6Hg7vvvtubP4aI+9bONr8O/QN0H33yc+th/b48Xl60A4DHJ/QlOtSzw2AiIi2BV5OcK664gkOHDjFjxgwyMzMZOHAg8+fPdxUjZ2RkVKm3KSkp4cEHH2Tnzp2EhIRwwQUX8K9//YuIiAgv/QQi9VBaCFu+MJ8PuMrzzVfYufPDNdgdBr/pH8+F/RM8fg8RkZbAq+vkeIPWyRGvW/cf+OR6iOwMf1zt8dlUf56/hVcX7SAqxI9v7jiLdsF+Hm1fRMQbWtQ6OSJt1voPza/9fufxBGdVxlH+/oM5TPXEhH5KcESkTVOSI9KUinJge2Wxff/LPdp0Sbmduz5ci8OACQMTGNe35lmKIiJthZIckaa08VNzZeP4gRDV3aNN/+XrdHYeKiIm1J9HL+7r0bZFRFoiJTkiTWndf8yvHu7FWbH7CG8s2QXA05f1IzzI16Pti4i0REpyRJrKkV2wbzlggT6XeqzZ4rIK7vpwLYYBvxvSgVE9Y099kYhIG6AkR6SprP/I/Nr5TAiL91izz8xPZ8/hYhLCA3joot4ea1dEpKVTkiPSFAwD1nt+qOqnHTm8/dNuAP782/6EBWiYSkTESUmOSFPIXAc5W8HmD70u8kiThaUV3P3ROgCuTu3IGd2jPdKuiEhroSRHpCk4C45TxkFAuEeafOrLzew7eowOkYHcf4Hn974SEWnplOSINDaHHTZ8bD7v55mhqsVbD/H+sgwAnvltf0L8vbpDi4hIs6QkR6Sx7f4RCg6aPTjdx3ikyWe/Tgdg8ohkRnSN8kibIiKtjZIckcbm3Mah93jwafhu4FuzCli/Pw9fm4U/nuvZBQVFRFoTJTkijam8BDZ9Zj730FDVx6v2AXBOSoz2phIROQklOSKNads3UJoHYYnQaWSDm7M7DOau3g/ApYM7NLg9EZHWTEmOSGNyro3T9zKwNvyv2087csjKLyUiyJdzemrKuIjIySjJEWksx3Jh69fm836/80iTn6wye3Eu6p+Av4/NI22KiLRWSnJEGsvm/4K9DKJ7Qly/BjdXWFrB/A2ZAFw6OLHB7YmItHZKckQai3Ooqt/vwGJpcHPzN2RyrNxOl6hgBiZFNLg9EZHWTkmOSGPIPwC7/mc+99hQlTmr6tLBiVg8kDSJiLR2SnJEGsOGjwEDkk6DyE4Nbm5/7jGW7jwMwCWaVSUiUidKckQag3Ovqv6e6cWZu3o/hgFpXdqTGBHokTZFRFo7JTkinnYo3dx13OoDvS9pcHOGYbgWAFTBsYhI3SnJEfE05zYOXc+F4PYNbm7tvjx2HioiwNfK+f3iG9yeiEhboSRHxJMM43iS098z2zg4C47H9YnTbuMiIm5QkiPiSftWwNHd4BsMKec3uLmyCgefrT0AaBsHERF3KckR8SRnwXGvC8EvuMHNfZ+eTW5xOTGh/ozsFtXg9kRE2hIlOSKeYi+HjZ+azz20Ns7HK82hqksGJWKzam0cERF3KMkR8ZSdi6A4B4KioMs5DW7uSFEZ36dnAxqqEhGpDyU5Ip7iHKrqeynYGl4g/Pm6A5TbDfomhpESF9rg9kRE2holOSKeUFYEW74wn/fzzKyqjyt3HL90kHpxRETqQ0mOiCds+RLKiyCyM3QY2uDmtmcXsnZvLjarhYsHJnggQBGRtkdJjogneHjH8U9XmwXHZ/eIJirEv8HtiYi0RUpyRBqqKAe2LzCfe2BWlcNh8KlzqEoFxyIi9aYkR6ShNn4Khh3iB0B0jwY39/POwxzIKyE0wIdze8V4IEARkbZJSY5IQzm3cfBwwfGF/RMI8LV5pE0RkbZISY5IQxzdDXuXARboe1mDmysuq+CrDQcBuEw7jouINIiSHJGGcPbidD4Twhq+Q/jXGzMpLrPTqX0QQzpFNrg9EZG2TEmOSH0ZBqxzDlV5ZhuHT05YG8figVlaIiJtmZIckfrKXA856WDzh94XN7y5vBJ+3J4DmHtViYhIwyjJEakv59o4PcZCQHiDm/t09X4MA4Ynt6Nj+6AGtyci0tYpyRGpr+0Lza99L21wU4Zh8MkqcwHAS1VwLCLiEUpyROqr2Bxaol3XBje1YX8+27IL8fOxckH/hhcwi4iIkhyR+jEMOHbUfB4Y0eDmPq7sxTmvdyxhAb4Nbk9ERJTkiNRP+TGwl5nPAyIa1pTdwWdrDwBwmbZxEBHxGCU5IvVRkmt+tdjAP7RBTf2QfogjRWVEhfhzRveohscmIiKAkhyR+jmWa34NCG/wruOfVO44PmFgAj42/ZUUEfEU/YsqUh/OnpwG1uPkFpfx3aZsQDuOi4h4mpIckfpw9uQENmzrhc/XHaTM7qBnXCi9E8IaHpeIiLgoyRGpD+fMqgYWHTvXxlHBsYiI5ynJEakPDwxX7copYlVGLlYLjB+Y4JGwRETkOCU5IvXhKjyOqHcTn1b24pzRPZqYsICGxyQiIlUoyRGpjwb25DgcBp+srtxxXNs4iIg0CiU5IvXRwMLjVRlH2Xf0GCH+PpzXO85zcYmIiIuSHJH6cPbk1HO4alH6IQDO6RlDoJ/NMzGJiEgVSnJE6qOB+1b9b5uZ5JypFY5FRBqNkhyR+mhA4fHRojLW7c8DzKJjERFpHEpyROqjAYXHS3bkYBjQIzaEuHDNqhIRaSxKckTcZRgN6sn539YcQL04IiKNTUmOiLvKi8FRbj53c3aVYRiuehztOC4i0riU5Ii4y9mLY/UBv2C3Lt1xqIgDeSX42aykdm7v+dhERMRFSY6Iu07ct8picevSHyt7cYZ1jtTUcRGRRub1JOfll18mOTmZgIAAUlNTWb58+UnPnzVrFikpKQQGBpKUlMQdd9xBSUlJE0UrQoOKjv+3TfU4IiJNxatJzpw5c5g+fToPP/wwq1atYsCAAYwdO5bs7Owaz3///fe59957efjhh9m8eTNvvPEGc+bM4f7772/iyKVNq2fRcVmFg6U7DwOqxxERaQpeTXKef/55rr/+eqZMmULv3r157bXXCAoK4s0336zx/J9++omRI0dy9dVXk5yczHnnncdVV111yt4fEY+qZ0/OqoyjFJfZiQrxo1dcmMfDEhGRqryW5JSVlbFy5UpGjx59PBirldGjR7N06dIarxkxYgQrV650JTU7d+7kyy+/5IILLqj1PqWlpeTn51d5iDRIPfetcs6qOr1bFFare7U8IiLiPh9v3TgnJwe73U5sbGyV47GxsWzZsqXGa66++mpycnI4/fTTMQyDiooKbrrpppMOV82cOZNHH33Uo7FLG1fPfatUjyMi0rS8XnjsjkWLFvHUU0/xyiuvsGrVKj755BO++OILHn/88Vqvue+++8jLy3M99u7d24QRS6tUj32rjhSVsd61lYPqcUREmoLXenKioqKw2WxkZWVVOZ6VlUVcXFyN1zz00EP8/ve/57rrrgOgX79+FBUVccMNN/DAAw9gtVbP2fz9/fH39/f8DyBtVz0Kj5dsN7dy6BkXSkyYtnIQEWkKXuvJ8fPzY8iQISxYsMB1zOFwsGDBAtLS0mq8pri4uFoiY7OZa40YhtF4wYqcqB6Fx1rlWESk6XmtJwdg+vTpTJo0iaFDhzJ8+HBmzZpFUVERU6ZMAWDixIkkJiYyc+ZMAC666CKef/55Bg0aRGpqKtu3b+ehhx7ioosuciU7Io3OzZ4ccysH1eOIiDQ1ryY5V1xxBYcOHWLGjBlkZmYycOBA5s+f7ypGzsjIqNJz8+CDD2KxWHjwwQfZv38/0dHRXHTRRTz55JPe+hGkLXL15NRtdtWOQ4UczCvBz8fK8M7tGi8uERGpwmK0sXGe/Px8wsPDycvLIyxMa5VIPTzTFYpz4OafILbPKU9/88ddPPb5Js7oHsW/rk1tggBFRFqf+vz+blGzq0S8zjCq7l1VByeujyMiIk1HSY6IO8oKwbCbz+tQeFxaYefnnUcA1eOIiDQ1JTki7nAWHVt9wTfolKev3HOUY+V2okL86RkX2rixiYhIFUpyRNxx4vRxy6m3Zjg+q0pbOYiINDUlOSLucHPfKq2PIyLiPUpyRNzhxr5VhwtL2bDf3BBWRcciIk1PSY6IO9zYt2rJjsOAtnIQEfEWJTki7nBjteP/bTWHqs7soVlVIiLeoCRHxB113Leq6lYOGqoSEfEGJTki7qhjT8727EIy80vw97EyLFlbOYiIeIOSHBF31HHfqsWVvTjDO7cjwFebx4qIeIOSHBF3uKaQR5z0NOfU8TO1yrGIiNcoyRFxRx32rTK3cjBnVp3RQ/U4IiLeoiRHxB11KDxeufsoJeUOokP9SYnVVg4iIt6iJEfEHXUoPF58wqwqSx22fhARkcahJEekrgwDSvLM5yfpyVE9johI86AkR6SuSgvAsJvPa5ldlVNYysYD5lYOI7WVg4iIVynJEakrZz2OzR98A2s8Zcl2c6iqd3wY0aH+TRSYiIjUREmOSF3VYd+qxVsr63E0q0pExOvcTnKSk5N57LHHyMjIaIx4RJqvUxQdm1s5qB5HRKS5cDvJuf322/nkk0/o0qULY8aMYfbs2ZSWljZGbCLNyymmj2/NKiS7oJQAXytDOp18RWQREWl89Upy1qxZw/Lly+nVqxe33nor8fHxTJs2jVWrVjVGjCLNg2u145oTGGcvTmrn9trKQUSkGah3Tc7gwYN58cUXOXDgAA8//DD//Oc/GTZsGAMHDuTNN9/EMAxPxinifc6enFqGqxZr13ERkWbFp74XlpeX8+mnn/LWW2/x7bffctppp3Httdeyb98+7r//fr777jvef/99T8Yq4l0n2beqpNzOssqtHM7soXocEZHmwO0kZ9WqVbz11lt88MEHWK1WJk6cyAsvvEDPnj1d51xyySUMGzbMo4GKeN1J9q1auecopRUOYsP86R4T0rRxiYhIjdxOcoYNG8aYMWN49dVXmTBhAr6+vtXO6dy5M1deeaVHAhRpNk5SeLy4sh7n9G7R2spBRKSZcDvJ2blzJ506dTrpOcHBwbz11lv1DkqkWTrJFPL/Va6Pc6bWxxERaTbcLjzOzs5m2bJl1Y4vW7aMX375xSNBiTRLrp6cqrOrDhWUsumgtnIQEWlu3E5ypk6dyt69e6sd379/P1OnTvVIUCLNUi2Fx86tHPokhBEVoq0cRESaC7eTnE2bNjF48OBqxwcNGsSmTZs8EpRIs1TLFHJnPc4ZWuVYRKRZcTvJ8ff3Jysrq9rxgwcP4uNT7xnpIs2bw1FjT465lUNlPY7WxxERaVbcTnLOO+887rvvPvLy8lzHcnNzuf/++xkzZoxHgxNpNkrzgcoFLk/oyUnPKuCQcyuHZG3lICLSnLjd9fKXv/yFM888k06dOjFo0CAA1qxZQ2xsLP/61788HqBIs+AcqvIJAN8A12HnrKrTurTH30dbOYiINCduJzmJiYmsW7eOf//736xdu5bAwECmTJnCVVddVeOaOSKtQi37VqkeR0Sk+apXEU1wcDA33HCDp2MRab5qKDouKbezfNcRQPU4IiLNUb0rhTdt2kRGRgZlZWVVjl988cUNDkqk2amh6HjF7iOUVjiICwugm7ZyEBFpduq14vEll1zC+vXrsVgsrt3GnUvZ2+12z0Yo0hzUsG/V/07YdVxbOYiIND9uz6667bbb6Ny5M9nZ2QQFBbFx40YWL17M0KFDWbRoUSOEKNIM/GrfKsMw+H5LNgBnaNdxEZFmye2enKVLl7Jw4UKioqKwWq1YrVZOP/10Zs6cyR//+EdWr17dGHGKeNev9q3aklnAtuxC/GxWzlKSIyLSLLndk2O32wkNDQUgKiqKAwcOANCpUyfS09M9G51Ic/GrfavmrtkPwKieMYQHalahiEhz5HZPTt++fVm7di2dO3cmNTWVZ555Bj8/P15//XW6dOnSGDGKeN8JhccOh8F/15jJ/fiBCd6LSURETsrtJOfBBx+kqKgIgMcee4wLL7yQM844g/bt2zNnzhyPByjSLJwwhXz57iMcyCshNMCHc3rGeDUsERGpndtJztixY13Pu3XrxpYtWzhy5AiRkZGaYSKtl3N2VWAE8yqHqs7vG0eAr1Y5FhFprtyqySkvL8fHx4cNGzZUOd6uXTslONK6VQ5XlfmG8sW6gwBMGJjoxYBERORU3EpyfH196dixo9bCkbancrhqRaZBfkkFsWH+pHZp792YRETkpNyeXfXAAw9w//33c+TIkcaIR6T5cTigJB+A/249BsDFAxKwWdV7KSLSnLldk/PSSy+xfft2EhIS6NSpE8HBwVVeX7VqlceCE2kWSvMAc2Xvz7cdA6yM11CViEiz53aSM2HChEYIQ6QZq6zHqbAFUlhipWt0MH0Swrwbk4iInJLbSc7DDz/cGHGINF+VM6vyMXstJwxMVKG9iEgL4HZNjkibU1l0fKg8EEBDVSIiLYTbPTlWq/Wk/4vVzCtpdSqHq3IJZnDHCDq2D/JuPCIiUiduJzmffvpple/Ly8tZvXo177zzDo8++qjHAhNpNip7cvKMYCYMUi+OiEhL4XaSM378+GrHfvvb39KnTx/mzJnDtdde65HARJqLwznZtAfyCeGCfvHeDkdEROrIYzU5p512GgsWLPBUcyLNxs695jYOIRFRRIX4ezkaERGpK48kOceOHePFF18kMVFd+dK6GIZBVpa5jUOnRO04LiLSkrg9XPXrjTgNw6CgoICgoCDee+89jwYn4m1r9uZiLc0DG3RJUhIvItKSuJ3kvPDCC1WSHKvVSnR0NKmpqURGRno0OBFvm7fmAGMoAsA/VHtViYi0JG4nOZMnT26EMESanwq7g8/XHeC3FjPJIVBJvIhIS+J2Tc5bb73Fhx9+WO34hx9+yDvvvOORoESagyU7DpNTWEaktdg8EBDh1XhERMQ9bic5M2fOJCoqqtrxmJgYnnrqKY8EJdIczFttzqpq50xyAiO8F4yIiLjN7SQnIyODzp07VzveqVMnMjIyPBKUiLcdK7Pz9cZMrDgIdBSaB9WTIyLSorid5MTExLBu3bpqx9euXUv79vUrzHz55ZdJTk4mICCA1NRUli9fXuu5Z599NhaLpdrjN7/5Tb3uLVKTbzdnUVRmp1fkCduUqCdHRKRFcTvJueqqq/jjH//I999/j91ux263s3DhQm677TauvPJKtwOYM2cO06dP5+GHH2bVqlUMGDCAsWPHkp2dXeP5n3zyCQcPHnQ9NmzYgM1m43e/+53b9xapjXOo6pKUEPOAXwjYfL0YkYiIuMvtJOfxxx8nNTWVc889l8DAQAIDAznvvPMYNWpUvWpynn/+ea6//nqmTJlC7969ee211wgKCuLNN9+s8fx27doRFxfnenz77bcEBQUpyRGPOVpUxg9bDwFwXtfKFY41VCUi0uK4PYXcz8+POXPm8MQTT7BmzRoCAwPp168fnTp1cvvmZWVlrFy5kvvuu891zGq1Mnr0aJYuXVqnNt544w2uvPJKgoODa3y9tLSU0tJS1/f5+fluxyltyxfrD1LhMOgdH0bHwDLzoIaqRERaHLeTHKfu3bvTvXv3Bt08JycHu91ObGxsleOxsbFs2bLllNcvX76cDRs28MYbb9R6zsyZM7U7urhl3hpzqGrCoAQoWWMeVE+OiEiL4/Zw1WWXXcaf//znasefeeaZJh8yeuONN+jXrx/Dhw+v9Zz77ruPvLw812Pv3r1NGKG0NPuOFrNi91EsFrh4QCIcO2q+oJ4cEZEWx+0kZ/HixVxwwQXVjp9//vksXrzYrbaioqKw2WxkZWVVOZ6VlUVcXNxJry0qKmL27Nlce+21Jz3P39+fsLCwKg+R2sxbcwCA0zq3Jy48AI7lmi+oJ0dEpMVxO8kpLCzEz8+v2nFfX1+36138/PwYMmQICxYscB1zOBwsWLCAtLS0k1774YcfUlpayv/93/+5dU+R2hiGUXWoCqAk1/yqnhwRkRbH7SSnX79+zJkzp9rx2bNn07t3b7cDmD59Ov/4xz9455132Lx5MzfffDNFRUVMmTIFgIkTJ1YpTHZ64403mDBhQr3X5hH5tS2ZBWzNKsTPZmVc33jzoLMnR0mOiEiL43bh8UMPPcSll17Kjh07GDVqFAALFizg/fff56OPPnI7gCuuuIJDhw4xY8YMMjMzGThwIPPnz3cVI2dkZGC1Vs3F0tPT+fHHH/nmm2/cvp9IbeZW9uKc0zOa8MDKNXGcPTkarhIRaXHcTnIuuugi5s6dy1NPPcVHH31EYGAgAwYMYOHChbRr165eQUybNo1p06bV+NqiRYuqHUtJScEwjHrdS6QmDofBfyvrcSYMTDz+gqsnRzuQi4i0NPWaQv6b3/zGtY1Cfn4+H3zwAXfddRcrV67Ebref4mqR5mf57iMcyCshNMCHc3rGHH9BhcciIi2W2zU5TosXL2bSpEkkJCTw3HPPMWrUKH7++WdPxibSZJwFx+f3jSPA13b8BRUei4i0WG715GRmZvL222/zxhtvkJ+fz+WXX05paSlz586tV9GxSHNQWmHni3UHgV8NVYGGq0REWrA69+RcdNFFpKSksG7dOmbNmsWBAwf429/+1pixiTSJH9IPkV9SQUyoP6ldTpitZ6+AsgLzuYarRERanDr35Hz11Vf88Y9/5Oabb27wdg4izYlzAcCLByRgs1qOv1CSd/x5QHgTRyUiIg1V556cH3/8kYKCAoYMGUJqaiovvfQSOTk5jRmbSKMrKCnnu83mitsTBv1qqMpZj+MXCrZ6b/MmIiJeUuck57TTTuMf//gHBw8e5MYbb2T27NkkJCTgcDj49ttvKSgoaMw4RRrF/A2ZlFY46BodTJ+EX235oX2rRERaNLdnVwUHB/OHP/yBH3/8kfXr13PnnXfy9NNPExMTw8UXX9wYMYo0mnknrI1jsViqvqjp4yIiLVq9p5CDuSjfM888w759+/jggw88FZNIk8jOL+GnHeaQ6/hfz6oCTR8XEWnhGpTkONlsNiZMmMBnn33mieZEmsS7S/fgMGBQxwg6tg+qfoKGq0REWjSPJDkiLU12fglv/LgLgBvO6FLzSdq3SkSkRVOSI23SXxds41i5nYFJEYzrG1fzSdqBXESkRVOSI23OzkOFzF6xF4B7z+9ZveDYSYXHIiItmpIcaXP+8k06dofBqJ4xnHbiCse/psJjEZEWTUmOtCmrM47y5fpMLBa4Z1zPk5+sfatERFo0JTnSZhiGwdNfbQHgssEdSIkLPfkFKjwWEWnRlORIm7Fo6yGW7TqCn4+VO8b0OPUFKjwWEWnRlORIm2B3GPy5shdn8ohkEiMCT32RenJERFo0JTnSJsxbs58tmQWEBvhwy9ldT32BvRzKCs3nqskREWmRlORIq1dSbue5b7YCcMvZ3YgI8jv1Rc6hKoCA8MYJTEREGpWSHGn13vt5D