Model_2_MultiClass_Classification_Model_with_FashionMnist_Dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOB/r0K6uUjYsLjHh28dHmT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pravincoder/Tensorflow_models/blob/main/Model_2_MultiClass_Classification_Model_with_FashionMnist_Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Working with a larger example (multiclass classification)\n"
      ],
      "metadata": {
        "id": "lTtdmYwpc5TG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Problem**:- Fashion/Dress Classification problem\n",
        "\n",
        "when we have more than 2 classes as an option , its know as **Multi-Class Classification**.\n",
        "\n",
        "#### In this problem we have to classify different images of clothing by building a neural network using multi-class classification."
      ],
      "metadata": {
        "id": "j8xagH0ksZJY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "# The data is already been sorted in training and test sets for us.\n",
        "(train_data,train_labels),(test_data,test_labels) = fashion_mnist.load_data()\n",
        "# More info link = https://www.tensorflow.org/datasets/catalog/fashion_mnist"
      ],
      "metadata": {
        "id": "M28fFdrxxrmB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Check the first training example\n",
        "print(f\"Training Sample:\\n{train_data[0]}\\n train Label:\\n {train_labels[0]}\" )\n",
        "print(f\"Test Sample: \\n {test_data[0]}\\n test Label: \\n {test_labels[0]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3xAxIYh32qY",
        "outputId": "9b964808-45e6-44c8-94fe-1fa49b7a7764"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Sample:\n",
            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  13  73   0\n",
            "    0   1   4   0   0   0   0   1   1   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   3   0  36 136 127  62\n",
            "   54   0   0   0   1   3   4   0   0   3]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   6   0 102 204 176 134\n",
            "  144 123  23   0   0   0   0  12  10   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0 155 236 207 178\n",
            "  107 156 161 109  64  23  77 130  72  15]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   1   0  69 207 223 218 216\n",
            "  216 163 127 121 122 146 141  88 172  66]\n",
            " [  0   0   0   0   0   0   0   0   0   1   1   1   0 200 232 232 233 229\n",
            "  223 223 215 213 164 127 123 196 229   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 183 225 216 223 228\n",
            "  235 227 224 222 224 221 223 245 173   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0 193 228 218 213 198\n",
            "  180 212 210 211 213 223 220 243 202   0]\n",
            " [  0   0   0   0   0   0   0   0   0   1   3   0  12 219 220 212 218 192\n",
            "  169 227 208 218 224 212 226 197 209  52]\n",
            " [  0   0   0   0   0   0   0   0   0   0   6   0  99 244 222 220 218 203\n",
            "  198 221 215 213 222 220 245 119 167  56]\n",
            " [  0   0   0   0   0   0   0   0   0   4   0   0  55 236 228 230 228 240\n",
            "  232 213 218 223 234 217 217 209  92   0]\n",
            " [  0   0   1   4   6   7   2   0   0   0   0   0 237 226 217 223 222 219\n",
            "  222 221 216 223 229 215 218 255  77   0]\n",
            " [  0   3   0   0   0   0   0   0   0  62 145 204 228 207 213 221 218 208\n",
            "  211 218 224 223 219 215 224 244 159   0]\n",
            " [  0   0   0   0  18  44  82 107 189 228 220 222 217 226 200 205 211 230\n",
            "  224 234 176 188 250 248 233 238 215   0]\n",
            " [  0  57 187 208 224 221 224 208 204 214 208 209 200 159 245 193 206 223\n",
            "  255 255 221 234 221 211 220 232 246   0]\n",
            " [  3 202 228 224 221 211 211 214 205 205 205 220 240  80 150 255 229 221\n",
            "  188 154 191 210 204 209 222 228 225   0]\n",
            " [ 98 233 198 210 222 229 229 234 249 220 194 215 217 241  65  73 106 117\n",
            "  168 219 221 215 217 223 223 224 229  29]\n",
            " [ 75 204 212 204 193 205 211 225 216 185 197 206 198 213 240 195 227 245\n",
            "  239 223 218 212 209 222 220 221 230  67]\n",
            " [ 48 203 183 194 213 197 185 190 194 192 202 214 219 221 220 236 225 216\n",
            "  199 206 186 181 177 172 181 205 206 115]\n",
            " [  0 122 219 193 179 171 183 196 204 210 213 207 211 210 200 196 194 191\n",
            "  195 191 198 192 176 156 167 177 210  92]\n",
            " [  0   0  74 189 212 191 175 172 175 181 185 188 189 188 193 198 204 209\n",
            "  210 210 211 188 188 194 192 216 170   0]\n",
            " [  2   0   0   0  66 200 222 237 239 242 246 243 244 221 220 193 191 179\n",
            "  182 182 181 176 166 168  99  58   0   0]\n",
            " [  0   0   0   0   0   0   0  40  61  44  72  41  35   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            " train Label:\n",
            " 9\n",
            "Test Sample: \n",
            " [[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   3   1   0   0   7   0  37   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   1   2   0  27  84\n",
            "   11   0   0   0   0   0   0 119   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   1   0   0  88 143\n",
            "  110   0   0   0   0  22  93 106   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   4   0  53 129 120\n",
            "  147 175 157 166 135 154 168 140   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   2   0  11 137 130 128\n",
            "  160 176 159 167 178 149 151 144   0   0]\n",
            " [  0   0   0   0   0   0   1   0   2   1   0   3   0   0 115 114 106 137\n",
            "  168 153 156 165 167 143 157 158  11   0]\n",
            " [  0   0   0   0   1   0   0   0   0   0   3   0   0  89 139  90  94 153\n",
            "  149 131 151 169 172 143 159 169  48   0]\n",
            " [  0   0   0   0   0   0   2   4   1   0   0   0  98 136 110 109 110 162\n",
            "  135 144 149 159 167 144 158 169 119   0]\n",
            " [  0   0   2   2   1   2   0   0   0   0  26 108 117  99 111 117 136 156\n",
            "  134 154 154 156 160 141 147 156 178   0]\n",
            " [  3   0   0   0   0   0   0  21  53  92 117 111 103 115 129 134 143 154\n",
            "  165 170 154 151 154 143 138 150 165  43]\n",
            " [  0   0  23  54  65  76  85 118 128 123 111 113 118 127 125 139 133 136\n",
            "  160 140 155 161 144 155 172 161 189  62]\n",
            " [  0  68  94  90 111 114 111 114 115 127 135 136 143 126 127 151 154 143\n",
            "  148 125 162 162 144 138 153 162 196  58]\n",
            " [ 70 169 129 104  98 100  94  97  98 102 108 106 119 120 129 149 156 167\n",
            "  190 190 196 198 198 187 197 189 184  36]\n",
            " [ 16 126 171 188 188 184 171 153 135 120 126 127 146 185 195 209 208 255\n",
            "  209 177 245 252 251 251 247 220 206  49]\n",
            " [  0   0   0  12  67 106 164 185 199 210 211 210 208 190 150  82   8   0\n",
            "    0   0 178 208 188 175 162 158 151  11]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]\n",
            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
            "    0   0   0   0   0   0   0   0   0   0]]\n",
            " test Label: \n",
            " 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Labels name and Number of each label\n",
        "![Screenshot 2023-04-27 060048.jpg](data:image/jpeg;base64,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)"
      ],
      "metadata": {
        "id": "1WER3ZhhHt9i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get shape of data\n",
        "train_data[0].shape,test_data[0].shape"
      ],
      "metadata": {
        "id": "xG10n_-d5vd1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bf4be59-d188-481a-db4a-e319d0560ccb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((28, 28), (28, 28))"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a sample\n",
        "plt.imshow(train_data[0]);\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "F0iQxFrTG0wy",
        "outputId": "4c453cff-f051-4030-f3c1-82efb2995dc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Check Sample label\n",
        "(train_labels[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w9jF8sMyHxQU",
        "outputId": "59174eb0-a0ec-4095-e954-437418369396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the labels human read able name of each number label\n",
        "label_names = [\"T-shirt/top\",\"Trouser\",\"Pullover\",\"Dress\",\"Coat\",\"Sandal\",\"Shirt\",\"Sneker\",\"Bag\",\"Ankle Boot\"]\n",
        "len(label_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAbVwcT1Ifm6",
        "outputId": "1d104ab6-c802-49ed-ffc7-f7ae2d4cc37d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def label_info(index_choice):\n",
        "  plt.imshow(train_data[index_choice],cmap=plt.cm.binary)\n",
        "  plt.title(label_names[train_labels[index_choice]])\n",
        "\n",
        "index_choice = 1\n",
        "label_info(index_choice)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "YLVrCvDlKGpr",
        "outputId": "e7afff2e-3450-47ce-cd1d-d258700bbc06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets plot multiple random images of fashion mnist\n",
        "import random\n",
        "plt.figure(figsize=(20,20))\n",
        "for i in range(10):\n",
        "  ax = plt.subplot(5,5,i+1)\n",
        "  rand_index = random.choice(range(len(train_data)))\n",
        "  label_info(rand_index)"
      ],
      "metadata": {
        "id": "jpzyH34TOZq8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "04b312d0-b7ee-4742-874b-68914ebec50e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 10 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABjsAAAKKCAYAAABrmekJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACp30lEQVR4nOzdeXjU9b3+/3uyTRKysYSEsIRFAYHigoAUEFQKYt2xFqotWKtWQUu1X6seFbWe4laLK2jPqbhWpUWtFFFAwKpgFUELHhEQBATCmoTsy3x+f/gjNbK8X4FJZj6Z5+O65rpgcuc170wy93wm78xMwPM8TwAAAAAAAAAAAD4VF+kFAAAAAAAAAAAAHA02OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2O+ArixcvViAQ0F//+tdILwUAmhwdCMAvNm7cqEAgoAceeMCZveOOOxQIBJpgVQAAAIgGgUBAkyZNcuZmzpypQCCgjRs3Nv6i0Cyw2YEj9u9//1sXXXSR8vPzlZycrPbt2+sHP/iBHnnkkUgvDQAaHR0IwM8CgYDptHjx4kgvtZ6ysjLdcccdh13X3r17lZCQoJdfflmS9Pvf/16vvvpq0ywQQLPk184EgEiI5GNljvuQEOkFwJ/ef/99nXbaaerUqZOuuOIK5ebmavPmzVq2bJkeeughXXvttZFeIgA0GjoQgN89++yz9f7/zDPPaP78+Qecf9xxxzX6Wm699VbddNNNpmxZWZnuvPNOSdLw4cMPmnnzzTcVCAQ0cuRISd886L3ooot0/vnnh2O5AGJQNHUmAESzcD9W/ulPf6qxY8cqGAya8hz3gc0OHJH//u//VmZmpj788ENlZWXV+9iOHTsis6gw8DxPFRUVSklJifRSAEQxOhCA31166aX1/r9s2TLNnz//gPObQkJCghISDv+wJBQKqaqqyjRv7ty5Gjx48AH9DABH6kg7s6ysTKmpqY25tEZRWlqqFi1aRHoZAHwo3I+V4+PjFR8ff9gMj2PxbbyMFY7I+vXr1bt374M+iGzbtm3dv/e/Bt+rr76qPn36KBgMqnfv3po3b94Bn/f111/r5z//uXJycupyf/7zn51rqays1Nlnn63MzEy9//77kr55QDxt2jT17t1bycnJysnJ0VVXXaW9e/fW+9zOnTvr7LPP1ptvvqmTTz5ZKSkpeuKJJxp4bQCINXQggFj30UcfadSoUWrTpo1SUlLUpUsX/fznPz9o9sknn1S3bt0UDAbVv39/ffjhh/U+frD37Njfn88//7x69+6tYDCoGTNmKDs7W5J055131r1szB133FH3eaFQSPPmzdMPf/jDujmlpaV6+umn6/ITJkyoy69YsUKjR49WRkaG0tLSdMYZZ2jZsmX11rL/taLfeecdXXXVVWrdurUyMjL0s5/97IBeBRC7hg8frj59+mj58uU69dRTlZqaqltuuUXSN7/gu/zyy5WTk6Pk5GQdf/zxevrpp+t9/v73ZvvuS2Htfw+kmTNn1p23fft2XXbZZerQoYOCwaDatWun884774DXtH/jjTc0dOhQtWjRQunp6frhD3+o1atX18tMmDBBaWlpWr9+vc466yylp6frkksuCdv1AiC2WB8r7+d6rHyw9+w41ONY13EfYgPP7MARyc/P19KlS7Vq1Sr16dPnsNl3331Xs2fP1jXXXKP09HQ9/PDDGjNmjDZt2qTWrVtLkgoKCnTKKafUPbDNzs7WG2+8ocsvv1zFxcWaPHnyQWeXl5frvPPO00cffaQFCxaof//+kqSrrrpKM2fO1GWXXabrrrtOGzZs0KOPPqoVK1bovffeU2JiYt2MNWvWaNy4cbrqqqt0xRVXqEePHuG5kgA0W3QggFi2Y8cOjRw5UtnZ2brpppuUlZWljRs3avbs2QdkX3jhBe3bt09XXXWVAoGA7rvvPl144YX68ssv63XRwbz99tt6+eWXNWnSJLVp00bHH3+8pk+frquvvloXXHCBLrzwQklS37596z7nww8/1M6dO3XWWWdJ+ualZ37xi19owIABuvLKKyVJ3bp1kyStXr1aQ4cOVUZGhm688UYlJibqiSee0PDhw7VkyRINHDiw3nomTZqkrKws3XHHHVqzZo2mT5+ur776qu4XlACwe/dujR49WmPHjtWll16qnJwclZeXa/jw4Vq3bp0mTZqkLl26aNasWZowYYIKCwv1q1/9qsGXM2bMGK1evVrXXnutOnfurB07dmj+/PnatGmTOnfuLOmb/hs/frxGjRqle++9V2VlZZo+fbqGDBmiFStW1OUkqaamRqNGjdKQIUP0wAMP+PLZKACiQ7gfKx/KwR7HHu64DzHEA47AW2+95cXHx3vx8fHeoEGDvBtvvNF78803vaqqqno5SV5SUpK3bt26uvM++eQTT5L3yCOP1J13+eWXe+3atfN27dpV7/PHjh3rZWZmemVlZZ7ned6iRYs8Sd6sWbO8ffv2ecOGDfPatGnjrVixou5z/vnPf3qSvOeff77erHnz5h1wfn5+vifJmzdv3lFfJwBiBx0IoLmZOHGiZ31o8Morr3iSvA8//PCQmQ0bNniSvNatW3t79uypO/+1117zJHmvv/563XlTpkw54LIleXFxcd7q1avrnb9z505PkjdlypSDXu5tt93m5efn1zuvRYsW3vjx4w/Inn/++V5SUpK3fv36uvO2bt3qpaene6eeemrdeU899ZQnyevXr1+9nr/vvvs8Sd5rr712yOsBQPN0sM4cNmyYJ8mbMWNGvfOnTZvmSfKee+65uvOqqqq8QYMGeWlpaV5xcbHnef85zlu0aFG9z9/fp0899ZTneZ63d+9eT5J3//33H3J9+/bt87Kysrwrrrii3vnbt2/3MjMz650/fvx4T5J30003mb9+ADiUcD9W3n8ctmHDhrrzDvc49lDHfYgdvIwVjsgPfvADLV26VOeee64++eQT3XfffRo1apTat2+vv//97/WyI0aMqLeT2rdvX2VkZOjLL7+U9M1r6/3tb3/TOeecI8/ztGvXrrrTqFGjVFRUpI8//rjezKKiIo0cOVKff/65Fi9erBNOOKHuY7NmzVJmZqZ+8IMf1JvVr18/paWladGiRfVmdenSRaNGjQrzNQSgOaMDAcSy/S9LMGfOHFVXVx82++Mf/1gtW7as+//QoUMlqa4DD2fYsGHq1atXg9Y2d+7cupewOpza2lq99dZbOv/889W1a9e689u1a6ef/OQnevfdd1VcXFzvc6688sp6z0a5+uqrlZCQoLlz5zZojQCar2AwqMsuu6zeeXPnzlVubq7GjRtXd15iYqKuu+46lZSUaMmSJQ26jJSUFCUlJWnx4sWHfCm9+fPnq7CwUOPGjat3PBgfH6+BAwcecDwofdNpAHC0wvlY+XB4HItD4WWscMT69++v2bNnq6qqSp988oleeeUV/fGPf9RFF12klStX1j047dSp0wGf27Jly7oDs507d6qwsFBPPvmknnzyyYNe1nffxGjy5MmqqKjQihUr1Lt373ofW7t2rYqKig76WoAHm9WlSxfbFwwA30IHAmjuSkpKVFJSUvf/+Ph4ZWdna9iwYRozZozuvPNO/fGPf9Tw4cN1/vnn6yc/+YmCwWC9Gd/twP0bH5b3umhoP23fvl0ff/yx7rrrLmd2586dKisrO+hL9x133HEKhULavHlzvY499thj6+XS0tLUrl27A14jH0Dsat++vZKSkuqd99VXX+nYY49VXFz9vzU97rjj6j7eEMFgUPfee69uuOEG5eTk6JRTTtHZZ5+tn/3sZ8rNzZX0zfGgJJ1++ukHnZGRkVHv/wkJCerQoUOD1gEAhxKux8qHw+NYHAqbHThqSUlJ6t+/v/r376/u3bvrsssu06xZszRlyhRJ3zwwPhjP8yR980aSknTppZdq/PjxB81++7WYJem8887Tiy++qHvuuUfPPPNMvQPHUCiktm3b6vnnnz/orP1vbLlfSkqK4asEgIOjAwE0Vw888IDuvPPOuv/n5+fXvVHuX//6Vy1btkyvv/663nzzTf385z/XH/7wBy1btkxpaWl1n+PqwMNpaD+98cYbSk5O1mmnndagzwOAcDma46pDvfdPbW3tAedNnjxZ55xzjl599VW9+eabuu222zR16lS9/fbbOvHEE+uOL5999tm6DZBvS0io/6ugYDB4wGYMAByto32sfDg8jsWhsNmBsDr55JMlSdu2bTN/TnZ2ttLT01VbW6sRI0aYPuf888/XyJEjNWHCBKWnp2v69Ol1H+vWrZsWLFigwYMHU34AmhQdCKA5+dnPfqYhQ4bU/f+7nXLKKafolFNO0X//93/rhRde0CWXXKIXX3xRv/jFLxptTYd7I/B//OMfOu200w5Y58E+Jzs7W6mpqVqzZs0BH/v8888VFxenjh071jt/7dq19TZSSkpKtG3btro3QweAg8nPz9enn36qUChUb0Ph888/r/u49J9nvhUWFtb7/EM986Nbt2664YYbdMMNN2jt2rU64YQT9Ic//EHPPfdc3cvCtG3b1nx8CQCN6UgeKx+Jwx0rIjawdY8jsmjRooPutO5/zeKDvSTAocTHx2vMmDH629/+plWrVh3w8Z07dx708372s5/p4Ycf1owZM/Tb3/627vyLL75YtbW1+t3vfnfA59TU1Bxw8AgADUUHAogFXbt21YgRI+pOgwcPlvTNS1B9twP3v3dQZWVlo64pNTVV0oG/DKyurtb8+fMP+n4dLVq0OCAfHx+vkSNH6rXXXqv3MlQFBQV64YUXNGTIkANe5uXJJ5+s9x4l06dPV01NjUaPHn10XxSAZu2ss87S9u3b9dJLL9WdV1NTo0ceeURpaWkaNmyYpG82PeLj4/XOO+/U+/zHH3+83v/LyspUUVFR77xu3bopPT29roNHjRqljIwM/f73vz/oeysd6vgSAI5WOB8rH4mDHfchtvDMDhyRa6+9VmVlZbrgggvUs2dPVVVV6f3339dLL72kzp07H/CmbC733HOPFi1apIEDB+qKK65Qr169tGfPHn388cdasGCB9uzZc9DPmzRpkoqLi/Vf//VfyszM1C233KJhw4bpqquu0tSpU7Vy5UqNHDlSiYmJWrt2rWbNmqWHHnpIF110UTiuBgAxig4EEMuefvppPf7447rgggvUrVs37du3T3/605+UkZHR6M9ySElJUa9evfTSSy+pe/fuatWqlfr06aOdO3equLj4oJsd/fr104IFC/Tggw8qLy9PXbp00cCBA3X33Xdr/vz5GjJkiK655holJCToiSeeUGVlpe67774D5lRVVemMM87QxRdfrDVr1ujxxx/XkCFDdO655zbq1wzA36688ko98cQTmjBhgpYvX67OnTvrr3/9q9577z1NmzZN6enpkqTMzEz96Ec/0iOPPKJAIKBu3bppzpw5B7zf2hdffFHXRb169VJCQoJeeeUVFRQUaOzYsZK+eU+O6dOn66c//alOOukkjR07VtnZ2dq0aZP+8Y9/aPDgwXr00Ueb/LoA0PyF+7FyQx3quA+xg80OHJEHHnhAs2bN0ty5c/Xkk0+qqqpKnTp10jXXXKNbb71VWVlZDZqXk5Ojf/3rX7rrrrs0e/ZsPf7442rdurV69+6te++997Cfe8stt6ioqKjul30TJ07UjBkz1K9fPz3xxBO65ZZblJCQoM6dO+vSSy+t+6tEADhSdCCAWDZs2DD961//0osvvqiCggJlZmZqwIABev7555vkzSL/53/+R9dee61+/etfq6qqSlOmTFFpaal69epV93Iw3/bggw/qyiuv1K233qry8nKNHz9eAwcOVO/evfXPf/5TN998s6ZOnapQKKSBAwfqueeeO+iD4kcffVTPP/+8br/9dlVXV2vcuHF6+OGHebkEAIeVkpKixYsX66abbtLTTz+t4uJi9ejRQ0899ZQmTJhQL/vII4+ourpaM2bMUDAY1MUXX6z7779fffr0qct07NhR48aN08KFC/Xss88qISFBPXv21Msvv6wxY8bU5X7yk58oLy9P99xzj+6//35VVlaqffv2Gjp0aKP/shFA7Ar3Y+WGOtRxH2JHwLO86wsAAAAARKlevXrp7LPPPugzMo7WzJkzddlll+nDDz+se71pAAAAANGHZ3YAAAAA8K2qqir9+Mc/1sUXXxzppQAAAACIIDY7AAAAAPhWUlKSpkyZEullAAAAAIiwuEgvAAAAAAAAAAAA4Gjwnh0AAAAAAAAAAMDXeGYHAAAAAAAAAADwNTY7AAAAAAAAAACAr0XdG5SHQiFt3bpV6enpCgQCkV4OgCjkeZ727dunvLw8xcU1rz1bOhCACx0IIFbRfwBiGR0IIJZZOzDqNju2bt2qjh07RnoZAHxg8+bN6tChQ6SXEVZ0IAArOhBArKL/AMQyOhBALHN1YKNtdjz22GO6//77tX37dh1//PF65JFHNGDAAOfnpaenS/pm4RkZGY21PAA+VlxcrI4dO9b1RbQ50v6T6EAAbnQgGqq6utqUS0xMdGbWr18ftlmdOnUyzfKzadOmmXJr1qxxZqZPn26aVVNT48wkJETd37yZRHv/STwO9qu9e/c6M7///e9Ns/Lz852Z+Ph406x27do5M3v27DHNKisrc2Ys3S1JLVu2dGYuvvhi0yzY0YFoqFWrVply//jHP5yZZcuWmWaNHDnSmamqqjLNqqiocGZCoZBpVo8ePZyZ1atXm2ZVVlY6M+PGjTPN6t27tykHewc2ylHuSy+9pOuvv14zZszQwIEDNW3aNI0aNUpr1qxR27ZtD/u5+5+ulpGRQcEBOKxofHrr0fSfRAcCsKMDYRXOzY60tDTTrKSkJGcmFr7HycnJplw4r6/mvNmxXzT2n8TjYD+rra11Ziy3U8l2u7dudqSmpjoz5eXlplmWXwhav0bLuvg5bjx0IKysx22W3rIeO1hmhfNl2Cz9Ldl6KxgMHu1y6live37eG87VgY3yIn8PPvigrrjiCl122WXq1auXZsyYodTUVP35z39ujIsDgKhB/wGIZXQggFhGBwKIZXQggGgQ9s2OqqoqLV++XCNGjPjPhcTFacSIEVq6dOkB+crKShUXF9c7AYAfNbT/JDoQQPNBBwKIZTwOBhDL6EAA0SLsmx27du1SbW2tcnJy6p2fk5Oj7du3H5CfOnWqMjMz6068IREAv2po/0l0IIDmgw4EEMt4HAwgltGBAKJFo7yMVUPcfPPNKioqqjtt3rw50ksCgCZDBwKIZXQggFhF/wGIZXQggMYS9nema9OmjeLj41VQUFDv/IKCAuXm5h6QDwaDYX0DGACIlIb2n0QHAmg+6EAAsYzHwQBiGR0IIFqE/ZkdSUlJ6tevnxYuXFh3XigU0sKFCzVo0KBwXxwARA36D0AsowMBxDI6EEAsowMBRIuwP7NDkq6//nqNHz9eJ598sgYMGKBp06aptLRUl112WWNcHABEDfoPQCyjA8OnqKjIlCsvL3dm9u7da5rVqVMnZ8b6V5h33323M9OyZUvTrKSkJGemb9++plmW6/Wf//ynadaaNWucmRUrVphmWS/TwvM8Z6aystI0i7+6bRg60L+WLVvmzLzyyiumWd99z4KD2bNnj2lWmzZtnBnL/YAk0xtAt2vXzjSre/fuzsy4ceNMswKBgCmH6BfLHRgKhUy5uDj335xbjwEvvPBCZ8Z6P37SSSc5M9XV1aZZkydPdma6du1qmpWYmOjMVFRUmGbt3r3bmWnRooVp1rnnnuvMPPjgg6ZZO3fudGbGjx9vmvWjH/3ImamtrTXNio+PN+WiUaNsdvz4xz/Wzp07dfvtt2v79u064YQTNG/ePNOdPgD4Gf0HIJbRgQBiGR0IIJbRgQCiQaNsdkjSpEmTNGnSpMYaDwBRi/4DEMvoQACxjA4EEMvoQACRFvb37AAAAAAAAAAAAGhKbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK8lRHoBAAAAAOpbs2aNKdezZ09nJiHBdshfVVXlzHTq1Mk069Zbb3VmnnnmGdOstWvXOjPLly83zUpOTnZmPv/8c9OsxMREZ2bx4sWmWQMGDHBmQqGQaZbl+11bW2uaBcSKv//9785M9+7dTbMsPVlaWmqaZckFg0HTLItdu3aZcjt37nRmli1bZpo1aNAgUw6IFRkZGabcSSed5MxYjlUkKS7O/bfw559/vmlWr169nJnPPvvMNOurr75yZtLS0kyzLMfMgwcPNs1KT093Zqx9ajlus3SuVSAQCNusaMUzOwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8LWESC8AAAAAiCXLly93ZmpqakyzPM9zZlq3bm2aVVlZGZbLk6ROnTo5M7feeqtp1iuvvOLMvPzyy6ZZJSUlzsyUKVNMs8aMGePMJCTYHm5ZrteqqirTrGAw6MzEx8ebZgGxwtINubm5plmhUMiZqaioMM1q1apVWC7PepnHH3+8adbXX3/tzCxdutQ0a9CgQaYcEM0CgUCTz7IcF9TW1ppmJScnOzN79uwxzWrTpo0zc+GFF5pmrV692pnp1auXaZbl+iosLDTN2rVrlzNj/T5arvv+/fubZllYj+X9jGd2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX0uI9AIAAAAA1Pfss8+acg888IAzs3PnTtOs7OxsZ6aystI0Kz4+3plJTEw0zbrgggvCkolmNTU1zkwwGAzb5QUCgbDNAqJZRUWFKWfpye9973umWaFQyJmxdKQkbdu2zZlJT083zcrKynJmUlJSTLOOOeYYZyY5Odk0C8CRSU1NdWZKSkpMs8rLy50ZS4dItn5bu3atadaqVaucmYKCAtOsvn37OjPWY609e/Y4My1atDDNsqy/ffv2plkWsXAMyDM7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwtYRILwAAAABoDnbs2GHK9enTx5lZsWKFadZXX33lzMTF2f6+qUWLFs5MaWmpaVbLli2dmeLiYtOs2tpaZyYtLc00q6SkJGyzKisrnRnLdSpJ8fHxply41NTUmHIJCTxchL998cUXplxVVZUzY72dWjorFAqZZpWVlTkziYmJplmW23NFRYVpluUyresCcGQsXWM9DsnKynJmCgsLTbPWr1/vzHTs2NE06/vf/74zs3HjRtOsTZs2OTPW66tt27bOjOV+RbIdk1ln4Rthf2bHHXfcoUAgUO/Us2fPcF8MAEQlOhBArKL/AMQyOhBALKMDAUSLRvlTnd69e2vBggX/uRD+IghADKEDAcQq+g9ALKMDAcQyOhBANGiU5klISFBubm5jjAaAqEcHAohV9B+AWEYHAohldCCAaNAob1C+du1a5eXlqWvXrrrkkksO+7polZWVKi4urncCAD+jAwHEqob0n0QHAmheOAYEEMvoQADRIOybHQMHDtTMmTM1b948TZ8+XRs2bNDQoUO1b9++g+anTp2qzMzMupP1TWoAIBrRgQBiVUP7T6IDATQfHAMCiGV0IIBoEfbNjtGjR+tHP/qR+vbtq1GjRmnu3LkqLCzUyy+/fND8zTffrKKiorrT5s2bw70kAGgydCCAWNXQ/pPoQADNB8eAAGIZHQggWjT6uwVlZWWpe/fuWrdu3UE/HgwGFQwGG3sZABARdCCAWOXqP4kOBNB8cQwIIJbRgQAipVHes+PbSkpKtH79erVr166xLwoAog4dCCBW0X8AYhkdCCCW0YEAIiXsmx2/+c1vtGTJEm3cuFHvv/++LrjgAsXHx2vcuHHhvigAiDp0IIBYRf8BiGV0IIBYRgcCiBZhfxmrLVu2aNy4cdq9e7eys7M1ZMgQLVu2TNnZ2eG+KACIOnQgot1rr73mzKxZs8Y068Ybbzza5aAZof+koqIiU66wsNCZadWqlWnW9OnTnZk//vGPplnvvfeeM9OlSxfTrJKSEmcmnC9fUV5ebsqlpKQ4M5WVlaZZoVDImamqqjLNiosL39+gJSYmOjMJCY3+asYxhw6MTlu2bDHlwnmbqKioCNuszMxMZ8baM57nOTNJSUmmWbW1tc6MtUvRPMR6BwYCgSa/TMtt2np8sXXr1rDN6tSpkzOTlpZmmmXpkb1795pmnXTSSc7M4sWLTbM2bNjgzAwZMsQ0yyKc9yuxIOxHuS+++GK4RwKAb9CBAGIV/QcgltGBAGIZHQggWjT6e3YAAAAAAAAAAAA0JjY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDXEiK9AMQ2z/NMuUAgELbLrKmpcWbWrl1rmnXccccd7XIaRSgUcmbi4mx7nZbvUTi/j+H8XgPRrrq62pRLTEx0Zvbu3WuadddddzkzW7duNc167LHHnJkxY8aYZnXq1MmZad++vWnWMccc48zk5eWZZpWXlzszGRkZplmtWrUy5eBfxx57rCm3cuVKZ6aiosI0a86cOc7Mgw8+aJqVlJTkzKSlpZlmWdafnp5ummU5rrFkJNvXuGvXLtOs7OzssFyelfUYyXJdbNiwwTSrW7duphwQrYqKiky51q1bOzPFxcWmWZbjNuvjp02bNjkzCQm2X+t07tzZmcnJyTHN+vrrr50Z6/0FgCNj6beUlBTTLMuxQ2pqqmmWxbZt20w5y7GWtQM3btzozJx33nmmWZ988okzU1paapplebxpPc61iIXfufHMDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GsJkV4AYlsgEDDlPM9zZlatWmWa9c9//tOZ2bRpk2nWrbfe6sykpaWZZoVCIWcmLs62P2nNWVi+R9bvI4D6ampqTLnExERn5qGHHjLN2r17tzOTn58ftllz5841zUpJSXFmkpOTTbMsKisrTbmKigpnpkWLFqZZxx57rDPzi1/8wpkpLS01XR6i1wknnODMDBs2zDRr0aJFzkxVVZVpVlZWljNTUFBgmtW+fXtnxnJ8J0nBYNCZKSoqMs2yHCMlJNgeIlnWb+15y3Gg5XqQpDVr1jgz1use8DtrZ6Wmpjoz1uOQsrIyZ8bymFSSFi5c6My89NJLpll///vfnRlLd1tZuxRoDiz341J4f1eza9cuZyYvL880q3Pnzs6MtU8tt33r9bVz505nJpzHR+np6aZZluO74uJi06ycnBxn5tNPPzXN6tWrlynX3PHMDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfC0h0gtAbNu9e7cp98QTTzgztbW1pllJSUnOTFlZmWlWWlqaKdfUampqnJmEhPDd/O+++25T7vLLL3dm2rVrd7TLAXwjJSUlbLMWLFhgylm6sqKiwjSrtLTUmQmFQqZZ5eXlzkwwGDTNio+Pd2Y8zzPNatmypTOzfv1606ycnBxn5sQTT3RmiouLTZcHf5syZYopt2TJEmfm5ptvNs2y5L744gvTrOOOO86ZKSkpMc1KTEx0Zlq1amWaVV1d7czk5uaaZlmOt+LibH9bZvkaLWu36tWrV9hmAdHM+rjO8hixqqrKNMtyXDBgwADTrK5duzoz1s6y9Pepp55qmmXpI+v1BeDIBAIBZ8b6e7Kvv/7amcnOzjbNsjwWsz7eTE5OdmZ27dplmvWvf/3LmbE8vpWkvLw8Z6Z79+6mWZb7qXXr1plmWVh+bvyOZ3YAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfS2joJ7zzzju6//77tXz5cm3btk2vvPKKzj///LqPe56nKVOm6E9/+pMKCws1ePBgTZ8+Xccee2w41w0fKC8vd2buuOMO06y9e/c6MwMHDjTN2rlzZ1guL9zi4sK395iQ0OCb9iG9/PLLzszHH39smlVRUeHM3H333aZZkUD/IVL27dvnzPz73/82zWrVqpUzU1paappl6fmWLVuaZqWnp5tyFlVVVc6MtXMtX6PlOpWkOXPmmHIu8fHxYZnTUHRgdHr77bedmdtvv900691333Vm+vXrZ5pluc+3Hq94nufMWG/ToVDIlAsX67pqamqcGev11bNnT1MODUMH+lNlZaUpZzkOSU5ONs366quvnJkrrrjCNMsiNzfXlNuzZ48zEwgETLMsvdzUfYvGQ/+5hfP3OVY9evRwZrZt22aaZbntf/LJJ6ZZLVq0cGby8vJMsyz9tmrVKtMsy2O21q1bm2Z16NDBmbH87lGyPbbr3r27aZaFpb8l+/1BNGrwrbG0tFTHH3+8HnvssYN+/L777tPDDz+sGTNm6IMPPlCLFi00atQo0wMeAIhm9B+AWEYHAohldCCAWEX/AfCTBv/59+jRozV69OiDfszzPE2bNk233nqrzjvvPEnSM888o5ycHL366qsaO3bs0a0WACKI/gMQy+hAALGMDgQQq+g/AH4S1udZbdiwQdu3b9eIESPqzsvMzNTAgQO1dOnScF4UAEQV+g9ALKMDAcQyOhBArKL/AESb8L2wv6Tt27dLknJycuqdn5OTU/ex76qsrKz3OprFxcXhXBIANIkj6T+JDgTQPNCBAGIZj4MBxCqOAQFEm6Z/B53vmDp1qjIzM+tOHTt2jPSSAKDJ0IEAYhkdCCBW0X8AYhkdCKCxhHWzIzc3V5JUUFBQ7/yCgoK6j33XzTffrKKiorrT5s2bw7kkAGgSR9J/Eh0IoHmgAwHEMh4HA4hVHAMCiDZh3ezo0qWLcnNztXDhwrrziouL9cEHH2jQoEEH/ZxgMKiMjIx6JwDwmyPpP4kOBNA80IEAYhmPgwHEKo4BAUSbBr9nR0lJidatW1f3/w0bNmjlypVq1aqVOnXqpMmTJ+vuu+/Wscceqy5duui2225TXl6ezj///HCuGwCaHP0HIJbRgQBiGR0IIFbRfwD8pMGbHR999JFOO+20uv9ff/31kqTx48dr5syZuvHGG1VaWqorr7xShYWFGjJkiObNm6fk5OTwrTrMPM8z5QKBQCOvxB9mz55tyn3xxRfOjPXnYuDAgc7M119/bZpVW1vrzHTv3t00a9q0ac7M97//fdOsk08+2Zmx/gwuW7bMmXn99ddNszZt2uTMtGnTxjRr69atzsx3n/56MPv27TNdXrg1x/5DfZZ+iI+PD9vl7dq1y5Q7++yznZmEBNtdeocOHZyZ1atXm2ZlZWU5M2lpaaZZln4rLCwM2yyrpKQkZ8bSbc0BHehf//Vf/2XKvfrqq85M27ZtTbMsxyKnnnqqaZbltl9WVmaa1apVK2emoqLCNMvSD+GcZbmPkuz3B2gYOtCfrMdtcXHuF70oKio62uXUGTx4cNhm9evXz5TLzMx0ZqxvEs3vRmIL/RedDvcyYftZXx7M0g/V1dWmWatWrXJmDvfm9d82ZMgQZ8bazX379nVm2rdvb5pl6cpgMGiaVVNT48zk5OSYZlnEQn83+Eh4+PDhh90cCAQCuuuuu3TXXXcd1cIAINrQfwBiGR0IIJbRgQBiFf0HwE/C+p4dAAAAAAAAAAAATY3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAryVEegFHyvO8Jp9VW1vrzCQkROdV+sorr5hy//d//+fMbN++3TQrFAo5M+Xl5aZZlut1x44dplnBYNCZiY+PN83auXOnM/P++++bZqWnpzsz27ZtM82y5Hr27GmalZub68xUV1ebZu3evduZqaysDEsG/mfpXMn285ecnGyaZb3tW2zZssWZOeuss0yzMjMznZnevXubZhUUFDgzlp6UpKSkJGemsLDQNMvCeh9rWX8476/T0tLCNgtoDMXFxaZcy5YtnZl9+/Yd7XLqlJSUhG2WtbfC+RgiLs79d2OW42HrrLKyMtMsOgn4jxYtWphye/bsCUtGsj1+at++vWmWRdu2bU25du3ahe0yLZ1bU1MTtssDmgvLcUggEDDNCuf9veVxnbVPO3bs6MxYfv8lSe+++64zYz3Ozc/Pd2asX6Pld1LWx5vr1693Zqy/v7OwHgtbfw6jEc/sAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA1xIivYAjFQgEmnxWXFzT7g2tW7fOlHvxxRedmdWrV5tmnXTSSc5MSUmJadb69eudmYyMDNOsgoICU84iMTHRmdm8ebNpVosWLZyZpKQk06zS0lJnpmvXrqZZI0eOdGaqq6tNs3bt2uXMWG9Dlu/3p59+6syUlZWZLg/h5XmeM1NTU2OaVVtb68wkJyebZsXHx5tyFpWVlc7MddddZ5r1l7/8xZnp3bu3aVbLli2dmR07dphmlZeXOzOWnpRs15eV5ftt+RmUwvs1pqenOzN79+41zbL83Ifz5xnYz3q/aenwZcuWmWZZest627EcZ1huX9ZcamqqaZbl2M16vxgKhZyZcD7+AWKFtRssty/rcUgwGDTlmlqbNm2cmcLCQtOs7OxsZ6ZPnz6mWUAssfSI9f6+VatWYZtlYf2daF5enjOze/du0yzLsVavXr1Msyzr3759u2lWZmamM5OQYPuVe0pKijOTk5NjmoVv8MwOAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADgawmRXkBj8zzPmdmyZYtp1qpVq5yZ3bt3m2Zt27bNmbGsXZK+/PJLZyY7O9s0KykpyZSzyM/Pd2YqKytNs0pLS50Zy3UqSXFx7j2+iooK06wWLVo4M7169TLNOuaYY5yZ4uJi06zly5c7M4mJiaZZ1dXVzkx6erppluW6Ly8vD0umuautrTXlQqGQMxMfH2+aZfn+WX+uLLmdO3eaZs2cOdOZWb9+vWnW3LlznRlrn1544YXOTFVVlWnWxx9/7MxYbquSlJKS4sxY738st0Xrz1dJSYkzU1NTY5qVnJzszFjvf9q0aePMtG7d2jRryZIlzszpp59umgU0RCAQMOW6du3qzKxevdo0y3JsYL0va9u2rSlnYbnMhATbQyRLV1pnWb5HwWDQNAvAf6SmpppyluMC62OQHj16mHJN7dhjj3Vmdu3aZZqVlpbmzLRq1co0C4gl1mMyC8sxhvU4xPKYzTrL8hjX+juk7t27OzM5OTmmWUVFRc6M9XeBlg603mdkZWWZcuESzp/BaMUzOwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8LWESC/gUHbv3q2qqqpDfvz55583zfn888+dmaysLNOsUCjkzBQWFppmFRUVOTPt27c3zRowYIAzs2/fPtOsJUuWODOZmZmmWfHx8c6M5XqQJM/znJl27dqZZh3u52o/68/E6aef7sysXbvWNMvy8/Xhhx+aZn366afOTG5urmmWZV1paWmmWZ988okz07dvX2emoqLCdHl+5nneYX/uLbevhuTCZfv27abc7NmznZm33nrLNKtt27bOzEcffWSadcoppzgzgUDANMtyO9ywYYNplqWTEhJsd+lxce6/c6ipqTHNslxmbW2taZal5y3fa0n63ve+58xUVlaaZpWWljoz1g588803nRnL/QrQUNb7AsvtsLq62jSrdevWzoy1Hyy51NRU06zdu3c7M9ZjXcsxZYsWLUyzysrKnBlrN2dkZJhyQCzIzs425SyP4y23ecl+W21qJ598sjPzt7/9zTTLcl+wadMm06xevXqZcgDqszyeSUpKMs2y3Kat3WZ5vGl97Go5BrQcQ0m24+GUlBTTLMu6rMfMHLeFX4Of2fHOO+/onHPOUV5engKBgF599dV6H58wYYICgUC905lnnhmu9QJAxNB/AGIZHQggltGBAGIV/QfATxq82VFaWqrjjz9ejz322CEzZ555prZt21Z3+stf/nJUiwSAaED/AYhldCCAWEYHAohV9B8AP2nwy1iNHj1ao0ePPmwmGAyaXyIHAPyC/gMQy+hAALGMDgQQq+g/AH7SKG9QvnjxYrVt21Y9evTQ1VdffdjXxq2srFRxcXG9EwD4VUP6T6IDATQvdCCAWMbjYACximNAANEi7JsdZ555pp555hktXLhQ9957r5YsWaLRo0cf8s1bpk6dqszMzLpTx44dw70kAGgSDe0/iQ4E0HzQgQBiGY+DAcQqjgEBRJMGv4yVy9ixY+v+/b3vfU99+/ZVt27dtHjxYp1xxhkH5G+++WZdf/31df8vLi6m5AD4UkP7T6IDATQfdCCAWMbjYACximNAANGkUV7G6tu6du2qNm3aaN26dQf9eDAYVEZGRr0TADQHrv6T6EAAzRcdCCCW8TgYQKziGBBAJDX6ZseWLVu0e/dutWvXrrEvCgCiCv0HIJbRgQBiGR0IIFbRfwAiqcEvY1VSUlJvd3bDhg1auXKlWrVqpVatWunOO+/UmDFjlJubq/Xr1+vGG2/UMccco1GjRoV14QDQ1Og/ALGMDgQQy+hAALGK/gPgJw3e7Pjoo4902mmn1f1//2vsjR8/XtOnT9enn36qp59+WoWFhcrLy9PIkSP1u9/9TsFgsEGXM2fOHKWkpBzy48nJyaY5J5xwgjOTmJhomlVUVOTMFBcXm2a1bdvWmUlLSzPNqqmpcWaSkpJMs8466yxn5rPPPjPN+ve//+3MHO4Nq74tFAo5M19//bVpVmFhoTNj/QuEd955x5lp3bq1aVZVVZUz84Mf/MA0y3L7sPw8Szrs7XA/6/fxxBNPdGYyMzOdGevPc7g1Vf9JUiAQUCAQOOTHLbcJSfr444+dmU8//dQ0a+vWrc7Ml19+aZr1s5/9zJnp3LmzaVZ8fLwzs379etOsNWvWODM7d+40zbJ0eKdOnUyzEhLcd9dlZWWmWSUlJc6MpY8kHfZndL/q6mrTrOzsbGemS5cuplkFBQXOjHVdnuc5M5aelGy3x2jWlB2I8MrKyjLlLMdS4fwrzdTUVFOuRYsWzkxcnO0J6zk5OaachWX9lg6xzqqoqDDNslympb9RHx3oT8OHDzflXnrpJWfG2jPWY+umZjmetD7OslwXlmNO+AP9Fz7hvP+1/E7HenmW3+lYHz9ZctausRzThPP4yPK7Bcl2vVq/RsvvdMPJemzq52PFBm92DB8+/LBXzJtvvnlUCwKAaEX/AYhldCCAWEYHAohV9B8AP2n09+wAAAAAAAAAAABoTGx2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvJUR6AYdSUVGhQCBwyI/v2rXLNCc1NdWZ2bx5s3ldLikpKWGbZV3XnDlznJmdO3eaZuXk5DgznTp1Ms1KSHD/eG3bts00q1evXs7MRRddZJrVt29fZ6ZDhw6mWePGjXNmVq9ebZqVnJzszLRo0cI0q6SkxJnxPM80q7y83Jmprq42zaqsrHRmPvroI2emqqrKdHl+VlhYqFAodMiPX3HFFaY5q1atcmZOOOEE06xzzz3XmbH87EnSM88848xY+kiS1q9f78yUlZWZZsXFuf8GoFWrVqZZNTU1zkxxcbFplsXh7jO/zdI1loyVpUMk2/foiy++MM0qKipyZoLBoGlWZmamM2O97sN5jAA0RGlpqSnX1PevhYWFplzLli2dGWtvWY5FrP1gue1b7gsk23FzONcFxArr8WTr1q2dmX379plmWY4Vw9kNVnv27HFmrP1RW1vrzMTCYzYgknbs2OHMJCYmmmZZjo8st3tJh/09RkMykpSUlOTMWL9Gy2Nv6+M1S79Z+9t63wI7ntkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvsdkBAAAAAAAAAAB8LSHSCziUq666ShkZGYf8+O9//3vTnLy8PGfmoosuMs36+uuvnZndu3ebZr333nvOTGZmpmnWZZdd5sx0797dNKtLly7OTLdu3UyzAoFAWDLR7IUXXnBmfvWrX5lmDRgwwJmZMmWKadbZZ5/tzGzevNk0a9++fc5Mjx49TLM+/fRTZ6Z///7OTHl5ueny/CwrK+uwHfjHP/7RNGfRokXOzEcffWSa9fTTTzszO3bsMM2yfA+rqqpMs/bs2ePMFBYWmmYlJLjvFkOhUNhmxcXZ/uYgOTnZmUlKSjLNsl6mhaXDrdeXJVdTU2OaZVFbW2vKWdaVmppqmpWSkmLKAeFWVFRkylnu860dUl1d7cyUlJSYZlnWZelJyXaf0a5dO9Msa7+Fa1Y4L89yHwXEEstxQVlZmWmW5Th35cqVplknn3yyKWexZs0aZ6a0tNQ0y3JsbT2WB5oDz/NMuXD+DszyO8r4+HjTLMtxm/UY0PK4tLKy0jTLcrySlpZmmmVZl/UxteX7bb2+LL1rvf+xPi5t7nhmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvpYQ6QUcqVtuucWUe/TRR52Z9957zzSrqKjImenZs6dp1u9+9ztnJjk52TQrFpSVlTkze/bsMc2yXK+BQMA0q1u3bs7MnDlzTLMsbr/99rDNioSdO3c6M4WFhc5MSUlJGFYT3bZs2aL09PRDfrxNmzamOT/96U/Dkgk3y/fw888/N8368ssvnZni4mLTrH379oUlI0lVVVXOTFJSkmlWMBh0Zlq0aGGalZiY6Mwc7mfv29LS0pwZ632ZJWf9Gi0567oyMjLCkpGk+Ph4Uw4IN8/zTLna2lpnprS01DRr7969zoy1T8vLy005i+rq6rDNCoVCzkw4b/dxcfydGtBYWrZs6cxYjjklWzdYfwdx8sknm3IWlvsC6zGz5djU+ngFQH2W339J0o4dO5yZvLw806yEBPevhy3HiZLtWCslJcU0q6KiwpmxdK5k60Dr12g5JrNcp5Lt+rL+buSkk04y5Zo7jpgBAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvJTQkPHXqVM2ePVuff/65UlJS9P3vf1/33nuvevToUZepqKjQDTfcoBdffFGVlZUaNWqUHn/8ceXk5IR98RaTJk0K26wPP/zQmdm4caNp1ty5c52ZoqIi06x///vfzkxSUpJpljVnsXr1amfmmmuuMc0644wzjnY5dUKhkDOTkGC7adTU1Dgz5eXlpln333+/M1NaWmqa1bp1a2emVatWplm7du0y5Szat2/vzPTt29eZKSkpCcdyGqwpOzA7O1sZGRmH/HhhYaFpztq1a52Z6upq06xAIODMpKSkmGYd7mvb76STTjLNOvnkk005REZtba0pZ/k5tM6qqKhwZiorK02zduzY4cwUFBSYZlnuW7p27erMWK+HcPPjcSC+Yb0vtxxnWO9/kpOTnZk9e/aYZnXr1s2ZsR43W1iPtyz3eZZjRcl2H+t5nmlWfHy8KQc7+q/5GzVqlDPzwQcfmGZZHlNbHiuHW1xc+P7W1dJtHTp0CNvlIbLowKa1efNmU27fvn3OjOX4QrI9vrD8Ls0qMTHRlLMe+1ikpqY6M5bHkZLtWMt63efm5jozH330kWmW5Xco1nX5WYPu7ZYsWaKJEydq2bJlmj9/vqqrqzVy5Mh6Dwh+/etf6/XXX9esWbO0ZMkSbd26VRdeeGHYFw4ATY0OBBDL6EAAsYr+AxDL6EAAftKgZ3bMmzev3v9nzpyptm3bavny5Tr11FNVVFSk//3f/9ULL7yg008/XZL01FNP6bjjjtOyZct0yimnhG/lANDE6EAAsYwOBBCr6D8AsYwOBOAnR/U8xv1PF9//cjjLly9XdXW1RowYUZfp2bOnOnXqpKVLlx7NRQFA1KEDAcQyOhBArKL/AMQyOhBANGvQMzu+LRQKafLkyRo8eLD69OkjSdq+fbuSkpKUlZVVL5uTk6Pt27cfdE5lZWW9180uLi4+0iUBQJOhAwHEMjoQQKyi/wDEMjoQQLQ74md2TJw4UatWrdKLL754VAuYOnWqMjMz604dO3Y8qnkA0BToQACxjA4EEKvoPwCxjA4EEO2OaLNj0qRJmjNnjhYtWqQOHTrUnZ+bm6uqqioVFhbWyxcUFBzy3eVvvvlmFRUV1Z02b958JEsCgCZDBwKIZXQggFhF/wGIZXQgAD9o0GaH53maNGmSXnnlFb399tvq0qVLvY/369dPiYmJWrhwYd15a9as0aZNmzRo0KCDzgwGg8rIyKh3AoBoRAcCiGV0IIBYRf8BiGV0IAA/adB7dkycOFEvvPCCXnvtNaWnp9e99l5mZqZSUlKUmZmpyy+/XNdff71atWqljIwMXXvttRo0aJBOOeWURvkCAKCp0IEAYhkdCCBW0X8AYhkdCMBPGrTZMX36dEnS8OHD653/1FNPacKECZKkP/7xj4qLi9OYMWNUWVmpUaNG6fHHHw/LYiOtf//+YcmgcaSmpkZ6CQeVnp5uyt11112NvJLmI1JvXtaUHRgMBhUMBg/58ZycHNMca86ivLzcmamurjbN2rdvnzOzdu1a06yysjJnJhAImGZZhEIhU87zPGcmPj7eNMuy/rg425M1wznLkrNeX+G6PEmHve00VKtWrZyZpKQk0yzLuiw/E9afm3CL9ePAcLL0Qzh7Kzk52ZT77huLHkxFRYVpVtu2bZ2Zzz77zDSrtrbWlLOwHC9aL8/Sb9bvo+VnApFD/zV//fr1c2as9/eW++mamhrTrHCy9FE4+xbNBx3YtP75z3+acod6ibBvC+fjButjMcsxjbUDLZ1k/R2E5bgtMTHRNMuyLuvXmJDg/tX8u+++a5p15ZVXmnLNXYM2Oyw/sMnJyXrsscf02GOPHfGiACAa0YEAYhkdCCBW0X8AYhkdCMBPjugNygEAAAAAAAAAAKIFmx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9LiPQCAAD+kJKSEpaMJGVkZBztcgDAtwKBQJNeXmJioimXnJzszOTm5ppmpaenOzOpqammWcFg0Jmpra01zWrdurUzs3XrVtMsy9do/V6HQiFTzqKpf76A5sDSDWlpaaZZe/fudWbKy8tNs8LpuOOOc2bef/990yxLZ5WUlJhmAc1BOO97V65cacpZjo8qKytNszzPc2asX2NcnPvv6uPj402zqqurnRnrMZRlluVYWLIdd1rXlZDg/tW85X5Fkr766itnJj8/3zTLz3hmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvpYQ6QUAAAAAscTzPGcmEAiYZoVCIWcmNTXVNKusrMyZSUpKMs2qra015Sws62rfvn3YLi8zM9OUq6ysdGaSk5NNsyzfR6v4+PiwzQLwH23atDHltm/f7sxUVVUd7XIaLD8/35mx3vdYeqZTp06mWUAs2bNnjzOzceNG06zOnTs7M5ZjTsl2m66oqDDNiotz/1299bjHcpnWrzEhwf0r8JqaGtMsy/qtx2MpKSnOTG5urmnWggULnJnLL7/cNMvPeGYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwtYRILwAAAADAkamurnZmysrKTLPi4tx/B2W5PEkqKChwZrKyskyztm7d6sx0797dNKumpsaZSU5ONs1qap7nRXoJgO9YbzeBQMCZ6dGjh2nWRx995MxkZGSYZoVTUlKSMxMKhUyzLNdrbm6uaRYQSxYvXuzMtG3b1jQrPj4+LBnJdnxk6UnJdqxoXZclZ11XOL/GhAT3r9OtX6Old1NTU02z3nvvPWfm8ssvN83yM57ZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAryVEegEAAAAAjkx8fLwz89VXX5lmtWrVyplJT083zdq5c6cz43meaVZZWZkzs23bNtOsdu3aOTPV1dWmWRY1NTWmXFyc+2/QQqGQaZbleg0EAqZZgN9Ze8Zymxg8eLBp1gsvvODMFBQUmGY1taqqKlOuZcuWzkxSUtLRLgfwjcrKSlPu3XffdWaSk5OPdjl1LMeJ1lxKSoppluW6sBz3SFJ5ebkzk5mZaZpVUVFhyllY7jOs173luNM6KzEx0Zl5+eWXTbMuvvhiUy4aNeiZHVOnTlX//v2Vnp6utm3b6vzzz9eaNWvqZYYPH65AIFDv9Mtf/jKsiwaASKADAcQyOhBArKL/AMQyOhCAnzRos2PJkiWaOHGili1bpvnz56u6ulojR45UaWlpvdwVV1yhbdu21Z3uu+++sC4aACKBDgQQy+hAALGK/gMQy+hAAH7SoJexmjdvXr3/z5w5U23bttXy5ct16qmn1p2fmpqq3Nzc8KwQAKIEHQggltGBAGIV/QcgltGBAPzkqN6gvKioSNKBr+/7/PPPq02bNurTp49uvvnmw77ObmVlpYqLi+udAMAP6EAAsYwOBBCr6D8AsYwOBBDNjvgNykOhkCZPnqzBgwerT58+def/5Cc/UX5+vvLy8vTpp5/qt7/9rdasWaPZs2cfdM7UqVN15513HukyACAi6EAAsYwOBBCr6D8AsYwOBBDtjnizY+LEiVq1apXefffdeudfeeWVdf/+3ve+p3bt2umMM87Q+vXr1a1btwPm3Hzzzbr++uvr/l9cXKyOHTse6bIAoEnQgQBiGR0IIFbRfwBiGR0IINod0WbHpEmTNGfOHL3zzjvq0KHDYbMDBw6UJK1bt+6gBRcMBhUMBo9kGQAQEXQggFhGBwKIVfQfgFhGBwLwgwZtdniep2uvvVavvPKKFi9erC5dujg/Z+XKlZKkdu3aHdECASBa0IEAYhkdCCBW0X8AYhkdCMBPGrTZMXHiRL3wwgt67bXXlJ6eru3bt0uSMjMzlZKSovXr1+uFF17QWWedpdatW+vTTz/Vr3/9a5166qnq27dvo3wBANBU6EAAsYwOBBCr6D8AsYwOBOAnDdrsmD59uiRp+PDh9c5/6qmnNGHCBCUlJWnBggWaNm2aSktL1bFjR40ZM0a33npr2BYMAJFCBwKIZXRg+AQCgbDNSkhwH867XmpivyeeeMKZmTBhgmlWmzZtnJl169aZZllew/vf//63aZblL0wLCwtNs7Kzs52ZsrIy06zk5GRTziIUCjkzcXFxYbu8WED/+ZfneWGbtWHDBlNu3759zoy1G/bs2ePMtGrVyjTro48+cmZKSkpMs1JSUkw5NA90oJvldi9JtbW1zkxaWppp1vr1652ZpKQk0yxLJ1lnWY5D4uPjTbMsnbR3717TLMvxdziP0a1fo+W6z8nJMc2qrq52Zjp16mSa5WcNfhmrw+nYsaOWLFlyVAsCgGhFBwKIZXQggFhF/wGIZXQgAD/hT3oAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL6WEOkFAAAAAGg8J5xwgil37bXXOjMVFRWmWR9++KEzU1paappVXFzszLRr1840a9euXc5MMBg0zaqsrHRmkpKSTLMsEhJ46AY0VCAQCNusSy65xJTbvHmzM5OVlWWa1apVK1PO4he/+IUzU11dbZrVpUuXo10O0Ky0adPGlHvooYfCdplVVVXOjOUYSpK2bdvmzFj7YefOnWGbVVZWZspZxMfHOzPWY0CLtLQ0U85yf9ChQwfTLOvPYXPHMzsAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPC1hEgv4Ls8z5MkFRcXR3glAKLV/n7Y3xfNCR0IwIUORGMpKSlxZioqKkyzysvLnZnKykrTrLKyMmfGsnZJ2rdvnzOTmJhomhUMBp2Z+Ph406y4OPffoFkyzR39h4YKhUKmnOX2ZZ1l6UlLR0rh/Xmw9J+1ly3r52c5/OhAfFtVVZUzY7ndS7bjqOrqatOs0tJSZ6ampsY0y3IMaGU5JqutrQ3b5QUCAVMuIcH9q3nr9zEpKcmU8ytrBwa8KGvJLVu2qGPHjpFeBgAf2Lx5szp06BDpZYQVHQjAig4EEKvoPwCxjA4EEMtcHRh1mx2hUEhbt25Venp63S5YcXGxOnbsqM2bNysjIyPCK2w4P6+ftUeOn9ff2Gv3PE/79u1TXl5es/srx+bWgX5eu+Tv9bP2yKEDj9x3O5Cfhcjx89olf6/fz2uXGnf9sdR/Ej8LkcTaI8fP6+cY8Mg1tw7089olf6/fz2uX/L3+aOnAqHsZq7i4uEPuzmRkZPjuG/1tfl4/a48cP6+/MdeemZnZKHMjrbl2oJ/XLvl7/aw9cujAhjtUB/KzEDl+Xrvk7/X7ee1S460/1vpP4mchklh75Ph5/RwDNlxz7UA/r13y9/r9vHbJ3+uPdAc2r61gAAAAAAAAAAAQc9jsAAAAAAAAAAAAvuaLzY5gMKgpU6YoGAxGeilHxM/rZ+2R4+f1+3nt0cjP16ef1y75e/2sPXL8vv5o4vfr0s/r9/PaJX+v389rl/y//mji9+vSz+tn7ZHj5/X7ee3RyM/Xp5/XLvl7/X5eu+Tv9UfL2qPuDcoBAAAAAAAAAAAawhfP7AAAAAAAAAAAADgUNjsAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga77Y7HjsscfUuXNnJScna+DAgfrXv/4V6SU53XHHHQoEAvVOPXv2jPSyDumdd97ROeeco7y8PAUCAb366qv1Pu55nm6//Xa1a9dOKSkpGjFihNauXRuZxX6Ha+0TJkw44Htx5plnRmax3zF16lT1799f6enpatu2rc4//3ytWbOmXqaiokITJ05U69atlZaWpjFjxqigoCBCK/4Py9qHDx9+wHX/y1/+MkIr9ic/9p/krw70c/9JdGCk0IFNgw5sfH7uQPovcujApuHHDvRT/0l0YKT4uQPpv6bhx/6T/NWBfu4/iQ6MFD90YNRvdrz00ku6/vrrNWXKFH388cc6/vjjNWrUKO3YsSPSS3Pq3bu3tm3bVnd69913I72kQyotLdXxxx+vxx577KAfv++++/Twww9rxowZ+uCDD9SiRQuNGjVKFRUVTbzSA7nWLklnnnlmve/FX/7ylyZc4aEtWbJEEydO1LJlyzR//nxVV1dr5MiRKi0trcv8+te/1uuvv65Zs2ZpyZIl2rp1qy688MIIrvoblrVL0hVXXFHvur/vvvsitGL/8XP/Sf7pQD/3n0QHRgod2PjowKbh5w6k/yKHDmx8fu5Av/SfRAdGip87kP5rfH7uP8k/Hejn/pPowEjxRQd6UW7AgAHexIkT6/5fW1vr5eXleVOnTo3gqtymTJniHX/88ZFexhGR5L3yyit1/w+FQl5ubq53//33151XWFjoBYNB7y9/+UsEVnho312753ne+PHjvfPOOy8i62moHTt2eJK8JUuWeJ73zfWcmJjozZo1qy7zf//3f54kb+nSpZFa5kF9d+2e53nDhg3zfvWrX0VuUT7n1/7zPP92oJ/7z/PowEiiA8OPDmx6fu5A+i+y6MDw82sH+rX/PI8OjCQ/dyD9F35+7T/P828H+rn/PI8OjKRo7MCofmZHVVWVli9frhEjRtSdFxcXpxEjRmjp0qURXJnN2rVrlZeXp65du+qSSy7Rpk2bIr2kI7JhwwZt37693vchMzNTAwcO9MX3QZIWL16stm3bqkePHrr66qu1e/fuSC/poIqKiiRJrVq1kiQtX75c1dXV9a77nj17qlOnTlF33X937fs9//zzatOmjfr06aObb75ZZWVlkVie7/i9/6Tm0YHNof8kOrAp0IHhRQdGh+bQgfRf06ADw8vvHdgc+k+iA5uSnzuQ/gsvv/ef1Dw6sDn0n0QHNoVo7MCEJrukI7Br1y7V1tYqJyen3vk5OTn6/PPPI7Qqm4EDB2rmzJnq0aOHtm3bpjvvvFNDhw7VqlWrlJ6eHunlNcj27dsl6aDfh/0fi2ZnnnmmLrzwQnXp0kXr16/XLbfcotGjR2vp0qWKj4+P9PLqhEIhTZ48WYMHD1afPn0kfXPdJyUlKSsrq1422q77g61dkn7yk58oPz9feXl5+vTTT/Xb3/5Wa9as0ezZsyO4Wn/wc/9JzacD/d5/Eh3YFOjA8KMDo4PfO5D+axp0YPj5uQObS/9JdGBT8XMH0n/h5+f+k5pPB/q9/yQ6sClEawdG9WaHn40ePbru33379tXAgQOVn5+vl19+WZdffnkEVxZ7xo4dW/fv733ve+rbt6+6deumxYsX64wzzojgyuqbOHGiVq1aFbWv53g4h1r7lVdeWffv733ve2rXrp3OOOMMrV+/Xt26dWvqZaIJ0YHRgw5sfHQgvosOjA70X9OgA/Ft9F/0oAMbH/2H76IDowcd2PiitQOj+mWs2rRpo/j4+APebb6goEC5ubkRWtWRycrKUvfu3bVu3bpIL6XB9l/XzeH7IEldu3ZVmzZtoup7MWnSJM2ZM0eLFi1Shw4d6s7Pzc1VVVWVCgsL6+Wj6bo/1NoPZuDAgZIUVdd9tGpO/Sf5twObW/9JdGC40YGNgw6MDs2tA+m/8KMDG0dz6kC/9p9EBzYFP3cg/dc4mlP/Sf7twObWfxIdGG7R3IFRvdmRlJSkfv36aeHChXXnhUIhLVy4UIMGDYrgyhqupKRE69evV7t27SK9lAbr0qWLcnNz630fiouL9cEHH/ju+yBJW7Zs0e7du6Pie+F5niZNmqRXXnlFb7/9trp06VLv4/369VNiYmK9637NmjXatGlTxK9719oPZuXKlZIUFdd9tGtO/Sf5twObW/9JdGC40IGNiw6MDs2tA+m/8KEDG1dz6kC/9p9EBzYmP3cg/de4mlP/Sf7twObWfxIdGC6+6MBIvTO61YsvvugFg0Fv5syZ3meffeZdeeWVXlZWlrd9+/ZIL+2wbrjhBm/x4sXehg0bvPfee88bMWKE16ZNG2/Hjh2RXtpB7du3z1uxYoW3YsUKT5L34IMPeitWrPC++uorz/M875577vGysrK81157zfv000+98847z+vSpYtXXl4e4ZUffu379u3zfvOb33hLly71NmzY4C1YsMA76aSTvGOPPdarqKiI9NK9q6++2svMzPQWL17sbdu2re5UVlZWl/nlL3/pderUyXv77be9jz76yBs0aJA3aNCgCK76G661r1u3zrvrrru8jz76yNuwYYP32muveV27dvVOPfXUCK/cP/zaf57nrw70c/95Hh0YKXRg46MDm4afO5D+ixw6sPH5tQP91H+eRwdGip87kP5rfH7tP8/zVwf6uf88jw6MFD90YNRvdnie5z3yyCNep06dvKSkJG/AgAHesmXLIr0kpx//+Mdeu3btvKSkJK99+/bej3/8Y2/dunWRXtYhLVq0yJN0wGn8+PGe53leKBTybrvtNi8nJ8cLBoPeGWec4a1Zsyayi/7/HW7tZWVl3siRI73s7GwvMTHRy8/P96644oqouZM82LoleU899VRdpry83Lvmmmu8li1beqmpqd4FF1zgbdu2LXKL/v+51r5p0ybv1FNP9Vq1auUFg0HvmGOO8f7f//t/XlFRUWQX7jN+7D/P81cH+rn/PI8OjBQ6sGnQgY3Pzx1I/0UOHdg0/NiBfuo/z6MDI8XPHUj/NQ0/9p/n+asD/dx/nkcHRoofOjDw/y8UAAAAAAAAAADAl6L6PTsAAAAAAAAAAABc2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAMAnFi9erEAgoL/+9a+RXgoAAACaEMeBAHCgmTNnKhAIaOPGjQ3+3AkTJqhz585hXxMii80ONEggEDCdFi9eHOmlAkCj+ve//62LLrpI+fn5Sk5OVvv27fWDH/xAjzzySKSXBgBRZ/8D0W+f2rZtq9NOO01vvPFGpJcHAA3CcSCAWEYHIpolRHoB8Jdnn3223v+feeYZzZ8//4DzjzvuuKZcFgA0qffff1+nnXaaOnXqpCuuuEK5ubnavHmzli1bpoceekjXXnttpJcIAFHprrvuUpcuXeR5ngoKCjRz5kydddZZev3113X22WdHenkA4MRxIIBYRgci2rHZgQa59NJL6/1/2bJlmj9//gHnf1dZWZlSU1Mbc2mNorS0VC1atIj0MgBEmf/+7/9WZmamPvzwQ2VlZdX72I4dOyKzqDDwPE8VFRVKSUmJ9FIANFOjR4/WySefXPf/yy+/XDk5OfrLX/7CZgcAX+A4EEAsa64diOaDl7FC2A0fPlx9+vTR8uXLdeqppyo1NVW33HKLpG+Kb/+D2uTkZB1//PF6+umn633+/tci/e5LYW3cuFGBQEAzZ86sO2/79u267LLL1KFDBwWDQbVr107nnXfeAa/V98Ybb2jo0KFq0aKF0tPT9cMf/lCrV6+ul5kwYYLS0tK0fv16nXXWWUpPT9cll1wStusFQPOxfv169e7d+4CDO0lq27Zt3b8DgYAmTZqkV199VX369FEwGFTv3r01b968Az7v66+/1s9//nPl5OTU5f785z8711JZWamzzz5bmZmZev/99yVJoVBI06ZNU+/evZWcnKycnBxdddVV2rt3b73P7dy5s84++2y9+eabOvnkk5WSkqInnniigdcGABy5rKwspaSkKCHhP3+D9cADD+j73/++WrdurZSUFPXr1++gr1FfXl6u6667Tm3atFF6errOPfdcff311woEArrjjjua8KsAEEs4DgQQy6wd+NRTT+n0009X27ZtFQwG1atXL02fPv2Az9nfRe+++64GDBig5ORkde3aVc8888wB2dWrV+v0009XSkqKOnTooLvvvluhUOiA3GuvvaYf/vCHysvLUzAYVLdu3fS73/1OtbW1R/fFwxd4Zgcaxe7duzV69GiNHTtWl156qXJyclReXq7hw4dr3bp1mjRpkrp06aJZs2ZpwoQJKiws1K9+9asGX86YMWO0evVqXXvttercubN27Nih+fPna9OmTXVvMvTss89q/PjxGjVqlO69916VlZVp+vTpGjJkiFasWFHvzYhqamo0atQoDRkyRA888IAvn40CoPHl5+dr6dKlWrVqlfr06XPY7LvvvqvZs2frmmuuUXp6uh5++GGNGTNGmzZtUuvWrSVJBQUFOuWUU+oeFGdnZ+uNN97Q5ZdfruLiYk2ePPmgs8vLy3Xeeefpo48+0oIFC9S/f39J0lVXXaWZM2fqsssu03XXXacNGzbo0Ucf1YoVK/Tee+8pMTGxbsaaNWs0btw4XXXVVbriiivUo0eP8FxJAHAQRUVF2rVrlzzP044dO/TII4+opKSk3rOEH3roIZ177rm65JJLVFVVpRdffFE/+tGPNGfOHP3whz+sy02YMEEvv/yyfvrTn+qUU07RkiVL6n0cABoDx4EAYpm1A6dPn67evXvr3HPPVUJCgl5//XVdc801CoVCmjhxYr3sunXrdNFFF+nyyy/X+PHj9ec//1kTJkxQv3791Lt3b0nf/LHzaaedppqaGt10001q0aKFnnzyyYM+G23mzJlKS0vT9ddfr7S0NL399tu6/fbbVVxcrPvvvz+8VwiijwcchYkTJ3rf/TEaNmyYJ8mbMWNGvfOnTZvmSfKee+65uvOqqqq8QYMGeWlpaV5xcbHneZ63aNEiT5K3aNGiep+/YcMGT5L31FNPeZ7neXv37vUkeffff/8h17dv3z4vKyvLu+KKK+qdv337di8zM7Pe+ePHj/ckeTfddJP56wcQm9566y0vPj7ei4+P9wYNGuTdeOON3ptvvulVVVXVy0nykpKSvHXr1tWd98knn3iSvEceeaTuvMsvv9xr166dt2vXrnqfP3bsWC8zM9MrKyvzPO8//Thr1ixv37593rBhw7w2bdp4K1asqPucf/7zn54k7/nnn683a968eQecn5+f70ny5s2bd9TXCQAczlNPPeVJOuAUDAa9mTNn1svu77z9qqqqvD59+ninn3563XnLly/3JHmTJ0+ul50wYYInyZsyZUqjfS0AYhvHgQBimbUDv3s853meN2rUKK9r1671ztvfRe+8807deTt27PCCwaB3ww031J03efJkT5L3wQcf1MtlZmZ6krwNGzYc9rKvuuoqLzU11auoqKg7b/z48V5+fr75a4c/8DJWaBTBYFCXXXZZvfPmzp2r3NxcjRs3ru68xMREXXfddSopKdGSJUsadBkpKSlKSkrS4sWLD3hK7n7z589XYWGhxo0bp127dtWd4uPjNXDgQC1atOiAz7n66qsbtA4AsecHP/iBli5dqnPPPVeffPKJ7rvvPo0aNUrt27fX3//+93rZESNGqFu3bnX/79u3rzIyMvTll19K+ub1kf/2t7/pnHPOked59bpq1KhRKioq0scff1xvZlFRkUaOHKnPP/9cixcv1gknnFD3sVmzZikzM1M/+MEP6s3q16+f0tLSDui9Ll26aNSoUWG+hgDg4B577DHNnz9f8+fP13PPPafTTjtNv/jFLzR79uy6zLf/Qm/v3r0qKirS0KFD63Xh/peBueaaa+rN500xATQ2jgMBxDJrB377eG7/M3uHDRumL7/8UkVFRfVm9urVS0OHDq37f3Z2tnr06FHXldI3v1M85ZRTNGDAgHq5g738/Lcve9++fdq1a5eGDh2qsrIyff7550d3BSDq8TJWaBTt27dXUlJSvfO++uorHXvssYqLq7/Hdtxxx9V9vCGCwaDuvfde3XDDDcrJydEpp5yis88+Wz/72c+Um5srSVq7dq0k6fTTTz/ojIyMjHr/T0hIUIcOHRq0DgCxqX///po9e7aqqqr0ySef6JVXXtEf//hHXXTRRVq5cqV69eolSerUqdMBn9uyZcu6TdqdO3eqsLBQTz75pJ588smDXtZ33+ht8uTJqqio0IoVK+qe1rvf2rVrVVRUVO/1Ug83q0uXLrYvGADCYMCAAfXeoHzcuHE68cQTNWnSJJ199tlKSkrSnDlzdPfdd2vlypWqrKysywYCgbp/f/XVV4qLizugw4455pjG/yIAxDyOAwHEMksHvvfee5oyZYqWLl2qsrKyep9fVFSkzMzMuv+7ulL65thv4MCBB+QO9vJ7q1ev1q233qq3335bxcXFB1w2mjc2O9AoDvaaeVbffiD7bQd7I6HJkyfrnHPO0auvvqo333xTt912m6ZOnaq3335bJ554Yt0bFT377LN1GyDf9u03w5S+2UD57mYMABxOUlKS+vfvr/79+6t79+667LLLNGvWLE2ZMkWSFB8ff9DP8zxPkup66tJLL9X48eMPmu3bt2+9/5933nl68cUXdc899+iZZ56p11uhUEht27bV888/f9BZ2dnZ9f5/NH0NAEcrLi5Op512mh566CGtXbtWe/bs0bnnnqtTTz1Vjz/+uNq1a6fExEQ99dRTeuGFFyK9XACoh+NAALHsUB146aWX6owzzlDPnj314IMPqmPHjkpKStLcuXP1xz/+8YA3FXd1ZUMUFhZq2LBhysjI0F133aVu3bopOTlZH3/8sX77298e9A3N0byw2YEmk5+fr08//VShUKjeAdn+p5Dl5+dL+mb3VvqmoL7tUM/86Natm2644QbdcMMNWrt2rU444QT94Q9/0HPPPVf3lOG2bdtqxIgR4f6SAKCe/X+tvG3bNvPnZGdnKz09XbW1teaeOv/88zVy5EhNmDBB6enpmj59et3HunXrpgULFmjw4ME8gAXgCzU1NZKkkpIS/e1vf1NycrLefPNNBYPBusxTTz1V73Py8/MVCoW0YcMGHXvssXXnr1u3rmkWDQDfwXEggFj27Q58/fXXVVlZqb///e/1nrVxsJeSt8rPz6979ZZvW7NmTb3/L168WLt379bs2bN16qmn1p2/YcOGI75s+At/wo4mc9ZZZ2n79u166aWX6s6rqanRI488orS0NA0bNkzSNwUWHx+vd955p97nP/744/X+X1ZWpoqKinrndevWTenp6XUveTBq1ChlZGTo97//vaqrqw9Y086dO8PytQGILYsWLTroX5nMnTtX0sGfSnso8fHxGjNmjP72t79p1apVB3z8UD31s5/9TA8//LBmzJih3/72t3XnX3zxxaqtrdXvfve7Az6npqbmgI1kAIik6upqvfXWW0pKStJxxx2n+Ph4BQKBes/o3bhxo1599dV6n7f/Nea/e3z4yCOPNPqaAcQ2jgMBxDJLB+5/psa3c0VFRQf88UpDnHXWWVq2bJn+9a9/1Z23c+fOA57JdrDLrqqqOuCYEc0Xz+xAk7nyyiv1xBNPaMKECVq+fLk6d+6sv/71r3rvvfc0bdo0paenS5IyMzP1ox/9SI888ogCgYC6deumOXPmHPD6ol988YXOOOMMXXzxxerVq5cSEhL0yiuvqKCgQGPHjpX0zXtyTJ8+XT/96U910kknaezYscrOztamTZv0j3/8Q4MHD9ajjz7a5NcFAH+79tprVVZWpgsuuEA9e/ZUVVWV3n//fb300kvq3LmzLrvssgbNu+eee7Ro0SINHDhQV1xxhXr16qU9e/bo448/1oIFC7Rnz56Dft6kSZNUXFys//qv/1JmZqZuueUWDRs2TFdddZWmTp2qlStXauTIkUpMTNTatWs1a9YsPfTQQ7rooovCcTUAQIO98cYbdc/q3bFjh1544QWtXbtWN910kzIyMvTDH/5QDz74oM4880z95Cc/0Y4dO/TYY4/pmGOO0aefflo3p1+/fhozZoymTZum3bt365RTTtGSJUv0xRdfSDr0y6ICwNHiOBBALLN0YEFBgZKSknTOOefoqquuUklJif70pz+pbdu2DXr227fdeOONevbZZ3XmmWfqV7/6lVq0aKEnn3yy7lVk9vv+97+vli1bavz48bruuusUCAT07LPPHtFLYsGf2OxAk0lJSdHixYt100036emnn1ZxcbF69Oihp556ShMmTKiXfeSRR1RdXa0ZM2YoGAzq4osv1v33368+ffrUZTp27Khx48Zp4cKFevbZZ5WQkKCePXvq5Zdf1pgxY+pyP/nJT5SXl6d77rlH999/vyorK9W+fXsNHTq0wQeiACBJDzzwgGbNmqW5c+fqySefVFVVlTp16qRrrrlGt956q7Kysho0LycnR//617901113afbs2Xr88cfVunVr9e7dW/fee+9hP/eWW25RUVFR3QPdiRMnasaMGerXr5+eeOIJ3XLLLUpISFDnzp116aWXavDgwUfxlQPA0bn99tvr/p2cnKyePXtq+vTpuuqqqyRJp59+uv73f/9X99xzjyZPnqwuXbro3nvv1caNG+s9kJWkZ555Rrm5ufrLX/6iV155RSNGjNBLL72kHj16KDk5uUm/LgCxg+NAALHM0oFZWVn661//qltvvVW/+c1vlJubq6uvvlrZ2dn6+c9/fkSX265dOy1atEjXXnut7rnnHrVu3Vq//OUvlZeXp8svv7wu17p1a82ZM0c33HCDbr31VrVs2bLuPUT2PzMYzVvAY2sLAAAAQDOwcuVKnXjiiXruued0ySWXRHo5AAAAAJoQ79kBAAAAwHfKy8sPOG/atGmKi4ur94aUAAAAAGIDL2MFAAAAwHfuu+8+LV++XKeddpoSEhL0xhtv6I033tCVV16pjh07Rnp5AAAAAJoYL2MFAAAAwHfmz5+vO++8U5999plKSkrUqVMn/fSnP9V//dd/KSGBv+kCAAAAYg2bHQAAAAAAAAAAwNd4zw4AAAAAAAAAAOBrbHYAAAAAAAAAAABfi7oXsw2FQtq6davS09MVCAQivRwAUcjzPO3bt095eXmKi2tee7Z0IAAXOhBArKL/AMQyOhBALDN3oNdIHn30US8/P98LBoPegAEDvA8++MD0eZs3b/YkceLEiZPztHnz5saqsKNypP3neXQgJ06c7Cc6kBMnTrF6itb+8zweB3PixKnxT3QgJ06cYvnk6sBGeWbHSy+9pOuvv14zZszQwIEDNW3aNI0aNUpr1qxR27ZtD/u56enpkqTNmzcrIyOjMZYXk+bPn2/KLVq0yJnp3r27aVZtba0z06ZNG9OssrIyZ2bnzp2mWUlJSc5MVVWVaZblMm+66SbTrBYtWphykIqLi9WxY8e6vogmR9N/Eh0IwI0OBBCrorn/JB4HA2hcdCCAWGbtwIDneV64L3zgwIHq37+/Hn30UUnfPB2tY8eOuvbaa52/+C0uLlZmZqaKiooouDCaN2+eKWfZFOnZs6dplmWzIzs72zSrtLTUmdmxY4dpVjAYdGYqKytNsyyXOWXKFNMsNjvsorknjqb/pOj+2gBEh2juCToQQGOK9o7gcTCAxhTtPUEHAmhM1p4I+4v8VVVVafny5RoxYsR/LiQuTiNGjNDSpUvDfXEAEDXoPwCxjA4EEMvoQACxjA4EEC3C/jJWu3btUm1trXJycuqdn5OTo88///yAfGVlZb2/oi8uLg73kgCgSTS0/yQ6EEDzQQcCiGU8DgYQy+hAANEi7M/saKipU6cqMzOz7tSxY8dILwkAmgwdCCCW0YEAYhX9ByCW0YEAGkvYNzvatGmj+Ph4FRQU1Du/oKBAubm5B+RvvvlmFRUV1Z02b94c7iUBQJNoaP9JdCCA5oMOBBDLeBwMIJbRgQCiRdg3O5KSktSvXz8tXLiw7rxQKKSFCxdq0KBBB+SDwaAyMjLqnQDAjxrafxIdCKD5oAMBxDIeBwOIZXQggGgR9vfskKTrr79e48eP18knn6wBAwZo2rRpKi0t1WWXXdYYFweD2bNnm3J/+tOfGnklRyY/P9+Z+eqrr8J2eXFxtn3AUCjkzJx00kmmWWPHjjXlEN3oPwCxjA4EEMvoQACxjA4EEA0aZbPjxz/+sXbu3Knbb79d27dv1wknnKB58+Yd8EZFANDc0H8AYhkdCCCW0YEAYhkdCCAaBDzP8yK9iG8rLi5WZmamioqKeBpbGF155ZWmHM/s+EY4n9nxl7/8xTSLZ3bYNeeeaM5fG4DwaM490Zy/NgBHrzl3RHP+2gCER3Puieb8tQEID2tPhP09OwAAAAAAAAAAAJoSmx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GsJkV4AmkZqaqopl5+f78y0bNnSNKu0tNSZqampMc2yaN++vSlnucycnBzTrC+//NKZqa6uNs0CAACIJZMnTzblzj33XGfm9NNPP8rVAAAAAPA7ntkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvJUR6AWga69evN+VqamqcmYKCAtOsiooKZyYYDJpmeZ7nzIRCobDNsqzdOuvLL780zQIAANHLcp8fCASaYCUNV1tba8rFx8eH7TJbt27tzFRVVZlmPfTQQ87Mvffea5p14403mnIAAABoHNbf31nExTX93/GXl5c7M5WVlaZZe/fudWZatmxpmlVUVBSWjCQtXLjQmTnmmGNMs8455xxTLlx4ZgcAAAAAAAAAAPA1NjsAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL7GZgcAAAAAAAAAAPC1hEgvAE2jsrLSlKutrXVmEhMTTbNatGjhzHieZ5qVkOD+Ua2qqjLNqqmpcWbKyspMs9LT052ZFStWmGYBAIDoFQgEIr2Eg7IcS8XHx4ft8qzXwyWXXOLMPPfcc0e7nDrWdaWmpjozkyZNOtrlAAAAxCTLsWlcXHT+7f3//u//mnJr1qxxZkaNGmWatXr1amfG+jvK1q1bOzMVFRWmWZbvY1FRkWlWU4vOny4AAAAAAAAAAAAjNjsAAAAAAAAAAICvsdkBAAAAAAAAAAB8jc0OAAAAAAAAAADga2x2AAAAAAAAAAAAX2OzAwAAAAAAAAAA+BqbHQAAAAAAAAAAwNfY7AAAAAAAAAAAAL6WEOkFoGns3LnTlPM8LywZSaqtrQ3brOrqamcmLs62d5eSkhKWjCQVFhY6M8XFxaZZAAAADRUIBMI2a+jQoc5Mly5dTLOee+65o11OgyxYsMCUGzFihDOzdu1a06yHHnrIlAMAAIgVlmPTUChkmlVZWenMrF+/3jTr/fffd2Y2bNhgmlVSUuLM/OMf/zDNOvfcc52ZZcuWmWZlZ2ebchZbtmxxZqzXV01NjTOTkBC+LQqe2QEAAAAAAAAAAHwt7Jsdd9xxhwKBQL1Tz549w30xABCV6EAAsYr+AxDL6EAAsYwOBBAtGuVlrHr37l3vqeThfCoKAEQ7OhBArKL/AMQyOhBALKMDAUSDRmmehIQE5ebmNsZoAIh6dCCAWEX/AYhldCCAWEYHAogGjfKeHWvXrlVeXp66du2qSy65RJs2bTpktrKyUsXFxfVOAOBndCCAWNWQ/pPoQADNC8eAAGIZHQggGoR9s2PgwIGaOXOm5s2bp+nTp2vDhg0aOnSo9u3bd9D81KlTlZmZWXfq2LFjuJcEAE2GDgQQqxrafxIdCKD54BgQQCyjAwFEi4DneV5jXkBhYaHy8/P14IMP6vLLLz/g45WVlaqsrKz7f3FxsTp27KiioiJlZGQ05tJiyoknnmjKbdu2zZlJTEw0zaqtrXVmrD9+oVDImYmLs+3dJSUlOTMpKSmmWYWFhc5Mr169TLPefvttUw7f9ERmZqYveoIOBBBufulAV/9JdGC0GTp0qDPz9ddfm2Z9+eWXR7ucBlm4cKEpN2LECGfmuuuuM8166KGHTDmEj1/6T+IYEED40YFoLiy/45NU72fkUNavX2+a9f777zszGzZsMM0qKipyZpKTk02zzj33XGdm2bJlplk9e/Y05Sy2bNnizOzdu9c06+abb3ZmLO/xY+3ARn+3oKysLHXv3l3r1q076MeDwaCCwWBjLwMAIoIOBBCrXP0n0YEAmi+OAQHEMjoQQKQ0ynt2fFtJSYnWr1+vdu3aNfZFAUDUoQMBxCr6D0AsowMBxDI6EECkhP2ZHb/5zW90zjnnKD8/X1u3btWUKVMUHx+vcePGhfui0ACWpwNJtpeCss6qqakx5SzS0tKcmbKyMtMsy9PlrC+vVVVV5cxY1o7mgw4EEKvov8ioqKhwZk499VTTrA8//NCZWbBggWmWhfUlDCzHp2eccYZp1tVXX+3MPPzww6ZZK1ascGbeeecd0ywL6/FpIBAI22XCjg4EEMvoQH+zHGNYjy+efvppZ2bt2rWmWa1bt3ZmLL+Xk6Q1a9Y4M4MHDzbN2rFjhzNjfUksy0u/Wq8v69sOWKSmpjoz1rdMsP4eOVzCfmlbtmzRuHHjtHv3bmVnZ2vIkCFatmyZsrOzw31RABB16EAAsYr+AxDL6EAAsYwOBBAtwr7Z8eKLL4Z7JAD4Bh0IIFbRfwBiGR0IIJbRgQCiRaO/ZwcAAAAAAAAAAEBjYrMDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK8lRHoBaBodO3Y05dauXevMtGjRwjQrPj7emamtrTXNKikpcWZqampMs0KhkDOTkGC7aVjW1a5dO9MsAADwjVAodNj767i46Px7HetxjeUYqaqqyjRrwIABzsyaNWtMs/7+9787M2eccYZplud5zkwkvo+PP/542GZNnz7dmbFeXwsXLnRmAoGAaZblurfOAgAAzZ/l92mJiYmmWVu3bnVmrL9XzMjIcGbKy8tNs4YNG+bMfPbZZ6ZZlmPYTp06mWatXLnSmdm2bZtp1vDhw52Z1q1bm2adddZZply4WH5Xa8lIPLMDAAAAAAAAAAD4HJsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4WkKkF4Cm0blzZ1OuuLjYmcnNzT3K1fxHbW2tKVdZWenMJCUlmWbFx8c7M8nJyaZZlvUfc8wxplkAACB6We7zLccYklRaWurM9O3b1zRr48aNzsy5555rmnXOOeeYchaBQCBss5ra448/bsotXrzYmXn77bdNs4477jhn5v/+7/9Ms/x83QNoPJbH1P/v//0/06wpU6Y4M61btzbN8jzPmbH2Wk1NjTOTkMCvwYDvCuftYvTo0c7Mzp07TbMsv0/LysoyzWrZsqUpFy5/+tOfTLnbbrvNmdmxY4dp1r59+5yZtm3bmmZZhEIhUy4uzv1ci3BlJJ7ZAQAAAAAAAAAAfI7NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAryVEegFoGp07dzblEhLcPxJJSUlHuZr/aNOmjSnXrl07Z+af//ynaVbHjh2dmbS0NNMsy/WVnZ1tmgWgvtraWlMuLs69bx8IBI52OY1i5cqVptxnn33mzLRv394067333nNmbrnlFtMshJ/neWHJ+F1cXJzptt2U4uPjnZni4mLTrMzMTGemZcuWplktWrRwZv7whz+YZllUVlaacomJiWG7TAvr7cKSsxzfSdJ5553nzKxfv940a8uWLc5M9+7dTbO++OILUy5campqTDnLbSha768Byd4z4fw5Xrp0qTNz0003mWbl5uY6M5s3bzbNuueee5yZ+++/3zTLcr1ar1Nrf4dLdXW1KdfU94lAQ4Wzt0444YSwzfKz0aNHm3KW37Favz+lpaWmXLhE2+O1/aJzVQAAAAAAAAAAAEZsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPgamx0AAAAAAAAAAMDX2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GsJkV4AmsYxxxxjyqWkpIQlI0nV1dXOjOd5pln5+fnOzDvvvGOalZqa6sxYv8bk5GRnpkOHDqZZQHNgvU0HAgFnJj4+/miX02B79uxxZt577z3TrD/84Q/OzL59+0yz4uLcf5vw1VdfmWZVVVU5M+PHjzfNat++vSkXjSzXgyRt2rTJmfn73/9umjV27FhnJi8vz5mx3H7wjdraWmcmnF2TmZlpyrVt29aZKS0tNc0aMWKEM9O1a1fTLItgMBi2WX43depUZ+aee+4xzcrNzXVm1q5da5p10kknOTMff/yxaZZFQoLtIWUoFHJm6DdEs3D+fN50002m3D/+8Q9nxnKfIkmJiYnOTOfOnU2z1q9fb8pZWI5zw8m69vPPP9+ZOeWUU0yz/vSnP5lyQLiF8/F5OC/TckxgZV27pWssjx0k2+OHV1991TRryJAhzozlvkCS+vTp48ykpaWZZllYHwf/9re/dWYs14P1MXyD71XeeecdnXPOOcrLy1MgEDjgm+d5nm6//Xa1a9dOKSkpGjFihPnAHACiGf0HIJbRgQBiGR0IIFbRfwD8pMGbHaWlpTr++OP12GOPHfTj9913nx5++GHNmDFDH3zwgVq0aKFRo0apoqLiqBcLAJFE/wGIZXQggFhGBwKIVfQfAD9p8MtYjR49WqNHjz7oxzzP07Rp03TrrbfqvPPOkyQ988wzysnJ0auvvmp6+QYAiFb0H4BYRgcCiGV0IIBYRf8B8JOwvjjihg0btH379nqvHZyZmamBAwdq6dKlB/2cyspKFRcX1zsBgN8cSf9JdCCA5oEOBBDLeBwMIFZxDAgg2oR1s2P79u2SpJycnHrn5+Tk1H3su6ZOnarMzMy6U8eOHcO5JABoEkfSfxIdCKB5oAMBxDIeBwOIVRwDAog2Yd3sOBI333yzioqK6k6bN2+O9JIAoMnQgQBiGR0IIFbRfwBiGR0IoLGEdbMjNzdXklRQUFDv/IKCgrqPfVcwGFRGRka9EwD4zZH0n0QHAmge6EAAsYzHwQBiFceAAKJNWDc7unTpotzcXC1cuLDuvOLiYn3wwQcaNGhQOC8KAKIK/QcgltGBAGIZHQggVtF/AKJNQkM/oaSkROvWrav7/4YNG7Ry5Uq1atVKnTp10uTJk3X33Xfr2GOPVZcuXXTbbbcpLy9P559/fjjXjQY69thjwzYrGAyacpad+fj4+LDNqq2tNc1KTEwMS0aSEhLcN6EePXqYZiH6RVP/WX/eq6urnRnrz7vl9hoIBEyzwmnWrFnOzB/+8AfTrJKSEmfmq6++Ms0aN26cM9OuXTvTrJdfftmZOe6440yzioqKnJk+ffqYZh3ur7X2y8rKMs3as2ePM1NVVRW2WdY3QbRcFzt37jTN+nZ/HMrjjz9umhUJ0dSBVpbeWrRokWnWz3/+c2fGclwg2W6vF1xwgWlWdna2M7N48WLTLMttrGXLlqZZaWlpzkxSUpJpVk1NjTNjvf/xPC9ssyzrP9ybszb0Mn/5y1+aZq1YscKZufjii02znn76aWdmyZIlpllnnnmmKRet/NiBCC/r7fmmm25yZvbt22eadeKJJzoz1mN5y/FK+/btTbOSk5Odmauvvto0a/r06aZcuJx99tmmnKXjrce5O3bscGbatm1rmhUJ9J9/ReLxueVYy/q7wKYWFxe+5wR88MEHppzld6wPPfSQadYDDzzgzLzwwgumWXfeeaczY/3diGXj8ze/+Y0zU1JSomeeecaZa/Bmx0cffaTTTjut7v/XX3+9JGn8+PGaOXOmbrzxRpWWlurKK69UYWGhhgwZonnz5pnuDAEgmtF/AGIZHQggltGBAGIV/QfATxq82TF8+PDD7tIFAgHddddduuuuu45qYQAQbeg/ALGMDgQQy+hAALGK/gPgJ2F9zw4AAAAAAAAAAICmxmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9jswMAAAAAAAAAAPhaQqQXgKbRtWtXUy4+Pt6ZSUxMNM1KTU11Zvbt22ealZWV5cxY1i7Z1h8KhUyzWrRo4cx07NjRNAv4ttraWtXW1h7y49afd2uuqS1YsMCZmThxomlW27ZtnRlrb1144YXOzNixY02zLP32P//zP6ZZ6enpzszhfl6+LS0tzZmx3mcUFBQ4M+Xl5aZZlu9RZmamaVbLli2dmaSkJNOs5ORkZ8ZyHyVJ//jHP0w5NK1rrrnGlNu5c6cz0759e9MsS4+UlJSYZr3//vvOzMcff2ya9eWXXzozRUVFplmW22tKSoppVlyc+++zrLdpi8rKSlPOcl106NDBNCsjI8OZOe6440yztmzZ4szMmjXLNMtyvS5ZssQ064MPPnBm8vLyTLMQHtZjB8/znBnL7VSSampqnJlw3p6tHn/8cWfm7rvvNs065ZRTnJnOnTubZpWVlTkzlsekkq2PNm7caJrVrl07Z2bbtm2mWWeeeaYzY/kZlKS33nrLmbF8fyTp/PPPd2Y2b95smnX77bc7MzNmzDDNAqKd9f4gGgUCgbDNKiwsNOWCwaAzY/0dxKeffurM/PrXvzbN6tOnjzNjPZ60/F7U8jiquLjYdHn+/QkEAAAAAAAAAAAQmx0AAAAAAAAAAMDn2OwAAAAAAAAAAAC+xmYHAAAAAAAAAADwNTY7AAAAAAAAAACAr7HZAQAAAAAAAAAAfI3NDgAAAAAAAAAA4GtsdgAAAAAAAAAAAF9LiPQC0DTi4+NNuYQE949EeXm5aZbneWHJSFKbNm1MOYu4OPceX21trWmW9XoFGio+Pj4sP19z5sxxZgoKCkyztm3b5szMnTvXNMtyGzv++ONNs/bu3evM7Nq1yzRr6dKlzsy//vUv06xPPvnEmQkGg6ZZeXl5zkxVVZVpVmpqqjNjXVf37t3DNst632KRmJjozFivr7KyMmfGelvdsWNHWC7Pkmnu7rzzTlPuueeec2aKi4tNs5KSkpyZRYsWmWZ16dLFlAOOhOV+aujQoaZZH3zwgTOTkpJimvX22287M5deeqlpFsIjEo9lLF1qtWXLFmfmtttuM81as2aNMzNq1CjTrIyMDGemqKjINOvYY491ZqyPqb/88ktnJj093TTL8rggNzfXNCstLc2ZSU5ONs064YQTnJnq6mrTrGXLljkz27dvN82yXF8AmpeWLVuacvn5+c7MxRdfbJp1zTXXODOdO3c2zXr++eedGetj6rfeesuZsdxnlJaWmi6PZ3YAAAAAAAAAAABfY7MDAAAAAAAAAAD4GpsdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfS4j0AhBdgsFg2GZlZGQ4MxUVFaZZnTp1cmZqa2tNswKBgClnUV5eHrZZwLdt3LhR6enph/z4tddea5pTVlbmzIRCIdOswsJCZ+Zwa/62zMxMZ2bt2rWmWRaJiYmm3M6dO52Z4uJi06z8/Hxnxnrdl5aWOjNVVVWmWdu3b3dmLP0tSXv37nVm9u3bZ5oVHx/vzKSkpJhmWbo5Ls729x6Wn53U1FTTLEvOch8Vzvsxv7rjjjtMOUsn1dTUmGaNHTvWmenSpYtplqVrWrZsaZpVVFTkzFiP7yy3Q+vxlud5zoz1dmiZZe1Ty+3Hcj1IUnV1dVguT7KtPyHB9tBtwIABzsygQYNMs5YtW+bMVFZWmmZ98cUXphyiz9dff+3MLFq0yDTrs88+c2ZWrlxpmrVhwwZn5phjjjHN6tChgzNjvT1bjsmSk5NNszZu3OjMWG+Dls61XA+S7b6zpKTENMtyP7ZlyxbTrG3btjkz1mNmy/Gw9djUer8CoPlo3769KTdp0iRn5uOPPzbNstznWY4TJendd991ZvLy8kyzLI+DLY+7rb/X4ZkdAAAAAAAAAADA19jsAAAAAAAAAAAAvsZmBwAAAAAAAAAA8DU2OwAAAAAAAAAAgK+x2QEAAAAAAAAAAHyNzQ4AAAAAAAAAAOBrbHYAAAAAAAAAAABfY7MDAAAAAAAAAAD4WkKkF4DokpiY6Mx4nmeaFR8f78xUVlaaZqWkpDgz1nUlJSU5M4FAwDTLmgMa6umnn1YwGDzkx5cvX26a06VLF2cmLs62752RkeHMJCTY7lZCoZAzk5OTE7ZZZWVlplmWrrGua+vWrc5MTU2NaVZycrIzY73uW7Ro4cyUl5ebZlmkpqaacof7ed/Pep+Rnp7uzFivL8v9ovX7WF1d7cxs3rzZmSkpKTFdXnN22mmnmXLvvvuuM9O1a1fTrD//+c+mnEVmZqYzY/15t3RzVVWVaZbl2M16vGVhPY6yXKb1viycx27Wy7Sw3P9Yv48Wc+fONeXat2/vzFh/Vt966y1n5q677jLNimUPPfSQKbdt2zZnZvv27aZZe/bscWastwfLY7GsrCzTrBNPPNGZadu2rWlWcXGxM1NUVGSaZTlWtB5r/X/t3Xts1fX9x/H36e303lJKb1Kg1AmM26KTimyIglzmmAguGjWBxYi4YhTcNJg5x0zWzSXTuTCXLA62RVDJRKYxbihQo4JThCFRO9tUKaOAFHuFXs/n94ejP6uln3fx2/M9n36fj6QJ7XnzPu/zPee8zuf003NOT0+PtSY9PV3VS5Px2oxsaGiw1miuaxHdGvA///mPZ70KCwtVvbTrOw3N7VCzTtTUABh63/72tz3rNWLECGvNoUOHVL1KS0utNZrHdBGRCy+80Fqj+T2FiO53Nl7+PmDQK/RXX31VFi9eLEVFRRIKheS5557rc/qKFSskFAr1+Vq4cOFgzwYAYg75ByDIyEAAQUYGAggq8g+ASwa92dHW1ibTp0+XDRs2nLNm4cKFUl9f3/u1ZcuWrzQkAMQC8g9AkJGBAIKMDAQQVOQfAJcM+m2sFi1aJIsWLRqwJhwOS0FBwXkPBQCxiPwDEGRkIIAgIwMBBBX5B8AlQ/IB5bt375a8vDyZMGGC3HHHHar3cwSA4YD8AxBkZCCAICMDAQQV+QcgVnj+AeULFy6UpUuXSklJidTU1Mj9998vixYtkj179vT7gVgdHR19PmBE88FgABCLBpt/ImQggOGDDAQQZDwPBhBUrAEBxBLPNztuvPHG3n9PnTpVpk2bJqWlpbJ7926ZO3ful+orKipk/fr1Xo8BAFE32PwTIQMBDB9kIIAg43kwgKBiDQgglgzJ21h93vjx4yU3N1eqq6v7PX3dunXS1NTU+1VXVzfUIwFAVNjyT4QMBDB8kYEAgoznwQCCijUgAD95/sqOLzpy5Ig0NDRIYWFhv6eHw2EJh8NDPQYARJ0t/0TIQADDFxkIIMh4HgwgqFgDAvDToDc7Wltb++zO1tbWyoEDByQnJ0dycnJk/fr1smzZMikoKJCamhq599575cILL5QFCxZ4OjgARBv5ByDIyEAAQUYGAggq8g+ASwa92fH222/LlVde2fv92rVrRURk+fLl8vjjj8vBgwflz3/+szQ2NkpRUZHMnz9fHnroIXZsHTFixAhrjTFG1aunp8da09XVpeqVnp5urQmFQqpecXH2d2+LRCKqXsnJyao6DA/RzL9Zs2ZJWlraOU9/8cUXVX1qa2utNe3t7apeCQn2hwzN/UtEVMdEe/8614fefZ42HzRzNTY2qnppsjIxMVHVS5OnmutHRCQpKclak5KSouqluYya2UV0uauZXevzH4j4Veu09/GRI0daaw4fPmytaWtrU52f12JpDXjFFVeo6nbt2mWtOXs5vNDS0qKq8/K23NnZaa3Rrms0t3dt1nR3d1trtPmgoc15zbHQHi/NY572cVHzWKw9Xp988om1ZtSoUape11xzjbXmySefVPXSHtdYFa0MvPfeewfMiD179qj6jBs3zlqjvU4GWpOe5eXaQXu/0dR5+ZiZn5/vWa+cnBxVneb58vHjx1W9Tp8+ba3RPHcQ0V2Pqampql6aty/Srts0zx+0z32ysrKsNV6ucTRrliCsASORyIDZpM2HWKW573i5DtGuj6JNs04U0c2v+X2A1l133aWqe+2116w1119/varXli1brDXa5ysnT5601hw7dkzVS3M79PLxWpMX2kwZ9GbHnDlzBrxz/uMf/xhsSwBwAvkHIMjIQABBRgYCCCryD4BL3N4SBQAAAAAAAAAAgcdmBwAAAAAAAAAAcBqbHQAAAAAAAAAAwGlsdgAAAAAAAAAAAKex2QEAAAAAAAAAAJzGZgcAAAAAAAAAAHAamx0AAAAAAAAAAMBpCX4PgNiSlZVlrWlsbFT1MsZYa3p6elS9CgoKrDUJCbqbs6aupaVF1SstLU1VBwzWvHnzJDMzc8DTNaqrq601b731lqrXzp07rTUffPCBqldNTY21pr6+XtWru7tbVRdtSUlJ1hpNToroLqO2FwYnFApZayZPnqzq9d///tdaM2XKFGuN9jFqOHvzzTdVddnZ2daalStXfsVp/l98fLyqzsvc0qylNLdjEV2OdHZ2qnq1t7dbazQ5KaJf43nVS3u8NNejdvaOjg5rjTbnU1NTVXUaf/3rX601L774oqrX0aNHrTW227P2uYPLvvWtbw14HY4aNUrVp7a21lpTV1en6qW5P8fF6f6OMiUlxbNemvuN5nFARHf/OnXqlKpXcnKyteb48eOqXk1NTdaavLw8Va8xY8ZYa7THa+TIkdaanJwcVa+5c+daa8aOHavqdfHFF1trNM9pREQqKiqsNaWlpapeGq+//rq1RnObd11cXJw6AwYSiUQ8mOYz0V5raXtp62KRl2s7rUcffdRa89hjj6l6LViwwFqzdetWVa+rr77aWqO9Pbe1tVlrNL/3FdFl+KeffqrqpVlHaC6j9jjwyg4AAAAAAAAAAOA0NjsAAAAAAAAAAIDT2OwAAAAAAAAAAABOY7MDAAAAAAAAAAA4jc0OAAAAAAAAAADgNDY7AAAAAAAAAACA09jsAAAAAAAAAAAATmOzAwAAAAAAAAAAOI3NDgAAAAAAAAAA4LQEvwdAbElNTbXWfPrpp6peoVDIWtPZ2anqlZGRYa1JTk5W9dKcZ3d3t6pXYmKiqg7wWnx8vKpuwoQJntSIiNxyyy2qOq/09PSo6o4ePWqtaWpqUvVqbGz0pEZEJC7O/vcEXmaI5vxEdMc1MzNT1SscDltrTp48qepljLHWaG/3I0eOtNZoHu9ERNLT06012rny8/NVdTba2Yezl19+WVV31113eXaeZ86csdZ4eT+MRCKqXpq67Oxsz3ppjoOIyKhRo6w12uOlmUu7dvNyHejlWldzm9A+ZmjytKGhQdVLk6fXX3+9qtdTTz1lrbEdB+3awGXf+973BnwczsrKUvW55pprrDVpaWmqXpr7/aFDh1S93nvvPWuNdu2guX9pH6M1a5oLLrhA1auoqMhaU1hYqOo1ceJEa8348eNVvTQ0+SGiu+1o76+a5/EdHR2qXpo1bG1traqX5rnP4sWLVb0mT55srUlJSbHWNDc3y69+9SvVeQ5X2vWRdo0R7V5e0txfNWsVP2jzQZPh9fX1ql4PPPCAtWbcuHGqXi+99JKqTuP73/++tWbGjBmqXprfnR45ckTVS3PsJ02apOqleczTPA5rakR4ZQcAAAAAAAAAAHAcmx0AAAAAAAAAAMBpbHYAAAAAAAAAAACnsdkBAAAAAAAAAACcxmYHAAAAAAAAAABwGpsdAAAAAAAAAADAaWx2AAAAAAAAAAAAp7HZAQAAAAAAAAAAnJbg9wCILZFIxFrT1dWl6hUXZ99L6+jo8KyXpkZ7ntrLmJmZqaoDMHjx8fGquuLiYk9qALhD+zh98803e3aePT091hrtWsQY40mNiG6uzs5OVa+kpCRrTVpamqqXRnNzs6pOc31rj30oFPKsl0Z3d7dnvbRzaY6X9jFW49FHH1XV/fGPf7TWfPTRRwOe3traqjqv4WzGjBmqupdfftlac/jwYVWvwsJCa83Xv/51Va9bbrlFVYfPNDU1WWtOnjyp6qXJBk1Gioh8+umn1ppwOKzqpcmjxMREVS/NY+JVV12l6jV37lxVncaBAwesNdXV1daa06dPezCN27S3US9p1lE1NTWqXvX19dYa7VqrrKxMVReLvFyHXHvttao6zf3niSee+Krj9NL8flVEN5dtfXSWZp2kXUu1tbVZa7SXMdr3W17ZAQAAAAAAAAAAnMZmBwAAAAAAAAAAcBqbHQAAAAAAAAAAwGlsdgAAAAAAAAAAAKex2QEAAAAAAAAAAJzGZgcAAAAAAAAAAHAamx0AAAAAAAAAAMBpbHYAAAAAAAAAAACnsdkBAAAAAAAAAACcluD3AIgt4XDYWtPU1KTqlZ6ebq0xxqh6hUIha01nZ6eqV1tbm7WmtbVV1auoqEhVBwAAvJOYmKiqW7VqlbVm7969ql6adUZ8fLyql2b9o1n7iIjExdn/dqm5uVnVKykpSVWnEYlErDXay5iQYH/KojkOgzlPjWhfxu7ublUvzbHQrsGPHTtmrSkoKFD1Sk5OttZ89NFHA56uWccPdxkZGaq66667zrPzPHDggLWmrq5O1euNN96w1pw6dUrVS3M7njx5sqqXpq6lpUXVS0N7f05LS7PWaJ7Di+jyqKurS9XLq/MTEXnrrbesNSdOnFD10uRMT0+PqpdmrZGTk6PqlZeXZ61ZvHixtUb7eO4yY8yA923NY6+Ibk2mfSz897//ba3p6OhQ9Zo5c6a1ZtmyZapef/vb36w1KSkpql6a+4WXa1OttWvXWms0GSIism7dOmvNVVddpeqloc1TzXHNzs5W9WpoaLDWaNcRmt/pan936uX6W2NQt8CKigq59NJLJSMjQ/Ly8mTJkiVSVVXVp6a9vV3Ky8tl5MiRkp6eLsuWLZPjx497OjQA+IEMBBBkZCCAoCL/AAQZGQjAJYPa7KisrJTy8nLZu3ev7NixQ7q6umT+/Pl9/sJmzZo18vzzz8vWrVulsrJSjh49KkuXLvV8cACINjIQQJCRgQCCivwDEGRkIACXDOptrF566aU+32/atEny8vJk3759Mnv2bGlqapInnnhCNm/e3PvSn40bN8qkSZNk7969ctlll3k3OQBEGRkIIMjIQABBRf4BCDIyEIBLvtIbqZ397Iaz71O4b98+6erqknnz5vXWTJw4UcaMGSN79uzpt0dHR4c0Nzf3+QIAF5CBAIKMDAQQVOQfgCAjAwHEsvPe7IhEInL33XfLrFmzZMqUKSLy2QfYJSUlfemDU/Lz88/54XYVFRWSlZXV+1VcXHy+IwFA1JCBAIKMDAQQVOQfgCAjAwHEuvPe7CgvL5dDhw7JU0899ZUGWLdunTQ1NfV+1dXVfaV+ABANZCCAICMDAQQV+QcgyMhAALFuUJ/Zcdbq1avlhRdekFdffVVGjx7d+/OCggLp7OyUxsbGPju6x48fl4KCgn57hcNhCYfD5zMGAPiCDAQQZGQggKAi/wAEGRkIwAWDemWHMUZWr14t27Ztk507d0pJSUmf0y+55BJJTEyUV155pfdnVVVVcvjwYZk5c6Y3EwOAT8hAAEFGBgIIKvIPQJCRgQBcMqhXdpSXl8vmzZtl+/btkpGR0fvee1lZWZKSkiJZWVly6623ytq1ayUnJ0cyMzPlzjvvlJkzZ8pll102JBcA3kpLS7PWtLa2qnolJiZ6UqOtM8aoerW1tVlrtJfx7AdyIRjIQABBFksZePnll6vqXn/9dWvN+++/r+o1adIka00kElH10qxZQqGQqldcnP1vl7q7u1W9WlparDXayxgfH2+tSU1NVfXy8jJ6eew1NMdBRHdctZdRc7x6enpUvZKTk1V1Gp//K+BzOXr06ICnnzlzxqtxBiWW8s8P3/jGNzypAfozYcIEv0dwhl+vhIhmBoZCoQEfh7WPq9rz0jh16pS1pqurS9Vr79691pqxY8eqei1fvtxa88wzz6h6eXlcNXbt2qWqe+SRR6w1V155parXL37xC1WdVzTrMRHdsdfeVpOSkqw12vWkppf2d7radadXBrXZ8fjjj4uIyJw5c/r8fOPGjbJixQoR+eyGGBcXJ8uWLZOOjg5ZsGCB/P73v/dkWADwExkIIMjIQABBRf4BCDIyEIBLBrXZoflLqOTkZNmwYYNs2LDhvIcCgFhEBgIIMjIQQFCRfwCCjAwE4JJBfWYHAAAAAAAAAABArGGzAwAAAAAAAAAAOI3NDgAAAAAAAAAA4DQ2OwAAAAAAAAAAgNPY7AAAAAAAAAAAAE5jswMAAAAAAAAAADiNzQ4AAAAAAAAAAOC0BL8HQGwJh8PWmlAopOpljPHk/LTa29tVdd3d3daarq4uVa/8/HxVHQAA8E5ZWZmqrrKy0lqzatUqz3rFxcXm3xElJOiW/CNGjBjiSYZOfHy83yP0y8u1ruuWLl1qrXn33XcHPL2jo8OrcQAAMWjfvn2Snp5+ztNffPFFVZ+CggJrTVZWlqqXZn2nXYd88skn1ppvfvObql6PPvqoqi7afvvb31prHnjgAVWv4uJia80///lPVS+NSCSiqtPcJjo7Oz3r1dzcrOqlmb+1tVXVq62tzVqTlJSk6hXt9VtsPiMDAAAAAAAAAABQYrMDAAAAAAAAAAA4jc0OAAAAAAAAAADgNDY7AAAAAAAAAACA09jsAAAAAAAAAAAATmOzAwAAAAAAAAAAOI3NDgAAAAAAAAAA4DQ2OwAAAAAAAAAAgNMS/B4AsSUcDltrjDGenV9cnG6/zcvz9LJXJBLxrBcAANCpqKhQ1f3lL3+x1rz77ruqXhMnTrTWhEIhVa/6+nprTWlpqapXUlKStSYjI0PVKyHB/tRAu45qb2/3pEZEpLu721rT09Oj6qVZu2kvo+bYx6r4+HjPeqWkpKjqDhw4YK258cYbBzy9s7NTdV4AADfV1dVJamrqOU//+9//rurT0NBgrfn4449VvW6//XZrzZo1a1S9GhsbrTW5ubmqXpr1SllZmarXyZMnrTVnzpxR9dKscydPnqzqtWPHDmuNZv0qoltPantpaH/fedFFF1lrrrjiClWv/fv3W2vy8/NVvSZNmuRZryNHjqjqvMIrOwAAAAAAAAAAgNPY7AAAAAAAAAAAAE5jswMAAAAAAAAAADiNzQ4AAAAAAAAAAOA0NjsAAAAAAAAAAIDT2OwAAAAAAAAAAABOY7MDAAAAAAAAAAA4jc0OAAAAAAAAAADgNDY7AAAAAAAAAACA0xL8HgCxJRwOW2uMMapemrr4+HhVL21dtMXFsV8IAEC0aR9/m5qarDVnzpzxrFckElH1Ki4uttacOnVK1UvjyJEjqrqenh5rjfbYa9ZuCQm6pyKaXqFQSNVLQ9urq6vLWqM5piIinZ2d1pqOjg5VLy/X4JrrW3McREQaGxutNQ899NCAp7e0tMif/vQn1fkBANyzZMkSyczMPOfp119/vWfnpV1r7du3z1rz9NNPq3rV1dVZaxITE1W9ioqKrDUlJSWqXsnJydaacePGqXpdfvnl1prbb79d1ctLXq5pNNdRQ0ODqtd3v/tda43mmIrontekpKSoemVnZ1trWltbVb1KS0utNY8//riqlwa/qQUAAAAAAAAAAE5jswMAAAAAAAAAADiNzQ4AAAAAAAAAAOA0NjsAAAAAAAAAAIDT2OwAAAAAAAAAAABOY7MDAAAAAAAAAAA4jc0OAAAAAAAAAADgNDY7AAAAAAAAAACA0xL8HgCxJSsry1oTCoU8O7/ExERVXXt7u2fnGRdn3+PTXsa0tLSvOg4AABgib7zxhrVm06ZNql4/+MEPrDXTp09X9Yo2Y4xndZp1FDBUkpKS/B4BADBM5OTkqOquvvpqT2rgL80a1st17ujRo1V177zzjrXmyJEjql6nTp2y1nR2dqp6JSR4t2WgPRZeGdS1WFFRIZdeeqlkZGRIXl6eLFmyRKqqqvrUzJkzR0KhUJ+vVatWeTo0APiBDAQQZGQggKAi/wAEGRkIwCWD2uyorKyU8vJy2bt3r+zYsUO6urpk/vz50tbW1qfutttuk/r6+t6vhx9+2NOhAcAPZCCAICMDAQQV+QcgyMhAAC4Z1GtSXnrppT7fb9q0SfLy8mTfvn0ye/bs3p+npqZKQUGBNxMCQIwgAwEEGRkIIKjIPwBBRgYCcMlXejOypqYmEfny+9w9+eSTkpubK1OmTJF169bJ6dOnv8rZAEBMIgMBBBkZCCCoyD8AQUYGAohl5/1pI5FIRO6++26ZNWuWTJkypffnN910k4wdO1aKiork4MGDct9990lVVZU8++yz/fbp6OiQjo6O3u+bm5vPdyQAiBoyEECQkYEAgor8AxBkZCCAWHfemx3l5eVy6NAhee211/r8fOXKlb3/njp1qhQWFsrcuXOlpqZGSktLv9SnoqJC1q9ff75jAIAvyEAAQUYGAggq8g9AkJGBAGLdeb2N1erVq+WFF16QXbt2yejRowesLSsrExGR6urqfk9ft26dNDU19X7V1dWdz0gAEDVkIIAgIwMBBBX5ByDIyEAALhjUKzuMMXLnnXfKtm3bZPfu3VJSUmL9PwcOHBARkcLCwn5PD4fDEg6HBzMGAPiCDAQQZGQggKAi/wAEGRkIwCWD2uwoLy+XzZs3y/bt2yUjI0OOHTsmIiJZWVmSkpIiNTU1snnzZvnOd74jI0eOlIMHD8qaNWtk9uzZMm3atCG5AAAQLWQggCAjAwEEFfkHIMjIQAAuCRljjLo4FOr35xs3bpQVK1ZIXV2d3HLLLXLo0CFpa2uT4uJiue666+QnP/mJZGZmqs6jublZsrKypKmpSf1/4J177rnHWvPII4+oeuXm5lprTp48qer1wQcfWGsmTJig6pWTk2OtaWtrU/X6/PtSnstjjz2m6gU9v3KCDAQQC8hAAEFF/gEIMjIQQJBpc2LQb2M1kOLiYqmsrBxMSwBwBhkIIMjIQABBRf4BCDIyEIBLzusDygEAAAAAAAAAAGIFmx0AAAAAAAAAAMBpbHYAAAAAAAAAAACnsdkBAAAAAAAAAACcxmYHAAAAAAAAAABwGpsdAAAAAAAAAADAaWx2AAAAAAAAAAAApyX4PQBiy4oVK6w1ra2tql4jRoyw1mRmZqp6XXTRRdaa++67T9UrPT3dWlNVVaXqtXTpUlUdAAAAAAAAAGDo8MoOAAAAAAAAAADgNDY7AAAAAAAAAACA09jsAAAAAAAAAAAATmOzAwAAAAAAAAAAOI3NDgAAAAAAAAAA4DQ2OwAAAAAAAAAAgNPY7AAAAAAAAAAAAE5jswMAAAAAAAAAADgtwe8BvsgYIyIizc3NPk8STK2trdaazs5OVa+Ojg5rTXt7u6qX5vagOT8RkYQE+81eexnb2tqsNdyWvXf2mJ7Ni+GEDARgQwYCCCryD0CQkYEAgkybgTG32dHS0iIiIsXFxT5Pgljy0EMP+T1Cv5555hm/Rwi0lpYWycrK8nsMT5GBALTIQABBRf4BCDIyEECQ2TIwZGJsSzgSicjRo0clIyNDQqGQiHy2c1NcXCx1dXWSmZnp84SD5/L8zO4fl+cf6tmNMdLS0iJFRUUSFze83o1vuGWgy7OLuD0/s/uHDDx/X8xAbgv+cXl2Ebfnd3l2kaGdP0j5J8JtwU/M7h+X52cNeP6GWwa6PLuI2/O7PLuI2/PHSgbG3Cs74uLiZPTo0f2elpmZ6dwV/Xkuz8/s/nF5/qGcfbj9JctZwzUDXZ5dxO35md0/ZODgnSsDuS34x+XZRdye3+XZRYZu/qDlnwi3BT8xu39cnp814OAN1wx0eXYRt+d3eXYRt+f3OwOH11YwAAAAAAAAAAAIHDY7AAAAAAAAAACA05zY7AiHw/Lggw9KOBz2e5Tz4vL8zO4fl+d3efZY5PLxdHl2EbfnZ3b/uD5/LHH9WLo8v8uzi7g9v8uzi7g/fyxx/Vi6PD+z+8fl+V2ePRa5fDxdnl3E7fldnl3E7fljZfaY+4ByAAAAAAAAAACAwXDilR0AAAAAAAAAAADnwmYHAAAAAAAAAABwGpsdAAAAAAAAAADAaWx2AAAAAAAAAAAApzmx2bFhwwYZN26cJCcnS1lZmfzrX//yeySrn/3sZxIKhfp8TZw40e+xzunVV1+VxYsXS1FRkYRCIXnuuef6nG6MkZ/+9KdSWFgoKSkpMm/ePPnwww/9GfYLbLOvWLHiS9fFwoUL/Rn2CyoqKuTSSy+VjIwMycvLkyVLlkhVVVWfmvb2dikvL5eRI0dKenq6LFu2TI4fP+7TxP9PM/ucOXO+dOxXrVrl08RucjH/RNzKQJfzT4QM9AsZGB1k4NBzOQPJP/+QgdHhYga6lH8iZKBfXM5A8i86XMw/Ebcy0OX8EyED/eJCBsb8ZsfTTz8ta9eulQcffFDeeecdmT59uixYsEBOnDjh92hWkydPlvr6+t6v1157ze+RzqmtrU2mT58uGzZs6Pf0hx9+WB577DH5wx/+IG+++aakpaXJggULpL29PcqTfpltdhGRhQsX9rkutmzZEsUJz62yslLKy8tl7969smPHDunq6pL58+dLW1tbb82aNWvk+eefl61bt0plZaUcPXpUli5d6uPUn9HMLiJy22239Tn2Dz/8sE8Tu8fl/BNxJwNdzj8RMtAvZODQIwOjw+UMJP/8QwYOPZcz0JX8EyED/eJyBpJ/Q8/l/BNxJwNdzj8RMtAvTmSgiXEzZsww5eXlvd/39PSYoqIiU1FR4eNUdg8++KCZPn2632OcFxEx27Zt6/0+EomYgoIC8+tf/7r3Z42NjSYcDpstW7b4MOG5fXF2Y4xZvny5ufbaa32ZZ7BOnDhhRMRUVlYaYz47zomJiWbr1q29Ne+//74REbNnzx6/xuzXF2c3xpgrrrjC3HXXXf4N5ThX888YdzPQ5fwzhgz0ExnoPTIw+lzOQPLPX2Sg91zNQFfzzxgy0E8uZyD55z1X888YdzPQ5fwzhgz0UyxmYEy/sqOzs1P27dsn8+bN6/1ZXFyczJs3T/bs2ePjZDoffvihFBUVyfjx4+Xmm2+Ww4cP+z3SeamtrZVjx471uR6ysrKkrKzMietBRGT37t2Sl5cnEyZMkDvuuEMaGhr8HqlfTU1NIiKSk5MjIiL79u2Trq6uPsd+4sSJMmbMmJg79l+c/awnn3xScnNzZcqUKbJu3To5ffq0H+M5x/X8ExkeGTgc8k+EDIwGMtBbZGBsGA4ZSP5FBxnoLdczcDjknwgZGE0uZyD55y3X809keGTgcMg/ETIwGmIxAxOidk7n4eTJk9LT0yP5+fl9fp6fny8ffPCBT1PplJWVyaZNm2TChAlSX18v69evl29/+9ty6NAhycjI8Hu8QTl27JiISL/Xw9nTYtnChQtl6dKlUlJSIjU1NXL//ffLokWLZM+ePRIfH+/3eL0ikYjcfffdMmvWLJkyZYqIfHbsk5KSJDs7u09trB37/mYXEbnppptk7NixUlRUJAcPHpT77rtPqqqq5Nlnn/VxWje4nH8iwycDXc8/ETIwGshA75GBscH1DCT/ooMM9J7LGThc8k+EDIwWlzOQ/POey/knMnwy0PX8EyEDoyFWMzCmNztctmjRot5/T5s2TcrKymTs2LHyzDPPyK233urjZMFz44039v576tSpMm3aNCktLZXdu3fL3LlzfZysr/Lycjl06FDMvp/jQM41+8qVK3v/PXXqVCksLJS5c+dKTU2NlJaWRntMRBEZGDvIwKFHBuKLyMDYQP5FBxmIzyP/YgcZOPTIP3wRGRg7yMChF6sZGNNvY5Wbmyvx8fFf+rT548ePS0FBgU9TnZ/s7Gy56KKLpLq62u9RBu3ssR4O14OIyPjx4yU3NzemrovVq1fLCy+8ILt27ZLRo0f3/rygoEA6OzulsbGxT30sHftzzd6fsrIyEZGYOvaxajjln4i7GTjc8k+EDPQaGTg0yMDYMNwykPzzHhk4NIZTBrqafyJkYDS4nIHk39AYTvkn4m4GDrf8EyEDvRbLGRjTmx1JSUlyySWXyCuvvNL7s0gkIq+88orMnDnTx8kGr7W1VWpqaqSwsNDvUQatpKRECgoK+lwPzc3N8uabbzp3PYiIHDlyRBoaGmLiujDGyOrVq2Xbtm2yc+dOKSkp6XP6JZdcIomJiX2OfVVVlRw+fNj3Y2+bvT8HDhwQEYmJYx/rhlP+ibibgcMt/0TIQK+QgUOLDIwNwy0DyT/vkIFDazhloKv5J0IGDiWXM5D8G1rDKf9E3M3A4ZZ/ImSgV5zIQL8+GV3rqaeeMuFw2GzatMm89957ZuXKlSY7O9scO3bM79EGdM8995jdu3eb2tpa8/rrr5t58+aZ3Nxcc+LECb9H61dLS4vZv3+/2b9/vxER85vf/Mbs37/ffPzxx8YYY375y1+a7Oxss337dnPw4EFz7bXXmpKSEnPmzBmfJx949paWFvOjH/3I7Nmzx9TW1pqXX37ZXHzxxeZrX/uaaW9v93t0c8cdd5isrCyze/duU19f3/t1+vTp3ppVq1aZMWPGmJ07d5q3337bzJw508ycOdPHqT9jm726utr8/Oc/N2+//bapra0127dvN+PHjzezZ8/2eXJ3uJp/xriVgS7nnzFkoF/IwKFHBkaHyxlI/vmHDBx6rmagS/lnDBnoF5czkPwbeq7mnzFuZaDL+WcMGegXFzIw5jc7jDHmd7/7nRkzZoxJSkoyM2bMMHv37vV7JKsbbrjBFBYWmqSkJHPBBReYG264wVRXV/s91jnt2rXLiMiXvpYvX26MMSYSiZgHHnjA5Ofnm3A4bObOnWuqqqr8Hfp/Bpr99OnTZv78+WbUqFEmMTHRjB071tx2220x8yDZ39wiYjZu3Nhbc+bMGfPDH/7QjBgxwqSmpprrrrvO1NfX+zf0/9hmP3z4sJk9e7bJyckx4XDYXHjhhebHP/6xaWpq8ndwx7iYf8a4lYEu558xZKBfyMDoIAOHnssZSP75hwyMDhcz0KX8M4YM9IvLGUj+RYeL+WeMWxnocv4ZQwb6xYUMDP1vUAAAAAAAAAAAACfF9Gd2AAAAAAAAAAAA2LDZAQAAAAAAAAAAnMZmBwAAAAAAAAAAcBqbHQAAAAAAAAAAwGlsdgAAAAAAAAAAAKex2QEAAAAAAAAAAJzGZgcAAAAAAAAAAHAamx0AAAAAAAAAAMBpbHYAAAAAAAAAAACnsdkBAAAAAAAAAACcxmYHAAAAAAAAAABwGpsdAAAAAAAAAADAaf8HUdnt+d7zokwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building a Multi-Class Classifattion Model\n",
        "\n",
        "#### for our multi-class classification , we can first try the simalar archictecture to our binary classifier :\n",
        "\n",
        "* Input Shape= 28X28 (Shape of one img)\n",
        "* Output Shape = 10 (one per class of clothing)\n",
        "* Loss Function = tf.keras.losses.CategoricalCrossentropy()\n",
        "  * If *labels are one_hot encoded*, use CategoricalCrossentropy()\n",
        "  * If *labels are integer form* , use SparseCategoricalCrossentropy()\n",
        "* Output Layer Activation = Softmax (Not Sigmoid)"
      ],
      "metadata": {
        "id": "FNcwDYMaTyH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Flatten Layer\n",
        "flatten_model = tf.keras.Sequential([tf.keras.layers.Flatten(input_shape=(28,28))])\n",
        "flatten_model.output_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-rK2zEtlNxI",
        "outputId": "71237bc9-6750-413a-f45f-fbf8ad99bebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "28*28"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rZCyK4hWlui_",
        "outputId": "d81ea22b-5f95-45fc-d733-2372dc4a2f91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "784"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create the model\n",
        "model_1 = tf.keras.Sequential([\n",
        "    # our data need to be flatten\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28,),name=\"FLatten_layer\"),\n",
        "    tf.keras.layers.Dense(10,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(20,activation='relu'),\n",
        "    tf.keras.layers.Dense(10,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation='Softmax')\n",
        "])\n",
        "# Compile\n",
        "model_1.compile(optimizer='Adam',loss=tf.keras.losses.CategoricalCrossentropy(),metrics=['accuracy'])\n",
        "\n",
        "# Fit\n",
        "non_norm_history = model_1.fit(train_data,\n",
        "                               tf.one_hot(train_labels,depth=10),\n",
        "                               epochs=10,\n",
        "                               validation_data=(test_data,tf.one_hot(test_labels,depth=10)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtmSMyPWfGbW",
        "outputId": "040b8f6a-898b-4322-8a4f-039f864b507a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 2.4157 - accuracy: 0.0965 - val_loss: 2.3030 - val_accuracy: 0.1000\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.3028 - accuracy: 0.0987 - val_loss: 2.3028 - val_accuracy: 0.1003\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 2.3005 - accuracy: 0.1021 - val_loss: 2.2858 - val_accuracy: 0.1077\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 2.1345 - accuracy: 0.1571 - val_loss: 1.7114 - val_accuracy: 0.2234\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 1.6825 - accuracy: 0.2299 - val_loss: 1.6468 - val_accuracy: 0.2444\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.5915 - accuracy: 0.2834 - val_loss: 1.5934 - val_accuracy: 0.2734\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.5682 - accuracy: 0.2891 - val_loss: 1.5431 - val_accuracy: 0.3051\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.5525 - accuracy: 0.2940 - val_loss: 1.5355 - val_accuracy: 0.3002\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.5348 - accuracy: 0.3058 - val_loss: 1.5194 - val_accuracy: 0.3089\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.5301 - accuracy: 0.3140 - val_loss: 1.5146 - val_accuracy: 0.3177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.summary()"
      ],
      "metadata": {
        "id": "n6A9-7EOkSNK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "704c86c2-0a72-4fe4-caf5-a487d0e46a42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " FLatten_layer (Flatten)     (None, 784)               0         \n",
            "                                                                 \n",
            " dense_32 (Dense)            (None, 10)                7850      \n",
            "                                                                 \n",
            " dense_33 (Dense)            (None, 20)                220       \n",
            "                                                                 \n",
            " dense_34 (Dense)            (None, 10)                210       \n",
            "                                                                 \n",
            " dense_35 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,390\n",
            "Trainable params: 8,390\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Normalization\n",
        "##### Neural network prefer data to be in scaled (NORMALIZED) , this means the generally have number in the from of tensor's between 0 & 1."
