Traffic_Sign_Classfication.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7da67836",
   "metadata": {},
   "source": [
    "# Traffic Sign Classification - LeNet Deep Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b87b84",
   "metadata": {},
   "source": [
    "### by ReDay Zarra"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ec8c0d1",
   "metadata": {},
   "source": [
    "This project utilizes the **LeNet deep network architecture** to classify 43 different types of traffic signs. LeNet refers to a convolutional neural network that can be used for computer vision and classification models. This project showcases a *step-by-step implementation* of the model as well as *in-depth notes* to customize the model further for higher accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3b4e7e7",
   "metadata": {},
   "source": [
    "## Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "358d581e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af26ea14",
   "metadata": {},
   "source": [
    "> Classic libraries that will help us read and analyze data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "191606ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76835ab1",
   "metadata": {},
   "source": [
    "> Libraries used for plotting and data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "090f8979",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbe3217f",
   "metadata": {},
   "source": [
    "> Using pickle package to open our data, not much use after that"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21f29fe",
   "metadata": {},
   "source": [
    "## Importing the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9604cdb0",
   "metadata": {},
   "source": [
    "Importing the data into three different sections: training, validation, and\n",
    "testing. **Training data is used to train** the network, **testing data is used to test**\n",
    "the trained network with data that it has never seen to see how it would perform in\n",
    "the real world. The **validation dataset ensures that we avoid overfitting** by showing\n",
    "the network validation data every epoch (or run) in a process called \n",
    "cross-validation to ensure that the network is not focusing on the details of the\n",
    "training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "3dbf755d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./traffic-signs-data/train.p\", mode = 'rb') as training_data:\n",
    "    train = pickle.load(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "9c14357b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./traffic-signs-data/valid.p\", mode = 'rb') as validation_data:\n",
    "    valid = pickle.load(validation_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "198fc2ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"./traffic-signs-data/test.p\", mode = 'rb') as testing_data:\n",
    "    test = pickle.load(testing_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1960590f",
   "metadata": {},
   "source": [
    "## Splitting the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee884025",
   "metadata": {},
   "source": [
    "Splitting the data into our individual training, validation, and testing set variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "d9f41d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = train['features'], train['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f1c816",
   "metadata": {},
   "source": [
    "> Assigning the features of the training set as X_train and the dependent variable (labels) as y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "364d7e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_validation, y_validation = valid['features'], valid['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "577b086a",
   "metadata": {},
   "source": [
    "> Assigning the features of the validation set as X_validation and the dependent variable (labels) as y_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "41b0abd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, y_test = test['features'], test['labels']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccf34b81",
   "metadata": {},
   "source": [
    "> Assigning the features of the testing set as X_test and the dependent variable (labels) as y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0e2e772",
   "metadata": {},
   "source": [
    "## Checking the dimensions of the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "9ed132e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34799, 32, 32, 3)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2d37363",
   "metadata": {},
   "source": [
    "> Gives us an output of a four element tuple. The first number is the quantity of images, the second is the width of image in pixels, the third is the height of the image, and the last number the depth - in this case the 3 tells us that the images are colored since they are being multiplied for both Red, Green, and Blue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "6ae13e69",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34799,)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5350975",
   "metadata": {},
   "source": [
    "> Gives us a tuple of one element, which is a label for each image in the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "9cb131ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4410, 32, 32, 3)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validation.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d2b7121",
   "metadata": {},
   "source": [
    "> A smaller subset only containing 4410 pictures, which are 32 x 32, and have a depth of 3 meaning they are colored with RGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "05560895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12630, 32, 32, 3)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "9048e3a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12630,)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e042b1a9",
   "metadata": {},
   "source": [
    "## Verifying the images from the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f597353f",
   "metadata": {},
   "source": [
    "Using matplotlib to display a randomly chosen image and see if it matches with its label, just to see what the images are and how the network will be classifying them. Each type of sign has its own class which will identify the type of sign to the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "0321d64c",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 23 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85e3cac",
   "metadata": {},
   "source": [
    "> The index of the image we want to look at, arbitrarily chose 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "9ccb3527",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2a36b68b0>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bb670ab",
   "metadata": {},
   "source": [
    "> Using matplotlib's .imshow method to show an image from the features training set at the index of 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "6b3f8aca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4985938",
   "metadata": {},
   "source": [
    " >Also show us the corresponding label from the same index in the label's training set - the label tells us that the sign is a \"End of No Passing\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "6cde0b5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uU++fZSVJcZN/nlVz1pZ9VVdniymxiAxZcNIl5KoZVCLD2MZpmJbSEK0iSeOn/WNeJOnUiH8Uz6QhhkeSjFFmJs6w0qM629NRU6t/HmVri+3YLIyaylUpFv3HLhRMY5cN+1YlwVgyU8KboZYzIQBAMDQhAEAwNCEAQDA0IQBAMDQhAEAwNCEAQDA0IQBAMDQhAEAwNCEAQDA0IQBAMLM2tsck8o+qiIyxFplM7F3b2NBkGjtriO1pqrdtqlTKfzkrFVueTcVZc14s49vmYooSMewnkqRy2bt0cnTcNPTJU3lT/YQh6qVSNq5DwzFhDWBydf6vc5tbbcdPq6E+rveP35KkjHKm+sr4pHdtIeMf8SNJxSn/461s2GclqWI5fhLaTzgTAgAEQxMCAARDEwIABEMTAgAEQxMCAARDEwIABEMTAgAEQxMCAARDEwIABEMTAgAEQxMCAAQze7Pjosie9eU1rq28zpB9lUnbVmeDITsunUmbxq4YMqSKU7YsK/N2MWTTRdZ0MkP2Vdo47zFDHthofsw09uSkbZ2XyiVDtW05TdlxhuNBkppaGr1rW5r8cxol27Fplamrt9XH/stZX2fLGUylDPtKyZYdZ9lXoiSej8WZEAAgIJoQACAYmhAAIBiaEAAgGJoQACAYmhAAIBiaEAAgGJoQACAYmhAAIBiaEAAgmNkb22OQssRJGKMn0nX+9XUZW0+vjzPetamUbVOVigXv2rRx7JTxpUsqbYgGMcb2lCsV79rxkdOmsUdOjnrXFgwxSZIpbUiSlEr57yvGoZUyJEI15FpMY7e0+EdT1adtO9bUlH+UUcU4dsqyUiSl6/1jflJ1tuPN9vRme36zHG8JpfZwJgQACIcmBAAIxtyE9uzZo7vvvludnZ2KokivvfbatMfXrVunKIqm3W699daZmi8AoIaYm9DY2JiWLFmibdu2nbfmrrvu0rFjx6q3N99887ImCQCoTeYLE/r6+tTX13fBmjiO1d7efsmTAgBcHRJ5T2j37t1asGCBrr/+ej3wwAMaHh4+b22hUFA+n592AwBcHWa8CfX19enFF1/Url279Mwzz2j//v1atWqVCoVzXzLc39+vXC5XvXV1dc30lAAAs9SMf07ovvvuq/578eLFWrp0qbq7u/XGG29ozZo1Z9Vv2rRJGzdurP6cz+dpRABwlUj8w6odHR3q7u7W4cOHz/l4HMeKY9t3ywMAakPinxM6ceKEjh49qo6OjqR/FQBgjjGfCZ0+fVq//vWvqz8fOXJEH3zwgebNm6d58+Zp8+bN+rM/+zN1dHTo008/1eOPP6758+fr29/+9oxOHAAw95mb0IEDB3TnnXdWf/7y/Zy1a9dq+/btOnjwoF544QWdOnVKHR0duvPOO/Xyyy8rm83O3KzPYMnKMuXMSYosQWnGfKp0nX995PxzsiSpXJr0ri1MTZnGrpT889okKTJsoLKzZbBNForetWN5W3ZcqWTI1UpZM7tsKhXDerHmI2b8c8+cs237ifEJ79qiMZQwMvwhp6HRmAVnzI5zhozJlKFWkiLD3mLNd7Mcm84QeGipNTehlStXXvAXvP3229YhAQBXKbLjAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADBJP5VDpcqFUWKfIOQLPlHxnkYIpAuIbjJUm+cecU/a64y5Z+/Jknlsi0/zBI3Vpjwz7yTpNPj4961pZItl06Rf35Yyvx6zrgODSFfmQb/LDhJamxq8K61PmGkyv7rvFKxrZNUnWU2xvXtjDl2hkPZ+jRhCRp0FdvzhK08mew4zoQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADB0IQAAMHM2tgei4SSJ/6Pf2ZG5Ix5HIb6KGV7vZBOZ7xr44xtpaTqbXOZGBvzry35xw1JUiryn0tdnW37lJ3/4WHcPJJh3pLU2BB717ZkW0xjN8T++0rauKApw/FTnpoyjV02zCVT5x/BJEl1xmydkiEVyBnjiSJTvTmYzFg/8zgTAgAEQxMCAARDEwIABEMTAgAEQxMCAARDEwIABEMTAgAEQxMCAARDEwIABEMTAgAEQxMCAARTE9lxkSH/yDlbVlKl7J/bVJ6yjV0slL1rG2Pbpso0GOqN8VFTBeNyGuLgKvLPMZOkdMb/dVRUtuXSSf55Y+l62/ZpafTPgpOkhqYG79qmZlt2XH3GkqtmzBk0lFdKk6axJ6f8j59M2rZ90hX/sSWpXPTPvStZDghJFUN2nDG9UpExIy8JnAkBAIKhCQEAgqEJAQCCoQkBAIKhCQEAgqEJAQCCoQkBAIKhCQEAgqEJAQCCoQkBAIKZtbE9kQwRFKnkoidKU/4RG1OlCdPYkxOnvGtbmr9qGjtT5x8LU6n4R45I0ujYaVP9uCGmpGLclpWKf71L2V5zZTL+h0dza6tp7K80N5nq62NDnFFkieGRLMktkfwjZKxShn1WkizJVCnjflUaL9jqi/6RQ1OGKDBJKhuixpw1hscSY2ZJbDLUciYEAAjG1IT6+/t1yy23KJvNasGCBbr33nv18ccfT6txzmnz5s3q7OxUY2OjVq5cqUOHDs3opAEAtcHUhAYHB7V+/Xrt27dPAwMDKpVK6u3t1djYWLXm6aef1tatW7Vt2zbt379f7e3tWr16tUZHR2d88gCAuc30ntBbb7017ecdO3ZowYIFeu+993T77bfLOadnn31WTzzxhNasWSNJev7559XW1qaXXnpJDz744MzNHAAw513We0IjIyOSpHnz5kmSjhw5oqGhIfX29lZr4jjWHXfcob17955zjEKhoHw+P+0GALg6XHITcs5p48aNuu2227R48WJJ0tDQkCSpra1tWm1bW1v1sTP19/crl8tVb11dXZc6JQDAHHPJTeiRRx7Rhx9+qH/7t38767Ezv63POXfeb/DbtGmTRkZGqrejR49e6pQAAHPMJX1O6NFHH9Xrr7+uPXv2aOHChdX729vbJX1xRtTR0VG9f3h4+Kyzoy/Fcaw4tn0+AABQG0xnQs45PfLII3rllVe0a9cu9fT0THu8p6dH7e3tGhgYqN5XLBY1ODioFStWzMyMAQA1w3QmtH79er300kv62c9+pmw2W32fJ5fLqbGxUVEUacOGDdqyZYsWLVqkRYsWacuWLWpqatL999+fyAIAAOYuUxPavn27JGnlypXT7t+xY4fWrVsnSXrsscc0MTGhhx9+WCdPntSyZcv0zjvvKJvNzsiEAQC1I3LOEh6UvHw+r1wup+bmpvNezHAlRSn/Pt1kzANrmXeNd23HgnbT2JEhn8p6WfzEhH9OliSVnSHfzTSy5CxZZnW2t0CzLf4vnK4xvshqiOtN9S4y5IeZRrZenWQb3XIEm5+KnP+2TxnnnT/5v6b648P+9SdP2j64P14oetdak/0s69xaWygUNDIyotaL5CqSHQcACIYmBAAIhiYEAAiGJgQACIYmBAAIhiYEAAiGJgQACIYmBAAIhiYEAAiGJgQACOaSvsphtrHESVijgFyl7F1bKBRMY6dHJ7xrhyvHTWOnDItZNiyjJDlTGIt1ndviVVIZ/124paXFNHYue+G4kd8VZzKmsaOUPaDIe+zI9toySjS5y3/slDWmyxAHVZ60RU1Njo3b6g1RVoWpkmnscoLbx3JsJpXwxpkQACAYmhAAIBiaEAAgGJoQACAYmhAAIBiaEAAgGJoQACAYmhAAIBiaEAAgGJoQACAYmhAAIJhZmx1niSlKNPrKkH1VKdkyoaam/POpRsdsY9fXN3nXZjL1prGt+W4VV/GujQxZcJItD+4rLVnT2I2xfx6cNU/PKjKt80QPCCP/9RIZ12Gl5J/VmD/5v6axR/N5U/3YuP9cSmX/40GSbXMmuBumUv7nLJacOc6EAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADBzNrYntnCkoJRKZdNY0+MT3jXxo22PI4omvSudcaYl3TKtttEmbR3bXO21TS2JYqnsd4/hkeSUpH/9rQnsRi3p214k4ohYsU6j8iwa5Wm/PdZSRr97W+9a0+eHDWNnR/zj9SSpKIhssuyviUpsqx0U7GSyzwjtgcAMBfQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwdCEAADB0IQAAMHQhAAAwcze7DhLnpUhLikyZiu5BHO13JR/3lSh4p8z98Xg/vOuGIPPUnX1pvp52a9617Y2NZvGrs/478KRNSer4p8dF1mz4CL/PL0vf4Mvyz4rGddLxX+flaSpov9+e+J//bPgJGl0JO9dOz5hy6Ubn5wy1ZctWWnmAD7/Uuu2tzxPWEa2zIMzIQBAMKYm1N/fr1tuuUXZbFYLFizQvffeq48//nhazbp16xRF0bTbrbfeOqOTBgDUBlMTGhwc1Pr167Vv3z4NDAyoVCqpt7dXY2Nj0+ruuusuHTt2rHp78803Z3TSAIDaYHpP6K233pr2844dO7RgwQK99957uv3226v3x3Gs9vb2mZkhAKBmXdZ7QiMjI5KkefPmTbt/9+7dWrBgga6//no98MADGh4ePu8YhUJB+Xx+2g0AcHW45CbknNPGjRt12223afHixdX7+/r69OKLL2rXrl165plntH//fq1atUqFQuGc4/T39yuXy1VvXV1dlzolAMAcEznzNX1fWL9+vd544w398pe/1MKFC89bd+zYMXV3d2vnzp1as2bNWY8XCoVpDSqfz6urq0tNjY3+l1ObLtG29d1EL9G2jJ22fTV13NTgXZvJ2C65Nl+i/VX/S7S/8pVrTGM3xf5zsX4eIYr8L9OtWLe++RJtf9ZD2lRvvES7NEcv0R6bsH29d7li+JiD9RnXdIm2cewEL9GeLBQ0MjKi1tbWC9Ze0ueEHn30Ub3++uvas2fPBRuQJHV0dKi7u1uHDx8+5+NxHCuO40uZBgBgjjM1IeecHn30Ub366qvavXu3enp6Lvp/Tpw4oaNHj6qjo+OSJwkAqE2mv02tX79e//qv/6qXXnpJ2WxWQ0NDGhoa0sTEF6fcp0+f1o9+9CP953/+pz799FPt3r1bd999t+bPn69vf/vbiSwAAGDuMp0Jbd++XZK0cuXKaffv2LFD69atUzqd1sGDB/XCCy/o1KlT6ujo0J133qmXX35Z2Wx2xiYNAKgN5j/HXUhjY6Pefvvty5pQVSTvN+SMaXD2uSTFlO9my7IqjhvecGzwz0iTpLqy7c3p8dMt3rX1mbGLF/2OtOGNctdgu6AiipLJL5Qk52zr3LTbVmz7eHHy3FeunsvY2Ihp7MmxE961I7+1XTwwOeV/TBSKtrFLtjhFYyal9cIRwzxMI0vOclGXcWxfZMcBAIKhCQEAgqEJAQCCoQkBAIKhCQEAgqEJAQCCoQkBAIKhCQEAgqEJAQCCoQkBAIK5pK9yuBJSqcg7CsMSa2H/rhVTuY0l6sM4j9JU0bu2XLFFAmUytu82Kk39xrt2cvykaeyT9f5zqW9sNI0d1/t/508qZQyPMsbClKb8Y37Khm0vSSVDbM/klH+tZPs+ocKkbT8sGeKJyuYD2bg9DeOnjBlPpqcJ8/cJGesTwJkQACAYmhAAIBiaEAAgGJoQACAYmhAAIBiaEAAgGJoQACAYmhAAIBiaEAAgGJoQACAYmhAAIJhZmx33RXaTX2hSZAhAMidIJRjcZBnbGWdumkrZFmQ2VbFlk00V/XPPSkX/vDZJmjTk2KVHR01j2yK+rNvHuK9YXi8ag+kqJf/tM1UumcYuG+qtuWemcuPYxng32/OEkWXq1ueJ5ObhjzMhAEAwNCEAQDA0IQBAMDQhAEAwNCEAQDA0IQBAMDQhAEAwNCEAQDA0IQBAMDQhAEAwsza2p1Jx/tEZhsQMa7yGNUrENLapOMGJWFbgJdS7iiW2x79WkkpTU/7FKdtrrowhEihl3K8qJcO89cXx4MsaCZRUHItkjaYyjp1YsT22x6KS6LFsZFjQpFYJZ0IAgGBoQgCAYGhCAIBgaEIAgGBoQgCAYGhCAIBgaEIAgGBoQgCAYGhCAIBgaEIAgGBoQgCAYGZtdpyrVPxzjSxBTwmGQtlz6ZLLkEolGKxlnbdlvVjXoYV13qlU2lBrzY4rmeotyWqJpgwmevzY6i3bMzKuFeuhaSk3r8MEnydmQ4odZ0IAgGBMTWj79u266aab1NraqtbWVi1fvlw///nPq48757R582Z1dnaqsbFRK1eu1KFDh2Z80gCA2mBqQgsXLtRTTz2lAwcO6MCBA1q1apXuueeeaqN5+umntXXrVm3btk379+9Xe3u7Vq9erdHR0UQmDwCY2yJ3mW9MzJs3Tz/+8Y/1/e9/X52dndqwYYP+6q/+SpJUKBTU1tamv/3bv9WDDz7oNV4+n1cul1NjQ4P/305nyXsO1pEtq96+mUx/pbaNPFffEzLWxw2N3rXW94RKhYKpvlKp+Ncm+X01SX7RjlGS7wlZzd33hJLZns45TUxMaGRkRK2trResveT3hMrlsnbu3KmxsTEtX75cR44c0dDQkHp7e6s1cRzrjjvu0N69e887TqFQUD6fn3YDAFwdzE3o4MGDamlpURzHeuihh/Tqq6/qm9/8poaGhiRJbW1t0+rb2tqqj51Lf3+/crlc9dbV1WWdEgBgjjI3oW984xv64IMPtG/fPv3gBz/Q2rVr9dFHH1UfP/NU0zl3wdPPTZs2aWRkpHo7evSodUoAgDnK/Dmh+vp6ff3rX5ckLV26VPv379dPfvKT6vtAQ0ND6ujoqNYPDw+fdXb0u+I4VhzH1mkAAGrAZX9OyDmnQqGgnp4etbe3a2BgoPpYsVjU4OCgVqxYcbm/BgBQg0xnQo8//rj6+vrU1dWl0dFR7dy5U7t379Zbb72lKIq0YcMGbdmyRYsWLdKiRYu0ZcsWNTU16f77709q/gCAOczUhH7zm9/oe9/7no4dO6ZcLqebbrpJb731llavXi1JeuyxxzQxMaGHH35YJ0+e1LJly/TOO+8om83aZxZFhstBk7sE2HjxsqnaflF3UpKdx2xZSuvlxVHK/w8FkfFvCuardG3liY1tvtTZcHlxxfwpBMMl2sZL6F2Sx7I5Eyi5yCbbfpjMkXzZnxOaadXPCTU2GhqG/8qxfp4jySZkWfNz+nNCpuIEPydkHLuhscm71tCvJEmlyUlbfdn/c0LW7WP7jItp6FnThKzHfZJNyLyHJ9qELLPxr3XOaTzpzwkBAHC5aEIAgGBoQgCAYGhCAIBgaEIAgGBoQgCAYGhCAIBgaEIAgGBoQgCAYMwp2kn78tPeSQU5JJsPMVcTE4wjz66QDW/mNWj5NlPr2NZUgwS/hTfZQ8Iy79kztj0xIUEJJiYkxfI8Puua0OjoqCRp0hhrAsy0ifHx0FMA5rTR0VHlcrkL1sy67LhKpaLPP/9c2Wx2Wq5RPp9XV1eXjh49etEsormM5awdV8MySixnrZmJ5XTOaXR0VJ2dnUpdJFhx1p0JpVIpLVy48LyPt7a21vQO8CWWs3ZcDcsosZy15nKX82JnQF/iwgQAQDA0IQBAMHOmCcVxrCeffFJxHIeeSqJYztpxNSyjxHLWmiu9nLPuwgQAwNVjzpwJAQBqD00IABAMTQgAEAxNCAAQzJxpQs8995x6enrU0NCgm2++Wf/xH/8RekozavPmzYqiaNqtvb099LQuy549e3T33Xers7NTURTptddem/a4c06bN29WZ2enGhsbtXLlSh06dCjMZC/DxZZz3bp1Z23bW2+9NcxkL1F/f79uueUWZbNZLViwQPfee68+/vjjaTW1sD19lrMWtuf27dt10003VT+Qunz5cv385z+vPn4lt+WcaEIvv/yyNmzYoCeeeELvv/++vvWtb6mvr0+fffZZ6KnNqBtuuEHHjh2r3g4ePBh6SpdlbGxMS5Ys0bZt2875+NNPP62tW7dq27Zt2r9/v9rb27V69epqfuBccbHllKS77rpr2rZ98803r+AML9/g4KDWr1+vffv2aWBgQKVSSb29vRobG6vW1ML29FlOae5vz4ULF+qpp57SgQMHdODAAa1atUr33HNPtdFc0W3p5oA/+qM/cg899NC0+37/93/f/fVf/3WgGc28J5980i1ZsiT0NBIjyb366qvVnyuVimtvb3dPPfVU9b7JyUmXy+Xc3//93weY4cw4czmdc27t2rXunnvuCTKfpAwPDztJbnBw0DlXu9vzzOV0rja3p3POXXPNNe6f/umfrvi2nPVnQsViUe+99556e3un3d/b26u9e/cGmlUyDh8+rM7OTvX09Og73/mOPvnkk9BTSsyRI0c0NDQ0bbvGcaw77rij5rarJO3evVsLFizQ9ddfrwceeEDDw8Ohp3RZRkZGJEnz5s2TVLvb88zl/FItbc9yuaydO3dqbGxMy5cvv+LbctY3oePHj6tcLqutrW3a/W1tbRoaGgo0q5m3bNkyvfDCC3r77bf105/+VENDQ1qxYoVOnDgRemqJ+HLb1fp2laS+vj69+OKL2rVrl5555hnt379fq1atUqFQCD21S+Kc08aNG3Xbbbdp8eLFkmpze55rOaXa2Z4HDx5US0uL4jjWQw89pFdffVXf/OY3r/i2nHUp2ufzu1/rIH2xg5x531zW19dX/feNN96o5cuX62tf+5qef/55bdy4MeDMklXr21WS7rvvvuq/Fy9erKVLl6q7u1tvvPGG1qxZE3Bml+aRRx7Rhx9+qF/+8pdnPVZL2/N8y1kr2/Mb3/iGPvjgA506dUr//u//rrVr12pwcLD6+JXalrP+TGj+/PlKp9NndeDh4eGzOnUtaW5u1o033qjDhw+Hnkoivrzy72rbrpLU0dGh7u7uObltH330Ub3++ut69913p33lSq1tz/Mt57nM1e1ZX1+vr3/961q6dKn6+/u1ZMkS/eQnP7ni23LWN6H6+nrdfPPNGhgYmHb/wMCAVqxYEWhWySsUCvrVr36ljo6O0FNJRE9Pj9rb26dt12KxqMHBwZrerpJ04sQJHT16dE5tW+ecHnnkEb3yyivatWuXenp6pj1eK9vzYst5LnNxe56Lc06FQuHKb8sZv9QhATt37nSZTMb98z//s/voo4/chg0bXHNzs/v0009DT23G/PCHP3S7d+92n3zyidu3b5/70z/9U5fNZuf0Mo6Ojrr333/fvf/++06S27p1q3v//ffdf//3fzvnnHvqqadcLpdzr7zyijt48KD77ne/6zo6Olw+nw88c5sLLefo6Kj74Q9/6Pbu3euOHDni3n33Xbd8+XL3e7/3e3NqOX/wgx+4XC7ndu/e7Y4dO1a9jY+PV2tqYXtebDlrZXtu2rTJ7dmzxx05csR9+OGH7vHHH3epVMq98847zrkruy3nRBNyzrm/+7u/c93d3a6+vt794R/+4bRLJmvBfffd5zo6Olwmk3GdnZ1uzZo17tChQ6GndVneffddJ+ms29q1a51zX1zW++STT7r29nYXx7G7/fbb3cGDB8NO+hJcaDnHx8ddb2+vu/baa10mk3HXXXedW7t2rfvss89CT9vkXMsnye3YsaNaUwvb82LLWSvb8/vf/371+fTaa691f/Inf1JtQM5d2W3JVzkAAIKZ9e8JAQBqF00IABAMTQgAEAxNCAAQDE0IABAMTQgAEAxNCAAQDE0IABAMTQgAEAxNCAAQDE0IABAMTQgAEMz/A0ET7y04O/TvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_validation[i])\n",
    "y_validation[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0ccbca",
   "metadata": {},
   "source": [
    ">Verifying images for the validation dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "d39084ae",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_test[i])\n",
    "y_test[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c10e49",
   "metadata": {},
   "source": [
    "> Verifying images for the testing dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dffb2ca4",
   "metadata": {},
   "source": [
    "## Preparing the data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a330303d",
   "metadata": {},
   "source": [
    "Preparing the data by **transforming the images so they are simpler for the network to understand**. Such as \n",
    "transforming the images from **RBG to grayscale (changes the depth from 3 to 1)**. I \n",
    "will also **perform data normalization**, where instead of using the pixel values \n",
    "ranging from 0 to 255, we can lower the range to use a more restricted range of\n",
    "pixel values. The **data needs to also be shuffled** so the images do not get trained\n",
    "in a certain order, we do not want the network to learn the order of these images.\n",
    "We want to make it as hard as possible for the network to learn, which is why we\n",
    "use low-quality images, more restricted pixel range, and grayscale images. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eae2662",
   "metadata": {},
   "source": [
    "### Shuffling the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "32410551",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a911398",
   "metadata": {},
   "source": [
    "> Importing the shuffle module from the sci-kit learn library to shuffle the dataset first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "28707682",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = shuffle(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fdaef39",
   "metadata": {},
   "source": [
    "> Used the shuffle function to reassign the variables of X_train and y_train into a different order, however the labels still correspond to the correct images because the shuffle function reorders them the same way"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1baa9d28",
   "metadata": {},
   "source": [
    "### Transforming the data from RBG to Grayscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "06ab131c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_gray = np.sum(X_train/3, axis = 3, keepdims = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbcea31f",
   "metadata": {},
   "source": [
    "> Use numpy to average the pixels to get the grayscale version of the image. We will simply add the pixel values from the three color channels (Red, Green, and Blue) and then divide them by 3. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "a31972ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34799, 32, 32, 1)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_gray.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5e96df4",
   "metadata": {},
   "source": [
    "> The depth of the features training set is no longer 3, its now 1. This means the images are now grayscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "683e6969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2d0b59220>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train_gray[i].squeeze(), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d69043d",
   "metadata": {},
   "source": [
    "> The .squeeze() method gets rid of the 1 (last number) at the end of our tuple when we call the .shape method. This is because the 1 represented the depth of each image, but because our images are now grayscale we no longer need to include that. We are also specifying that we want our colormap, or cmap, to be in grayscale so we give it the 'gray' value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "2082c642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4410, 32, 32, 1)"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_validation_gray = np.sum(X_validation/3, axis = 3, keepdims = True)\n",
    "X_validation_gray.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1c29c1",
   "metadata": {},
   "source": [
    "> Averaging the RGB values to reduce the depth from 3 to 1 for the feature's validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "fada6cb5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2a3542c10>"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_validation_gray[i].squeeze(), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57bcb475",
   "metadata": {},
   "source": [
    "> Grayscale sample image from the feature's validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "3f13fbec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12630, 32, 32, 1)"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_gray = np.sum(X_test/3, axis = 3, keepdims = True)\n",
    "X_test_gray.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "82880bfb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2b0795d90>"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_test_gray[i].squeeze(), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98c01abc",
   "metadata": {},
   "source": [
    "### Normalizing the data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842963dd",
   "metadata": {},
   "source": [
    "**Normalization is restricting the pixel value scale for each dataset**. It is important because we want all our **pixels in a similar range** so the weight at one part of the image is not a lot more than the weight at another part of an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "a505c20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_gray_norm = (X_train_gray - 128) / 128"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61aa3ca",
   "metadata": {},
   "source": [
