Image_classification_in_fashion_MNIST.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Tulu-Ragot/D.S/blob/main/Image_classification_in_fashion_MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Io7_9kPpxwyl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Processing"
      ],
      "metadata": {
        "id": "zZq03Xs-xc_-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "from tensorflow.keras.utils import to_categorical"
      ],
      "metadata": {
        "id": "kHjkXn63x4c_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the fashion MNIST dataset"
      ],
      "metadata": {
        "id": "0m8GOOjWyjKD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "id": "KvwollNryvUD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06723e79-7334-499c-aa28-a30df27b6c2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 2s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalize the image data to range between 0 and 1"
      ],
      "metadata": {
        "id": "D6pJA4NXzOBP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = train_images/255.0\n",
        "test_images = test_images/255.0"
      ],
      "metadata": {
        "id": "pf1h85IkzXPM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reshape the data to fit the model input requirements"
      ],
      "metadata": {
        "id": "LGzXRIn9z7_E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_images = train_images.reshape((train_images.shape[0], 28, 28, 1))\n",
        "test_images = test_images.reshape((test_images.shape[0], 28, 28, 1))"
      ],
      "metadata": {
        "id": "nNNJDik20HjK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert the labels into one-hot vectors"
      ],
      "metadata": {
        "id": "_rvJpyGm1Nro"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = to_categorical(train_labels)\n",
        "test_labels = to_categorical(test_labels)"
      ],
      "metadata": {
        "id": "ZLvkTi1y1LN0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Development**"
      ],
      "metadata": {
        "id": "XN4Ro7Rn12q0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense"
      ],
      "metadata": {
        "id": "NvjJZLpX2d7f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Designing a CNN"
      ],
      "metadata": {
        "id": "TlyuhEdq3OOH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential([\n",
        "    Conv2D(32,(3,3), activation='relu', input_shape=(28, 28,1)),\n",
        "    MaxPooling2D((2,2)),\n",
        "    Conv2D(64,(3,3), activation='relu'),\n",
        "    MaxPooling2D((2,2)),\n",
        "    Conv2D(64, (3,3), activation='relu'),\n",
        "    Flatten(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dense(10, activation='softmax')\n",
        "])"
      ],
      "metadata": {
        "id": "tla6O62E3VpD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Training**"
      ],
      "metadata": {
        "id": "pGlfKxT570bG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compiling the model"
      ],
      "metadata": {
        "id": "14SAMrvC78Kj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "LO9vGhxc8Ei2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model on the training data"
      ],
      "metadata": {
        "id": "yl6socue-WDe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(train_images, train_labels, epochs=10, batch_size=64, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQxOovbP9uSz",
        "outputId": "f3af8f67-8fe3-4e80-fa84-a3cdb26637a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "750/750 [==============================] - 44s 58ms/step - loss: 0.1515 - accuracy: 0.9447 - val_loss: 0.2663 - val_accuracy: 0.9080\n",
            "Epoch 2/10\n",
            "750/750 [==============================] - 43s 57ms/step - loss: 0.1387 - accuracy: 0.9484 - val_loss: 0.2849 - val_accuracy: 0.9076\n",
            "Epoch 3/10\n",
            "750/750 [==============================] - 46s 61ms/step - loss: 0.1256 - accuracy: 0.9536 - val_loss: 0.2856 - val_accuracy: 0.9048\n",
            "Epoch 4/10\n",
            "750/750 [==============================] - 46s 61ms/step - loss: 0.1153 - accuracy: 0.9560 - val_loss: 0.2900 - val_accuracy: 0.9058\n",
            "Epoch 5/10\n",
            "750/750 [==============================] - 44s 59ms/step - loss: 0.1066 - accuracy: 0.9603 - val_loss: 0.3369 - val_accuracy: 0.8982\n",
            "Epoch 6/10\n",
            "750/750 [==============================] - 45s 60ms/step - loss: 0.0982 - accuracy: 0.9643 - val_loss: 0.3401 - val_accuracy: 0.9047\n",