/tzjxEXFsCUkcl1u8g5VOUfDlZbY4UmIiKNSEmOtGr5JeW89P12AO4Y050A3zomLK6iY/XiiIi0VEpypFX7+w87yC0up1tMCJcN7lD3C1V0LCLS4inJkVYr64RNOO8em4KPzY2Pu6aPi4i0eEpypNWa9d1WSsodDOkUyZjese5dfOyo+VU9OSIiLZaSHGmVtmcXMqcum3DWxrVvlaaPi4i0VEpypFV69ustOAwY3SuWYcnt3G9Aw1UiIi2ekhxpdVbuOcrXG7OwWuDucSn1a0SFxyIiLZ6SHGlVDOP49g2/HdKBHrGn2ISzNurJERFp8ZTkSKuycEs2y3cfwd/Hyu2j67AJZ23UkyMi0uIpyZFWw+4w+PP8yk04RyaTUJdNOGvjnF2lnhwRkRZLSY60Gp+s2sfWrELCAny45axuDWvMNVyl2VUiIi2VkhxpFUrK7Tz/rbkJ59RzuhEe5NvABnPNrxquEhFpsZTkSKvw7tLdHMwrIT48gEkjkhvWWEUZlBebzzVcJSLSYinJkRYvr7icl7/fAcD0MT3qvglnbZy9OFjMXchFRKRFUpIjLd6rP+wg71g5PWJDuNSdTThr49rSIQys+isiItJS6V9wadEO5h3jrSXmJpz3jOuJzerm9g01cRYdqx5HRKRFU5IjLdqsb7dRWuFgeHI7RvWM8Uyj2rdKRKRVUJIjLVZZhYO5a/YD8KdxKe5vwlkbrXYsItIqKMmRhsvZBofSm/y2mw7mU1rhICLIl6GdPNjrounjIiKtgo+3A5AWrqIU3jgPHBVwxwYIaLrZSKv2mAXCg5IiPNeLA+rJERFpJdSTIw2TsxWOHYHSfNj3S5PeelWGmeQM7ujh2hnX7KoIz7YrIiJNSkmONEz2luPP9y5v0luvzsgFYLAnh6rghMLjCM+2KyIiTUpJjjTMoc3Hn+9ruiQnK7+E/bnHsFpgQFKEZxvXvlUiIq2CkhxpmBN7cvb9Ag5Hk9x2deVQVY/YUEL8PVxapsJjEZFWQUmONMyJPTml+XBoS+3netCqxhqqAhUei4i0EkpypP7Kj8ERc7VhonuZX/cua5JbO2dWebzoGNSTIyLSSijJkfrL2QoYENgOel5gHtu3otFvW1bhYN3+PAAGdYzw/A2cs6vUkyMi0qIpyZH6y64cqorpBUmp5vMm6MnZdDCfsspFALtEBXu28fISqCgxn6snR0SkRVOSI/XnTHKie0KHYebzw9uh6HCj3rbRFgGE40NVFiv4h3m2bRERaVJKcqT+nEXGMb0gqB1E9TC/b+Qhq0ZbBBBO2IE8HKz66yEi0pLpX3GpvxOHqwA6DDe/NvJ6OY22CCCo6FhEpBVRkiP1U1YEuXvM586ZVUmVSU4jrnzcqIsAgqaPi4i0IkpypH6cu44HR0Nwe/O5M8nZvxLsFY1y20ZdBBC0b5WISCuiJEfqx1mPE93z+LGoFPAPh/JiyNrQKLdt1EUAQftWiYi0IkpypH6yN5lfnfU4YBbqdhhqPm+k4uNGXQQQtG+ViEgroiRH6ie7hp4caNT1ck5cBHBwYywCCCo8FhFpRZTkSP24po/3rno8qXK9nEYoPj5xEcDOnl4E0EmFxyIirYaSHHFfaQHk7TWfx/yqJydxKGAxZ14VZHn0to26CKCTenJERFoNJTniPufMqpC46rUrAWHHe3c8vF5Ooy4C6KR9q0REWg0lOeI+1yKAPWt+3bVejmfrchp1EUAn14rHEY13DxERaRJeT3JefvllkpOTCQgIIDU1leXLT/6//9zcXKZOnUp8fDz+/v706NGDL7/8somiFeCE6eO9an7dleR4boZVoy8C6OSaQq7ZVSIiLV0jrKZWd3PmzGH69Om89tprpKamMmvWLMaOHUt6ejoxMTHVzi8rK2PMmDHExMTw0UcfkZiYyJ49e4iIiGj64Nsy1/Tx2npyKmdYHVgNFWXg49fgW9a6CGBBFpQVQvuuDb4HoMJjEZFWxKs9Oc8//zzXX389U6ZMoXfv3rz22msEBQXx5ptv1nj+m2++yZEjR5g7dy4jR44kOTmZs846iwEDBjRx5G1c9il6ctp1gaD2YC+FzHUeuWWNiwA67PDWOHh1JOTtb/hNyo+ZMYOGq0REWgGvJTllZWWsXLmS0aNHHw/GamX06NEsXbq0xms+++wz0tLSmDp1KrGxsfTt25ennnoKu91e631KS0vJz8+v8pAGOJYLBQfM59EpNZ9jsRzfrNNDdTk1LgKYsRSO7ISKY7Drh4bfxNmLY7GBf2jD2xMREa/yWpKTk5OD3W4nNja2yvHY2FgyMzNrvGbnzp189NFH2O12vvzySx566CGee+45nnjiiVrvM3PmTMLDw12PpKQkj/4cbY5zZlVY4smHdDy4Xk6tiwBu/PT48z1LGnyf4/tWhZuJmoiItGheLzx2h8PhICYmhtdff50hQ4ZwxRVX8MADD/Daa6/Ves19991HXl6e67F3794mjLgVOlQ5s+rXKx3/mmvl44YnOTUuAmivgE3zjp+056cG30dFxyIirYvXCo+joqKw2WxkZVVdMC4rK4u4uLgar4mPj8fX1xebzeY61qtXLzIzMykrK8PPr3qBq7+/P/7+/p4Nvi1z1uPE1FKP45QwyBz2KTgAefsgvEO9b1njIoB7lkDRIXND0NJ8c9iqIBNCa/7s1ImKjkVEWhWv9eT4+fkxZMgQFixY4DrmcDhYsGABaWlpNV4zcuRItm/fjsPhcB3bunUr8fHxNSY40gjq2pPjFwxx/cznDazLqXERQOdQVZ/xENfXfN7Q3hytdiwi0qp4dbhq+vTp/OMf/+Cdd95h8+bN3HzzzRQVFTFlyhQAJk6cyH333ec6/+abb+bIkSPcdtttbN26lS+++IKnnnqKqVOneutHaHtcCwGeoicHPLZeTrVFAO0VsPkz83mfS6DTSPN5Rs0F63WmnhwRkVbFq+vkXHHFFRw6dIgZM2aQmZnJwIEDmT9/vqsYOSMjA6v1eB6WlJTE119/zR133EH//v1JTEzktttu45577vHWj9C2FB+BwsrhxdpmVp0oKRWWv96gnpwaFwHcvRiKD5vT1JPPhJJ8WPaaenJERKQKryY5ANOmTWPatGk1vrZo0aJqx9LS0vj5558bOSqpkXOl4/Ckuk2x7lA5wypznbkGjW+g27escRFA51BVr4vB5gOdRpjfZ200Z0jVt3BY+1aJiLQqLWp2lXiZO0NVABEdzU08HRXm6sf1UG0RQHs5bP6v+bzPJebXkBho3w0wIKMB9T+u4SrNrhIRaQ2U5EjdufasOkXRsZPFckJdTv2mkldbBHDnD2aPS3D08VocON6b05D1cjRcJSLSqijJkbpztycHGpTk1LgIoHOoqvd4c6jKyZnwNKQuR4XHIiKtipIcqTt3e3Lg+KKA+5aDYbh1O+cigJHORQArymDLr4aqnDpWLjtwcA2UFbl1Hxf15IiItCpKcqRuinLMxfegbjOrnOIHgM3PvPboLrdu6VoEsGOkuQjgzu+hJA9CYo8nNU4RHSGsg1n/s6+eU9bVkyMi0qooyZG6cQ5VRXQyF/qrKx9/iB9oPndzvRznIoCDnFPHXUNVE8Bqq3qyxXJCXU491ssxjBP2ropw/3oREWl2lORI3Ryq43YONUmq347kVRYBrCiFLV+YL/x6qMqpU2XvTn2Kj8uLwVFuPtfsKhGRVkFJjtTNSYqONx3IZ0NlgXCNnEnOvroXH1dbBHDHQnOPqtD443U+v+YsPt63wqzfcYdzqMrq415PlYiINFtKcqRuXEXHVZOcXTlFTHhlCZe8soQdhwprvrZDZZKTtRFKC+p0u2qLAG74xHyh9wSw1vKxjephroJcUWIWILvjxKJj5yagIiLSoinJkVMzjBN6cnqecNjg0f9upKzCQbndYOaXW2q+PiwewjuC4YD9K+t0yyqLAJYfg/QvzRf6Xlr7RRbL8YJkd4esVHQsItLqKMmRUys6BMeOgMVq9pZU+m5zNovSD+Frs2CzWvhucxY/bc+puY2kyi0e6lh8XGURwO0LoKzQnD2VOPTkF9Z3vRxNHxcRaXWU5MipOXtxIpNd+0+VlNt57PONAFx3Rhf+L7UjAE98sRm7o4b1cJx1NHUoPq62CODGyqGqPhNqH6pychYfZywDh/2U93LRvlUiIq2Okhw5NWeSc0I9zt9/2MneI8eICwtg2jnduG10D0IDfNh0MJ+PV+2r3oZzs859K8DhOOntqiwCGG6B9PnmC31OMlTlFNsP/EKhNM+sAaor7VslItLqKMmRUztUtR5n75FiXlm0HYAHftOLYH8f2gX78cdR3QH4y9fpFJdVVG0jrh/4BJrDQoe3nfR2VRYB3P4dlBeZNT2Jg08dq80HOlb2GrkzZKXhKhGRVkdJjpxatnONnN4APP75JkorHKR1ac+F/eNdp00c0YmO7YLILijl7z/srNqGzfd4knKKfayciwAO7hhxfFZVnwl1n/XkLD7OcCPJUeGxiEiroyRHTs4wjvfkRPdkUXo232zKwma18Oj4PuZ2C5X8fWzce77Z2/P3xTvIzCup2lYdFwV0LgI4JMEftn5tHqxtAcCanFh8XNf9stSTIyLS6ijJkZMryDT3i7LYKI3owqP/3QTA5BHJ9IgNrXb6+X3jGNopkpJyB89+nV71Red6OSfZW+rERQAHlSyHimNmwXPCoLrHnDgYbP7mrLDDO+p2jXpyRERaHSU5cnLOXpx2XXjz54PsyikiKsSf20Z3r/F0i8XCgxeaw1ofr9pXdSVkZ0/OoS3HZzP9yomLAAakzzMP9rnEvQX6fPyhQ+VU87qul6N9q0REWh0lOXJylfU4xyJ78LeFZsHw/Rf0JCzAt9ZLBiZFMGFgAgBPfLEJwzlkFBwF7bqYz/fVvCigcxHA0zr4w7ZvzIPuDFU5uTbrrGNdjnO4SrOrRERaDSU5cnLZ5vDUoiPtKC6zM7RTJJcMSjzlZX8a1xN/Hys/7zzCt5uyjr9wivVynDOrzvdbY27P0K4rxPV3P253i481XCUi0uooyZGTq9yz6ovMCKwWqhUb1yYxIpDrzugMwMyvtlBWUbk2jmu9nOozrE5cBLDv0YXmQXeHqpyShoPFBrkZkLv35OcahgqPRURaISU5UjvDwKhMctKNJK5J7USfhPA6X37z2d2ICvFjV04R7/28xzzo7MnZ90u1FYmdiwB2CCwnKON782B9hqoA/EMhfoD5PGPpyc8tKwJH5bo+6skREWk1lORI7fL3YyktoNywkReYxJ3n9Tj1NScI8ffhzvNSAPjrgm3kFpdBTC9zReKywuMrKVdyDlVNbL8Zi70U2neH2D71j7+udTnOXhyrL/gG1f9+IiLSrCjJkVrl7lkHwC4jjunj+hIR5Od2G5cPTaJnXCh5x8p5ccF2sNqgwxDzxV/V5TgXARzjqJwR1ffS+g1VOdU1yTlx36qG3E9ERJoVJTlSqx+W/A+A7IDOXD40qV5t2KwW7r/A3PPqXz/vZldO0QlDVlXXy1mdkUsYRXTK/dk8UN+hKidn8XFOOhTVsjs6aN8qEZFWSkmO1OiX3Uco3W9ucNmtzzCs1vr3cJzZI5qzU6Iptxs8/dXm44sCntCT41wE8DzbSqyOcojuaQ5tNURQu+Obip6sLkdFxyIirZKSHKnG7jCYMW8jPazmrKS4bgMb3OYDF/TCZrXw9cYsVti7mgeP7HT1sDgXAbw88BfztbrsOF4XdRmy0vRxEZFWSUmOVPP+sj1sPphLD+t+80BDe1SA7rGhXDXcHPJ69Nt9GNHmHlfOzTpXZeQSRiFDKlabx/tMaPA9gbolOerJERFplZTkSBWHC0t59ut0Ei2HCaIUbH7HVyluoNtH9yDE34cN+/PZHVg5a6pyvZxVe44y1vYLNuwQ0weiUzxyT1ddTuY6KMmv+Rz15IiItEpKcqSKv3yTTn5JBee2P2IeaN8dbLVv4eCOqBB/pp7TDYB/748zD+5d7loE8EKrhwqOTxSeaG7waThcvUbVaN8qEZFWSUmOuKzdm8vsFWYdzvUpJebBmJ4evceUkckkRgTyfXGyeWD/KjbtP0xQRR4jbRvMY55McgA6Vg5Z1bbFg/atEhFplZTkCAAOh8GMzzZiGHDpoEQ6VGSYL0Q3vB7nRAG+Nu49vyc7jXhyjWCoOMaejcsYZ1uBDw6I6wdR3Tx6z1PW5Wi4SkSkVVKSIwB8uHIva/fmEuLvw73n9zy+GrGHe3IALuwfz8CO7Vjl6A5A2a6f+U1jDFU5OZOc/SuhvKT66yo8FhFplZTkCHnF5fx5fjoAt4/uTkyIHxwyv/d0Tw6AxWLhwd/0diU5SYcWMcJqrsnTKElOuy4QEgv2MjPR+TX15IiItEo+3g5AvCu3uIyb3lvJkaIyuseEMGlEMuTuhopjYPOHdp0b5b5DOkWypPNpsO9DTmM9WMAeNwCbh2ZyVWGxmL05Gz81h6ySR1Z9XT050kTsdjvl5eXeDkOk2fLz88Nq9Vz/i5KcNmzHoUKufXsFuw8XE+xn4+nL+uNrs0K2ufM40T3MvaYayaUXjsf+6l3YLAYAtr4eWgCwJh0rk5xfFx8bhrZ1kEZnGAaZmZnk5uZ6OxSRZs1qtdK5c2f8/NzfK7EmSnLaqCXbc7j5vZXkl1SQGBHIG5OH0jMuzHzxUGU9TiMMVZ2oQ1w0WcHdiS3eah7w1AKANXHW5WQsA3sF2Co/+qUFYNjN5xqukkbiTHBiYmIICgrCoo1gRapxOBwcOHCAgwcP0rFjR4/8PVGS0wb9e9keZszbiN1hMLhjBK9PHEpUiP/xE5w9OY1QdPxrMb3PgF+2Yk8YjC0yuTFvBAHhUJIHmWshsXIndOdQlc0ffAMb7/7SZtntdleC0759e2+HI9KsRUdHc+DAASoqKvD1bfgabSo8bkPsDoNH/7uRBz7dgN1hMGFgAu9ff1rVBAearCcHwDLsWogfgG3UA417I6v1+OrHe07YrFNFx9LInDU4QUFBXo5EpPlzDlPZ7XaPtKckp40oKCnn2ndW8NaS3QDcdV4PXrhiIAG+v6q5cdjhUOXwURP05BDbB25cDN1GN/69alovR0XH0kQ0RCVyap7+e6LhqjZg75Firn1nBVuzCgnwtfL85QO5oF98zScf2QX2UvAJhIjkJo2z0XWqnFWV8RM4HGbvjnpyRERaLfXktHK/7D7C+JeXsDWrkJhQf/5zY1rtCQ6cMFTVw0wCWpP4AeAbZO5VlVO5DpBz3yrNrBKpl5EjR7J+/XqOHj3KaaedxoYNG1yv7d69G4vFwpo1a07axtlnn83tt9/euIF6wF133cU///lPSkpKuPXWW3nppZc80u7bb79NREREs4mnNWllv8XkRJ+s2sfV/1jGkaIy+iSEMW/aSPp3iDj5Ra6i496NHl+Ts/lCh2Hm8z1LzK8arhKp0eTJk7FYLNUe48aNq3Le9OnTSUtLo127dnTr1o2+ffu6XktKSuLgwYOuY4sWLcJisVSbSv/JJ5/w+OOPe/xnSE5OdsUdGBhIcnIyl19+OQsXLqxXezfccANPPPEEgYGBLF68mKuuusrDEbfseJojJTmtkMNh8OzXW5j+n7WU2R2M7RPLhzelER9eh9lDrp6cJqjH8QbnkJWzLkfDVSK1GjduHAcPHqzy+OCDD6qcc9lll5GXl0dhYSHvvfdelddsNhtxcXH4+Jy8MqJdu3aEhoZ6PH6Axx57jIMHD5Kens67775LREQEo0eP5sknn3S7rR49erB7927y8vJYu3btKWfLlZWV1TfsRomnLVKS08oUl1Uw9f1VvPz9DgBuObsrr14zhCC/OpZfuXpyGn9mlVd0OmGGlWGoJ0eanGEYFJdVeOVhGIZbsfr7+xMXF1flERl5fGh3y5YtnH766QQHBzNs2DC+++47LBYLc+fOBaoOV+3evZtzzjkHgMjISCwWC5MnTwaqD1clJyfzxBNPMHHiREJCQujUqROfffYZhw4dYvz48YSEhNC/f39++eWXU/4MoaGhxMXF0bFjR84880xef/11HnroIWbMmEF6errrvB9++IHhw4fj7+9PfHw89957LxUVFa7XCwoKuOaaawgODiYlJYUXXnihxrgff/xxJk6cSFhYGDfccEONvVdr1qzBYrGwe/fuGmOePHkyEyZMqHLs9ttv5+yzz3YrnhP/LJwiIiJ4++23T/m+tRYqPG5FMvNKuO7dFWzYn4+vzcLTl/bnsiEd6t6AvQIObzOft9aenMShYPWFggNwdLd6cqTJHSu303vG116596bHxtb9PzynYLfbmTBhAh07dmTZsmUUFBRw55131np+UlISH3/8MZdddhnp6emEhYURGFh77/ILL7zAU089xUMPPcQLL7zA73//e0aMGMEf/vAHnn32We655x4mTpzIxo0b3Z6Rc9ttt/H4448zb9487r77bvbv388FF1zA5MmTeffdd9myZQvXX389AQEBPPLII4A5LLdkyRI+++wzYmNjmTFjBqtWrWLgwIFV2v7LX/7CjBkzePjhhwHYu3evW7HVVV3jaeuU5LQS6/flcd27K8jKL6VdsB9///0QhiW3c6+RIzvNTSx9gyE8qXEC9Ta/IEgYBPuWQ8ZS9eSInMTnn39OSEhIlWP3338/999/P99++y07duxg0aJFxMXFAfDkk08yZsyYGtuy2Wy0a2f+mxQTE3PKQtsLLriAG2+8EYAZM2bw6quvMmzYMH73u98BcM8995CWlkZWVpbr/nXVrl07YmJiXD0pr7zyCklJSbz00ktYLBZ69uzJgQMHuOeee5gxYwZFRUW88847vP/++5x77rkAvPXWWyQkJFRre9SoUVWSvcZIcgoKCuocT1unJKeFMwyD95Zl8MTnmyitcNA9JoQ3Jg2jY/t6LDyWvcn8Gp3S+mZWnajTCDPJ2bNEs6ukyQX62tj02Fiv3dsd55xzDq+++mqVY85EJT09naSkpCoJxvDhwxseZKX+/fu7nsfGxgLQr1+/aseys7PdTnLA/LfT2QO0efNm0tLSqvQIjRw5ksLCQvbt28fRo0cpLy+v8vOFh4eTkpJSrd2hQ4e6HYu7du7cWed42jolOS3YkaIy7v5oHd9tzgLgnJRo/nrVIMIC6rkU9qFWXo/j1GkkLJllFh87axQ0XCVNxGKxeGzIqLEFBwfTrVs3r9z7xCX9nclHTcccDofbbR8+fJhDhw7RuXPnBkZZXXBwcJXvnTtqn1gPdaqd6K1Wa7X6qfrsXm+xWDzSTkvWiv+73rr9uC2HcbMW893mLPxsVh66sDdvTBpW/wQHILtyZlVrT3KShgMWc3guf795TMNVIm5JSUlh7969ZGVluY6tWLHipNd4esn++vrrX/+K1Wp1Fff26tWLpUuXVkkIlixZQmhoKB06dKBLly74+vpW+fny8vLYunXrKe8VHR0NwMGDB13HTrVuUHR0dJXzf31NXeP5dTvbtm2juLj4lDG3JkpyWpiyCgdPfbmZ/3tjGdkFpXSLCWHu1JFce3pnrNYGLoft7Mlpgj2rvCowAuIq1/Kwlx0/JiJVlJaWkpmZWeWRk5MDwJgxY+jatSuTJ09m48aN/PzzzzzwgLkHXW2FwJ06dcJisfD5559z6NAhCgsLG/1nKCgoIDMzk71797J48WLX2jJPPvmkq5fqlltuYe/evdx6661s2bKFefPm8fDDDzN9+nSsViuhoaFMmjSJP/3pTyxevJgtW7Zw3XXXYbVaT1n03K1bN5KSknjkkUfYtm0bX3zxBc8999xJrxk1ahS//PIL7777Ltu2bePhhx+usshiXeMZNWoUL730EqtXr+aXX37hpptu8simly2JkpwWZMehQi55ZQmvL94JwP+d1pH/Tjud3glhDW+8ogwObzefN8WeVd7mXC/HST05ItXMnz+f+Pj4Ko/TTz8dMAuJ586dS0FBAYMHD2bKlCmuJCcgIKDG9hITE3n00Ue59957iY2NZdq0aY3+M8yYMYP4+Hi6devG73//e/Ly8liwYAH33HNPlbi+/PJLli9fzoABA7jpppu49tprefDBB13nPP/886SlpXH++edzzjnnkJaWRq9evWr9WZ18fX354IMP2LJlC/379+fPf/4zTzzxxEmvGTt2LA899BB33303w4YNo6CggIkTJ1Y5py7xPPfccyQlJXHGGWdw9dVXc9ddd7W5jWIthrsLJ7Rw+fn5hIeHk5eXR1iYB5KDJmAYBnNW7OXR/27iWLmdyCBf/nxZf87r436xXa2yN8Mrp4F/GNybAa19M8GNc+HDSeZznwB4MOukp4vUV0lJCbt27aJz586n/IXY0i1ZsoTTTz+d7du307VrV2+H06iKiopITEzkueee49prr/V2OM0unvo62d+X+vz+bhnVb21YbnEZ9368nvkbMwEY2a09z18+kNgwD/9j6azHiU5p/QkOHN+RHDSzSqSePv30U0JCQujevTvbt2/ntttuY+TIka0ywVm9ejVbtmxh+PDh5OXl8dhjjwEwfvx4xdOMKclpxpbuOMwdc9aQmV+Cr83CXeelcP0ZXRpee1OT7Fa+ncOvhcRA+27mEJ2GqkTqpaCggHvuuYeMjAyioqIYPXr0KetNWrK//OUvpKen4+fnx5AhQ/jf//5HVFSU4mnGlOQ0Q+V2By98u5VXf9iBYUCXqGD+euUg+nUIb7ybHmojM6tO1GmEmeSo6FikXiZOnFitVqS1GjRoECtXrvR2GC7NLZ7mSklOM7M7p4jbZq9m7b48AK4clsSMi3o3/roarX3Pqpp0Gw2r3oV2XbwdiYiINAIlOc2EYRh8tHIfD3+2keIyO+GBvjx9aT/O7xff+DevKDXXjIHWP338RL0uhomfQcJAb0ciIiKNQElOM1BUWsEDn65n7poDAJzWpR3PXz6QhIjaN6/zqJxtYNghIBxCPThjq7mzWKDLWd6OQkREGomSHE+pKIWiQ25ftjuniPs/Xc+unGI6WC1cf0Zn/u+0DtgshyGvEeKsScZS82t0r7Yxs0pERNoEJTmecnAdvDHa7cuSgfcBnDPCl1U+vKEtLAIoIiJtRrNY8fjll18mOTmZgIAAUlNTWb58ea3nvv3221gsliqPZrHAlsUCNv86PQybP+UWP0oNX0oNX8rwxajjtY32CGoPfS/z9rsoIiLiMV7vyZkzZw7Tp0/ntddeIzU1lVmzZjF27FjS09OJiYmp8ZqwsDDS09Nd359q75Am0WEoPJR9ytP2HS1m6vurWbs3F4BbR3Xj9tE9sDTG2jciIs3E559/zscff8wrr7zCZ599xocffshHH33U4HZ3795N586dWb16NQMHDmx4oM3cb3/7W2688UZGjBjB73//e/7whz9w4YUXNrjdRx55hLlz555y89CmisdTvN6T8/zzz3P99dczZcoUevfuzWuvvUZQUBBvvvlmrddYLBbi4uJcj9jY2CaMuP6+T8/mwr/9yNq9uYQH+vLW5GHceV4KNiU4ItLMTJ482dVb7uvrS2xsLGPGjOHNN9/E4XC43d6YMWPYtWsXQUFBTJs2jTvvvLMRom5+Thx1CA4Opnv37kyePLnea9zccccdXH311YSEhJCbm8t5553n4Yhbdjy/5tUkp6ysjJUrVzJ69PFaFqvVyujRo1m6dGmt1xUWFtKpUyeSkpIYP348GzdurPXc0tJS8vPzqzyamt1h8Nw36Ux5awW5xeUM6BDOF388nXN61txTJSLSHIwbN46DBw+ye/duvvrqK8455xxuu+02LrzwQioqKtxqy9/fn0WLFpGfn092djZpaWknPb+srKwhoTcrb731FgcPHmTjxo28/PLLFBYWkpqayrvvvut2WyNHjiQ7O5uCggIWLlyIn5/fSc8vLy+vb9iNEk9T82qSk5OTg91ur9YTExsbS2ZmZo3XpKSk8OabbzJv3jzee+89HA4HI0aMYN++fTWeP3PmTMLDw12PpKQkj/8cJ5NTWMrEN5fxt4XmDt8T0zrxn5vS6BDZtnaCFZFKhgFlRd55uLkfs7+/P3FxcSQmJjJ48GDuv/9+5s2bx1dffcXbb7/tOi8jI4Px48cTEhJCWFgYl19+OVlZVTe9feKJJ4iJiSEhIYHrr7+ee++9t8rw0uTJk5kwYQJPPvkkCQkJpKSkAGZPyNy5c6u0FRERUeX+J7Lb7Vx77bV07tyZwMBAUlJS+Otf/1rlHOe9nnrqKWJjY4mIiOCxxx6joqKCP/3pT7Rr144OHTrw1ltvVbnunnvuoUePHgQFBdGlSxceeuihOiURERERxMXFkZyczHnnncdHH33ENddcw7Rp0zh69KjrvI8//pg+ffrg7+9PcnJytS0yDh48yG9+8xuCgoLo168f77//PsnJycyaNct1jsVi4dVXX+Xiiy8mODiYJ598krfffpuIiIgqbc2dO/ekpR5nn302t99+e5VjEyZMYPLkyXWOZ/fu3VgslipDYLm5uVgsFhYtWnTK980TvF6T4660tLQq/wMYMWIEvXr14u9//zuPP/54tfPvu+8+pk+f7vo+Pz+/yRKdX3YfYer7q8jKLyXIz8bMS/sxfmBik9xbRJqp8mJ4KsE7977/APgFN6iJUaNGMWDAAD755BOuu+46HA6HK8H54YcfqKioYOrUqVxxxRWuX2T//ve/efLJJ3nllVcYOXIks2fP5rnnnqNz585V2l6wYAFhYWF8++239Y7P4XDQoUMHPvzwQ9q3b89PP/3EDTfcQHx8PJdffrnrvIULF9KhQwcWL17MkiVLuPbaa/npp58488wzWbZsGXPmzOHGG29kzJgxdOjQAYDQ0FDefvttEhISWL9+Pddffz2hoaHcfffdbsd5xx138O677/Ltt99y+eWXs3LlSi6//HIeeeQRrrjiCn766SduueUW2rdv70osJk6cSE5ODosWLcLX15fp06eTnV29FvSRRx7h6aefZtasWfj4+LBw4cL6vZmnUNd4vMmrSU5UVBQ2m61axp+VlUVcXN0WpfP19WXQoEFs3769xtf9/f3x9/dvcKzuMAyDN37cxcyvtmB3GHSLCeHVawbTPTa0SeMQEWkMPXv2ZN26dYCZmKxfv55du3a5/gP57rvv0qdPH1asWMGwYcP429/+xrXXXsuUKVMAmDFjBt988w2FhYVV2g0ODuaf//xng4Y8fH19efTRR13fd+7cmaVLl/Kf//ynSpLTrl07XnzxRaxWKykpKTzzzDMUFxdz//33A+Z/kJ9++ml+/PFHrrzySgAefPBB1/XJycncddddzJ49u15JTs+e5pIdu3fvBsz61HPPPZeHHnoIgB49erBp0yaeffZZJk+ezJYtW/juu+9YsWIFQ4cOBeAf//gHPXr0qNb21Vdf7XqvG4s78XiTV5Mc586pCxYsYMKECYCZhS9YsIBp06bVqQ273c769eu54IILGjHSussvKedPH67l641m4nbxgARmXtqPYP8W12kmIo3BN8jsUfHWvT3AMAzXUMfmzZtJSkqq0kPeu3dvIiIi2Lx5M8OGDSM9PZ1bbrmlShvDhw+v1sPQr18/j9R0vPzyy7z55ptkZGRw7NgxysrKqs286tOnD1br8YqN2NhY+vbt6/reZrPRvn37Kj0Tc+bM4cUXX2THjh0UFhZSUVFBWFhYvWI0KocOT3wfx48fX+WckSNHMmvWLOx2O+np6fj4+DB48GDX6927dycyMrJa286kozG5E483ef037/Tp05k0aRJDhw5l+PDhzJo1i6KiIlcWOnHiRBITE5k5cyYAjz32GKeddhrdunUjNzeXZ599lj179nDdddd588cAYNOBfG7590p2Hy7Gz2bloYt683+pHZvHFHcRaR4slgYPGXnb5s2bqw01eUJwcPX3xWKxuBICp5PVwcyePZu77rqL5557jrS0NEJDQ3n22WdZtqzqKqu+vr7V7lPTMedMsqVLl3LNNdfw6KOPMnbsWMLDw13DbvWxefNmgCZ5H61Wq1vvYX2vqakNoEo7jV0I/WteT3KuuOIKDh06xIwZM8jMzGTgwIHMnz/fVYyckZFRJds+evQo119/PZmZmURGRjJkyBB++uknevfu7a0fAYBvN2Ux7f1VlFY4SIwI5JVrBjMgKcKrMYmIeNrChQtZv349d9xxBwC9evVi79697N2719Wbs2nTJnJzc13/LqekpLBixQomTpzoamfFihV1ul90dDQHDx50fb9t2zaKi4trPX/JkiWMGDGiSs/Rjh076v4D1uKnn36iU6dOPPDAA65je/bsqXd7s2bNIiwszDW7uFevXixZsqTKOUuWLKFHjx7YbDZSUlKoqKhg9erVDBkyBIDt27dXKVyuTXR0NAUFBRQVFbkSoFOth/Pr991ut7NhwwbOOeccgDrFEx0dDZgFyoMGDarTfT3N60kOwLRp02odnvp1BfYLL7zACy+80ARRuadnXCgBvjZGdG3P85cPJDK4eU2jExFxV2lpKZmZmdjtdrKyspg/fz4zZ87kwgsvdCUso0ePpl+/flxzzTXMmjWLiooKbrnlFs466yzXsMmtt97K9ddfz/Dhwzn99NP54IMPWLt2LV27dj1lDKNGjeKll14iLS0Nu93OPffcU63H5UTdu3fn3Xff5euvv6Zz587861//YsWKFQ3uMenevTsZGRnMnj2bYcOG8cUXX/Dpp5/W6drc3FwyMzMpLS1l69at/P3vf2fu3Lm8++67rllPd955J8OGDePxxx/niiuuYOnSpbz00ku88sorgFnDM3r0aG688Ub+/ve/4+vry+23305gYOApRwtSU1MJCgri/vvv549//CPLli2rdXaa06hRo5g+fTpffPEFXbt25fnnnyc3N9f1el3iCQwM5LTTTuPpp5+mc+fOZGdnV6lragpeXwywtUhqF8Snt4zgjUnDlOCISKswf/584uPjSU5OZty4cXz//fe8+OKLzJs3D5vNBphDOvPmzSMyMpIzzzyT0aNH06VLF+bMmeNq55prrnHNdO3fvz87duxgypQpddqS57nnniMpKYkzzjiDq6++mrvuuougoNpri2688UYuvfRSrrjiClJTUzl8+HC1eqD6uPjii7njjjuYNm0aAwcO5KeffnIVCZ/KlClTiI+Pp2fPntx8882EhISwfPlyrr76atc5gwcP5j//+Q+zZ8+mb9++zJgxg8cee6zKlO13332XmJgYRo4cycUXX8x1111HaGjoKd/Hdu3a8d577/Hll1/Sr18/PvjgAx555JGTXvOHP/yBSZMmMXHiRM466yy6dOni6sVxJ54333yTiooKhgwZwu23384TTzxRp/fMUyzGrwfdWrn8/HzCw8PJy8urd8GYiEhdlZSUsGvXLjp37tw89tlrJsaMGUNcXBz/+te/vB1Ki7Vv3z6SkpL47rvvOPfcc70djkfiOdnfl/r8/m4Ww1UiItJ6FRcX89prrzF27FhsNhsffPAB3333XYPWw2mLFi5cSGFhIf369ePgwYPcfffdJCcnc+aZZyqeWijJERGRRmWxWPjyyy958sknKSkpISUlhY8//rjKlj5yauXl5dx///3s3LmT0NBQRowYwb///e+T1ii1pXhqouEqEZFGpOEqkbrz9HCVCo9FRESkVVKSIyLSBNpYp7lIvXj674mSHBGRRuSsTzjZAnYiYiorKwNwLVHQUCo8FhFpRDabjYiICNceSEFBQdrqRaQGDoeDQ4cOERQUhI+PZ9ITJTkiIo0sLi4OoMpmjyJSndVqpWNHz+35qCRHRKSRWSwW4uPjiYmJafINCkVaEj8/vyr7VTaUkhwRkSZis9k8VmsgIqemwmMRERFplZTkiIiISKukJEdERERapTZXk+NcaCg/P9/LkYiIiEhdOX9vu7NgYJtLcgoKCgBISkryciQiIiLiroKCAsLDw+t0bpvboNPhcHDgwAFCQ0M9viBXfn4+SUlJ7N27V5t/ukHvm/v0ntWP3rf60ftWP3rf3Hey98wwDAoKCkhISKjzNPM215NjtVrp0KFDo94jLCxMH+h60PvmPr1n9aP3rX70vtWP3jf31fae1bUHx0mFxyIiItIqKckRERGRVklJjgf5+/vz8MMP4+/v7+1QWhS9b+7Te1Y/et/qR+9b/eh9c5+n37M2V3gsIiIibYN6ckRERKRVUpIjIiIirZKSHBEREWmVlOSIiIhIq6Qkx0NefvllkpOTCQgIIDU1leXLl3s7pGbtkUcewWKxVHn07NnT22E1O4sXL+aiiy4iISEBi8XC3Llzq7xuGAYzZswgPj6ewMBARo8ezbZt27wTbDNyqvdt8uTJ1T5/48aN806wzcTMmTMZNmwYoaGhxMTEMGHCBNLT06ucU1JSwtSpU2nfvj0hISFcdtllZGVleSni5qEu79vZZ59d7fN20003eSni5uHVV1+lf//+rkX/0tLS+Oqrr1yve+qzpiTHA+bMmcP06dN5+OGHWbVqFQMGDGDs2LFkZ2d7O7RmrU+fPhw8eND1+PHHH70dUrNTVFTEgAEDePnll2t8/ZlnnuHFF1/ktddeY9myZQQHBzN27FhKSkqaONLm5VTvG8C4ceOqfP4++OCDJoyw+fnhhx+YOnUqP//8M99++y3l5eWcd955FBUVuc654447+O9//8uHH37IDz/8wIEDB7j00ku9GLX31eV9A7j++uurfN6eeeYZL0XcPHTo0IGnn36alStX8ssvvzBq1CjGjx/Pxo0bAQ9+1gxpsOHDhxtTp051fW+3242EhARj5syZXoyqeXv44YeNAQMGeDuMFgUwPv30U9f3DofDiIuLM5599lnXsdzcXMPf39/44IMPvBBh8/Tr980wDGPSpEnG+PHjvRJPS5GdnW0Axg8//GAYhvnZ8vX1NT788EPXOZs3bzYAY+nSpd4Ks9n59ftmGIZx1llnGbfddpv3gmohIiMjjX/+858e/aypJ6eBysrKWLlyJaNHj3Yds1qtjB49mqVLl3oxsuZv27ZtJCQk0KVLF6655hoyMjK8HVKLsmvXLjIzM6t89sLDw0lNTdVnrw4WLVpETEwMKSkp3HzzzRw+fNjbITUreXl5ALRr1w6AlStXUl5eXuXz1rNnTzp27KjP2wl+/b45/fvf/yYqKoq+ffty3333UVxc7I3wmiW73c7s2bMpKioiLS3No5+1NrdBp6fl5ORgt9uJjY2tcjw2NpYtW7Z4KarmLzU1lbfffpuUlBQOHjzIo48+yhlnnMGGDRsIDQ31dngtQmZmJkCNnz3na1KzcePGcemll9K5c2d27NjB/fffz/nnn8/SpUux2WzeDs/rHA4Ht99+OyNHjqRv376A+Xnz8/MjIiKiyrn6vB1X0/sGcPXVV9OpUycSEhJYt24d99xzD+np6XzyySdejNb71q9fT1paGiUlJYSEhPDpp5/Su3dv1qxZ47HPmpIc8Yrzzz/f9bx///6kpqbSqVMn/vOf/3Dttdd6MTJpC6688krX8379+tG/f3+6du3KokWLOPfcc70YWfMwdepUNmzYoDo5N9X2vt1www2u5/369SM+Pp5zzz2XHTt20LVr16YOs9lISUlhzZo15OXl8dFHHzFp0iR++OEHj95Dw1UNFBUVhc1mq1b1nZWVRVxcnJeiankiIiLo0aMH27dv93YoLYbz86XPXsN16dKFqKgoff6AadOm8fnnn/P999/ToUMH1/G4uDjKysrIzc2tcr4+b6ba3reapKamArT5z5ufnx/dunVjyJAhzJw5kwEDBvDXv/7Vo581JTkN5Ofnx5AhQ1iwYIHrmMPhYMGCBaSlpXkxspalsLCQHTt2EB8f7+1QWozOnTsTFxdX5bOXn5/PsmXL9Nlz0759+zh8+HCb/vwZhsG0adP49NNPWbhwIZ07d67y+pAhQ/D19a3yeUtPTycjI6NNf95O9b7VZM2aNQBt+vNWE4fDQWlpqWc/a56tjW6bZs+ebfj7+xtvv/22sWnTJuOGG24wIiIijMzMTG+H1mzdeeedxqJFi4xdu3YZS5YsMUaPHm1ERUUZ2dnZ3g6tWSkoKDBWr15trF692gCM559/3li9erWxZ88ewzAM4+mnnzYiIiKMefPmGevWrTPGjx9vdO7c2Th27JiXI/euk71vBQUFxl133WUsXbrU2LVrl/Hdd98ZgwcPNrp3726UlJR4O3Svufnmm43w8HBj0aJFxsGDB12P4uJi1zk33XST0bFjR2PhwoXGL7/8YqSlpRlpaWlejNr7TvW+bd++3XjssceMX375xdi1a5cxb948o0uXLsaZZ57p5ci969577zV++OEHY9euXca6deuMe++917BYLMY333xjGIbnPmtKcjzkb3/7m9GxY0fDz8/PGD58uPHzzz97O6Rm7YorrjDi4+MNPz8/IzEx0bjiiiuM7du3ezusZuf77783gGqPSZMmGYZhTiN/6KGHjNjYWMPf398499xzjfT0dO8G3Qyc7H0rLi42zjvvPCM6Otrw9fU1OnXqZFx//fVt/j8lNb1fgPHWW2+5zjl27Jhxyy23GJGRkUZQUJBxySWXGAcPHvRe0M3Aqd63jIwM48wzzzTatWtn+Pv7G926dTP+9Kc/GXl5ed4N3Mv+8Ic/GJ06dTL8/PyM6Oho49xzz3UlOIbhuc+axTAMo549SyIiIiLNlmpyREREpFVSkiMiIiKtkpIcERERaZWU5IiIiEirpCRHREREWiUlOSIiItIqKckRERGRVklJjoiIiLRKSnJEpM2zWCzMnTvX22GIiIcpyRERr5o8eTIWi6XaY9y4cd