D4_Python_Kutuphaneleri_SLTPK.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "YAPAY ZEKA dersinin D2 Bölümünde;\n",
        "\n",
        "\"Scikit-Learn\" kütüphanesi:\n",
        "\n",
        "\"Tensorflow\" kütüphanesi:\n",
        "\n",
        "\"Pytorch\" kütüphanesi:\n",
        "\n",
        "\"Keras\" kütüphanesi:\n",
        "\n",
        "YAPAY ZEKA dersi kütüphane kullanım ve örneklerini göreceğiz.\n",
        "\n",
        "İGÜ - Öğr. Gör. Ali ÇETİNKAYA - 2024"
      ],
      "metadata": {
        "id": "fG2R7eT-_glD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2e8XKjoM38rr",
        "outputId": "e516195d-6b13-4482-eba9-ac63fd6ad977"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Merhaba, İGÜ - YAPAY ZEKA geliştirilmesi için kullanılacak kütüphaneler - D3\n"
          ]
        }
      ],
      "source": [
        "print(\"Merhaba, İGÜ - YAPAY ZEKA geliştirilmesi için kullanılacak kütüphaneler - D3\") # print ekrana çıktı veren bir koddur."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys # import python programlama dilinde yazılıma kütüphane eklemeye yarayan bir kod blogudur.\n",
        "print(sys.version)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhlhMuWCAKHz",
        "outputId": "4e391fae-0a25-47af-def7-08e4cd640319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive # google colab - drive bağlantısı\n",
        "\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "es1snaCgALR4",
        "outputId": "5646a1f5-abad-4932-d6f6-825c749ceeb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.chdir('/content/drive/My Drive/YAPAYZEKA_PYTHON')\n",
        "\n",
        "!pwd # bize kodun çıktısını dosya yolu olarak veren kod blogudur."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "G-1ZS52uAShZ",
        "outputId": "8c1da569-f7eb-40ed-b921-4b5caab5abe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/YAPAYZEKA_PYTHON\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## PYTHON PROGRAMLAMA DİLİ KÜTÜPHANELERİ - Scikit-Learn, TensorFlow, PyTorch, Keras\n",
        "\n",
        "--------------------------------------------------------------------------------\n"
      ],
      "metadata": {
        "id": "2sw9XZYHAaAS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CPU ve GPU teknolojisinin kullanımına göre olması gereken veri sayısı tablosu\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "tablo = {\n",
        "    \"Aralık\": [\"Küçük ölçekli\", \"Orta ölçekli\", \"Büyük ölçekli\", \"Hiper ölçekli\", \"Exascale\"],\n",
        "    \"Veri Sayısı\": [\"100 - 10,000\", \"10,000 - 100,000\", \"100,000 - Milyon\",  \"Milyon - Milyar\", \"Milyar - Trilyon\"],\n",
        "    \"CPU\": [\"Evet\", \"Evet\", \"Opsiyonel\", \"Hayır\", \"Hayır\"],\n",
        "    \"GPU\": [\"Hayır\", \"Opsiyonel\", \"Evet\", \"Evet\", \"Evet\"]\n",
        "}\n",
        "\n",
        "print(pd.DataFrame(tablo))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJpIN7KVbl8E",
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      "execution_count": null,
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          "text": [
            "          Aralık       Veri Sayısı        CPU        GPU\n",
            "0  Küçük ölçekli      100 - 10,000       Evet      Hayır\n",
            "1   Orta ölçekli  10,000 - 100,000       Evet  Opsiyonel\n",
            "2  Büyük ölçekli  100,000 - Milyon  Opsiyonel       Evet\n",
            "3  Hiper ölçekli   Milyon - Milyar      Hayır       Evet\n",
            "4       Exascale  Milyar - Trilyon      Hayır       Evet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "--------------------------------------------------------------------------------\n",
        "Kütüphane Kurulumunda dikkat edilmesi gereken en önemli konu:\n",
        "--------------------------------------------------------------------------------\n",
        "\n",
        "Genel kullanım bakımından python kütüphanesi kurucağımız zaman dikkat edilmesi gereken iki yapı bulunmaktadır. Bu yapılar \"pip\" ve \"pip3\"'tür.\n",
        "\n",
        "pip yapısı -> Python 2.x sürümlerini destekler.\n",
        "\n",
        "pip3 yapısı -> Python 3.x sürümlerini destekler.\n",
        "\n"
      ],
      "metadata": {
        "id": "argYlBm-bmpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN)\n",
        "matris_bilgisi = {\n",
        "    \"Gerçek Pozitif\": [\"Doğru Pozitif (TP)\", \"Yanlış Negatif (FN)\"],\n",
        "    \"Gerçek Negatif\": [\"Yanlış Pozitif (FP)\", \"Doğru Negatif (TN)\"]\n",
        "}\n",
        "\n",
        "# DataFrame oluşturma\n",
        "matris = pd.DataFrame(matris_bilgisi, index=[\"Tahmin Pozitif\", \"Tahmin Negatif\"])\n",
        "\n",
        "# Tabloyu gösterme\n",
        "print(matris)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuvKsDPdvJlu",
        "outputId": "28432526-f2cb-4ea5-c585-17adb7f3ee12"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "                     Gerçek Pozitif       Gerçek Negatif\n",
            "Tahmin Pozitif   Doğru Pozitif (TP)  Yanlış Pozitif (FP)\n",
            "Tahmin Negatif  Yanlış Negatif (FN)   Doğru Negatif (TN)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karmaşıklık matrisinden sistemin doğruluğunu bulacağımız formül ->\n",
        "\n",
        "Dogruluk = TP+TN+FP+FN / TP+TN 'dir.\n",
        "\n",
        "Örneğin: TP = 50, TN = 40, FP = 5, FN = 5 ise;\n",
        "Dogruluk = 50 + 40 + 5 + 5 / 50 + 40 --> 0.9 -> % 90 olarak hesaplanmaktadır.\n",
        "\n",
        "%90 Model Doğruluk Oranı - %10 Modelin Hata Oranı 'dır.\n",
        "\n",
        "Hata oranını azaltmak için->\n",
        "\n",
        "* Modelin iyileştirilmesi,\n",
        "* Veri sayısının arttırılması,\n",
        "* Eğitim adım sayısının değiştirilmesi,\n",
        "* Veri seti içerisindeki farklı değişlenlerin - özelliklerin algoritmaya dahil edilmesi."
      ],
      "metadata": {
        "id": "9W4UdRCLwS4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scikit-Learn kütüphanesinin sisteme dahil edilmesi ve veri setinin işleme alınması\n",
        "\n",
        "!pip install -U scikit-learn"
      ],
      "metadata": {
        "id": "EZuBUrsNyJNd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "1. Scikit-Learn kütüphanesi:\n",
        "\n",
        "* !pip install -U scikit-learn\n",
        "\n"
      ],
      "metadata": {
        "id": "9yhyHAlXveYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "import pandas as pd\n",
        "\n",
        "# California Housing veri setini yükleme\n",
        "housing = fetch_california_housing()\n",
        "\n",
        "# Özellikleri ve hedef değişkeni almak\n",
        "X = housing.data  # Bağımsız değişkenler (özellikler)\n",
        "y = housing.target  # Bağımlı değişken (fiyatlar)\n",
        "\n",
        "# Verileri bir DataFrame'e dönüştürme\n",
        "df = pd.DataFrame(X, columns=housing.feature_names)\n",
        "df['PRICE'] = y\n",
        "\n",
        "# İlk birkaç satırı görüntüleme\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50CQkAjOqcjg",
        "outputId": "23cec87a-1b84-4f44-d5ca-c511b2f3efa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
            "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
            "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
            "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
            "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
            "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
            "\n",
            "   Longitude  PRICE  \n",
            "0    -122.23  4.526  \n",
            "1    -122.22  3.585  \n",
            "2    -122.24  3.521  \n",
            "3    -122.25  3.413  \n",
            "4    -122.25  3.422  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import pandas as pd\n",
        "\n",
        "# California Housing veri setini yükleme\n",
        "housing = fetch_california_housing()\n",
        "df = pd.DataFrame(housing.data, columns=housing.feature_names)\n",
        "df['PRICE'] = housing.target\n",
        "\n",
        "# Özellikler ve hedef değişkeni ayırma\n",
        "X = df.drop('PRICE', axis=1)\n",
        "y = df['PRICE']\n",
        "\n",
        "# Veri setini bölme\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Model oluşturma\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Tahmin yapma\n",
        "predictions = model.predict(X_test)\n",
        "print(\"Tahmin Sonucları: \", predictions[:5])\n",
        "print(\"Gerçek Değerler: \", y_test[:5].values)\n",
        "\n",
        "# Sistemin Ortalama Hatası (MSE)\n",
        "mse = mean_squared_error(y_test, predictions)\n",
        "print(\"Mean Squared Error (MSE) Sonucu:\", mse)\n",
        "\n",
        "# Sistemin R-kare Hatası (R²)\n",
        "r2 = r2_score(y_test, predictions)\n",
        "print(\"R-squared (R²) Sonucu:\", r2)"
      ],
      "metadata": {
        "id": "BtviwHBKAfbE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16bbfb32-d8f2-4cbb-e92a-ee1a163d213a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tahmin Sonuclar:  [0.72604907 1.76743383 2.71092161 2.83514727 2.60695807]\n",
            "Gerçek Değerler:  [0.477   0.458   5.00001 2.186   2.78   ]\n",
            "Mean Squared Error (MSE) Sonucu: 0.5305677824766758\n",
            "R-squared (R²) Sonucu: 0.595770232606166\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TensorFlow kütüphanesinin sisteme dahil edilmesi ve veri setinin işleme alınması\n",
        "\n",
        "#!pip install pydot\n",
        "#!apt-get install graphviz\n",
        "\n",
        "# Bu iki kütüphane ile oluşturulan yapay zeka modelinin yapısını çıktı alabileceğiz."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIVsR0IuydLu",
        "outputId": "732089bc-ad6b-4ab0-97fd-264c47b986d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pydot in /usr/local/lib/python3.10/dist-packages (3.0.1)\n",
            "Requirement already satisfied: pyparsing>=3.0.9 in /usr/local/lib/python3.10/dist-packages (from pydot) (3.1.4)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "graphviz is already the newest version (2.42.2-6ubuntu0.1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------------------------------------------------------------------------\n",
        "\n",
        "2. TensorFlow kütüphanesi:\n",
        "\n",
        "* !pip install tensorflow\n",
        "\n",
        "* !pip install tensorflow-gpu\n",
        "\n",
        "--------------------------------------------------------------------------------\n"
      ],
      "metadata": {
        "id": "Fo30iK6BAfwq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.utils import plot_model\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data() # MNIST veri setini yükleme\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "!pwd"
      ],
      "metadata": {
        "id": "2kOxWAd1C3l6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4e7d898-e6a4-4858-b2ee-83e357a2fac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/YAPAYZEKA_PYTHON\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Yapay zeka modeli oluşturmada aşamalar;\n",
        "--------------------------------------------------------------------------------\n",
        "\n"
      ],
      "metadata": {
        "id": "aCZbSO56AF2g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.Sequential([ # Modeli oluşturma\n",
        "    layers.Flatten(input_shape=(28, 28)),\n",
        "    layers.Dense(128, activation='relu'),\n",
        "    layers.Dropout(0.2),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "el_USd1L_4h-",
        "outputId": "ac8ce000-882c-4636-9574-31e9b8966184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelin eğitim süreci\n",
        "\n"
      ],
      "metadata": {
        "id": "S4zs3qC6CF8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', # Modeli derleme\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "xmx_AMS0CFB4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelin eğitim süreci\n",
        "\n",
        "--------------------------------------------------------------------------------\n",
        "\n",
        "5. model eğitim işlemi --> model.fit()\n",
        "\n",
        "--------------------------------------------------------------------------------\n",
        "\n",
        "6. model eğitim işlemi --> model.fit(x_train, y_train, ...)\n",
        "\n",
        "--------------------------------------------------------------------------------\n",
        "\n",
        "7. Eğitim Adım Sayısı --> epochs=5\n",
        "\n",
        "--------------------------------------------------------------------------------"
      ],
      "metadata": {
        "id": "gLM7IOvDD7HU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=5) # Modeli Eğitilmesi Adımı\n",
        "\n",
        "# Modeli görselleştirmek ve çalışma alanına kaydetmek için plot_model kullanımı\n",
        "plot_model(model, to_file='YZ_Model/yz_tensoflow_model_ciktisi.png', show_shapes=True, show_layer_names=True) # Modelin Çıktısı"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-kBT6tF-D6S_",
        "outputId": "05e1ee29-e465-4f1f-acda-636c321cceb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.8577 - loss: 0.4793\n",
            "Epoch 2/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.9555 - loss: 0.1505\n",
            "Epoch 3/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - accuracy: 0.9655 - loss: 0.1112\n",
            "Epoch 4/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.0896\n",
            "Epoch 5/5\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.9774 - loss: 0.0713\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test seti üzerinde modelin performansını değerlendirme\n",
        "\n",
        "hata_oranı, basarı_oranı = model.evaluate(x_test, y_test, verbose=2)\n",
        "\n",
        "print(\" Modelin Doğruluk Oranı: %\", basarı_oranı*100)\n",
        "print(\" Modelin Hata Oranı: %\", hata_oranı*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLym7xVA7Ode",
        "outputId": "0cda8b07-7d5a-4c35-cfbb-a7a96652acf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - 2ms/step - accuracy: 0.9772 - loss: 0.0737\n",
            " Modelin Doğruluk Oranı: % 97.71999716758728\n",
            " Modelin Hata Oranı: % 7.369861751794815\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. PyTorch kütüphanesi:\n",
        "\n",
        "* !pip install torch torchvision torchaudio\n",
        "\n",
        "--------------------------------------------------------------------------------"
      ],
      "metadata": {
        "id": "tHG0qFMPAuyt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# MNIST veri setini yükleme\n",
        "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
        "train_set = datasets.MNIST('./data', train=True, download=True, transform=transform)\n",
        "test_set = datasets.MNIST('./data', train=False, transform=transform)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)\n",
        "\n",
        "# Modeli oluşturma\n",
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(28 * 28, 128)\n",
        "        self.fc2 = nn.Linear(128, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 28 * 28)\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = self.fc2(x)\n",
        "        return x\n",
        "\n",
        "model = Net()\n",
        "\n",
        "# Kayıp fonksiyonu ve optimizer\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
        "\n",
        "# Eğitim için boş listeler tanımla\n",
        "train_losses = []\n",
        "train_accuracies = []\n",
        "\n",
        "# Eğitme döngüsü\n",
        "for epoch in range(50):  # 50 epoch boyunca eğit\n",
        "    running_loss = 0.0\n",
        "    correct = 0\n",
        "    total = 0\n",
        "\n",
        "    for images, labels in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        output = model(images)\n",
        "        loss = criterion(output, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        # Kayıp (loss) değerini topla\n",
        "        running_loss += loss.item()\n",
        "\n",
        "        # Doğruluğu hesapla\n",
        "        _, predicted = torch.max(output, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "    # Her epoch sonunda ortalama kayıp ve doğruluk\n",
        "    epoch_loss = running_loss / len(train_loader)\n",
        "    epoch_accuracy = 100 * correct / total\n",
        "    train_losses.append(epoch_loss)\n",
        "    train_accuracies.append(epoch_accuracy)\n",
        "\n",
        "    print(f'Epoch {epoch+1}, Loss: {epoch_loss}, Accuracy: {epoch_accuracy}%')\n",
        "\n",
        "print('Eğitim tamamlandı!')\n",
        "\n",
        "# Eğitim sonuçlarını grafikle göster\n",
        "epochs = range(1, 51)  # 50 epoch olduğu için 1'den 51'ya kadar olan epoch'lar\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Kayıp grafiği\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs, train_losses, 'bo-', label='Eğitim Kaybı (Loss)')\n",