            "Epoch 7/10\n",
            "750/750 [==============================] - 47s 63ms/step - loss: 0.0905 - accuracy: 0.9669 - val_loss: 0.3165 - val_accuracy: 0.9093\n",
            "Epoch 8/10\n",
            "750/750 [==============================] - 43s 57ms/step - loss: 0.0840 - accuracy: 0.9691 - val_loss: 0.3389 - val_accuracy: 0.9090\n",
            "Epoch 9/10\n",
            "750/750 [==============================] - 46s 62ms/step - loss: 0.0777 - accuracy: 0.9712 - val_loss: 0.3630 - val_accuracy: 0.9078\n",
            "Epoch 10/10\n",
            "750/750 [==============================] - 44s 59ms/step - loss: 0.0695 - accuracy: 0.9738 - val_loss: 0.4007 - val_accuracy: 0.9057\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Evaluation and analysis**"
      ],
      "metadata": {
        "id": "gAwRPM5FArtz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "BhE_W0vqApga"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluate the models performance on the test dataset"
      ],
      "metadata": {
        "id": "do9T-ym9BUNc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy=model.evaluate(test_images, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbxWNTreBaU1",
        "outputId": "dacd523b-dac2-441b-9564-fe64455c4674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 10ms/step - loss: 0.4124 - accuracy: 0.9068\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot the training and validation accuracy and loss over epochs"
      ],
      "metadata": {
        "id": "x6MT6KR0BrSj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "plt.plot(history.history['val_loss'],label='Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "HwdIfIY4Bz0y",
        "outputId": "e1da38e0-dbb6-455d-db66-5f8fe3b48936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JZJgzZw6mTZum3TZmzBhIpVJs2rSpxveYMWMGfvzxR1y5cuWxEjRvgyfizzrRo+QXKbStOsJ4nUL8dbOk2kroAGBuaoRuFa05lWGni7MVJI28pqOhazW3wZeVlSEpKQnR0dHabWKxGEOHDsWRI0dqPEahUFT7ZSyVSnHw4MFa32PTpk2IjIysNfwoFAooFArtc31M9kdERC2TWq1B2u27OJ9VqA07KVly5BUpatzfycpM25rTQ2aD7jJreNibt4nlI9oSvQagmzdvQqVSwdnZWWe7s7MzLl68WOMxISEhWLJkCQYOHAgvLy8kJiZix44dOl1e94uPj0dBQQEmT55cax2xsbFYuHBhva/DkAwaNAj+/v5YunQpAGG81YwZMzBjxoxajxGJRNi5cydGjRrVoPdurPMQEdWmVKnC5dyi+1p15LiQLa+2AjoAiERARwcLIeTc14XlaGWmh8qprvQ+Bqiuli1bhqlTp8LHxwcikQheXl4IDw/HunXratx/7dq1GD58OGQyWa3njI6ORmRkpPa5XC6Hu7t7o9euTyNGjIBSqURCQkK11/744w8MHDgQZ86cQa9evep03hMnTlRb7byhFixYgPj4eCQnJ+tsz87Ohp2dXaO+V23u3bsHNzc3iMViZGZmwsyMv9CI2po7JWW4kF01KDklS46r+cXV5tcBADNjMXxcrbWDkrvLrOHjYtUqV0EngV6/cw4ODjAyMkJubq7O9tzcXLi4uNR4jKOjI+Lj41FaWopbt25BJpMhKioKnTp1qrZvWloa9u3bhx07djy0DjMzszb/ATdlyhSMGTMGN27cQPv27XVei4uLQ2BgYJ3DDyB8P5pLbT8TTWH79u3o0aMHNBoN4uPjMX78+GZ77wdpNBqoVCoYG/MXLVF9aDQa3Lhz776gI3RlZdWw8jkA2FuY6ozV6SGzhmc7CxgbiZu5cmpKev1umpqaIiAgAImJidptarUaiYmJ6Nev30OPlUgkcHNzQ3l5ObZv346RI0dW2ycuLg5OTk54/vnnG7321uaFF16Ao6Mj1q9fr7O9uLgY27Ztw5QpU3Dr1i1MmDABbm5uMDc3h6+vL7755puHntfT01PbHQYAV65cwcCBAyGRSNC9e3fs3bu32jGzZ89Gly5dYG5ujk6dOmHu3LlQKpUAgPXr12PhwoU4c+YMRCIRRCKRtmaRSIT4+Hjtec6ePYtnnnkGUqkU7dq1w+uvv47i4mLt65MnT8aoUaPwySefwNXVFe3atcO0adO07/Uwa9euxSuvvIJXXnkFa9eurfb6+fPn8cILL8Da2hpWVlYYMGAArl27pn193bp16NGjB8zMzODq6oqIiAgAwvpdIpFIp3WroKAAIpEIv//+OwDg999/h0gkwu7duxEQEAAzMzMcPHgQ165dw8iRI+Hs7AxLS0sEBQVh3759OnUpFArMnj0b7u7uMDMzQ+fOnbF27VpoNBp07twZn3zyic7+ycnJEIlEuHr16iP/TohaA3XFLeffJ2di8c8X8Pc1R+G38BcM+Og3vLEpCZ8nXsG+C3na8OPRzhzDe7pg5rNdsG5yII5GD0FSzFBsnBKM6NBuGOnvhs5OVgw/bZDe/0sZGRmJsLAwBAYGom/fvli6dClKSkoQHh4OAJg0aRLc3NwQGxsLADh27BgyMzPh7++PzMxMLFiwAGq1GrNmzdI5r1qtRlxcHMLCwpr+f84aDaCsefKqJmViLnRCPwZjY2NMmjQJ69evx5w5c7QDwrdt2waVSoUJEyaguLgYAQEBmD17NqytrfHTTz/h1VdfhZeXV63zMt1PrVbjb3/7G5ydnXHs2DEUFhbWODbIysoK69evh0wmw9mzZzF16lRYWVlh1qxZGD9+PM6dO4eEhATth7uNjU21c5SUlCAkJAT9+vXDiRMnkJeXh3/84x+IiIjQCXm//fYbXF1d8dtvv+Hq1asYP348/P39MXXq1Fqv49q1azhy5Ah27NgBjUaDf/3rX0hLS4OHhwcAIDMzEwMHDsSgQYPw66+/wtraGocOHUJ5ubAOz6pVqxAZGYkPPvgAw4cPR2FhYb1mso6KisInn3yCTp06wc7ODhkZGQgNDcX7778PMzMzfPXVVxgxYgQuXbqEDh06ABD+vRw5cgSff/45/Pz88Ndff+HmzZsQiUR47bXXEBcXh5kzZ2rfIy4uDgMHDkTnzp3rXB+RvpWr1LiWX4JzmYU4lyVMKHg+qxAlNYzXMTUSo4uLpdCq42qNHm428HGxgpXERA+VU0ug9wA0fvx45OfnY968ecjJyYG/vz8SEhK0A6PT09MhFlcl79LSUsTExCA1NRWWlpYIDQ3Fxo0bYWtrq3Peffv2IT09Ha+99lrTX4TyLrC49jFGTebdLMD08cffvPbaa/j444+xf/9+DBo0CIDwAThmzBjY2NjAxsZG58Nx+vTp2LNnD7799tvHCkD79u3DxYsXsWfPHu2Yq8WLF2P48OE6+8XExGi/9vT0xMyZM7FlyxbMmjULUqkUlpaWMDY2fmiX1+bNm1FaWoqvvvpKOwZpxYoVGDFiBD788EPtz4+dnR1WrFgBIyMj+Pj44Pnnn0diYuJDA9C6deswfPhw7XijkJAQxMXFYcGCBQCEiTttbGywZcsWmJgIvzy7dOmiPf4///kP/v3vf+Odd97RbgsKCnrk39+DFi1ahGHDhmmf29vbw8/PT/v8vffew86dO7Fr1y5ERETg8uXL+Pbbb7F3714MHToUAHS6hidPnox58+bh+PHj6Nu3L5RKJTZv3lytVYioJSorV+NybhHOZxXiXKYc57IKcSG75vl1JCZidHe1Rk83G/SU2aCHmzW8naxgasxWHKqi9wAEABEREdouggdVdgtUevrpp5GSkvLIcz777LNcL+kBPj4+ePLJJ7Fu3ToMGjQIV69exR9//IFFixYBAFQqFRYvXoxvv/0WmZmZKCsrg0KhgLm5+WOd/8KFC3B3d9cZcF5TV+bWrVvx+eef49q1ayguLkZ5efkj52uo6b38/Px0BmD3798farUaly5d0gagHj16wMioap4NV1dXnD17ttbzqlQqbNiwAcuWLdNue+WVVzBz5kzMmzcPYrEYycnJGDBggDb83C8vLw9ZWVkYMmRIna6nJoGBgTrPi4uLsWDBAvz000/Izs5GeXk57t27h/T0dABCd5aRkRGefvrpGs8nk8nw/PPPY926dejbty9++OEHKBQKThBKLU6pUoWLOUU4l1moDTyXcopqXA/L0swY3WXW6CmzQU83IfR0cuB4HXq0FhGAWj0Tc6E1Rh/vW0dTpkzB9OnTsXLlSsTFxcHLy0v7gfnxxx9j2bJlWLp0KXx9fWFhYYEZM2agrKzm6drr48iRI5g4cSIWLlyIkJAQbUvKp59+2mjvcb8HQ4pIJIJaXf2XaKU9e/YgMzOz2qBnlUqFxMREDBs2DFJp7evvPOw1ANrWzPvDeW1jkh68u27mzJnYu3cvPvnkE3Tu3BlSqRQvvfSS9vvzqPcGgH/84x949dVX8dlnnyEuLg7jx49/7IBL1BRKFOW4kC2v6MYS/rySV/OdWDZSEyHkyGzQw80GPSsGJ3N+HaoPBqDGIBLVqStKn8aNG4d33nkHmzdvxldffYU333xTOx7o0KFDGDlyJF555RUAwpiey5cvo3v37o917m7duiEjIwPZ2dlwdXUFABw9elRnn8OHD8PDwwNz5szRbntw3TZTU9Na53W6/73Wr1+PkpISbVA4dOgQxGIxunbt+lj11mTt2rV4+eWXdeoDgPfffx9r167FsGHD0KtXL2zYsAFKpbJawLKysoKnpycSExMxePDgauevvGsuOztbu3TLg7f71+bQoUOYPHkyRo8eDUBoEbp+/br2dV9fX6jVauzfv1/bBfag0NBQWFhYYNWqVUhISMCBAwce672JGoO8VKkdp3MusxBnMwuRWsvMye0sTIUuLLfK1h0brodFjYoByMBYWlpi/PjxiI6Ohlwu15kg0tvbG9999x0OHz4MOzs7LFmyBLm5uY8dgIYOHYouXbogLCwMH3/8MeRyebUg4e3tjfT0dGzZsgVBQUH46aefsHPnTp19PD098ddffyE5ORnt27eHlZVVtWkKJk6ciPnz5yMsLAwLFixAfn4+pk+fjldffbXaxJqPKz8/Hz/88AN27dqFnj176rw2adIkjB49Grdv30ZERASWL1+Ol19+GdHR0bCxscHRo0fRt29fdO3aFQsWLMAbb7wBJycnDB8+HEVFRTh06BCmT58OqVSKJ554Ah988AE6duyIvLw8nTFRD+Pt7Y0dO3ZgxIgREIlEmDt3rk5rlqenJ8LCwvDaa69pB0GnpaUhLy8P48aNAwAYGRlh8uTJiI6Ohre39yPvtiSqr9slZVXjdSoGKde20rmLtQQ93YRZk3u62cDXzQbO1mYMO9SkGIAM0JQpU7B27VqEhobqjNepHFweEhICc3NzvP766xg1ahQKCwsf67xisRg7d+7ElClT0LdvX3h6euLzzz/Hc889p93nxRdfxL/+9S9ERERAoVDg+eefx9y5c7UDjAFhbbcdO3Zg8ODBKCgoQFxcXLWZvM3NzbFnzx688847CAoKgrm5OcaMGYMlS5bU+++lckB1TeN3hgwZol1v7u2338avv/6K//u//8PTTz8NIyMj+Pv7o3///gCAsLAwlJaW4rPPPsPMmTPh4OCAl156SXuudevWYcqUKQgICEDXrl3x0Ucf4dlnn31kfUuWLMFrr72GJ598Eg4ODpg9e3a1ZVtWrVqFd999F2+99RZu3bqFDh064N1339XZZ8qUKVi8eLH2TkuihsorKsX5iqBzNrMQ57PkyCy4V+O+7e2k2vE6PSoGKXPmZNIHvS+G2hJxMVRqy/744w8MGTIEGRkZD20t4886PUij0SC7sFRo0blvzE5ta2IJy0RYa1t1esisYWvOlc6p6bSaxVCJqPkoFArk5+djwYIFGDt2bL27CslwZBXcQ3JGAc5WBJ7zWXLcLql+U4RYBHg5WlaM2REGJ3eXWXOOHWrRGICIDMQ333yDKVOmwN/fH1999ZW+y6EWRqXW4EK2HElpd3Ay7Q6Srt+ucakIY7EI3s5W6Cmzhm97G/SQ2aCbK9fEotaHP7FEBmLy5MnVxlKR4SpWlCM5vQAn024jKe0OTqXdqTaDspFYhG6uVvB1s4VvxR1ZXZytIDExquWsRK0HAxARkQHILryHk9fv4OT12ziZdgcXsuV4cKodKzNj9PawQ2DFw7+DLVt2qM3iT3Y9cew4tXX8GW+9VGoNLuZUdGddv4OktDs13pXlZitFoKcdAj3tEehhhy7OVjDipIJkIBiA6qhy4ru7d+8+1sy7RK3V3bvCnC01LflBLUuJohzJGQVCC0/abZxOL0CxolxnHyOxCN1drRHgYSeEHg97uNjw7j4yXAxAdWRkZARbW1vk5eUBEOaj4WRd1JZoNBrcvXsXeXl5sLW11VlLjVqGnMJSnEy7rQ08F7KLqi0dYWlmjN4dbBHoYY9ATzv4u9vCwoy/8okq8V9DPVSuUl4ZgojaIltbW+3POumPSq3B5dwi7didk9dr7866v3Wnqwu7s4gehgGoHkQiEVxdXeHk5FTrQpZErZmJiQlbfvTkblnl3VnC7ein0+6g6IHuLLEI6OZqjSBPe23ocbVhlzxRXTAANYCRkRE/JIioQXLlpdqurJPX7yAlW16tO8vC1Ah9POyEsONhD/8OtrBkdxZRg/BfEBFRM9F2Z1VMNHgy7Q5u3KnenSWzkSCg4s6sQE87+LhYszuLqJExABERNZG7ZcLdWUnXhe6sU+l3UFRavTvLx8Va53Z0mS27s4iaGgMQEVEjuppXjF3Jmdh/OR/ns+Qof6A7y9zUCH062GnH7vi723LNLCI9YAAiImqgnMJS/HAmC/HJmTifJdd5zdVGUjF2R2jh8XGxgrGRWE+VElElBiAionoovKvE7nPZiE/OxLG/bqNy4mxjsQhPd3FEqK8rnvBqBzd2ZxG1SAxARESPqVSpQuKFPHyfnInfL+WjTKXWvhbkaYeR/m4I9XWFvYWpHqskosfBAERE9BDlKjUOX7uF75OzsOd8js4SEz4uVnjRX4YX/WRob2euxyqJqK4YgIiIHqDRaHDmRiHiT2fixz+zcbNYoX3NzVaKF/1lGOkvg4+LtR6rJKKGYAAiIqpwLb8Y35/OxPdnspB26652u525CZ7v5YqR/m4I6GAHMefkIWr1GICIyKDlFJbixz+FO7jOZVbdwSU1McKzPZwx0l+GAd6OMOGdW0RtCgMQERmcwntKJJzLRvzpLBz965b2Di4jsQgDvR0wqrcbhnZz5urpRG0Y/3UTkUEoVarw60XhDq7fLurewRXoYYeR/jKE+rqinaWZHqskoubCAEREbZZKrcGRa7cQn5yJPedydFZV7+JsiZH+bnjRTwZ3e97BRWRoGICIqE3RaDT480Yh4pOFO7jyi6ru4JLZSPCivxtG+svQzZV3cBEZMgYgImoTUvOL8X1yFnadycJfN0u0223NTfC8r3AHV6AH7+AiIgEDEBG1WrlyYQ2uXWey8OeNQu12iYkYz3Z30d7BZWrMO7iISBcDEBG1KvJSJRLO5uD7M5k4fE33Dq4B3g4Y5e+GYd15BxcRPRx/QxBRi1eqVOG3i3n4PjkLv17KQ1l51R1cARV3cD3PO7iIqA4YgIioRVKpNTiaegvxpzOR8MAdXN5OlhjVm3dwEVH96b1jfOXKlfD09IREIkFwcDCOHz9e675KpRKLFi2Cl5cXJBIJ/Pz8kJCQUG2/zMxMvPLKK2jXrh2kUil8fX1x8uTJprwMImoEwh1cBVj0QwqeiE3ExP8dw7akGyhSlENmI8E/n+6En98egF/+NRDTBndm+CGietNrC9DWrVsRGRmJL774AsHBwVi6dClCQkJw6dIlODk5Vds/JiYGmzZtwpo1a+Dj44M9e/Zg9OjROHz4MHr37g0AuHPnDvr374/Bgwdj9+7dcHR0xJUrV2BnZ9fcl0dEjylXXoodpzLxXVIGruXr3sEV6uuKkX4yBHna8w4uImo0Io2mcghh8wsODkZQUBBWrFgBAFCr1XB3d8f06dMRFRVVbX+ZTIY5c+