Soft_Computing_(Offline_2)_Experiment2(2nd_Adjustment).ipynb

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  "metadata": {
    "colab": {
      "name": "Copy of Copy_of_Soft_Computing_(Offline_2).ipynb",
      "provenance": [],
      "collapsed_sections": [
        "XUK0viGsDhGI",
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5-4GFDXuChh"
      },
      "source": [
        "# **Assignment 2 (Deep Neural Networks) - 160204107**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAmqIkdlcBDx"
      },
      "source": [
        "**Adjustment - 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2Bxhm2GuIDF"
      },
      "source": [
        "**Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cOFePX-pp9fi"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "import cv2\n",
        "import os\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "import re\n",
        "from PIL import Image\n",
        "import pickle"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUK0viGsDhGI"
      },
      "source": [
        "## **Step - 1**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCWQ8eX8Q8vO"
      },
      "source": [
        "### **Drive and Directories**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUz2ADpFD2gQ"
      },
      "source": [
        "**Mount Drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZlYhojBD5aM",
        "outputId": "5fc5655c-4095-4c9a-f451-86a51436ec3d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIp8dq1oDsWx"
      },
      "source": [
        "**Directories**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7LE6KS82DwC5",
        "outputId": "d977da75-45cf-4c85-b401-b41dd6d361c0"
      },
      "source": [
        "directory = r'/content/drive/MyDrive/Soft Computing 4.2/Deep Neural Networks Dataset/'\n",
        "img_folder = r'/content/drive/MyDrive/Soft Computing 4.2/Deep Neural Networks Dataset/training-c'\n",
        "df = pd.read_csv('/content/drive/MyDrive/Soft Computing 4.2/Deep Neural Networks Dataset/training-c.csv')\n",
        "df2_Train = pd.read_csv('/content/drive/MyDrive/Soft Computing 4.2/Deep Neural Networks Dataset/verification_train.csv')\n",
        "df2_Test = pd.read_csv('/content/drive/MyDrive/Soft Computing 4.2/Deep Neural Networks Dataset/verification_test.csv')\n",
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>original filename</th>\n",
              "      <th>scanid</th>\n",
              "      <th>digit</th>\n",
              "      <th>database name original</th>\n",
              "      <th>contributing team</th>\n",
              "      <th>database name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>c00000.png</td>\n",
              "      <td>Scan_280_digit_6_num_7.png</td>\n",
              "      <td>280</td>\n",
              "      <td>6</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>c00001.png</td>\n",
              "      <td>Scan_29_digit_1_num_1.png</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c00002.png</td>\n",
              "      <td>Scan_131_digit_3_num_1.png</td>\n",
              "      <td>131</td>\n",
              "      <td>3</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>c00003.png</td>\n",
              "      <td>Scan_160_digit_2_num_2.png</td>\n",
              "      <td>160</td>\n",
              "      <td>2</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>c00004.png</td>\n",
              "      <td>Scan_202_digit_7_num_0.png</td>\n",
              "      <td>202</td>\n",
              "      <td>7</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24293</th>\n",
              "      <td>c24293.png</td>\n",
              "      <td>Scan_167_digit_3_num_4.png</td>\n",
              "      <td>167</td>\n",
              "      <td>3</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24294</th>\n",
              "      <td>c24294.png</td>\n",
              "      <td>Scan_174_digit_2_num_8.png</td>\n",
              "      <td>174</td>\n",
              "      <td>2</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24295</th>\n",
              "      <td>c24295.png</td>\n",
              "      <td>Scan_152_digit_7_num_4.png</td>\n",
              "      <td>152</td>\n",
              "      <td>7</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24296</th>\n",
              "      <td>c24296.png</td>\n",
              "      <td>Scan_123_digit_2_num_2.png</td>\n",
              "      <td>123</td>\n",
              "      <td>2</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24297</th>\n",
              "      <td>c24297.png</td>\n",
              "      <td>Scan_2_digit_7_num_6.png</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>OngkoDB</td>\n",
              "      <td>Buet_Backpropers</td>\n",
              "      <td>training-c</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24298 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         filename           original filename  ...  contributing team  database name\n",
              "0      c00000.png  Scan_280_digit_6_num_7.png  ...   Buet_Backpropers     training-c\n",
              "1      c00001.png   Scan_29_digit_1_num_1.png  ...   Buet_Backpropers     training-c\n",
              "2      c00002.png  Scan_131_digit_3_num_1.png  ...   Buet_Backpropers     training-c\n",
              "3      c00003.png  Scan_160_digit_2_num_2.png  ...   Buet_Backpropers     training-c\n",
              "4      c00004.png  Scan_202_digit_7_num_0.png  ...   Buet_Backpropers     training-c\n",
              "...           ...                         ...  ...                ...            ...\n",
              "24293  c24293.png  Scan_167_digit_3_num_4.png  ...   Buet_Backpropers     training-c\n",
              "24294  c24294.png  Scan_174_digit_2_num_8.png  ...   Buet_Backpropers     training-c\n",
              "24295  c24295.png  Scan_152_digit_7_num_4.png  ...   Buet_Backpropers     training-c\n",
              "24296  c24296.png  Scan_123_digit_2_num_2.png  ...   Buet_Backpropers     training-c\n",
              "24297  c24297.png    Scan_2_digit_7_num_6.png  ...   Buet_Backpropers     training-c\n",
              "\n",
              "[24298 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1jxaCh4LUZv"
      },
      "source": [
        "### **Read Dataset-1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nEoAt-TqFdnx",
        "outputId": "d8af4452-449a-4951-c03d-cc452fec98ed"
      },
      "source": [
        "df = df[['filename','digit']]\n",
        "df"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>digit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>c00000.png</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>c00001.png</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>c00002.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>c00003.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>c00004.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24293</th>\n",
              "      <td>c24293.png</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24294</th>\n",
              "      <td>c24294.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24295</th>\n",
              "      <td>c24295.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24296</th>\n",
              "      <td>c24296.png</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24297</th>\n",
              "      <td>c24297.png</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24298 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         filename  digit\n",
              "0      c00000.png      6\n",
              "1      c00001.png      1\n",
              "2      c00002.png      3\n",
              "3      c00003.png      2\n",
              "4      c00004.png      7\n",
              "...           ...    ...\n",
              "24293  c24293.png      3\n",
              "24294  c24294.png      2\n",
              "24295  c24295.png      7\n",
              "24296  c24296.png      2\n",
              "24297  c24297.png      7\n",
              "\n",
              "[24298 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aoBgG9eIRMVs"
      },
      "source": [
        "### **Dataset-1 Creation and Save (One Time Run)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUGgAwzIb95S"
      },
      "source": [
        "**Image and Digit Data Creation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MamFtFry1dqN"
      },
      "source": [
        "img_digit = []\n",
        "dimention = (180,180)\n",
        "\n",
        "transformer=transforms.Compose([\n",
        "    transforms.Resize(dimention),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.5], [0.5])                         \n",
        "])\n",
        "\n",
        "for name in os.listdir(img_folder):\n",
        "    # Image name and digit from csv file\n",
        "    index = re.split('c| |\\.',name)\n",
        "    index = int(index[1])\n",
        "    digit_label = df['digit'][index]\n",
        "    \n",
        "    # Open an image using PIL library\n",
        "    image_dir = os.path.join(img_folder, name)\n",
        "    image = Image.open(image_dir)\n",
        "\n",
        "    # Tensor transformation and normalization\n",
        "    image_Ttrans = transformer(image)\n",
        "    img_digit.append((image_Ttrans,digit_label))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oc3dSF1YCvvi"
      },
      "source": [
        "**Save Image and Digit Data in a Binary File**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4C6O-ZQHCGlJ"
      },
      "source": [
        "with open(directory+'image_digit_data', 'wb') as file:\n",
        "  pickle.dump(img_digit, file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oesdshjxRdxP"
      },
      "source": [
        "### **Load Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nS60gFeIDMBM"
      },
      "source": [
        "**Load Image and Digit Data from a Binary File**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Q8PGrQDDLAV"
      },
      "source": [
        "with open(directory+'image_digit_data', 'rb') as file:\n",
        "  img_digit = pickle.load(file)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u36_d4q2LfwM"
      },
      "source": [
        "## **Step - 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "36BOfAEhLjyr"
      },
      "source": [
        "### **Train & Test Creation (Dataset - 1)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8slH0Uvij-r",
        "outputId": "8c49d248-fd76-4b74-cf16-073e87ed4118"
      },
      "source": [
        "df_Train, df_Test = torch.utils.data.random_split(img_digit, (round(len(img_digit)*0.8),round(len(img_digit)*0.2)), generator=torch.Generator().manual_seed(107))\n",
        "\n",
        "print('Total Data: ', len(img_digit))\n",
        "print('Train Data: ', len(df_Train))\n",
        "print('Test Data: ', len(df_Test))\n",
        "\n",
        "df_Train[0]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Data:  24298\n",
            "Train Data:  19438\n",
            "Test Data:  4860\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[[0.8902, 0.9922, 1.0000,  ..., 0.9922, 1.0000, 1.0000],\n",
              "          [0.9137, 0.9922, 1.0000,  ..., 0.9922, 0.9922, 0.9922],\n",