    "> There are many kinds of normalization methods. This one works by **subtracting the image pixels by 128** because we want to center the data from its range of 0 to 255 (256 total, so half is 128). We then **divide by 128** to so the data is between -1 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "40c1c96f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2a368a370>"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_train_gray_norm[i].squeeze(), cmap = 'gray')\n",
    "# X_train_gray_norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "26dc6590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2a3a305b0>"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation_gray_norm = (X_validation_gray - 128) / 128\n",
    "plt.imshow(X_validation_gray_norm[i].squeeze(), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "4f48e3da",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2d2a3a53790>"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_gray_norm = (X_test_gray - 128) / 128\n",
    "plt.imshow(X_test_gray_norm[i].squeeze(), cmap = 'gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3015e5e6",
   "metadata": {},
   "source": [
    "## Building the Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f5a0cb",
   "metadata": {},
   "source": [
    "To train the model we will implement six fundamental steps that will ensure we have\n",
    "correctly implemented the LeNet-5 architecture. The original LeNet-5 architecture\n",
    "was proposed by Yann LeCun and he developed it to recognize handwritten numbers. \n",
    "The dataset with the handwritten numbers is called the MNIST dataset and we will\n",
    "be working with that in some different project. \n",
    "\n",
    "For our use, we will need to cutomize the LeNet architecture a bit because we have\n",
    "more classes (or types of things) to differentiate from. Yann LeCun had 10 classes \n",
    "which were numbers ranging from 0 to 1, while we have over 43 different traffic \n",
    "signs. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf0289dc",
   "metadata": {},
   "source": [
    "### First Convolutional Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6703cc",
   "metadata": {},
   "source": [
    "For our first step, we will take our input image (which is 32 x 32 x 1) and **apply\n",
    "6 filters** that are 5 x 5 with input depth of 3. The output of these filters will be \n",
    "28 x 28 x 6 after the image has been processed through it. This is because there is \n",
    "an equation the ouptut has to follow that applies when we run images to filters: \n",
    "\n",
    "> **Output = ((Input - Filter) + 1) / Stride**\n",
    "\n",
    "the equation above returns 28 by: \n",
    "\n",
    "> **Output = ((32 - 5) + 1) / 1 = 28**\n",
    "\n",
    "The **stride is simply how much the kernel is shifted by each time when it passes over the image.** The kernel is essentially a feature map that scans the image by essentially gliding over it. The stride is how much the kernel shifts, so a **stride of 1 means that the kernel is moving 1px at a time** which is slow. While a stride of 2 means its moving 2px each time.\n",
    "\n",
    "The **output depth is determined by the number of filters we apply**, since we applied\n",
    "six filters the output of the image after the filters have been applied becomes\n",
    "28 x 28 x 6.\n",
    "\n",
    "Then we need to **apply a ReLU function on the output**. The rectified linear \n",
    "unit, or ReLU, an activation function simply takes in input and **converts all negative numbers \n",
    "into 0's while maintaining all the positive numbers**.\n",
    "\n",
    "Finally, a **pooling layer (or subsampling layer) will be applied to simply shrink \n",
    "the feature map by 2**. This means the output depth will remain the same (the output\n",
    "from the filters is still maintained), however the size of the output will be \n",
    "divided from 28 to 14. The result of the pooling layer is 14 x 14 x 6."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404e470f",
   "metadata": {},
   "source": [
    "#### Importing the Keras classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "8a007c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18932bb4",
   "metadata": {},
   "source": [
    "> We are going to use **Keras which will sit on top of TensorFlow** and help us build our network. From the keras.models module we will import the Sequential class which will allow us to **build our network in a sequential fashion (building it one step at a time)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "f10d3689",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Dense, Flatten, Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85abb026",
   "metadata": {},
   "source": [
    "> Import the following **necessary classes from Keras to build our model** sequentially:\n",
    "\n",
    "> 1. The **Conv2D** class will be used to **perform our convolutions in the convolutional layers**.\n",
    "\n",
    "> 2. The **MaxPooling2D** class is going to help us in the **downsampling layers by selecting the largest pixel value** in the pooling window.\n",
    "\n",
    "> 3. The **AveragePooling2D** class will also perform **downsampling but it will take the average of the pixels** in the pooling window. \n",
    "\n",
    "> 4. The **Dense** class helps **build the dense layers**. \n",
    "\n",
    "> 5. The **Flatten** class will help us **flatten the matrix down to a vector** of pixels. \n",
    "\n",
    "> 6. The **Dropout** class implements a regularization technique that **reduces overfitting by forcing some of the neurons to have an input of zero** - which reduces dependency on any one feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "4782a50e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fb8f8e",
   "metadata": {},
   "source": [
    ">The **Adam** class is optimization algorithm **used to update the weights of the neural network**. Adam maintains a running average of the gradients and uses them to update the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "607ed96a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import TensorBoard"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20a2fbf2",
   "metadata": {},
   "source": [
    "> Basically using TensorFlow as the backend of the Keras API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4a5ce4",
   "metadata": {},
   "source": [
    "#### Applying the First Convolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "32f001dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model = Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72437685",
   "metadata": {},
   "source": [
    "> Create the Sequential class instance object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "d8631a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(Conv2D(filters = 6, kernel_size = (5, 5), activation = 'relu', input_shape = (32, 32, 1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa377ca",
   "metadata": {},
   "source": [
    "> Use the Sequential class .add method to start building. The **Conv2D is class is then called to build the first convolution layer**, it takes 4 parameters. The first parameter is the **number of filters** which we know is 6. The **kernel_size is the size of the filters** which we know is 5 x 5 so we input the tuple (5,5). We specify the **activation function as 'relu'** to ensure ReLU is used. The final parameter is the **input shape which will be the shape of the image** so the tuple (32, 32, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35e7b86",
   "metadata": {},
   "source": [
    "#### Applying First Pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "cde8dd8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(AveragePooling2D())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883213b5",
   "metadata": {},
   "source": [
    "> Using the .add method from the Sequential class, call the AveragePooling2D class which will automatically apply average downsampling (average pixel value is chosen from pooling window) to our output. This means our output will go from (28, 28, 6) to (14, 14, 6). It will divide the image shape by 2 but will not affect the output depth. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d99b760",
   "metadata": {},
   "source": [
    "### Second Convolutional Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e449e1b5",
   "metadata": {},
   "source": [
    "For our second step, we will simply repeat the process above but implementing some slight changes. We will take the output from the previous convolutional layer, which was 14 x 14 x 6 and then **apply 16 filters**. The filters will follow the same equation from above:\n",
    "\n",
    "> **Output = ((14-5) + 1) / 1 = 10**\n",
    "\n",
    "The **stride is 1px** (kernel is shifting by 1px each time) and because we have applied 16 filters, the **output of the filters will be 10 x 10 x 16**.\n",
    "\n",
    "Similar to the previous layer, we **apply a ReLU funciton** which will simply *convert all the negative values into 0's*.\n",
    "\n",
    "A subsampling layer aka **pooling layer is applied** to reduce the size of the images by 2. Meaning the images are shrunk down from 10 x 10 to 5 x 5, however the output depth from the filters has not changed. This means that the **output after the second convolutional layer is 5 x 5 x 16**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179ce514",
   "metadata": {},
   "source": [
    "#### Applying the Second Convolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "573d4725",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(Conv2D(filters = 16, kernel_size = (5,5), activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26aab314",
   "metadata": {},
   "source": [
    "> Similar to the last filter except we no longer have to specify the input shape because the model already accepted the input from the last layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f32e10",
   "metadata": {},
   "source": [
    "#### Apply Second Pooling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "f00c74aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(AveragePooling2D())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecdaece",
   "metadata": {},
   "source": [
    "### Flatten Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4132f97",
   "metadata": {},
   "source": [
    "The **flatten layer is simply used to convert** our matrix of data, which in our case is 5 x 5 x 16 **into a single array** so 400 x 1. This array *basically stores the individual pixels of an image in a single line*. It is important to flatten our data because the fully connected layers expects a one-dimensional vector of data as input. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "08650a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(Flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9793da",
   "metadata": {},
   "source": [
    "> Call the Flatten class to automatically flatten the output from the last convolutional layer into a vector of 400 x 1 containing pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ce070c6",
   "metadata": {},
   "source": [
    "### First Dense Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b3bf06",
   "metadata": {},
   "source": [
    "A fully connected layer or a **dense layer is simply a layer that connects all of the\n",
    "nodes from it's layer to all of the nodes in the next layer**. Dense layers are often\n",
    "used at the end of neural networks because they *allow the network to learn the \n",
    "complex mapping from the input to the output data*. They function by receiving \n",
    "input from all of the nodes and then multiplying weights and adding biases to each\n",
    "input value. These values are then passed down to the next dense layer which adds \n",
    "its own weights and biases. The **network learns to control these weights and biases\n",
    "so that it can eventually make highly accurate predictions**.\n",
    "\n",
    "In our case, the **first dense layer will have 400 nodes** which will each be assigned a \n",
    "pixel from our 400 x 1 flattened vector. The nodes will then connect to a second \n",
    "layer which only has 120 nodes (or neurons). The **dense layer will then apply a ReLU activation function to the output** before sending it off to the next layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "cb995318",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(Dense(units = 120, activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a36af4",
   "metadata": {},
   "source": [
    "> Use the .add method from the Sequential class, and then call the Dense class. This class takes the parameters units which is the number of nodes it needs to connect to for the next layer, and then the activation function which is ReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0447df0e",
   "metadata": {},
   "source": [
    "### Second Dense Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf48f2c",
   "metadata": {},
   "source": [
    "The second dense layer is similar to the first. It will **take the input from the 120 nodes** from the previous layer, **manipulate the input with its own weights & biases, and then pass on the output to the next layer**.\n",
    "\n",
    "The second dense layer will have 120 nodes to recieve the input from the previous layer and then it's output will be passed on to another dense layer with 84 nodes. The dense layer will then **apply a ReLU activation function to the output** before sending it off to the next layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "e5017fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(Dense(units = 84, activation = 'relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c385fe",
   "metadata": {},
   "source": [
    "### Third Dense Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07798465",
   "metadata": {},
   "source": [
    "The third and **final dense layer will recieve input from the previous layer with its 84 nodes**. This layer will be **responsible for manipulating the input recieved and sending it to the final output layer**. The dense layer will then **apply a ReLU activation function to the output** before sending it off to the output layer.\n",
    "\n",
    "Our **output layer needs to have nodes equal to the number of classes**. In the MNIST experiment, Yann LeCun only had 10 classes because he was trying to recognize the handwritten digits 0 to 9. However, we are trying to classify 43 different kinds of signs so our **output layer needs to have 43 nodes**. One node for class, or type of sign. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "08ae01e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.add(Dense(units = 43, activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35e2d10",
   "metadata": {},
   "source": [
    "> The activation function of the last layer **can't use ReLU because the output needs to be categorical** (ReLU just gives us numbers that aren't negatives). The **softmax activation function takes in all the input and squashes it between 0 and 1**, meaning the final values of all the numbers add up to 1. **These numbers between 0 and 1 act as probability values** (if you multiply by 100 you get the percentage) which is perfect for our project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4053d572",
   "metadata": {},
   "source": [
    "## Compiling the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75edcbb",
   "metadata": {},
   "source": [
    "To train the model, we will **first have to compile it**. We will use the .compile \n",
    "method from the Sequential class. The method accepts the following classes which\n",
    "are used to compile and train the model effeciently: **loss, optimizer, and metrics**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "041d6942",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn_model.compile(loss = 'sparse_categorical_crossentropy', optimizer = Adam(learning_rate = 0.001), metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4455fe6d",
   "metadata": {},
   "source": [
    "> 1. The loss parameter allows us to specify what kind of loss function we want to use to better train the model. The categorical cross entropy loss function measures the difference between the true probability distribution and predicted probability distribution.\n",
    ">> The loss function does this by comparing the predicted probability for each class with the true class label\n",
    "\n",
    "> 2. The optimizer parameter will specify what kind of optimizer we want to use. For my project, I want to use the Adam optimizer. The Adam optimizer takes in the parameter learning_rate which is set to 0.001 - this is the step size at which the optimizer makes updates to the parameters (weights and biases).\n",
    ">> The learning rate of 0.001 is good for Adam. However, if the learning rate is too small than the model can be train too slow. If it is too big than the model can speed up the training process but increase the risk of overfitting.\n",
    "\n",
    "> 3. The metrics parameter lets us specify how we want to evaluate the model. The accuracy metric allows us to evaluate the performance of the model based on the number of correct predictions divided by the total predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "126fb847",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1682b4f",
   "metadata": {},
   "source": [
    "Finally, to train the model we will use the .fit method from the Sequential class. This class has a number of parameters that we will need to address for the network to train effeciently, such as: **features training** dataset, **dependent variable training** dataset, **batch_size, epochs, verbose, validation_data**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "522057c6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 3.0740 - accuracy: 0.2160 - val_loss: 2.3989 - val_accuracy: 0.3515\n",
      "Epoch 2/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 1.4447 - accuracy: 0.5961 - val_loss: 1.2297 - val_accuracy: 0.6279\n",
      "Epoch 3/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.8250 - accuracy: 0.7689 - val_loss: 0.9237 - val_accuracy: 0.7263\n",
      "Epoch 4/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.6088 - accuracy: 0.8324 - val_loss: 0.7595 - val_accuracy: 0.7789\n",
      "Epoch 5/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.4921 - accuracy: 0.8665 - val_loss: 0.7310 - val_accuracy: 0.7771\n",
      "Epoch 6/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.4152 - accuracy: 0.8886 - val_loss: 0.6348 - val_accuracy: 0.8181\n",
      "Epoch 7/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.3497 - accuracy: 0.9099 - val_loss: 0.6115 - val_accuracy: 0.8288\n",
      "Epoch 8/45\n",
      "70/70 [==============================] - 3s 46ms/step - loss: 0.3041 - accuracy: 0.9225 - val_loss: 0.5979 - val_accuracy: 0.8417\n",
      "Epoch 9/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.2823 - accuracy: 0.9261 - val_loss: 0.5555 - val_accuracy: 0.8485\n",
      "Epoch 10/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.2413 - accuracy: 0.9403 - val_loss: 0.5561 - val_accuracy: 0.8503\n",
      "Epoch 11/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.2234 - accuracy: 0.9419 - val_loss: 0.5119 - val_accuracy: 0.8624\n",
      "Epoch 12/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.1947 - accuracy: 0.9523 - val_loss: 0.5276 - val_accuracy: 0.8603\n",
      "Epoch 13/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.1813 - accuracy: 0.9550 - val_loss: 0.5193 - val_accuracy: 0.8658\n",
      "Epoch 14/45\n",
      "70/70 [==============================] - 3s 43ms/step - loss: 0.1641 - accuracy: 0.9592 - val_loss: 0.5059 - val_accuracy: 0.8707\n",
      "Epoch 15/45\n",
      "70/70 [==============================] - 3s 43ms/step - loss: 0.1525 - accuracy: 0.9632 - val_loss: 0.4986 - val_accuracy: 0.8701\n",
      "Epoch 16/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.1354 - accuracy: 0.9681 - val_loss: 0.5335 - val_accuracy: 0.8633\n",
      "Epoch 17/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.1267 - accuracy: 0.9690 - val_loss: 0.5191 - val_accuracy: 0.8707\n",
      "Epoch 18/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.1209 - accuracy: 0.9695 - val_loss: 0.5343 - val_accuracy: 0.8544\n",
      "Epoch 19/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.1099 - accuracy: 0.9730 - val_loss: 0.4953 - val_accuracy: 0.8705\n",
      "Epoch 20/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.1016 - accuracy: 0.9754 - val_loss: 0.5007 - val_accuracy: 0.8850\n",
      "Epoch 21/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0928 - accuracy: 0.9772 - val_loss: 0.5004 - val_accuracy: 0.8807\n",
      "Epoch 22/45\n",
      "70/70 [==============================] - 3s 43ms/step - loss: 0.0865 - accuracy: 0.9790 - val_loss: 0.5130 - val_accuracy: 0.8755\n",
      "Epoch 23/45\n",
      "70/70 [==============================] - 3s 46ms/step - loss: 0.0801 - accuracy: 0.9804 - val_loss: 0.5177 - val_accuracy: 0.8805\n",
      "Epoch 24/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0741 - accuracy: 0.9824 - val_loss: 0.5068 - val_accuracy: 0.8769\n",
      "Epoch 25/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0696 - accuracy: 0.9826 - val_loss: 0.5291 - val_accuracy: 0.8785\n",
      "Epoch 26/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0655 - accuracy: 0.9841 - val_loss: 0.5034 - val_accuracy: 0.8855\n",
      "Epoch 27/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0582 - accuracy: 0.9866 - val_loss: 0.5168 - val_accuracy: 0.8857\n",
      "Epoch 28/45\n",
      "70/70 [==============================] - 4s 54ms/step - loss: 0.0549 - accuracy: 0.9878 - val_loss: 0.5157 - val_accuracy: 0.8864\n",
      "Epoch 29/45\n",
      "70/70 [==============================] - 3s 46ms/step - loss: 0.0538 - accuracy: 0.9873 - val_loss: 0.5481 - val_accuracy: 0.8850\n",
      "Epoch 30/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0505 - accuracy: 0.9870 - val_loss: 0.5228 - val_accuracy: 0.8914\n",
      "Epoch 31/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0445 - accuracy: 0.9901 - val_loss: 0.5326 - val_accuracy: 0.8893\n",
      "Epoch 32/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0436 - accuracy: 0.9895 - val_loss: 0.5855 - val_accuracy: 0.8760\n",
      "Epoch 33/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.0440 - accuracy: 0.9891 - val_loss: 0.5884 - val_accuracy: 0.8773\n",
      "Epoch 34/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.0418 - accuracy: 0.9894 - val_loss: 0.5953 - val_accuracy: 0.8778\n",
      "Epoch 35/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0398 - accuracy: 0.9902 - val_loss: 0.6660 - val_accuracy: 0.8798\n",
      "Epoch 36/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0336 - accuracy: 0.9923 - val_loss: 0.5299 - val_accuracy: 0.8862\n",
      "Epoch 37/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0361 - accuracy: 0.9907 - val_loss: 0.5780 - val_accuracy: 0.8844\n",
      "Epoch 38/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.0296 - accuracy: 0.9927 - val_loss: 0.5916 - val_accuracy: 0.8805\n",