        "plt.title('Eğitim Kaybı')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "# Doğruluk grafiği\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs, train_accuracies, 'go-', label='Eğitim Doğruluğu (Accuracy)')\n",
        "plt.title('Eğitim Doğruluğu')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy (%)')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "V4XS349MDAWU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e763e414-f86a-4cec-a63a-4889c2e39b6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1, Loss: 0.7402880122221863, Accuracy: 82.16%\n",
            "Epoch 2, Loss: 0.3646549315753776, Accuracy: 89.52833333333334%\n",
            "Epoch 3, Loss: 0.31957723606211036, Accuracy: 90.78%\n",
            "Epoch 4, Loss: 0.2925242402358477, Accuracy: 91.53666666666666%\n",
            "Epoch 5, Loss: 0.2701682077605587, Accuracy: 92.23%\n",
            "Epoch 6, Loss: 0.25112159168923587, Accuracy: 92.77166666666666%\n",
            "Epoch 7, Loss: 0.23383397759913382, Accuracy: 93.36333333333333%\n",
            "Epoch 8, Loss: 0.21850239026791124, Accuracy: 93.79%\n",
            "Epoch 9, Loss: 0.20503345177943771, Accuracy: 94.235%\n",
            "Epoch 10, Loss: 0.19280135948290386, Accuracy: 94.50833333333334%\n",
            "Epoch 11, Loss: 0.1817067781272632, Accuracy: 94.85333333333334%\n",
            "Epoch 12, Loss: 0.17227108058120524, Accuracy: 95.11166666666666%\n",
            "Epoch 13, Loss: 0.16364776450735546, Accuracy: 95.40166666666667%\n",
            "Epoch 14, Loss: 0.15582736536884295, Accuracy: 95.65%\n",
            "Epoch 15, Loss: 0.14837546172990665, Accuracy: 95.835%\n",
            "Epoch 16, Loss: 0.14197038593632516, Accuracy: 96.02833333333334%\n",
            "Epoch 17, Loss: 0.13588902171947428, Accuracy: 96.18833333333333%\n",
            "Epoch 18, Loss: 0.13041029982904254, Accuracy: 96.30333333333333%\n",
            "Epoch 19, Loss: 0.12515179008078664, Accuracy: 96.50333333333333%\n",
            "Epoch 20, Loss: 0.1204687255625325, Accuracy: 96.63166666666666%\n",
            "Epoch 21, Loss: 0.11581222353967777, Accuracy: 96.75%\n",
            "Epoch 22, Loss: 0.1119299744730438, Accuracy: 96.88666666666667%\n",
            "Epoch 23, Loss: 0.10808611153634881, Accuracy: 97.00333333333333%\n",
            "Epoch 24, Loss: 0.10488912013412983, Accuracy: 97.09%\n",
            "Epoch 25, Loss: 0.10104204647278767, Accuracy: 97.21333333333334%\n",
            "Epoch 26, Loss: 0.09797503498158475, Accuracy: 97.27833333333334%\n",
            "Epoch 27, Loss: 0.09490915087720891, Accuracy: 97.33666666666667%\n",
            "Epoch 28, Loss: 0.0922616542076696, Accuracy: 97.46666666666667%\n",
            "Epoch 29, Loss: 0.0893275170112406, Accuracy: 97.57333333333334%\n",
            "Epoch 30, Loss: 0.08706732698158225, Accuracy: 97.63%\n",
            "Epoch 31, Loss: 0.08458245849943777, Accuracy: 97.66666666666667%\n",
            "Epoch 32, Loss: 0.08209866562437242, Accuracy: 97.765%\n",
            "Epoch 33, Loss: 0.07999701889803701, Accuracy: 97.79166666666667%\n",
            "Epoch 34, Loss: 0.07784911176896711, Accuracy: 97.845%\n",
            "Epoch 35, Loss: 0.07580400512579566, Accuracy: 97.98166666666667%\n",
            "Epoch 36, Loss: 0.07404161936768305, Accuracy: 97.97166666666666%\n",
            "Epoch 37, Loss: 0.07214128850961227, Accuracy: 98.02666666666667%\n",
            "Epoch 38, Loss: 0.07028205854855954, Accuracy: 98.10166666666667%\n",
            "Epoch 39, Loss: 0.06851290072650035, Accuracy: 98.16%\n",
            "Epoch 40, Loss: 0.06702372053665902, Accuracy: 98.17333333333333%\n",
            "Epoch 41, Loss: 0.06535656977720908, Accuracy: 98.24666666666667%\n",
            "Epoch 42, Loss: 0.0641508738198546, Accuracy: 98.26666666666667%\n",
            "Epoch 43, Loss: 0.06274786662322276, Accuracy: 98.29%\n",
            "Epoch 44, Loss: 0.0610362534984144, Accuracy: 98.36666666666666%\n",
            "Epoch 45, Loss: 0.059825231024284546, Accuracy: 98.37833333333333%\n",
            "Epoch 46, Loss: 0.05855929693514938, Accuracy: 98.42666666666666%\n",
            "Epoch 47, Loss: 0.05762828825645323, Accuracy: 98.44333333333333%\n",
            "Epoch 48, Loss: 0.05641103605739772, Accuracy: 98.47%\n",
            "Epoch 49, Loss: 0.054917110594362814, Accuracy: 98.53%\n",
            "Epoch 50, Loss: 0.05379937892295182, Accuracy: 98.55833333333334%\n",
            "Eğitim tamamlandı!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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iNm7cqFdffbXQJX7d3f/+9z/9/vvvmjJlinr16sUqegAAuCmugYrWsWNHRUZGqmXLljp69Kjmz5+vjIyMCjl7vDLFClQ2lO8BcLilS5dq+vTp2rt3r86fP69mzZppxIgRGjVqlKtDc4nIyEht3LhRt9xyi9577z2FhIS4OiQAAOAAXAMV7bnnnlNCQoLS0tJkMBh0ww036MUXX1RUVJSrQyugMsUKVDYkpQAAAAAAAOB09JQCAAAAAACA05GUAgAAAAAAgNNVuUbneXl5Onz4sPz9/WUwGFwdDgAAqEDMZrOysrIUHBwsDw++uysO11QAAKAo9l5TVbmk1OHDhxUWFubqMAAAQAWWmpqq0NBQV4dRoXFNBQAASlLSNVWVS0r5+/tLsrwwAQEBLo4GAABUJJmZmQoLC7NeL6BoXFMBAICi2HtNVeWSUvnTywMCAriAAgAAhaIcrWRcUwEAgJKUdE1FswQAAAAAAAA4HUkpAAAAAAAAOB1JKQAAAAAAADhdlespBQAovby8POXk5Lg6DOCyeXp6ymg0ujqMKsVkMik3N9fVYQAoZ15eXsUu8w4A9iApBQAoVk5Ojvbv36+8vDxXhwKUi5o1a6pBgwY0M3cws9msI0eO6NSpU64OBYADeHh4qHHjxvLy8nJ1KAAqMZJSAIAimc1mpaeny2g0KiwsjG9EUamZzWadPXtWx44dkyQFBQW5OCL3lp+Qqlevnnx9fUkCAm4kLy9Phw8fVnp6uho2bMjfN4AyIykFACjShQsXdPbsWQUHB8vX19fV4QCXzcfHR5J07Ngx1atXj1I+BzGZTNaE1BVXXOHqcAA4QN26dXX48GFduHBBnp6erg4HQCXFV94AgCKZTCZJYmo+3Ep+gpU+R46T/9qSzAbcV/61Qf61AgCUBUkpAECJmJYPd8L/n52H1xpwX/x9AygPJKUAAAAAAADgdCSlAABV1i233KLt27fr77//1k033aQdO3ZY9x04cEAGg0G//PJLseeIjIzUmDFjHBuogyxatEg1a9Ysl3NNmDBBw4cPL5dzXY45c+aoR48erg4DVUBVev946qmn9J///Efnz5/X448/rlmzZpXLecv6HuSoeE6ePKl69erpwIED5XK+yu6+++7T9OnTXR0GADdHUqocmUxSUpK0bJnlJ+XVAGDh7PfHwYMHy2AwFLh169bNZtzYsWPVsWNH1a5dW82aNdO1115r3RcWFqb09HTrtqSkJBkMhgLL2ycmJmry5Mnl/hzCw8MVHx9vvW82m/XUU08pICBASUlJ5f54l+PIkSOaOXOmnn/+eeu2wYMHq1evXk6P5aGHHtLWrVuVnJzs9MeGY5jyTEo6kKRl25cp6UCSTHmOfQNxl/eP/Lh9fHwUHh6uvn376n//+1+Zzjd8+HC98sor8vHx0TfffKP+/fuXc8QVI54pU6aoZ8+eCg8PL7AvOjpaRqNRP/74Y7k8VmXwwgsvaMqUKcrIyHB1KABKwZ7PTWd/thaH1ffKSWKiFBcnpaX9sy00VJo5U4qJcV1cAOBqrnp/7NatmxYuXGizzdvb2+Z+nz591KtXL50/f15+fn42+4xGoxo0aFDi49SuXfvygy2ByWTSsGHD9Nlnn2nDhg1q27atwx+zNP7zn//o5ptvVqNGjVwdiry8vDRgwAC99dZbioiIcHU4uEyJOxMVtzpOaZn/vIGEBoRqZreZimnpuDcQd3j/ePnllzVs2DDl5OTowIEDeu+99xQVFaXJkyfbJJDtcdVVV+nAgQPKzMxUQEBAieNzcnIcukBGaeOxx9mzZzV//nytWbOmwL6UlBRt3LhRo0aN0oIFC3TjjTeWy2OWVW5urlNWu7v22mvVtGlTvffeexo5cqTDHw/A5bPnc9NVn61FYaZUOUhMlGJjbf/BJUmHDlm2Jya6Ji4AcDVXvj96e3urQYMGNrdatWpZ9//xxx+69dZb5efnpxtvvFHr1q2TwWDQxx9/LMm2/ObAgQPq1KmTJKlWrVoyGAwaPHiwpILlN+Hh4XrllVc0cOBA1ahRQ40aNdInn3yi48ePq2fPnqpRo4auu+46/fTTT3Y9j+zsbN17771at26dkpOTrQkpk8mkhx9+WI0bN5aPj4+aN2+umTNnWo/75ptv5OnpqSNHjticb8yYMQWSNR9//LGuvPJKVa9eXdHR0UpNTbXue+mll9SmTZtiY1y+fHmpS+a+/vprtW/fXt7e3goKCtKzzz6rCxcuWPcnJCSoVatW8vHx0RVXXKGoqCidOXNGkmXWSfv27eXn56eaNWvqlltu0cGDB63H9ujRQ5988onOnTtXqphQsSTuTFTsylibi2ZJOpR5SLErY5W403FvIO7w/uHv768GDRqoYcOGuu222zR37lxNmDBBEydO1K5du6zjSvpbzMrK0v333y8/Pz81b95cb775ZqFxT548WQMHDlRAQICGDx9e6OywX375RQaDocjyuMJmWI4ZM0aRkZGliufi/xb5atasqUWLFhX5en3xxRfy9vbWTTfdVGDfwoULdffdd2vEiBFatmxZgfeWU6dO6ZFHHlH9+vVVvXp1XXvttfrss8+s+7/77jtFRkbK19dXtWrVUnR0tP7++2/ra3fxrFhJatOmjV566SWb5zN79mzdc8898vPz05QpU0r8DMi3YMECXXPNNdb/vqNGjZJkmVV6991324zNzc1VvXr1NH/+fOu2Hj16aPny5UW+bgAun72zlkoaZ8/npis/W4tCUuoymUyWGQBmc8F9+dvGjKGUD4B7MJulM2fsu2VmSqNHF//+GBdnGWfP+Qo7T1mZTCb16tVLvr6++uGHHzR37txiZw6EhYXpww8/lCTt2rVL6enphV7853vzzTd1yy236Oeff9Zdd92lBx98UAMHDtQDDzygrVu3qmnTpho4cKDMJTyp06dP66677tLvv/+u7777Ts2bN7fuy8vLU2hoqD744AP9/vvvmjhxop577jmtXLlSknTbbbepSZMmWrJkifWY3Nxcvf/++3rooYes286ePaspU6bov//9r7777judOnVK9913X/Ev4EX++usv/f7772rXrp3dxxw6dEh33nmnbrzxRm3btk2zZ8/W/Pnz9corr0iS0tPT1b9/fz300EPauXOnkpKSFBMTI7PZrAsXLqhXr166/fbb9euvv2rTpk0aPny4zSpQ7dq104ULF/TDDz/YHRMcz2w260zOGbtumeczNfrL0TKr4N9I/ra4L+OUeT6zxHOV9HdWWpXl/aMwcXFxMpvNWrVqlaSS/xYlS5nid999p08++URr165VcnKytm7dWuDcb7zxhlq3bq2ff/5ZEyZMKHVs9rI3ntK6OOl/MbPZrIULF+qBBx5QixYt1KxZMyUkJFj35+XlqXv37vruu+/03nvv6ffff9drr70mo9EoyZKI69y5s66++mpt2rRJ3377rXr06CFTKf9x8NJLL6l3797avn27HnrooRI/AyRp9uzZGjlypIYPH67t27frk08+UbNmzSRJQ4cO1erVq5Wenm4d/9lnn+ns2bPq16+fdVv79u21efNmZWdnlypeABb2JJLCZ4ar0+JOGpA4QJ0Wd1L4zPACyaGSxpnyTIpbHVfs5+boL0dr1Bejih0zZvUYp5fyUb53mZKTC84AuJjZLKWmWsZd9CUPAFRKZ89KNWqUz7nMZsv7Z2CgfeNPn5YuqZAp1meffaYalwT73HPP6bnnntPatWu1b98+JSUlWUtspkyZoi5duhR6LqPRaC2zqVevXomNee+880498sgjkqSJEydq9uzZuvHGG3XvvfdKksaNG6eOHTvq6NGjxZb4TJ48Wf7+/tq5c6fq1q1rs8/T01OTJk2y3m/cuLE2bdqklStXqm/fvpKkhx9+WAsXLtTTTz8tSfr00091/vx5637JkqiaNWuWOnToIElavHixWrZsqc2bN6t9+/bFPk/JUtZiNpsVHBxc4th877zzjsLCwjRr1iwZDAa1aNFChw8f1rhx4zRx4kSlp6frwoULiomJsZYEtmrVSpIlCZaRkaG7775bTZs2lSS1bNnS5