              "          [0.9529, 0.9922, 0.9922,  ..., 1.0000, 1.0000, 0.9922],\n",
              "          ...,\n",
              "          [1.0000, 0.9922, 1.0000,  ..., 0.9922, 1.0000, 0.9922],\n",
              "          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 0.9922, 0.9922],\n",
              "          [0.9922, 0.9922, 1.0000,  ..., 0.9922, 0.9922, 0.9922]]]), 0)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FSnI6DjWasfn"
      },
      "source": [
        "### **Model Creation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4R3aFoE1I2it"
      },
      "source": [
        "#Hidden layers\n",
        "class DeepNeuralNetworkModel(nn.Module):\n",
        "    def __init__(self, input_size, num_classes, num_hidden):\n",
        "        super().__init__()\n",
        "        ### 1st hidden layer: input_size --> 200\n",
        "        self.linear_1 = nn.Linear(input_size, num_hidden)\n",
        "        ### Non-linearity in 1st hidden layer\n",
        "        self.relu_1 = nn.ELU()\n",
        "\n",
        "        ### 2nd hidden layer: 200 --> 200\n",
        "        self.linear_2 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 2nd hidden layer\n",
        "        self.relu_2 = nn.ELU()\n",
        "\n",
        "        ### 3rd hidden layer: 200 --> 200\n",
        "        self.linear_3 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 3rd hidden layer\n",
        "        self.relu_3 = nn.ELU()\n",
        "\n",
        "        ### 4th hidden layer: 200 --> 200\n",
        "        self.linear_4 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 4th hidden layer\n",
        "        self.relu_4 = nn.ELU()\n",
        "\n",
        "        ### 5th hidden layer: 200 --> 200\n",
        "        self.linear_5 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 5th hidden layer\n",
        "        self.relu_5 = nn.ELU()\n",
        "\n",
        "        ### 6th hidden layer: 200 --> 200\n",
        "        self.linear_6 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 6th hidden layer\n",
        "        self.relu_6 = nn.ELU()\n",
        "\n",
        "        ### 7th hidden layer: 200 --> 200\n",
        "        self.linear_7 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 7th hidden layer\n",
        "        self.relu_7 = nn.ELU()\n",
        "\n",
        "        ### 8th hidden layer: 200 --> 200\n",
        "        self.linear_8 = nn.Linear(num_hidden, num_hidden)\n",
        "        ### Non-linearity in 8th hidden layer\n",
        "        self.relu_8 = nn.ELU()\n",
        "\n",
        "        ### Output layer: 200 --> output_size(10)\n",
        "        self.linear_out = nn.Linear(num_hidden, num_classes)\n",
        "\n",
        "    def forward(self, x):\n",
        "        ### 1st hidden layer\n",
        "        out  = self.linear_1(x)\n",
        "        ### Non-linearity in 1st hidden layer\n",
        "        out = self.relu_1(out)\n",
        "        \n",
        "        ### 2nd hidden layer\n",
        "        out  = self.linear_2(out)\n",
        "        ### Non-linearity in 2nd hidden layer\n",
        "        out = self.relu_2(out)\n",
        "\n",
        "        ### 3rd hidden layer\n",
        "        out  = self.linear_3(out)\n",
        "        ### Non-linearity in 3rd hidden layer\n",
        "        out = self.relu_3(out)\n",
        "\n",
        "        ### 4th hidden layer\n",
        "        out  = self.linear_4(out)\n",
        "        ### Non-linearity in 4th hidden layer\n",
        "        out = self.relu_4(out)\n",
        "\n",
        "        ### 5th hidden layer\n",
        "        out  = self.linear_5(out)\n",
        "        ### Non-linearity in 5th hidden layer\n",
        "        out = self.relu_5(out)\n",
        "\n",
        "        ### 6th hidden layer\n",
        "        out  = self.linear_6(out)\n",
        "        ### Non-linearity in 6th hidden layer\n",
        "        out = self.relu_6(out)\n",
        "\n",
        "        ### 7th hidden layer\n",
        "        out  = self.linear_7(out)\n",
        "        ### Non-linearity in 7th hidden layer\n",
        "        out = self.relu_7(out)\n",
        "\n",
        "        ### 8th hidden layer\n",
        "        out  = self.linear_8(out)\n",
        "        ### Non-linearity in 8th hidden layer\n",
        "        out = self.relu_8(out)\n",
        "        \n",
        "        # Linear layer (output)\n",
        "        probas  = self.linear_out(out)\n",
        "        return probas"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YplbD1mSR3aR"
      },
      "source": [
        "### **Dataset - 1 (Model Build, Accuracy, Prediction)**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZowiiPvSSeK"
      },
      "source": [
        "**Model Build**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "caRlgwe8OwFE"
      },
      "source": [
        "batch_size = 60\n",
        "num_iters = 60000\n",
        "input_dim = 180*180 # num_features = 32400\n",
        "num_hidden = 200\n",
        "output_dim = 10\n",
        "learning_rate = 0.01\n",
        "\n",
        "model = DeepNeuralNetworkModel(input_size = input_dim,\n",
        "                               num_classes = output_dim,\n",
        "                               num_hidden = num_hidden)\n",
        "\n",
        "# Device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# To enable GPU\n",
        "model.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.ASGD(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMnIvIAESWrM"
      },
      "source": [
        "**Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5qMslDYm2ja",
        "outputId": "811f2502-45d7-42f8-d90a-29398f55edd8"
      },
      "source": [
        "loss_data,iterations = [],[]\n",
        "accuracy_data = []\n",
        "iter = 0\n",
        "\n",
        "num_epochs = math.ceil(num_iters / (len(df_Train) / batch_size))\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset=df_Train, \n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=True)   # It's better to shuffle the whole training dataset! \n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(dataset=df_Test, \n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False) \n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "  for i, (images, labels) in enumerate(train_loader):\n",
        "\n",
        "      images = images.view(-1, 180*180).to(device)\n",
        "      labels = labels.to(device)\n",
        "\n",
        "      # Clear gradients w.r.t. parameters\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # Forward pass to get output/logits\n",
        "      outputs = model(images) \n",
        "\n",
        "      # Calculate Loss: softmax --> cross entropy loss\n",
        "      loss = criterion(outputs, labels)\n",
        "\n",
        "      # Getting gradients w.r.t. parameters\n",
        "      loss.backward()\n",
        "\n",
        "      # Updating parameters\n",
        "      optimizer.step()\n",
        "\n",
        "      iter += 1\n",
        "      if iter > num_iters:\n",
        "        break;\n",
        "\n",
        "      if iter % 500 == 0:\n",
        "          # Calculate Accuracy         \n",
        "          correct = 0\n",
        "          total = 0\n",
        "          # Iterate through test dataset\n",
        "          for images, labels in test_loader:\n",
        "              \n",
        "              images = images.view(-1, 180*180).to(device)\n",
        "\n",
        "              # Forward pass only to get logits/output\n",
        "              outputs = model(images)\n",
        "\n",
        "              # Get predictions from the maximum value\n",
        "              _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "              # Total number of labels\n",
        "              total += labels.size(0)\n",
        "              # print(predicted)\n",
        "\n",
        "\n",
        "              # Total correct predictions\n",
        "              if torch.cuda.is_available():\n",
        "                  correct += (predicted.cpu() == labels.cpu()).sum() \n",
        "              else:\n",
        "                  correct += (predicted == labels).sum()\n",
        "\n",
        "          accuracy = 100 * correct.item() / total\n",
        "\n",
        "          # save the loss and iteration\n",
        "          loss_data.append(round(loss.item(),4))\n",
        "          iterations.append(iter)\n",
        "\n",
        "          # Print Loss\n",
        "          print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, round(loss.item(),4), round(accuracy,4)))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 500. Loss: 2.3051. Accuracy: 10.3292\n",
            "Iteration: 1000. Loss: 2.2995. Accuracy: 12.037\n",
            "Iteration: 1500. Loss: 2.3038. Accuracy: 10.0412\n",
            "Iteration: 2000. Loss: 2.3. Accuracy: 9.7531\n",
            "Iteration: 2500. Loss: 2.2931. Accuracy: 13.0247\n",
            "Iteration: 3000. Loss: 2.2989. Accuracy: 17.3457\n",
            "Iteration: 3500. Loss: 2.2847. Accuracy: 21.5844\n",
            "Iteration: 4000. Loss: 2.4048. Accuracy: 13.6831\n",
            "Iteration: 4500. Loss: 2.2171. Accuracy: 18.251\n",
            "Iteration: 5000. Loss: 2.1616. Accuracy: 19.2181\n",
            "Iteration: 5500. Loss: 2.2119. Accuracy: 12.4897\n",
            "Iteration: 6000. Loss: 2.1955. Accuracy: 16.0082\n",
            "Iteration: 6500. Loss: 2.105. Accuracy: 21.5021\n",
            "Iteration: 7000. Loss: 1.973. Accuracy: 25.144\n",
            "Iteration: 7500. Loss: 2.0837. Accuracy: 28.2099\n",
            "Iteration: 8000. Loss: 1.9747. Accuracy: 26.7078\n",
            "Iteration: 8500. Loss: 1.9555. Accuracy: 36.5021\n",
            "Iteration: 9000. Loss: 1.9053. Accuracy: 34.4856\n",
            "Iteration: 9500. Loss: 2.216. Accuracy: 32.7572\n",
            "Iteration: 10000. Loss: 1.8081. Accuracy: 26.4609\n",
            "Iteration: 10500. Loss: 2.0861. Accuracy: 35.6173\n",
            "Iteration: 11000. Loss: 1.7849. Accuracy: 38.2305\n",
            "Iteration: 11500. Loss: 1.7794. Accuracy: 26.3992\n",
            "Iteration: 12000. Loss: 1.8874. Accuracy: 43.0247\n",
            "Iteration: 12500. Loss: 1.9731. Accuracy: 34.4033\n",
            "Iteration: 13000. Loss: 1.7339. Accuracy: 39.9588\n",
            "Iteration: 13500. Loss: 1.9004. Accuracy: 38.3951\n",
            "Iteration: 14000. Loss: 1.4713. Accuracy: 44.5473\n",
            "Iteration: 14500. Loss: 1.3261. Accuracy: 45.8848\n",
            "Iteration: 15000. Loss: 1.6936. Accuracy: 38.9095\n",
            "Iteration: 15500. Loss: 1.4308. Accuracy: 44.9177\n",
            "Iteration: 16000. Loss: 1.2133. Accuracy: 46.1728\n",
            "Iteration: 16500. Loss: 1.5744. Accuracy: 44.5473\n",
            "Iteration: 17000. Loss: 1.4491. Accuracy: 53.9095\n",
            "Iteration: 17500. Loss: 1.3249. Accuracy: 54.3827\n",
            "Iteration: 18000. Loss: 1.6961. Accuracy: 44.0329\n",
            "Iteration: 18500. Loss: 1.4957. Accuracy: 51.2757\n",
            "Iteration: 19000. Loss: 1.2364. Accuracy: 51.4198\n",
            "Iteration: 19500. Loss: 1.166. Accuracy: 60.1235\n",