      "Epoch 39/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0336 - accuracy: 0.9912 - val_loss: 0.6234 - val_accuracy: 0.8834\n",
      "Epoch 40/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0260 - accuracy: 0.9938 - val_loss: 0.6210 - val_accuracy: 0.8787\n",
      "Epoch 41/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0243 - accuracy: 0.9944 - val_loss: 0.7653 - val_accuracy: 0.8655\n",
      "Epoch 42/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0331 - accuracy: 0.9913 - val_loss: 0.6192 - val_accuracy: 0.8766\n",
      "Epoch 43/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.0271 - accuracy: 0.9928 - val_loss: 0.5908 - val_accuracy: 0.8825\n",
      "Epoch 44/45\n",
      "70/70 [==============================] - 3s 45ms/step - loss: 0.0218 - accuracy: 0.9949 - val_loss: 0.5957 - val_accuracy: 0.8832\n",
      "Epoch 45/45\n",
      "70/70 [==============================] - 3s 44ms/step - loss: 0.0191 - accuracy: 0.9956 - val_loss: 0.6326 - val_accuracy: 0.8850\n"
     ]
    }
   ],
   "source": [
    "history = cnn_model.fit(X_train_gray_norm,\n",
    "              y_train,\n",
    "              batch_size = 500,\n",
    "              epochs = 45,\n",
    "              verbose = 1,\n",
    "              validation_data = (X_validation_gray_norm, y_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c22bcc34",
   "metadata": {},
   "source": [
    "> 1. The first parameter of the .fit method is the features training dataset. Here we will just **input the features training data** which are the images that we prepared by shuffling, grayscaling, and normalizing. The images are **stored in the variable X_train_gray_norm**\n",
    "\n",
    "> 2. The second parameter is the dependent varible training dataset (what we want to predict). We can just **input the dependent variable training data** which are the labels that correspond to every image in the features training data. These labels are **stored in the variable y_train**\n",
    "\n",
    "> 3. The third parameter is the **batch_size** with just **means the number of images that will be fed into the network at once**.\n",
    "\n",
    "> 4. The fourth parameter is the **epochs** which **means the number of epochs or a single pass through the *entire dataset***. At the end of each epoch, the model's performance is evaluated and recorded. Another epoch starts and the **optimizer aims to perform better each time** using the evaluations. \n",
    "\n",
    "> 5. The fifth parameter is **verbose** which just means how much information the program shows us during the training process. Setting the **value to 1 will show us all the background information, and the value will 0 will show us nothing**.\n",
    "\n",
    "> 6. The sixth parameter is the **validation_data**. This is the dataset we will **use to avoid overfitting by showing the network validation data every epoch** so that the network is not focusing on the details of the training data. The **validation images are stored in the variable X_validation_gray_norm** and the **validation labels are stored in the variable y_validation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcde2f4e",
   "metadata": {},
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abf0d42c",
   "metadata": {},
   "source": [
    "Now that we have trained our model, it **needs to be evaluated so we know how well it performs in the real world**. This can be done by **showing the model the testing dataset** which it has not seen before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1315b7e9",
   "metadata": {},
   "source": [
    "### Testing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "4c01cc23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 1s 2ms/step - loss: 1.0775 - accuracy: 0.8773\n"
     ]
    }
   ],
   "source": [
    "score = cnn_model.evaluate(X_test_gray_norm, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54a9f501",
   "metadata": {},
   "source": [
    "> Using the .evaluate method from the Sequential class that allows us to evaluate the model on the testing sets. The features testing set or the **testing images are stored in the variable X_test_gray_norm** and the **labels are stored in y_test**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "5cec2063",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 87.73%\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Accuracy: {}%\".format(round(score[1] * 100, 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d34c50e",
   "metadata": {},
   "source": [
    "### Extracting Values from the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "61bd2e8a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history.history.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbc13281",
   "metadata": {},
   "source": [
    "> The history variable is calling on the .history method which produces a dictionary containing the **keys: loss, accuracy, val_loss, val_accuracy** which **store values about the all the accuracy and losses** for training and validation data for every epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "617d859b",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = history.history['accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7dae14",
   "metadata": {},
   "source": [
    "> **Extracting the accuracy values** from the history dictionary object. Storing the values **in the accuracy variable**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "e89752f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = history.history['loss']\n",
    "valid_loss = history.history['val_loss']\n",
    "valid_accuracy = history.history['val_accuracy']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0ed99a",
   "metadata": {},
   "source": [
    "> **Extracting the loss, validation loss, and validation accuracy** values from the history dictionary object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "821cc446",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(1, 46)\n"
     ]
    }
   ],
   "source": [
    "epochs = range(1, len(accuracy) + 1)\n",
    "print(epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef01d2ad",
   "metadata": {},
   "source": [
    "> This allows us to create a **range starting from 1 to the number of epochs** (45)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdc8acc",
   "metadata": {},
   "source": [
    "### Visualizing the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a08cd60",
   "metadata": {},
   "source": [
    "Visualizing the model by plotting the data values extracted and **creating scatter\n",
    "plots** to better understand the performance of our network. We want to observe the **accuracy and losses for both the training and validation datasets** so we can **make the model more accurate** (with more epochs) **or avoid overfitting** (reducing number of epochs)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7688d292",
   "metadata": {},
   "source": [
    "#### Training Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "2315ad2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(epochs, accuracy, color = 'black', sizes = [5])\n",
    "plt.plot(epochs, accuracy, 'blue')\n",
    "plt.title('Training Accuracy over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156b1017",
   "metadata": {},
   "source": [
    "> The graph above is exactly what we should be expecting. The **network learns fast in the beginning and then the growth in accuracy slows down**. I did not want the network to overfit so I allowed it to run at 45 epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "838723dc",
   "metadata": {},
   "source": [
    "#### Training Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "cf12b86a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(epochs, loss, color = 'black', sizes = [5])\n",
    "plt.plot(epochs, loss, color = 'red')\n",
    "plt.title('Training Loss over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388d091d",
   "metadata": {},
   "source": [
    "> Exactly what we shold be looking at. The loss is the measure of how well the model is making correct predictions, basically an error rate. The network is **minimizing loss through loss functions** (we used **categorical cross-entropy** which rewards the network when it produces a high probability for the correct label)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e9ee0a3",
   "metadata": {},
   "source": [
    "#### Validation Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "e88a3822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(epochs, valid_accuracy, color = 'black', sizes = [5])\n",
    "plt.plot(epochs, valid_accuracy, color = 'green')\n",
    "plt.title('Validation Accuracy over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f04fb8",
   "metadata": {},
   "source": [
    "> The validation accuracy closely resembles the training accuracy plot as it starts off with **high accuracy growth until it eventually starts slowing down**. Unlike the training curve, it is **more volatile** and is most likely going to bounce around in the high 80's"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "694c64ee",
   "metadata": {},
   "source": [
    "#### Validation Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "d3d4e94f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(epochs, valid_loss, color = 'black', sizes = [5])\n",
    "plt.plot(epochs, valid_loss, color = 'orange')\n",
    "plt.title('Validation Loss over Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f992842",
   "metadata": {},
   "source": [
    "> Unfortunately, our **model has symptoms of overfitting**. The validation **loss is decreasing fast in the beginning** which is good. However, the validaiton **loss starts to increase at the end** which means the network is *overfitting*. To fix this issue, I would most likely **decrease the number of epochs down to between 20 and 30 epochs**, because the validation loss is at its most stable low during that range. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75913fbc",
   "metadata": {},
   "source": [