vy+vr4KDAy0mT0F1zube1Y1ppbPG4hZZqVlpSnw9ZLfQE6PPy0/r1K8ecg93j8KU7t2bZtG3iX9LZ45c0aLFy/W0qVL1blzZ0mWWUOF/b3fcccdevLJJ633L55xWV6ysrLsjqe0Dh48WOh51q1bp7Nnzyo6OlqS9MADD2j+/Pl68MEHrfs3b96snTt36qqrrpIkNWnSxHr8tGnT1K5dO73zzjvWbddcc02p4xswYICGDBlis62kz4BXXnlFTz75pOLi4qzj8ksPb775ZjVv3lxLlizRM888I8nyWt577702/98PDg5WTk6Ojhw5UiFKtAFHM+WZlJySrPSsdAX5BymiYYSMHsYyjSupTC5/1tKlSaL8WUsJfRPsGrcidoVyTDkFZj9dzCyzDmUdKva5m2VWamaqklOSFRkeWezY8kRS6jJd9OVCuYwDAJSPTp06afbs2Tbb8v9huGvXLoWFhdn8g86eBIy9rrvuOuvv9evXl/RPUuXibceOHSv2H5Vdu3bVunXr9Oqrr+rNN98ssP/tt9/WggULlJKSonPnziknJ8em1G7w4MF64YUX9P333+umm27SokWL1LdvX5v+N9WqVbPpj9KiRQvVrFlTO3futOs1yS9jqV69eolj8+3cuVMdO3a0md10yy236PTp00pLS1Pr1q3VuXNntWrVStHR0eratatiY2NVq1Yt1a5dW4MHD1Z0dLS6dOmiqKgo9e3bV0FBQTaP4ePjo7Nnz9odE3Axd3j/KIrZbLb+7ZX0t/j3338rNzfX5vkFBgbazNrMV5rZkmX1559/2h1PaZ07d67Q97EFCxaoX79+qlbN8s+m/v376+mnn9a+ffvUtGlT/fLLLwoNDbUmpC71yy+/WBOKl6Ow17e4z4Bjx47p8OHD1uRdYYYOHaq5c+fqmWee0dGjR/Xll18WaIbv4+MjSbyfokqwt9eSvX2bSkokjf1qbJGzlgwyKG51nG4KuUkjvxhZ7Oymvgl9C+y7HOlZzk1ekJS6TJdcA1/2OACoyHx9LTOW7PHNN9Kdd5Y87osvpNtus++xS8PPz89apuBsFzegzf/HXmHb8vLyij1P586d9fjjj6tnz57Ky8uzKflZvny5nnrqKU2fPl0dO3aUv7+//vWvf9mUrNWrV089evTQwoUL1bhxY3355ZflvnJfnTp1JEl///13gdlcZWU0GrV27Vpt3LhRX331lf7973/r+eef1w8//KDGjRtr4cKFGj16tFavXq0VK1bohRde0Nq1a216wfz111/lFg/Kh6+nr06Pt+8N5JuD3+jOpSW/gXwx4Avd1qj4NxBfz1K+ecg93j8Kc/LkSR0/flyNGze+zCgLurTZu4eHpUvIxWWGubm5xZ7Dw8OjQFliSccUxmAwlPo8derUsfZ5yvfXX3/po48+Um5urk2S0mQyacGCBZoyZYo1aVOUkvbb+5wvfX1L+gwo6XElaeDAgXr22We1adMmbdy4UY0bNy7Qc/Cvv/6SJN5PUaGVx+ym8pq1lNA3QT2b9yyxBH3gxwN1/sL5Ip+TWWalZaYp5M0Qu14Do8Eok7l8yu6C/J2bvKCn1GWKiLCsInXRF0w2DAYpLMwyDgAqO4PBUkJnz61rV/veH7t2te98RZ2nLJo3b67U1FQdPXrUuq2kZb7zV5IqbR+Qy9W1a1d9+umnmjdvnkaPHm3d/t133+nmm2/WY489puuvv17NmjXTvn37Chw/dOhQrVixQnPnzlXTpk11yy232Oy/cOGCTdPkXbt26dSpUwVK4orStGlTBQQE6Pfff7f7ObVs2VKbNm2y+YfYd999J39/f4WGhkqy/KPylltu0aRJk/Tzzz/Ly8tLH330kXX89ddfr/Hjx2vjxo269tprtXTpUuu+ffv26fz587r++uvtjgmOZzAY5OflZ9eta9OuCg0IlUGF/+EbZFBYQJi6Nu1a4rkM5fnmocr1/nGpmTNnysPDw9pMvKS/xSZNmsjT09Pm+WVkZGj37t0lPlZ+EuPinkW//PJLicekX1JecPEx9sZz6Xn27NlT4kyf66+/vsD72Pvvv6/Q0FBt27ZNv/zyi/U2ffp0LVq0SCaTSdddd53S0tKKfE2uu+46rV+/3u7nnJmZqf379xcbq1TyZ4C/v7/Cw8OLfewrrrhCvXr10sKFC7Vo0aIC5YGStGPHDoWGhlq/gACcyZ4G4OXRk8mefkwjvxipLYe2aMTnI4ocZ5ZZ9yXcp8DXAksslSsuIVUWC3stLPFzM9Q/VKH+JX+2RjR0bvKCpNRlMhoty5pLBf/BlH8/Pt4yDgCqEle/P2ZnZ+vIkSM2txMnTkiSunTpoqZNm2rw4MH67bff9P3331sbFRf1D9hGjRrJYDDos88+0/Hjx3Xa3ilj5SAqKkqfffaZ5s+fb1056corr9RPP/2kNWvWaPfu3ZowYUKh/zCOjo5WQECAXnnllUL/weHp6anHH39cP/zwg7Zs2aLBgwfrpptusrscycPDQ1FRUfr2228L7MvIyLD5h9wvv/yi1NRUPfbYY0pNTdXjjz+uP/74Q6tWrdKLL76osWPHysPDQz/88INeffVV/fTTT0pJSVFiYqKOHz+uli1bav/+/Ro/frw2bdqkgwcP6quvvtKePXtskmjJyclq0qSJtecUKh+jh1Ezu1neQC69eM6/H98tvtBvwsuDO7x/ZGVl6ciRI0pNTdU333yj4cOH65VXXtGUKVOss8BK+lv09/fXoEGD9PTTT+ubb77RH3/8oaFDh8rDw6PEZF+zZs0UFhaml156SXv27NHnn3+u6dOnF3vMHXfcoZ9++kn//e9/tWfPHr344ovasWOHdb+98dxxxx2aNWuWfv75Z/3000969NFHbWabFSY6Olq//fabzWyp+fPnKzY2Vtdee63N7eGHH9aJEye0evVq3X777brtttvUp08frV27Vvv379eXX36p1atXS5LGjx+vH3/8UY899ph+/fVX/fHHH5o9e7b1/0933HGHlixZouTkZG3fvl2DBg2yNkkvjj2fAS+99JKmT5+ut956S3v27NHWrVv173//22bM0KFDtXjxYu3cuVODBg0q8DjJycnq2rVrifEApVFeySZ7V5IradyIz0eU2I/pyOkjavefdjp25lixzy03L1dncs+U+BrY640ub9g1LiwgrMTPzZndZ2pmd9d9thaFpFQ5iImREhKkkEtm1oWGWrbHxBR+HAC4O1e+P65evVpBQUE2t1tvvVWSpTzs448/VlZWlm644QYNGTLE+o/KonojhYSEaNKkSXr22WdVv359a3LIWe644w59/vnnWrRokUaOHKlHHnlEMTEx6tevnzp06KCTJ0/qscceK3Cch4eHBg8eLJPJpIEDBxbY7+vrq3HjxmnAgAG65ZZbVKNGDa1YsaJUsQ0dOlTLly8vUE6UlJSk66+/3uY2adIkhYSE6IsvvtDmzZvVunVrPfroo3r44Yf1wgsvSJICAgL0zTff6M4779RVV12lF154QdOnT1f37t3l6+urP/74Q3369NFVV12l4cOHW1+PfMuWLdOwYcNK9RxQ8cS0jFFC3wSFBNi+gYQGhFrLKBzFHd4/Jk6cqKCgIDVr1kwPPvigMjIytH79eo0bN84mruL+FiVpxowZ6tixo7p3765OnTqpY8eOatmyZYl95Dw9PbVs2TL98ccfuu666/T666/brOpXmOjoaE2YMEHPPPOMbrzxRmVlZRV437InnunTpyssLEwREREaMGCAnnrqKfmWUAPeqlUr3XDDDdbV67Zs2aJt27apT58+BcYGBgaqc+fOmj9/viTpww8/1I033qj+/fvr6quv1jPPPGOdFXfVVVfpq6++0rZt29S+fXt17NhRq1atsvaoGj9+vG6//Xbdfffduuuuu9SrVy+7Eur2fAYMGjRI8fHxeuedd3TNNdfo7rvv1p49e2zGREVFKSgoSNHR0QUavZ8/f14ff/wx76colfJYbc6eZJM9s5vivozTwb8PauTnRfdkMsuseVvn2fXcfKqVXBYrSY/c8EjJgyTV9a1b4qylx9s/btfM4YiGEXZ9brrys7UoBnN5r5NbwWVmZiowMFAZGRkKCAgo13ObTFLr1tJvv0kvvyw99xwzpABUbufPn9f+/fvVuHHjUjWyvpTJZFmFND3d0mMvIqLivT9+9913uvXWW7V37163m2Hz8MMP6/jx4/rkk08ccn6z2awOHTroiSeeUP/+/R3yGPb67bffdMcdd2j37t0KLGJpx+L+f+3I6wR3U9xrVV7vHZL9vUJcyZ3fPy515swZhYSEaPr06Xr44YddHU65xvP555/r6aef1o4dO6w9sdzd6dOnFRISooULFyrmkm+KZs+erY8++khfffVVoceW5985Kj5HrjaXn3DJ78cUPjO82JlL/l7+ur3R7fpsz2fl+AxL9mb0m3pizRMljlv34DoNXjVYhzIPFZoMM8ig0IBQzeg6w9qk/OJxF78eF79uJY3LZ89/K2d8ttp7TUWj83JkNFpmA/z2m9SoUcX7BxcAuIrRKEVGujoKWx999JFq1KihK6+8Unv37lVcXJxuueUWt/oHZUZGhrZv366lS5c6LCElWUqW5s6dq+3btzvsMeyVnp6u//73v0UmpFD5GD2MTl2a2h5V4f0j388//6w//vhD7du3V0ZGhl5++WVJUs+ePd0unrvuukt79uzRoUOHFBYWdtnnq8jy8vJ04sQJTZ8+XTVr1tQ999xTYIynp2eBcj9UTc5YbU6Shn4yVEsaLSk2ISVJWTlZ5Z6Qqu1TW3+f+7vYJNJj7R7T9E3TS0w2RYZHama3mYpdGSuDDIUmkuK7xVtmLXkkFPra5u+X/pk5XNK4fPZ8blakz1aSUuXM39/y04mtRgAAZZCVlaVx48YpJSVFderUUVRUVIn9Tiqbnj17avPmzXr00UfVpUsXhz5WmzZtrEuRu1JUVJSrQ0AVUBXePy72xhtvaNeuXfLy8lLbtm2VnJzs0sbXjoxnzJgx5XKeii4lJUWNGzdWaGioFi1aZC0nvNjQoUNdEBmcraQZM/auNldSKd2gjwfp3IVzxcby9/m/9fGuj+2K+7aGt+mblG9KHDej6wyN/WpsiePiOsTppaSXik0ieVXzsivZZPQw2p1IimkZo57Ne5Y4a8necZUR5XvlbPBgafFi6fXXpWeeKffTA4BTMTUf7ojyvfLhrPI9ABUTf+euY2/plT0Jp+JmQJnyTCWW0lWvVl31fOspJTOlXJ7bTSE36ftD35c4zt4Sub2P71XTfzctcdz+uP1atWtVgdcjLCCswGykwl63wsZJlaME3VEo33ORGjUsP7OyXBsHAAAAAMC92FNKZ8+4kmZAzeo+S3+d/6vEUrrzF86XW0JKkl654xW7kk32lsiVdnZTec9aqkhlchVV1ejg50T5SSnK9wAAAAAA5cWeVensGffBbx8UW25nllkjvxypCRsm2BVX/2vtW+TEntXm8pNN+dsuHSMVLJEraSW50qw4l59E6t+qvyLDI4uc1WTvOJSMmVLljJ5SANxRFav0hpvLy8tzdQhVBq814L64Nih/xZV6mfJMJfZteuSzR+Tl4aVHP3u02HH9P+wvk9lUYjzBNYJ1+PThEsc9fP3DSk5Jtnu1ufLqxyTRk8kdkJQqZ5TvAXAnnp6eMhgMOn78uOrWrSuDofBvt4DKwGw2KycnR8ePH5eHh4e8vLxcHZLb8vLykoeHhw4fPqy6devKy8uL9w/AjZjNZh0/flwGg0Genp6uDqdSuNz+Tl/t+6rEUroTZ0+ox/IeJcdiR0JKkqZ1maZn1z/r9NXmJMeUyFFKVzGRlCpnlO8BcCdGo1GhoaFKS0vTgQMHXB0OUC58fX3VsGFDeXjQxcBRPDw81LhxY6Wnp+vw4ZK/ZQdQ+RgMBoWGhsporNozTexpZH05/Z36rOyjtkFtte3INrviqeNTRyfOnbj8JyYpJCDEZavNSSSRqgqSUuWM8j0A7qZGjRq68sorlZub6+pQgMtmNBpVrVo1Zu04gZeXlxo2bKgLFy7IZLLvW3kAlYenp2eVT0jZ03S8pIbiK2JXaOxXY4stt9uSvsXumJ6/7Xk9seaJEsfV9a2rE2dPFDsDKj9ZVN6ldCSbcDGDuYoVAzt6qecvvpDuuku64QZpi/3vHQAAoAJw9HWCO+G1AuDO7Cm3KyzZlD+DKKFvgno276nwmeHFlt15engqN6/kL/7m3T1Pk76ZVGIp3d7H96rpv5va3d9JUqEzoC5tAG7PjDDgYvZeJzBTqpxRvgcAAAAAFdfl9neyp+n4fQn3KdA7sMRSOnsSUpLk5+VnVymdVzWvcu/vJDG7CY5TIZopvP322woPD1f16tXVoUMHbd68ucixkZGRMhgMBW533XWXEyMuGuV7AAAAAOB8pjyTkg4kadn2ZUo6kCRTXsHS4cSdiQqfGa5OiztpQOIAdVrcSeEzw5W4M9G6P3ZlbIHZTfnlduPWjtOADweU2HQ8Ny+33Ho7SVKQf5C1b1NIQIjNvtCAUJuZTaUZdyDugDYM2qClMUu1YdAG7Y/bXyAhBTiSy8v3VqxYoYEDB2rOnDnq0KGD4uPj9cEHH2jXrl2qV69egfF//fWXcnJyrPdPnjyp1q1b6z//+Y8GDx5c4uM5eqr5nj3SVVdZklOZmeV+egAA4ECUpNmP1wpARXI5/Z3yZxCtiF2hJ9Y8oUNZh8olpofaPKQFvywocZw9/Z32x+23zuayt5SOkju4kr3XCS6fKTVjxgwNGzZMQ4YM0dVXX605c+bI19dXCxYU/sdbu3ZtNWjQwHpbu3atfH19de+99zo58sJdXL5Xtbp1AQAAR8nKytKYMWPUqFEj+fj46Oabb9aPP/5o3V/YLHKDwaB//etfRZ7zpZdeKjC+RYsWzng6AFAqJc2AKml2U+LOROVcyNGoL0YVWXJnlll9E/ralZDqGNrRrrgHtBqg0IBQa9LrUgYZFBYQpnfufMd6/9L90j8r3OXLL6Xr36q/IsMji0w02TsOcCWX9pTKycnRli1bNH78eOs2Dw8PRUVFadOmTXadY/78+brvvvvk5+dX6P7s7GxlZ2db72c6ePpSfvme2SydOyf5+jr04QAAQBUwdOhQ7dixQ0uWLFFwcLDee+89RUVF6ffff1dISIjS09Ntxn/55Zd6+OGH1adPn2LPe80112jdunXW+9Wq0W4UQMVSHv2d+n3QT2aZZTKXz0qgI28cqdTM1BKbiUeGRzqkvxPgTlx65XHixAmZTCbVr1/fZnv9+vX1xx9/lHj85s2btWPHDs2fP7/IMVOnTtWkSZMuO1Z7XZyEOn2apBQAALg8586d04cffqhVq1bptttuk2SZ5fTpp59q9uzZeuWVV9SgQQObY1atWqVOnTqpSZMmxZ67WrVqBY4FgIqiqHK7/BlQK+9dqYzzGSX2d7pgvlCucYUEhNiVbDJ6GK39nUpKOMW0jFHP5j0pt0OVU6m/Dps/f75atWql9u3bFzlm/PjxGjt2rPV+ZmamwsLCHBaTh4fk5yedOSNlZUmFtMUCAACw24ULF2QymVS9enWb7T4+Pvr2228LjD969Kg+//xzLV68uMRz79mzR8HBwapevbo6duyoqVOnqmHDhoWOdfbscwBVQ1F9j+yZAdX3g76F7i/MqBtHadaPs0ocZ09/p/wY7Uk2SfYnnFjhDlWRS5NSderUkdFo1NGjR222Hz16tMRv7c6cOaPly5fr5ZdfLnact7e3vL29LzvW0vD3tySlWIEPAABcLn9/f3Xs2FGTJ09Wy5YtVb9+fS1btkybNm1Ss2bNCoxfvHix/P39FRNTfLlHhw4dtGjRIjVv3lzp6emaNGmSIiIitGPHDvnn9yO4iLNnnwOo3Oxpsl1caV5tn9olzoAyyyxPD0/l5uWWGE+vFr308a6PSyy5m9F1hvom9C1xBpRUutlNJJyAwrm00bmXl5fatm2r9evXW7fl5eVp/fr16tix+OZxH3zwgbKzs/XAAw84OsxSu7jZOQAAwOVasmSJzGazQkJC5O3trbfeekv9+/eXh0fBS7kFCxbo/vvvLzCz6lLdu3fXvffeq+uuu07R0dH64osvdOrUKa1cubLQ8ePHj1dGRob1lpqaWi7PDYD7SdyZqPCZ4eq0uJMGJA5Qp8WdFD4zXIk7E23GFNec/IX/vWDXY83rMc+uZuL5/Z3yt106RrIknGKviVVC3wSFBITYjAkNCFVC34QC/Z1oJg5cHpeX740dO1aDBg1Su3bt1L59e8XHx+vMmTMaMmSIJGngwIEKCQnR1KlTbY6bP3++evXqpSuuuMIVYRcrPymVleXaOAAAgHto2rSpvv76a505c0aZmZkKCgpSv379CvSMSk5O1q5du7RixYpSP0bNmjV11VVXae/evYXud8XscwAVT0kzoErqA5XQN0E9m/cssTTvu9Tv7IqnUc1G9HcCKjGXJ6X69eun48ePa+LEiTpy5IjatGmj1atXW5ufp6SkFPgWcNeuXfr222/11VdfuSLkEuXPeGemFAAAKE9+fn7y8/PT33//rTVr1mjatGk2++fPn6+2bduqdevWpT736dOntW/