            "Iteration: 20000. Loss: 0.9733. Accuracy: 58.8272\n",
            "Iteration: 20500. Loss: 1.1474. Accuracy: 53.2305\n",
            "Iteration: 21000. Loss: 0.9697. Accuracy: 60.7819\n",
            "Iteration: 21500. Loss: 1.1975. Accuracy: 58.6831\n",
            "Iteration: 22000. Loss: 1.1744. Accuracy: 55.5556\n",
            "Iteration: 22500. Loss: 0.9006. Accuracy: 65.679\n",
            "Iteration: 23000. Loss: 1.1629. Accuracy: 39.8148\n",
            "Iteration: 23500. Loss: 0.9586. Accuracy: 60.7407\n",
            "Iteration: 24000. Loss: 0.9795. Accuracy: 65.7819\n",
            "Iteration: 24500. Loss: 0.9435. Accuracy: 52.1399\n",
            "Iteration: 25000. Loss: 1.0851. Accuracy: 61.4198\n",
            "Iteration: 25500. Loss: 0.8982. Accuracy: 71.2551\n",
            "Iteration: 26000. Loss: 0.7005. Accuracy: 73.1481\n",
            "Iteration: 26500. Loss: 0.8384. Accuracy: 67.4897\n",
            "Iteration: 27000. Loss: 0.7045. Accuracy: 66.5638\n",
            "Iteration: 27500. Loss: 0.6667. Accuracy: 71.5021\n",
            "Iteration: 28000. Loss: 0.725. Accuracy: 69.5473\n",
            "Iteration: 28500. Loss: 0.6951. Accuracy: 69.7531\n",
            "Iteration: 29000. Loss: 0.7248. Accuracy: 66.6255\n",
            "Iteration: 29500. Loss: 0.4784. Accuracy: 75.9671\n",
            "Iteration: 30000. Loss: 0.8594. Accuracy: 76.3786\n",
            "Iteration: 30500. Loss: 0.656. Accuracy: 72.8601\n",
            "Iteration: 31000. Loss: 1.106. Accuracy: 71.214\n",
            "Iteration: 31500. Loss: 0.5769. Accuracy: 74.6091\n",
            "Iteration: 32000. Loss: 0.8393. Accuracy: 71.8724\n",
            "Iteration: 32500. Loss: 0.4881. Accuracy: 73.2305\n",
            "Iteration: 33000. Loss: 0.4957. Accuracy: 75.8642\n",
            "Iteration: 33500. Loss: 0.3983. Accuracy: 77.0576\n",
            "Iteration: 34000. Loss: 0.501. Accuracy: 74.0947\n",
            "Iteration: 34500. Loss: 1.1202. Accuracy: 72.6955\n",
            "Iteration: 35000. Loss: 0.6422. Accuracy: 69.856\n",
            "Iteration: 35500. Loss: 0.3396. Accuracy: 79.0741\n",
            "Iteration: 36000. Loss: 0.6752. Accuracy: 69.0123\n",
            "Iteration: 36500. Loss: 0.4532. Accuracy: 78.0247\n",
            "Iteration: 37000. Loss: 0.4211. Accuracy: 78.5597\n",
            "Iteration: 37500. Loss: 0.3147. Accuracy: 81.2963\n",
            "Iteration: 38000. Loss: 0.463. Accuracy: 74.8148\n",
            "Iteration: 38500. Loss: 0.3681. Accuracy: 80.8025\n",
            "Iteration: 39000. Loss: 0.345. Accuracy: 77.9835\n",
            "Iteration: 39500. Loss: 0.4055. Accuracy: 80.5556\n",
            "Iteration: 40000. Loss: 0.7682. Accuracy: 73.3333\n",
            "Iteration: 40500. Loss: 0.5581. Accuracy: 81.3992\n",
            "Iteration: 41000. Loss: 0.5565. Accuracy: 73.8477\n",
            "Iteration: 41500. Loss: 0.3678. Accuracy: 81.4815\n",
            "Iteration: 42000. Loss: 0.2042. Accuracy: 81.6667\n",
            "Iteration: 42500. Loss: 0.3232. Accuracy: 79.1358\n",
            "Iteration: 43000. Loss: 0.3935. Accuracy: 81.07\n",
            "Iteration: 43500. Loss: 0.6321. Accuracy: 72.963\n",
            "Iteration: 44000. Loss: 0.4306. Accuracy: 80.2469\n",
            "Iteration: 44500. Loss: 0.2358. Accuracy: 82.8395\n",
            "Iteration: 45000. Loss: 0.169. Accuracy: 81.6255\n",
            "Iteration: 45500. Loss: 0.3347. Accuracy: 80.823\n",
            "Iteration: 46000. Loss: 0.2879. Accuracy: 81.3169\n",
            "Iteration: 46500. Loss: 0.1787. Accuracy: 80.4733\n",
            "Iteration: 47000. Loss: 0.1766. Accuracy: 83.1481\n",
            "Iteration: 47500. Loss: 0.4065. Accuracy: 79.3004\n",
            "Iteration: 48000. Loss: 0.1848. Accuracy: 81.5432\n",
            "Iteration: 48500. Loss: 0.1852. Accuracy: 84.0123\n",
            "Iteration: 49000. Loss: 0.5323. Accuracy: 70.1852\n",
            "Iteration: 49500. Loss: 0.15. Accuracy: 81.5432\n",
            "Iteration: 50000. Loss: 0.3926. Accuracy: 79.1975\n",
            "Iteration: 50500. Loss: 0.1537. Accuracy: 83.3745\n",
            "Iteration: 51000. Loss: 0.2505. Accuracy: 80.9671\n",
            "Iteration: 51500. Loss: 0.1498. Accuracy: 84.1358\n",
            "Iteration: 52000. Loss: 0.1242. Accuracy: 83.1893\n",
            "Iteration: 52500. Loss: 0.0888. Accuracy: 84.3416\n",
            "Iteration: 53000. Loss: 0.2286. Accuracy: 83.5185\n",
            "Iteration: 53500. Loss: 0.1242. Accuracy: 83.4774\n",
            "Iteration: 54000. Loss: 0.2693. Accuracy: 82.0782\n",
            "Iteration: 54500. Loss: 0.1725. Accuracy: 84.5679\n",
            "Iteration: 55000. Loss: 0.2171. Accuracy: 81.1934\n",
            "Iteration: 55500. Loss: 0.1191. Accuracy: 84.5473\n",
            "Iteration: 56000. Loss: 0.2329. Accuracy: 82.9218\n",
            "Iteration: 56500. Loss: 0.1657. Accuracy: 84.4033\n",
            "Iteration: 57000. Loss: 0.098. Accuracy: 82.4486\n",
            "Iteration: 57500. Loss: 0.0644. Accuracy: 84.7119\n",
            "Iteration: 58000. Loss: 0.0686. Accuracy: 84.2181\n",
            "Iteration: 58500. Loss: 0.3107. Accuracy: 81.1317\n",
            "Iteration: 59000. Loss: 0.0664. Accuracy: 85.0823\n",
            "Iteration: 59500. Loss: 0.185. Accuracy: 82.0165\n",
            "Iteration: 60000. Loss: 0.1623. Accuracy: 83.5185\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNwkm3CuSjny"
      },
      "source": [
        "**Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "uqBohu_O2b-f",
        "outputId": "06ada50f-d4cf-46c2-8e37-fb2006f62292"
      },
      "source": [
        "num_predictions = 10\n",
        "\n",
        "for images, labels in test_loader:\n",
        "    break\n",
        "\n",
        "predictions = model.forward(images[0:num_predictions].view(-1, 180*180).to(device))\n",
        "predictions = torch.argmax(predictions, dim=1)\n",
        "\n",
        "print('Actual Values for Dataset 1: ')\n",
        "plt.figure(figsize=(14,14))\n",
        "for i in range(num_predictions):\n",
        "    ax=plt.subplot(1,num_predictions,i+1)\n",
        "    ax.title.set_text(labels[i].item())\n",
        "    plt.imshow(images[i].view(180, 180), cmap=plt.cm.binary)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print('Predicted Values for Dataset 1: ')\n",
        "plt.figure(figsize=(14,14))\n",
        "for i in range(num_predictions):\n",
        "    ax=plt.subplot(1,num_predictions,i+1)\n",
        "    ax.title.set_text(predictions[i].item())\n",
        "    plt.imshow(images[i].view(180, 180), cmap=plt.cm.binary)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual Values for Dataset 1: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1008 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Values for Dataset 1: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1008 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0K2xEH_S6YN"
      },
      "source": [
        "### **Loss Curve of Dataset -1**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "hd67iRRq6Zlo",
        "outputId": "3930e46f-4a92-4735-c1c8-8bdc0cb6b33e"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.plot(iterations,loss_data,marker='o')\n",
        "plt.title('Loss vs Iterations Curve')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mBCUxqrPRp-S"
      },
      "source": [
        "## **Step - 3**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4garZF0TMEC"
      },
      "source": [
        "### **Train & Test Creation (Dataset - 2)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bU0Cf-5CBfTt"
      },
      "source": [
        "# label store\n",
        "Train2_digits = np.array(df2_Train['label'])\n",
        "Test2_digits = np.array(df2_Test['label'])\n",
        "\n",
        "# label drop\n",
        "df2_Train.drop(['label'],axis=1,inplace=True)\n",
        "df2_Test.drop(['label'],axis=1,inplace=True)\n",
        "\n",
        "# Image pixel convertion into numpy(uint8 datatype)\n",
        "Train2_img = np.array(df2_Train, dtype=np.uint8)\n",
        "Test2_img = np.array(df2_Test, dtype=np.uint8)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7ZUVONFUrOv"
      },
      "source": [
        "### **Image Data Creation (Dataset - 2)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8qqVAOGBjot",
        "outputId": "ae9b7955-ba2f-4b53-c99c-ccd210a2e1a8"
      },
      "source": [
        "Train2_imgTN = []\n",
        "Test2_imgTN = []\n",
        "\n",
        "transformer=transforms.Compose([\n",
        "    transforms.ToPILImage(),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize([0.5], [0.5])                         \n",
        "])\n",
        "\n",
        "matrixNum = int(math.sqrt(len(df2_Test.columns)))         #features=784, Sqrt(784)=28, 28*28 image\n",
        "\n",
        "# Tensor transformation and normalization for Train Data\n",
        "for index, data in enumerate(Train2_img,start=0):\n",
        "  image = data.reshape(matrixNum,matrixNum)\n",
        "  image_Ttrans2 = transformer(image)\n",
        "  Train2_imgTN.append((image_Ttrans2,Train2_digits[index]))\n",
        "\n",
        "# Tensor transformation and normalization for Test Data\n",
        "for index, data in enumerate(Test2_img,start=0):\n",
        "  image = data.reshape(matrixNum,matrixNum)\n",
        "  image_Ttrans2 = transformer(image)\n",
        "  Test2_imgTN.append((image_Ttrans2,Test2_digits[index]))\n",
        "\n",
        "print('Train Data: ', len(Train2_imgTN))\n",
        "print('Test Data: ', len(Test2_imgTN))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data:  60000\n",
            "Test Data:  10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_XHfIGSrU4bm"
      },
      "source": [
        "### **Dataset - 2 (Model Build, Accuracy, Prediction)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dM3jRaXU6r3"
      },
      "source": [
        "**Model Build**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrqolLk2IKiU"
      },
      "source": [
        "batch_size = 60\n",
        "num_iters = 60000\n",
        "input_dim = matrixNum*matrixNum\n",
        "num_hidden = 200\n",
        "output_dim = 10\n",
        "learning_rate = 0.01\n",
        "\n",
        "\n",
        "model2 = DeepNeuralNetworkModel(input_size = input_dim,\n",
        "                               num_classes = output_dim,\n",
        "                               num_hidden = num_hidden)\n",
        "\n",
        "# Device\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# To enable GPU\n",
        "model2.to(device)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.SGD(model2.parameters(), lr=learning_rate)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M28wmzznU-vi"
      },
      "source": [
        "**Accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVFsUZO4I1k-",
        "outputId": "879b35c1-9556-461b-e6ca-e2dd965a3341"
      },
      "source": [
        "loss_data2,iterations2 = [],[]\n",
        "accuracy_data2 = []\n",
        "iter = 0\n",
        "\n",
        "num_epochs = round(num_iters / (len(Train2_imgTN) / batch_size))\n",
        "\n",
        "train_loader2 = torch.utils.data.DataLoader(dataset=Train2_imgTN, \n",