Zg2rRp2m1jxoyBVCrFpk2bAABRUVE4dOgQ/vjjj8euQ6FQQKGomitELpfD3d0dhYWFsLbmXCFETUFRrkLihTxsO5mB/Zfzoa74TSQxEWNYdxeM9JNhYBfewUVEj08ul8PGxuaxPr/11gJUVlaGpKQkREdHa7eJxWIMHToUR44cqfEYhUIBiUSis00qleLgwYPa57t27UJISAjGjh2L/fv3w83NDW+99RamTp1aay2xsbFYuHBhA6+IiB7HucxCfJd0A/HJmSi4q9RuD/Sww9jA9gj1dYWVxESPFRKRIdBbALp58yZUKhWcnZ11tjs7O+PixYs1HhMSEoIlS5Zg4MCB8PLyQmJiInbs2AGVSqXdJzU1FatWrUJkZCTeffddnDhxAm+//TZMTU0RFhZW43mjo6MRGRmpfV7ZAkREjeN2SRniT2diW9INXMiuWnHdxVqCv/Vxw0sB7dHJ0VKPFRKRoWlVd4EtW7YMU6dOhY+PD0QiEby8vBAeHo5169Zp91Gr1QgMDMTixYsBAL1798a5c+fwxRdf1BqAzMzMYGbG22eJGlO5So39l/Ox7eQNJF7MhVIl9HGZGokxrIczxga0xwBvRxhxXA8R6YHeApCDgwOMjIyQm5ursz03NxcuLi41HuPo6Ij4+HiUlpbi1q1bkMlkiIqKQqdOnbT7uLq6onv37jrHdevWDdu3b2/8iyCiaq7mFWHbyRvYcTpTZx0uXzcbjA1sjxf9ZLA1N9VjhUREegxApqamCAgIQGJiIkaNGgVAaL1JTExERETEQ4+VSCRwc3ODUqnE9u3bMW7cOO1r/fv3x6VLl3T2v3z5Mjw8PBr9GohIIC9V4oczWdh28gaSMwq029tZmGJUb6GLi4uPElFLotcusMjISISFhSEwMBB9+/bF0qVLUVJSgvDwcADApEmT4ObmhtjYWADAsWPHkJmZCX9/f2RmZmLBggVQq9WYNWuW9pz/+te/8OSTT2Lx4sUYN24cjh8/ji+//BJffvmlXq6RqK1SqzU4fO0WtiVlIOFcDhQVszMbiUUY3NUJYwPbY3BXJ97FRUQtkl4D0Pjx45Gfn4958+YhJycH/v7+SEhI0A6MTk9Ph1hc9cuztLQUMTExSE1NhaWlJUJDQ7Fx40bY2tpq9wkKCsLOnTsRHR2NRYsWoWPHjli6dCkmTpzY3JdH1Cal37qL75IysP1UJjIL7mm3d3G2xNgAd4zsLYOTleQhZyAi0j+9zgPUUtVlHgEiQ3C3rBy7z+ZgW1IGjqbe1m63khjjRT8ZxgW6o1d7G4hEHNBMRPrTKuYBIqKWTaPRICntDradvIGfzmajuGItLpEIeKqzA14KaI+QHi6QmBjpuVIiorpjACIiHTmFpdh+6ga2J91A6s2qZSk82pnjpT7t8beA9nCzleqxQiKihmMAIiKUKlXYdyEX207ewB9XqpalMDc1QqivK8YGtEffjvbs4iKiNoMBiMhAaTQanMuUY1tSBr5PzkLhvaplKfp62uOlwPZ43tcVFmb8NUFEbQ9/sxEZmFvFCuw8nYnvkm7gYk6RdrurjQRj+rTHSwHt4elgoccKiYiaHgMQkQFQqtT4/VI+tp3MwK8X81Be0cdlaixGSA8XjA1oj/6dHbgsBREZDAYgojbscm4Rtp3MwM7TWbhZXLUshV97G7wU6I4Xe8lgY86V14nI8DAAEbUxhfcql6XIwJkbhdrtDpamGN3bDS8FuKOri5UeKyQi0j8GIKI2QKXW4NDVm/gu6Qb2nK9alsJYLMIzPk4YG+iOQV0dYWLEZSmIiAAGIKJWSV6qxJmMApxOL0ByRgFOp9/BnbtVd3H5uFjhpYD2GNXbDQ6WZnqslIioZWIAImrhVGoNLucWaYPO6fQCXM0vxoOL2NhITTDSX4axAe7o6WbNOXuIiB6CAYiohckvUuiEnT9vFKCkTFVtvw725vB3t0XvDrbo3cEO3V2tufI6EdFjYgAi0iNFuQopWXKcTi/A6YwCJGfcQcbte9X2szA1gl9l2HG3g38HW3ZtERE1AAMQUTPRaDS4cedeRetOAU5n3MH5TDnKVGqd/UQiwNvJEr3d7dC7gy38O9jC28mKc/QQETUiBiCiJlKiKMefNwpxOkPoyjqdXqAzF08lewtT9Ha3rejOskMvdxtYSzg3DxFRU2IAImoEarUGqTeLcSq96s6sSzly7aKilYzFInSXWaN3Rdjp3cEWHezNOWCZiKiZMQAR1cOdkjIk36joykq/g+SMAhSVllfbT2Yj0Qad3h1s0UNmA4mJkR4qJiKi+zEAET2CUqXGpZwi7V1ZpzMK8NfNkmr7SUzE6NW+cqCyLfzd7eBiI9FDxURE9CgMQEQPyCksFcJORgGS0wvwZ2YBSpXqavt1crCAf8Ut6L3dbdHVxYozLRMRtRIMQGTQSpUqnMss1N6VdTq9ANmFpdX2s5YYw78i6PTuIAxYtjU31UPFRETUGBiAyCDlFJYi7tBf2HwsHUUK3bE7YhHg42KtDTq9O9ihk4MFxLwNnYiozWAAIoNyJbcIXx5IRXxyJpQq4RYtB0sz9OlQdVeWr5sNLMz4T4OIqC3jb3lq8zQaDU5cv4PV+68h8WKedntfT3v88+lOGNzVia07REQGhgGI2iyVWoO9KblYfeAaTqcXABBmWX62uzNeH+iFAA87/RZIRER6wwBEbU6pUoWdpzOx5kAqUituVzc1FmNMn/aYOqAjOjla6rlCIiLSNwYgajMK7yqx6Vga4g5d1y45YS0xxqv9PBD2pCecrDgnDxERCRiAqNXLLLiHdQf/wjfH03G3TAUAcLWRYMpTHfFy3w6w5IBmIiJ6AD8ZqNW6mCPHl/tTsetMFsorFt3ycbHC6wM7YYSfjJMSEhFRrRiAqFXRaDQ4knoLXx5Ixe+X8rXb+3Vqh38+3QlPd3HkwqJERPRIDEDUKqjUGiScy8HqA9fw541CAMKEhcN9XfHPgZ3Qq72tfgskIqJWhQGIWrRSpQrbkm7gf3+kIu3WXQCAmbEY4wLd8Y8BHeHRzkLPFRIRUWvEAEQt0p2SMmw8moYNh6/jVkkZAMDW3AST+nkirJ8H2lma6blCIiJqzRiAqEXJuH0Xaw/+ha0nMnBPKdzR5WYrxdQBHTEuyB3mpvyRJSKihmsRt8msXLkSnp6ekEgkCA4OxvHjx2vdV6lUYtGiRfDy8oJEIoGfnx8SEhJ09lmwYAFEIpHOw8fHp6kvgxrgXGYh3v7mNAZ98jvWH76Oe0oVesis8fmE3tj/f4MwuX9Hhh8iImo0ev9E2bp1KyIjI/HFF18gODgYS5cuRUhICC5dugQnJ6dq+8fExGDTpk1Ys2YNfHx8sGfPHowePRqHDx9G7969tfv16NED+/bt0z43Ntb7pdIDNBoNDl29hdUHruGPKze12wd4O+CfA73Qv3M73tFFRERNQqTRaDT6LCA4OBhBQUFYsWIFAECtVsPd3R3Tp09HVFRUtf1lMhnmzJmDadOmabeNGTMGUqkUmzZtAiC0AMXHxyM5ObleNcnlctjY2KCwsBDW1tb1OgfVrlylxk9ns/HlgVScz5IDAIzEIjzv64rXB3ZCTzcbPVdIREStUV0+v/XaLFJWVoakpCRER0drt4nFYgwdOhRHjhyp8RiFQgGJRHdJA6lUioMHD+psu3LlCmQyGSQSCfr164fY2Fh06NCh1nMqFArtc7lcXt9Looe4W1aOb09k4H8H/8KNO/cAAFITI4wPcseUpzrC3d5czxUSEZGh0GsAunnzJlQqFZydnXW2Ozs74+LFizUeExISgiVLlmDgwIHw8vJCYmIiduzYAZVKpd0nODgY69evR9euXZGdnY2FCxdiwIABOHfuHKysrKqdMzY2FgsXLmzciyOtW8UKbDiShq+OXEfBXSUAwN7CFJOf9MSrT3jAzsJUzxUSEZGhaXUDY5YtW4apU6fCx8cHIpEIXl5eCA8Px7p167T7DB8+XPt1r169EBwcDA8PD3z77beYMmVKtXNGR0cjMjJS+1wul8Pd3b1pL8QApN0qwf/++AvfnsyAolwNAOhgb46pAzvhpT7tITU10nOFRERkqPQagBwcHGBkZITc3Fyd7bm5uXBxcanxGEdHR8THx6O0tBS3bt2CTCZDVFQUOnXqVOv72NraokuXLrh69WqNr5uZmcHMjPPKNJY/bxRg9f5U7D6XjYolutCrvQ3+OdALz/V0gZGYA5uJiEi/9BqATE1NERAQgMTERIwaNQqAMAg6MTERERERDz1WIpHAzc0NSqUS27dvx7hx42rdt7i4GNeuXcOrr77amOXTfTQaDfZfzsfq/ak4knpLu31QV0f8c6AXnuhkzzu6iIioxdB7F1hkZCTCwsIQGBiIvn37YunSpSgpKUF4eDgAYNKkSXBzc0NsbCwA4NixY8jMzIS/vz8yMzOxYMECqNVqzJo1S3vOmTNnYsSIEfDw8EBWVhbmz58PIyMjTJgwQS/X2JYpVWr8+GcWVu9PxcWcIgCAsViEF/1kmDqwE7q58i46IiJqefQegMaPH4/8/HzMmzcPOTk58Pf3R0JCgnZgdHp6OsTiqvkaS0tLERMTg9TUVFhaWiI0NBQbN26Era2tdp8bN25gwoQJuHXrFhwdHfHUU0/h6NGjcHR0bO7La7NKFOXYciIDa/9IRVZhKQDA3NQIE/p2wGtPdYSbrVTPFRIREdVO7/MAtUScB6hmpUoV9l/Ox+6z2dh3IQ/FinIAgIOlGcL7e+KVYA/YmJvouUoiIjJUrWYeIGr57paV47eL+fj5XDZ+u5iHu2VV0w10dLDA6wM7YXRvN0hMeEcXERG1HgxAVE1RqRK/XszD7rM5+P1yHkqVau1rMhsJhvu6ItTXBb3d7SDmHV1ERNQKMQARAKDwnhL7UnKx+1w2Dly+iTJVVejpYG+O4b4uCO3pil7tbXg3FxERtXoMQAbsdkkZ9qbkYPe5HBy6ehNKVdVwsE4OFgj1dcVwXxd0d7Vm6CEiojaFAcjA5Bcp8EtKDnafzcGR1FtQqatCT1dnKzzX0wWhvq7o4mzJ0ENERG0WA5AByJWXIuFcDn4+m40T12/jvsyD7q7WCPV1wXM9XdHZyVJ/RRIRETUjBqA2KrPgHnafzUbCuRycTLuj85pfexsM93XF8J4u8GhnoacKiYiI9IcBqA1Jv3UXu89l4+dzOTiTUaDzWp8Otgj1dUVIDxe425vrp0AiIqIWggGolUvNL8buiu6t81ly7XaRCAjytEdoTxeE9HSBqw1nZiYiIqrEANTKaDQaXMkrxs8V3VuV628BgFgE9PNqh+E9XfFsD2c4WUn0WCkREVHLxQDUCmg0GlzILhK6t85m41p+ifY1Y7EIT3Z2QGhPFwzr7ox2lmZ6rJSIiKh1YABqoTQaDc5mFuLnsznYfS4babfual8zNRJjgLcDhvu6Ylg3Z66/RUREVEcMQC2IWq3B6YwCJJzLxs9nc5BZcE/7mpmxGIO6OmJ4T1c8080J1hKGHiIiovpiANIzlVqDpLQ72jE9OfJS7WtSEyM84+OE4b4uGNzVCRZm/HYRERE1Bn6i6kG5So3jf93Gz+eysed8LvKLFNrXLM2MMaSbE4b3dMXTXRwhNeUq60RERI2NAagZnb1RiM3H07DnfC5ul5Rpt1tLjDGsuwuG93TBU94OkJgw9BARETUlBqBmdCFHjm+OZwAA7MxN8Gx3Fwz3dcGTXg4wNRbruToiIiLDwQDUjJ7t7owzwR0Q6uuK4I72MDZi6CEiItIHBqBmZGtuivdH++q7DCIiIoPHJggiIiIyOAxAREREZHAYgIiIiMjgMAARERGRwWEAIiIiIoPDAEREREQGhwGIiIiIDA4DEBERERmcOgcgT09PLFq0COnp6U1RDxEREVGTq3MAmjFjBnbs2IFOnTph2LBh2LJlCxQKxaMPJCIiImoh6hWAkpOTcfz4cXTr1g3Tp0+Hq6srIiIicOrUqaaokYiIiKhRiTQajaYhJ1Aqlfjvf/+L2bNnQ6lUwtfXF2+//TbCw8MhEokaq85mJZfLYWNjg8LCQlhbW+u7HCIiInoMdfn8rvdiqEqlEjt37kRcXBz27t2LJ554AlOmTMGNGzfw7rvvYt++fdi8eXN9T09ERETUZOocgE6dOoW4uDh88803EIvFmDRpEj777DP4+Pho9xk9ejSCgoIatVAiIiKixlLnABQUFIRhw4Zh1apVGDVqFExMTKrt07FjR7z88suNUiARERFRY6vzIOjU1FQkJCRg7NixNYYfALCwsEBcXNxjn3PlypXw9PSERCJBcHAwjh8/Xuu+SqUSixYtgpeXFyQSCfz8/JCQkFDr/h988AFEIhFmzJjx2PUQERFR21bnAJSXl4djx45V237s2DGcPHmyzgVs3boVkZGRmD9/Pk6dOgU/Pz+EhIQgLy+vxv1jYmKwevVqLF++HCkpKXjjjTcwevRonD59utq+J06cwOrVq9GrV68610VERERtV50D0LRp05CRkVFte2ZmJqZNm1bnApYsWYKpU6ciPDwc3bt3xxdffAFzc3OsW7euxv03btyId999F6GhoejUqRPefPNNhIaG4tNPP9XZr7i4GBMnTsSaNWtgZ2f30BoUCgXkcrnOg4iIiNquOgeglJQU9OnTp9r23r17IyUlpU7nKisrQ1JSEoYOHVpVkFiMoUOH4siRIzUeo1AoIJFIdLZJpVIcPHhQZ9u0adPw/PPP65y7NrGxsbCxsdE+3N3d63QdRERE1LrUOQCZmZkhNze32vbs7GwYG9dtTPXNmzehUqng7Oyss93Z2Rk5OTk1HhMSEoIlS5bgypUrUKvV2Lt3L3bs2IHs7GztPlu2bMGpU6cQGxv7WHVER0ejsLBQ+6iphYuIiIjajjoHoGeffVYbGCoVFBTg3XffxbBhwxq1uJosW7YM3t7e8PHxgampKSIiIhAeHg6xWLiUjIwMvPPOO/j666+rtRTVxszMDNbW1joPIiIiarvqHIA++eQTZGRkwMPDA4MHD8bgwYPRsWNH5OTkVBuH8ygODg4wMjKq1qKUm5sLFxeXGo9xdHREfHw8SkpKkJaWhosXL8LS0hKdOnUCACQlJSEvLw99+vSBsbExjI2NsX//fnz++ecwNjaGSqWq6yUTERFRG1PnAOTm5oY///wTH330Ebp3746AgAAsW7YMZ8+erfPYGVNTUwQEBCAxMVG7Ta1WIzExEf369XvosRKJBG5ubigvL8f27dsxcuRIAMCQIUNw9uxZJCcnax+BgYGYOHEikpOTYWRkVNdLJiIiojamXkthWFhY4PXXX2+UAiIjIxEWFobAwED07dsXS5cuRUlJCcLDwwEAkyZNgpubm3Y8z7Fjx5CZmQl/f39kZmZiwYIFUKvVmDVrFgDAysoKPXv2rFZvu3btqm0nIiIiw1TvtcBSUlKQnp6OsrIyne0vvvhinc4zfvx45OfnY968ecjJyYG/vz8SEhK0A6PT09O143sAoLS0FDExMUhNTYWlpSVCQ0OxceNG2Nra1vdSiIiIyMDUeTX41NRUjB49GmfPnoVIJELl4ZUrv7eFMTZcDZ6IiKj1qcvnd53HAL3zzjvo2LEj8vLyYG5ujvPnz+PAgQMIDAzE77//Xt+aiYiIiJpNnbvAjhw5gl9//RUODg4Qi8UQi8V46qmnEBsbi7fffrvGJSmIiIiIWpI6twCpVCpYWVkBEG5jz8rKAgB4eHjg0qVLjVsdERERUROocwtQz549cebMGXTs2BHBwcH46KOPYGpqii+//FI7Fw8RERFRS1bnABQTE4OSkhIAwKJFi/DCCy9gwIABaNeuHbZu3droBRIRERE1tjrfBVaT27dvw87OTnsnWGvHu8CIiIhanya7C0ypVMLY2Bjnzp3T2W5vb99mwg8RERG1fXUKQCYmJujQoUObmOuHiIiIDFed7wKbM2cO3n33Xdy+fbsp6iEiIiJqcnUeBL1ixQpcvXoVMpkMHh4esLCw0Hn91KlTjVYcERERUVOocwAaNWpUE5RBRERE1Hwa5S6wtoZ3gREREbU+TboWGBEREVFrV+cuMLFY/NBb3nmHGBEREbV0dQ5AO3fu1HmuVCpx+vRpbNiwAQsXLmy0woiIiIiaSqONAdq8eTO2bt2K77//vjFOp1ccA0RERNT66GUM0BNPPIHExMTGOh0RERFRk2mUAHTv3j18/vnncHNza4zTERERETWpOo8BenDRU41Gg6KiIpibm2PTpk2NWhwRERFRU6hzAPrss890ApBYLIajoyOCg4NhZ2fXqMURERERNYU6B6DJkyc3QRlEREREzafOY4Di4uKwbdu2atu3bduGDRs2NEpRRERERE2pzgEoNjYWDg4O1bY7OTlh8eLFjVIUERERUVOqcwBKT09Hx44dq2338PBAenp6oxRFRERE1JTqHICcnJzw559/Vtt+5swZtGvXrlGKIiIiImpKdQ5AEyZMwNtvv43ffvsNKpUKKpUKv/76K9555x28/PLLTVEjERERUaOq811g7733Hq5fv44hQ4bA2Fg4XK1WY9KkSRwDRERERK1CvdcCu3LlCpKTkyGVSuHr6wsPD4/Grk1vuBYYERFR61OXz+86twBV8vb2hre3d30PJyIiItKbOo8BGjNmDD788MNq2z/66COMHTu2UYoiIiIiakp1DkAHDhxAaGhote3Dhw/HgQMHGqUoIiIioqZU5wBUXFwMU1PTattNTEwgl8sbpSgiIiKiplTnAOTr64utW7dW275lyxZ07969UYoiIiIiakp1DkBz587Fe++9h7CwMGzYsAEbNmzApEmT8J///Adz586tVxErV66Ep6cnJBIJgoODcfz48Vr3VSqVWLRoEby8vCCRSODn54eEhASdfVatWoVevXrB2toa1tbW6NevH3bv3l2v2oiIiKjtqXMAGjFiBOLj43H16lW89dZb+Pe//43MzEz8+uuv6Ny5c50L2Lp1KyIjIzF//nycOnUKfn5+CAkJQV5eXo37x8TEYPXq1Vi+fDlSUlLwxhtvYPTo0Th9+rR2n/bt2+ODDz5AUlISTp48iWeeeQYjR47E+fPn61wfERERtT31ngeoklwuxzfffIO1a9ciKSkJKpWqTscHBwcjKCgIK1asACBMquju7o7p06cjKiqq2v4ymQxz5szBtGnTtNvGjBkDqVSKTZs21fo+9vb2+PjjjzFlypTHuibOA0RERNS61OXzu84tQJUOHDiAsLAwyGQyfPrpp3jmmWdw9OjROp2jrKwMSUlJGDp0aFVBYjGGDh2KI0eO1HiMQqGARCLR2SaVSnHw4MEa91epVNiyZQtKSkrQr1+/Ws8pl8t1HkRERNR21WkixJycHKxfvx5r166FXC7HuHHjoFAoEB8fX68B0Ddv3oRKpYKzs7POdmdnZ1y8eLHGY0JCQrBkyRIMHDgQXl5eSExMxI4dO6q1PJ09exb9+vVDaWkpLC0tsXPnzlprjI2NxcKFC+tcPxEREbVOj90CNGLECHTt2hV//vknli5diqysLCxfvrwpa6vRsmXL4O3tDR8fH5iamiIiIgLh4eEQi3UvpWvXrkhOTsaxY8fw5ptvIiwsDCkpKTWeMzo6GoWFhdpHRkZGc1wKERER6cljtwDt3r0bb7/9Nt58881GWwLDwcEBRkZGyM3N1dmem5sLFxeXGo9xdHREfHw8SktLcevWLchkMkRFRaFTp046+5mammoHZQcEBODEiRNYtmwZVq9eXe2cZmZmMDMza5RrIiIiopbvsVuADh48iKKiIgQEBCA4OBgrVqzAzZs3G/TmpqamCAgIQGJionabWq1GYmJireN1KkkkEri5uaG8vBzbt2/HyJEjH7q/Wq2GQqFoUL1ERETUNjx2AHriiSewZs0aZGdn45///Ce2bNkCmUwGtVqNvXv3oqioqF4FREZGYs2aNdiwYQMuXLiAN998EyUlJQgPDwcATJo0CdHR0dr9jx07hh07diA1NRV//PEHnnvuOajVasyaNUu7T3R0NA4cOIDr16/j7NmziI6Oxu+//46JEyfWq0YiIiJqW+q8GryFhQVee+01vPbaa7h06RLWrl2LDz74AFFRURg2bBh27dpVp/ONHz8e+fn5mDdvHnJycuDv74+EhATtwOj09HSd8T2lpaWIiYlBamoqLC0tERoaio0bN8LW1la7T15eHiZNmoTs7GzY2NigV69e2LNnD4YNG1bXyyUiIqI2qMHzAAHCreY//PAD1q1bV+cA1BJxHiAiIqLWpy6f340SgNoaBiAiIqLWp1kmQiQiIiJqrRiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcFpEAFq5ciU8PT0hkUgQHByM48eP17qvUqnEokWL4OXlBYlEAj8/PyQkJOjsExsbi6CgIFhZWcHJyQmjRo3CpUuXmvoyiIiIqJXQewDaunUrIiMjMX/+fJw6dQp+fn4ICQlBXl5ejfvHxMRg9erVWL58OVJSUvDGG29g9OjROH36tHaf/fv3Y9q0aTh69Cj27t0LpVKJZ599FiUlJc11WURERNSCiTQajUafBQQHByMoKAgrVqwAAKjVari7u2P69OmIioqqtr9MJsOcOXMwbdo07bYxY8ZAKpVi06ZNNb5Hfn4+nJycsH//fgwcOLDa6wqFAgqFQvtcLpfD3d0dhYWFsLa2buglEhERUTOQy+WwsbF5rM9vvbYAlZWVISkpCUOHDtVuE4vFGDp0KI4cOVLjMQqFAhKJRGebVCrFwYMHa32fwsJCAIC9vX2Nr8fGxsLGxkb7cHd3r+ulEBERUSui1wB08+ZNqFQqODs762x3dnZGTk5OjceEhIRgyZIluHLlCtRqNfbu3YsdO3YgOzu7xv3VajVmzJiB/v37o2fPnjXuEx0djcLCQu0jIyOjYRdGRERELZrexwDV1bJly+Dt7Q0fHx+YmpoiIiIC4eHhEItrvpRp06bh3Llz2LJlS63nNDMzg7W1tc6DiIiI2i69BiAHBwcYGRkhNzdXZ3tubi5cXFxqPMbR0RHx8fEoKSlBWloaLl68CEtLS3Tq1KnavhEREfjxxx/x22+/oX379k1yDURERNT66DUAmZqaIiAgAImJidptarUaiYmJ6Nev30OPlUgkcHNzQ3l5ObZv346RI0dqX9NoNIiIiMDOnTvx66+/omPHjk12DURERNT6GOu7gMjISISFhSEwMBB9+/bF0qVLUVJSgvDwcADApEmT4ObmhtjYWADAsWPHkJmZCX9/f2RmZmLBggVQq9WYNWuW9pzTpk3D5s2b8f3338PKyko7nsjGxgZSqbT5L5KIiIhaFL0HoPHjxyM/Px/z5s1DTk4O/P39kZCQoB0YnZ6erjO+p7S0FDExMUhNTYWlpSVCQ0OxceNG2NraavdZtWoVAGDQoEE67xUXF4fJkyc39SURERFRC6f3eYBaorrMI0BEREQtQ6uZB4iIiIhIHxiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcPQegFauXAlPT09IJBIEBwfj+PHjte6rVCqxaNEieHl5QSKRwM/PDwkJCTr7HDhwACNGjIBMJoNIJEJ8fHwTXwERERG1NnoNQFu3bkVkZCTmz5+PU6dOwc/PDyEhIcjLy6tx/5iYGKxevRrLly9HSkoK3njjDYwePRqnT5/W7lNSUgI/Pz+sXLmyuS6DiIiIWhmRRqPR6OvNg4ODERQUhBUrVgAA1Go13N3dMX36dERFRVXbXyaTYc6cOZg2bZp225gxYyCVSrFp06Zq+4tEIuzcuROjRo2qU11yuRw2NjYoLCyEtbV13S6KiIiI9KIun996awEqKytDUlIShg4dWlWMWIyhQ4fiyJEjNR6jUCggkUh0tkmlUhw8eLBBtSgUCsjlcp0HERERtV16C0A3b96ESqWCs7OzznZnZ2fk5OTUeExISAiWLFmCK1euQK1WY+/evdixYweys7MbVEtsbCxsbGy0D3d39wadj4iIiFo2Y30XUBfLli3D1KlT4ePjA5FIBC8vL4SHh2PdunUNOm90dDQiIyO1z+VyOUMQEVHeReDGcUBsDBibAUZmgLEEMDat+LNyW+VDAhhVvGbUqj5eyADp7SfUwcEBRkZGyM3N1dmem5sLFxeXGo9xdHREfHw8SktLcevWLchkMkRFRaFTp04NqsXMzAxmZmYNOgcRUZtQrgBSdgEn1wHph+t/HpFRVTAyui8gVYYnneD0QHiqts/92+4LX7Udd/97io0a7++G2hS9BSBTU1MEBAQgMTFRO0hZrVYjMTERERERDz1WIpHAzc0NSqUS27dvx7hx45qhYiKiNux2KpC0Hji9Cbh7S9gmMgI8nhSCRLmi4lEq/KlS3Let4rm6vOp8GhWgvCs89ElsLIQiEylg5QJYtwesZYCNG2DtJnxd+aeJVL+1UrPSaxtlZGQkwsLCEBgYiL59+2Lp0qUoKSlBeHg4AGDSpElwc3NDbGwsAODYsWPIzMyEv78/MjMzsWDBAqjVasyaNUt7zuLiYly9elX7/K+//kJycjLs7e3RoUOH5r1AIqKWTFUOXN4ttPZc+7Vqu5UMCAgDer8qBIW6nE/1QCi6PySVl9YcnB57n1KgvOzR+2jUVTWpy4GyYuFRkg/knK29fvN29wWiilBk054hqY3SawAaP3488vPzMW/ePOTk5MDf3x8JCQnagdHp6ekQi6vGaZeWliImJgapqamwtLREaGgoNm7cCFtbW+0+J0+exODBg7XPK8f2hIWFYf369c1yXURELVrhDeDUV8KjqPImEhHQeQgQ+BrgHVK/MTxGxsLD1KJRy60zVXlFSCqrarEqKwbk2YA8s+KRJfw9yLOE58q7QsvX3VsPD0lS+xpaj9x0tzEktQp6nQeopeI8QETU5qjVQivPyXVCq09lK4m5A9D7FSBgMmDfUa8l6o1GA5QWVISi+wJSZViq3Pa43XlS+/tCkazi0b6qRcnKFTA1b9JLMlR1+fzmMP3mlH9JGFxoUtEfbSwV/qx81PhcIvwp1vuqJUTUGhXnA6c3CuN7CtKqtns8BQSGA91GCGN8DJlIBEjthIdzj5r30WiA0sIaWo+yAPmNqvCkLAHu3RYeuQ9rSbJ7YDzS/V1vFc8ZkpoUA1BzyjkL/Paf+h1rZPaIsCQBTMyrAlNtr5mY1/L8vvMybBG1bhoNkHZIaO1J2QWolcJ2iQ3g93ch+Dh21W+NrY1IBEhthccjQ9KDrUcPPFeWAPfuCI9HhqQHutts2gvfO0cfBqQGYgBqTrYeQJ8wQHkPKL8n/Kksve/re7qvqcqqjlVVDPIrLWj6Oo3MHiMsmQPtOgt3iLgFCK8TkX7duwOc2SIEn5uXq7a7BQCBU4Aeo/mh2ZR0QlL3mvfRCUkPtB7VGpLO1fRmQDsvwKm7EMicewhf23Xkf2IfE8cA1aDFjAFSqyoCUWnF7aQVf5aXVg9L2uc17Vv5vHL/Gl5TKepfp5EZ0D4Q8OgvBCL3vvofBElkKDQaIDNJCD3