      ],
      "metadata": {
        "id": "FGR8ZXudszA1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check the min and max value of training data\n",
        "train_data.min(),train_data.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSX8LBwJueo1",
        "outputId": "cba4b6b8-83ee-4cce-fb73-4473af371d5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 255)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We can get our training and testing data between 0 & 1 by dividing by the maximum\n",
        "norm_train_data = train_data/255.0\n",
        "norm_test_data = test_data/255.0\n",
        "norm_train_data[4421].shape"
      ],
      "metadata": {
        "id": "nmomK9pVuvUk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09969d55-6a71-4d4a-82fb-d40970a77008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create Model\n",
        "Model_2 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28,),name=\"FLatten_layer\"),\n",
        "    tf.keras.layers.Dense(10,activation='relu'),\n",
        "    tf.keras.layers.Dense(20,activation='relu'),\n",
        "    tf.keras.layers.Dense(10,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation='Softmax')\n",
        "])\n",
        "# Compile Model\n",
        "Model_2.compile(optimizer='Adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(),metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "norm_history = Model_2.fit(norm_train_data,train_labels,epochs=25,validation_data=(norm_test_data,test_labels))"
      ],
      "metadata": {
        "id": "-y0XZoMDPNtI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f0908e5-71f9-44b4-95db-054602d109ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.7553 - accuracy: 0.7245 - val_loss: 0.5389 - val_accuracy: 0.8105\n",
            "Epoch 2/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4935 - accuracy: 0.8263 - val_loss: 0.5219 - val_accuracy: 0.8118\n",
            "Epoch 3/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4497 - accuracy: 0.8404 - val_loss: 0.4813 - val_accuracy: 0.8276\n",
            "Epoch 4/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4266 - accuracy: 0.8495 - val_loss: 0.4638 - val_accuracy: 0.8374\n",
            "Epoch 5/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4138 - accuracy: 0.8538 - val_loss: 0.4746 - val_accuracy: 0.8315\n",
            "Epoch 6/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4039 - accuracy: 0.8560 - val_loss: 0.4687 - val_accuracy: 0.8334\n",
            "Epoch 7/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3951 - accuracy: 0.8592 - val_loss: 0.4489 - val_accuracy: 0.8385\n",
            "Epoch 8/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3880 - accuracy: 0.8626 - val_loss: 0.4229 - val_accuracy: 0.8498\n",
            "Epoch 9/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3814 - accuracy: 0.8647 - val_loss: 0.4331 - val_accuracy: 0.8489\n",
            "Epoch 10/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3780 - accuracy: 0.8648 - val_loss: 0.4219 - val_accuracy: 0.8482\n",
            "Epoch 11/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3748 - accuracy: 0.8648 - val_loss: 0.4303 - val_accuracy: 0.8450\n",
            "Epoch 12/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3697 - accuracy: 0.8680 - val_loss: 0.4310 - val_accuracy: 0.8479\n",
            "Epoch 13/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3667 - accuracy: 0.8685 - val_loss: 0.4344 - val_accuracy: 0.8469\n",
            "Epoch 14/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3651 - accuracy: 0.8693 - val_loss: 0.4279 - val_accuracy: 0.8470\n",
            "Epoch 15/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3604 - accuracy: 0.8706 - val_loss: 0.4284 - val_accuracy: 0.8467\n",
            "Epoch 16/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3590 - accuracy: 0.8696 - val_loss: 0.4192 - val_accuracy: 0.8491\n",
            "Epoch 17/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3540 - accuracy: 0.8724 - val_loss: 0.4216 - val_accuracy: 0.8504\n",
            "Epoch 18/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3547 - accuracy: 0.8712 - val_loss: 0.4189 - val_accuracy: 0.8515\n",
            "Epoch 19/25\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3514 - accuracy: 0.8734 - val_loss: 0.4232 - val_accuracy: 0.8463\n",
            "Epoch 20/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3475 - accuracy: 0.8741 - val_loss: 0.4215 - val_accuracy: 0.8505\n",
            "Epoch 21/25\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3453 - accuracy: 0.8757 - val_loss: 0.4125 - val_accuracy: 0.8533\n",
            "Epoch 22/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3422 - accuracy: 0.8762 - val_loss: 0.4109 - val_accuracy: 0.8508\n",
            "Epoch 23/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3410 - accuracy: 0.8764 - val_loss: 0.4260 - val_accuracy: 0.8479\n",
            "Epoch 24/25\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3397 - accuracy: 0.8761 - val_loss: 0.4168 - val_accuracy: 0.8522\n",
            "Epoch 25/25\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3376 - accuracy: 0.8774 - val_loss: 0.4393 - val_accuracy: 0.8447\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NOTE**- Neural network tend to prefer data in numerical form as well as scaled/normalized (numbers b/w 0 & 1)"
      ],
      "metadata": {
        "id": "M-7OL4qT_JDR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "# Comparing both models using graph\n",
        "# plot Non-Normalized data loss curve\n",
        "pd.DataFrame(non_norm_history.history).plot(title='Non-normalized data')\n",
        "# plot Normalized data loss curve\n",
        "pd.DataFrame(norm_history.history).plot(title='Normalized Data')"
      ],
      "metadata": {
        "id": "SbDXw37xPQYS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "outputId": "ae52686b-1f5c-41ae-f841-15feb5fb2b82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Normalized Data'}>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ideal Learning Rate"
      ],
      "metadata": {
        "id": "0hOGL8iwFiB4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Check the best learning rate for the model\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create Model\n",
        "Model_3 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28,),name=\"FLatten_layer\"),\n",
        "    tf.keras.layers.Dense(10,activation='relu'),\n",
        "    tf.keras.layers.Dense(20,activation='relu'),\n",
        "    tf.keras.layers.Dense(10,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation='Softmax')\n",
        "])\n",
        "# Compile Model\n",
        "Model_3.compile(optimizer='Adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(),metrics=['accuracy'])\n",
        "\n",
        "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-6 * 10**(epoch/20))\n",
        "# Fit the model\n",
        "fit_lr_history = Model_3.fit(norm_train_data,train_labels,epochs=100,validation_data=(norm_test_data,test_labels),callbacks=[lr_scheduler])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mrbSxWh2BVaK",
        "outputId": "dd00cfce-43c4-42ba-9051-5202ea9046c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 2.3122 - accuracy: 0.1108 - val_loss: 2.2958 - val_accuracy: 0.1107 - lr: 1.0000e-06\n",
            "Epoch 2/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.2812 - accuracy: 0.1063 - val_loss: 2.2699 - val_accuracy: 0.1063 - lr: 1.1220e-06\n",
            "Epoch 3/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.2585 - accuracy: 0.1081 - val_loss: 2.2499 - val_accuracy: 0.1111 - lr: 1.2589e-06\n",
            "Epoch 4/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.2388 - accuracy: 0.1119 - val_loss: 2.2285 - val_accuracy: 0.1121 - lr: 1.4125e-06\n",
            "Epoch 5/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.2148 - accuracy: 0.1128 - val_loss: 2.2020 - val_accuracy: 0.1148 - lr: 1.5849e-06\n",
            "Epoch 6/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 2.1868 - accuracy: 0.1192 - val_loss: 2.1728 - val_accuracy: 0.1248 - lr: 1.7783e-06\n",
            "Epoch 7/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.1564 - accuracy: 0.1342 - val_loss: 2.1408 - val_accuracy: 0.1503 - lr: 1.9953e-06\n",
            "Epoch 8/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 2.1178 - accuracy: 0.1882 - val_loss: 2.0948 - val_accuracy: 0.2234 - lr: 2.2387e-06\n",
            "Epoch 9/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 2.0696 - accuracy: 0.2406 - val_loss: 2.0461 - val_accuracy: 0.2504 - lr: 2.5119e-06\n",
            "Epoch 10/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.0219 - accuracy: 0.2587 - val_loss: 1.9995 - val_accuracy: 0.2609 - lr: 2.8184e-06\n",
            "Epoch 11/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.9760 - accuracy: 0.2677 - val_loss: 1.9535 - val_accuracy: 0.2683 - lr: 3.1623e-06\n",
            "Epoch 12/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.9293 - accuracy: 0.2766 - val_loss: 1.9057 - val_accuracy: 0.2810 - lr: 3.5481e-06\n",
            "Epoch 13/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.8797 - accuracy: 0.2952 - val_loss: 1.8552 - val_accuracy: 0.3043 - lr: 3.9811e-06\n",
            "Epoch 14/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.8272 - accuracy: 0.3224 - val_loss: 1.8014 - val_accuracy: 0.3350 - lr: 4.4668e-06\n",
            "Epoch 15/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.7709 - accuracy: 0.3585 - val_loss: 1.7443 - val_accuracy: 0.3704 - lr: 5.0119e-06\n",
            "Epoch 16/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.7103 - accuracy: 0.4025 - val_loss: 1.6821 - val_accuracy: 0.4196 - lr: 5.6234e-06\n",
            "Epoch 17/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.6445 - accuracy: 0.4421 - val_loss: 1.6152 - val_accuracy: 0.4447 - lr: 6.3096e-06\n",
            "Epoch 18/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.5738 - accuracy: 0.4641 - val_loss: 1.5433 - val_accuracy: 0.4628 - lr: 7.0795e-06\n",
            "Epoch 19/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.4976 - accuracy: 0.4823 - val_loss: 1.4665 - val_accuracy: 0.4837 - lr: 7.9433e-06\n",
            "Epoch 20/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4152 - accuracy: 0.5069 - val_loss: 1.3826 - val_accuracy: 0.5108 - lr: 8.9125e-06\n",
            "Epoch 21/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.3268 - accuracy: 0.5423 - val_loss: 1.2940 - val_accuracy: 0.5527 - lr: 1.0000e-05\n",
            "Epoch 22/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.2334 - accuracy: 0.5877 - val_loss: 1.2018 - val_accuracy: 0.5982 - lr: 1.1220e-05\n",
            "Epoch 23/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.1374 - accuracy: 0.6349 - val_loss: 1.1083 - val_accuracy: 0.6493 - lr: 1.2589e-05\n",
            "Epoch 24/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.0406 - accuracy: 0.6822 - val_loss: 1.0151 - val_accuracy: 0.6947 - lr: 1.4125e-05\n",
            "Epoch 25/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.9465 - accuracy: 0.7239 - val_loss: 0.9270 - val_accuracy: 0.7288 - lr: 1.5849e-05\n",
            "Epoch 26/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8607 - accuracy: 0.7539 - val_loss: 0.8505 - val_accuracy: 0.7525 - lr: 1.7783e-05\n",
            "Epoch 27/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7900 - accuracy: 0.7739 - val_loss: 0.7909 - val_accuracy: 0.7689 - lr: 1.9953e-05\n",
            "Epoch 28/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.7352 - accuracy: 0.7863 - val_loss: 0.7439 - val_accuracy: 0.7760 - lr: 2.2387e-05\n",
            "Epoch 29/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6923 - accuracy: 0.7939 - val_loss: 0.7064 - val_accuracy: 0.7841 - lr: 2.5119e-05\n",
            "Epoch 30/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6572 - accuracy: 0.7994 - val_loss: 0.6761 - val_accuracy: 0.7883 - lr: 2.8184e-05\n",
            "Epoch 31/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6281 - accuracy: 0.8032 - val_loss: 0.6481 - val_accuracy: 0.7897 - lr: 3.1623e-05\n",
            "Epoch 32/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6028 - accuracy: 0.8070 - val_loss: 0.6231 - val_accuracy: 0.7968 - lr: 3.5481e-05\n",
            "Epoch 33/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5808 - accuracy: 0.8119 - val_loss: 0.6038 - val_accuracy: 0.7998 - lr: 3.9811e-05\n",
            "Epoch 34/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5618 - accuracy: 0.8155 - val_loss: 0.5847 - val_accuracy: 0.8009 - lr: 4.4668e-05\n",
            "Epoch 35/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5442 - accuracy: 0.8196 - val_loss: 0.5680 - val_accuracy: 0.8089 - lr: 5.0119e-05\n",
            "Epoch 36/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5285 - accuracy: 0.8229 - val_loss: 0.5545 - val_accuracy: 0.8076 - lr: 5.6234e-05\n",
            "Epoch 37/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5147 - accuracy: 0.8263 - val_loss: 0.5415 - val_accuracy: 0.8138 - lr: 6.3096e-05\n",
            "Epoch 38/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5022 - accuracy: 0.8283 - val_loss: 0.5303 - val_accuracy: 0.8144 - lr: 7.0795e-05\n",
            "Epoch 39/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4909 - accuracy: 0.8313 - val_loss: 0.5164 - val_accuracy: 0.8193 - lr: 7.9433e-05\n",
            "Epoch 40/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4800 - accuracy: 0.8348 - val_loss: 0.5081 - val_accuracy: 0.8224 - lr: 8.9125e-05\n",
            "Epoch 41/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4707 - accuracy: 0.8387 - val_loss: 0.4998 - val_accuracy: 0.8268 - lr: 1.0000e-04\n",
            "Epoch 42/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4623 - accuracy: 0.8405 - val_loss: 0.5033 - val_accuracy: 0.8255 - lr: 1.1220e-04\n",
            "Epoch 43/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4545 - accuracy: 0.8432 - val_loss: 0.4888 - val_accuracy: 0.8275 - lr: 1.2589e-04\n",
            "Epoch 44/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4473 - accuracy: 0.8449 - val_loss: 0.4805 - val_accuracy: 0.8314 - lr: 1.4125e-04\n",
            "Epoch 45/100\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4411 - accuracy: 0.8479 - val_loss: 0.4731 - val_accuracy: 0.8338 - lr: 1.5849e-04\n",
            "Epoch 46/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4347 - accuracy: 0.8488 - val_loss: 0.4736 - val_accuracy: 0.8337 - lr: 1.7783e-04\n",
            "Epoch 47/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4281 - accuracy: 0.8514 - val_loss: 0.4770 - val_accuracy: 0.8320 - lr: 1.9953e-04\n",
            "Epoch 48/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4231 - accuracy: 0.8526 - val_loss: 0.4709 - val_accuracy: 0.8315 - lr: 2.2387e-04\n",
            "Epoch 49/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4189 - accuracy: 0.8546 - val_loss: 0.4663 - val_accuracy: 0.8376 - lr: 2.5119e-04\n",
            "Epoch 50/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4148 - accuracy: 0.8554 - val_loss: 0.4601 - val_accuracy: 0.8366 - lr: 2.8184e-04\n",
            "Epoch 51/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4088 - accuracy: 0.8567 - val_loss: 0.4471 - val_accuracy: 0.8428 - lr: 3.1623e-04\n",
            "Epoch 52/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4061 - accuracy: 0.8584 - val_loss: 0.4467 - val_accuracy: 0.8420 - lr: 3.5481e-04\n",
            "Epoch 53/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4029 - accuracy: 0.8600 - val_loss: 0.4518 - val_accuracy: 0.8437 - lr: 3.9811e-04\n",
            "Epoch 54/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3998 - accuracy: 0.8599 - val_loss: 0.4493 - val_accuracy: 0.8438 - lr: 4.4668e-04\n",
            "Epoch 55/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3981 - accuracy: 0.8613 - val_loss: 0.4341 - val_accuracy: 0.8496 - lr: 5.0119e-04\n",
            "Epoch 56/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3943 - accuracy: 0.8619 - val_loss: 0.4422 - val_accuracy: 0.8475 - lr: 5.6234e-04\n",
            "Epoch 57/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3910 - accuracy: 0.8624 - val_loss: 0.4468 - val_accuracy: 0.8444 - lr: 6.3096e-04\n",
            "Epoch 58/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3906 - accuracy: 0.8649 - val_loss: 0.4307 - val_accuracy: 0.8519 - lr: 7.0795e-04\n",
            "Epoch 59/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3884 - accuracy: 0.8639 - val_loss: 0.4395 - val_accuracy: 0.8482 - lr: 7.9433e-04\n",
            "Epoch 60/100\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3862 - accuracy: 0.8645 - val_loss: 0.4291 - val_accuracy: 0.8439 - lr: 8.9125e-04\n",
            "Epoch 61/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3834 - accuracy: 0.8643 - val_loss: 0.4369 - val_accuracy: 0.8459 - lr: 0.0010\n",
            "Epoch 62/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3843 - accuracy: 0.8635 - val_loss: 0.4240 - val_accuracy: 0.8497 - lr: 0.0011\n",
            "Epoch 63/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3839 - accuracy: 0.8636 - val_loss: 0.4263 - val_accuracy: 0.8505 - lr: 0.0013\n",
            "Epoch 64/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3815 - accuracy: 0.8655 - val_loss: 0.4231 - val_accuracy: 0.8534 - lr: 0.0014\n",
            "Epoch 65/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3793 - accuracy: 0.8648 - val_loss: 0.4337 - val_accuracy: 0.8478 - lr: 0.0016\n",
            "Epoch 66/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3821 - accuracy: 0.8632 - val_loss: 0.4204 - val_accuracy: 0.8517 - lr: 0.0018\n",
            "Epoch 67/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3788 - accuracy: 0.8638 - val_loss: 0.4142 - val_accuracy: 0.8526 - lr: 0.0020\n",
            "Epoch 68/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3799 - accuracy: 0.8640 - val_loss: 0.4815 - val_accuracy: 0.8264 - lr: 0.0022\n",
            "Epoch 69/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3816 - accuracy: 0.8633 - val_loss: 0.4253 - val_accuracy: 0.8546 - lr: 0.0025\n",
            "Epoch 70/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3798 - accuracy: 0.8633 - val_loss: 0.4668 - val_accuracy: 0.8290 - lr: 0.0028\n",
            "Epoch 71/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3804 - accuracy: 0.8629 - val_loss: 0.4259 - val_accuracy: 0.8471 - lr: 0.0032\n",
            "Epoch 72/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3814 - accuracy: 0.8627 - val_loss: 0.4859 - val_accuracy: 0.8297 - lr: 0.0035\n",
            "Epoch 73/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3821 - accuracy: 0.8615 - val_loss: 0.4527 - val_accuracy: 0.8411 - lr: 0.0040\n",
            "Epoch 74/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3848 - accuracy: 0.8617 - val_loss: 0.4466 - val_accuracy: 0.8449 - lr: 0.0045\n",
            "Epoch 75/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3836 - accuracy: 0.8623 - val_loss: 0.4691 - val_accuracy: 0.8420 - lr: 0.0050\n",
            "Epoch 76/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3860 - accuracy: 0.8612 - val_loss: 0.4629 - val_accuracy: 0.8373 - lr: 0.0056\n",
            "Epoch 77/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3936 - accuracy: 0.8580 - val_loss: 0.4502 - val_accuracy: 0.8388 - lr: 0.0063\n",
            "Epoch 78/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3957 - accuracy: 0.8578 - val_loss: 0.4666 - val_accuracy: 0.8425 - lr: 0.0071\n",
            "Epoch 79/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4011 - accuracy: 0.8579 - val_loss: 0.4585 - val_accuracy: 0.8447 - lr: 0.0079\n",
            "Epoch 80/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4059 - accuracy: 0.8541 - val_loss: 0.4654 - val_accuracy: 0.8409 - lr: 0.0089\n",
            "Epoch 81/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4154 - accuracy: 0.8522 - val_loss: 0.5084 - val_accuracy: 0.8178 - lr: 0.0100\n",