        "                                           batch_size=batch_size, \n",
        "                                           shuffle=True)   # It's better to shuffle the whole training dataset! \n",
        "\n",
        "test_loader2 = torch.utils.data.DataLoader(dataset=Test2_imgTN, \n",
        "                                          batch_size=batch_size, \n",
        "                                          shuffle=False) \n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    for i, (images, labels) in enumerate(train_loader2):\n",
        "\n",
        "        images = images.view(-1, matrixNum*matrixNum).to(device)\n",
        "        labels = labels.to(device)\n",
        "\n",
        "        # Clear gradients w.r.t. parameters\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Forward pass to get output/logits\n",
        "        outputs = model2(images) \n",
        "\n",
        "\n",
        "        # Calculate Loss: softmax --> cross entropy loss\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        # Getting gradients w.r.t. parameters\n",
        "        loss.backward()\n",
        "\n",
        "        # Updating parameters\n",
        "        optimizer.step()\n",
        "\n",
        "        iter += 1\n",
        "        if iter > 20000:\n",
        "          break;\n",
        "\n",
        "        if iter % 500 == 0:\n",
        "            # Calculate Accuracy         \n",
        "            correct = 0\n",
        "            total = 0\n",
        "            # Iterate through test dataset\n",
        "            for images, labels in test_loader2:\n",
        "                images = images.view(-1, matrixNum*matrixNum).to(device)\n",
        "\n",
        "                # Forward pass only to get logits/output\n",
        "                outputs = model2(images)\n",
        "\n",
        "                # Get predictions from the maximum value\n",
        "                _, predicted = torch.max(outputs, 1)\n",
        "\n",
        "                # Total number of labels\n",
        "                total += labels.size(0)\n",
        "                # print(predicted)\n",
        "\n",
        "\n",
        "                # Total correct predictions\n",
        "                if torch.cuda.is_available():\n",
        "                    correct += (predicted.cpu() == labels.cpu()).sum() \n",
        "                else:\n",
        "                    correct += (predicted == labels).sum()\n",
        "\n",
        "            accuracy = 100 * correct.item() / total\n",
        "\n",
        "\n",
        "            # Print Loss\n",
        "            print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, round(loss.item(),4), accuracy))"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 500. Loss: 2.2772. Accuracy: 19.53\n",
            "Iteration: 1000. Loss: 1.6556. Accuracy: 21.98\n",
            "Iteration: 1500. Loss: 1.4613. Accuracy: 46.64\n",
            "Iteration: 2000. Loss: 1.1894. Accuracy: 63.57\n",
            "Iteration: 2500. Loss: 0.8954. Accuracy: 64.54\n",
            "Iteration: 3000. Loss: 0.6994. Accuracy: 69.97\n",
            "Iteration: 3500. Loss: 0.8982. Accuracy: 70.1\n",
            "Iteration: 4000. Loss: 0.9477. Accuracy: 75.37\n",
            "Iteration: 4500. Loss: 0.6978. Accuracy: 75.8\n",
            "Iteration: 5000. Loss: 0.5029. Accuracy: 79.59\n",
            "Iteration: 5500. Loss: 0.6303. Accuracy: 79.31\n",
            "Iteration: 6000. Loss: 0.4513. Accuracy: 80.73\n",
            "Iteration: 6500. Loss: 0.5375. Accuracy: 82.58\n",
            "Iteration: 7000. Loss: 0.3927. Accuracy: 82.5\n",
            "Iteration: 7500. Loss: 0.5922. Accuracy: 83.53\n",
            "Iteration: 8000. Loss: 0.5335. Accuracy: 82.53\n",
            "Iteration: 8500. Loss: 0.5478. Accuracy: 83.99\n",
            "Iteration: 9000. Loss: 0.5363. Accuracy: 82.78\n",
            "Iteration: 9500. Loss: 0.6352. Accuracy: 84.14\n",
            "Iteration: 10000. Loss: 0.476. Accuracy: 84.2\n",
            "Iteration: 10500. Loss: 0.3756. Accuracy: 84.79\n",
            "Iteration: 11000. Loss: 0.4347. Accuracy: 84.33\n",
            "Iteration: 11500. Loss: 0.198. Accuracy: 85.29\n",
            "Iteration: 12000. Loss: 0.5386. Accuracy: 85.01\n",
            "Iteration: 12500. Loss: 0.3279. Accuracy: 85.03\n",
            "Iteration: 13000. Loss: 0.6901. Accuracy: 80.8\n",
            "Iteration: 13500. Loss: 0.3671. Accuracy: 85.89\n",
            "Iteration: 14000. Loss: 0.3291. Accuracy: 86.11\n",
            "Iteration: 14500. Loss: 0.3352. Accuracy: 86.39\n",
            "Iteration: 15000. Loss: 0.3779. Accuracy: 86.3\n",
            "Iteration: 15500. Loss: 0.2494. Accuracy: 86.02\n",
            "Iteration: 16000. Loss: 0.2369. Accuracy: 86.24\n",
            "Iteration: 16500. Loss: 0.4743. Accuracy: 86.08\n",
            "Iteration: 17000. Loss: 0.3625. Accuracy: 86.03\n",
            "Iteration: 17500. Loss: 0.2876. Accuracy: 86.07\n",
            "Iteration: 18000. Loss: 0.3845. Accuracy: 86.4\n",
            "Iteration: 18500. Loss: 0.368. Accuracy: 86.15\n",
            "Iteration: 19000. Loss: 0.2084. Accuracy: 86.56\n",
            "Iteration: 19500. Loss: 0.243. Accuracy: 86.73\n",
            "Iteration: 20000. Loss: 0.377. Accuracy: 86.89\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "04wyArcMVBsa"
      },
      "source": [
        "**Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "eiqZaKwcKPrJ",
        "outputId": "bb26df51-079b-4a71-b9a0-f545504ec68e"
      },
      "source": [
        "num_predictions = 10\n",
        "\n",
        "for images, labels in test_loader2:\n",
        "    break\n",
        "\n",
        "predictions = model2.forward(images[0:num_predictions].view(-1, matrixNum*matrixNum).to(device))\n",
        "predictions = torch.argmax(predictions, dim=1)\n",
        "\n",
        "print('Actual Values for Dataset 2: ')\n",
        "plt.figure(figsize=(14,14))\n",
        "for i in range(num_predictions):\n",
        "    ax=plt.subplot(1,num_predictions,i+1)\n",
        "    ax.title.set_text(labels[i].item())\n",
        "    plt.imshow(images[i].view(matrixNum, matrixNum), cmap=plt.cm.binary)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print('Predicted Values for Dataset 2: ')\n",
        "plt.figure(figsize=(14,14))\n",
        "for i in range(num_predictions):\n",
        "    ax=plt.subplot(1,num_predictions,i+1)\n",
        "    ax.title.set_text(predictions[i].item())\n",
        "    plt.imshow(images[i].view(matrixNum, matrixNum), cmap=plt.cm.binary)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual Values for Dataset 2: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x1008 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Values for Dataset 2: \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gBEy2vFgs4p133lGpMKfTiUQigWg0qpxXXm8fjPqe5dcvNJtNpFIpZDIZVaakaZpiF5kS5G8qP8tNmFGhsaZBrlarWF9fRyQSQTAY7OdGuq+yo37VajU4nU6EQiE4HA46X+rey+9PhnV1dRWVSgUulwsejwftdlvpHp0au90On88Hp9OJWCyGQCCAxcVF3Lx5E06nE88//zzi8ThcLhc0TYPT6YTP54Ou6+q32+1W12fORh6m/Mgyc1NbWlrq+v5cRyzDi0QiePHFFxGNRtW6rVQqWF9fV78ZHPP7Hz9+HDabDQsLC8hms/D5fF2ldA6HQzmD8l4VCgUYhoHFxUV8+OGHiEQimJqaetzA+tDWbaPRwNWrV5FMJnHr1i3F3HJdUR9J3DQaDQCAy+VSjH+z2ezKZPHvyCguLi4qB4mERD8ypsBmhhv7ILtqtYpUKoV8Pg9gc/8hkUB5kHBgENJoNGCz2VRpGYkX2iQZtMi/455IfWy1WopQYCafZRiE3EOp9z6fD9lsFu12GzabbVvW1oSBsRlHEBUcsOza7Tbq9TqazSbS6TSq1Sry+TxyuZwiG2RQTIeyWq1iY2MDlUoFwWAQwWAQxWJR7XPAps7b7XbEYjE4HA7FkBcKBcW2l0olVVESDAb3Wi75I6F7nU5HETYkglZWVpQ8bTZbl10fGhpSlTlerxe1Wg0LCwvI5/OKGOee8BDsq+7JUvZisagycww08vk8qtWq2uuATZug6zoSiYQiqeU+SZBMDAQCMAwDsVgMmqZhaGgIwWBQlbKzYoR2hLbJ5XKhWCzC6/Uqcogk8F6x6+DFMIy2pmm/BuBvAdgB/FvDMD583PeRi2kn2RfeqHQ6jVKphPPnz+P73/8+MpkMrl69CsMw8PM///N47bXXMDQ0hKefflqV39TrdayuruLixYuYm5vDiy++iOHhYUxMTCASieDb3/42/uIv/gK6rmN6ehrhcBgvv/wyXnjhBfh8PnWj+4F+ya8fqFQquHbtGjY2NlAoFFQ5DZ0br9fblSEj+82FzmCH98/j8QAAstksrly5gsnJSYyPj++UXXwk9lt2NALlclmxB9z4ZeB2/1rQbDaVQVpdXYWu6wiFQhgeHkapVMLbb7+Ne/fuIZvNIpPJwOfzYWJiAqFQCGfPnsWxY8fw7rvv4q233kIikcBrr72G48ePK+aEJS8AVG0zA3EGmYMiv0ajgXv37iGXy+Hy5cs4f/48yuUyMpkMDMNQaWU60GNjY/iFX/gFzM3NIRwOw+v14lvf+hb+5E/+BNlsVmVqHA4HHA4H5ufn8ZnPfAbBYBDf+973cPPmzS4DziDT4XCoTCod93v3Nku26axOTk7i85///GMFL4e5bovFIt588028//77WFpawurqKux2uyqLajQa0HUd1WoVpVIJmqZhY2NDrdN2u91VekvHnD+VSgXnzp2Dy+XCyZMn8dprr8HpdKogeq+4/x59l10mk8H169cV8eL3+xEKhRCNRpURB6CMZrvdVuVr/F7FYhF2u12V08mSMuoTIfuxCAZ5mqb1lBcdo2AwiKGhITSbTdy9exedTgculwujo6OP/J6DZDOOKPZVdrI3DwBqtRo2NjZQKpVw6dIlpFIppTvM1tOJZIaQWZgPP/wQmqYhFoshFospRxrYLAnK5XIYHR3F3NwcAKBUKqnS70ajofTT6/Xi05/+NJ544gm13nf53Y687hmGoYLJQqGAVCqFjY0NvPnmm1heXlYltyyTcrvd+NSnPoW5uTnE43GMjY0hnU6r1y8sLKiM9g6wb7JjRq9er+PatWvKzgHoKvt3uVxwu91wOBzw+XzQNA3BYFAFccViUdkPlsY1m004HA5EIhE4HA6MjY0hEokogoh9MMxCN5tN1Go1rK6uwmazYWVlBXa7HWNjY5ibm4PX61XE7F6xJ0/cMIw3Aby556vofs8utpTCoWFut9toNptYXV1FuVzGysoK1tfXkclksL6+DgBIp9PIZDJwu91oNpuK3aJjw/IzMmosoSiVSlhfX4dhGHC73ahWq1hdXcXo6Cj8fr+KLunIyl6O3Rj3/ZDfbsCSh1wu18U+8js5HI4u1pFlE6wD5+vMpXTcTKPR6G6yAw/FfsqO35X3m03kZiZaDiqgDMisNptN1Ot1NBoNFYRUKhWUy2VomqZKmsiGsz+BJWOyYZP6Kh0prode5Ys7/I77Ij+W3DHL4fP5lDHtdDpdDcsAlBFPJBIIBoOq94DGnN/N5XIpp5Sv9fl8XayjWR502hlY8/FarYZ8Pg+/349sNqtqxHdaendY65ZlR8lkUhkaqSPUTfM6vX/ND8hHZkIpIxr3SqWCarUKj8fz2L1BD8N+yI6ZJjKH5rUje1fsdnvPYJ96wvVLmLPK4nuoXkqSGrJOnHujfB9em9vtVr1GdBL4+Y+S86DYjKOIg5Yds9DlclmtJ65ROUBINk0HAgFle6hTZL9JotFhpv9CUH/5I8mMcrkMj8ezJ/L1KOierMoBttYp9z/6koVCAZlMBtlsFtlsFrlcTjn33E88Hg+SyST8fr96v2w2i1QqhVwupzLbsgyL99WM/ZSdbIeoVCqqTNY8XAnYIlm4H8kMsezXk5lnVorQBjMDSLJH2mCpj9zjAKheG8MwEI1G+/K9971hf7dgY3S9Xse5c+fw0UcfqbRro9FQpSi5XA6ZTEYpl6ZpOH/+PNbW1jA1NYUzZ87A4/GozMn8/DwcDgei0Sg0TUO5XMby8jKq1SouXryoGPRKpQK73Y7FxUX83d/9nfp7RpxOpxNnzpzB66+/rkoU+lVecdAolUr44IMPsLi4iEwmo8puyEgzJSkNcqVSAQAld6YdGekbhoHV1VXFdu6QnRgI0Olrt9twOBzwer2qNpkpZbkp2u12xONxFVzTSQc2A7/p6WkEg0GVAfR6varPY2JiAn6/H6+99hrOnDmjSkjY2C5L+AB0BeEulwsOh0N9bj9LfHYjM8MwUCwWcfnyZSSTSRw7dgxPPvkk3n//fbzxxhuqVIebHQC1gcopagCUDJmxO3bsGObm5jAzM4MXXngBdrsd586dUxs3B0cUi0XUarUulpEGhaWkTqcTmUwGrVYLf/u3f4tIJIKXXnoJzzzzzKHIbqfodDpIpVJYWlqCYRjKmQG20vuUp3mSooQ0ZLI0SrJ0q6uruHTpEoaGhjA/P6+yqYMG6ty9e/fUOiTZUK/Xu0gTBv7SeaTO8XFmXoCtwEWSUwwy+HfmYTEMaLhXMpDk4A273a4cBkl0sPyvX9lpCwcPs9Oaz+fx0UcfdQUubGqWJGggEIDT6cTExISaXsk1SjvkdrtVAz7LFLlm6UDKckU66ABw48YNrK2tYW5uDqdOnTqQiaqHhUqlgnfffRfr6+tdpXgM5OjLsHmdBCMrG5ipL5fLKJfLOH/+PK5fv95FdKfTabVmnU6nGuzT6XSQSCT2rcfjYd/5ww8/RD6fV4Nb5ORc7kuyx5MBDvdHl8uFaDSqSsNkFlFORmTrBGUq/SCn06laBkgEslytUCjg6tWriEajiMVifbEnAxu80JBWq1XcvXsXFy9eRKlUQiqVQr1eRzKZRK1WU8KV4LjfSqUCn8+HcDiM+fl5eL1exGIx1XwObC7ytbU1pFIprK+vK2VmWcHGxgY0TesKXlgDGQwG8dJLL6lN6agGL41GA+vr61hbW+uSqazT5m9u0AxG5GNyJChHQKbTaRQKhccubTpMkK2Qjopk9+XrOp2OKlGSzJcMKMLhMBwOhwpsvF4vEonEA5OwhoeH0Wg0sLCw0FXWIh1UPibHafI6+tlc/bigzEgspFIpnDlzBsePH0c2m1VlTb1G1ZqzVwRl5/V6MTMzg1OnTmFsbAyjo6Oq74fGhoaIqW4+x/fnGGVu7HRsb968CZ/Ph2PHjh2ovHYDXddRqVRQKBTg9XpV6p/oFbyyVIzyNWemZEaAj3c6HZWFpoM0yGBDqcfjUXs0CQjqpQxaCBm08DfXUi/ILDONtHyO+4NZ1r2ek9PLZLZnJ6XTFo4G6vU6UqkUqtVql/NoLkN0u92qt3JoaKjL6axUKqp3hdOgOBCHDrTM+kkSiKW52WwW+XwekUhkV1n6owSWB9++fVuVMbVaLVQqla6MNLOeAJTc5ERLBohra2tqIEqz2VQ9HtxnuW+WSiV4PB6Ew+Gu6zmItSx7lgOBgBqmRB3jPsNrliXE3I9YOixtMd/DXFki+/2ALZJH7qVm4puVTXysHxi44IVCKhQKOHfuHJLJJN5//33cuXOnK4L2+XwqCjQ7QmQfNjY28IMf/ABerxe3b9+Gz+dDLpdDLpeD2+1GPB6HzWZTDU3pdFqdNSH7OAB0OZ/VahWdTgeXLl3C8PAwhoeH8fLLLyMWix28wPoEKiUdcdZG8jkqPx1yyZwb9xtS2dQWj8dVM2E8Hu/byMb9Bjc0BnAyQyADMwaq0skxp6n59wCQSCQQCoXUqFo24PN5jmZm0z9ZWrJ0nDbGgIplJ7xHzDT4fL5+T3XbMarVKgqFApaXl3HlyhU1uODevXu4evWqWrcMsugQZ7NZvPXWW7hx4wZmZmbU+OcvfOEL6m8oQxp9MtSnT59WjI/X60UqlcKVK1e6GHBunJRPIpFQ07gajQZu376NTqeDl19+eeAdRzrWMoiWgWuv8lUZlADo2tP4717BI4kilrEMIqTzzx8GKLJkjq/lpDAJfm9zNnW7z3uYjkgije9nLr2V+yAdhmaziWKxqPbdQdZBC48GS5PYh8L9h/aTjrEkCVnOyF41rjn2I0giTZKJkiiTDqXsy5Tr40cleJF7mASntm1sbKimfDkWXv7IYIZ/y/2V/Rxco/K1vGfMiqXTaVy4cAHDw8NqzP9BgKXp7ItlRZC5dI16JjMwfr+/y14wqDbbEfl9+ZmtVktN8TQMQ2XrJRnGvZZ/LzPOuVwOwOZUzL1kqQ49eNluo87lcnjrrbewsLCAq1evYnl5WZ0vQuFL55JgVK3rOtbW1nDlyhUAUNPDOHKVGRQZcTJilCM0pdKzfyaTyai0W7VaxfHjx3Hy5MkHgpftFtiggZtfq9Xqki0ZTOk0bQe73a6Y7dHRUfh8PuVMh8PhI5GVYiMfU/xycAHL4gAo/eD/6Rjput5Vy0zdYkDBhU/ILFa5XO5qLGbqNRgMolAoqDIybqzyLA8G0/KzDhrlchmrq6u4e/cu3n//fSwsLGBlZQXDw8OqPpgOpXSgk8kkvvGNbyAWi+GTn/wkTp48iZGREfzSL/0SOp0O7t27h1Kp1LVuqWvPP/88pqenlW4uLCzgm9/8pjLudALY8B8KhTA2NqbGOWYyGVy7dg25XA6f//znD0VujwPKj2yWNLoAuhhBGZj0clik0ZbZAD7H+xkKhQY2eJHOBkkBOnhcs+Yxx/IxYKskTNaF7wbmbAodR7Ns5f3h/tBoNJDL5RAKhY7MXmmhN7h2isWisiXMBANQPoTUv0AgoJzhYrHYlamTEwTJ/PcqV6Ru93LM5YS8H5XgZTvQP+MYapZeM3jkniGdc7PcWGkDdNtoOcjD4XAoe5xMJvH9738fIyMjePrppzF7QIdzcux9pVKB2+1WvrGZKJYEDf0ETlClDNgqIElb+iv0dzRNQ61WU9+fww3YK03dlK0DwFbZNn/Tfx4fHz/awQs3eW7YXKTlclmdwm0YhjqsiUGFdBJlip7Cb7fbalwta/XkqFtZ9yyVmQZIMpIyLQZAMd+6riOfz6sDMTkG9yhkGSRkFoX1uJKFlAwi75csJeHfsfE5EAjA6/WqYKZf04r2G3Q8zEEnjQkDVjIOkvU1Oz9SP/levUqjzMYGQJezSEeMZ0lIPZYbsrmkbb9hlhGHMxSLRcW6eDweRCIRdd38jgyCuZ4qlUrXEAKbzabG3HKEbKFQwMrKitqo2TQYCASUDDm60ayb5vIxqcu8jywL2MtEnv2CDJ6lbkjdArb0V+rYds6K/BtJTvD1PL+EfV6DBl6zZJjlns7X8PuYhxU8bF3Kv+8Fs9x7Pd6r0Z//Nv/ey+ANC4MFw9jsb+I5YTIAkVk5OpNm38Wc/XtcvTDbbf7QoaxUKooA+1EMkun/sWyYmQaSXpQFm/PpmNOOygwXYRibfXU8rBRbfB8AACAASURBVNpsa3V9a1rcQRI9HKtdr9cfCEzNvgewdc6V3W7vOhKDz5FY5X4k+3zkGWwsu+Z5gOasiwxi5GfI7KDct3eLQ7XSNEDAlrALhQKSySQWFhbUOFCevi0b0nq9F50iOeefjfl0+hgcSeezXC6jXq93MXa8MTxVnpNA+F7M7iwtLcHpdGJjY0MdjLbDmf0DBS7icDgMv9+vhiNIw8o6WylrBiehUEgdXMQTV1nmQ6dy0CHTyzIoZvCwtramWBlOCuMEDW4esrbefNZIL8eT7A3Tq+bHuKksLy+rDAIZZgby3AgelhnrN8xOIEeVLywsqPNcTpw4gU984hO4c+eOMuacQS/ZrWQyqTZhrnEaV6biz507h7/+67+GpmkqQP7yl7+MT33qU10sEIkFACrdz+tlYMJUNxlQl8uFer2OXC6nTqYeJH1ttVoolUooFotdzeTSWABbQzPIjAHdpU69nG3qOA0g9bhQKODevXsYGhoayMyLYRioVCpqOhpLKLnnmLMfvYJarlUZrPQq/TKDzo3ZwZHvwX9L51E+JtleM2li4ehC13WkUincuXNHDQWRQbZkwamn1AU5sESShHQCZV8WIQkZrmVJqLFihETr4uIiAoEAJiYmDi1L3w9sR4ba7ZsHzg4PD6tBCRwNzL5Hh8OBWCymmP/Z2Vn4/f4HysKArezuysoKbt26hXQ6jfPnz6uDHZ1OJzqdjir7pKx77bf9RrlcVn3K7PeUPYwyywtAjYKu1WpYW1vrIkMlGZTNZtFsNjE1NYXJyUnk83ncuHEDrVYLJ06cwPDwsBoc43K5MD09rXxjuc/KvZDlk+w/ol7uBYcevMgvCGymVUulEsrlsropwWBQ9RlIRksaBmmY+DqZrmLkTQPH2lMJ9nuwTl46hfK9mElgtF2tVtWPTIMdhWwD0L0BynGBZhmb2SGCTjYzLVwkdCT6OWp1v2E2DMBWWp4T6CRjRmdPGhkZ9MhSEvme8vP4mdIwycc4HYUblHxOvv9hOj+cblMul5WeeL3erpHGdPaYujZnQRi48DkOwuBGl0qlVJ1xOBxW6Wr2vDDLSvTSOZk+l045ZWx2DgYBJA1oGIFu55vXTOaPz5kh91pzRornTcjpPJVKpavvaJAgjS2/F/XLzGCbf4CtTD9fb5bHXnTAnPnqlQkzv7/5+g4DstSoVwagF7bLPvV6zkx49MpKPQzmzJY5kzoo4Nphrwr3ZslQ95IxsKV75mC7F7b73r1sAdc2nflBXNP9gM22dc6THPXL0jySqolEAiMjIwgGg2oaKPBgj4ecHsqSKZKX0kfiXsQhCvR79hPsj2WvrAyCgQczxPxehmGofixO4JQZkWw2i3q9jkgkgnq9jmq1imw2q8rbQ6EQSqWSGi89PDys/D1z8G2ujqI/3Y9KkUMNXszpOWDzjJYPP/wQS0tLaDQaqqyLNc1UCCoMU7TAVv2yZOAYTPDMCD7earVQLBbRbrcRCAQQjUbx7LPP4vXXXwewObGsVCrhhz/8IS5fvgzDMNQUBzo9MnNz5coVlMtlnDlzBpFI5KBEuCfw+gEoGUQiEYRCIeWg06nTdV05o1RiLp5qtYpIJKKUkkGh1+tVp6YPEpO9EzBI4XfK5/Oq7+qpp56C3+9XGQQyOpKR5UZhLgsg+DiZG76PYWw1FjNzIDdG/pizLAfB9Jg/jzAMQzXLJ5NJGMbmsAEyfRsbG13MEIPjZrOJRCKBs2fPYmhoCC+99BKee+45uFwuFShyUwwGgwgEAmpkdb1exxtvvIFz587h1Vdfxc/8zM90OTPMDvbKdtVqNeRyOTWoo9FoYGVlBVeuXMHo6OjADZio1+tqghvX1sjICJ566il4PB51jtK5c+dw/fp1+Hw+NYpSOkjSiFHPWq0WTp8+jddffx2lUgnf+ta3sLq6ilqthvX1deRyuYF0dJg940QlrjVm78wnX8ueE/MaBbYYbhm4yH1eZq9kdkZej9lp7JVxkf+WU9HK5XLXhLyDRrVaVUcGZDIZFItFlVkmmWdmlM0sL+vmSULIYRHy+5udGVY6yCoM+Xq+PzOyPDXe6/VibGxMHd44CKBelkoldd6IYRhdJanm8ppeASCfkxPzzOcYyewM5cXyV6mPlDF9JfOwmR8lBAIBvPrqqzh9+rSymzJrxbXv8XgQDAbhdDrVoYuE1HGu0cnJSfj9fuRyOdhsNmQyGVy6dAk3b95Uf9NqtfD++++jVCrh+PHjOHXq1L76PSQMOWyq3W4jHo93DbLSNK0rqKhWq1hbW8N3vvMd5PN5TE1NIR6PK72RWcJ6vY6FhQU1UETXdVy8eBEffvghKpUKisUibDYb1tbW4Ha78cILL+D06dNqP5MldiTeWH4ve4h3i4ELXorFIhYXF9WcbuBBAywNDMca8ybxN5lcGmqWRbAETNc3R/nquq6mYj333HP42Z/9WdhsNiwtLSGXy6FYLOLChQsA0BVp87oAqJvcaDQwNTV1UOLbM2Tw4vP5EAgE1A9lys241WrB7/cjHA6rEYQA1KnA8lRfbrIOh0P1vxyl4EUaD7IZlUoFyWRSTSGR09jokEuniBsl0H3+g9lQkQXj6+VCZ6DMhU5WvFfvw0Gzj+bPKxQKWFhYUA2nDodDySyfz6tNjPKRDfhPPvkkJicnMT8/j2PHjqFSqSCXy6kJYRxEIMebNxoNfO9731OldD/1Uz/1gCxkvbm87kajgWw2qxw0Toy5e/cuNE3D008/fUBS3BmazSby+Tzy+bxad9FoFHNzcwiFQpiYmECn08FHH32Eer3e1dsnYXaMGUCOjY3hs5/9LNLpNN57772ukfGlUmlggxdeP/VK07bOH5DlgtzHzGU1AHo6gIR0/mQtvMzeSZjrznsx69KpJAtKR4FO52GABz/ncjllf7kOydhzX5f7owxUOETEbrerwTdSDpQjA01JDnGoh7TvnGxJwtHr9cLv9yMYDOLEiRMIh8Pw+XwDFbwAm+tK3k8ZFMsSr+0yRzI4llkqSXIRMrPD15in7FFXGYySaT/qMGesgM0y9tOnT/f9sziGnZUFmUwGqVQKN27cALDpA7RaLdy6dQv5fF6RnPvp97AahIFEu91GKBQC0D0ohOuTdjOTyeCHP/whUqkUKpUKpqam1OQyYOs8rHQ6rbJOJMNWV1fV67iGuS9MT0/D7/crn1wSiHIvpS++1zL3gepMNYytA8dyuVxXIz1TrsBWg1S73YbT6cTQ0BB8Ph/GxsZgt9vVJiwNjCw3k82vmqZhfHwcc3NzCIfDSKVSyrkCNiP50dHRLuabkawMlDjdglmgowAqvzzJnMooy6EYrITDYZw4cQKlUkllnHiyNZ1AOtdUbFnqN+iQBpbBBBceRyi3Wi0sLi4C2Aw8wuFwV+Asy6NkSZcsKTOzl+aSRx6yVy6Xkc1msbKyolj3crmMcDis1gOzY5LlPQywZ4TlEgDUmSS6riMcDisnnIEudYSn3dMx5xqVWSh+NxrhTqeDaDSq6pfN5aTAg847nQoAqsY5kUiotZ7NZjE0NDRwhp2H5srmeZZ6cSIdnUKChqsXw0oZcZSo3W5XpAQZRPOhoYMGstmyVE461A9bE9KhlK+TemPWOX6GuW9BOtu9AhWz/GUmhk48mcpgMLhvwYvMvOm6rqZgLS4u4tatW6hUKlhfX0etVkM+n0epVFKHv9LplrognQ+W0ZTLZSSTSTgcDkQiETXmnec7mfVJ9p/yh04/AyCbzab0nhUVXq9XlWgvLy/jvffeQyKRwMmTJ+H1ehEKhdR0r4MGHTZmms3kCV/DIFgeNiyDPP6btpevl/1tcp+k/ZWN0OYMFjNohxkk9xN7Jey2sxMPe43NZlNBNLBp91iRY7PZkEwmUSwW8cQTT6jge78gs3AkPum/kdCRQa+cYMr+5HA4rNaaz+dThIKu611EvyTNWEJXLpeV7fB6vRgZGVFBDtefPOCcj/EzSMhKovdxMFDBC7DZvHv58mXlKPKmyH93Oh0UCgXU63WMjIxgbGwMY2NjeO211+ByufDGG2/g/PnzytGhU0nI8huXy4Wnn34ar776KhwOB27dutUVaSYSCczPzyOdTuPmzZtoNptd2QQOAbh69So8Hg9ef/31B0qEBhUsEZCHSFKhqHSc/EZZv/rqq0gmk2g2m4p9YBkPnR9OqNB1/YHD3AYZXFTsydA0TRkFjiXsdDp47733cPfuXZw5cwYvvPACACiDQKdPGmTKQgYvsryJr2HQx0W+srKCO3fu4N69e7h58yZ0XcfExAQikUiXQWMwcJilAKVSCSsrK8rRsNlsyGaz6vuMjY2pwwQ5EppnXKysrKimdABqs2QZEPUJ2JrmAwAnTpzA5OQkZmZmuozEdgFMrVbrOvCNJ9RzY15cXEQ0Gh24kopemReW0ASDQYyMjChjAzw4VY0wl9ORbXM4HBgbG1NOD5v+B5l4MAwD1WoVpVIJzWZTDSmQ5zLwcfN+LMsZKCtZGmbuU+N+KOUqsz3b7W9yAEKv65f9XOVyGX6/v++Bs7lqgYFdKpVCPp/H17/+dfz5n/85ACAcDsPlcsHr9cLlcnUdVDo6Ogq/369kR53UdR3RaBSBQAC5XA6rq6twOp2YmpqC3+9HJpNBOp1Wa46yZja7VCrB5XKpBvJkMolMJgO/34+pqSnY7XbkcjlFVvI+0D6z1/Ts2bP45V/+ZYyNjeHkyZOHFrwAWz0v5nHHJAyq1apyChmQSV2SBCJ1j/4LB7TIzJVks4Fuh1vqHslKr9c7cHvcQcNMVnB/MJdDm9cOnXWiUCgA2JqcefPmTZTLZTzxxBPKH9ovsP9aBr8sD5TT1riXMXs5MjKC559/Xq1fXdfVYe7tdluRGLFYDIlEApVKBcvLy9B1HVNTUxgaGsL6+jru3LkDn8+HT3ziExgaGsLJkyfVCGaWfbPvi5N/OQ6cGXJ5Rs3jympgghey9jSo5tM7ZfpTLkg248uGfPMY5V5lNebULVn2arXaVZvKekkaRJn+4udzUyJrKRvzBhmsP6Rh4EbYaxSgruvweDzqcD866ZK95OKhgzVoDPajYGZqzWUmNCbValWxikSvshOJnSxMyZgB3edY8L7Ia5SZSLkZHyRYygZAldJRN+LxuDqwlNkk6otkYavVqio9lP08dLQNw4DX68XQ0BDcbrcaQUoSAdgMnkqlUs9UND+HTD0P2Gq1WmrkMoAHBiIMCsiYytn5PPOKh/XKzLQZlLvUQemwc/+TLLC5rEcOLRkEUDfMB6TJoE2W3EjbQR0EHiwb2855YUAuMzYy69JLLtvtB3xMXittS79hdmD5WZlMRgUKxWJRZQFk9pN9Lrz/3IvkbznZStoQBo7yJHNm+XgfGIyw5IaDdDg9ju8h7zNJDd6HYrGIYrGITCaD9fV12O12TE9P912OO4XUS5l1N9sHyk2uQz4nMyqUNTN1DJolQUbd3i4gkVUVvEeDtscdNHqti53+HfdJSVpw72V2n8TKXvs6ekHed7MfSp9O7oc89Jp+LQ+1pg9DXZNZPZ7H5na71WROrnNz3yCwlc1lYz+vjetZErf8Dvyc3frJAxG8tNttpFIplEolZLNZtbg41UuyEDxrg0bW6/Wqwydv3rwJm82GcrkMr9fbNenKbNAYCTocDlXny0CGTYcch8sFD3TXQbN0Q5YF1Wo1ZLNZNc51kAOYYrGI5eVlrK6uIp1OqzN1uPnKMy8Mw8DIyAieeeYZeL1enDt3Tt0fufGyDIj3Yb+M8n5ANqYCW3X1LGWgo84At1AoIJvNAoAyVjzrxuwEyd/S+SHI1kknS9d1RCIRFAoFeL3eLsPNoJplfVJHDwpkaUqlErxeL1555RV4PB4cO3YMgUAAiUQC4XAY6+vr+Oijj5DJZBSrLcvjMpkMdF3H8vIy7t27B2DLGHAU9dNPP43R0VFsbGzg4sWLiql2Op3IZDJ45513sLa2ppgwgmymYRhIp9Oo1+s4deoUZmdn1dpmSVYul1N9cIOEer2OZDKpSgc1TcPo6Cief/55+Hw+RKNRNUWIkKVNHCHNXhjzVBrC6XRidHQU09PTyOVy6pytZDKpmHMy3ocNXdeVvWDvFMt55doA0OVIAlDBqyzvNGdegAenYnFtyrIy/jC7T/3mezEQlPeD7yEPLtxP8Jr4fSqVCr71rW/hwoULSKfTmJyc7NqbZJkIB96wtE323klnmexqMBjsyvBR3gxkpNPDcejMBvEsK2DTCSwUCrDb7SqLwb+RcuUUqWKxiDfffFNVYoyPjx9KoG0YmyO8s9msqsyQJAzQvTbl38mMM3WT593ZbDa1xqPRaNdwB/6dHJVrJuLY/F8ul+Hz+T72mRe5zuVj5v+byQl5wGM4HO4i0Pi8y+XCxsYG3n333b5PHCOpwCCFZbM8TJgZ1enpaUxNTaHdbuPGjRtIp9Ndw344TIIDawCoQCWZTKJcLiORSMDn86FcLiOTyagJZA7H5sHYXL+3b9/G0tISrl27poY0HT9+HHa7XfmVsVhMnd1G2XLggN/v7+qR2ykGInjhgi8Wi139F9zYZPqVESE3L/5utVrI5XIAoErMZBaGoEIysGFkyo2STA+jRckmSSeUCi1rUVm/XKvVoGmaKuMYRHARmMdSM6tiznxpmqaaI9PpdNdp3r3el47/YWUEdgMZOEhGQZZ/SLZRNrnxO0vm+mFMOCENGeUlR+Jyug6NoPx7Wd7Sj9GDjwtd3xx6kc/n4XA4MD4+jlAohGeffRahUAixWEyNOV9YWEC5XO7SJ14/mapyuYxSqaRICWCrHC8ej2NsbAyxWEyxxSQ1arUalpeXkUwm1f2Q4Pqv1+tK1qFQCM1mUwWFDGAGsR6cARYzTsBmzw5HVHK9yhGZZscb6B4P3CtbwAEJoVBIDTPgvalWqwN1fpVhGOr6ZCZPklRy4IVcI9zXAXQRBXxOZqokQ2vOesrgUL6Wr5PGWOo7X2vuY9hPyHvNvr0PP/xQHS5MEkRm0AEo50tmXGSfCuUgm/cJyoUEosz08W/kVDyzjZWZGknYSOeen9doNHD79m1ViibLdA8azPDKIwJkkLsd6y8DW+ozCTUG2CxbkkEKgC49l6V5wJYfRZs1iNnlw8BOdcPsP7LcUx50KZ8ncbuysrIvBx6bB/h0Oh1ForM0e2hoSJUg5vN5NYCjWq3CZrOpkmmZ6eR7ygMoSVBzsqMMmFj6SNKbP+Pj4xgdHVVVEtVqVe0xUu/42SQxHxcDEby0Wi2srKxgZWUFmUymq5TLnDWRRgPYbLKtVCrqoEhN01CtVpWCyVNuAagSMNb5aZqGpaUlFItFjIyMYGZmBgDUtKPV1VUsLy93pdp4DeYGxE6ng9XVVVy+fBljY2MDN3LVDFmOIqNyc9BBhptMoXnyCxskpYPPjZqTKwal3ORhIEMm7y3r0dvtzVOJw+EwXn/9dczOzqraTsLsmPdKlfK3OdBgQM0F3W63MTw8jHg8Dr/fj+vXr6sJcOVy+YHJPPL9DwrMmK6urqLRaCCRSCAej+PYsWMIh8OqqS+fz6tRvyyvpPPp8/kwPj6OeDyOQCCgghmOYSSzmsvlVA3+vXv3lINiGJtjLNloTzbInJ52uVw4ffo0ZmdnMTo6CmDT4Umn0yoY4kGQg5Z5qdVq2NjYQDKZVCyZz+dTJXl8rBe5IvVPsl691qPNZkMwGEQkEkEqlVKBYSqVgtvtHrgDeOnsAVBZD/ZKEuxXM5eDmvdu6RAT5h417mn8PDkp0EykScfCZrN1ZXr4eQe9J0rChU26vMe99qZeZXTA1nTFeDwOl8uF0dFRhMNhLCwsYHV1VX13WWMvpyTy/KBIJKKafBOJBNxut+orpbNNmcspbyTGZHYLgJqOtrCwgOvXryMWi2FkZOTA5SwbkBnosTyHDi+wpUO8PgZnwJbDzCZrWTYsy9fl30o5yUBaZnVIrg7aHncUYfb9+JjT6US9Xsfa2lrf/b9Op6NsYaFQUP4KAwCXywWn04lsNosf/OAHyp76/X5171kiDED18obDYUxPT6uMp8fjURlMm82GmZkZVCoVDA8PIxaLIZ/PK1vJfYEkuNPpRDqdhsfj6Sp15h5KkDyXI9UfB48MXjRN+7cA/isAScMwTt9/LAbg/wUwC2ABwD82DCP32J9+H+12G/fu3cONGzeQSqUAbC1kOpCSrQG2yrdyuZzKdDCTwgZCNgTRSTcMQx2kyMyIruvqs5966imMjIwA2AyKDMPA6uoqlpaWuhxxyaxL5pyvv3TpEur1Oubn5/Erv/Ir+Ju/+RsMDw/jypUr6vtqmvaf+iW/3YLGTJZEyd80HD6fTx0CCGwxtAxewuEwPB6PChIle81MxG7wla985UBlJ5kvl8sFYGsUd7vdViU6P/7jP45PfOITuHLlCn74wx92lTPIwMWclTPXPpszMGQyGEwODw/jzJkzcLvduHDhgipRKxaLXRkG83sdlPw6nQ42NjawsLAAh8OBoaEhjI6OqjGmN2/eRCaTQTabxdramhq7yuZCTgtk8MHghYMkeKiY2+1GNpvF3bt3kUwmsbS01BU0joyMYHh4WK1v7hvyOu12O5577jl88pOfVMxorVZDMplUrFStVlPT0Q5a9x4GnojMiYaapimHT9d1ZDKZB/5GlqjQgFK35UQ8YCuosdvtCAaDiEajcLlcyvFfX19X92mn2G/50SHjd6Ixpi3gvsPvYBhGl16wfEkSY2ZIe0MWk04hbY257l3TtoZ80Mnm/mD+vO0Y+P2SHQMVjtP2er1K7wGo7yQDGF6j/D/PXWJ56NTUFGKxmDpHhAfnuVwuhEIhNayD45A3NjaQy+UQCAQwMjKCUCiE+fl5ZbPX1tZUJlpeF7CVwWB5L+0SHaFsNotf//VfRzwexze+8Q0MDQ2xlPQJTdNu7lZ2OwUzS9Q9/rBMJxAIqHM4ZG8tsJUhkcSLw+FQrDiDDnMliCR7zeQuZcbfbOjeLngZpH1vkCGzp8CmbK9fv450Og273Y7JyUkVyKOPutfpdJDNZpFOp1XwItsjuO9lMhk1fIpn1HBvZEAMbAUvc3NzmJ2dRbvdVoHHxMQEpqen4XK5MDc3h3K5rIgKVunout5FHqTTaTidTiSTSfh8vgcOVpYZQ+4Xuz3faif56j8H8JOmx34TwLcNw3gCwLfv/3/X0HVdsSYU8MPqkOUPMwL8oYNNo+Hz+RAMBhGPxxGLxR447RvYCpQajQbW19exsrKChYUFdW6FPPXbzJiZmQ+WoDHt9ou/+Iv45je/2fV919fX0U/57RaSxSHkdyObEAqFEI1GVSkPHSAOSTCn5/m+kuHcDft10LIzBx805Ky3llkm6hB/98q0ENsFF1KP5WfK9+UPNyUablmzvJ1s91t+vM88lffu3bu4efMmLl++jA8++ADXr1/HnTt3kEqlejLbMgNDRlgO6+Amy8CQzC0Nejwex8TEBIaHhxGNRrvq7XvJm9eaz+dx7949rK6uqkl6MvNoGMZArVs6cnKtSieFpTLbNYfKYPlhRoJONvdIfrYcGrFTHIT8ZIZJrkPzepDlSnJNmTOiO8V2gY7ZLhiGoYJkOX5eGnB5T0iyfelLX8Kbb77