fPj344IPlFS4AN3O5K+EZZNCjnz2qb1O+LbE0T5JqeNXQmZwz9HcC3JjBbDbb1xnOTWRmZiowMFAZGRkKCAhwyGPceaf05ZfSwoXS4MEOeQgAAOAAzrhOKIs1a9bIbDarefPm2rt3r55++mlVr15dycnJ8vT0lCTrDKrp06fr0UcfLXCOzp07q3fv3ho1apQk6amnnlKPHj3UqFEjHT58WC+++KJ++eUX/f7776pbt26JMVXU1wqAYxQ1Ayp/NlJC3wQFegcqaklUuT3mmA5jNPMHS8ldYTOgLi2ns6ePFQDnsPc6weUzpdwR5XsAAKA8ZWRkaPz48UpLS1Pt2rXVp08fTZkyxZqQkqTly5fLbDarf//+hZ5j3759OnHihPV+Wlqa+vfvr5MnT6pu3bq69dZb9f3339uVkALgni5nJbx+Cf1k73yHprWaat/f+0oc17NFT0U0irBrBpTE7CagMmKmlAM8/LC0YIH06qvS+PEOeQgAAOAAzP6xH68V4F6KK82rVb2W7vjvHeX2WOseXKfBqwaXuBLe/rj91qQYM6CAyoWZUi7E6nsAAAAAKoqyNidPy0xTn5V9FOBlX+J5RtcZmrFphg5lFZ9syl8Jz57m5BIzoAB3VnAdYVw2yvcAAAAAVASJOxMVPjNcnRZ30oDEAeq0uJPCZ4YrcWeiJBVbmpcvMyfTrse6Puh6zexu6QGVn1zKV9RKeCEBITbjQgNCC/SKAuC+mCnlAKy+BwAAAMDRyjoD6lDmIfVZ2UdDrx+qtMw0u1bCq+NbRyfPnnTJSngA3BdJKQegfA8AAACAIxXXAyqmZYxdzcn/8/N/7H68B1o9oJk/zLSr3K40ySZK84CqjaSUA1C+BwAAAMBRipsBFbsyVot6LdL+v/fbNQOqS5MuWvvn2hLHsRIeAEcgKeUAlO8BAAAAuBxFlebZMwNq0MeD7H6cQa0HaeeJnSWuhJf/+JTbAShPJKUcgPI9AAAAAGVVXGlebZ/ads2AqudbT8fOHitxXEhACCvhAXAZVt9zAMr3AAAAAJRFfmnepYmn/NK8SUmT7DrPjOgZCg0ILbAKXj6DDAoLCFNEwwhWwgPgMsyUcgDK9wAAAAAUprgV8+wpzUs6mGTX45R2BhQr4QFwBZJSDkD5HgAAAIBLlbRiXnJKsl2leX6efjqbe9auHlAJfRNoTg6gwiIp5QD5SakzZ6S8PMmDIkkAAACgSitpxbwHrntAPx/52a5zDbthmGb+MJMZUAAqPZJSDpBfvidZElMX3wcAAADgfi63LG/Jr0vsfqyeLXoqolEEM6AAVHokpRygenXL7Ki8PEsJH0kpAAAAwH2VVJb3zcFv7CrLG9FuhD7a+ZGOnjlqV2keM6AAVHYkpRzAYLCU8GVmWlbgCwpydUQAAAAAHKG4srw+K/uoS+Mu+jH9R7vOFdEwQlFNouxuTs4MKACVHd2OHIQV+AAAAAD3Zk9Z3tr9a3Xq/Cm7zhfkH6SYljFK6JugkIAQm32hAaFK6JtQoDQPACozZko5CCvwAQAAAJVfcb2i7F0tb2rnqZq1eZYOZx0usSxPojk5gKqDpJSDkJQCAAAAKreSekUlH0y26zyNAhvpre5v2V2WJ1GaB6BqICnlIPlJqaws18YBAAAAoPRK6hV1dd2r9fvx3+06V5B/kCLDI5XQN8HuFfMAoCogKeUg9JQCAAAAKraiSvPs6RX1+/HfZZRR3p7eOpd7jrI8ACgDklIOQvkeAAAAUHEVV5pX26e2Xb2ilsUuk9HDSFkeAJQRq+85COV7AAAAQMWUX5p3aeIpvzRv/Prxdp3nQt4FVssDgMvATCkHoXwPAAAAcI3iVsyzpzTv+7Tv7XqcIP8gSZTlAUBZkZRyEMr3AAAAAOcrccW8lGS7SvNqeNXQmZwzdvWKkijLA4CyoHzPQSjfAwAAAJyruLK82JWxGrdunJ5e+7Rd5xp6/VBJ//SGyldUrygAQOmRlHIQyvcAAAAA5ympLM8ss6Z9N00/Hf7JrvP1bNGTXlEA4GCU7zkI5XsAAACA89hblhfbMlbJKck6duZYiaV5Rg8jvaIAwIFISjkI5XsAAABA+SqugXl6Vrpd54hpGaP+rfordmWsDDLYJKYKK82jVxQAOA5JKQehfA8AAAAoP8U1MA8LCNO8rfPsOk+Qf5AiwyOV0Deh0PPFd4unNA8AnISklINQvgcAAACUj/wG5peW26VlpqnPyj52nePSFfNiWsZQmgcALkZSykEo3wMAAAAuX3ENzC92f6v7dX2D662r65VUlidRmgcArkZSykEo3wMAAADsV1S/KHsbmA+9YagiwyPVuFZjyvIAoJIgKeUg+TOlzp+XLlyQqvFKAwAAAIUqql9UfHS8tqZvtesc+Y3OKcsDgMqDVImD5CelJMtsqZo1XRYKAAAAUGEV1y8q9oNYu88T5B9k/Z2yPACoHDxcHYC78vaWPD0tv1PCBwAAABRkb78oP08/a1+oSxlkUFhAmLWBOQCg8iAp5UCswAcAAAAUzd5+Uc/c8owkFUhMFdXAHABQOZCUciBW4AMAAEBVZ8ozKelAkpZtX6akA0ky5Zms+1IzUu06x5W1r1RC3wSFBITYbA8NCFVC3wQamANAJUVPKQdiBT4AAABUZUU1MJ/ZbaZ8PX31wv9esOs8Qf5BigyPpIE5ALgZklIORPkeAAAAqqqiGpgfyjykPiv7WO97GDyUZ84r9BwGGRQaEGrtF0UDcwBwL5TvORDlewAAAKiKimtgfvG2MR3GaFHPRTL8//8uRr8oAHB/JKUciPI9AAAAVEX2NjDv2aKnHmz9IP2iAKCKcnlS6u2331Z4eLiqV6+uDh06aPPmzcWOP3XqlEaOHKmgoCB5e3vrqquu0hdffOGkaEuH8j0AAABURelZ6aUaF9MyRgfiDmjDoA1aGrNUGwZt0P64/SSkAMDNubSn1IoVKzR27FjNmTNHHTp0UHx8vKKjo7Vr1y7Vq1evwPicnBx16dJF9erVU0JCgkJCQnTw4EHVrFnT+cHbgfI9AAAAVEUNajSwa1yQf5D1d/pFAUDV49Kk1IwZMzRs2DANGTJEkjRnzhx9/vnnWrBggZ599tkC4xcsWKC//vpLGzdulKenpyQpPDzcmSGXCuV7AAAAcFemPFOhK+GlZabp9e9eL/bYSxuYAwCqJpclpXJycrRlyxaNHz/eus3Dw0NRUVHatGlTocd88skn6tixo0aOHKlVq1apbt26GjBggMaNGyejseI1P6R8DwAAAO4ocWei4lbH2fSNCg0IVc/mPfXer+8pIztD1Tyq6ULeBRlksGluTgNzAEA+l/WUOnHihEwmk+rXr2+zvX79+jpy5Eihx/z5559KSEiQyWTSF198oQkTJmj69Ol65ZVXinyc7OxsZWZm2tychaQUAAAA3E3izkTFrowt0Mg8LTNNb//4tjKyM9QhpIO2j9iuD/t+SANzAECRXN7ovDTy8vJUr149zZ07V23btlW/fv30/PPPa86cOUUeM3XqVAUGBlpvYWFhTos3v3yPnlIAAOByZGVlacyYMWrUqJF8fHx0880368cff7TuHzx4sAwGg82tW7duJZ63tAvOAKY8k+JWx9nMfLpUoHegvh78tVrUaUEDcwBAsVyWlKpTp46MRqOOHj1qs/3o0aNq0KDwxohBQUG66qqrbEr1WrZsqSNHjignJ6fQY8aPH6+MjAzrLTU1tfyeRAmYKQUAAMrD0KFDtXbtWi1ZskTbt29X165dFRUVpUOHDlnHdOvWTenp6dbbsmXLij1n/oIzL774orZu3arWrVsrOjpax44dc/TTQSWWnJJcYIbUpTKyM7Qp7Z92HPkNzPu36q/I8EhK9gAAVi5LSnl5ealt27Zav369dVteXp7Wr1+vjh07FnrMLbfcor179yovL8+6bffu3QoKCpKXl1ehx3h7eysgIMDm5iwkpQAAwOU6d+6cPvzwQ02bNk233XabmjVrppdeeknNmjXT7NmzreO8vb3VoEED661WrVrFnvfiBWeuvvpqzZkzR76+vlqwYIGjnxIqsfSs9HIdBwCo2lxavjd27FjNmzdPixcv1s6dOzVixAidOXPGuhrfwIEDbRqhjxgxQn/99Zfi4uK0e/duff7553r11Vc1cuRIVz2FYlG+BwAALteFCxdkMplUvXp1m+0+Pj769ttvrfeTkpJUr149NW/eXCNGjNDJkyeLPGf+gjNRUVHWbSUtOOPKPp1wPlOeSUkHkrRs+zIlHUiSKc8ks9msPX/tsev4IP8gB0cIAHAHLlt9T5L69eun48ePa+LEiTpy5IjatGmj1atXW5ufp6SkyMPjn7xZWFiY1qxZoyeeeELXXXedQkJCFBcXp3HjxrnqKRSLmVIAAOBy+fv7q2PHjpo8ebJatmyp+vXra9myZdq0aZOaNWsmyVK6FxMTo8aNG2vfvn167rnn1L17d23atKnQFYqLW3Dmjz/+KDSOqVOnatKkSeX/BFHhFLayXoMaDVTfr762Hd1W7LEGGRQaEKqIhhGODhMA4AYMZrO56C6FbigzM1OBgYHKyMhweCnfzp3S1VdLtWtLxXxZCQAAKghnXieUxr59+/TQQw/pm2++kdFo1A033KCrrrpKW7Zs0c6dOwuM//PPP9W0aVOtW7dOnTt3LrD/8OHDCgkJ0caNG23aJjzzzDP6+uuv9cMPPxQ4Jjs7W9nZ2db7mZmZCgsLq3CvFS5P/sp6RTUyr+ZRTb2a99KHOz+UJJtxBhkkiZX1AAB2X1NVqtX3KhvK9wAAQHlo2rSpvv76a50+fVqpqanavHmzcnNz1aRJk0LHN2nSRHXq1NHevXsL3V+WBWdc2acTzmHPynp1fetqeexyJfRNUEhAiM2+0IBQElIAgFJxafmeu8sv38vNlXJypCJ6sQMAANjFz89Pfn5++vvvv7VmzRpNmzat0HFpaWk6efKkgoIK7+tz8YIzvXr1kvTPgjOjRo1yVPio4OxZWS/9dLqSU5IV0zJGPZv3VHJKstKz0hXkH6SIhhGsrAcAKBWSUg7k5/fP76dPW8r4AAAASmvNmjUym81q3ry59u7dq6efflotWrTQkCFDdPr0aU2aNEl9+vRRgwYNtG/fPj3zzDNq1qyZoqOjrefo3LmzevfubU06jR07VoMGDVK7du3Uvn17xcfH2yw4g6qntCvrGT2MigyPdGBEAAB3R1LKgTw9JW9vKTvbUsJHUgoAAJRFRkaGxo8fr7S0NNWuXVt9+vTRlClT5OnpqQsXLujXX3/V4sWLderUKQUHB6tr166aPHmyvL29refYt2+fTpw4Yb1f0oIzqHqOnzlu1zhW1gMAlBcanTtY3brSiRPSjh3SNdc4/OEAAMBlqKiNzisiXiv3YTab9c6P7yhudZxMZlOR4/JX1tsft58yPQBAsey9TmCmlIPVqGFJSp0+7epIAAAAUNWZ8kw2faBuDL5Rj3/5uBb+slCSdEvYLdqYulFS4SvrxXeLJyEFACg3JKUcLL/ZOSvwAQAAwJUSdyYqbnWcTTNzTw9P5eblysPgodejXteTHZ/UR398VGBcaECo4rvFs7IeAKBckZRyMH9/y09mSgEAAMBVEncmKnZlrM3sJ0nKzcuVJL0Q8YKeuvkpSWJlPQCA05CUcrD8mVIkpQAAAOAKpjyT4lbHFUhI5TPIoIW/LNTE2ydaE0+srAcAcAYPVwfg7ijfAwAAgCslpyTblOJdyiyzUjNTlZyS7MSoAAAgKeVwlO8BAADAlQ5mHLRrXHpWuoMjAQDAFuV7Dkb5HgAAABzt0lX18ntAfX3ga0343wS7zhHkH+TgKAEAsEVSysEo3wMAAIAjFbaqXrB/sFrWaan1+9dLkjwMHsoz5xV6vEEGhQaEKqJhhFPiBQAgH0kpB6N8DwAAAI5S1Kp6h7MO63DWYUnSI20f0S1ht2jQx4MkyWasQQZJUny3eFbXAwA4HUkpB6N8DwAAAI5Q0qp6klTPr57evvNtGT2M8vPyKzCjKjQgVPHd4hXTMsYZIQMAYIOklINRvgcAAABHKGlVPUk6duaYklOSFRkeqZiWMerZvGehvacAAHAFklIORvkeAAAAHMHe1fIuHmf0MCoyPNJBEQEAUDoerg7A3VG+BwAAAEewd7U8VtUDAFRUJKUcjKQUAAAAHKHFFS1UzaPowgeDDAoLCGNVPQBAhUVSysHyy/foKQUAAIDy8te5v9Tt/W66kHdB0j+r6OVjVT0AQGVAUsrBmCkFAACA8pRxPkPR70Vr29Ftqu9XX291e0shASE2Y0IDQpXQN4FV9QAAFRqNzh3s4qSU2SwZDMWPBwAAAC5myjNZV8wL9A7U5G8m66fDP+kKnyu0buA6XVvvWj1242OsqgcAqHRISjlYfvmeySSdPy/5+Lg2HgAAAFQeiTsTFbc6TmmZaTbbfT19tfbBtbq23rWSWFUPAFA5Ub7nYL6+//xOCR8AAADslbgzUbErYwskpCTpbO5Z7T+13wVRAQBQfkhKOZjR+E9iiqQUAAAA7GHKMyludZzMMhe63yCDxqweI1OeycmRAQBQfkhKOQEr8AEAAKA0klOSC50hlc8ss1IzU5WckuzEqAAAKF8kpZyAFfgAAABQGulZ6eU6DgCAioiklBOQlAIAAEBp1Papbde4IP8gB0cCAIDjsPqeE1C+BwAAAHudzT2rNza+UewYgwwKDQhVRMMIJ0UFAED5Y6aUEzBTCgAAAPY4m3tWPZb10Lr96+Rt9JZkSUBdLP9+fLd4GT2MTo8RAIDywkwpJyApBQAAgEuZ8kxKTklWela6gvyDdEODG9RzRU8lHUhSDa8a+vL+L3XszDHFrY6zaXoeGhCq+G7ximkZ48LoAQC4fCSlnIDyPQAAAFwscWdigWSTl9FLOaYc+Xv5a/UDq3Vz2M2SpJ7Ne9okryIaRjBDCgDgFkhKOQEzpQAAAJAvcWeiYlfGyiyzzfYcU44k6bmI56wJKUkyehgVGR7pzBABAHAKeko5AUkpAAAASJaSvbjVcQUSUhd758d3ZMozOTEqAABcg6SUE1C+BwAAAElKTkm2KdkrTGpmqpJTkp0UEQAArkNSygmYKQUAAABJSs9KL9dxAABUZiSlnICkFAAAACQpyD+oXMcBAFCZkZRyAsr3AAAAIEkRDSMUVKPohJNBBoUFhCmiYYQTowIAwDVISjkBM6UAAACQr7ZP7UK3G2SQJMV3i5fRw+jMkAAAcAmSUk5AUgoAAACSNPXbqfrt+G/yNnqrQY0GNvtCA0KV0DdBMS1jXBQdAADOVc3VAVQFlO8BAADgu5Tv9FLSS5KkuT3m6v5W9ys5JVnpWekK8g9SRMMIZkgBAKoUklJOwEwpAACAqu3vc39rQOIAmcwmPXDdAxrYeqAkKTI80rWBAQDgQpTvOUF+UurMGSkvz7WxAAAAwLnMZrOGfTpMKRkpala7md658x1XhwQAQIVQIZJSb7/9tsLDw1W9enV16NBBmzdvLnLsokWLZDAYbG7Vq1d3YrSll5+UMpulc+dcGwsAAAAcy5RnUtKBJC3bvkxJB5I056c5+nDnh/L08NTyPsvl7+3v6hABAKgQXF6+t2LFCo0dO1Zz5sxRhw4dFB8fr+joaO3atUv16tUr9JiAgADt2rXLet9gMDgr3DLx9ZUMBktSKitL8vNzdUQAAABwhMSdiYpbHae0zLQC+6Z2nqq2wW1dEBUAABWTy2dKzZgxQ8OGDdOQIUN09dVXa86cOfL19dWCBQuKPMZgMKhBgwbWW/369Z0YcekZDPSVAgAAcHeJOxMVuzK20ISUJDWq2cjJEQEAULG5NCmVk5OjLVu2KCoqyrrNw8NDUVFR2rRpU5HHnT59Wo0aNVJYWJh69uyp3377rcix2dnZyszMtLm5AkkpAAAA92XKMyludZzMMhe63yCDxq4ZK1OeycmRAQBQcbk0KXXixAmZTKYCM53q16+vI0eOFHpM8+bNtWDBAq1atUrvvfee8vLydPPNNystrfBvpKZOnarAwEDrLSwsrNyfhz38/791QFaWSx4eAAAADpScklzkDClJMsus1MxUJackOzEqAAAqNpeX75VWx44dNXDgQLVp00a33367EhMTVbduXb377ruFjh8/frwyMjKst9TUVCdHbMFMKQAAAPeVnpVeruMAAKgKXNrovE6dOjIajTp69KjN9qNHj6pBgwZ2ncPT01PXX3+99u7dW+h+b29veXt7X3asl4ukFAAAgPsK8g8q13EAAFQFLp0p5eXlpbZt22r9+vXWbXl5eVq/fr06duxo1zlMJpO2b9+uoKCK/QFP+R4AACirrKwsjRkzRo0aNZKPj49uvvlm/fjjj5Kk3NxcjRs3Tq1atZKfn5+Cg4M1cOBAHT58uNhzvvTSSzIYDDa3Fi1aOOPpuKWIhhEKDQgtcr9BBoUFhCmiYYQTowIAoGJzefne2LFjNW/ePC1evFg7d+7UiBEjdObMGQ0ZMkSSNHDgQI0fP946/uWXX9ZXX32lP//8U1u3btUDDzyggwcPaujQoa56CnZhphQAACiroUOHau3atVqyZIm2b9+url27KioqSocOHdLZs2e1detWTZgwQVu3blViYqJ27dqle+65p8TzXnPNNUpPT7fevv32Wyc8G/dk9DDq1rBbC91nkEGSFN8tXkYPozPDAgCgQnNp+Z4k9evXT8ePH9fEiRN15MgRtWnTRqtXr7Y2P09JSZGHxz+5s7///lvDhg3TkSNHVKtWLbVt21YbN27U1Vdf7aqnYBeSUgAAoCzOnTunDz/8UKtWrdJtt90myTLL6dNPP9Xs2bP1yiuvaO3atTbHzJo1S+3bt1dKSooaNmxY5LmrVatmd8sEFO+z3Z9pxW8rJEm1qtfS3+f/tu4LDQhVfLd4xbSMcVV4AABUSC5PSknSqFGjNGrUqEL3JSUl2dx/88039eabbzohqvJF+R4AACiLCxcuyGQyqXr16jbbfXx8ipzZlJGRIYPBoJo1axZ77j179ig4OFjVq1dXx44dNXXq1GKTWCjc7pO7dX/i/TLLrJE3jtTMbjOVnJKs9Kx0BfkHKaJhBDOkAAAoRIVISlUFzJQCAABl4e/vr44dO2ry5Mlq2bKl6tevr2XLlmnTpk1q1qxZgfHnz5/XuHHj1L9/fwUEBBR53g4dOmjRokVq3ry50tPTNWnSJEVERGjHjh3yz/827SLZ2dnKzs623s/MzCyfJ1jJZWVnqdfyXsrMztStDW/VjOgZMnoYFRke6erQAACo8FzeU6qqICkFAADKasmSJTKbzQoJCZG3t7feeust9e/f36bFgWRpet63b1+ZzWbNnj272HN2795d9957r6677jpFR0friy++0KlTp7Ry5cpCx0+dOlWBgYHWW1hYWLk9v8oqz5ynQR8P0s4TOxXsH6wP7v1AXkYvV4cFAEClQVLKSSjfAwAAZdW0aVN9/fXXOn36tFJTU7V582bl5uaqSZMm1jH5CamDBw9q7dq1xc6SKkzNmjV11VVXae/evYXuHz9+vDIyMqy31NTUy3pOlZUpz6SkA0latn2Zhn86XB/98ZG8jF76sO+HalCD/lwAAJQG5XtOwkwpAABwufz8/OTn56e///5ba9as0bRp0yT9k5Das2ePNmzYoCuuuKLU5z59+rT27dunBx98sND93t7e8vb2vqz4K7vEnYmKWx2ntMw0m+0PX/+wbgq9yUVRAQBQeZGUchKSUgAAVA15eXn6+uuvlZycrIMHD+rs2bOqW7eurr/+ekVFRZWp7G3NmjUym81q3ry59u7dq6efflotWrTQkCFDlJubq9jYWG3dulWfffaZTCaTjhw5IkmqXbu2vLws5WSdO3dW7969rYvLPPXUU+rRo4caNWqkw4cP68UXX5TRaFT//v3L78VwI4k7ExW7MlZmmQvsm/PTHEU1iWJ1PQAASonyPSehfA8AAPd27tw5vfLKKwoLC9Odd96pL7/8UqdOnZLRaNTevXv14osvqnHjxrrzzjv1/fffl+rcGRkZGjlypFq0aKGBAwfq1ltv1Zo1a+Tp6alDhw7pk08+UVpamtq0aaOgoCDrbePGjdZz7Nu3TydOnLDeT0tLU//+/dW8eXP17dtXV1xxhb7//nvVrVu33F4Td2HKMyludVyhCal8Y1aPkSnP5MSoAACo/Jgp5STMlAIAwL1dddVV6tixo+bNm6cuXbrI09OzwJiDBw9q6dKluu+++/T8889r2LBhdp27b9++6tu3b6H7wsPDZTYXnSzJd+DAAZv7y5cvt+uxISWnJBco2buYWWalZqYqOSWZVfcAACgFklJOQlIKAAD39tVXX6lly5bFjmnUqJHGjx+vp556SikpKU6KDJcrPSu9XMcBAAALyvechPI9AADcW0kJqYt5enqqadOmDowG5SnIP6hcxwEAAAtmSjlJ/kypc+ckk0kyGl0bDwAAcLwLFy7o3XffVVJSkkwmk2655RaNHDlS1atXd3VoKIWIhhEK9A5URnZGofsNMig0IFQRDSOcHBkAAJUbSSknyU9KSdKZM1JAgOtiAQAAzjF69Gjt3r1bMTExys3N1X//+1/99NNPWrZsmatDQykcyjqkcxfOFbrPIIMkKb5bvIwefOsIAEBpkJRyEm9vqVo16cIFSwkfSSkAANzPRx99pN69e1vvf/XVV9q1a5eM/z9FOjo6WjfddJOrwkMZPf7l48ox5ahFnRY6nXPapul5aECo4rvFK6ZljAsjBACgciIp5SQGg2W21KlTNDsHAMBdLViwQIsXL9Y777yj4OBg3XDDDXr00UfVp08f5ebmat68ebrxxhtdHSZK4eM/PtYnuz5RNY9qSrg3QS3qtFBySrLSs9IV5B+kiIYRzJACAKCMSEo5EUkpAADc26effqoVK1YoMjJSjz/+uObOnavJkyfr+eeft/aUeumll1wdJuyUlZ2lUV+MkiQ9c/MzuqbeNZKkyPBIF0YFAID7ICnlRPkr8JGUAgDAffXr10/R0dF65plnFB0drTlz5mj69OmuDgtlMHHDRB3KOqQmtZrohdtecHU4AAC4HQ9XB1CV5Dc7z8pybRwAAMCxatasqblz5+pf//qXBg4cqKefflrnz593dVgoha3pW/XW5rckSe/c+Y58PH1cHBEAAO6HpJQT5SelmCkFAIB7SklJUd++fdWqVSvdf//9uvLKK7Vlyxb5+vqqdevW+vLLL10dIuxgyjNp+KfDlWfO033X3qfoZtGuDgkAALdEUsqJKN8DAMC9DRw4UB4eHvrXv/6levXq6ZFHHpGXl5cmTZqkjz/+WFOnTlXfvn1dHSYKYcozKelAkpZtX6Yxq8doS/oWBXoH6s3oN10dGgAAboueUk5E+R4AAO7tp59+0rZt29S0aVNFR0ercePG1n0tW7bUN998o7lz57owQhQmcWei4lbHKS0zzWZ7v2v6qUGNBi6KCgAA98dMKSeifA8AAPfWtm1bTZw4UV999ZXGjRunVq1aFRgzfPhwF0SGoiTuTFTsytgCCSlJmrd1nhJ3JrogKgAAqgaSUk5E+R4AAO7tv//9r7Kzs/XEE0/o0KFDevfdd10dEophyjMpbnWczDIXOWbM6jEy5ZmcGBUAAFUH5XtORPkeAADurVGjRkpISHB1GLBTckpyoTOk8pllVmpmqpJTkhUZHum8wAAAqCKYKeVElO8BAOC+zpw549DxKH/pWenlOg4AAJQOSSknonwPAAD31axZM7322mtKTy86gWE2m7V27Vp1795db731lhOjQ2GC/IPKdRwAACgdyveciPI9AADcV1JSkp577jm99NJLat26tdq1a6fg4GBVr15df//9t37//Xdt2rRJ1apV0/jx4/XII4+4OuQqL6JhhEIDQoss4TPIoNCAUEU0jHByZAAAVA0kpZyI8j0AANxX8+bN9eGHHyolJUUffPCBkpOTtXHjRp07d0516tTR9ddfr3nz5ql79+4yGo2uDheSjB5GjbxxpMavH19gn0EGSVJ8t3gZPfjvBQCAI5CUciKSUgAAuL+GDRvqySef1JNPPunqUFACs9msVbtWSZJ8PX11NvesdV9oQKjiu8UrpmWMq8IDAMDtkZRyovyeUpTvAQAAuN7yHcv1fdr38vP0086RO7Xv731Kz0pXkH+QIhpGMEMKAAAHIynlRMyUAgAAqBjO5p7VuHXjJEnjbx2vsMAwhQWGuTgqAACqFlbfcyKSUgAAABXD9I3TlZqZqoaBDTW241hXhwMAQJVEUsqJ8sv3srOl3FzXxgIAAFBVHco8pNe+e02SNC1qmnw8fVwcEQAAVRNJKSfy8/vnd2ZLAQAAuMZz/3tOZ3PP6uawm9X3mr6uDgcAgCqLpJQTeXlZbhJJKQAA3Fl4eLhefvllpaSkuDoUXOLHQz/qv9v+K0mKj46XwWBwcUQAAFRdJKWcjBX4AABwf2PGjFFiYqKaNGmiLl26aPny5crOznZ1WFWe2WzWmDVjJEkDWw/UjSE3ujYgAACqOJJSTkazcwAA3N+YMWP0yy+/aPPmzWrZsqUef/xxBQUFadSoUdq6daurw6tSTHkmJR1I0rLty/Ri0ovamLpRvp6+mtp5qqtDAwCgyqvm6gCqGpJSAABUHTfccINuuOEGTZ8+Xe+8847GjRun2bNnq1WrVho9erSGDBlC+ZgDJe5MVNzqOKVlptlsv6f5PQr2D3ZRVAAAIB8zpZyM8j0AAKqO3NxcrVy5Uvfcc4+efPJJtWvXTv/5z3/Up08fPffcc7r//vtdHaLbStyZqNiVsQUSUpK0YscKJe5MdEFUAADgYmWaKZWamiqDwaDQ0FBJ0ubNm7V06VJdffXVGj58eLkG6G6YKQUAgPvbunWrFi5cqGXLlsnDw0MDBw7Um2++qRYtWljH9O7dWzfeSE8jRzDlmRS3Ok5mmYscM2b1GPVs3lNGD6MTIwMAABcr00ypAQMGaMOGDZKkI0eOqEuXLtq8ebOef/55vfzyy+UaoLshKQUAgPu78cYbtWfPHs2ePVuHDh3SG2+8YZOQkqTGjRvrvvvuc1GE7i05JbnQGVL5zDIrNTNVySnJTowKAABcqkwzpXbs2KH27dtLklauXKlrr71W3333nb766is9+uijmjhxYrkG6U4o3wMAwP39+eefatSoUbFj/Pz8tHDhQidFVLWkZ6WX6zgAAOAYZZoplZubK29vb0nSunXrdM8990iSWrRoofR0PtyLw0wpAADc37Fjx/TDDz8U2P7DDz/op59+ckFEVUuQf1C5jgMAAI5RpqTUNddcozlz5ig5OVlr165Vt27dJEmHDx/WFVdcUa4BuhuSUgAAuL+RI0cqNTW1wPZDhw5p5MiRLoioaoloGKHQgFAZVPjKhgYZFBYQpoiGEU6ODAAAXKxMSanXX39d7777riIjI9W/f3+1bt1akvTJJ59Yy/pK4+2331Z4eLiqV6+uDh06aPPmzXYdt3z5chkMBvXq1avUj+kq+eV7JKUAAHBfv//+u2644YYC26+//nr9/vvvLoioajF6GDWz28xC9+UnquK7xdPkHAAAFytTUioyMlInTpzQiRMntGDBAuv24cOHa86cOaU614oVKzR27Fi9+OKL2rp1q1q3bq3o6GgdO3as2OMOHDigp556ShERlesbrvyZUvSUAgDAfXl7e+vo0aMFtqenp6tatTK19EQpxbSM0aPtHi2wPTQgVAl9ExTTMsYFUQEAgIuVKSl17tw5ZWdnq1atWpKkgwcPKj4+Xrt27VK9evVKda4ZM2Zo2LBhGjJkiK6++mrNmTNHvr6+NsmuS5lMJt1///2aNGmSmjRpUpan4DKU7wEA4P66du2q8ePHKyMjw7rt1KlTeu6559SlSxcXRla17D65W5I09PqhWhqzVBsGbdD+uP0kpAAAqCDK9FVdz549FRMTo0cffVSnTp1Shw4d5OnpqRMnTmjGjBkaMWKEXefJycnRli1bNH78eOs2Dw8PRUVFadOmTUUe9/LLL6tevXp6+OGHlZxcuZbypXwPAAD398Ybb+i2225To0aNdP3110uSfvnlF9WvX19LlixxcXRVw5HTR7ThwAZJ0viI8WpSq3J9kQkAQFVQpplSW7dutZbNJSQkqH79+jp48KD++9//6q233rL7PCdOnJDJZFL9+vVtttevX19Hjhwp9Jhvv/1W8+fP17x58+x6jOzsbGVmZtrcXInyPQAA3F9ISIh+/fVXTZs2TVdffbXatm2rmTNnavv27QoLC3N1eFXCB799oDxznjqEdCAhBQBABVWmmVJnz56V//9P+fnqq68UExMjDw8P3XTTTTp48GC5BnixrKwsPfjgg5o3b57q1Klj1zFTp07VpEmTHBZTaVG+BwBA1eDn56fhw4e7Oowqa/lvyyVJ9117n4sjAQAARSlTUqpZs2b6+OOP1bt3b61Zs0ZPPPGEJOnYsWMKCAiw+zx16tSR0Wgs0Aj06NGjatCgQYHx+/bt04EDB9SjRw/rtry8PMsTqVZNu3btUtOmTW2OGT9+vMaOHWu9n5mZ6dJvKCnfAwCg6vj999+VkpKinJwcm+333HOPiyKqGg6eOqiNqRtlkEF9r+nr6nAAAEARypSUmjhxogYMGKAnnnhCd9xxhzp27CjJMmsqv2+CPby8vNS2bVutX79evXr1kmRJMq1fv16jRo0qML5Fixbavn27zbYXXnhBWVlZmjlzZqHJJm9vb3l7e5fi2TkW5XsAALi/P//8U71799b27dtlMBhkNpslSQaDQZJl0RY4zorfVkiSbg+/XcH+wS6OBgAAFKVMSanY2FjdeuutSk9PV+vWra3bO3furN69e5fqXGPHjtWgQYPUrl07tW/fXvHx8Tpz5oyGDBkiSRo4cKBCQkI0depUVa9eXddee63N8TVr1pSkAtsrqovL98xm6f+vTQEAgBuJi4tT48aNtX79ejVu3FibN2/WyZMn9eSTT+qNN95wdXhub/mO/y/du4bSPQAAKrIyJaUkqUGDBmrQoIHS0tIkSaGhoWrfvn2pz9OvXz8dP35cEydO1JEjR9SmTRutXr3a2vw8JSVFHh5l6sdeIeWX7124IOXkSBVoEhcAACgnmzZt0v/+9z/VqVNHHh4e8vDw0K233qqpU6dq9OjR+vnnn10dotvadWKXfj7ys6p5VFOfq/u4OhwAAFCMMmV78vLy9PLLLyswMFCNGjVSo0aNVLNmTU2ePNna46k0Ro0apYMHDyo7O1s//PCDOnToYN2XlJSkRYsWFXnsokWL9PHHH5fhWbiGn98/v1PCBwCAezKZTNZFYerUqaPDhw9Lkho1aqRdu3aV+nxZWVkaM2aMGjVqJB8fH91888368ccfrfvNZrMmTpyooKAg+fj4KCoqSnv27CnxvG+//bbCw8NVvXp1dejQQZs3by51bBVN/iypLk26qI6vfQvjAAAA1yhTUur555/XrFmz9Nprr+nnn3/Wzz//rFdffVX//ve/NWHChPKO0a0YjZKPj+V3mp0DAOCerr32Wm3btk2S1KFDB02bNk3fffedXn75ZTVp0qTU5xs6dKjWrl2rJUuWaPv27eratauioqJ06NAhSdK0adP01ltvac6cOfrhhx/k5+en6OhonT9/vshzrlixQmPHjtWLL76orVu3qnXr1oqOjtaxY8fK9qQrALPZzKp7AABUIgZzfufNUggODtacOXMKrByzatUqPfbYY9YLpIooMzNTgYGBysjIKNVKgeWpfn3p2DFp+3apkrTCAgCgSiiv64Q1a9bozJkziomJ0d69e3X33Xdr9+7duuKKK7RixQrdcccddp/r3Llz8vf316pVq3TXXXdZt7dt21bdu3fX5MmTFRwcrCeffFJPPfWUJCkjI0P169fXokWLdN99hSdnOnTooBtvvFGzZs2SZJkJHxYWpscff1zPPvtsiXFVhGuqS207sk1t3m0jb6O3jj19TAHeFSMuAACqGnuvE8o0U+qvv/5SixYtCmxv0aKF/vrrr7KcskphBT4AANxbdHS0YmJiJEnNmjXTH3/8oRMnTujYsWOlSkhJ0oULF2QymVS9enWb7T4+Pvr222+1f/9+HTlyRFFRUdZ9gYGB6tChgzZt2lToOXNycrRlyxabYzw8PBQVFVXkMZXBsh3LJEl3XXUXCSkAACqBMiWlWrdubf1W7WKzZs3Sddddd9lBubuLV+ADAADuJTc3V9WqVdOOHTtstteuXVuGMiy76+/vr44dO2ry5Mk6fPiwTCaT3nvvPW3atEnp6ek6cuSIJFkXiclXv359675LnThxQiaTqVTHZGdnKzMz0+ZWkZjNZlbdAwCgkinT6nvTpk3TXXfdpXXr1qljx46SLKvMpKam6osvvijXAN1R/gp8JKUAAHA/np6eatiwoUwmU7mdc8mSJXrooYcUEhIio9GoG264Qf3799eWLVvK7TFKMnXqVE2aNMlpj1daPxz6QQczDqqGVw3dddVdJR8AAABcrkwzpW6//Xbt3r1bvXv31qlTp3Tq1CnFxMTot99+05IlS8o7RrdD+R4AAO7t+eef13PPPVdubQ2aNm2qr7/+WqdPn1Zqaqo2b96s3NxcNWnSRA0aNJAkHT161OaYo0ePWvddqk6dOjIajaU6Zvz48crIyLDeUlNTy+GZlZ9l2y2lez2b95Svp6+LowEAAPYo00wpydLsfMqUKTbbtm3bpvnz52vu3LmXHZg7o3wPAAD3NmvWLO3du1fBwcFq1KiR/Pz8bPZv3bq1TOf18/OTn5+f/v77b61Zs0bTpk1T48aN1aBBA61fv15t2rSRZGku+sMPP2jEiBGFnsfLy0tt27bV+vXr1atXL0mWRufr16/XqFGjCj3G29tb3t7eZYrb0Ux5Jq38faUkVt0DAKAyKXNSCmVH+R4AAO4tP9FTXtasWSOz2azmzZtr7969evrpp9WiRQsNGTJEBoNBY8aM0SuvvKIrr7xSjRs31oQJExQcHGwTR+fOndW7d29r0mns2LEaNGiQ2rVrp/bt2ys+Pl5nzpzRkCFDyjV2Z/jm4Dc6cvqIalWvpa5Nu7o6HAAAYCeSUi7g+/8zyjdtkpKSpIgIyWh0aUgAAKAcvfjii+V6voyMDI0fP15paWmqXbu2+vTpoylTpsjT01OS9Mwzz+jMmTMaPny4Tp06pVtvvVWrV6+2WbFv3759OnHihPV+v379dPz4