    "#### Comparing Training and Validation Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "9cdfd060",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mTpzIjBkzGDBgAIMHD2bBggV07NiRESNG0KhRIy5fvszq1av517/+RatWrXjqqaeYNWsW/fr1Y8+ePbRq1YqkpCQiIiKoUaMGTz75JDabja5duzJz5kzuvPNO6taty4YNG9KdiZYRPz8/mjdvzrvvvktgYCAVKlRg9erVzJgxgyJFiqRad/To0SxdupTmzZvz8ssvc9ddd3Hu3DmWLVvGkCFDqF69un3drl27MnLkSNasWcMrr7yS7rmR24xzxzOLOF5Gs6Vq1aqV7vrr1q0zTZo0Md7e3qZ48eKmT58+ZsuWLWlmhmQ0W6p9+/Zp9nn9zJ6MZktdX8+MjhMZGWk6d+5sChcubHx9fc2jjz5qlixZYgDz/fffZ/RWpLJ3714DpDtz5sqVK6Zfv36mTp06xs/Pz3h5eZlq1aqZ119/3Vy8eDFN3a59HTeyfft2AxhXV1dz7NixNM/PnDnTVKtWzXh4eJhKlSqZsWPHmhkzZqSZ3ZSV2VLGGPPbb7+Zxo0bGw8PD1OyZEnz0ksvmenTp6fZX1bPe1xcnOnTp48pXry4sdlsqfZz/WwpY4w5dOiQefrpp01AQIApVKiQqVatmnn33XdNYmKifZ2UmUfvvvtumvcjvdd0rcGDBxvAbNu2LcN1UmZtbd682RhjzNmzZ80LL7xgypUrZwoVKmRKlChh2rdvb3bv3m3f5vLly+a1114zVapUMe7u7iYgIMDcf//9Zt26dfZ1YmJiTJ8+fUxQUJDx8fExHTp0MAcPHsxwttSpU6fS1O3IkSPm0UcfNUWLFjW+vr7moYceMn/88Ue67+Xhw4fNs88+a0qWLGkKFSpkSpcubZ544glz4sSJNPvt2bOncXNzM0eOHMnwfZHbh82YLFwpS0TynHfeeYdXXnmFyMjIHL8FgUheFh8fT4UKFbjvvvv4v//7P2dXR/IAdUuJ5AOTJ08GoHr16ly9epUVK1bw0Ucf0bVrVwUbuW2dOnWKPXv2MGvWLE6cOJFqkLLc3hRuRPIBb29vPvjgAw4ePEhcXBzlypUjNDSUV155xdlVE3GaH3/8kV69elGqVCmmTp2q6d9ip24pERERKVA0FVxEREQKFIUbERERKVAUbkRERKRAue0GFCclJXHs2DF8fX3TvRy/iIiI5D3GGM6fP5+l+6DdduHm2LFjlC1b1tnVEBERkZtw+PDhG14C47YLNyl3Iz58+DB+fn5Oro2IiIhkRWxsLGXLlrV/jmfmtgs3KV1Rfn5+CjciIiL5TFaGlGhAsYiIiBQoCjciIiJSoCjciIiISIGicCMiIiIFisKNiIiIFCgKNyIiIlKgKNyIiIhIgaJwIyIiIgWKU8PNmjVr6NChA6VLl8Zms/Hdd9/dcJvVq1dTv359PD09qVSpEp988knOV1RERETyDaeGm4sXL1K3bl0mT56cpfUPHDhAu3btaNasGVu3buXll19m0KBBfPPNNzlcUxEREckvnHr7hZCQEEJCQrK8/ieffEK5cuWYNGkSADVq1GDTpk289957PProozlUSxEREclP8tW9pdavX0/btm1TlT344IPMmDGDq1evUqhQoTTbxMXFERcXZ38cGxub4/UUERHJCREREezdu5eqVasSHBycJ4/tzDqmyFcDio8fP05QUFCqsqCgIBISEjh9+nS624wdOxZ/f3/7UrZs2dyoqoiI5BERERHMnTuXiIiIPLm/rO4zNDSUxo0b0717dxo3bkxoaGiuHPdGxzYG4uMhNhYGDhxN48aP0b37Kw6r482wGWOMU458HZvNxqJFi+jUqVOG61StWpVevXoxcuRIe9lvv/3GfffdR1RUFCVLlkyzTXotN2XLliUmJkZ3BRcRSUde+OZ9I1mtY2hoKBMmTLA/Hj58OOPHj7/pfTpyf1evQlQUvPzyFObNWwF4A+60afMv2rV7hLg4iIuzgsPBg8eYN28h4A6cAw4Af/P11+/x8MN3c33HRfZfiyd9+75G794jOXECTp60lhMnYOfO0/z881ageHIdPQBPChcO4OpVN675iL3GYaAcAOHh4Q75PYqNjcXf3z9Ln9/5qluqZMmSHD9+PFXZyZMncXNzIyAgIN1tPDw88PDwyI3qiYg4lDNCRnY+vMHxXRU3EzCGDQvlpZfG2T+MUz6ct249xpw5VYHvgMvAGSZMOMvly4e4++7yFC0KxYpB0aIwbdo7fPLJO1jhoQRPPTWYzp37cerUPx/0u3dHs2JFCNCDlJAxYcI5Nm2KplKlAIoWhSJFrGXp0i/54Ye5QBxQihYtnqFOnXYcOYJ9OX7cavWA/smLJSzMWlIrDbyQ5r147DFwcYGyZaFiRahUCfbu/Ylff/0ciAGK8MADj9O8eSfOnYOzZ60lMjKWLVueAv4DFAN8+fRT+PTT9N7xQKBNmtILF9I9Pcmv+ar90d69e3M9JOerlpvQ0FD+97//sXPnTnvZ888/z7Zt21i/fn2WjpOd5Cci4mjOanHIaL2EBOuD9sAB+OWXv3nnnflARaAUkADE0aJFU0qVKoa7O3h4WIu7O6xbt4Lw8DVYH2QJtGzZjE6d/oWbG6mWhQvn8+OP3wE2wJV27R7msce6kJhoHT8x0Vq+++5/rFy5GigEeNGoUXMaNWrJ5ctw5QpcvgxRUedYv34L4AUUBkpgffi6ZvEM5C2urokkJkYCx4DzQDwQR5MmDahevaL9PY+OPsa8eTOx3utArHNUCQ+P6sTFOWqEyRWKFUukUiUfSpTAvly+fIiPP34FOAVcBK4Acfzf/82lUaO6eHpadfz99w20aNEYSB0rnNFy49Rwc+HCBf766y8A7rnnHt5//31atWpFsWLFKFeuHCNHjuTo0aPMmTMHsKaC165dm759+/Lvf/+b9evX069fP+bPn5/l2VIKNyKSHY5qcTAGBg16k8mTvwWCAF/atXuCdu26cOECqZZDh04TFrYe8AGSsD7QrtKy5X2ULFmMQoWwLxs3/saWLeFYQSSRJk0a8eCDD+DmBq6u1uLmBj/++D3Ll/+MFQgqUaFCK1xdq3LokBUw8jubzRAQYKNECQgKsj6UjTnO//3fR0A0VhgqChTlwQefxtU1kLNn4cwZiIq6TGysK1ZrDMBZ4BRVqhShVq0S9g/5ixcP8sEHI4CTWCHEHyjC0KFvU6RIBXvLyO+/H2LTpr+Sj+cJRAFH6NChHg89dBdlymBf9u+PoGnTxmleT3qB4PrAGxoaytix4zh+3AqnBw7At99u49tvtwCVAN/k13KWli3vplGjKhQpYrVUnT69j1df7Y/VxXUm+TWdzzCIpHfscePG3fR6NyNbn9/GiVauXGmwIl6qpUePHsYYY3r06GFatGiRaptVq1aZe+65x7i7u5sKFSqYadOmZeuYMTExBjAxMTEOehUikleEh4ebOXPmmPDwcIesO3z48FR/m4YPH55mnStXjOnff7SBagbuM9DFtGz5P/Pii8Z06WLMffcZU7GiMe7uicaKOHlvKVTImCpVjGnc+KyBKQaGGnjSwDMGepmXXvrbTJpkzPjxxrz1ljGvvGJM+/Y7DHyQvP6nBmYY+Nw0bvy3efxxYx55xJgOHYypU+eIgZ8MrDCw3MDPBpaaunUPmw4drPUee8yY4OADBr4w8LmBzwx8bOBd8/DD283bbxszcaIxU6ca88orfxnoYuBhAw8YqGMgyPz6a/rn8fpzGBoamu7vgvW8j4FC9nXT+93I3v5SLxn9rmVln9fuO7Pf2+wcOzvHzcqxs7tedmXn8zvPdEvlFrXciOQ/tzrQMz4e+9iJU6fgo4/m8+OP4Vjf1F1p1qwlbdo8RFKS1T2SlASHDx9jzpy5WN0dPkARoAi1a99HQoIv587BuXNWd0n2nMT6Jh8LXKBhw5rUqlWewoWxL9HRB/ngg7ewugBsyfUsxEsvjaJMmYrEx1uDUTdt2sa33/6A1Y3jllxXN+6/vw2VKlWzd/fs23eA9esjkte5AvwNHODll5+kX78HKV3aauFJ733M6Jt3REQEjRvfuMXB0etlp47X7ju7vz+Z7dPR+8vqPrPK0a8lr8g3LTfOoJYbkbwje60nNgN+pl+/d8z27casWWPM4sXGzJ1rzNChBwy8bGCCgf8a+M7Ab6ZMmcvGzy83Wj8SDZwx8JeB1Qa+NCEhf5j33zdmwQJjfv3VmG+/3WLA3aHf5rP6LT27LQlZ/ead1W/+jl4vO3XMDkfvM6daMPL6sXOKWm4yoZYbkZx3o2+DV67AkCFvM23aAqzBkQG0afMULVp0Jjoa+3Lw4AX+/DMKCMBqObn5gZOurlC8OBQqdJbDhzcDp7FmdSQCSbRq1Zxq1ari4mKte/JkFAsWfIk15uUi1tiEc4wf/zKNGlW1z4r5669NtGnTCLIwiNKZLQ45NRbCGbOl5PaUbwYUO4PCjcjNyexD5+pVa1rrsWMwbtwcvvsuHOuaGAHUqNGMsmXv4fRpK7CcPg0XL95KTeLw80uiZEkv/P2tgJGYGM2KFd9iTX09hzWr4xSffPIWLVvWonhxaz0Xl5zp/nB2N4CCg9wOFG4yoXAjtwtHfeAlJUH//mP55JPlWDMwSlOnTghlywZz9KgVaE6dguz/JUnAmqVxGms2SzTNm9cmOLgygYEQEADR0XsJDe2d/PwZrOASd8utIjkRRhQcRHKWwk0mFG4kv3PkVVRTrxfEU0+9TuvWz7NvH+zdC/v2wb59SVm6joabG/j5XeTMmR1YA2ZPYoWS0zz33KM8/PC99tBy8GDOdOVkJ2AojIjkLwo3mVC4kfzshRfe4KOPVmGNP/HmwQc7067dY1y6hH05cOAE3367lH8uk14IcOeeexrh6enH1atWN1Js7CUOHDia/HwxILN/D/FYM2z+Ao4Ax3j22RAefbQJpUtD6dIQGAgbN+bMjBcFERFRuMmEwo3kRel9eF+8CFu2wMaN1vLrr1c4csQzB2uRCBzirrs8aNHiDqpWhSpV4NKlbTz6aIPk5/9xqxf7AoUWEck6hZtMKNxIXmOFgY+AWkBD7rrrWYxpyM6d1niXtPYDx7HulXOJ4OA61KxZAW9v8PaGM2eOMGPGR8nPXyHl6rZjxrxBrVpV7Fe2/euvXfTv/2+sVplYrBvxxefaLB8RkexQuMmEwo3kpus/5C9fht274c8/reW3386ydm001kDdtONaSpeGhg2txdd3Ny+80BTrcur/yM+zfEREskrhJhMKN+IIN/qgj4+Hfv0+YNasTVgtMrUoUuReYmICM5lVdBrYDGzkhRfuZfjwVpQunXoNzfIRkduVwk0mFG7kVl0fMPr1e4d//Wskv/8OO3ZYy65dSSQmpj/DqFgxqFXLWry9D/L++88Cf2LNLrJkdhddhRERuR1l5/PbLZfqJJIvpBccLl2Cw4etZfXq/UyY4AFMB2oAtfnkkyJ88sn1e3LBuqDcDuAPrPDyJx9//Dz9+z+OzZayXgXc3BoyYcJK+5ahoaGZhpbg4GCFGhGRTKjlRiTZgAFvMWVKNFAFKEuJEg1ISCjNmTM32vIqZcpcoFmzotx1F9x1FyQmbqVTp3pp1syoRUatMSIimVO3VCYUbuR627bBqFGnWLLED+u6MGkVLgxly4Kf3zkiIr4GDmNd8+V3YA/h4WtveYaRiIhkTN1SIjeQmAj/+x9MmgSrV4N1HySA9cByrPBymLfffp7//KcD/v4kdyUVITR0X5rQkl5ry/jx4+ncubNaZEREcplabqTAu7bLp0aNYGbOhI8+ggMHrOfd3OD++0/z88/tgQ2ptlU3kohI3qCWG7ktZO8eS5WAgbi71yU+3rrKb7Fi0Lcv/Oc/UKZMIKGhLZkwYUOqbTParwb1iojkXWq5kXzJCi1zgHuBYrRo0YnGjdtx5gycPWsthw9fZO/ek0BRrHsxWSpUuMzIkV507Wpd0fdaapEREcmbNKA4Ewo3+d/atRto3vw7YBTgk40tlwCT+PzzbnTv3i1H6iYiIjlD3VJSYC1bBj17VgfeSS7ZgXVPpDM8+GAwTZvWoFgxKFoUTp7czZAhPbFuV3ASOAdAtWpv5X7FRUQk1yjcSL5w8CAMHgzffw/gB0QBw4Av7eu8+WY4qXuSqnP8eIsszWwSEZGCQ+FG8rQrV2DCBBg71vrZ1RVeeAESE6fx4Yf/BBtNxxYRkRQacyN5Tsqg3pMnGzF1ajX+/tsqb9kSJk+27sl07XoKLSIiBZ/G3EielJUw8tJLI3jvvR+AsUA1AEqXhokToUsXrrknk6Zji4hI+hRuJFdcfyuCQYNe56mn3mDPHuzLtm2X+PvvN4CUWxRcBT5g7txW3H9/QyfUWkRE8iOFG8lxERERTJjwHfAxUBeoxkcfleCjj65fM+WiM1ewboEwDNjN0aNzAIUbERHJGoUbyVFHjkBoaDHgT67/dSta9CL33ONDtWpQrRoYs5sXXwwBIoEk+3pVq1bNzSqLiEg+p3AjOSI6GsaNg48/hri4Ksmli4H5wB5gL0uXLr9uzEx1oqKe0NRtERG5JQo34lAXLsCHH1rTt2NjrbJmzaBcuanMm9ffvp6mbouISE7RVHC5JSkzoCpWrMb27Y146y04ccJ6rm5d6/o0Dz1kzXLS1G0REblZmgouucKaAfUu8BTWDSwtlSrBmDHW1G0Xl3/W19RtERHJDQo3clOsGVAfA98BDyeXRvHSS1cYM6Yi7u7Oq5uIiNzeXG68Ss6aOnUqFStWxNPTk/r167N27dpM158yZQo1atTAy8uLatWqMWfOnFyqqVxr8+ZDWNO1HwYuAyOBytx1168KNiIi4lRObblZsGABgwcPZurUqdx77718+umnhISEsHPnTsqVK5dm/WnTpjFy5Ej++9//0rBhQzZs2MC///1vihYtSocOHZzwCm5PBw7A+PEdAC/gDNABWAdo2raIiDifUwcUBwcHU69ePaZNm2Yvq1GjBp06dWLs2LFp1m/atCn33nsv7777rr1s8ODBbNq0iV9//TVLx9SA4luzdSu0awfHj4Of31liY5sCuwFrDM64ceMy34GIiMhNyBcDiuPj49m8eTMjRoxIVd62bVvWrVuX7jZxcXF4enqmKvPy8mLDhg1cvXqVQoUKpbtNXFyc/XFsyvxkybawMOjc2ZruXacOLF1alMOHZ2sGlIiI5ClOG3Nz+vRpEhMTCQoKSlUeFBTE8ePH093mwQcf5LPPPmPz5s0YY9i0aRMzZ87k6tWrnD59Ot1txo4di7+/v30pW7asw1/L7WDePKvF5sIFaNUK1qyxbmgZHBxMt27dFGxERCTPcPqAYtu1t3kGjDFpylK8+uqrhISE0LhxYwoVKkTHjh3p2bMnAK6uruluM3LkSGJiYuzL4cOHHVr/gs4Y64J8XbtCQgI8+SQsXQr+/s6umYhkV0REBHPnziUiIsLZVRHJUU4LN4GBgbi6uqZppTl58mSa1pwUXl5ezJw5k0uXLnHw4EEiIyOpUKECvr6+BAYGpruNh4cHfn5+qRa5sYiICD7//AuefPI4oaFW2ZAhVguOh4dz6yYi2RcaGsq/Gjfmve7deaRxY0YOG5bhugpBkt85bcyNu7s79evXJywsjEceecReHhYWRseOHTPdtlChQpQpUwaAr776in/961+4uDi9EarAsC7O9yEwFygJwMSJVrgRkfRl9Qrc2blS9y1f1fvSJVizhqi5c3nqyy8Zf+1zEyeS8N//4laqFJQoYV/Ctm9n0bp1RAI/AUOGD2f8+PHp7z+f0NXRb0PGib766itTqFAhM2PGDLNz504zePBg4+PjYw4ePGiMMWbEiBGmW7du9vX37Nlj5s6da/bu3WsiIiJMly5dTLFixcyBAweyfMyYmBgDmJiYGEe/nAIhPDzcgK+BFcbqlIoz0MWEh4c7u2qSj4SHh5s5c+bcNr83w4cPN4B9GT58+C2tZxITzctDhhhfMG43Wtdc837/9psxERHGjBljTMuWxri7m+R/yPblBJir15VltOwC0x5M+Pr1jnibnCLL77lx/O9tQft3kNXXk1OvOzuf304NN8YYM2XKFFO+fHnj7u5u6tWrZ1avXm1/rkePHqZFixb2xzt37jR333238fLyMn5+fqZjx45m9+7d2Tqewk3mpkxZYGBj8t+2GAOtDGDmzJnj7KpJPvHqCy+Y3mCeAFMdzIhhwzJctyD88Q//7TfTFMw7YL4GswjM92DONG1qzEMPGdOmjTGtW5uYevXMKjBrwawDsxnMH2Au33GHMXfcYUxgoDGFCxtTqFCakHERzDEwl8qVM6ZhQ2MeeMCYzp2N6dXLrK1f34wG8w2YM+mFlHLlzIkOHUwXMIHJH/A2MMXAbJs/35hVq4z5v/8zZvJks61TJzM1+XWcvGYfx2rWNGbbNme/1VmXlGTMH3+Yg4MHm/+BOZ38/u0AsxJMdMuWxjz3nDEjRxozcaIxn39uZj76qGkEpmQWQlBWZCdUOVNW/w2m+3oSE42JjjZmzx5j1q0zZvFisyAkxAwF80IOvO7sfH7rxplid+QI3HffZQ4d8gJOAQ8BWwAIDw9Xc65kzhj2vf02Pq++Sulrii8DidWrU7hJE+tuqnXqQJ06hE6YwIQJE+zrDXdA90dOdD+ku8+zZ+Gnn+CHH4j77js8Ll50yLEc4RyQ1KIFxZ54Atq0gcqVwWZL7m7+5/1O77pUERERNG7cGAA/rOuOvwh4gHX322efhbfeglKlcufFZCLNeTl6FJYvt65Z8csv1sW4btJZYBdQuUMHSrRoATVrQo0aUK4cuLikPbYxcOYMHDwIhw5xaPVqvv/oI8oDRYA/gc3A4DlzuOvJJyGdy5Y4o+ts5LBhrJo4kbuwLsn6YIsWtLv/foiLgytXrP/HxXH6yBFWLFuGJ1AMCAQCgEAXF2xJSenu+yhQJvlnR31+ZOvz22GRKp9Qy0369u41plw564uar+9ZA9XsCT00NNTZ1ZO8bs8eqzUh+Zv+X2B+A3M+ky6Pw2B+BPNWctdHANx8C05iopnQp495BswrYLqB+bhrV2POns1wk6x8Y73222oNMD+0aGFM8+bGuLqmei3RYL4A0x/Mv8E8C+avV14xZvZsY+bONWbePLP3rbfMY2A6g+kI5iEwrcD8MX26MZs2GfPHH8bs22dMZKTZtHSp8QXjmdzCUgFMXTB/TptmzOLF1j6nTDFbHn/cvANmCpiXwTQC45pJS2t2XzNgxvXta0yXLv+8Xh8fY956y5iLF7O8T4eul5hoXh840FQD8y8wk8AcDwhI+zvm6WnOBgeboWDqJ79/9ye3KB4YNsyYN980ZuBAY55+2hytXdtsBHMQTEImv7PG29scDgoyc8BMBvM/MMdSWtwy2+7axcPDmEaNjHn+eWM++8yYrVvNiKFDHd7Kk+77GBdnzK+/GjNmjDnXqJG5kNU632jx9TWmYkVzqmJFswTMXDATrnk9jmr5z1fdUrlN4SatbduMKVHC+h2tUsWYgwcLRneB5IJLl4x59VX72I5Ed3fzKhgP/un+qARmz9ixxrz+ujGdOhlTqVKGfyRjgoKM6drVmMmTrQ/8+HhjTDq/j8eOGfPdd8a8/LIxbdqYq76+Gf/hLVHCmGbNjOnTx5h33zVm8WIzoU8f44o1nqU4mAm9exuzfr0xS5YY88UXxnz8sYl87jkzEcxsMPvT22+tWsaEhhqzZo0ZMWxYqg+njL4QXB8cMvvikJV1rTFypFlu9d9tuv/+160zJjj4n9dfpoyZ3769sd3oQzkuzrwxYICpBKZmcsj4qFs3Y377zeoSCwszZulSYxYvNp936mSewAqHI8CsqV/fmKeftoJznTrGlCplEq8LlilLks1mddmNHGnMihXGXL58U++jB5jaySHocO/exjz+uHWu0+kuTLOULGlM48bm9AMPmLFg+oJ5Bsx4ML+AuZpBCLoMZj2Y57DC6a2ew5TX7A7mPjDL7rvPmNatjfHySnPsU2B+APMlmJlg9rRqZcygQcYMH2792x4zxhwcOND0B9MHK5y3AFMLzOYffrACUzrvoyN/H1Mo3GRC4Sa1X381xt/f+j2/+25jTpxwdo0k3/jxR2MqVvznD+VDDxnz119Z+jDZsHy5aQKmH5gZYHZm9GHh6Wn+LlPGTAAzEmtcydkMgswlrNaiWWCWgzlygw+iTL+hZ/ABtARMePfuxqQziSEnBlveTEtLjra0JiUZM3/+P828YDYmfyh+AyYMzPkaNaxvSUFBxnh6OqZlIJ3lDNaYpanJH7hfTZ2aYbUd8j5evWq+Gz/edEz+XRyD1UrXBsyi8ePtYSrT/SUlWa1zX31lzEsvGdOqlYnz9k71uv4E0w7MnM8/v6lTFB4eboKxxk1dSu+9Cww05tFHzYGhQ01tSBVOMwsiWf09y8nfR425yYTG3Pxj2TLrdgqXL8N998H//gdFiji7VgVLTkwPdvSxs7u/2v7+3DN7NixaZD1xxx3w4YfWL1PyBTizcuzrx4C88cILvB4SAuHh/yznzqW7rXFxwVarFjRsCI0ascPLi3o9epBw3Xobli+nob8/7NljX86sX49HZCQ+16x3DnALDKRw2bJQtCgULcrJq1eZvXgxZ7HGTCwHLpE3x5/l+niNy5fZ0qMHlRcuJKt/RS9gjb+6mrwUK1EC36JFrfEnhQpxOiaG3X//zVWs9/lk8tL6qadoEBJin6q+5cgRGj/8MFev278jzsuN3sdrxyRl5dhZOS8R69fzTNOmdABGYY1nAYipXx//6dOhXr2sVd4Y+Oknjr/4IiV377YXnwBWARW6dyc4NNQaO5T87zQr47Cy+3qys152acxNJtRyY1mw4J9W1pAQe/e5ONDIoUNNfTAPg3kQzCddulhN8Zs3/zO+4vBh8+aAAVme8ptVocOGmfpgSjlon8OHDzduYIZxzTgaV1djhg41Jjb2pveb6TfqxETz3fjxpjuYaWDmgRkKphmYL6dPT7eOZOEbY3h4uLFhzYopBsYlk2+sudoqks+Eh4ebEsm/E6FYrXBPg9n93nvGrFlj9XcfOGA2/vyzvavl2uX69zs7XRrOPC85ceyUffqDGQcm/tqut65djTl0KOONr1415ssvjalb175NHFaLaJ0stMjkpyEI6pbKxO0ebsLDw02vXuuMzZZkwBoneE2XqWRRmj8IiYnG7NplzJw5xgwYYM7XrGmuZLOZ/SqYNVhjDX6fM8dqws6Oy5eN+d//zIkOHezTeK8kBwKXTP64ZeW11saaupxS17Vgtn/xxU3tL7vHzuoHXsr6NzOtNbMPqPz0xz+3ObqrIr+cl5w4dqp9HjxozDPP/PP3wcPDmBEjjDl3zr5exKpVxkydmrpr2MfHmCFDzNvPP18gQ7nCTSZu53Bj/eEYav93ULfuepOQ4Oxa5TxH/yEaPny4KQWmE5i3wewtX94YP790A0s0mHD+uaZJTIkS/1zTxNfXJGQwONKAMaVKGfPss8YsXGjMuXPpv56zZ42ZN8+Yxx6z/rBds/21/e2rwXz73nvZf7FXr5rNjz9uD2rRYHpi9dPn1rWPcupbukKLY+T6bKnbycaN1sUYk//9nffyMgOxxvwcv/ZvRWCgNYMtOtq+aUF8HxVuMnG7hhvrG3C7az773sn0G3BBkd0LaaX7ByEhwZitW42ZMsWcatvWHMgojHh6GnPvvcYMHmz2jR5tKmWhxSGli8QDTEWspv3vwSRcPxDT1dWYZs3MkubNTTDWLIxlYK66uKRer0wZE/XYY+Z+rG6u3mBik59L8PIyZvr0rLcI7d6danbMYv65wFlu/+4UxD/UIlmSlGTM4sXmUvnyaf7m/E3ytPbbZFyBwk0mbtdwM3nyAgNRyf8mJtk/oArylYfDw8ONC5iuYP4Pqw96LJiDgwZZ1wn56ScrtBw9akx8vD0I+YJ5AMzPTZtaU1DTmb6ZAGY7mOlY0yP/99Zb9mnLKW6pKf7KFWN+/tmYF180pnr1TLuzLlasaE2J3rjRHlyu3WdFMH+XKfPPNu3aWVOpM5KYaMwHH/wz08XPzywICSmQzdwi+cXcWbNMX6xZgFvBPEXm1zQqiBRuMnE7hpukJGOaN49O/mz73YCHU75956qEBLOmXz+zO5NQkF4X0n4ymCLs62tM27bmcJ8+pnVyAHLkGJAbrvf33ya8Rw+zOLme68C8BKZKJn/cUu0zIcG6xkvKvYaKFbMuuX+9/futi9SlvO42bYyJjMzWaxERx8vpa8jkBwo3mbgdw82MGdbnlIvLVQN1Cva378RE6xoSNWrYP6BPgxmN1U/9AZhTbdtaF7S66y7rWhzXd+0kh5y5YMJ79LBmfVwzOMlZMzUc8sdtxw5j7rnnn9f69NNm488/mzmff27+Hj78n3E7Pj7GfPJJ9gc1i0iOud1n7yncZOJ2Czf79//TqzJ+fAH+9p2YaLVE1Kr1zwd30aJmabNmqVpZ0v1jkJhoNi1bZmpgXc2zVBaCg7PeR4f8cYuLM+aVV+y3EDiCddE7+/vWvLn1iyMieU6B/RueBbqIXyZup4v4JSRAixawbh00bw4rVoCrq7NrdWPZulDU7t0ER0VR9csvYccO6wl/fxg6FAYNAn//LO8vuxe0chZHXSDrjxkzKNSnD9WSH1/GulHiU+vWEdykiSOqKiLiMNn5/HbLpTqJE0yYYAUbX1/4/PP8EWyuDxjDhw9n/FtvQXS0tZw+DdHRfDt9Opt//pnHgKopK/v5wYsvwuDBqS61HBwcnKUQMH78eDp37pzrd+bNrqy+nhvZ6u5OX+ANoDJWsNkL1P/rL4UbEcnX1HJTQG3eDI0bW603n38O3bs7u0Y3tmXJEj5q354HgTuxLkMeAPjfYLtY4EMg5OefadCmTQ7XsuDI7qXkRUScSS03t7lLl6BrVyvYPPYYdOvm7BpZ0nSnJCRY9w9auhSWLqXe1q3Mzmhjmw2KFYPAQE4mJbF+3z6igX3AdOAMUOH4cRrk0mspCIKDgxk+fHiarjgFGxHJ7xRuCqARI2D3bihVCj75xH6PNKdK6W4KAh4CPKtVo+6JE2lujLgRWApsAk4nL/N/+on6rVvb+9UORETQKZ0Wh6pVq6Ypk8zll644EZHscHF2BcSxfv4ZPv7Y+nnWLAgIcG59ACLCw9k9YQIRwHFgNlB3zx4r2BQtCk8+afWdHT/O18OH8zrwP2A90Dk0lPpt26YaMJTS4nAttTjcvODgYLp166b3T0QKDI25KUCio+GuuyAqCvr3h8mTnV0jYN06TvbsSYl9++xFm7BaZxq8+iohr7+eZqRztmZLqcVBROS2kJ3Pb4WbAiI8PIJBg0qycWN5qle3BhR7ezuxQnv2wMiRsGgRAJeAD4CPgRPJq2jgqoiIZFV2Pr/VLVUAhIaG0qTJZDZuLA9cpWHDj5wXbKKioF8/qFXLCjYuLtCnDx88/zyv8E+wUTeSiIjkFLXc5HPWdN5HgT+xJk2/Aryda60iKV1D1e+4g4arVsHEidZ0LYCHH4axY6FmzVTrqhtJRESyS1PBbyN79+4FnscKNhHAOHt5TgeI0NBQPpgwgX8DD177ROPG1hUEmzVLtb6jLj4nIiKSGYWbfO7OO6sCLZMfTQQSgZyfFr3p559xmTCBg0Dp5LK9AO+8Q9URI/LG/HMREbktacxNPnfpUjBQFjgLLAZyeDzL/v0wcCB3d+jAWKxgcxSr7agWEFGmjIKNiIg4lVpu8rnZs63/d+4cT6dO/3XYeJZU42MaNbJuUjVxInz3HRiDG7ANq61oAXA1eTtdSE9ERJxN4SYfi4mBb7+1fg4NDaJRI8fcZyHlasKuQGfgvVKlKBcV9c8K7drBkCHM/+knvnj33VTbaUyNiIg4m2ZL5WOffQb//jfUqAF//umY3qCUmyl2xLouTcXk8iR3d1y6d7fuup08+yllfc2AEhGRnKbZUreJzz+3/t+jh+OGuezdu5eywHzACzgFTAVqTpzI4wMGpFlfM6BERCSvUbjJp/76C3791bpGXteujttv1apVeRcr2KzBmuJ9BQhv2NBxBxEREclBCjf5VEqrTdu2cMcdjttvcFwcwVgTygdhBRuNpRERkfxE4SYfSkqCOXOsn3v2dOCOExPhhRcAOP3IIwx95BGNpRERkXzH6de5mTp1KhUrVsTT05P69euzdu3aTNefN28edevWxdvbm1KlStGrVy+io6NzqbZ5w6pVEBkJ/v7QsaMDdzxzJmzbBv7+BH36Kd26dVOwERGRfMep4WbBggUMHjyYUaNGsXXrVpo1a0ZISAiRkZHprv/rr7/SvXt3evfuzZ9//snChQvZuHEjffr0yeWaO1fKtW2efBI8PR2003Pn4OWXrZ/ffBOKF3fQjkVERHKXU8PN+++/T+/evenTpw81atRg0qRJlC1blmnTpqW7fnh4OBUqVGDQoEFUrFiR++67j759+7Jp06ZcrrnzxMbC119bPzu0S2r0aDh9GqpXh//8x4E7FhERyV1OCzfx8fFs3ryZtm3bpipv27Yt69atS3ebpk2bcuTIEZYsWYIxhhMnTvD111/Tvn37DI8TFxdHbGxsqiU/+/pruHwZqlUDh/UY7d4NH39s/TxpEhQq5KAdi4iI5D6nhZvTp0+TmJhIUFBQqvKgoCCOHz+e7jZNmzZl3rx5dOnSBXd3d0qWLEmRIkX4OOWDOR1jx47F39/fvpQtW9ahryO3pXRJ9ezpwFs4DRkCCQnQoQM8+OCN1xcREcnDnD6g2HbdJ7QxJk1Zip07dzJo0CBee+01Nm/ezLJlyzhw4AD9+vXLcP8jR44kJibGvhw+fNih9c9N+/fD2rVWqHHYtW1+/BGWLrVaayZOdNBORUREnMdpU8EDAwNxdXVN00pz8uTJNK05KcaOHcu9997LSy+9BECdOnXw8fGhWbNmjBkzhlKlSqXZxsPDAw8PD8e/ACdImf7dpg2UKeOAHcbHW7dTABg8GKpUccBORUREnMtpLTfu7u7Ur1+fsLCwVOVhYWE0bdo03W0uXbqEi0vqKru6ugJWi09BlpT0z4X7HDaQ+OOPYd8+CAqCV15x0E5FREScy6ndUkOGDOGzzz5j5syZ7Nq1ixdffJHIyEh7N9PIkSPp3r27ff0OHTrw7bffMm3aNP7++29+++03Bg0aRKNGjShdurSzXkauWL0aDh0CPz/o1MkBOzxxwpohBTB2rLVjERGRAsCpVyju0qUL0dHRjB49mqioKGrXrs2SJUsoX748AFFRUamuedOzZ0/Onz/P5MmTGTp0KEWKFOH+++9n/PjxznoJuSal1ebJJ8HLywE7HDXKmlfeoIF1500REZECwmYKen/OdbJzy/S84sIFKFkSLl6E336D9HrtIiIi2Lt3b9Zul7B5MzRsCMbAunXQpEnOVFxERMRBsvP57fTZUnJjX39tBZsqVdLPIaGhoTRu3Jju3bvTuHFjQkNDM9xXRHg4J596ygo2zzyjYCMiIgWObpyZD2R2bZuIiAg+mzCBLwEP4DQQPWEChy5fpny9ehAYCAEBEBjIGx9/zN6PP+ZL4CLwoZ8fL+fi6xAREckN6pbK4w4cgEqVrFBz6BBcfw3CuXPncrx7d17K5n5HAe9g3dJCN8cUEZG8Ljuf32q5yeNSrm3zwANpgw1AjTvu4OHkn98FYoFA4JkHHyQQIDoaTp8m/vhx3K9cAWAX8H7yNnv37lW4ERGRAkXhJg9bvz6CKVNqAr4ZTmhqsG0bYAWWUMBgjcEJHDcu1XpbIyJo1rgxAUA0cDW5vGrVqjlRdREREafRgOI8KjQ0lKZNh3PqlC8Qy+bN6Vxk7+pV60aXgMfIkXw+Zw7h4eGMuy7YAAQHB/Pi8OEc559gExoaqlYbEREpcDTmJg+KiIigcePGwIfAIOAz4N9px8fMnw9PPw0lSlgDcjw9s7TvLE8ZFxERySM05iaf27t3b/JP1ZP//6u93B5IjIH33rN+HjAgS8EGrBYchRoRESnI1C2VB/0zDqZy8v/3X1cOrFoFW7ZYlyt+/vncrJ6IiEiepnCTBwUHBzN06AigXHLJ/rTjYyZOtP7fq5d1LRsREREBNObG2dXJ0F9/WVckdndPYPXqzTRufE2w2bkTatWyLn6zdy9UrpzxjkRERAoA3X6hANhv9URRtapb6mAD8H7yVWo6dVKwERERuY7CTR7111/W/++887onjh+HuXOtn4cNy9U6iYiI5AcKN3lUSstNmnAzZQrEx1s3vEzv9uAiIiK3OYWbPCrdcHPxIkydav08dGiu10lERCQ/ULjJo9INN7Nnw5kz1p00O3VyQq1ERETyPoWbPCgp6Z9wYx8vnJgIH3xg/TxkCLi6OqVuIiIieZ3CTR4UFQVXrlj5pVzKpW6+/95KPMWKQc+ezqyeiIhInqZwkweltNqULw+FCiUXptxq4fnnwcfHKfUSERHJDxRu8qA0423WrYP168Hd3bqPlIiIiGRI4SYPSjPeJuVWC926QcmSTqmTiIhIfqFwkweluoDfX3/BokVWwZAhTquTiIhIfqFwkweltNwkJu5l9/PPgzHQrh3UrOnciomIiOQDCjd5UEq4GR/akXK//ALAp76+TqyRiIhI/qFwk8ecOQNnz1o//5v9eANbgH4LFhAREeHMqomIiOQLCjd5TEqrDRyjHVcB+DS5ZO/evc6okoiISL6icJPHpIQbD3YSnFy2Mvn/VatWdUaVRERE8hWFmzwmJdw8WfFvPIDjwD4gNDSU4ODgTLYUERERADdnV0BSSwk3PSqdggNwpVEjwj/6SMFGREQkixRu8piUa9xUP7kGgArdulFBwUZERCTL1C2Vx+zfD64kUOKv36yC5s2dWyEREZF8RuEmD7l8GY4dg3vYiuvli1CkCNSu7exqiYiI5CtODzdTp06lYsWKeHp6Ur9+fdauXZvhuj179sRms6VZatWqlYs1zjl//239/0FPq0uKZs3AxemnSEREJF9x6ifnggULGDx4MKNGjWLr1q00a9aMkJAQIiMj013/ww8/JCoqyr4cPnyYYsWK8fjjj+dyzXNGynibNh7XhBsRERHJFqeGm/fff5/evXvTp08fatSowaRJkyhbtizTpk1Ld31/f39KlixpXzZt2sTZs2fp1atXLtc8Z+zfDzaSqHf5V6tA421ERESyzWnhJj4+ns2bN9O2bdtU5W3btmXdunVZ2seMGTN44IEHKF++fIbrxMXFERsbm2rJq/bvh5rsxDf+DHh7Q716zq6SiIhIvuO0cHP69GkSExMJCgpKVR4UFMTx48dvuH1UVBRLly6lT58+ma43duxY/P397UvZsmVvqd45af9+aE5yl1TTplCokHMrJCIikg85fbSqzWZL9dgYk6YsPbNnz6ZIkSJ06tQp0/VGjhxJTEyMfTl8+PCtVDdHpQo36pISERG5KU67iF9gYCCurq5pWmlOnjyZpjXnesYYZs6cSbdu3XB3d890XQ8PDzw8PG65vjktIQEOHjAKNyIiIrfIaS037u7u1K9fn7CwsFTlYWFhNG3aNNNtV69ezV9//UXv3r1zsoq5KjISyifupzRRmEKFoFEjZ1dJREQkX3Lq7ReGDBlCt27daNCgAU2aNGH69OlERkbSr18/wOpSOnr0KHPmzEm13YwZMwgODqZ2AbrA3f790AzrGj+2Ro3Ay8vJNRIREcmfnBpuunTpQnR0NKNHjyYqKoratWuzZMkS++ynqKioNNe8iYmJ4ZtvvuHDDz90RpVzjMbbiIiIOIbNGGOcXYncFBsbi7+/PzExMfj5+Tm7OnbDhsHzE+/kTv6GpUvhoYecXSUREZE8Izuf306fLSWWc38c4U7+JsnmYk0DFxERkZuicJNHBOy0xtucv/MeyEMtSiIiIvmNwk0eYAxUjrLG2yTdp/E2IiIit0LhJg84fhyaJljhpnA73SxTRETkVijc5AGHNp+mFjsBKNTyPifXRkREJH/LdripUKECo0ePTjNFW27epZ+tu4Af9KkJxYs7uTYiIiL5W7bDzdChQ/n++++pVKkSbdq04auvviIuLi4n6nbb8NxgdUkdKqfxNiIiIrcq2+Fm4MCBbN68mc2bN1OzZk0GDRpEqVKlGDBgAFu2bMmJOhZ4JfdZ4Sb2boUbERGRW3XTY27q1q3Lhx9+yNGjR3n99df57LPPaNiwIXXr1mXmzJncZtcGvHmxsZQ/sxUAt1YaTCwiInKrbvr2C1evXmXRokXMmjWLsLAwGjduTO/evTl27BijRo3il19+4csvv3RkXQumdetwJYn9VKJ0ozLOro2IiEi+l+1ws2XLFmbNmsX8+fNxdXWlW7dufPDBB1SvXt2+Ttu2bWmu+yNlyZVf1uIJrKUZj1Zydm1ERETyv2yHm4YNG9KmTRumTZtGp06dKFSoUJp1atasyZNPPumQChZ0Ccut8TbbfJvT09fJlRERESkAsh1u/v77b/tduzPi4+PDrFmzbrpSt43Ll/H6YwMAxyqrpUtERMQRsj2g+OTJk0RERKQpj4iIYNOmTQ6p1G1jwwZcE+I5Rik8a93p7NqIiIgUCNkON/379+fw4cNpyo8ePUr//v0dUqnbxhqrS2oNzbmzss3JlRERESkYsh1udu7cSb169dKU33PPPezcudMhlbptXBtu1HAjIiLiENkONx4eHpw4cSJNeVRUFG5uNz2z/PZz9SqsXw9Y4aZyZSfXR0REpIDIdrhp06YNI0eOJCYmxl527tw5Xn75Zdq0aePQyhVoW7fCxYucoSg7qamWGxEREQfJdlPLxIkTad68OeXLl+eee+4BYNu2bQQFBTF37lyHV7DASu6SWkszCvu6EBjo5PqIiIgUENkON3fccQe///478+bNY/v27Xh5edGrVy+eeuqpdK95Ixm4bryNTeOJRUREHOKmBsn4+Pjw3HPPObout4+kJFi7FtB4GxEREUe76RHAO3fuJDIykvj4+FTlDz/88C1XqqD7/csvqXPuHFdcvdmaeA+tNd5GRETEYW7qCsWPPPIIO3bswGaz2e/+bUvuV0lMTHRsDQuY0NBQLkyYwBRgdWJ1EnHTYGIREREHyvZsqRdeeIGKFSty4sQJvL29+fPPP1mzZg0NGjRg1apVOVDFgiMiIoIJEyaQcqOFtTQDID5+l/MqJSIiUsBkO9ysX7+e0aNHU7x4cVxcXHBxceG+++5j7NixDBo0KCfqWGDs3bsXgJRLIK4jBFC4ERERcaRsh5vExEQKFy4MQGBgIMeOHQOgfPny7Nmzx7G1K2CqVq0KQFDy48NUAuIIDr7DaXUSEREpaLIdbmrXrs3vv/8OQHBwMBMmTOC3335j9OjRVKpUyeEVLEiCg4N5+cUX8Ut+fJISFCsWQ9OmwU6tl4iISEGS7XDzyiuvkJSUBMCYMWM4dOgQzZo1Y8mSJXz00UcOr2BB8/bgwQBcdSlELH40blzCuRUSEREpYLI9W+rBBx+0/1ypUiV27tzJmTNnKFq0qH3GlGTi5EkALvoEwXmbZkqJiIg4WLZabhISEnBzc+OPP/5IVV6sWDEFm6xKDjfRrlaLjS7gJyIi4ljZCjdubm6UL19e17K5FcnhJirRCjdquREREXGsmxpzM3LkSM6cOZMT9Sn4ksNN5GWFGxERkZyQ7XDz0UcfsXbtWkqXLk21atWoV69eqiW7pk6dSsWKFfH09KR+/fqsTb7nUkbi4uIYNWoU5cuXx8PDgzvvvJOZM2dm+7hOkxxujiaUwGaDihWdXB8REZECJtsDijt16uSwgy9YsIDBgwczdepU7r33Xj799FNCQkLYuXMn5cqVS3ebJ554ghMnTjBjxgwqV67MyZMnSUhIcFidclxyuDlJCcqWBQ8PJ9dHRESkgLGZlJtDOUFwcDD16tVj2rRp9rIaNWrQqVMnxo4dm2b9ZcuW8eSTT/L3339TrFixmzpmbGws/v7+xMTE4Ofnd+MNHO2hh+Cnn+jBbA636sGKFblfBRERkfwmO5/f2e6WcpT4+Hg2b95M27ZtU5W3bduWdevWpbvN4sWLadCgARMmTOCOO+6gatWqDBs2jMuXL2d4nLi4OGJjY1MtTnVNy42v70nn1kVERKQAyna4cXFxwdXVNcMlq06fPk1iYiJBQUGpyoOCgjh+/Hi62/z999/8+uuv/PHHHyxatIhJkybx9ddf079//wyPM3bsWPz9/e1L2bJls1zHnBCzbx9ghZvFi98nNDTUqfUREREpaLI95mbRokWpHl+9epWtW7fy+eef8+abb2a7AtdfH8cYk+E1c5KSkrDZbMybNw9/f38A3n//fR577DGmTJmCl5dXmm1GjhzJkCFD7I9jY2OdFnAiwsO558IFwAo3sJ8JE76mc+fOBAfrFgwiIiKOkO1w07FjxzRljz32GLVq1WLBggX07t07S/sJDAzE1dU1TSvNyZMn07TmpChVqhR33HGHPdiANUbHGMORI0eoUqVKmm08PDzwyCOjdg9s20ZKhDlFceAwYN0tXOFGRETEMRw25iY4OJhffvkly+u7u7tTv359wsLCUpWHhYXRtGnTdLe59957OXbsGBeSWz/ACgYuLi6UKVPm5iqei2oEBAAQgy9xeAJngX/uFi4iIiK3ziHh5vLly3z88cfZDhhDhgzhs88+Y+bMmezatYsXX3yRyMhI+vXrB1hdSt27d7ev//TTTxMQEECvXr3YuXMna9as4aWXXuLZZ59Nt0sqr6lbqhQAJ0lpmTpHaGioWm1EREQcKNvdUtffINMYw/nz5/H29uaLL77I1r66dOlCdHQ0o0ePJioqitq1a7NkyRLKly8PQFRUFJGRkfb1CxcuTFhYGAMHDqRBgwYEBATwxBNPMGbMmOy+DOe4ZqYUwOrV/6N580bOrJGIiEiBk+3r3MyePTtVuHFxcaF48eIEBwdTtGhRh1fQ0Zx6nZtPPoHnn2cRnXjacxGZzGAXERGRa2Tn8zvbLTc9e/a82XrJNS03RYo4tyoiIiIFVbbH3MyaNYuFCxemKV+4cCGff/65QypVYCnciIiI5Lhsh5tx48YRGBiYprxEiRK88847DqlUgaVwIyIikuOyHW4OHTpExXRuZV2+fPlUg38lHQo3IiIiOS7b4aZEiRL8/vvvacq3b99OQPJ1XCQDCjciIiI5Ltvh5sknn2TQoEGsXLmSxMREEhMTWbFiBS+88AJPPvlkTtSx4FC4ERERyXHZni01ZswYDh06ROvWrXFzszZPSkqie/fuGnOTmYQEiI4GFG5ERERyUrbDjbu7OwsWLGDMmDFs27YNLy8v7rrrLvuF9yQDp08DkGRz4YwpRj64JJCIiEi+lO1wk6JKlSrp3qhSMpDcJRXrHkhSnKtabkRERHJItsfcPPbYY4wbNy5N+bvvvsvjjz/ukEoVSMnh5oybdesFhRsREZGcke1ws3r1atq3b5+m/KGHHmLNmjUOqVSBlBxuTtkUbkRERHJStsPNhQsXcHd3T1NeqFAhYmNjHVKpAunECet/SQo3IiIiOSnb4aZ27dosWLAgTflXX31FzZo1HVKpAim55eZoQhCgcCMiIpJTsj2g+NVXX+XRRx9l//793H///QAsX76cL7/8kq+//trhFSwwksPNkXi13IiIiOSkbIebhx9+mO+++4533nmHr7/+Gi8vL+rWrcuKFStueAvy21pyuDmBFW78/Z1ZGRERkYLrpqaCt2/f3j6o+Ny5c8ybN4/Bgwezfft2EhMTHVrBAuOaqxN7eYGHh5PrIyIiUkBle8xNihUrVtC1a1dKly7N5MmTadeuHZs2bXJk3QoW3XpBREQkV2Sr5ebIkSPMnj2bmTNncvHiRZ544gmuXr3KN998o8HEN6JwIyIikiuy3HLTrl07atasyc6dO/n44485duwYH3/8cU7WreC4eBEuXQIUbkRERHJalltufv75ZwYNGsTzzz+v2y5kV3KrTYK7FxfjfRRuREREclCWW27Wrl3L+fPnadCgAcHBwUyePJlTp07lZN0KjuRwc8mnBGBTuBEREclBWQ43TZo04b///S9RUVH07duXr776ijvuuIOkpCTCwsI4f/58TtYzf0sON+e9dI0bERGRnJbt2VLe3t48++yz/Prrr+zYsYOhQ4cybtw4SpQowcMPP5wTdcz/ksPNOXeFGxERkZx201PBAapVq8aECRM4cuQI8+fPd1SdCp7kcBOtO4KLiIjkuFsKNylcXV3p1KkTixcvdsTuCp6UO4KjcCMiIpLTHBJu5AaSw81x3RFcREQkxync5Ab7HcEVbkRERHKawk1uSA43h68o3IiIiOQ0hZvckBxuDl5SuBEREclpCjc5LSkJki92+PcFhRsREZGcpnCT086ehcREAE4RCIC/vzMrJCIiUrAp3OS05C6pRP+iXMUdLy/w8HBynURERAowp4ebqVOnUrFiRTw9Palfvz5r167NcN1Vq1Zhs9nSLLt3787FGmdTcri5WlRdUiIiIrnBqeFmwYIFDB48mFGjRrF161aaNWtGSEgIkZGRmW63Z88eoqKi7Euevkt5cri54qdwIyIikhucGm7ef/99evfuTZ8+fahRowaTJk2ibNmyTJs2LdPtSpQoQcmSJe2Lq6trLtX4JiSHm4s+CjciIiK5wWnhJj4+ns2bN9O2bdtU5W3btmXdunWZbnvPPfdQqlQpWrduzcqVK3Oymrcu5Y7gngo3IiIiucHNWQc+ffo0iYmJBAUFpSoPCgri+PHj6W5TqlQppk+fTv369YmLi2Pu3Lm0bt2aVatW0bx583S3iYuLIy4uzv44NjbWcS8iK3RHcBERkVzltHCTwmazpXpsjElTlqJatWpUq1bN/rhJkyYcPnyY9957L8NwM3bsWN58803HVTi7Uu4I7qpwIyIikhuc1i0VGBiIq6trmlaakydPpmnNyUzjxo3Zt29fhs+PHDmSmJgY+3L48OGbrvNNSQ43J3VHcBERkVzhtHDj7u5O/fr1CQsLS1UeFhZG06ZNs7yfrVu3UqpUqQyf9/DwwM/PL9WSq5LDTVSiwo2IiEhucGq31JAhQ+jWrRsNGjSgSZMmTJ8+ncjISPr16wdYrS5Hjx5lzpw5AEyaNIkKFSpQq1Yt4uPj+eKLL/jmm2/45ptvnPkyMpdyR/CrCjciIiK5wanhpkuXLkRHRzN69GiioqKoXbs2S5YsoXz58gBERUWluuZNfHw8w4YN4+jRo3h5eVGrVi1+/PFH2rVr56yXkLn4eDh3DoBI3RFcREQkV9iMMcbZlchNsbGx+Pv7ExMTk/NdVEePQpky4ObGPTXj2Pa7Cz/9BNfNfhcREZEbyM7nt9Nvv1CgJXdJUbw4Z2Ost1otNyIiIjlL4SYnpYSbEiVSeqcUbkRERHKYwk1OSg43pngJUq4dqHAjIiKSsxRuctI1dwRPGdnk7+/E+oiIiNwGFG5yUnK4uZx8R3AvL/DwcGaFRERECj6Fm5yUckdwb00DFxERyS0KNznpxAkAYnVHcBERkVyjcJOTkltuzhZSuBEREcktCjc5SXcEFxERyXUKNznFGHu4OWEUbkRERHKLwk1OOX8e4uIAiEooDijciIiI5AaFm5yScnViHx9OXfIBFG5ERERyg8JNTtGtF0RERJxC4SanKNyIiIg4hcJNTlG4ERERcQqFm5yicCMiIuIUCjc5ReFGRETEKRRucorCjYiIiFMo3OSU5HCTFFiC2FirSOFGREQk5ync5JSUO4L7lLAX+fs7qzIiIiK3D4WbnJIcbmI8rHDj5QUeHs6skIiIyO1B4SYnJCbC6dOA7gguIiKS2xRuckJ0tHXjTCDaFggo3IiIiOQWhZuckDJTKiCAs+fdAIUbERGR3KJwkxPSmQZetKjTaiMiInJbUbjJCbrGjYiIiNMo3OQEhRsRERGnUbjJCQo3IiIiTqNwkxMUbkRERJxG4SYnKNyIiIg4jcJNTlC4ERERcRqFm5ygcCMiIuI0Tg83U6dOpWLFinh6elK/fn3Wrl2bpe1+++033NzcuPvuu3O2gjdD4UZERMRpnBpuFixYwODBgxk1ahRbt26lWbNmhISEEBkZmel2MTExdO/endatW+dSTbPh8mU4f976WeFGREQk1zk13Lz//vv07t2bPn36UKNGDSZNmkTZsmWZNm1aptv17duXp59+miZNmuRSTbPh1Cnr/+7uJBb2JzbWeqhwIyIikjucFm7i4+PZvHkzbdu2TVXetm1b1q1bl+F2s2bNYv/+/bz++us5XcWbc02XVOx5m73Y399J9REREbnNuDnrwKdPnyYxMZGgoKBU5UFBQRw/fjzdbfbt28eIESNYu3Ytbm5Zq3pcXBxxcXH2x7EpTSk5JZ3xNt7e4O6es4cVERERi9MHFNtstlSPjTFpygASExN5+umnefPNN6latWqW9z927Fj8/f3tS9myZW+5zpnSYGIRERGnclq4CQwMxNXVNU0rzcmTJ9O05gCcP3+eTZs2MWDAANzc3HBzc2P06NFs374dNzc3VqxYke5xRo4cSUxMjH05fPhwjryea16A9X+FGxEREadwWreUu7s79evXJywsjEceecReHhYWRseOHdOs7+fnx44dO1KVTZ06lRUrVvD1119TsWLFdI/j4eGBh4eHYyufGYUbERERp3JauAEYMmQI3bp1o0GDBjRp0oTp06cTGRlJv379AKvV5ejRo8yZMwcXFxdq166