ntgv/rgHAxALoNRYICAdk/notkepIowEU8vtaj+4brH0nDci7ANy9WfOxJhaAk09FIOohBDHnnoC5ffNeg57U5fObAagGLSYANSe1SjdYPRiuHgxWZcVAVjKQdhgoydM9l9gYkPWuCET9gQ7BQtM7ETUeRTFwdpsQfHL+rNru1AMIeg3wHQdIDOT3lyEqzhNahnJTgNzzQN55Yebu2v4za+lS0VJUEYicugtdaW1s/BcDUAMZZACqL40GuHVVGG+Qdhi4fkj438r9RGLAxbcqEHk8aTD/GyFqdDnnhNDz57dAWZGwzchM6N4KmgK0DxK6YsjwqMqFCS3zzguhKDdFCEn3D36/n8gIcPCu6j6r7EqzcW+1P0MMQA3EANQAGg1QkF4RiA4JgejOX9X3c+ouBKHKUGTlXH0fIhIo7wEp3wMn1gprc1Wy9xLm7fH/O/9TQbVTFAndZrnngbyKFqPc87WPKTWzrghE3XW70lpBSz4DUAMxADUyeZbQOlQZiG5eqr5P5YBqj6eEP225GC0Rbl4FkuKA5K+FwbCA0MXs84IQfDoObLX/Uyc902iE3815KbpdaTcvV901+CAbd92WIucewu9uI5Pmrf0hGIAaiAGoiRXnC4ssVnaZ5Z4D8MCPoW0H3S4z+078RU+GobwMuPST0M3114Gq7TYdqpanYIspNZXyMuDWFSEQ3d+V9uDQhkpGpoBD1+qtRVauevmdzQDUQAxAzezeHSD9aFULUfYZYSHF+1m56naZOXZlIKK2pSAdSNogLE+hvbFABHQJEW5h7zyEK5uT/ty7U9WNVvnIu1A1Du1BUruKMHTfwGtHH8DMsknLZABqIAYgPVMUARnHqlqIMpOqN8mat9PtMnPu0XY/HFTlwvpEJXnCYo7F+TV/3a4zMOy9ui1eSfqlVgFX9gqtPVd+gbYl1NIZ6DNJeNhyEWdqoSrHfFbehVbZWnTriu6CtPez61g16LpDMNB5aKOWxADUQAxALYzyHnDjREUgOih8XTnXSSWJDdChX1ULkatf/RZzbC7KUiG0lOQBJTeFW1pL8ioCTb7u13dvoVoXYW3MbIDnYoVBsWwha7mKcoBTG4FTG4DCjKrtnQYJY3u6hraocRVEdaIsFcZ6aluKKsYXFefq7tdtBDB+U6O+NQNQAzEAtXDlZUDWqaous4xjwrxE9zOxEP53UdlK5Nanaee70GiElquSitBSnPfwrxXyOr6BSGj1snQCLBwAC6eKrx2Fh8QaOLRMaC0DgC7PAS8sBaxdG/tKqb7UauD6AaG15+JPgLpc2C61A/wnCsGnnZd+ayRqSiU37wtE5wD3YKGVsxExADUQA1AroyoHcs5UdZmlHxammr+fsUSYH6VyHFH7oEcvCaBWC/3e2haZ2kJNxesPtko9ithECC+WjkKgqe1rSydhdelHtWipyoHDnwO/xwrLqEhsgdCPAd+xbA3Sp7u3hbu4TsYBt69VbXd/Qgg93UdyKRmiRsIA1EAMQK2cWi30R1d2maUdrj5tvNhEaBXyeFL4H/j9QUbb9XSz6n/pj8vEvCK8OFUEGYeav7Z0FAJKUwST3BQg/k0gO1l47vMC8MJnwntT89BogIzjwMm1wPn4qtl5Ta0Av5eFxUhrW1CTiOqNAaiBGIDaGI0GuHkFSDtY1UpUlPX4x0tsqnc5VX794LaWsgaaSgkcXArs/1AYQC61B57/FOj5N31X1vbdSAISonQnLHTpJczS3POlJr8LhsiQMQA1EANQG6fRAHeuC2OI0o8IY4oe7HKqHGdj4QgYm+q74vrLOQvsfBPIPSs87z4KeH4JYNFOr2W1SfJsIHERcGaz8NxYCviOEbq5ZH3YDUnUDBiAGogBiNqU8jLgj0+AA58I8ytZOApdYt1G6LuytkFZChxdCRz4FFCWCNv8/g4MmcdB6ETNjAGogRiAqE3KOi20BuVfEJ77jgOGf8g1pOpLowEu/AD8ElO12GT7IOC5D4H2AfqtjchA1eXzW9xMNRGRvsl6A//cDzwVCYjEwNlvgf8+AVxK0HdlrU/OOWDDCODbV4XwYyUD/rYGmLKX4YeolWALUA3YAkRt3o2Twp1iNy8Lz/3+LkygKLXVa1ktXskt4Lf/AEnrhZlujSXAk28DT81oOQPgiQwYu8AaiAGIDILyHvDb+8DhFQA0QivGi8sB78admr5NUCmBE/8T5liqnGOqx2hg2CIuVUHUgjAANRADEBmU9KNA/FtVk/T1mQQ8+74wuzQBV/YBe6KrWstcfIVxPp799VsXEVXDMUBE9Pg6PAG8cRAIflN4fuorYNWTwLXf9FuXvt28Cnw9Dvh6jBB+zB2AEcuA1/cz/BC1AWwBqgFbgMhgXT8otAZV3tUUOEXo5jGkyfvuFQAHPgaOfSHMBC42AYL/CTw9S5gUk4haLHaBNRADEBk0RTGwb74w5gUAbD2AUf8FPJ/Sb11NTa0SWr9+/U/V0ildnhO6Ax0667c2InosDEANxABEBCD1d+D7CKAwQ3ge/IYwuV9bvNvp+kFgd1TVjNkOXYCQWA4IJ2plOAaIiBqu0yDgzcNAnzDh+bEvgC+eEgZNtxV30oBvw4D1zwvhR2IjDHB+8zDDD1EbxxagGrAFiOgBV/cB30+vWERWBPSbBjwTA5hI9V1Z/ZSVAAc/Aw59LqzULhIDAeHA4DlcJ42oFWMXWAMxABHV4F4BsOddIPlr4Xk7b2D0F0D7QL2WVSdqNXB2G7BvQUWYA+A5QFgSxLmHXksjooZjAGogBiCih7iUAPzwDlCcI7Sc9H8HGBQNGJvpu7KHu5EEJMwGbpwQntt6ACHvAz4vcKV2ojaCAaiBGICIHuHubSAhCvhzq/DcsRswepWw3lhLI88GEhcCZ74RnptYAANnAk+8BZhI9FsbETUqBqAGYgAiekwXfgB+/BdQkg+IjIAB/wYG/h9gbKrvygBlKXB0JXDgU0BZImzznyjcyWblot/aiKhJMAA1EAMQUR2U3AJ+ngmc3yE8d+4JjFoFuPbSTz0ajRDMfompmtCxfZAwzseNK7UTtWWt7jb4lStXwtPTExKJBMHBwTh+/Hit+yqVSixatAheXl6QSCTw8/NDQkJCg85JRA1g0Q4YGweMXQ9I7YHcc8CawcD+j4RFRJtTzjlgwwjg21eF8GMlA/62Bpiyl+GHiHToPQBt3boVkZGRmD9/Pk6dOgU/Pz+EhIQgLy+vxv1jYmKwevVqLF++HCkpKXjjjTcwevRonD59ut7nJKJG0GM0MO2YMKhYXS6sNP+/IUBuStO/d8lNoStu9QDg+h+AsQQYOAuYfhLoNY6DnImoGr13gQUHByMoKAgrVqwAAKjVari7u2P69OmIioqqtr9MJsOcOXMwbdo07bYxY8ZAKpVi06ZN9TqnQqGAQqHQPpfL5XB3d2cXGFF9aDTA2e+EbrHSAsDIFBgUBTz5DmBk3LjvpVIKS3b8HguUFgrbeowW1i+z7dC470VELV6r6QIrKytDUlIShg6tmnFVLBZj6NChOHLkSI3HKBQKSCS6d25IpVIcPHiw3ueMjY2FjY2N9uHu7t7QSyMyXCIR0Gus0BrU5TlAVQYkLgLWPQvkX2q897myT1i1PiFKCD8uvsDkn4WuOIYfInoEvQagmzdvQqVSwdnZWWe7s7MzcnJyajwmJCQES5YswZUrV6BWq7F3717s2LED2dnZ9T5ndHQ0CgsLtY+MjIxGuDoiA2flAkzYAoz6AjCzATKTgC8GAIeXCwuP1tfNK8DXY4GvxwA3LwPmDsCIZcDr+wHP/o1XPxG1aXofA1RXy5Ytg7e3N3x8fGBqaoqIiAiEh4dDLK7/pZiZmcHa2lrnQUSNQCQC/CcAbx0BOg8Vlp34JQaIGw7cula3c90rAPbMAf77BHDlF0BsAvSLAN4+BQRMBsRGTXEFRNRG6TUAOTg4wMjICLm5uTrbc3Nz4eJS8zwdjo6OiI+PR0lJCdLS0nDx4kVYWlqiU6dO9T4nETUxGzdg4nfAi8sBUysg4xiwqj9wdJWwPMXDqFXAyThgeR/gyAphgHWX54C3jgozOUtsmucaiKhN0WsAMjU1RUBAABITE7Xb1Go1EhMT0a9fv4ceK5FI4ObmhvLycmzfvh0jR45s8DmJqAmJRECfScBbh4GOTwPl94TxOxteAG7/VfMx1w8Cq58GfpwB3L0FOHQBJm4H/r4VcOjcrOUTUdvSyLdk1F1kZCTCwsIQGBiIvn37YunSpSgpKUF4eDgAYNKkSXBzc0NsbCwA4NixY8jMzIS/vz8yMzOxYMECqNVqzJo167HPSUR6ZNsBmPQ9cHId8MtcIO2Q0Bo0bCEQOAUQi4E7acDeuUDK98IxEhtg0LtA0BTAyES/9RNRm6D3ADR+/Hjk5+dj3rx5yMnJgb+/PxISErSDmNPT03XG95SWliImJgapqamwtLREaGgoNm7cCFtb28c+JxHpmUgkhBmvZ4DvI4C0g8Jt8xd+ANz6AEf+K4wXEomBgHBg8BxhwkUiokai