4OTURaOB9vByAiMm7cON56660qx/z9/b0UjYi0FurJERGv8/f3Jy4ursojMjISMIeSXn31Vc4//3wCAwPp0qULH330UZXr169fz6hRowgMDKR9+/bccMMNFBYWVjnnzTffpE+fPvj7+xMfH8+0adOqvJ6Tk8Mll1xCUFAQ3bt357PPPmvcH1pEGp2SHBFp9h566CEuu+wy1q5dyzXXXMOVV17J5s2bASgqKmLs2LFERkayYsUKPvzwQ7777rsqScyrr77K1KlTueGGG1i/fj2fffYZ3bp1q3KPRx99lMsvv5x169ZxwQUXcM0113DkyJEm/TlFxMM8t3G6iIj7Jk2aZNhsNiM4OLjK48knnzQMwzAA46abbqpyTWpqqnHzzTcbhmEYr7/+uhEZGWkUFha6Xv/iiy8Mq9VqZGZmGoZhGAkJCcYDDzxQawyA8eCDD7q+LywsNADjq6++8tjPKSJNTzU5IuJ155xzDq+++mqVY+3atXM9T0tLq/JaWloaa9asAWDz5s0MGDCA4OBg1+sjR47E4XCQnp6OxWLhwIEDnHvuuSeNoX///q7nwcHBhIWFkZ2dXd8fSUSaASU5IuJ1wcHB1YaPPCUwMLBO5/n6+lb53mKx4HA4GiMkEWkiqskRkWbv559/rvZ9r169AOjVqxdr166lqKjI9fqSJUuwWq2kpKQQGhpKcnIyCxYsaNKYRcT71JMjIl5XWlpKZmZmlWM+Pj5ERUUB8OGHHzJ06FBOP/10/v3vf7N8+XLeeOMNAK655hoefvhhJk2axCOPPMKhQ4e49dZb+f3vf09sbCwAjzzyCDfddBMxMTGcf/75FBQUsGTJEm699dam/UFFpEkpyRERr5s/fz7x8fFVjqWkpLBlyxbAnPk0e/ZsbrnlFuLj4/nggw/o3bs3AEFBQXz99dfcdtttDBs2jKCgIC677DKef/55V1uTJk2ipKSEF154gbvuuouoqCh++9vfNt0PKCJeYTEMw/B2ECIitbFYLHz66adMmDDB26GISAujmhwRERFplZTkiIiISKukmhwRadY0oi4i9aWeHBEREWmVlOSIiIhIq6QkR0RERFolJTkiIiLSKinJERERkVZJSY6IiIi0SkpyREREpFVSkiMiIiKt0v8Dx/n8CXfHL60AAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Eğitim ve Doğrulama Kaybı\n",
+        "plt.plot(history.history['loss'], label='Eğitim Kaybı')\n",
+        "plt.plot(history.history['val_loss'], label='Doğrulama Kaybı')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()\n",
+        "\n",
+        "# Eğitim ve Doğrulama Doğruluğu\n",
+        "plt.plot(history.history['accuracy'], label='Eğitim Doğruluğu')\n",
+        "plt.plot(history.history['val_accuracy'], label='Doğrulama Doğruluğu')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WTTfsUN6EnhW",
+        "outputId": "08742c57-6788-4cc8-8511-db54b4e0b898"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "20/20 [==============================] - 1s 25ms/step - loss: 0.1939 - accuracy: 0.9536\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "0.9535999894142151"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "loss,acc=model.evaluate(x_test,y_test)\n",
+        "acc"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 474
+        },
+        "id": "GWcfZDd2EnfD",
+        "outputId": "b6392290-6c5e-4a1b-d4e6-becb37f235bf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "20/20 [==============================] - 0s 8ms/step\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "predict=model.predict(x_test)\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "import numpy as np\n",
+        "\n",
+        "\n",
+        "sns.heatmap(confusion_matrix(y_test, np.argmax(predict, axis=1)), annot=True, fmt='d', annot_kws={\"size\": 16})\n",
+        "plt.title('Confusion Matrix', fontsize=18)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "DdaZnEo4Euyn"
+      },
+      "outputs": [],
+      "source": [
+        "model.save('MalariaModel95.h5')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T9w4IGt9MxuZ"
+      },
+      "source": [
+        "# VGG - 16 ile"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "KED3Hm6HMsHD"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import shutil\n",
+        "import random\n",
+        "\n",
+        "def create_small_dataset(source_dir, target_dir, target_size):\n",
+        "    classes = os.listdir(source_dir)\n",
+        "    for cls in classes:\n",
+        "        os.makedirs(os.path.join(target_dir, cls), exist_ok=True)\n",
+        "\n",
+        "    for cls in classes:\n",
+        "        cls_path = os.path.join(source_dir, cls)\n",
+        "        all_files = os.listdir(cls_path)\n",
+        "        sample_files = random.sample(all_files, min(len(all_files), target_size // len(classes)))\n",
+        "\n",
+        "        for file_name in sample_files:\n",
+        "            shutil.copy(os.path.join(cls_path, file_name), os.path.join(target_dir, cls, file_name))\n",
+        "\n",
+        "# Kaynak ve hedef dizinler\n",
+        "source_path = '/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN/cell_images/cell_images'\n",
+        "small_dataset_path = '/content/drive/MyDrive/Sıtma Hastalığı veri Ve Ipnd dosyası CNN/cell_images/small_dataset'\n",
+        "create_small_dataset(source_path, small_dataset_path, 1500)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AeQJfuuKNB9d",
+        "outputId": "d82825fa-6de8-4649-c264-339afec080a5"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Found 1500 images belonging to 2 classes.\n",
+            "Found 1500 images belonging to 2 classes.\n",
+            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+            "80134624/80134624 [==============================] - 1s 0us/step\n",