            "Epoch 82/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4252 - accuracy: 0.8514 - val_loss: 0.4408 - val_accuracy: 0.8463 - lr: 0.0112\n",
            "Epoch 83/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4412 - accuracy: 0.8481 - val_loss: 0.5224 - val_accuracy: 0.8233 - lr: 0.0126\n",
            "Epoch 84/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4511 - accuracy: 0.8452 - val_loss: 0.5154 - val_accuracy: 0.8199 - lr: 0.0141\n",
            "Epoch 85/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4771 - accuracy: 0.8402 - val_loss: 0.4858 - val_accuracy: 0.8410 - lr: 0.0158\n",
            "Epoch 86/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4967 - accuracy: 0.8348 - val_loss: 0.5417 - val_accuracy: 0.8317 - lr: 0.0178\n",
            "Epoch 87/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.5195 - accuracy: 0.8291 - val_loss: 0.5489 - val_accuracy: 0.8169 - lr: 0.0200\n",
            "Epoch 88/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5437 - accuracy: 0.8206 - val_loss: 0.5549 - val_accuracy: 0.8160 - lr: 0.0224\n",
            "Epoch 89/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.6533 - accuracy: 0.7708 - val_loss: 0.6197 - val_accuracy: 0.7782 - lr: 0.0251\n",
            "Epoch 90/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.6580 - accuracy: 0.7701 - val_loss: 0.7315 - val_accuracy: 0.7594 - lr: 0.0282\n",
            "Epoch 91/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.7995 - accuracy: 0.7202 - val_loss: 0.7879 - val_accuracy: 0.7394 - lr: 0.0316\n",
            "Epoch 92/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.8643 - accuracy: 0.6887 - val_loss: 0.8566 - val_accuracy: 0.6998 - lr: 0.0355\n",
            "Epoch 93/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.8597 - accuracy: 0.6796 - val_loss: 0.9580 - val_accuracy: 0.6209 - lr: 0.0398\n",
            "Epoch 94/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.3256 - accuracy: 0.4553 - val_loss: 1.2744 - val_accuracy: 0.5058 - lr: 0.0447\n",
            "Epoch 95/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.3427 - accuracy: 0.4196 - val_loss: 1.6849 - val_accuracy: 0.3851 - lr: 0.0501\n",
            "Epoch 96/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.3965 - accuracy: 0.3920 - val_loss: 1.3477 - val_accuracy: 0.3870 - lr: 0.0562\n",
            "Epoch 97/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.3162 - accuracy: 0.4119 - val_loss: 1.2898 - val_accuracy: 0.4354 - lr: 0.0631\n",
            "Epoch 98/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.3473 - accuracy: 0.4103 - val_loss: 1.5248 - val_accuracy: 0.3902 - lr: 0.0708\n",
            "Epoch 99/100\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 1.8986 - accuracy: 0.2553 - val_loss: 2.3254 - val_accuracy: 0.1880 - lr: 0.0794\n",
            "Epoch 100/100\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 2.1076 - accuracy: 0.1509 - val_loss: 2.2977 - val_accuracy: 0.1040 - lr: 0.0891\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "lrs = 1e-6 * (10**(tf.range(100)/20))\n",
        "plt.semilogx(lrs,fit_lr_history.history['loss'])\n",
        "plt.xlabel(\"learning  Rate\")\n",
        "plt.ylabel('Loss')\n",
        "plt.title(\"Finding the ideal learning rate\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "iy5Yc6iDGawx",
        "outputId": "354b8a06-5c91-4180-aa97-a3ca31a7ad12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Finding the ideal learning rate')"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above graph we can know that the ideal learning rate\n",
        "---\n",
        "1e-3 or 10**3"
      ],
      "metadata": {
        "id": "DFVcibyWWWqs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " # Random Seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Create Model\n",
        "Model_4 = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28,),name=\"FLatten_layer\"),\n",
        "    tf.keras.layers.Dense(10,activation='relu'),\n",
        "    tf.keras.layers.Dense(20,activation='relu'),\n",
        "    tf.keras.layers.Dense(10,activation=\"relu\"),\n",
        "    tf.keras.layers.Dense(10,activation='Softmax')\n",
        "])\n",
        "# Compile Model\n",
        "Model_4.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001),loss=tf.keras.losses.SparseCategoricalCrossentropy(),metrics=['accuracy'])\n",
        "\n",
        "# Fit the model\n",
        "history_4 = Model_4.fit(norm_train_data,train_labels,epochs=20,validation_data=(norm_test_data,test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BZbagcgqPvkX",
        "outputId": "aa4269b2-5f89-4947-e0f0-8429b3795395"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 0.6977 - accuracy: 0.7533 - val_loss: 0.5363 - val_accuracy: 0.8147\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4990 - accuracy: 0.8258 - val_loss: 0.5101 - val_accuracy: 0.8206\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4626 - accuracy: 0.8378 - val_loss: 0.5004 - val_accuracy: 0.8200\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.4381 - accuracy: 0.8464 - val_loss: 0.4647 - val_accuracy: 0.8340\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4208 - accuracy: 0.8523 - val_loss: 0.4677 - val_accuracy: 0.8304\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4075 - accuracy: 0.8564 - val_loss: 0.4692 - val_accuracy: 0.8336\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3999 - accuracy: 0.8591 - val_loss: 0.4526 - val_accuracy: 0.8367\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3915 - accuracy: 0.8626 - val_loss: 0.4228 - val_accuracy: 0.8501\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3852 - accuracy: 0.8643 - val_loss: 0.4239 - val_accuracy: 0.8524\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3816 - accuracy: 0.8641 - val_loss: 0.4232 - val_accuracy: 0.8489\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3773 - accuracy: 0.8662 - val_loss: 0.4159 - val_accuracy: 0.8527\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3727 - accuracy: 0.8671 - val_loss: 0.4214 - val_accuracy: 0.8528\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3692 - accuracy: 0.8688 - val_loss: 0.4229 - val_accuracy: 0.8513\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3656 - accuracy: 0.8696 - val_loss: 0.4363 - val_accuracy: 0.8459\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.3636 - accuracy: 0.8691 - val_loss: 0.4337 - val_accuracy: 0.8432\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.3593 - accuracy: 0.8708 - val_loss: 0.4173 - val_accuracy: 0.8515\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 10s 6ms/step - loss: 0.3577 - accuracy: 0.8714 - val_loss: 0.4087 - val_accuracy: 0.8552\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.3549 - accuracy: 0.8720 - val_loss: 0.4051 - val_accuracy: 0.8550\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3526 - accuracy: 0.8735 - val_loss: 0.4198 - val_accuracy: 0.8504\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3509 - accuracy: 0.8739 - val_loss: 0.4108 - val_accuracy: 0.8557\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating our Multi-class Classification Model\n",
        "\n",
        "To Evaluate our multi-class classification model :\n",
        "* Evalaute its performance using other classification metrics (such as a confusion matrix).\n",
        "* Asses some of its prediction (through visualization).\n",
        "* Improve its results (by training it for longer or changing the architecture).\n",
        "* Save and Export it for in an applications."
      ],
      "metadata": {
        "id": "ucHRkzBSXOso"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a confusion matrix\n",
        "import itertools\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "def make_confusion_matrix(y_test,y_pred,classes=None,figsize=(10,10),text_size=15):\n",
        "  # Create the confusion matrix\n",
        "  cm = confusion_matrix(y_test,y_pred)\n",
        "  cm_norm = cm.astype(\"float\")/cm.sum(axis=1)[:,np.newaxis] #normailize\n",
        "  n_classes =cm.shape[0]\n",
        "  #lets prettify it\n",
        "  fig,ax = plt.subplots(figsize=figsize)\n",
        "  #create a matrix plot\n",
        "  cax = ax.matshow(cm,cmap=plt.cm.Blues) # first cm = confusion matrix , plt.cm refers colorMap\n",
        "  fig .colorbar(cax)\n",
        "\n",
        "  #Check Classes\n",
        "\n",
        "  if classes:\n",
        "    label = classes\n",
        "  else:\n",
        "    label = np.arange(cm.shape[0])\n",
        "\n",
        "  #label the axis\n",
        "  ax.set(title = \"Confusion Matrix\",\n",
        "        xlabel = \"Prediction label\",\n",
        "        ylabel = \"True label\",\n",
        "        xticks = np.arange(n_classes),\n",
        "        yticks = np.arange(n_classes),\n",
        "        xticklabels = label,\n",
        "        yticklabels = label)\n",
        "\n",
        "  # set x-axis labels to bottom\n",
        "  ax.xaxis.set_label_position('bottom')\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  #Adjust label size\n",
        "  ax.yaxis.label.set_size(text_size)\n",
        "  ax.xaxis.label.set_size(text_size)\n",
        "  ax.title.set_size(text_size)\n",
        "\n",
        "  #set Threshold for different color\n",
        "  threshold = (cm.max()+cm.min())/2\n",
        "\n",
        "  #plot the text on each cell\n",
        "  for i,j in itertools.product(range(cm.shape[0]),range(cm.shape[1])):\n",
        "    plt.text(j, i,s= f\"{cm[i,j]} ({cm_norm[i,j]*100:.2f}%)\",\n",
        "            horizontalalignment=\"center\",\n",
        "            color='white' if cm[i,j] > threshold else\"black\",\n",
        "            size=text_size)"
      ],
      "metadata": {
        "id": "IWtG3DbbYrHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8nRNFNJUb2q8",
        "outputId": "ee783e53-dc88-4225-c798-04f6d4d78594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make some Prediction on test data\n",
        "y_tpred = Model_4.predict(test_data)\n",
        "\n",
        "#View 5 test pred\n",
        "y_tpred[0:5]\n",
        "print(\"ERROR as the training was on Normalized data so test data should be in Normalized form\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXrDw0SqcAor",
        "outputId": "7c643616-cdf8-4313-c696-e8c37a11e715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 1ms/step\n",
            "ERROR as the training was on Normalized data so test data should be in Normalized form\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make some Prediction on test data\n",
        "y_tpred = Model_4.predict(norm_test_data)\n",
        "\n",
        "#View 5 test pred\n",
        "y_tpred[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z6mGx1-fdDc6",
        "outputId": "4e00c4eb-4a7d-433b-cc6a-d0c374896d94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 1ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.94896274e-05, 1.12724025e-04, 3.53096038e-05, 7.27243198e-04,\n",
              "        1.07614296e-05, 4.00650278e-02, 1.03200118e-05, 8.74175802e-02,\n",
              "        2.43192967e-02, 8.47282290e-01],\n",
              "       [1.31211011e-04, 9.83272912e-08, 9.67216372e-01, 1.73212993e-05,\n",
              "        9.60860215e-03, 3.04003041e-19, 2.29952782e-02, 3.18791268e-15,\n",
              "        3.11181248e-05, 8.31797959e-15],\n",
              "       [3.25232509e-06, 9.99994695e-01, 1.14245822e-07, 1.28186139e-06,\n",
              "        5.55143117e-07, 7.15980712e-27, 9.93799709e-09, 1.72454915e-16,\n",
              "        6.60601795e-09, 5.45279533e-13],\n",
              "       [5.57144460e-07, 9.99978006e-01, 7.95133772e-08, 1.93599226e-05,\n",
              "        1.97391932e-06, 9.90504994e-25, 3.65169317e-09, 6.56888891e-14,\n",
              "        1.02917328e-08, 5.46964661e-11],\n",
              "       [1.39887556e-01, 1.00281170e-06, 5.62389642e-02, 6.45871926e-03,\n",
              "        8.12110305e-03, 3.64214242e-10, 7.80254364e-01, 1.73616059e-13,\n",
              "        9.03817825e-03, 1.49000621e-11]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NOTE**- Remember to make prediction on same kind of data your model was trained on."
      ],
      "metadata": {
        "id": "YHShbfx1dZ38"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_tpred[0],tf.argmax(y_tpred[0]),label_names[tf.argmax(y_tpred[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMtej_mDZpZ9",
        "outputId": "3c528515-fd72-47a2-e499-d866105b5a3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1.94896274e-05, 1.12724025e-04, 3.53096038e-05, 7.27243198e-04,\n",
              "        1.07614296e-05, 4.00650278e-02, 1.03200118e-05, 8.74175802e-02,\n",
              "        2.43192967e-02, 8.47282290e-01], dtype=float32),\n",
              " <tf.Tensor: shape=(), dtype=int64, numpy=9>,\n",
              " 'Ankle Boot')"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert all of the prediction probabilities into integers\n",
        "y_tpred = y_tpred.argmax(axis=1)\n",
        "# View the first 10 prediction labels\n",
        "y_tpred[:10]"
      ],
      "metadata": {
        "id": "q4pKGxTSebRU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a011612-a983-4240-c9c3-968d1c14603c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_true=test_labels,y_pred=y_tpred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qe-UGhdGo17Q",
        "outputId": "941d8b2f-25f7-4ec2-c1fc-90ff0feaad85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[812,   5,  12,  42,   5,   2, 109,   0,  13,   0],\n",
              "       [  2, 963,   6,  23,   6,   0,   0,   0,   0,   0],\n",
              "       [ 19,   0, 736,  11, 159,   0,  66,   0,   9,   0],\n",
              "       [ 26,  15,   6, 854,  55,   0,  38,   0,   6,   0],\n",
              "       [  0,   2,  79,  27, 845,   0,  44,   0,   3,   0],\n",
              "       [  0,   0,   0,   0,   0, 933,   0,  38,  11,  18],\n",
              "       [144,   1, 104,  40, 123,   1, 570,   0,  17,   0],\n",
              "       [  0,   0,   0,   0,   0,  27,   0, 948,   1,  24],\n",
              "       [  2,   0,   4,   3,  14,   2,  11,   4, 960,   0],\n",
              "       [  0,   0,   0,   0,   0,  21,   0,  41,   2, 936]])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make Confusion Matrix\n",
        "make_confusion_matrix(y_test=test_labels,y_pred=y_tpred,classes=label_names,figsize=(10,10),text_size=6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "KerBN_qws9nH",
        "outputId": "32a5b8df-d007-430b-e6cb-4473bf94a897"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Note :** Often when working with images and other forms of visual data,it's good idea to visualize as much as possible!\n",
        "\n",
        "Play Around task:\n",
        "* Plot a random img.\n",
        "* Make Prediction .\n",
        "* LAbel the plot with Truth Label and Prediction."
      ],
      "metadata": {
        "id": "9f741w0IunhV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "# point 1\n",
        "def pick_random_img(model,images,true_labels,classes):\n",
        "  # Set random integer\n",
        "  rand = random.randint(0,len(images))\n",
        "  #Create Prediction and Target\n",
        "  target = images[rand]\n",
        "  pred_pro=model.predict(target.reshape(1,28,28))\n",
        "  pred_label=classes[pred_pro.argmax()]\n",
        "  true_label=classes[true_labels[rand]]\n",
        "  #Plot the target\n",
        "  plt.imshow(target,cmap=plt.cm.binary)\n",
        "  # Change the color of the title\n",
        "  if pred_label==true_label:\n",
        "    color=\"green\"\n",
        "  else:\n",
        "    color='red'\n",
        "  # Add xlabel info(pred/true_label)\n",
        "  plt.xlabel(\"Pred: {} {:2.0f}% (True:{})\".format(pred_label,100*tf.reduce_max(pred_pro),true_label),color=color)"
      ],
      "metadata": {
        "id": "kmI1OdcJyx77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pick_random_img(model=Model_4,images=test_data,true_labels=test_labels,classes=label_names)\n"
      ],
      "metadata": {
        "id": "cKzSSUc5zJ9K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "caceedb4-24d7-41fd-9ebb-37397ff882f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 23ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Model_4.layers"
      ],
      "metadata": {
        "id": "ORofPuVY3CLj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "630402f0-5f0c-48df-f3b9-edc7eea053a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.reshaping.flatten.Flatten at 0x7fe2590af8e0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fe2590ac6d0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fe2590afb20>,\n",
              " <keras.layers.core.dense.Dense at 0x7fe2590ad7e0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fe2590ada20>]"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights,biases =Model_4.layers[1].get_weights()"
      ],
      "metadata": {
        "id": "YbhV2fyG4k1J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weights[0].shape"
      ],
      "metadata": {
        "id": "kkt8n5lV5t2y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dee8c160-ce77-4142-94cb-df8dcf1c5302"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10,)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weights.shape #(shape = 28*28 = 748 ,layer = 10 layers)"
      ],
      "metadata": {
        "id": "LITUqGRB6NTG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "340e4e7d-dc9b-4f46-a1c9-21dbd25831eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(784, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Model_4.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrJUv5IVtaDI",
        "outputId": "becafbea-46d0-487a-f66b-1b313de93a8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " FLatten_layer (Flatten)     (None, 784)               0         \n",
            "                                                                 \n",
            " dense_44 (Dense)            (None, 10)                7850      \n",
            "                                                                 \n",
            " dense_45 (Dense)            (None, 20)                220       \n",
            "                                                                 \n",
            " dense_46 (Dense)            (None, 10)                210       \n",
            "                                                                 \n",
            " dense_47 (Dense)            (None, 10)                110       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,390\n",
            "Trainable params: 8,390\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now Check the bias Vector"
      ],
      "metadata": {
        "id": "ZjhL8LyMtxy6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "biases"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IcCqPe8vTGH",
        "outputId": "8a9c0109-a003-4518-f742-65f8005dde59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.04666971,  0.6312996 , -0.18669847,  0.02323548,  0.20834897,\n",
              "       -0.04425796,  0.5030214 ,  0.16643292,  0.07192811,  1.0047475 ],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "biases.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66DvF3eOvWjn",
        "outputId": "170e9fec-5f44-46de-ba20-f0fbdf085be3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10,)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Every Neuron has a bias vector.Each of it is paired with a weight matrix.\n",
        "\n",
        "The bias vector get intialized as zeros(at least in the case of Tensorflow Dense Layer).\n",
        "\n",
        "The bias vector dictates how much the pattern within the corresponding weight matrix should influence the next layer."
      ],
      "metadata": {
        "id": "dDxtSB1cvgCW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(Model_4,show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "TcOD6MQMxg-G",
        "outputId": "12c2e77e-58b3-406f-ac5e-e47f20d8b939"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "E9LBnAElyCRe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}