Z9f79kF0ul8OlS5dw+fJl5HI5dS0kCHgNvN9ynGm9XlcZGpbKVCoVVerJbAoPhp6ZmcH8/DzGxsYQCAQQi8UwMzOD2dlZjIyMqFHKfN9isajOg2J1A3VPTsBkNqtXpsjpdMLv9+PHfuzHlC03DAO///u/DwCl/V63ZjIK2BoVSx01T/Ez6wD1Qg5CYYM4fzRN62m3H1V+KActbKfzg7TvDSLoD5gPjNZ1HcPDwzh+/DiArT37gw8+APZB96TfIAkG2WfFMi9mSnlOXzQaxfDwMEZHRzE7O4uTJ09ifHxc6RcJaU4LMwwDoVAI8XhcHWLOMu7x8XFMTk5iampKre/R0dGuoS/UZdmCIeX5KLu0HR5JiRuG8Z81TZs1PfxTAD5z/99/AeBtAP/8sT/9PlqtFpaXl3Hjxg1VLsIbwrQ7HTqzkXA4HPD7/SpFrWmaOriHrI/b7YbP51MRYSqVUk3pAFRmIZ/P45133lFpXqbheK4GmxglywF0R5TZbBZ37txBLBZDu93Gpz/9aSwsLHR933w+T7n1RX67BQ0DN0nJCnKROhwOTE5OIhQKIZFIKCfU7/cjEAioiJ8bMjMvdK7Zr7Gb4OWgZceFT0MjmSq3242ZmRkVAAObRkX+u1epo7mUh+iV7mdGwufzKSMObOk4MxKsI5UGr1fQtN/y4zqpVqu4fPkyLly4AJ/Ph+9+97vwer0qKACgdIY17DTiLD3rdDpIJpOqnpvMcCwWQzgcxu3bt1GtVtHpdNRBqqdPn1bs0Pz8fBdLaXa0dV1HsVhEKpXC9evXcf78eTUdhaWTkqUfpHVLxrxXuUe9XsfKygo2Nja6zpiiIZP/p7O6naFwOByIx+MolUqqPIzrWTJoO8FByI8sPO8792jDMLomMsn+DZbtcG3thAR4XHAt80A4mRFkQE5nQxJIHD/8zDPPqP5NYq+yMwwDH330Ef7wD/+wayIQnRWWULIcSzq4nc7mFM52u42JiQnE43FVsUDCrlgsIpfLwel0IhwO46d/+qdx/PhxpNNppNNpBINBzM7OwjAMvPPOO7h58yba7TY2NjaQyWRQq9XgdruRz+cRCARQq9XUepfj1yXjTXvDUlRmbpmlfvfdd/H666/jr/7qrwCA6cl9X7e8RjbIA+g6bJfkF7MoDBhpO7ivcsIfK0RIzFLmQLf9kNlIs63hc5Ix74VB2vcGEezz5CCFfD4Pn8+nfuRAj2q1ygE0fdM9+locDECdIOlGfSkUCshkMmq/i8fjaqx+IBDA7Ows3G43XnzxRVUNwQFVMzMzSCQSavCAruuYm5uDpmnqiIHx8XGcOHFCVebY7XYUi0WUy2VV0sXRvwAAIABJREFU2dBut1UfO31yYKvPcDcBi8Rue15GDMNYu//vdQAj271Q07SvAvgqgG1HGEonyDyC8mGsNYCu+eiSLeSG5vf7Vfra6XSiWq2qqSbScPAaOG6VGyanJMhrlf/uxZjQaTM3KBH3Fe2R8tuJ7PYCaUDNzi9lw8VgzrxIFrMX48j3NNeW7xU7ld39a3ls+clAFNgaL0ldMk8pkv/eLsO0nf6aDYyUK+XH9+Y0JWYqzNNidup49Vv3yEKVy2WVMqYzye/OGfKSdZRrnO9hLk2RP8z0kYigsxIMBtXEl0ed7UC95IGaNOQ7HR2637r3sOs2l4rIayJr3SvzYt6v7l9bl34xm8cyPk5qJHo5RLtBP3XPbBskkWSG7FUxZ1X5/OPUpveySXLPkyw4CTI6qXKogswa8DqYYeh1n3crOwZ55XIZy8vLSKfTCIfDqnzJ4XAo3TEHLlJGxv0yJo/Ho4JEkjvsw2NwlkgkMDY2phx4v9+PRCIBYPM8GZ/Pp0gYTj3insZeHLlH9iJ/JEFEW8PP47owDAMbGxsAwMWxr+vWvMYkMSj3P5l5Mf8t0D1KuVeWkM+ZCSyiV3Ai/Z3HwaD4K4cBKV/+X2ZeSP4yuJb3lfsq+qx7tIU8H4VBCwNcn88Hj8fTNfqeuscR+4FAoGtypJwwSz+P+wLXNG0DM68kbH0+n3pvr9eLWq2GTCajBgGQBDf7R+aKpsfFnhv2DcMwNE3b1qoZhvFnAP4MAM6ePWuYnutiHch6k51l9MbUHIXI11K4HMFIlpdN/NFoVE09oHCOHz+OkydPqvGjtVoNt27dwsbGBpxOJ6LRqBot2ul01N8ypW+efEJjRFaTisLI2+v1PnSE7cPk9zDZ9QNkKilbGfxVq1Ukk0nEYjE89dRTmJ+fx9TUlNokpVMpF5Ou60phPR6PUvZ+sZoSe9G9XpDBCDefarWKYrEIv9+PsbExRCIR9X0k40VIx4VOkzl4YzpVGhz5OrOhdrvdGB0dhd1uV/0Zkj3brlztUdir7nU6HdWMX6/XVeaToz158i43UWDTMfF6vV3fkc7H5OQkzpw5o5xl6la73cbc3BxCoZA6CbxeryOTyeDKlSvQdR2Tk5Ndjce8N7yfDocDExMTeOqpp5DL5VQwRcabQWkoFNpRsN1v3XsYuEeyBEUim83i7bffxuLioppu06vklg4Q16rMzKysrOCtt95S5UPMtPaDHdsOu9U9Og908mU2XDaaS9tCx1z2m20Hs1Ms/0b2YcjPoZxZ0sN9UWbL2Gsigy2+N50QlmmwWb0fsjMMQ02U5CQ/OSGp0WgovaJd5Vrlv2n/gM3+sqGhITgcDpWdW19fx+rqKhl6AFDTA0le0J4y2OHzxWJR6V0oFMLExASCwSDW1tZUPw6b+Xl2hqZpKnshJ5FyzPDQ0BBarRaGh4cfWMv7uW7Na46ZDgBqX6EP0ys7z3+bJ+DRfzAHlL2IVH42SS7gQZ02k5aPg8P0V/YLMittlofZtrdaLdXHWSgUVIO62RZv81571j2HY/PA2FgshvHxcaVf9CmSySQKhQLm5+dx6tQpRczQL+P5NKlUStkC+qvUi2AwqCqV5HPSDyZJT12z2+3qrDa3240TJ050BdeBQADhcFjZIE3b7NuUR5Y8LnYbvGxomjZmGMaapmljAJK7eRNpYLjp0+mQZ1cwyjUbYArTfAo3a8ANw1DN5GxSY3Mv550XCgXcvXsX1WoVwWCw65RjTgehEsoSq+0mzcjom+UoZqbj/mv3LL+9QjKqlDsZo2azqWZwT05O4qmnnsLw8HCX0y5/pIMkx1jK0cH9wH7LTjas0iBWKhX4/X5EIhFEIhHVHLxdwCCzT/Ix82t6wWxkgE05RiIR5ThxpC+v92HvZ0Y/5Uc2N5/Pq7M2GPAxMyKzJrquqyCX9df8rpqmIZFIYGZmRm2M/H6dTgfDw8OYmJhApVJBIpFAsVjE3//932NpaQlDQ0Mq2ymDF2lANE1DPB7H5OQkEokEPB6PIkiArZHUzOrst+weF2TuzSiXy/joo49w8+ZNNbKakM6JdK65dxEMAiORiGrCltnmfqEf8qMzJ39IGpkDYvl6GvedsM7mNSj3A9n8zNfQ8ednyh44GWSxD9Msfz7G1/cq0duL7HiGUa1WU+Uh3LvkMBvaYO7lTqcTgUCga3/n2U2BQAATExMAgHQ6rd6fMuEaNgyjq19PTrhkvwt10uv1qjp6u92OS5cuodVqqTp7NvyzT8fhcHRVOTBzGIlEVAmqpmkYGRlBoVBw3r+3+7JuzYGsDCDkHi1ZcPM+IzOE8jH6R3wP+Xnydb0CY9lQ3eta5Xtuh0HxV/YL5ntnhvTzOp0OSqUS8vm8IhElocG/5+vvE7l90z2bzYZIJNLzOV3XEQ6HVRb+5MmTqNVquH37trINnDrG88xILEh7yYmdsjRTHjzOPYpkAV9LItzn86kpghxQwaoJ9t/0oxpnt8HLXwP4JwB+//7vv9rNm3DDcTgcePbZZxEOh7s2uPX1dTVph3W6zMYwC8K6eJlFCIfDanTc4uJiF0NTqVSQz+dRr9eRSqVUuprMl/mcBzoNspY6GAzC6/WqsaKcvuJ0OjE0NIRoNIrZ2VkEg8Ge56FEIhFsbGzsWX79gOxN4b+5GDkOmU1ectqY2+3uOlhIZi3YyC+baPuF/ZQd07zm7AmwNWHNXDZG3TI7OPw/0DvI2c5wyPchnE4nQqGQaoiVNaMygyNHIZI9NaOf8pPBi2SAZLDK2nqSEayJpdPHINlms+HGjRuKWOAYRupSOBxWTYRutxvhcBiRSATRaFT1HUm5ms80oGNGx01O1uN+w/2hWCyqvqP9kt3jQDKlsieL31PWE9NQUC8kwypPPZf6V6vVsLKygk6ng6effhqxWKyrvv5hJZGPg37IT2ZezAMMAKgghs4zSwNJstBwmteo2RGU7wl0y4HPmR0Vvo+sDKA9q9VqqieJZRby9VyvciJQP2RnGIb6bPPJ2wBU1qfVaqkSLmZmmAlhX4zT6USpVEIymVQZEJvNps5Pol52Oh1cuXIFGxsbiMViiEajXRnae/fuIZfLKbvLUcDNZhPr6+vI5/MoFApq9PrMzAyi0SjW1ta6slL8G6njhmGow5GLxSKq1So+97nP4caNG/H7ItmXdcs9jpk22j7uc9xreL+3W0vbZQbNj5mzg/w37wN/ZFaeP9SJcrms2O+HYZD8lf3Cw4hIMwlGn0ZWnHC9E/QXpqamcO3atX3VPXmtrGpg5YvT6VR2lwSBDMbMpW4y88dsFPcwAD3tAHVQDnZhsMLpvnKyXt+I7B0I5GvYbM5PaJq2DOC3sanE/17TtP8OwCKAf7ybD+eCdzqd+Imf+ImuBVkqlXDnzh0UCgVcvHhRjV29c+eOCmKAzTT2iRMn1EjPRqMBn8+nnKaLFy8C2Kp19vv98Pv9XWUYHGEIoMsokjlhkMWNJx6Pq0lHJ06cUOMifT4fIpEIAoEA/H4/YrEYvvzlL+Odd95RJxn/zu/8Dk8K/+xe5bdXSCeZJT80+MDWlLHh4WGMjY0pJbfb7QgEAl2netM5lAuEjtRuMy9f/OIX8fbbbx+Y7KhXrN+UYCMqe6eArV4UAEpmzCRyA3lUSReflw4qNwLeG7fbjaGhoS7nQJ4hQcjRgw6HA1/60pf2VX6dTgeZTAYrKytdpzvLci027/F07kqlothINjXTGfrBD36AfD6PbDaLlZUVVcPrdDoxPT2N6elpjI2N4aWXXkIkEsHY2Biq1apiWKWBpzMm+wd4sjodrnq93sXgc2zkxsYGfuM3fgPnz59HJpM59HUrM9ScvEb9ZBlDOp1GKBRS34/GRv622WyKLKJMHI7N08lv3LiBer2Oz33uc6psTDrnj0tA7Nfalcy92SG02+0q6Eqn06qxlmtCZhJkKZkZkimXPwAeCCpkEEiDz4lbPDiOATozNPF4/AF50qGtVqv47d/+bVy5cgXZbHbPsqMt5UAHMp90XDjyngfTUU9cLhfi8TiefvppFcT4fD68/fbbuHjxIlwuF4rFIhwOhyIfWPPebDbxne98B7qu49VXX8Urr7yCZDKJd999V63vcrmMcDjclQWtVquq/CwUCuHYsWMIBoN45plnMDY2hmvXrsHhcKhSXtkYzHvAk8R1Xcfv/d7vQdM0fPWrX8Uf//Efh7TNcbX7sm47nQ7K5bKSoRw7zWCaGS5Zns3rlu9j1klzeZi5r0L+0EGkgy0DGpmh5IAFjkUnDtrmDgJkeZMZZttN+8ZsopwCd/36dXUcwHe/+128/PLL+OQnP4lr167tq+7JawuFQggGg8q/5UCabDarSHZ+Zxm8mLPKUmfoy8jAjZ/H10vCiHbK5/N1ZfH7FbQQO5k29sVtnvov9vrhkuGWTaJcoLFYDA6HA2NjY2o2fTabVQ2C7NXgQXaBQKCrzEuegM7P4qbCzZZsJtNiAFRqkI2EZCw5JndsbAwjIyPqN5sRmY1hKs5ut+NrX/vaAzftT//0T2EYxp7l1y/QKaZhpgHjFDbWScrXy3Ix83vJptS94Gtf+9oDjx2U7MwlKNQdOTmtl3Mj2S2JXo+Zn9/OQWSZBO8FP6vXpCRZPnAQ8mNgLwdt8HEAisFlVrVQKCiGslKpqDQ1e2O4B5BE4PtyHGu1WlUbJNP32WwWyWRSnYPjdrtVIz+wef/Yq8RsjHnKHuuB6Uj90R/9EcbHx9Vmvx+y2yloPOj4yLOTzBk4OieP6JlQ/2agw3G+3Bd7revHWc/7pXsMBpg9ksE/vw+w1YDKbDwJFLmPmde41F3p0Dzse5vfQ5YNyb9juROvW/bSyOvqdDr4rd/6LZw6dQrDw8N9kx0/x+VydY1tlrrAe0zHh+UhMnvF9cPrlUNbpO7wc+g45fN5Ve5K2yjLU2TPJR14kgosMTMfqGwe8sGyNq7rL37xi/jCF77AQR43DMM4u1v57US+vF4ZMJjxsH3e/H78bQ5ytitv6vVZvTIzALocTYnDtLmHiZ3sb9RJ2i4AyibbbDacOnUK1WoVjUYDk5OTOHbsGO/1vuqeBL+H3F/MGRUJqYu99rte+5t8TO695r1EXs9+YM8N+3v68Ps1eMAW80T4/X7Mzm4emjM9PY1arYabN2/i+9//PjY2NvDd734XqVRKHZ7GcjFd32zejcfj6oBBNi0Bm7Pu8/k8PB6POqEa6E73l8tlXLp0SR30s7a2hsnJSbzyyisYGhrCK6+8guPHjyumiRNYZFrsYanhQYJkErjxMns1NDSEiYmJB+rf6UzT+EhHlc4oAFW+Ih3qQQblII0E0SvzQkNkbgx3uVzKKEi24mE19zKrIvupgM0Ncnx8HF6vV51nwgY5GvzHmZbUL0iDDUB972q1CgCq1OvZZ5/F5z//edjtdty6dQvZbBbvvPMO3nrrLfh8Pjz11FOIx+N48cUX8eSTT2JhYUGlujOZjJoCWCgUYLfbce3aNei6ju985zu4cOECxsfHceXKFVQqFdhsNkxNTeGZZ57Bk08+qepvmb1aWVnB+vo6UqkUWq2W0uP5+XnMzMxgbm4OhmH0NOyHBZIojUYDU1NTSg8Z3AHdhIJs8iVkHwb1S/YikTXmRBmp44MElhHLZnBm0phZ07TNoRErKyvKBjDQ4ThQWR4qsybS+APbjyEnZE9Nr94Glg5tbGwop396ehqapinbxF5LOb6/n7rHbIvf70c8Hofb7e6atqdpmiICnU6nOqyVgYrX68Xo6Ci8Xi9u3bqFWq0Gn8+HyclJ+Hw+JQPZfDs/Pw+Xy4VUKoVvfOMbKnCKRCLqUEtmbVnHHwwG1Z6r67o6/LZYLCISiWBhYQFXr15V0xa5R9J55H3y+/3KDjEg32/ouq6cWmbY5FqTWUxOjTSvUbOT2KsntVfZjdRRGRhTJ2WPJHU7l8sp4jcaje67fI462Hudz+dx/fp1rK6uwjAMzM3NKZsv/QW/34+RkZEDs8vmwIPXWigUujLTvcpkzZknmXWhvlC3uC/xPYHuQSjyb8w+307IoMfBoQYvmrZ1MrIZDodDsZ6xWEw9zhnkZIFYCibTrZOTkypbIw0XAFVfGwgE1BkwFCbr9AqFApaXl1WPAY0OmwmffPJJzM/Pd0WgskfiKDjqhLx+yXyxtycYDD7Q8MeN1FwOJlPT/P9RgmRAzQyDnMTTy3hIJpKbQa/yk4d9dq9MDjcJv9+PZrOpnAM6HPLvZTnBQYEBGTdH6VBTZqOjo3j55ZdV7fzGxgauX7+ugkWWJo6MjGBkZASlUkk5c8b90k2ywDybpd1uY2VlBffu3UOz2VRDABwOByKRCCYmJvDEE08gHA5jZmYGdrtdNVmy1EROMIxGo4r0IPs8KOtYlq2yCZJBh5kFe1gJhNRH2ftAGTPLRRKGGKR13G63VY8P1waNqjTMDFpkX5NcI8wum9eM/K6SlNkO5rHC8n3kOubkMXm6Oa/N//+396YxbqXXmfBzubO4b7VvklqtpaVuqd12t3qLG3Y7seOg4zQ8DpJMHCSB89OT+EeMCRAEQWDMDGAnAfwrgH94ggE+NzCDOEAQwI7jMdxW0vDn3tXaVaqdrCKLLO7FKvLOj9Lz1uGry1KpFhZLuQ9A1ELyLuee9z37OYGAEvrkyf3iPWnUci6VbGDAdSDpwXQsp9OJTCaj2rH29fWhWCyq7zPlTE6M55pOpVLw+/1YXFzE9PQ0/H4/RkdH24bLmqaJdDqtvkPjhw12isUigE25XKlUkMlksLy83OYA8vv9KpWS+yIzBaisdcMJwbQZmaYqjQ3pieZ17SSix/1Vpvd04kfd6caoKmvE6Gjj+mDarI3O4HOgY4HyJ5vNwjRNhEKhtmdD0DF/GE5FoD1CzeuhAbKTLBBdr+NPq0hgJ5230zl2Gjl8EA7VeHkQrG6QC9Dn8yEQCGB9fR3pdBrNZlNZma1WCwsLC6qGQVqEpVIJpVJJGSb07LDVaygUUjn63KRZeMTN2qqtmy60truHXgE7tVnNFZDMb7X5c1Pl5ijvn0WxMu2sG96vvUJ6r7jhc6Gyiw0LV4GtTjAyZUoKEOmx0FM0CN0DIju4UVGjgi2jZKa52YqbKUT8PGt2uqV489xM06In2TRNxGIxNWdJ5mT7fD4cO3YMly5dQiwWw7lz5xCPxzE6OopUKoXZ2VlVYExjjQpKrVbD9PS0Gqw1NDSEM2fO4IUXXlDFiQAQi8UQjUbhcDhUoXSrtTmU7/z58/jyl7+sPut0OnHy5ElMTEyornK7bd94EKC3lj362XaVhlinaF4nSOWGvEIvvC5YaBj0ijHHYYrFYhGzs7NYXV1FLBZDIpFQz7/ZbCKfzyOdTquCdNnSnTPAdGMDaN/Hd1JcKusKpDFEhdPv9yu60qDKZrNtaWwyslYqlVTEYL/AqEs0GkV/fz88Hg8KhYKqWWQ0StaBsuEFa0vJewMDA/B4PHC73ZiZmYHT6UQ2m1VpcawnyuVycDqdqtZnfX1dpWLn83l4vV61Ljc2NrC4uKiK+Om4ZLtu7oNMGQXQ5sThXivTHtnoYzf1WrsBz880RWnASOOWDlsZVeH3gfsVQvnSoy9WeoaVrKHiTZoAndPG/iOi0xpvNBqYmppCLpfD8vIyZmdnUSwW8eGHH6qOXnyGusMnGAxieHi4o3P+oEFj3uPxIBAIKCe0Ve0KIeWIrs90+ikdR/L72+k8j0TkZbegUAiFQlhfX8fS0hI2NjZQKBRUSJRpUBQC+oJnj2rDMFT3k3A4rDqXlUolFdlhZ61gMKgm5QL3Czz5v6MAbmoUrgS9cvpGTOhhbZkaxs2ZhfvsznVU6CI9krrxEg6HEQwG27y1Mn1BFyi68LGCFEzSG04loFwuw+v1KoOQSiyNF0YRGS1gbVg3FE1eMz26/f39aLVaqkUqh9HJKAGVycl7KaGhUAgnT55URcGpVAo+nw/lchnValUpXjTearWaSvliNPTMmTP4pV/6JYRCIcTjcbjdbmSzWTXvKZ1Oo9VqqeM88cQTuHDhAjY2NlAqldBsNjE0NKSun17ow/Ka6SCNSQvyIJW57YwXPSoglSM+PzZOkcaLLpx6xXhpNBpqzgL3bafTiZGRETgcjrb0jnQ6rWYNycgpU7OALbpYpY/txHghraSiLD2cjCQwusVUSJ/Pp1JQ6TxitEHWY+0HqMCEw2FlfMzOzrYpOHJPZwSBqak0CLh+4vE4yuUyZmdnFf/RMUBvfzabRavVQqlUUkYFU6ry+bwqvGe0KZ1OwzAMxGIx1XgikUjANDc7hnJSN40kOhFpHLC+hrWpshanG7KH10CZyf1OtsXm34wSc+1JR5d+TL4nHVtWSiV/16Ox5DvSjbKLjkk78mINPs+bN2/i1q1buHXrFt577z3lYGi1WsqZLetg+V3OhXtQJ7f9gs47zICRxothGG3t2vU9j9DlyXYGs5QPVsbLdte4Vxwp44WKBQ0SwzCwtraGQqHQpohw7ovM/dYFEXt0OxwO5HI5tclSgLNTkVzcVhuD7lXZr5BYN0CliEq6HPjldDpVSF5X4ijM2NWKoNeMi6NTjm6vgoJCN85k3rG8F3on9boX3Zh7kAGjg+lXjKRQKAHt6WF6brM0pg4aPEc0GsXAwADGxsZw4sQJAFB8RJr09fVhcXERXq9XpZ/09/crwTo1NaWiS/Smnjp1qi0qyOGWoVBIfZdTvQcHB9umA/PcnNnEhhzFYhHNZlO1YDVNU7UIp/eZvKt70noBUtkkT+gh+048xs9SWeRnqWx2qseS3b16wUvLWSAejwdTU1NtexaAtj1fRls6PU+5fwPWrcs7QXrXdeWFtGbUHoBSIil3mJ5Lg4GRREas9wM0rKhgMV1I5q7rDii2xud11Wo1ZDIZrK2tIRAIKIeUjKZyn2PbZNaCsssRa0xZJ8n5LLxGGQ2ks6NQKChjh0bq+Pj4ffLXNE21f9CA5fG7tR/qIA/IdfegjAwpL4D2wavyM9LA7qQgMmIlI2qU5XR0SeecjS2DjhPiS6US7t69i9nZWTUxHgCSyaRyzvp8PuXoldkodLB3y3jRocsxq8gy/35QqUMn+bIbZ+1+omeNF6toBrviyEnF+XxeCTCmUOhWpPQ4MJJgZYRwo3M6nWpWDM/F9/VIxE6EXq8q7/SY09MmN3yPx4PBwUGVZqB/LxqNqpkAQPvMHpmqYRWe7FVIHqDRQoNWGsz8LAU7NzJ2emLqijQ4tuMTKpF8n0p/rVZDNptVygTQ3raW5+B3qGxYRcv2EzTaTNPEY489hmaziWeeeQYvvfTSfUXK+XwepVIJly9fhtfrxZNPPomBgQGMjo7C7XbjnXfewV/8xV8gk8ng9ddfx3PPPYdEIoHf+Z3fwcbGBu7evau80evr64jH4zhz5gz8fr96Rvl8HouLi3A6N4fb+v1+RCIRDA0NwefzoVAooFgs4vr161hcXMSlS5fwzDPPqOhsq9VCLpdDPp9HIBBAKpW6r2//YYPPmBFg1uJZeU71vYc8Rh6WdV18n12cdGVmY2MDq6urCAaD+5rKtFukUim8+OKLWF5expUrV3Dz5k01QwiA8n5TgWb01+/337cPUXATeq0aZYcO6eCgI0cKcsoJp9OJaDSqDM1araamxDebTTWTgbQfHx/H888/rwr49wv0vhqGgZWVFTUzjffC6yUNH3vsMYyOjmJmZgbvvvsuisUiVlZW4HA48OSTT+LixYvKAGL0NBgMolAoYHFxET6fDxMTE4hGoyiXy8hkMkilUrh48SJ8Ph/S6TRyuZyin27shUIhpThWKhUsLCxgdXUVp0+fxssvv4y1tTVcu3YNq6urysHBNtgAVDdDpod3G1K/kB0NdyILyVtco3Sc6WlmgPXMDcoH1mcw2ler1VTkzO12q8yTXljTvQDTNLG6uopcLofZ2Vn85Cc/wcrKCu7evYtcLqf22kgkgqeffhqxWEzJuuXlZdVqns4J1l5SP+o2KCu4NqSzQeoi0mm9HXTDRDpruffJKEw3jJjekc47AIkjiUIvC724Vpak9FZLA0SPypBB6S2mh4Kw8nQeZdDLxdC27tWRaRD695jKJIv2dYX9KEVdJCQtrEKiQPtgJhnKt/KG8fM7pYWezqMPFeT5pNCX5+kGf/J+GA1JJBIYHBxsK5Jl6lelUlHpJXKjpPe5XC5jdXVVfc7tdqO/v1/lx7daLRSLRVQqFZXOwsgfUzAWFxfbPF+6A4N584VCQQ3KlLTkDBryc6/xrnyuerhfd8LI7+g8qUfxpDFuxTeybW4vRF6YvtloNNTMLqZYAmhrVytTMK1SV/VIue7N1o0biU700p8J6y+4nukNdzqdbY4xGkGcXr+fDh+ZAslnKVOZZITANDejkcFgUM1oYESFMlbuhzw2eZJrT8oWrjM5sK6TfOY1Ua7zvFTGotEoqtWqJX1k5NvKEO8W5LrSlUU95auTR9wKO9nX5feZ+iz5WEZedqK0PuqQzsZSqYR8Po9sNqtqsNj0g47eaDSKVCqlZjXRUJQp9NxHOFrhMMA1Jfe9Tny3Ex6QeyMd/FZO2W7qxz1rvEghy7+lZ5z/A7Y2LW60UmBLgU9lSSeuNIjobZO5qmRAfePXr/dBYeFeA/OKqZTwJ8P6S0tL983AATaLJYeHh1XeNiMz9HIFg8EjuTHqC5neanqwyCP09MpGBy6Xq83Lws2LvwPWnjKeB9hKxeIcBCr2zJmnZ5JeZOaj68roQfMeFS2+5IThYrGIt99+G4VCAfPz81heXsbg4CBOnToFh8OB6elpXL9+HZlMRg2VpTf3E5/4BE6fPo1YLKY8sgzj//znP8fly5cRDAYxPj6ORCKBz3/+87hw4QJCoRBOnToFwzCUMlssFpHNZpHNZvHRRx9hdXUVMzMzSKfT+Pd//3cV0Tp9+jQCgQBu3bqFmZkZTExMqBbqveKkkPOm9D1R93pZGW91gd/oAAAgAElEQVTA1v7HvQ1AW+Smk2Om2dycVN4rkRcAqubhk5/8JE6ePIl0Oo3p6WnldCItKMDJn1R4SQMpH6TwlYq5lRNHd5Ax3ZCyQ6ZTsXkF60GYguV0OjE3NweXy4VLly7hwoULiMfjKvVpPyGNA6au8R4lbzFzgd3cEokEPvOZz2B9fR2ZTEY1CLl16xZarc0Bsky7LJVKME2zrXFCsVhEuVxGvV5XHTxZIzA8PIxqtYp8Pg8AKp2sVqthbm5OOSkikQjOnTvX5sVeW1tDPp9HLpdTz2Z9fR3FYlEZWjRgGW3oBqQs4NDncrkMAEgkEm0DJIH2GRvSCSXXstRLpAHSiUeYWkrDT3YU477Nwu1eSQXtBjoZh/V6Hbdu3UKhUMBHH32E69evo1QqYWFhAQBw7tw5JJNJ9Pf3Y2JiAn6/H6lUStVgGoaBX/ziF/jpT3+qOvE5HA6lGzFltNugg1nWOpE3dGer1CH0PVHXwyU/UieRw3gpe/Q99yDQE8bLTrwOnT4vX3woXNwU5EC7x0FaxgBU4RofJJVWbigyjaBT4epRMFZ0cC4CjT4Z4qYwiEQi96WmuFwuRCIRAGjrysS87V4q8N0N5HXLIky+R+8pPTbAVtctuaitPL1WfCI9GjwWC1xl3Qc3n+2M6W4YL7wO+aJ3u16v48aNG0in05ifn0cul1Ptkk3TxO3bt7GwsIArV67go48+Qn9/P1566SX09/fj+PHjGBwcVIoMab2+vo6pqSn89Kc/hdfrRX9/PxKJBJ566imcP38efr9fpcWQF3O5HJaWlrC0tISFhQUUi0WVx8zZTclkUrVunZ6exu3bt5Wx3kv8ywjcg3LUZTRQ31Olh5zOBqm4bBd5oRLUS4oOZ/OMjo7i8uXL+OCDDywL5mX3Pul5lGtVj1bxPrn/y5oO+Tm5ZqXyKWWMz+dTQ44BtCnZ5XIZLpcLL730Eo4fPw6fz3cgnlpp4HLP4vVRKZZDH1kXk0wmMTk5iWaziampKeUQyGQyau4V17wcWyA7H8qUZHZo6u/vV53EVlZWlCx2uVyqIBrYmid26tQpTE5OIp/Pq2YdTL9j3YFMZZVGPiO03QTr0uiVB7bqy6ThQr2i09qzSoEn9Gi7BJ8rW8xLY5yy5T9S5EWnr6Rdo9HA4uIi0uk0rly5gvfee0/pL2zvferUKZw4cQLnz5+3bOIyPz+vShqknPb7/YeWNsY9Ttb66ZEY+VnyBHB/KruEVYZOp8jrQdeN9oTx0gl6WF9/D2j3ZusLUf5NT5wkPCMKst2tfIB6OLuTUn4UDRdazOFwGIVC4b6oFhshhEIhy2FaTCehMKSSyboDeneOKvQIn/SK1et1NSuE+aS6F3ynkFEX3bNGfpReOOaEsyZGCsNuGC76dXNQa71ex+LiIpaXl5VHT26gLNZn6sjAwABqtRqGhoZw7tw5pFIpJJPJNnpS0WY+eyKRALBV+zYzM4MrV64gkUhgdHQUjUYDd+7cQalUwvT0NObm5tBsNtVclJmZGWUImuZm55+FhQVUq1XMzc1hfn4e8XgcxWJRdfc6rLC/RCqVwnPPPQfTNPHcc89hYmJCFa3rdTk673Efk/V8ANoEDYA2xd/hcOD06dP4/Oc/j1gshjNnziAejyuHRS/AMAxVOJ5KpTA2NqZqHagst1qbzRrYwUqmNFrVsAHtjQ3kWqIxrRuFcr3TAUavNq+x1drsCMi9dGlpCaFQSNF1YmJCdeU7iPVbqVSwsrKiBjTTQ8w1wGiP1+tFo9HAysqKMmJSqRScTifC4TC8Xq9Ku/T7/SrKQj6q1WooFotqb+L6OX78uPLCrq2tYWFhAYuLi1hbW1ODUePxOHw+n7ou7netVktlCCwvL+PWrVvI5/NtaZ/kXXq5V1dXVeSL+003QZpybwTQ1jSCiqIuV6j0SblD5wH1EGl0y+/p6xjYShuTIw94TvJ/LzkkOsnNva4Jq4yYQqGApaUlrKys4KOPPkImk1F1XYlEAidPnkQoFML58+cxMjKi5n9ZgSmRrMOU++xhgTzBvYhDfKVOK3lId+RY0Ux3yjLSrOsg0lF2kOh542Wnn5ORA0J/GPqmqFvkUnDJAmp63qyMl6NouBB+vx/xeFy1mJYRhlqthpWVFeU5lGBkSkYfpAHDDlm9kmbysJDGGXD/4Lpyuaxaacvw6G54QU8LoBeSx9LrDZh3C0ANcTNN8752hgfJl9LjLGcj3bp1C6urq6hWq/elKjCNgm0bx8fHEY/HMT4+jpdffhnJZFJt/FLIMzoYi8XU4Fmmo3z44YcwTRPnz5/H6OgoarUa3nrrLczMzGB2dhbz8/MYGhrCs88+C8MwcOXKFdUNifUzN2/eRDAYxLVr1zA1NYVQKIRsNguHw4FQKLRvXZ/2grGxMbz22mvKSz86OqoM6AcpvLqSw3Uq/wegrQDT6XTi0qVLGBwcVA4Or9eLVCp1wHe6c/D5BINBjI6O4syZM8hms3jnnXfUMNJmswmv14uhoSEEg0GV3sA9irVTUjboaWP8KVMopBDXoy8A2maRRKNR1eAkFothaWkJMzMzGB8fxwsvvIDHH38ck5OTqpXpfsM0NwuRaTBwRg7lGAe01mo1rK6uwjAMLCws4M6dO6jVaura4vE4XC4XpqenkcvlEIlEVB0AacICZwBqVtLIyAhOnDih5vPQeMnn86r5RDAYVE4ZOoXIp4zyDw8P4/3338c777yjmvbQ0cFOY8FgsC1tjNfXbeOFexZrskzTbKt9Ara83XJ9ArjPodBoNNpq8aSxSMjfpTLJFEG2mKZzUa/T6CVIw0zSaq/Qj7G0tIS33noLS0tL+NnPfqZmL7lcLkxMTOA3f/M3VYSQadqdlHGmh3IQLfn2MCGzFmq1mhrqy+uS2UT8qZdj6HqE/jd5h3uodOJ2qjPcT/SE8bLdDT7szXey3q0iODv1kOsKpr5xHFUDRranlW2idejeSJlmIA0XLloZKTiq0A1U6X2QeeMUBtID1ikyJ3nFynOmF//r1yOjgSz+Pcz0JhlZocfZMDZnNXi9XlQqFayurmJtbU15tWictFottakWCgV1HNaaOBwOy65pdCYwMsKOYblcDsViUXmWWRQNbM10cjgcyjtGg5CKJg1xdunppenTfr8fAwMDSkiyzkAqI4RVGogVf8goNGkqhQ27rjH94TCLTzuB6yQQCCAej6s1SYOfwyjpfZfGB6Erj1bQ1+p2n+M5mJJDhQjYejY0msLhsFLyD8pwkR3mZIqndDrpk+G5F9XrdRXloLFXqVSU0ssBlpVKRaUosRaAhj+VbcMwlAHZ19enhiaSLnR4cd0xDaxer6sBgdlstq1Oa7usCOnEOWgPMKHLR9M01T1yr5INbnS5QsgIjJQLD/JmWxkx0rFrFZXtNWyXbbMf4D6/urqKpaUl1QK51WohEokgHo9jeHgY0WgUwWBwx8OKqR/xOffCXtlJH7GCjCDvxPnJ+9X1pN06cXeDnjBedgrpObQitFUYVgfft8o37eS15iaiF2nL6zqK6OvrQzKZxPz8PAqFAnK5nMrTlDmbzDFmgfb6+roqmGTnJir0wFa4+qjm05LPZL0U+aXZbKJQKCCbzaoCYSrjemcX4H6e0iN+UkDJojn5PRpMfI8FqpVKRXmTuwlptLDehIMx2crYNE18//vfx+LiImZnZ/HP//zPyhMdDAZx8+ZNvP3225iamkKlUkE0GsXZs2dVC2VOGaeBTKPC4XAgmUwiGAzi5MmTOH/+PMrlMn74wx+iWCzi2rVrKBQKCAaDOH78OAzDwNTUFExzs3j3scceU8/P4XCo+RE8dr1eVwXDQ0NDXaVrJ6RSKVy6dEkphQBUTr1sAWwV9pdNOKjMULAyvY/PkrVGhmEgmUwiHA4rgcTC917E4OAgwuEwbty4gX/5l39R6VHlchmmaSKZTCqFmbU7sjOVNGo6GX/bvU/wOVDB9ng8yuvPOWTAZvH2wMAAxsfHcezYsQObBbGxsaGcBvF4HKlUCoODg/B6vZienlathDnTTEYswuEwSqUS/u3f/k01D2m1WmpAX7lcxu3bt9WwybW1NfT19WFychJ+v18Nnp2bm8P09DSi0ShOnToFj8eD4eFhlMtl5PN5ZDIZrK+v4/r16/ddO42n733ve/inf/onNYtN8jAzIiijgE2e93g8bSllBw3pACiVSqrBAVN8Y7GYGtpLSO+3dJDyPa5h8lIoFFL3ZSVXpGHucrnU8GLZal4qtL3mdJX3dBDX1mw2kU6nkc/n8d577+HNN99U6aXhcBivvPIKXnnlFYRCIQwMDKiotlUaqYR0JCaTSeVM6ZbRbAX5nDulcUnjng4G+X8ex8qRynVH+kjZQmPvoPXiI2W8ANt7ymS+6HbQQ2HbpYHJ88nJuY8CqLCwPoV5kcDWZkzGZjE+sFVArIf4uYE+CnTqZNTSkKjX6/D5fEqJlMLHyovW6dj6752uRT8ujavDirxIASg99kzTcTqdau5CpVJRnYY45G5jY0MNoZudnUW5XMbQ0JDqfsdIji44mJvf19eHYDCIcDiM1dVVLC4uolQqoVgsol6vIxwOIxAIYG1tDaurq2g2m6p2ptFoKD5noXGz2VT8zfbOvRJ5YVtyCdJcH0JGyH2NuclAe068FFZ6mN/qnL0KOcCUQpX7ELB1L+wSqKdH7MRD+aDP6AKf10DFQdbBMFUqGAzu6zwXq2vmRG1GnwKBgIo2MlIqoylSEdnY2EA2m1VREFnH1mw2UalUAEB1K/N4PKq2J5FIIBKJYHFxEZVKBYFAAMFgUKWZsb6F0ZTV1VU1N4zRGnrE2cmMxfedFHc6eKioHXTaig5eE2Up9xzKWXaR1D/PZ6UbL3pqj2zWIo9B6DJLRv0oJ2RjiV6ElSzcr+s1TROVSkU5XjOZDJrNpqq3Gh4extmzZ7eto+4Eymefz6fSIA8bD6KhlRO+0+esMkZk9P5hojb7hSNlvOgecUL3avOznY7xICVRfwD8v1VEoZc3ggeB3py+vj5loEhPJAVuJpPB3NwcBgYG1ERZpt1wU9zY2FA90T0eT1u04KhBKh8svqTQkMYL0+6oMOlGhs5nutFMSEEl0wWArTbgeo4yDUh6z7mZdMPTWK1WVeeu6elppNNpDA8PIx6Pq8gcPbiMFMg8e7ZKvXjxIoLBIE6cOKGUOX32UjKZRDwex7PPPguPx4NCoYBbt26h0Wjg6tWrqFQqKjUIAMLhMFwuF8rlsupuxPSWSCSihNT58+fRaDQwMzOjPKTNZhMDAwPKIO2lta3XYfBZs2aB9Shy/9KNEul9k8oSFf9IJHLoudp7QSgUwtmzZxEKhTA1NYXFxUVVJC8VW+B+IWwFGh9S4dOVGendZE0DDSSmVnG9pNNpnDt3Ds8//zxGR0fV4FkrubMfvOd0bg7J5D6dTqdVnYhsb+52u9v2c3mP8v70a6NhJD2xrNG4c+cOvF4v7t69q5p4sKiZoNEEQHm5raaCy3Q7vRCanzVNUx2bvM69uVsgT7ndbtXEpFwuK0cJ5YgsqKcCqPOVdAQys4Fp2boXXXrQpZLJVG6+yM/kVSt6HiZarRaWlpZQKBSU3HW5XKqFOLB7o4ZpkB999BE+/PBDTE9PwzRNRCIRvPLKKxgeHsbp06fvi4Y9KArBz9KJy9qtXjBeOumw8rmT72TTKqv71OVGJ6OZx6J+dJD1Zg+UVIZhjAH4nwAGAJgA/s40zb81DCMO4HsAJgHcBfCfTNPMH9iVol2plJaebPPGz23H2NtZ050MG1mQrnvuOmF2dha/+7u/i0wmA8Mw8JWvfAVf/epXuWn9EF2knRWYBkaDRCrBwBYTcmhTX18fUqmUeg6kBT189FZLr/ZeYEU/AOgG73GxcwHKdqmsD/J6vW25nzJ0Lxe0HjWRgobKokyF0IsWTdO8z3hhKpVuvFCwzc7O4vd///cPhPdqtRoWFxdRKBQwNzeHdDqtDAPOeKCREgwGVRci3hdbJzP/fXR0VHkmZVtoj8eDZDKpNsChoSHcvn1bnfvmzZuYm5tT7SzZFcnhcGB+fh6Li4tKoZJK/8DAAF588UWUSiVsbGxgeXlZXTvnFuXzeXzpS19SRczd5D0dUrnRFVyPx4N4PK4GqjF1U0aqZBcY/bgAVEF5KBSyNF7kXrsTHNa+19fXh1OnTiEejytPfCAQaEv3lQrxg4x80n27mjb5bJh+J4tgabxks1mk02m8+OKL+PSnP63SiPg5aZzOzc3h937v9/a873E9ECyyt2pzTrlqZbjwWFaGry5L6bhgehm7ODG1jMZ2IBBoM154fDkLRUYZdJnOfVAfhLm+vo65uTlsbGzg29/+Nvx+P772ta8BgLNbMpf7WqFQUM1LZH2pHvnsZLzQACFN+dLrLAG0GZE8ptRZZNMXyhqrVKLDlrnLy8uYnp6Gz+dTdVOc1aN7+B/muNRlrl+/jsuXLyt5GgqF8Pzzz+OJJ55QTWX4eQBtNUpWirt+/B/84AdYX1/HG2+8ga9+9av4kz/5E6CLvKdD6ibS8S8dM1ZruZPBptOAjm697rJer6v654PCTtxsGwC+Zprm24ZhhAD84t6D+D0APzJN878ZhvF1AF8H8KcHdqXY2uT1wjz+lEaMFZNbhcm2+1v/vp5X+iC4XC5885vfxNNPP41SqYSPfexjePXVV5FOp4Eu0247UEFn4aisK6KyzrQBgrSwegGdBzI+DKzod0+gfR0HQD8ZOZGKCwW8/AyNt7W1NVVkrodRrYwX6c0hrTvlZsvCYyqlrDOpVquWHjee4yB5j0K11WopLyO9imzlScPP7/cjHA5jbGwMPp8PkUhE0ZRpICxkzefzqNVqCIfDSKVSMAxDpSYuLS1hamoK8/PzSkkPBoNIpVKqNgPYMsilMKd3mecxza2J4SzO5GdooNfrdfzlX/4lXn311a7w3oOwnUeez588KQ1hYKtNPF+kgZxGb2Xc7BaHte95PB5lFC8tLamIWq1WQ7PZvG+dyIiKlBukhzQY5fqVv0vwHPw/HQyNRgMDAwN4/PHHMTY2hmg0qlo38/jy5Xa792Xf47ph/c+NGzdQLBZVjQujnDQU6JhidyrKARoGcn+TRrTcH9nNj/QxDAN+v1/VagBQ9SkyhY88adUxUT4nXfGXkSLS/uTJkxgYGMBnP/tZfPvb38ZnP/tZABgC8L2D4j0qsLpuYhVJ4ud1Q1jenzSAdb1Dfpf/140XrmdpoEi6dnLwdlPmkr/q9ToymQxqtRpmZ2extLSkIi+BQAADAwNtRvjD6hW1Wk11uMtmsyiVSkp2jI6OttUSEQ+zF0pj56WXXsLFixfxxBNP4Gtf+xp+5Vd+BThg3tvumnR+pJ4h+YfQ96FO+rDOY/J56MfYqw64HR5ovJimuQhg8d7vJcMwrgIYAfAagE/e+9h3AfxfdMF4oTDQ0yMIKXQ6KdBWtTEPMlxM01QdVXZqvAwNDamiX/b1Z3E8NmnGn/8Xh2i8uFwuRKNR1YKXXgmGrCuVimoNDLR7dfQ2yfrQwr3Ain5Xr14FDpD39HszDEMp5uQtpo3xbxp/nYST7qnUFUkqBrJw1zRNVKtVVKtVOJ1OlMtllbueSCSU0SRfVGCplI+NjbXRbr94j0rZ+vq6qjOQkRd6+fx+P5LJJI4fP47nn38ehmFgdnYWpVJJ5b77/X5VxHrz5k1cv34djz32GJ599lkVNTFNE7/4xS9w+fJlFAoFLCwswOFwYHh4GE899VRbvj6H5DFsz6YK/MlGAEtLSwgEArh06ZJKmwQ2FeBAINA2iLVbvNcJVB63e491a5VKRRnTQHv6A7DVuhpA22BCWWhpdY6HwWHte6FQCBcvXsTa2ho8Ho9KmWLRejQaVffOaINssmGlSHJN6bKG35H7HPcOHoczU5rNJi5cuICJiQl8/OMfx7Fjx5QXnueRytLw8DCGh4fb6Lcb3qvVanjnnXfwzjvv4MaNG6hUKlheXlaR0Gq1qjqCBQIBTExMwOPxYHZ2VnVhYpMBGv56jR35j47DfD6v9jKmNdIRQdpx6rw8BlO8rOjK/VgaAdzvSqUSqtWqon0gEMDHP/5xTE5O4sSJE4r3AERxgLynO/mYcsyGLjJdTJcHMnrCtSs94txP9e9JZ6HejYzPgFFnaczI/UHHfspcK689rw2ASqtbWFjAD37wA1VfBUClrCcSCZw4cQIDAwP36Xr6PXTap7LZLH784x8jnU7jo48+QjqdxoULF/DKK69gYGAAAwMD6Ovra1PKH0Z3Ie+xzXI0GkUikcDp06e7wntWoJ7C/d00TdXlUF4zecqqnoqQdJXOBWCr9lJ+lvTrqVbJhmFMArgI4C0AA/cMGwBIYzOtzOo7XwHwFQAYHx/f7XUCQNvCfZjviGvZ9v0HHaeTkNvuvDzn3bt38c477+DZZ5+lt7OrtNsOVGg4pExa7DLaRbqT8fUUKX6WnvX9ZFzS796gwgPhPSuDg4qhXmsgi0L1Ba1dw30pP1aeCSsPhoz6SK+sNKb0zdzqGvab96y8OpIW0jvL6AsHTGYyGQBb7WLZVpLCv1artQlc8h67IrGdt2EYqjCY7ZZlZy1JD3p+Q6EQwuGwSpPke8FgsM0D7PP52pSnbvDebiG9u9KQle/Ln8D9ex6VrJ22BX0YdHPfczg2u6/J+2GHMRkRkdD3LysHhLiutlxxfr5Tww5Gt2jIt1otVcy7085tD8t7knbJZFIN45ufn29bX7xO2WxF96TqESZJK/0zAJSzQTpmJH1kNIHH0GtpJM1lbQhpyWPK7/JzjCAx1WhlZQXvvvsunn32WQBwdWvdSh7gNdFA3c6jbyVDOkVo9M9YrXNJI/24O/GM74X3xsfHO8oj/drX19dVt1PuY2zP7fF4UKlUUKvV7lOwO0UHCK7RWq2GbDar5gwZhqEi7Kyn2Y2uoq8LGt0ejweZTKbrvKevS6kr68/bKpLHz+k01Z9jp9+tznOQ2LGkMgwjCOB/A/gvpmkWtU3eNAzDkotM0/w7AH8HAM8888ye2iLRC6HnLwPWURZp5FgJr07CzArcPLnxbwf9GOVyGa+//jr+5m/+pi38ee+zXaHddnC73ejv70etVkM6nUahUGjz3Nbr9ba5F81mE9VqVRXo37tWRZe+vj5EIpF9K1qT9PvGN77R9t5+0o/RJmmocUBiIBBQyksymYRpmqrIXC54fVPg/4CtFAeryIs0+GSxOmuIGAUwjM1uReVyWRlVvG4eU7YSPwjeI0+Uy2XVenJwcFC12uV9Mt2NCqRhGColhREOtk82TRMDAwMoFAoYGxvDyMgI3G63miHR39+PY8eOoVwuqy5v586dw8WLF3H16lXcuHFDGd6maapo4eDgoJpk/tRTT2FkZKSt7fLq6qqaGO7z+ZRRxrSFtbW1rvDeg6A7QwiPx4NUKqUmmq+uriollcKUfOb1elV0hlEvj8eDyclJXLp0CSMjI6rBAs+5FyF0WPueaZooFApIp9NtSrR0xMjuiNIYl2tTOmt0ZVtXKlutlvJq8hzAVpcnve5gJ9jNvidpNzIyYl65cgU3btzAnTt31P7FeyE/5HI5FAoF5PN5OBwONeeIaxiAanphBV1hMgxDzSDSGwBImkrHjR6VkE4jfTq8bI7A1B+fz4dYLAafz4fBwUGYpom/+qu/wje/+c2u8B4dS263W+2PAFTxdl9fX1uzAqv6At0Qpizie6SJjLCQ3roTSTowmKIrm09w7XcyovfKe88884ypG7r6XuL1euFwOFRXSnZno8HH73/wwQdYXl7GiRMncPLkyW2VZ4l8Po+VlRXcvHkT77//PrLZLPx+P86dO4fz58/j4sWLCIVCu+74p9dBOxwO9PX1we1248/+7M/wrW99q2v7nq5zslkIa09YN8ToqUwXlt+X+5o8pnxf3xclPz7Imbuf2JHxYhiGG5uGy/8yTfP/3Pt3xjCMIdM0Fw3DGAKwtNuLsDIYrG5cCplORoZeOCSFy4O8Hg/ybsgw207uAdjM73399dfx27/92/iN3/gNAKoIbF9otx/g8LtQKITl5eW2yfJkVpmfLEOSMlec9PZ6vcoDulfo9Lu3ke4b70lIbwV/Z/GlbNEZCATQaDQQDocRiUSUckIesUpJ5PEohGXBPgWT7JUu+Z91LkyblB5mwzDuU7z4TA6K98gPbHGaz+extrbW1lqbdJSFpgBUqg5bSvp8PpVCFgqFEI1GEYlEEI1GlfIDbCoByWQSHo9HFf4ODg5iZGREtVqW9UfkT6aXDQwM4IknnsDx48eRyWQwMzPTpuST7pIH1tbW8KUvfakrvLdbcO2Gw2HFr+VyWdGbPELjrF6vq5Q/KjWxWAzHjh1Df3+/ao4ghdVuhNBh7numudngolgsqrx5XcGWe7l85lKBJGSkQN/naeTwpyyC1QW+VEYfhP3Y99bX15FOp7G8vIxsNguPx6OKkg3DUGupWq2qlK/9BB0pu2k5rqc36so+09iGh4eRSqUQCoUwMjKiornf/e538elPfxpf+MIX+LWNg+Q9uYdTHgBQjUhoKEg+0++XvCfljzy+jN7oqe+6AaR/T9YsAVCZFlbpUfvBe9vpUgQjxnonMd4fm1/Mz8+jVCohGo3iscce29F+ZJqbtVsrKytYXl7GwsICVlZWcPLkSfT392NkZEQZvTuFlXPcKgPhr//6r/Grv/qrat/DAfOeFaijscED04Ilf7HeVu6N0jGj/49/8yd/l3uazn8HiZ10GzMAfAfAVdM0vyXe+kcAXwbw3+79/P5uL+JhhGOntC254GVe7E4WkbyOTgVIXAws/tzJPZimiT/4gz/AmTNn2HUCABCNRpHJZPaFdvsBKkCRSOS+gWkMu1YqFbUhM/JCr7hu0O1X6LAT/bCPvKefT88jpoJnlQYnF6o09KRnQh5bCiRpvOhFqtK7y+PJaCONQhbdWnmBTTXwwIgAAB8hSURBVNPEH/7hHx4I73m9XiSTSXi9XpRKJWXEcao9hQ4jM+Qvt9utwvaZTAZ3795FLBZrE+6sfykUCorugUAAk5OT8Pl8qu1qs9lENpvFjRs3AEDVyJBmTM2ZnJzE2bNnEY1GUSwW8eGHH6pIkGmaylBko4FarYZSqQSXy4U//dM/7Rrv7RbRaBSXLl3CmTNnVFodFVLTNNuUIsMwsLKygitXrqBeryMWi6Gvrw9PPvkkTp48qeqW9opu7nudjCsaC/TQ06iXThh+Tq4zabjINat7kPX9DoBS0qXxIqM92zm/9Hvaj30vGAzi5ZdfxvHjx5FOp9ucKzQq2FJcTwvudL+8J6aDcm0HAgGlJANb+xsjTro31soIlKDCRQNIz6Kgh7e/vx+JRKKtGcjf/u3f4ty5c/jjP/5jqUgVHoZ2DwvuIYy+0FChIi5pyfUoI4I6pPOHzgYpb/Tv6LVA8rzS6ct5PJyTpWdH7BfvPYz8j0QieO6555DNZnH37l2VWkzaLCwsIJPJYGRkBMViUXVIpSIuaSHvdXZ2Fu+//z5mZ2fVfZ8+fRpnz57FyZMnH7omlzzL++K6luf/h3/4Bxw7dgx/9Ed/JL96oLzX6Vr5kmueNOX61tPipfNWHkvnO6lrW9VpPSjIsB/YiWv8BQD/GcAHhmG8e+9//xWbD+INwzD+AMA0gP90MJe4Bfkg9PCWLoB068/K6NE3Uj5M+R6VRaZbyFQp+Tkr/OxnP8Pf//3f4/z587hw4QIA4Bvf+AYGBweRyWRe7SbttgNbpZbL5fs2MxoqnMYMbE2LLhaLyitmVci5V+PFin73BqMdCO9J/uL10xtvFV7XhQSP0cl4sfq+3Dj0z0ihzWYJvCYAanYC+V5Gxg6S9/r6+jAyMoJarQan04lisYhEIqFylNn9q1Ao4O7du1hZWVEF8qdOnUI0GsX8/DyuX7+uPGBs48rvMsTf39+vamaefPJJ3L59G/Pz81hdXcX8/Dzq9TqOHTuGX/7lX1a50uvr64hEIggEAjh+/LiaEfPWW29henoawWAQ0WhUtXKmgttsNlEqlTA1NYU7d+7gjTfe6BrvPQid1lIqlcLnPve5+7xlOi8x9fPu3bvw+/0ol8vKC3ns2DE8+eSTKmVjr+jWvmfleSYYGfR4PGot09C3ihZLw1ceX5crQPssJlmsLyeY85p048VKUdWxX/teLBbDF7/4RdTrdVVDkM/nVbevRqOhIqeMiMr7lsoy70nOs0ilUvD7/ZicnER/fz/cbrcyfrl30UiSUWZ5PJ5HylvSmMaLlaFISFlvGAYuX76MH/3oRzh37hy++MUvAgAjB4sADkzmsjkBlWS29mW9lbxH8gzpoaeHAVuKMZvD0HjR5Yx0aOmF+TKKaJrtw5XD4TBisVhbKhuwvzJ3px74RCKBz372s6hWq3jjjTdw+/ZtZaCw9XW1WsX4+DguXLiAQCCg0uBkZI/rjfx+/fp1/OQnP0G5XEaj0UBfXx+eeeYZfOYzn1EG5k6hr2v+T2YZVCoVXL16FYVCAa+99hrcbndXeG+7a5ZGMHD/nknnrL4XSh1IrkGpe7O7op5tQSeRTF8/COyk29ibADppoZ/a38t5MOTm1UkhBLZyQa1SACS2UzD1ENpOhQ/x4osvWl7jn//5n8M0za7TrhMcDodK37Fq+ysFrxRq0rMDtNN2Jx7GB8GKfs888wxM08zhgHhPN7qkl4/vU0hLry5fUsnZyf3LzcHK+8hrYLSFx6eiKSM0PKdpmnjhhRcOjPcoqJmmRCVDV9DYjtLhcKBcLqvCeyo/kUgEfr8ftVoNDodDFW0yh9jr9aqOSAMDA0pB0tuPsruYXOuhUAgDAwOqw5RhGMjn85ifn0d/f7/yFPMZSKXW6XTiiSeewMzMjOrYBhw87+0GhmHcFy3t9DkAqnW1w+FAIBBQKZH6zAddaXwY9MK+J4Wx/J/VviQVacB6DpiV8ajLIioJevReXstOaLpf+55hGGpdMN+d10jF0Ov1qrUsDThpVEheoGLi9XpVsXMsFlPDYen8kkX2pImeomJFS2m87Cbt+JOf/GQnGjcPkvdkNoKsJ5ANVwgrRViHPp9FZgPozlurqL2VY4x7HNNlOVBYYr94j8Y8u8NKGaU795hZUKvVsLq6quqFPB5Pm/Kdz+dx9+5d5XhiTaKs92w2m+oYKysrKJVKqitmMBhUjTx241jVZbSMvJimiVAohO985zv4xCc+gUQigcHBQX7+QHmv07Va7Td6NlKn9bgdpL6iR2f1YMJhR156BnIzIKTHmZszN2EqeYC1R1wXVno4jZ/Rc/itHsiDNqNehtvtVsXhPp+vjem5gcq8Y6fTiWAw2DbUTjKsVXrGUYAUAvybAkCmF8XjcdViVN4/cP/msBNI3pECiAYiO5gYhqGEGSMVrAuReeUHTXe3263SwDhJGoAq8mVI//z580ilUrh9+zbefPNN1UqSgylPnTqFjY0NpNNp1Ot1vP3225iamlLGIWngcrnw2muv4dd+7ddUvc/Gxkab95DPjMoSozlMjSiXy3j77bfxr//6r7h48WLbdHNga8Kwy+XC4OBgmxe51yG7rHXaf7h3eb1eTExMoFarIR6Pq85q+jwP4ijuZ8CWQiRbnsuUCRmtlC+5/sgbNIolLawUUknDTg1kDlKYdwJlJmvndC8r946dGFdyr6LzRnY/JCg/5d7F723ndDyqYFRzZWUFKysrihdY8yIjIJ34QBqKbDjC5glcux6PB/V6vU0nAbaMPcmzbMhAucHIC7DZlr6vrw/JZBLHjh3bd3qwhmpqagoLCwsq/a3VarVla9ChlcvlFA0XFxfVHCTWZXi9Xrz77ru4ffs2RkdH8bnPfQ6xWEzNLGLKXr1ex61bt5DP5/HBBx9gdnYW0WgUp0+fRjKZRCqV2lN0WT43zqihcQZsOYcOW3ZIpzOwlSUi9RgZGdb5UupCVk4M8rPu3OVz5d57kLrIkTNerISCbkl2sjrlZzv9tMp75s8Hee6OKjinRBbZy/vWjRFulFJg6XQ6aoYLoSsk0pDg+/R0SyVnJ940K2znPZPtb6kAMEQrJyTLc3eD9rw2eX1yI6QSyKL75eVllc7FqEogEMDQ0BDK5TLy+TwqlQpWV1eRy+Xa7osFh9ls9r78Zl4LAOXd5fs0Lum9Y4rM0tISisWiug55DD5vdjPbjxSqg8ZOvfmEw+FQEVYqnToPEUdZoey0b1u9dpLmqUdk9b95HLlX6OvysOSE3Ff2uxX2dueUP63ee5TA7k6yNhSApSGr00b+JO/I1BvZzUpG2zutWemAlQ4wKa+YAq5HXvYLprmZMpfP57G4uIj19XXl3GL0na9araaGVBYKBdTrdWV4Sbrk83mk02lsbGwgm83CMAyVUkfjvFqtIp1OI5fLIZfLoVarIRKJIB6PqzrN/eA/6aiVKfM0og56xokOq3Pp0V7Z7EHqeQ8TFbbaA62iiqTNQeJIGS8MUbdaLTXQiNEWCbm4uUg75V9u5wniopHKWadi/aNswPh8PoyNjcHv96uhg8BWVKtSqaBYLCqvja5YE3LT1D1xRwFUchnuNk1T5cbKHGKm2gC7C7tKWIVwrSIwMp2CP3lNzI0mzz/MINXdgHSiNzAUCinjly2QHY7NNsQ0KjisLRqNIhaLIZVKYWhoCBsbG4hGo6jX66pGhW1uW61WW3vQ2dlZTE1N4erVq1hZWcG1a9fgcrlw+vRpXLp0CeFwGOPj4+jr60O9XkexWEStVkMul0OlUkE8HsdLL72E0dFRpRjIOS/cdNfX1x86J/qwwPWmRwZ00Phld7VWa3PmCFN+yOO6MX1Uwfa0Ulmh4JZGtF7rQoMZsK7boxyw+r98BnItSo+mTDWRMukoR+5tbK4vprVyb65Wq8jlcsrZJFvySiWe6OvrQzgcRr1ex+LiIpxOpxosyki73+9va9QCbDkYAbQZLABUtDuRSODxxx/HhQsX1NrweDwYGRk5EHo0Gg3Mzc3h2rVruHLlyn2GmVSY19bWUCgUlEyhc7BarQLYqp9aW1tDqVRCJpPBe++9h0QigeHhYcRiMZRKJSwvL6NcLuPOnTtYWVlBvV5HJBLB2NgYnn/++bYBnLuBVSRVj27oBfCHhY2NDTXkmtcnnYuylXsnZ6fuwJewMlxoFNHB+rBlFg+LI2W8sLB8Y2MDgUBAdQzSPaj8XXqByVg7Dd3r3hBpoBxlQ8UKHo8Hg4ODSvGU99pqtdRsDRqCQLvyrEN6fY4SmFojQ55MDZONHPZrfs1uQH6UNUcM05Lfu2G8kEYs9mPNlOwYxk3MMAwVCWFXu3g8jv7+fhiGgZGREZWyODY2hunpabX5+f1+lXaxuLiIubk53L59G5lMBoVCQc0m6uvrw9DQEIaHh1UTg0qlgqWlJbz//vtKkD399NPweDzq+BTk0mCnwdUtL/VesRNDg8YYC4lbrVbbDCPZdpvHPKpgShONaX0vlwa+VV2MjDDvlA7SQJLfkfNkeHy9IHa3UVsbvQO32414PA632632YqYVmaapshu4b3Pgpoys+P1+RKNRNbOJzoZsNouhoSGVyspBo5JvyU8yEs5I8tDQEJxOJ86cOYMLFy6oNvVMIzwIbGxsIJPJKGcT16OuF3AtFotFlSJGJwpnC9HB0Gg01Hyza9euIRaLqdrGer2O2dlZrK6uYnp6GoVCQd3nwMAAnn76aQwPD+/6fqzWpVWWA6//sHWfZrOpmrTIyJBeLyWNZz3TQOqButNedhmTe5d0qPZCt7GegUxhkqEwoLPhws/o4VYej8eUx9Pz/KjI+Hw+lSP+KIEpJDJtTHoQqazKhaoXq8mFEAgEEA6HD1XJ3w30hcjnrtdZ9QIkn9Lbw2e2X6HxTuA5ZQc+DutcW1tTQqfVaiESiaioS6vVwuzsrJqYzHXHVqBTU1NYXl7G0tISSqWSSimgMtpoNFT+NO/Z7XajVqvh7t27KBaLaujlysqKagBw+/ZtNJtNTE5Oqnk5nNhcKpWUkcXjMnJ4VFMfHwRp+HaKIBxlkKc4jFMfxqdH0PU0HCtnWCd0Sp2Q0RkqXlQmmObyMMaRjd6Gy+VqGzUQDAbhcDgwMzOjis3lXi2bl7DzG42cRqMBw9isl0kmkxgfH0c8Hkej0VA1HozcyYYMcp4HAJRKJbRaLTz++ONIpVIYGxtTtYpsQ39QSrbL5cLAwIByKEm9TQ4TtupSBUDV6ADtzmLqX5QL165dw8rKikpPK5fLWF1dRa1Ww+DgIE6cOIFjx47tqKnJdrByMDDjQTYMoEw6bH1B0hZo1+e4P0n5ZlXXJ/c1q5RaK2e+rjceJI6U8cIFL4vHZRGSDEtKK15CTxGQD9NKcLndbpUeE41GVfciHUdZCHHjBbY2B3rMAai5GLKVphxSKQ0cAEgmkxgeHlaTc48KJD+Qb2jUPSgNbj8Uvk7eHav3DMNQA9kojNiik4LzoEBjt9lsYnl5Gel0GolEAgMDA1hfX8fKygpM08SxY8cwNjaG5eVl+Hw+FItFXL58GWtrazh16hTOnj2LRqOBbDaLer2ObDaLUqmEjY0NJRSq1SparRbu3LmjCvXr9To8Hg+CwSA8Hg/y+TzefPNNOJ1O/OxnP1PRIAAoFouYnp6Gx+NBNBrFyMgIyuUyisUi1tbWkE6nUS6XUa1WUavV0NfXpwrZWdT/KEF6Zzs5fo46TNNEqVTC0tKSMp5lHSOVDmDL0JdKlPzcg9aRlREilQYer1qtolAoKF4DoFKMbBx9eL1ejI6Oqn2v1Wrh5z//Od577z1Uq1Xcvn0bMzMzalBoq9VSURnC7XarWg5gc/7JiRMnkEgkEAqFVCczYJPH2U6Y+7F0tFGZNwwDv/7rv47nn39eRV915+xBgPOjSqUSnE4nVlZWMDU1peqCqE/IdDcAaoiuTF+lMeB0OhGNRgFA1UB++OGHqmW/z+dDs9lEsVhEq9XCpz71Kbz++utIJBL7oovo6aJra2sol8sqnZ7d9mQk/7DAyB/5xarrHCNzbPTA/U7yhmxApDv++Xm5T0rDzk4bE2AorFarqbxlPbRlVSwkGUlGCqSg0r/HxSQjDGSGR9EjK6NT0nrmveshQMnI+qJmekq3i9b2Clmrw+dPo8BqSOVhgkX87EAjfz/odCeZflMsFrG8vKzWxPr6OnK5HEzTxPDwsCp8p8KYz+dVgSZrXXK5nMp7rlQqbWuTBbD1el150sl7XIv0aDudTjVXgcKOhorsvENvJ42vSqWiBGo4HIbf728r/n+UIIVSp9TPo1Lvsl2USN6D3uqd0KNP292z/p7ubdRlDI8vZYqVF3O7c9g4OqCSLREKhVTTElnjoqf3SiWRiiDXJQdKcsAv6wBZuyVlFdN4mbpWr9eVQUMDvlvGMlOFo9EokskkDMNQERHDMFSXLirXvAdGoZgKDWzNw2FUA9jS42hAsAUyDRzDMJBMJpFMJlXnsv0GdULZTKGbNN4OlI+8PqvUWBovMgrDeyFf6s1M5LH4Po8hU693Wp6xFxwp46VYLOL69evIZrPIZDKoVqvo6+tDIBBQKSySsJJ4XADywUmjRxKcC4/dK5jmwt7hxWJRHfdRylXmBsGwNjeParUKl8vVlmcrc1MlLdnKloOkjhIikQgmJycBtLdCNQxDDU/shIN6/p0Uy1AohPHxcTUkLhqNKj6moDooOBwONSPgJz/5Cd588001sZnrkHNczp07h/X1dWQyGSwvL2N+fh6VSgW5XA5Xr15VQl02ASCk0Wx1P1YbpDTAnU6nGsgnjZ18Po8bN25gbW0NP/7xj9vqhU6cOIFXX30VqVTqwIpZDwukYyAQUANBI5FIW1v4o7SPdTKyDMNAPB7H+Pi4MoDX1tZUKo7T6UQoFLrvexTE3PckL+n8J4W/brjQscYUIAAqBYizjThkb7v7sHG0cfz4cfzWb/0WCoUC3nrrLSwuLqJYLCKXyymeYsQAgGoWA6BtjhVr+Obm5gBsGkVerxelUgmFQgHAVipoKpVCf38/FhcXUSgU4Ha7sby8jMXFRTWcV6ZJHrSceOyxx9Df36/qVejEYhYN04fpTKKuVavVVFfISqWi0i4ZtSSSySTW19cRDoeVoXL+/HkkEgkV+ZepdHuB7thtNBqoVCpoNpuqJX+vNCqq1+tYWlpSRfuyq5yUufJaJS90SofV35epi3JPtFsla2A/8Gw2q9I+qHCvr6+rlBMufFrGANqEhZ73J71y7AzC79CrKycFs3DdKg/yqENPvzMMoy1FjJ+RP/WCLaYxHXbe58OCqUL0eDkcjrauWr30jFnbwaLrSCSivFnd2EC5vu7cuYP33ntPPXMKpUAgoNoes+lDsVjE6uoqyuWyEroHBW6uXKNUAJjCk8vlsLq6itu3b6NUKqnPr6+v42Mf+5hKUXtUINco9zfW8Mn3HwUYhqHWRbVaxerqaltrVhrf0pElo4m6UWHlTdVr4wC0yRCmxPBzTNnx+Xw9WUNnY/8Ri8UQi8WQz+dx69Yt5HI5tFotFIvFNgcp+YGDGoEt48Xr9apmOSxqN00TgUAAhUIBc3NzqpaEdYgul0sNamTjklKp1BYZkhknB+l4SyQSSCQSlu/TGGk0GigWi2g0GiiVSqjVaiiXy8hms0rnq1arKBaLWFlZAbClaDMdLBqNYnh4GIlEAi+//DLGxsb2fZ3pdOI6N01TZT30QtQF2Oo2xswFq6Yh5CXuefq1b8cXsmkQ67horHC2lt0qWWB4eBif+cxn1DwIpopQyczn81hfX29TvmXOn7QO9YIiPc+P7UNp2DDHnouE33mUQI9+KpVSmx/bUycSCZXLyda3bEUqOxkFAgHVU/2gOpkcFKh4A1BFk71aVMuuXm63W3m0dIX9oOFyuTA6OorHH38c1WoV5XJZ8QIjbw6HA8PDw/jUpz6F5eVlvP3228hkMsrYIfQIHiG937qxLBVI3TsuZ8U0Gg2EQiE8+eSTOHfuHFqtFpaWltDX16e8oHxFo1EkEgnEYrEj0Sp5t+hFnt4vOJ1OTExMwOVyqandUkmjZ1fmfjOdUPKY5CudXjK1gi86DSg73G43gsGg8q67XC6MjY2p+oRH+RnY2ILT6UQsFsPg4KBKk2WtrkxdlE4n2WCIzlMaOuRVWQRP/pKOicHBQVUDqReR89iHyYNsgiSHLQeDQTWqgDWUNHDW1tZU5EWXBRwO2dfXp1L19vPerDIg4vE4TNNEMBhUhmivROv9fj+GhoZUDVGr1bpv7pnUhXX5+TC0Y+SF0RbqgGzHfVA4UsbL5OQkvvjFL6oNgCGxcrmsLHSZAsaprhJWxfmMEnDxu1wuhMNh1UqQRXacrD4xMaGO9ygJIIfDgUgkgsHBQTgcDtVucGBgAP39/aqo3+l0qnQ90oebZDgcRiqVwuDg4JEr2N/Y2GibgwFsdk7Tp+Xq4dPDABUj0zRVNIPPo1u1Gm63G8eOHUMul8PCwgLu3r0LACoth5va5OQkvvCFLyCXy8HlcmFqagrBYFDxh0zfpLeGGyoNIApufo6foddI97Ax95sdywKBAJ577jk89dRTME0TCwsLCAQCqqiVL86gSaVShz4l2cbu4HQ6cerUKZw8eRL1eh2lUgmNRkPJjOXlZWQyGVU/wJRCeq51A9oqbQxAm9B3OBxKgUwmk0p4J5NJeDwexGKxrtcd2OgNuFwupFIpVUy+sLBw394GQI0qALb2Nuo4unEj2+MzikPvP7BpvIyOjsLn86kuaLrxctiQe7ouC+Tv+s9O2M7ZsB+QxzQMA/39/UilUjBNE8899xwA9Ex7/WAwiImJCZVut7GxoYwXqfvqTnzg/o67EuRLWYzP/Ywd4gKBAPr7++Hz+do6zu53llJvUHqHoLeCkRAuYCopHo8HjUajrdhWz4und016zWi48EUDRu/kxNejLHykMJbpYfrf8qfeevWo5nBL7/9RSKOR3qfDumYpfPXr4jph+gO90bKgnmuR36NHm+uT6RNcl/y/nvagR0lkZx2uZzlY1YpneQ1HrdGEjfvB58tnL4tqrbyoumKym7UklSfyme4Us/EfA3pUo5NS3WmfsVIa5fet+NPqfNsp8r20x+k6xVGA3Dd6LQ1Uf/6yoQDfl3JWfk8+i07GiwTPwbIKvRHKgd1jNxUewzCWAVQAZLt20v1FEvtz7ROmaaYe5guPAO2A/aHfQ9MOeCToZ/Pe3mDz3u5h897ecCi8d4920/t0/sOCzXu7x2HTzua9Tdi8t3t0pF1XjRcAMAzj/zdN85munnSfcNjXftjn3ysO+/oP+/x7wWFf+2Gff6847Os/7PPvBYd97Yd9/r3isK//sM+/Fxz2tR/2+feCXrj2XriG3eKwr/2wz78XdOPaH938Jxs2bNiwYcOGDRs2bDxSsI0XGzZs2LBhw4YNGzZsHAkchvHyd4dwzv3CYV/7YZ9/rzjs6z/s8+8Fh33th33+veKwr/+wz78XHPa1H/b594rDvv7DPv9ecNjXftjn3wt64dp74Rp2i8O+9sM+/15w4Nfe9ZoXGzZs2LBhw4YNGzZs2NgN7LQxGzZs2LBhw4YNGzZsHAl01XgxDONXDMO4bhjGLcMwvt7Ncz8sDMMYMwzjx4ZhfGQYxhXDML567/9xwzB+aBjGzXs/Y126Hpt2u7+eI0M7wKbfXmDTbm+w6bd72LTbPXqNdvfObdNv99dj025v12TT70HQB/Md1AuAE8BtAMcBeAC8B+Bst86/i+sdAvD0vd9DAG4AOAvgfwD4+r3/fx3Af7dpZ9POpl9vvGza2fSzaWfTzqafzXtHkXY2/Xb+6mbk5RMAbpmmecc0zQaA/w/Aa108/0PBNM1F0zTfvvd7CcBVACPYvObv3vvYdwH8ehcux6bd7nGkaAfY9NsLbNrtDTb9dg+bdrtHj9EOsOm3F9i02xts+u0A3TReRgDMir/n7v2v52EYxiSAiwDeAjBgmubivbfSAAa6cAk27XaPI0s7wKbfXmDTbm+w6bd72LTbPXqAdoBNv73Apt3eYNNvB7AL9h8AwzCCAP43gP9immZRvmduxsPsdm0dYNNub7Dpt3vYtNsbbPrtHjbtdg+bdnuDTb/dw6bd3tBt+nXTeJkHMCb+Hr33v56FYRhubD6M/2Wa5v+59++MYRhD994fArDUhUuxabd7HDnaATb99gKbdnuDTb/dw6bd7tFDtANs+u0FNu32Bpt+O0A3jZefAzhpGMYxwzA8AH4TwD928fwPBcMwDADfAXDVNM1vibf+EcCX7/3+ZQDf78Ll2LTbPY4U7QCbfnuBTbu9wabf7mHTbvfoMdoBNv32Apt2e4NNv51gvyr/d/IC8DlsdiK4DeDPunnuXVzri9gMc70P4N17r88BSAD4EYCbAP4FQNymnU07m3698bJpZ9PPpp1NO5t+Nu8dVdrZ9NvZy7h3chs2bNiwYcOGDRs2bNjoadgF+zZs2LBhw4YNGzZs2DgSsI0XGzZs2LBhw4YNGzZsHAnYxosNGzZs2LBhw4YNGzaOBGzjxYYNGzZs2LBhw4YNG0cCtvFiw4YNGzZs2LBhw4aNIwHbeLFhw4YNGzZs2LBhw8aRgG282LBhw4YNGzZs2LBh40jANl5s2LBhw4YNGzZs2LBxJPD/AAQkY/jWebO1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x1008 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}