+            "Epoch 1/10\n",
+            "46/46 [==============================] - 31s 556ms/step - loss: 0.8565 - accuracy: 0.6587 - val_loss: 0.5083 - val_accuracy: 0.8159\n",
+            "Epoch 2/10\n",
+            "46/46 [==============================] - 25s 552ms/step - loss: 0.4796 - accuracy: 0.7841 - val_loss: 0.4367 - val_accuracy: 0.7840\n",
+            "Epoch 3/10\n",
+            "46/46 [==============================] - 22s 472ms/step - loss: 0.3916 - accuracy: 0.8317 - val_loss: 0.3565 - val_accuracy: 0.8512\n",
+            "Epoch 4/10\n",
+            "46/46 [==============================] - 23s 493ms/step - loss: 0.3297 - accuracy: 0.8651 - val_loss: 0.3094 - val_accuracy: 0.8838\n",
+            "Epoch 5/10\n",
+            "46/46 [==============================] - 25s 552ms/step - loss: 0.3113 - accuracy: 0.8733 - val_loss: 0.2852 - val_accuracy: 0.8832\n",
+            "Epoch 6/10\n",
+            "46/46 [==============================] - 22s 481ms/step - loss: 0.2888 - accuracy: 0.8753 - val_loss: 0.2610 - val_accuracy: 0.8906\n",
+            "Epoch 7/10\n",
+            "46/46 [==============================] - 22s 483ms/step - loss: 0.2732 - accuracy: 0.8883 - val_loss: 0.2781 - val_accuracy: 0.8764\n",
+            "Epoch 8/10\n",
+            "46/46 [==============================] - 25s 557ms/step - loss: 0.2626 - accuracy: 0.8856 - val_loss: 0.2464 - val_accuracy: 0.8961\n",
+            "Epoch 9/10\n",
+            "46/46 [==============================] - 22s 484ms/step - loss: 0.2631 - accuracy: 0.9033 - val_loss: 0.2180 - val_accuracy: 0.9192\n",
+            "Epoch 10/10\n",
+            "46/46 [==============================] - 27s 598ms/step - loss: 0.2353 - accuracy: 0.9149 - val_loss: 0.2287 - val_accuracy: 0.9015\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+        "from tensorflow.keras.applications import VGG19\n",
+        "from tensorflow.keras.models import Model\n",
+        "from tensorflow.keras.layers import Dense, Flatten\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from sklearn.metrics import confusion_matrix, classification_report\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Verileri hazırlama\n",
+        "train_datagen = ImageDataGenerator(\n",
+        "    rescale=1./255,\n",
+        "    shear_range=0.2,\n",
+        "    zoom_range=0.2,\n",
+        "    horizontal_flip=True\n",
+        ")\n",
+        "\n",
+        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
+        "\n",
+        "train_generator = train_datagen.flow_from_directory(\n",
+        "    small_dataset_path,\n",
+        "    target_size=(150, 150),\n",
+        "    batch_size=32,\n",
+        "    class_mode='binary'\n",
+        ")\n",
+        "\n",
+        "validation_generator = test_datagen.flow_from_directory(\n",
+        "    small_dataset_path,\n",
+        "    target_size=(150, 150),\n",
+        "    batch_size=32,\n",
+        "    class_mode='binary'\n",
+        ")\n",
+        "\n",
+        "# VGG19 modelini yükleme (önceden eğitilmiş ağırlıklarla)\n",
+        "base_model = VGG19(weights='imagenet', include_top=False, input_shape=(150, 150, 3))\n",
+        "\n",
+        "# Modelin üst kısmını oluşturma\n",
+        "x = base_model.output\n",
+        "x = Flatten()(x)\n",
+        "x = Dense(256, activation='relu')(x)\n",
+        "predictions = Dense(1, activation='sigmoid')(x)\n",
+        "\n",
+        "# Yeni model\n",
+        "model = Model(inputs=base_model.input, outputs=predictions)\n",
+        "\n",
+        "# Önceden eğitilmiş katmanları dondurma\n",
+        "for layer in base_model.layers:\n",
+        "    layer.trainable = False\n",
+        "\n",
+        "# Modeli derleme\n",
+        "model.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Modeli eğitme\n",
+        "history = model.fit(\n",
+        "    train_generator,\n",
+        "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
+        "    epochs=10,\n",
+        "    validation_data=validation_generator,\n",
+        "    validation_steps=validation_generator.samples // validation_generator.batch_size\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "id": "r-H-3KynUIFT"
+      },
+      "outputs": [],
+      "source": [
+        "model.save('MalariaModelVGG19.h5')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5lbKPxUbUgDZ"
+      },
+      "source": [
+        "# Sonuç olarak  Modellerimiz CNN ile Hemde VGG19 ile başarılı bir sonuca getirebildik  accuracy 91 VGG19 için"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b4-DfCZiUj8r"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}