cU2cOFFHjhxRmzZttHr16gLNzyuD/AbnfVr2kZfRy8XRAAAAexnMZrO5vE62bds23XDDDeXa2LO8ZWZmKjAwUBkZGQoIcP5SwYmJ0uDBtv2kQkOlmTOl/185GgAAuIirrxMqk4rwWpnyTPrf/v+p94reOpN7RmvuX6OuzZgpBQCAq9l7nVCqmVIxJWRNTp06VZrTVTmJiVJsrHRpGvDQIcv2hAQSUwAAuAMPDw8ZDIYi91fkL/Aqi8SdiYpbHae0zDTrtoc/eVgzu89UTEsuqAAAqAxKlZQKDAwscf/AgQMvKyB3ZTJJcXEFE1KSZZvBII0ZI/XsSSkfAACV3UcffWRzPzc3Vz///LMWL16sSZMmuSgq95G4M1GxK2Nllu2F1aGsQ4pdGauEvgkkpgAAqATKtXyvMnDVVPOkJKlTp5LHbdggRUY6OhoAAFAYR18nLF26VCtWrNCqVavK/dzO5qprKlOeSeEzw21mSF3MIINCA0K1P26/jB580wcAgCvYe53g4cSYqrT09PIdBwAAKp+bbrpJ69evd3UYlVpySnKRCSlJMsus1MxUJackOzEqAABQFiSlnCQoqHzHAQCAyuXcuXN66623FBIS4upQKrX0LPu+wbN3HAAAcJ1S9ZRC2UVEWFbZO3So8L5SBoNlf0SE82MDAADlq1atWjaNzs1ms7KysuTr66v33nvPhZFVfkH+9n2DZ+84AADgOiSlnMRolGbOtKyyZzAUnpiKj6fJOQAA7uDNN9+0SUp5eHiobt266tChg2rVquXCyCq/iIYRCg0I1aHMQwUanUv/9JSKaMg3fQAAVHQkpZwoJkZKSLCswpd2USsEHx/pvfcs+wEAQOU3ePBgV4fgtoweRs3sNlOxK2ML7DPIkgiM7xZPk3MAACoBeko5WUyMdOCAZZW9V1+1bMvNlW691aVhAQCAcrRw4UJ98MEHBbZ/8MEHWrx4sQsici8xLWOU0DdB3kZvm+2hAaFK6JugmJZ80wcAQGVAUsoFjEYpMlIaP15q3166cEFatMjVUQEAgPIydepU1alTp8D2evXq6dX8b6VwWWJaxqhlnZaSpGdufkYbBm3Q/rj9JKQAAKhESEq52PDhlp/z5kl5ea6NBQAAlI+UlBQ1bty4wPZGjRopJSXFBRG5p8ycTElSzxY9FRkeSckeAACVDEkpF7vvPikgQNq711LSBwAAKr969erp119/LbB927ZtuuKKK1wQkXvKOJ8hSQr0DnRxJAAAoCxISrmYn5/0wAOW399917WxAACA8tG/f3+NHj1aGzZskMlkkslk0v/+9z/FxcXpvvvuc3V4bsFsNisj+/+TUtVJSgEAUBmRlKoA8kv4PvpIOnrUtbEAAIDLN3nyZHXo0EGdO3eWj4+PfHx81LVrV91xxx30lCon5y6c04W8C5KYKQUAQGVFUqoCaN1a6tCBhucAALgLLy8vrVixQrt27dL777+vxMRE7du3TwsWLJCXl5erw3ML+aV7HgYP1fCq4eJoAABAWVRzdQCweOQR6YcfpLlzpaefljxIFwIAUOldeeWVuvLKK10dhlvKL90L8A6QwWBwcTQAAKAsSH1UEH37Whqe//mn9L//uToaAABwOfr06aPXX3+9wPZp06bp3nvvdUFE7ocm5wAAVH4kpSoIPz/pwQctv9PwHACAyu2bb77RnXfeWWB79+7d9c0337ggIvdDk3MAACo/klIVyCOPWH5+/LF05IhLQwEAAJfh9OnThfaO8vT0VGZmpgsicj/MlAIAoPIjKVWBtGol3XSTpeH5woWujgYAAJRVq1attGLFigLbly9frquvvtoFEbkfZkoBAFD50ei8gnnkEen776V586Rx42h4DgBAZTRhwgTFxMRo3759uuOOOyRJ69ev17Jly/TBBx+4ODr3wEwpAAAqP1IeFUzfvlJgoLR/v7RunaujAQAAZdGjRw99/PHH2rt3rx577DE9+eSTSktL07p169SrVy9Xh+cWrDOlSEoBAFBpMVOqgvH1tTQ8nzVLeuUV6eRJKShIioiQjEZXRwcAAOx111136a677iqwfceOHbr22mtdEJF7sc6UonwPAIBKi5lSFVDTppafycnSgAFSp05SeLiUmOjSsAAAQBllZWVp7ty5at++vVq3bu3qcNwCM6UAAKj8SEpVMImJ0tixBbcfOiTFxpKYAgCgMvnmm280cOBABQUF6Y033tAdd9yh77//3tVhuQUanQMAUPlRvleBmExSXJxkNhfcZzZLBoM0ZozUsyelfAAAVFRHjhzRokWLNH/+fGVmZqpv377Kzs7Wxx9/zMp75YhG5wAAVH7MlKpAkpOltLSi95vNUmqqZRwAAKh4evTooebNm+vXX39VfHy8Dh8+rH//+9+uDsstMVMKAIDKj5lSFUh6evmOAwAAzvXll19q9OjRGjFihK688kpXh+PWmCkFAEDlx0ypCiQoqHzHAQAA5/r222+VlZWltm3bqkOHDpo1a5ZOnDjh6rDcEjOlAACo/EhKVSAREVJoqKV3VFHCwizjAABAxXPTTTdp3rx5Sk9P1yOPPKLly5crODhYeXl5Wrt2rbKyslwdolswm83MlAIAwA2QlKpAjEZp5kzL70UlpqZMock5AAAVnZ+fnx566CF9++232r59u5588km99tprqlevnu655x5Xh1fpnb9wXrl5uZKYKQUAQGVGUqqCiYmREhKkkBDb7dX+v/tXUpLTQwIAAJehefPmmjZtmtLS0rRs2TJXh+MW8kv3DDKohlcNF0cDAADKiqRUBRQTIx04IG3YIC1davm5bp1l9tSCBdKXX7o6QgAAUFpGo1G9evXSJ5984upQKr380r0A7wB5GLicBQCgsqoQn+Jvv/22wsPDVb16dXXo0EGbN28ucmxiYqLatWunmjVrys/PT23atNGSJUucGK1zGI1SZKTUv7/l5+23S3Fxln3DhkmnTrkwOAAAABeiyTkAAO7B5UmpFStWaOzYsXrxxRe1detWtW7dWtHR0Tp27Fih42vXrq3nn39emzZt0q+//qohQ4ZoyJAhWrNmjZMjd74pU6Qrr5QOHZLGjnV1NAAAAK5Bk3MAANyDy5NSM2bM0LBhwzRkyBBdffXVmjNnjnx9fbVgwYJCx0dGRqp3795q2bKlmjZtqri4OF133XX69ttvnRy58/n6SgsXWsr4Fi6UvvjC1REBAAA4HzOlAABwDy5NSuXk5GjLli2KioqybvPw8FBUVJQ2bdpU4vFms1nr16/Xrl27dNtttxU6Jjs7W5mZmTa3yuyWW6QxYyy/DxsmnTxpaX6+bJnlp8nkwuAAAACcgJlSAAC4h2qufPATJ07IZDKpfv36Ntvr16+vP/74o8jjMjIyFBISouzsbBmNRr3zzjvq0qVLoWOnTp2qSZMmlWvcrvbKK9Jnn0l79kgNG0pnz/6zLzRUmjnT0iwdAADAHTFTCgAA9+Dy8r2y8Pf31y+//KIff/xRU6ZM0dixY5WUlFTo2PHjxysjI8N6S01NdW6wDuDrKw0ebPn94oSUZOk3FRsrJSY6PSwAAACnYKYUAADuwaUzperUqSOj0aijR4/abD969KgaNGhQ5HEeHh5q1qyZJKlNmzbauXOnpk6dqsjIyAJjvb295e3tXa5xu5rJJM2eXfg+s9nSc2rMGKlnT8sqfgAAAO7EOlOKpBQAAJWaS2dKeXl5qW3btlq/fr11W15entavX6+OHTvafZ68vDxlZ2c7IsQKKTlZSksrer/ZLKWmWsYBAAC4G8r3AABwDy6dKSVJY8eO1aBBg9SuXTu1b99e8fHxOnPmjIYMGSJJGjhwoEJCQjR16lRJlh5R7dq1U9OmTZWdna0vvvhCS5Ys0eyipg65ofT08h0HAABQmVC+BwCAe3B5Uqpfv346fvy4Jk6cqCNHjqhNmzZavXq1tfl5SkqKPDz+mdB15swZPfbYY0pLS5OPj49atGih9957T/369XPVU3C6oKDyHQcAAFCZMFMKAAD3YDCbzWZXB+FMmZmZCgwMVEZGhgICAlwdTpmYTFJ4uKWpeWH/9QwGyyp8+/fTUwoAgNJwh+sEZ3Hla9VubjttSd+iz/p/pruuusupjw0AAEpm73VCpVx9r6ozGqWZMy2/GwwF95vN0rRpJKQAAIB7YqYUAADugaRUJRUTIyUkSCEhttvzk1RffVX4LCoAAFC5mEwmTZgwQY0bN5aPj4+aNm2qyZMn6+LJ7gaDodDbv/71ryLP+9JLLxUY36JFC2c8pctGTykAANyDy3tKoexiYqSePS2r7KWnW3pInT8v3XWXtHChdP310uOPuzpKAABwOV5//XXNnj1bixcv1jXXXKOffvpJQ4YMUWBgoEaPHi1JSr9kdZMvv/xSDz/8sPr06VPsua+55hqtW7fOer9atcpxachMKQAA3EPluPJAkYxGKTLSdtu0adJTT0lPPCFde63UqZNLQgMAAOVg48aN6tmzp+66y9I7KTw8XMuWLdPmzZutYxo0aGBzzKpVq9SpUyc1adKk2HNXq1atwLEV3fkL55VjypHETCkAACo7yvfc0Nix0gMPWBqi33uvdOCA5fekJGnZMstPk8nFQQIAALvcfPPNWr9+vXbv3i1J2rZtm7799lt179690PFHjx7V559/rocffrjEc+/Zs0fBwcFq0qSJ7r//fqWkpJRr7I6QX7pnkEH+3v4ujgYAAFwOZkq5IYNBmjtX2rlT2rLFMpPqwgXLan35QkMtzdJjYlwWJgAAsMOzzz6rzMxMtWjRQkajUSaTSVOmTNH9999f6PjFixfL399fMSV8yHfo0EGLFi1S8+bNlZ6erkmTJikiIkI7duyQv3/BZE92drays7Ot9zMzMy/viZVRfumev7e/PAx8vwoAQGXGJ7mb8vGRPvpICgiQDh60TUhJlvuxsVJiomviAwAA9lm5cqXef/99LV26VFu3btXixYv1xhtvaPHixYWOX7Bgge6//35Vr1692PN2795d9957r6677jpFR0friy++0KlTp7Ry5cpCx0+dOlWBgYHWW1hY2GU/t7KgyTkAAO6DpJQbCw6WvL0L35e/YM+YMZTyAQBQkT399NN69tlndd9996lVq1Z68MEH9cQTT2jq1KkFxiYnJ2vXrl0aOnRoqR+nZs2auuqqq7R3795C948fP14ZGRnWW2pqaqkfozzQ5BwAAPdBUsqNJSdLx48Xvd9sllJTLeMAAEDFdPbsWXl42F6yGY1G5eXlFRg7f/58tW3bVq1bty7145w+fVr79u1TUFBQofu9vb0VEBBgc3MFZkoBAOA+SEq5sUtWh77scQAAwPl69OihKVOm6PPPP9eBAwf00UcfacaMGerdu7fNuMzMTH3wwQdFzpLq3LmzZs2aZb3/1FNP6euvv9aBAwe0ceNG9e7dW0ajUf3793fo87lczJQCAMB90OjcjRXxRWeZxwEAAOf797//rQkTJuixxx7TsWPHFBwcrEceeUQTJ060Gbd8+XKZzeYik0r79u3TiRMnrPfT0tLUv39/nTx5UnXr1tWtt96q77//XnXr1nXo87lczJQCAMB9GMzm/O5CVUNmZqYCAwOVkZHhsmnnzmIySeHhlqbmhf1XNhgsq/Dt3y8ZjU4PDwCACqcqXSdcLle9Vi8lvaRJX0/So20f1ey7ZzvtcQEAgP3svU6gfM+NGY3SzJmW3w2GgvvNZunZZ0lIAQCAysM6U4ryPQAAKj2SUm4uJkZKSJBCQmy3e3pafk6bZml2DgAAUBlYe0pRvgcAQKVHUqoKiImRDhyQNmyQli61/DxwQLrySungQSkqSjp61NVRAgAAlIxG5wAAuA8anVcRRqMUGWm7bd06KSJC2r1b6trVcv+33yyr8QUFWfZR2gcAACoSGp0DAOA+SEpVYQ0bSuvXW5JPv/5qaXqek/PP/tBQS0+qmBjXxQgAAHAxZkoBAOA+KN+r4po1k8aNs/x+cUJKsqzaFxsrJSY6Py4AAIDCMFMKAAD3QVKqijOZpOnTC99nNlt+jhljGQcAAOBqzJQCAMB9kJSq4pKTpbS0ovebzZbV+ZKTnRcTAABAUZgpBQCA+yApVcWlp5fvOAAAAEfJvpCtbFO2JGZKAQDgDkhKVXFBQeU7DgAAwFHyS/ckyd/L34WRAACA8kBSqoqLiLCssmcwFD3G01O6+mrnxQQAAFCY/NI9fy9/GT2MLo4GAABcLpJSVZzRKM2cafn90sRU/v3cXKlzZ+nIEefGBgAAcDGanAMA4F5ISkExMVJCghQSYrs9NNSSsAoOlnbskG67zdL03GSSkpKkZcssP1mZDwAAOANNzgEAcC/VXB0AKoaYGKlnT8sqe+nplh5SERGWmVR33WWZKbVnj9S2rWXbxbOm8pNXMTGuix8AALg/ZkoBAOBeSErBymiUIiMLbm/a1JKs6tCh8FX4Dh2SYmMts61ITAEAAEdhphQAAO6F8j3YJTi46H1ms+XnmDGU8gEAAMdhphQAAO6FpBTskl/WVxSz2dJvKjnZeTEBAICqhZlSAAC4F5JSsEtxCamyjAMAACgt60wpklIAALgFklKwS1BQ+Y4DAAAoLetMKcr3AABwCySlYJeICMsqewZD0WN8fKQbbnBeTAAAoGphphQAAO6FpBTsYjRKM2dafi8qMXXunGX1vkOHLPdNJikpSVq2zPKTJugAAOBy0OgcAAD3QlIKdouJkRISpJAQ2+1hYdKUKVLdutLPP0vt20tvvCGFh0udOkkDBlh+hodLiYmuiBwAALgDGp0DAOBeqrk6AFQuMTFSz57/rMYXFGQp7TMapf79pbvvln7/XXr66YLHHjokxcZaElsxMc6PHQAAVG7MlAIAwL2QlEKpGY2WMr1LNW5sSVYFB0vZ2QX3m82W0r8xYyyJLaPR0ZECAAB3wkwpAADcC+V7KFe//lp4Qiqf2SylplqSVwAAAKXBTCkAANwLSSmUq/T08h0HAAAgSTmmHJ2/cF4SM6UAAHAXJKVQroKCynccAACA9E/pniQFeAe4MBIAAFBe6CmFchURIYWGWpqam82Fj/H1la6/3vK7yVR403QAAICL5Zfu1fCqIaMHFwsAALgDZkqhXBmN0syZlt8NhsLHnD0rtW8vzZghhYdLnTpJAwZYfoaHS4mJzooWAABUFjQ5BwDA/ZCUQrmLiZESEqSQENvtYWHS5MmW7bt3S08+KaWl2Y45dEiKjSUxBQAAbNHkHAAA90NSCg4REyMdOCBt2CAtXWr5uX+/9MIL0pYtUvXqhR+XX/I3ZoyltA8AAEBiphQAAO6oQiSl3n77bYWHh6t69erq0KGDNm/eXOTYefPmKSIiQrVq1VKtWrUUFRVV7Hi4jtEoRUZK/ftbfub3itq5Uzp/vujjzGYpNdXSawoAAEBiphQAAO7I5UmpFStWaOzYsXrxxRe1detWtW7dWtHR0Tp27Fih45OSktS/f39t2LBBmzZtUlhYmLp27apDhw45OXKUVXp6+Y4DAADuL3+mVM3qNV0bCAAAKDcuT0rNmDFDw4YN05AhQ3T11Vdrzpw58vX11YIFCwod//777+uxxx5TmzZt1KJFC/3nP/9RXl6e1q9f7+TIUVZBQeU7DgAAuD/rTCnK9wAAcBsuTUrl5ORoy5YtioqKsm7z8PBQVFSUNm3aZNc5zp49q9zcXNWuXbvQ/dnZ2crMzLS5wbUiIqTQ0KJX58uXlCTl5lp+N5ks95cts/yk3xQAAFULPaUAAHA/Lk1KnThxQiaTSfXr17fZXr9+fR05csSuc4wbN07BwcE2ia2LTZ06VYGBgdZbWFjYZceNy2M0SjNnWn6/NDF18f1Jk6SOHaX4eCk8XOrUSRowwPIzPJwV+gAAqEroKQUAgPtxefne5Xjttde0fPlyffTRR6pexHJu48ePV0ZGhvWWmprq5ChRmJgYKSFBCgmx3R4aatn+/vtSrVqWlfqeeEJKS7Mdd+iQFBtLYgoAgKqC8j0AANxPNVc+eJ06dWQ0GnX06FGb7UePHlWDBg2KPfaNN97Qa6+9pnXr1um6664rcpy3t7e8vb3LJV6Ur5gYqWdPyyp76emWHlIREf+s0hcRIV11VeEr9ZnNlllVY8ZYzpF/DAAAcE/W8j1mSgEA4DZcOlPKy8tLbdu2tWlSnt+0vGPHjkUeN23aNE2ePFmrV69Wu3btnBEqHMRolCIjpf79LT8vTi7t21d4Qiqf2SylplqSWgAAwL0xUwoAAPfj0plSkjR27FgNGjRI7dq1U/v27RUfH68zZ85oyJAhkqSBAwcqJCREU6dOlSS9/vrrmjhxopYuXarw8HBr76kaNWqoRo0aLnseKH/p6eU7DgAAVF7MlAIAwP24PCnVr18/HT9+XBMnTtSRI0fUpk0brV692tr8PCUlRR4e/0zomj17tnJychQbG2tznhdffFEvvfSSM0OHgwUF2Tfu4upMk6nockAAAFB5MVMKAAD34/KklCSNGjVKo0aNKnRfUlKSzf0DBw44PiBUCBERlsbnhw5ZSvWKMmSIdOSIVK9ewabooaGWlf5iYhwfLwAAcBxmSgEA4H4q9ep7cG9GoyWhJFmaml8s/36TJlJmpjRypHTvvazSBwCAO8o15erchXOSmCkFAIA7ISmFCi0mRkpIkEJCbLeHhkoffijt3i299VbBpFW+/BlWY8ZYSvsAAEDlk1+6J0kB3gEujAQAAJSnClG+BxQnJkbq2bPoXlGtWhVf3nfxKn2RkU4JGQAAlKP80r0aXjVk9KBZJAAA7oKZUqgUjEZLQql/f8vPi5uXl3aVPpNJSkqSli2z/GQGFQCgIjOZTJowYYIaN24sHx8fNW3aVJMnT5b5om9kBg8eLIPBYHPr1q1bied+++23FR4erurVq6tDhw7avHmzI59KmdHkHAAA98RMKVR69q7S9+uvkqcnzdABAJXL66+/rtmzZ2vx4sW65ppr9NNPP2nIkCEKDAzU6NGjreO6deumhQsXWu97X7w8bSFWrFihsWPHas6cOerQoYPi4+MVHR2tXbt2qV69eg57PmVBk3MAANwTM6VQ6eWv0ldUX6l8r71GM3QAQOWzceNG9ezZU3fddZfCw8MVGxurrl27FpjV5O3trQYNGlhvtWrVKva8M2bM0LBhwzRkyBBdffXVmjNnjnx9fbVgwQJHPp0yYaYUAADuiaQUKr2SVukzGKQBA2iGDgConG6++WatX79eu3fvliRt27ZN3377rbp3724zLikpSfXq1VPz5s01YsQInTx5sshz5uTkaMuWLYqKirJu8/DwUFRUlDZt2uSYJ3IZmCkFAIB7onwPbiF/lb64uIKlefHxUu3a0tKlRR9PM3QAQEX17LPPKjMzUy1atJDRaJTJZNKUKVN0//33W8d069ZNMTExaty4sfbt26fnnntO3bt316ZNm2Q0FmwMfuLECZlMJtWvX99me/369fXHH38UGkd2drays7Ot9zMzM8vpGZaMmVIAALgnklJwG8Wt0rdsmX3nuDihZTIVveIfAADOsnLlSr3//vtaunSprrnmGv3yyy8aM2aMgoODNWjQIEnSfffdZx3fqlUrXXfddWratKmSkpLUuXPncolj6tSpmjRpUrmcq7SsM6VISgEA4FZISsGt5K/Sdyl7m6GPGyedOycFBEhPPUVDdACA6z399NN69tlnrYmnVq1a6eDBg5o6dao1KXWpJk2aqE6dOtq7d2+hSak6derIaDTq6NGjNtuPHj2qBg0aFHrO8ePHa+zYsdb7mZmZCgsLK+vTKhXrTCnK9wAAcCv0lEKVYE8zdA8P6fBhafhw6b77aIgOAKgYzp49Kw8P20s2o9GovLy8Io9JS0vTyZMnFVTEtzJeXl5q27at1q9fb92Wl5en9evXq2PHjoUe4+3trYCAAJubszBTCgAA90RSClWCPc3QlyyRpk+3JKcKQ0N0AIAr9OjRQ1OmTNHnn3+uAwcO6KOPPtKMGTPUu3dvSdLp06f19NNP6/vvv9eBAwe0fv169ezZU82aNVN0dLT1PJ07d9asWbOs98eOHat58+Zp8eLF2rlzp0aMGKEzZ85oyJAhTn+OJWGmFAAA7onyPVQZJTVDj4mRkpKkYr54LrQhOr2nAACO9O9//1sTJkzQY489pmPHjik4OFiPPPKIJk6cKMkya+rXX3/V4sWLderUKQUHB6tr166aPHmyvL29refZt2+fTpw4Yb3fr18/HT9+XBMnTtSRI0fUpk0brV69ukDz84qARucAALgng9mcP/+jasjMzFRgYKAyMjKcOu0cFUdxSaRly6QBA0o+x5gx0r/+JX3ySeFJLnpPAUDlxHWC/Zz5Wt30n5v0w6EftOq+Vbqn+T0OfSwAAHD57L1OYKYUqpyimqFL9jdEj4+X3n9fOn684L783lMJCSSmAAAoD8yUAgDAPdFTCrhISQ3RDQbLyny1ahWekJLoPQUAQHmzNjqnpxQAAG6FpBRwkZIaokvSwoWWMr/iXNx7Kp/JZOlZtWyZ5ScJKwAA7MNMKQAA3BNJKeAS+Q3RQ0Jst4eG/lOS99df9p1rxw7Lz8REKTxc6tTJ0rOqUyfL/cTE8owcAAD3k2vK1dncs5KYKQUAgLuhpxRQiJgYqWfPohui29t7avRo6b//lX78seA+ek8BAFCyzOxM6+8B3jSfBwDAnZCUAopQXEP0/N5Thw7900PqUt7eUnZ24QkpyXKcwWDpPdWzp+XxilsZEACAqujU+VOSJD9PP1Xz4NIVAAB3QvkeUAYl9Z4yGKSlS6VFi4o/z8W9pyjxAwCgIGs/KUr3AABwOySlgDKyp/eUl5d953rzTUspX1qa7fb8Ej8SUwCAqsq68h5NzgEAcDvMgQYuQ3n1nvrkk8K3F1biJ1HmBwCoOpgpBQCA+yIpBVymy+09Vb26dP580ee/uMQvMtIyayouznZWVWiopZyQhukAAHfDTCkAANwX5XuAA9nTe+rRR+0713ffSR9+SJkfAKBqYaYUAADui6QU4GAl9Z7q2dO+87zwgtSvX+EzrvK3jRljKe3LZzJJSUnSsmWWnxfvAwCgMmCmFAAA7ovyPcAJius9ZTKVXOLn42MZl5NT9GNQ5gcAcEfWmVIkpQAAcDskpQAnKar3VH6JX2yspZzv4sRUfsnfe+9JGRnSQw+V/Dj790t//WU536VJrvwyv/zVAQEAqOisM6Uo3wMAwO1QvgdUACWV+MXESI0b23eu4cOl+++3v8yPEj8AQEXGTCkAANwXM6WACqK4Ej/JvpX8jEbpwgXLrSgXl/n99RclfgCAio1G5wAAuC9mSgEVSH6JX//+lp/5Can8fSWt5LdihTRtmn2PtWIFK/kBACo+Gp0DAOC+SEoBlUhJZX59+kg33mjfuebMYSU/AEDFx0wpAADcF+V7QCVTHmV+Hh5SXl7Rj8FKfgCAioKZUgAAuC9mSgGV0OWW+Y0ebd/jvPKK9OSTpSvzY0YVAPxfe3cfHHV173H8s0lICJEkQDAPJBhQC0pLbBHSqIwKuSJ1FARUNKORztULBIVip+K1QBhlQpWhoNKoUxVnUB4Fnyq2EAGRigiIoDwUbRAqCRGVPIAkkJz7x95dWchmf8ju77fZvF8zO2T3nOye/R0z852v3/P9IZiolAIAIHKRlAIiUKBjfsOGWXufsjJpzhzrx/xWrJCys6Xrr5fuusv9b3Y2/akAAD/NycaTOn7yuCQqpQAAiEQc3wMiVEvH/BobWz7i53JJXbq4569c6f8zPMf8Vq+Wjh93V06d+X6eiqrly3886tfY6P/4IQAAHjX1Nd6fE+MSHVwJAAAIBZJSQATzHPNr7vV589zJIpfLN5HkOfL33HNSfX3LSSmPm26SYmL8V1S5XO6KqmHDpDfeoD8VAMAaz9G9Du06qF10O4dXAwAAgo3je0AbFeiI34gR7iomK5qapIYG/+OeiqqZM+lPBQCwjibnAABENpJSQBs2YoS0f7+0dq306qvuf8vLf6xY8tzJ78yG6R4ul5SVJc2ebe3zHn+c/lQAAOtocg4AQGTj+B7Qxvk74ucZC3TMb+5cqXNna5918qT/MU811YYN0nffWe9PJdGjCgAiFZVSAABENiqlALTIyjE/KxVViRb7044dKxUWUlEFAKBSCgCASEdSCkBAgY75eSqqpLMTU57nDz1k7bP27pXq6vyPn15RtWKF9R5V9KcCgNaHSikAACKb40mp+fPnKzs7W+3bt1dubq42b97sd+7nn3+ukSNHKjs7Wy6XS3PnzrVvoUAb5znmd+ed7n/PPB4XqKLq0UcDV1OlpUn33WdtPXff7X5YqaiimgoAWidvpRRJKQAAIpKjSaklS5Zo8uTJmj59urZt26acnBwNGTJEVVVVzc4/fvy4evbsqVmzZiktLc3m1QIIpKWKKivVVPPnu5NGVvznP9Lx4/7HueMfALR+3kopju8BABCRHE1KzZkzR/fdd5/GjBmjyy+/XM8++6w6dOigF198sdn5/fv315NPPqnRo0crLi7O5tUCsKKliqpg9adKT5cmT7a2nhkzQtOfiuQVAIQelVIAAEQ2x5JSDQ0N2rp1q/Lz839cTFSU8vPz9eGHHzq1LAAhFoz+VM88I918s7XPa2ryP+applq0yJ0Us1pRxXFAALAHjc4BAIhsMU598JEjR9TY2KjU1FSf11NTU7Vnz56gfU59fb3q6+u9z2tqaoL23gB+Gk81lT+eiqqJE32TRJmZ0ty57vHGRvfzr79uvhLK5ZKSk6Xvvw+8nrvv9j9mjPu9Jk2Shg2T3njDnaQ68zM9yStPxZfkXuOGDVJFhbu6a+DAs3txAQD8o9E5AACRzfFG56FWUlKipKQk7yMrK8vpJQGwIBgVVZMmWfusmADpeU9F1fjx0v/8T/Cbq3MUEACaR6UUAACRzbGkVEpKiqKjo3X48GGf1w8fPhzUJuaPPPKIqqurvY+DBw8G7b0BhJYdd/zLypL8tLE7y/PPS0eO+B//Kc3VOQoIAP5RKQUAQGRzLCkVGxurfv36qayszPtaU1OTysrKlJeXF7TPiYuLU2Jios8DQOQ43zv+zZ3rTkxZcdll1uYVF1uvpuLOgADgH5VSAABENsd6SknS5MmTVVhYqCuvvFIDBgzQ3LlzdezYMY0ZM0aSdM8996hbt24qKSmR5G6OvmvXLu/PX3/9tbZv364LLrhAl1xyiWPfA4CzWupRFaz+VJmZ0tNPS6fdm8Gv5t7j9LGDB6X//m/prbf8J69O72MVHe1OUDX3HebN+/FIo0QfKwCRhUopAAAim6NJqTvuuEPffPONpk2bpsrKSl1xxRV69913vc3PDxw4oKioH4u5Dh06pF/+8pfe57Nnz9bs2bN17bXXat26dXYvH0ArMWKEO7njL1njqagaNcqdDDo9UXR6RdV11wWvufqCBS2Pe5JXq1ZJDQ3WmqtbTVxJJK8AhL9TTad07OQxSVRKAQAQqVzGtPT/9CNPTU2NkpKSVF1dzVE+AD6aS+pkZf1YUeWZM2qU++fmklfFxdL06YE/q29faccOa+s6M1F25lhmpjRnjnT77WfP86zr9LsCkrwC/CNOsC7U1+q7H75Tlye6SJIa/tigdtHtgv4ZAAAgNKzGCRF/9z0AsCrQHf88c4LRXH3OHOvrsnIc8K67gt/HymoTdnpdAQgFz9G9+Jh4ElIAAEQoklIAcJpAd/yTgtNc3XMUMFDy6oUXrK375En/Y57E1b33untZBTN5xd0DAYQKTc4BAIh8jvaUAoDW6nybq0vW+lh17hy8NS9c2PK4J3n11FPSE08EbsLe1NT8kcEze115cBQQwLmgyTkAAJGPSikACIFgHAUcMcKduAlUUdW1q7U1XXGFtXmTJ0uVlf7HPcmrsWOtVV1J51ZRxXFAABKVUgAAtAVUSgFAiLRUTeURjDsDzp/vTiS1dFfAzExp9mwpPz/wupOTpaNHA8/79lv/Y57EVVmZVFdn7e6BEk3YAfyISikAACIflVIA4LBAfawCVVTddltw+1gtX35+3+d0Q4a419dSRdWDD0qnTjnbhJ3qLCD8UCkFAEDkIykFAK1AoOOAVo4CBrMJu9Ujg5K795Q/xriTTu3bN9+fyjNHOvfkldXEFUcLEe4aGxs1depU9ejRQ/Hx8br44ov12GOPyfz/H8fJkyf18MMP6xe/+IUSEhKUkZGhe+65R4cOHWrxfYuLi+VyuXwevXv3tuMrWUKlFAAAkY/jewDQSgQ6DhjoKKBnTjCasFs9Mvi//yuNGxf4uwVK7niSVwkJ7iRXS03YJ0603oTdk+AK5tFCjhUi2P70pz+ptLRUL7/8svr06aMtW7ZozJgxSkpK0oMPPqjjx49r27Ztmjp1qnJycvT9999r4sSJuuWWW7Rly5YW37tPnz5as2aN93lMTPiEht5KKZJSAABErPCJPAAA5y0Yfaw8cwIlr6Kjg3f3wAkTpGeeCTyvoaHlcWPc6w1UdTVhgnTDDe7vF+gug8OGSW+8YS15RU8shMI///lPDRs2TDfddJMkKTs7W4sWLdLmzZslSUlJSVq9erXP7zzzzDMaMGCADhw4oO7du/t975iYGKWlpYVu8efBWynF8T0AACIWx/cAoA0K1MdKCs6RQSt3D8zKkoYPt7bu8eOtzWsu0XS6igqpY8ezjwCe+R4HD0pLlriPDga60+Dy5cHviSVZOzLIscLIdtVVV6msrEz/+te/JEmffvqpPvjgAw0dOtTv71RXV8vlcik5ObnF9963b58yMjLUs2dPFRQU6MCBA8Fc+nmhUgoAgMhHpRQAwK/zPTJo5e6Bp/exCnQccMQI6S9/CdKXs6igoOVxT/LqvvuCW3UlWau8ojor8k2ZMkU1NTXq3bu3oqOj1djYqJkzZ6rAz3+cJ06c0MMPP6w777xTiYmJft83NzdXCxYsUK9evVRRUaEZM2Zo4MCB+uyzz9SxY8ez5tfX16u+vt77vKam5vy/XAtodA4AQOSjUgoAcF7O9+6BTjVhf+wxa/OsJm2OHvU/5klcjR4tjRkTuOqqsdFaQ/dQ3LFQojor3CxdulSvvPKKXn31VW3btk0vv/yyZs+erZdffvmsuSdPntTtt98uY4xKS0tbfN+hQ4fqtttuU9++fTVkyBC98847Onr0qJYuXdrs/JKSEiUlJXkfWVlZQfl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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Test verisi üzerinde modelin performansını değerlendirme\n",
        "test_loss = 0.0\n",
        "correct = 0\n",
        "total = 0\n",
        "\n",
        "# Test verisi üzerinde modelin kaybını ve doğruluğunu hesapla (gradyan hesaplama yok)\n",
        "with torch.no_grad():\n",
        "    for images, labels in test_loader:\n",
        "        output = model(images)\n",
        "        loss = criterion(output, labels)\n",
        "        test_loss += loss.item()\n",
        "\n",
        "        _, predicted = torch.max(output, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum().item()\n",
        "\n",
        "# Test seti kaybı ve doğruluğu\n",
        "test_loss /= len(test_loader)\n",
        "test_accuracy = 100 * correct / total\n",
        "\n",
        "print(\"Test Seti Kaybı: \", test_loss)\n",
        "print(\"Test Seti Doğruluğu: \", test_accuracy , \"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70qh1M2_Ir7I",
        "outputId": "57c602c7-472d-4799-8b95-bce23e748c97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Seti Kaybı:  0.08279869516171029\n",
            "Test Seti Doğruluğu:  97.55 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. Keras kütüphanesi:\n",
        "\n",
        "* !pip install keras\n",
        "\n",
        "--------------------------------------------------------------------------------"
      ],
      "metadata": {
        "id": "HUvgQCnDDAs9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "\n",
        "# Modeli oluşturma\n",
        "model = Sequential()\n",
        "model.add(Dense(128, activation='relu', input_shape=(28*28,)))\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "# Modeli derleme\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Eğitme\n",
        "model.fit(x_train, y_train, epochs=5)"
      ],
      "metadata": {
        "id": "uW_Y7k9dN3Ni"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}