93mAWiIuhUHUjNRq4MQaYO98oVusUseBwHMfAM499FcbEbUqdfn81nsLEBEZOLEYCP6ncJfYj/8CinOBZ+YCPs9zBmciajIMQETUMrTzAsJ26bsKIjIQrW4eICIiIqKGYgAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAaHAYiIiIgMDgMQERERGRwGICIiIjI4xvouoCXSaDQAALlcrudKiIiI6HFVfm5Xfo4/DANQDYqKigAA7u7ueq6EiIiI6qqoqAg2NjYP3UekeZyYZGDUajWysrJgZWUFkUjUqOeWy+Vwd3dHRkYGrK2tG/XcVHf8frQs/H60LPx+tDz8njycRqNBUVERZDIZxOKHj/JhC1ANxGIx2rdv36TvYW1tzR/eFoTfj5aF34+Whd+Ploffk9o9quWnEgdBExERkcFhACIiIiKDwwDUzMzMzDB//nyYmZnpuxQCvx8tDb8fLQu/Hy0PvyeNh4OgiYiIyOCwBYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAmtHKlSvh6ekJiUSC4OBgHD9+XN8lGazY2FgEBQXBysoKTk5OGDVqFC5duqTvsgjABx98AJFIhBkzZui7FIOWmZmJV155Be3atYNUKoWvry9Onjyp77IMkkqlwty5c9GxY0dIpVJ4eXnhvffee6z1rqh2DEDNZOvWrYiMjMT8+fNx6tQp+Pn5ISQkBHl5efouzSDt378f06ZNw9GjR7F3714olUo8++yzKCkp0XdpBu3EiRNYvXo1evXqpe9SDNqdO3fQv39/mJiYYPfu3UhJScGnn34KOzs7fZdmkD788EOsWrUKK1aswIULF/Dhhx/io48+wvLly/VdWqvG2+CbSXBwMIKCgrBixQoAwnpj7u7umD59OqKiovRcHeXn58PJyQn79+/HwIED9V2OQSouLkafPn3w3//+F//5z3/g7++PpUuX6rssgxQVFYVDhw7hjz/+0HcpBOCFF16As7Mz1q5dq902ZswYSKVSbNq0SY+VtW5sAWoGZWVlSEpKwtChQ7XbxGIxhg4diiNHjuixMqpUWFgIALC3t9dzJYZr2rRpeP7553X+nZB+7Nq1C4GBgRg7diycnJzQu3dvrFmzRt9lGawnn3wSiYmJuHz5MgDgzJkzOHjwIIYPH67nylo3LobaDG7evAmVSgVnZ2ed7c7Ozrh48aKeqqJKarUaM2bMQP/+/dGzZ099l2OQtmzZglOnTuHEiRP6LoUApKamYtWqVYiMjMS7776LEydO4O2334apqSnCwsL0XZ7BiYqKglwuh4+PD4yMjKBSqfD+++9j4sSJ+i6tVWMAIoM3bdo0nDt3DgcPHtR3KQYpIyMD77zzDvbu3QuJRKLvcgjCfwoCAwOxePFiAEDv3r1x7tw5fPHFFwxAevDtt9/i66+/xubNm9GjRw8kJydjxowZkMlk/H40AANQM3BwcICRkRFyc3N1tufm5sLFxUVPVREARERE4Mcff8SBAwfQvn17fZdjkJKSkpCXl4c+ffpot6lUKhw4cAArVqyAQqGAkZGRHis0PK6urujevbvOtm7dumH79u16qsiw/d///R+ioqLw8ssvAwB8fX2RlpaG2NhYBqAG4BigZmBqaoqAgAAkJiZqt6nVaiQmJqJfv356rMxwaTQaREREYOfOnfj111/RsWNHfZdksIYMGYKzZ88iOTlZ+wgMDMTEiRORnJzM8KMH/fv3rzYtxOXLl+Hh4aGnigzb3bt3IRbrflwbGRlBrVbrqaK2gS1AzSQyMhJhYWEIDAxE3759sXTpUpSUlCA8PFzfpRmkadOmYfPmzfj+++9hZWWFnJwcAICNjQ2kUqmeqzMsVlZW1cZeWVhYoF27dhyTpSf/+te/8OSTT2Lx4sUYN24cjh8/ji+//BJffvmlvkszSCNGjMD777+PDh06oEePHjh9+jSWLFmC1157Td+ltWq8Db4ZrVixAh9//DFycnLg7++Pzz//HMHBwfouyyCJRKIat8fFxWHy5MnNWwxVM2jQIN4Gr2c//vgjoqOjceXKFXTs2BGRkZGYOnWqvssySEVFRZg7dy527tyJvLw8yGQyTJgwAfPmzYOpqam+y2u1GICIiIjI4HAMEBERERkcBiAiIiIyOAxAREREZHAYgIiIiMjgMAARERGRwWEAIiIiIoPDAEREREQGhwGIiIiIDA4DEBHRYxCJRIiPj9d3GUTUSBiAiKjFmzx5MkQiUbXHc889p+/SiKiV4mKoRNQqPPfcc4iLi9PZZmZmpqdqiKi1YwsQEbUKZmZmcHFx0XnY2dkBELqnVq1aheHDh0MqlaJTp0747rvvdI4/e/YsnnnmGUilUrRr1w6vv/46iouLdfZZt24devToATMzM7i6uiIiIkLn9Zs3b2L06NEwNzeHt7c3du3a1bQXTURNhgGIiNqEuXPnYsyYMThz5gwmTpyIl19+GRcuXAAAlJSUICQkBHZ2djhx4gS2bduGffv26QScVatWYdq0aXj99ddx9uxZ7Nq1C507d9Z5j4ULF2LcuHH4888/ERoaiokTJ+L27dvNep1E1Eg0REQtXFhYmMbIyEhjYWGh83j//fc1Go1GA0Dzxhtv6BwTHBysefPNNzUajUbz5Zdfauzs7DTFxcXa13/66SeNWCzW5OTkaDQajUYmk2nmzJlTaw0ANDExMdrnxcXFGgCa3bt3N9p1ElHz4RggImoVBg8ejFWrVulss7e3137dr18/ndf69euH5ORkAMCFCxfg5+cHCwsL7ev9+/eHWq3GpUuXIBKJkJWVhSFDhjy0hl69emm/trCwgLW1NfLy8up7SUSkRwxARNQqWFhYVOuSaixSqfSx9jMxMdF5LhKJoFarm6IkImpiHANERG3C0aNHqz3v1q0bAKBbt244c+YMSkpKtK8fOnQIYrEYXbt2hZWVFTw9PZGYmNisNROR/rAFiIhaBYVCgZycHJ1txsbGcHBwAABs27YNgYGBeOqpp/D111/j+PHjWLt2LQBg4sSJmD9/PsLCwrBgwQLk5+dj+vTpePXVV+Hs7AwAWLBgAd544w04OTlh+PDhKCoqwqFDhzB9+vTmvVAiahYMQETUKiQkJMDV1VVnW9euXXHx4kUAwh1aW7ZswVtvvQVXV1d888036N69OwDA3Nwce/bswTvvvIOgoCCYm5tjzJgxWLJkifZcYWFhKC0txWeffYaZM2fCwcEBL730UvNdIBE1K5FGo9HouwgiooYQiUTYuXMnRo0ape9SiKiV4BggIiIiMjgMQERERGRwOAaIiFo99uQTUV2xBYiIiIgMDgMQERERGRwGICIiIjI4DEBERERkcBiAiIiIyOAwABEREZHBYQAiIiIig8MARERERAbn/wHUZbVk30hfUgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Display sample images with their predicted and actual labels"
      ],
      "metadata": {
        "id": "DhSZnIFLEEup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(test_images)\n",
        "predicted_labels = tf.argmax(predictions, axis=1)\n",
        "actual_labels = tf.argmax(test_labels, axis=1)\n",
        "\n",
        "sample_images=test_images[:10]\n",
        "sample_predicted_labels=predicted_labels[:10]\n",
        "sample_actual_labels=actual_labels[:10]\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "for i in range(10):\n",
        "  plt.subplot(2,5, i+1)\n",
        "  plt.imshow(sample_images[i].reshape(28, 28), cmap='gray')\n",
        "  plt.title(f\"Predicted:{sample_predicted_labels[i]}, Actual:{sample_actual_labels[i]}\")\n",
        "  plt.axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "0MBpcD1MECQf",
        "outputId": "465f3b50-5082-4ba0-b315-eebd3a7886e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 4s 12ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}