davsSJUrg6emZptypFG5EREScyqnhpkuXLkRHRzN69GiioqKoXbs2S5YsoXz58gBERUXd8Jo3ec614eaM9aPCjYiISO6xGWOMsyuRm2JjY/H39ycmJgY/Pz/HH6BePdi6FZYs4fXwEEaPhv/8B6ZMcfyhREREbhfZ+fx2+mypAkfdUiIiIk6lcONIxijciIiIOJnCjSOdOwdXr1o/Fy+ucCMiIuIECjeOlNJq4+cHnp4KNyIiIk6gcONI13RJAQo3IiIiTqBw40gKNyIiIk6ncONICjciIiJOp3DjSNeEm8RESLkBucKNiIhI7lG4caRrwk1KsAHw93dOdURERG5HCjeOlM41bry9wd3daTUSERG57SjcOJIu4CciIuJ0CjeOpHAjIiLidE69K3hBc/XYMQoBvx8/zjlPq0zhRkREJHep5cZBRg4bRqHkUcStn36a//53IaBwIyIiktsUbhwgIiKC2RMnApAInAGWLl0HKNyIiIjkNnVLOcDevXtJAN4FvIEkAIpY/y3ipEqJiIjcphRuHKBq1aqcBoanKi1i/bdIrldHRETktqZuKQcIDg5m+PDU0aZ27fsAhRsREZHcpnDjIOPHjyc8PJw5c+YQHh5OpUr1AYUbERGR3KZuKQcKDg4mODgY0E0zRUREnEUtNzlE4UZERMQ5FG5yiMKNiIiIcyjc5BCFGxEREedQuMkBiYmQfLFihRsREZFcpnCTA1KCDYC/v/PqISIicjtSuMkBKV1S3t7g7u7UqoiIiNx2FG5ygMbbiIiIOI/CTQ5QuBEREXEehZscoHAjIiLiPAo3OUDhRkRExHkUbnKAwo2IiIjzKNzkAIUbERER53F6uJk6dSoVK1bE09OT+vXrs3bt2gzX/fXXX7n33nsJCAjAy8uL6tWr88EHH+RibbPm7Fnr/wo3IiIiuc+pdwVfsGABgwcPZurUqdx77718+umnhISEsHPnTsqVK5dmfR8fHwYMGECdOnXw8fHh119/pW/fvvj4+PDcc8854RWkTy03IiIizmMzxhhnHTw4OJh69eoxbdo0e1mNGjXo1KkTY8eOzdI+OnfujI+PD3Pnzs3S+rGxsfj7+xMTE4Ofn99N1ftGHn4Y/vc/mD4d/v3vHDmEiIjIbSU7n99O65aKj49n8+bNtG3bNlV527ZtWbduXZb2sXXrVtatW0eLFi1yooo3TS03IiIizuO0bqnTp0+TmJhIUFBQqvKgoCCOHz+e6bZlypTh1KlTJCQk8MYbb9CnT58M142LiyMuLs7+OPbaGz/lEIUbEZHckZSURHx8vLOrIQ7i7u6Oi8utt7s4dcwNgM1mS/XYGJOm7Hpr167lwoULhIeHM2LECCpXrsxTTz2V7rpjx47lzTffdFh9s0LhRkQk58XHx3PgwAGSkpKcXRVxEBcXFypWrIj7Ld6Y0WnhJjAwEFdX1zStNCdPnkzTmnO9ihUrAnDXXXdx4sQJ3njjjQzDzciRIxkyZIj9cWxsLGXLlr3F2mdO4UZEJGcZY4iKisLV1ZWyZcs65Nu+OFdSUhLHjh0jKiqKcuXK3bChIzNOCzfu7u7Ur1+fsLAwHnnkEXt5WFgYHTt2zPJ+jDGpup2u5+HhgYeHxy3VNTsSEuD8eetnhRsRkZyRkJDApUuXKF26NN7e3s6ujjhI8eLFOXbsGAkJCRQqVOim9+PUbqkhQ4bQrVs3GjRoQJMmTZg+fTqRkZH069cPsFpdjh49ypw5cwCYMmUK5cqVo3r16oB13Zv33nuPgQMHOu01XO/aIT0KNyIiOSMxMRHglrsvJG9JOZ+JiYn5N9x06dKF6OhoRo8eTVRUFLVr12bJkiWUL18egKioKCIjI+3rJyUlMXLkSA4cOICbmxt33nkn48aNo2/fvs56CWmkdEn5+MAtnBcREcmCW+m6kLzHUefTqde5cYacvs7Nli1Qvz7ccQccOeLw3YuICHDlyhUOHDhgv8L97axly5bcfffdTJo0KUvrHzx4kIoVK7J161buvvvuHK1bdmV2XrPz+e302VIFjQYTi4hIem7UKtGjRw9mz56d7f1+++232erCKVu2LFFRUQQGBmb7WPmFwo2DKdyIiEh6oqKi7D8vWLCA1157jT179tjLvLy8Uq1/9erVLIWWYsWKZaserq6ulCxZMlvb5DeaO+dgCjciIpKekiVL2hd/f39sNpv98ZUrVyhSpAj/93//R8uWLfH09OSLL74gOjqap556ijJlyuDt7c1dd93F/PnzU+23ZcuWDB482P64QoUKvPPOOzz77LP4+vpSrlw5pk+fbn/+4MGD2Gw2tm3bBsCqVauw2WwsX76cBg0a4O3tTdOmTVMFL4AxY8ZQokQJfH196dOnDyNGjMhz3VopFG4cTOFGRCT3GQMXLzpnceTI1dDQUAYNGsSuXbt48MEHuXLlCvXr1+eHH37gjz/+4LnnnqNbt25ERERkup+JEyfSoEEDtm7dyn/+8x+ef/55du/enek2o0aNYuLEiWzatAk3NzeeffZZ+3Pz5s3j7bffZvz48WzevJly5cqlui9kXqNuKQdTuBERyX2XLkHhws459oUL1gxZRxg8eDCdO3dOVTZs2DD7zwMHDmTZsmUsXLiQ4ODgDPfTrl07/vOf/wBWYPrggw9YtWqV/VIq6Xn77bft92ocMWIE7du358qVK3h6evLxxx/Tu3dvevXqBcBrr73Gzz//zIULF276teYktdw4mMKNiIjcrAYNGqR6nJiYyNtvv02dOnUICAigcOHC/Pzzz6kuk5KeOnXq2H9O6f46efJklrcpVaoUgH2bPXv20KhRo1TrX/84L1HLjYMp3IiI5D5vb6sFxVnHdhSf65qAJk6cyAcffMCkSZO466678PHxYfDgwTe8Wej1A5FtNtsN78F17TYpM7uu3Sa9e0HmVQo3DqZwIyKS+2w2x3UN5SVr166lY8eOdO3aFbDCxr59+6hRo0au1qNatWps2LCBbt262cs2bdqUq3XIDnVLOZjCjYiIOErlypUJCwtj3bp17Nq1i759+6a54XRuGDhwIDNmzODzzz9n3759jBkzht9//z3PXiFaLTcOpnAjIiKO8uqrr3LgwAEefPBBvL29ee655+jUqRMxMTG5Wo9nnnmGv//+m2HDhnHlyhWeeOIJevbsyYYNG3K1Hlml2y84WLlycPgwbNwI140LExERB9HtF5yvTZs2lCxZkrlz5zpsn7r9Qh6llhsRESloLl26xCeffMKDDz6Iq6sr8+fP55dffiEsLMzZVUuXwo0DJSTA+fPWzwo3IiJSUNhsNpYsWcKYMWOIi4ujWrVqfPPNNzzwwAPOrlq6FG4cKDb2n5/9/Z1XDxEREUfy8vLil19+cXY1skyzpRwopUvKxweycYNWERERcSCFGwfSeBsRERHnU7hxIIUbERER51O4cSCFGxEREedTuHEghRsRERHnU7hxIIUbERER51O4cSCFGxERyUktW7Zk8ODB9scVKlRg0qRJmW5js9n47rvvbvnYjtpPblC4cSCFGxERyUiHDh0yvOjd+vXrsdlsbNmyJVv73LhxI88995wjqmf3xhtvcPfdd6cpj4qKIiQkxKHHyikKNw6kcCMiIhnp3bs3K1as4NChQ2memzlzJnfffTf16tXL1j6LFy+Ot7e3o6qYqZIlS+Lh4ZErx7pVCjcOpHAjIiIZ+de//kWJEiWYPXt2qvJLly6xYMECOnXqxFNPPUWZMmXw9vbmrrvuYv78+Znu8/puqX379tG8eXM8PT2pWbNmuvd+Cg0NpWrVqnh7e1OpUiVeffVVrl69CsDs2bN588032b59OzabDZvNZq/v9d1SO3bs4P7778fLy4uAgACee+45Lly4YH++Z8+edOrUiffee49SpUoREBBA//797cfKSbr9ggMp3IiIOIkxcOmSc47t7Q022w1Xc3Nzo3v37syePZvXXnsNW/I2CxcuJD4+nj59+jB//nxCQ0Px8/Pjxx9/pFu3blSqVIng4OAb7j8pKYnOnTsTGBhIeHg4sbGxqcbnpPD19WX27NmULl2aHTt28O9//xtfX1+GDx9Oly5d+OOPP1i2bJn9dgv+6dxP6NKlSzz00EM0btyYjRs3cvLkSfr06cOAAQNShbeVK1dSqlQpVq5cyV9//UWXLl24++67+fe//33D13MrFG4cSOFGRMRJLl2CwoWdc+wLF6z77mTBs88+y7vvvsuqVato1aoVYHVJde7cmTvuuINhw4bZ1x04cCDLli1j4cKFWQo3v/zyC7t27eLgwYOUKVMGgHfeeSfNOJlXXnnF/nOFChUYOnQoCxYsYPjw4Xh5eVG4cGHc3NwoWbJkhseaN28ely9fZs6cOfgkv/bJkyfToUMHxo8fT1BQEABFixZl8uTJuLq6Ur16ddq3b8/y5csVbvIThRsREclM9erVadq0KTNnzqRVq1bs37+ftWvX8vPPP5OYmMi4ceNYsGABR48eJS4ujri4OHt4uJFdu3ZRrlw5e7ABaNKkSZr1vv76ayZNmsRff/3FhQsXSEhIwM/PL1uvY9euXdStWzdV3e69916SkpLYs2ePPdzUqlULV1dX+zqlSpVix44d2TrWzdCYGweKjk4E4ODBbc6tiIjI7cbb22pBccaSzQG9vXv35ptvviE2NpZZs2ZRvnx5WrduzcSJE/nggw8YPnw4K1asYNu2bTz44IPEx8dnab/GmDRltuu6y8LDw3nyyScJCQnhhx9+YOvWrYwaNSrLx7j2WNfvO71jFrruLtI2m42kpKRsHetmKNw4yEsvjeTSJSudPv74A4SGhjq5RiIitxGbzeoacsaShfE213riiSdwdXXlyy+/5PPPP6dXr17YbDbWrl1Lx44d6dq1K3Xr1qVSpUrs27cvy/utWbMmkZGRHDt2zF62fv36VOv89ttvlC9fnlGjRtGgQQOqVKmSZvaWu7s7iYmJNzzWtm3buHjxYqp9u7i4ULVq1SzXOaco3DhAREQE77336TUlMUyYMIGIiAin1UlERPKmwoUL06VLF15++WWOHTtGz549AahcuTJhYWGsW7eOXbt20bdvX44fP57l/T7wwANUq1aN7t27s337dtauXcuoUaNSrVO5cmUiIyP56quv2L9/Px999BGLFi1KtU6FChU4cOAA27Zt4/Tp08TFxaU51jPPPIOnpyc9evTgjz/+YOXKlQwcOJBu3brZu6ScSeHGAfbu3QsUBs4B54GEa8pFRERS6927N2fPnuWBBx6gXLlyALz66qvUq1ePBx98kJYtW1KyZEk6deqU5X26uLiwaNEi4uLiaNSoEX369OHtt99OtU7Hjh158cUXGTBgAHfffTfr1q3j1VdfTbXOo48+ykMPPUSrVq0oXrx4utPRvb29+emnnzhz5gwNGzbkscceo3Xr1kyePDn7b0YOsJn0OukKsNjYWPz9/YmJicn2AKqMRERE0Lhx4+RHNsB6S8PDw7M0wl1ERLLnypUrHDhwgIoVK+Lp6ens6oiDZHZes/P5rZYbBwgODmb48OHJj6xgExoaqmAjIiLiBE4PN1OnTrUntPr167N27doM1/32229p06YNxYsXx8/PjyZNmvDTTz/lYm0zNn78eMLDw5kzZw7h4eGMGzfO2VUSERG5LTk13CxYsIDBgwczatQotm7dSrNmzQgJCSEyMjLd9desWUObNm1YsmQJmzdvplWrVnTo0IGtW7fmcs3TFxwcTLdu3dRiIyIi4kROHXMTHBxMvXr1mDZtmr2sRo0adOrUibFjx2ZpH7Vq1aJLly689tprWVo/J8bciIhI7tKYm4Ip34+5iY+PZ/PmzbRt2zZVedu2bVm3bl2W9pGUlMT58+cpVqxYhuvExcURGxubahEREZGCy2nh5vTp0yQmJqaZDx8UFJTlef0TJ07k4sWLPPHEExmuM3bsWPz9/e1L2bJlb6neIiKSd9xmE34LPEedT6cPKL7+8s2ZXdL5WvPnz+eNN95gwYIFlChRIsP1Ro4cSUxMjH05fPjwLddZREScK+V+Rdm9bYDkbSnn89r7Ud0Mp904MzAwEFdX1zStNCdPnrzh1Q0XLFhA7969WbhwIQ888ECm63p4eODh4XHL9RURkbzDzc0Nb29vTp06RaFChXBxcfp3dblFSUlJnDp1Cm9vb9zcbi2eOC3cuLu7U79+fcLCwnjkkUfs5WFhYXTs2DHD7ebPn8+zzz7L/Pnzad++fW5UVURE8hibzUapUqU4cOBAmnsjSf7l4uJCuXLlstSDkxmnhRuAIUOG0K1bNxo0aECTJk2YPn06kZGR9OvXD7C6lI4ePcqcOXMAK9h0796dDz/8kMaNG9tbfby8vPD393fa6xARkdzn7u5OlSpV1DVVgLi7uzukFc6p4aZLly5ER0czevRooqKiqF27NkuWLKF8+fIAREVFpbrmzaeffkpCQgL9+/enf//+9vIePXowe/bs3K6+iIg4mYuLi6aCSxq6t5SIiIjkefniOjciIiIiOUHhRkRERAoUp465cYaUXjhdqVhERCT/SPnczspomtsu3Jw/fx5AVyoWERHJh86fP3/DGdK33YDipKQkjh07hq+vb5bn0cfGxlK2bFkOHz6sQch5iM5L3qVzkzfpvORdOjc3Zozh/PnzlC5d+obTxW+7lhsXFxfKlClzU9v6+fnply4P0nnJu3Ru8iadl7xL5yZzWb2mnQYUi4iISIGicCMiIiIFisJNFnh4ePD666/rBpx5jM5L3qVzkzfpvORdOjeOddsNKBYREZGCTS03IiIiUqAo3IiIiEiBonAjIiIiBYrCjYiIiBQoCjc3MHXqVCpWrIinpyf169dn7dq1zq7SbWfNmjV06NCB0qVLY7PZ+O6771I9b4zhjTfeoHTp0nh5edGyZUv+/PNP51T2NjJ27FgaNmyIr68vJUqUoFOnTuzZsyfVOjo3uW/atGnUqVPHfjG4Jk2asHTpUvvzOid5w9ixY7HZbAwePNhepnPjOAo3mViwYAGDBw9m1KhRbN26lWbNmhESEkJkZKSzq3ZbuXjxInXr1mXy5MnpPj9hwgTef/99Jk+ezMaNGylZsiRt2rSx30dMcsbq1avp378/4eHhhIWFkZCQQNu2bbl48aJ9HZ2b3FemTBnGjRvHpk2b2LRpE/fffz8dO3a0f0jqnDjfxo0bmT59OnXq1ElVrnPjQEYy1KhRI9OvX79UZdWrVzcjRoxwUo0EMIsWLbI/TkpKMiVLljTjxo2zl125csX4+/ubTz75xAk1vH2dPHnSAGb16tXGGJ2bvKRo0aLms88+0znJA86fP2+qVKliwsLCTIsWLcwLL7xgjNG/F0dTy00G4uPj2bx5M23btk1V3rZtW9atW+ekWsn1Dhw4wPHjx1OdJw8PD1q0aKHzlMtiYmIAKFasGKBzkxckJiby1VdfcfHiRZo0aaJzkgf079+f9u3b88ADD6Qq17lxrNvuxplZdfr0aRITEwkKCkpVHhQUxPHjx51UK7leyrlI7zwdOnTIGVW6LRljGDJkCPfddx+1a9cGdG6caceOHTRp0oQrV65QuHBhFi1aRM2aNe0fkjonzvHVV1+xefNmNm3alOY5/XtxLIWbG7DZbKkeG2PSlInz6Tw514ABA/j999/59ddf0zync5P7qlWrxrZt2zh37hzffPMNPXr0YPXq1fbndU5y3+HDh3nhhRf4+eef8fT0zHA9nRvHULdUBgIDA3F1dU3TSnPy5Mk0yVqcp2TJkgA6T040cOBAFi9ezMqVKylTpoy9XOfGedzd3alcuTINGjRg7Nix1K1blw8//FDnxIk2b97MyZMnqV+/Pm5ubri5ubF69Wo++ugj3Nzc7O+/zo1jKNxkwN3dnfr16xMWFpaqPCwsjKZNmzqpVnK9ihUrUrJkyVTnKT4+ntWrV+s85TBjDAMGDODbb79lxYoVVKxYMdXzOjd5hzGGuLg4nRMnat26NTt27GDbtm32pUGDBjzzzDNs27aNSpUq6dw4kLqlMjFkyBC6detGgwYNaNKkCdOnTycyMpJ+/fo5u2q3lQsXLvDXX3/ZHx84cIBt27ZRrFgxypUrx+DBg3nnnXeoUqUKVapU4Z133sHb25unn37aibUu+Pr378+XX37J999/j6+vr/0bp7+/P15eXvZreOjc5K6XX36ZkJAQypYty/nz5/nqq69YtWoVy5Yt0zlxIl9fX/t4tBQ+Pj4EBATYy3VuHMh5E7XyhylTppjy5csbd3d3U69ePfs0V8k9K1euNECapUePHsYYawrl66+/bkqWLGk8PDxM8+bNzY4dO5xb6dtAeucEMLNmzbKvo3OT+5599ln736zixYub1q1bm59//tn+vM5J3nHtVHBjdG4cyWaMMU7KVSIiIiIOpzE3IiIiUqAo3IiIiEiBonAjIiIiBYrCjYiIiBQoCjciIiJSoCjciIiISIGicCMiIiIFisKNiNyWbDYb3333nbOrISI5QOFGRHJdz549sdlsaZaHHnrI2VUTkQJA95YSEad46KGHmDVrVqoyDw8PJ9VGRAoStdyIiFN4eHhQsmTJVEvRokUBq8to2rRphISE4OXlRcWKFVm4cGGq7Xfs2MH999+Pl5cXAQEBPPfcc1y4cCHVOjNnzqRWrVp4eHhQqlQpBgwYkOr506dP88gjj+Dt7U2VKlVYvHix/bmzZ8/yzDPPULx4cby8vKhSpUqaMCYieZPCjYjkSa+++iqPPvoo27dvp2vXrjz11FPs2rULgEuXLvHQQw9RtGhRNm7cyMKFC/nll19ShZdp06bRv39/nnvuOXbs2MHixYupXLlyqmO8+eabPPHEE/z++++0a9eOZ555hjNnztiPv3PnTpYuXcquXbuYNm0agYGBufcGiMjNc/adO0Xk9tOjRw/j6upqfHx8Ui2jR482xlh3HO/Xr1+qbYKDg83zzz9vjDFm+vTppmjRoubChQv253/88Ufj4uJijh8/bowxpnTp0mbUqFEZ1gEwr7zyiv3xhQsXjM1mM0uXLjXGGNOhQwfTq1cvx7xgEclVGnMjIk7RqlUrpk2blqqsWLFi9p+bNGmS6rkmTZqwbds2AHbt2kXdunXx8fGxP3/vvfeSlJTEnj17sNlsHDt2jNatW2dahzp16th/9vHxwdfXl5MnTwLw/PPP8+ijj7Jlyxbatm1Lp06daNq06U29VhHJXQo3IuIUPj4+abqJbsRmswFgjLH/nN46Xl5eWdpfoUKF0myblJQEQEhICIcOHeLHH3/kl19+oXXr1vTv35/33nsvW3UWkdynMTcikieFh4eneVy9enUAatasybZt27h48aL9+d9++w0XFxeqVq2Kr68vFSpUYPny5bdUh+LFi9OzZ0+++OILJk2axPTp029pfyKSO9RyIyJOERcXx/Hjx1OVubm52QftLly4kAYNGnDfffcxb948NmzYwIwZMwB45plneP311+nRowdvvPEGp06dYuDAgXTr1o2goCAA3njjDfr160eJEiUICQnh/Pnz/PbbbwwcODBL9XvttdeoX78+tWrVIi4ujh9++IEaNWo48B0QkZyicCMiTrFs2TJKlSqVqqxatWrs3r0bsGYyffXVV/znP/+hZMmSzJs3j5o1awLg7e3NTz/9xAsvvEDDhg3x9vbm0Ucf5f3337fvq0ePHly5coUPPviAYcOGERgYyGOPPZbl+rm7uzNy5EgOHjyIl5cXzZo146uvvnLAKxeRnGYzxhhnV0JE5Fo2m41FixbRqVMnZ1dFRPIhjbkRERGRAkXhRkRERAoUjbkRkTxHveUicivUciMiIiIFisKNiIiIFCgKNyIiIlKgKNyIiIhIgaJwIyIiIgWKwo2IiIgUKAo3IiIiUqAo3IiIiEiBonAjIiIiBcr/A0oqYpKT+kPXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, accuracy, color = 'blue')\n",
    "plt.plot(epochs, valid_accuracy, color = 'red')\n",
    "\n",
    "plt.scatter(epochs, accuracy, color = 'black', sizes = [10])\n",
    "plt.scatter(epochs, valid_accuracy, color = 'black', sizes = [10])\n",
    "\n",
    "\n",
    "plt.title('Training vs. Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "\n",
    "plt.legend(['Training', 'Validation'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c5434c",
   "metadata": {},
   "source": [
    "#### Comparing Training and Validation Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "4d9d7559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BhpRoQLGIiIiUKQo3IiIiUqYo3IiIiEiZct2NuRERkbIjOzub9PR0Z1dD7MTDw+Oql3kXhMKNiIiUSunp6ezfv5/s7GxnV0XsxMXFhVq1auHh4XFN+1G4ERGRUscwDOLi4nB1daV69ep2+WtfnCtnkt24uDjCwsKuaaJdhRsRESl1MjMzOX/+PFWqVMHHx8fZ1RE7CQ4O5ujRo2RmZuLu7l7k/SjqiohIqZOVlQVwzd0XUrLknM+c81tUCjciIlJq6R6BZYu9zqfCjYiIiJQpCjciIiKlWKdOnRg6dGiByx84cACLxcL27dsdVidn04BiO4qJiWHv3r2Eh4cTERHh7OqIiEgJcrUul/79+zN79uxC7/ebb74p1ODb6tWrExcXR8WKFQt9rNJC4cZO/i/q/5j04STwAE5DZGQkEyZMcHa1RESkhIiLi7P+PH/+fEaPHs2ePXusy7y9vW3KZ2RkFCi0VKhQoVD1cHV1JTQ0tFDblDbqlrKDmJgYJk2fBMOBZ81lEydOJCYmxqn1EhGRkiM0NNT6CAgIwGKxWJ+npqZSvnx5vv76azp16oSXlxeff/45p06d4qGHHqJatWr4+Phw44038uWXX9rs9/JuqZo1a/LWW2/x2GOP4efnR1hYGNOnT7euv7xbau3atVgsFlatWkWrVq3w8fGhXbt2NsEL4M033yQkJAQ/Pz+eeOIJRowYQbNmzRz1dl0ThRs72Lt3L6ReeOIGuF6yXEREHM4wDJLTk53yMAzDbq8jKiqK5557jtjYWO644w5SU1Np2bIl3333Hb///jtPPfUUffv2veofz++88w6tWrXi119/5dlnn+WZZ55h9+7d+W7z8ssv884777Blyxbc3Nx47LHHrOu++OILxo4dy4QJE9i6dSthYWFMmzbNLq/ZEdQtZQfh4eGQdskCT+D8heUiIuJw5zPO4zvO1ynHThqZRDmPcnbZ19ChQ+ndu7fNsuHDh1t/HjJkCMuXL2fBggX5ju3s3r07zz5rdiVERUXx3nvvsXbtWurXr3/FbcaOHUvHjh0BGDFiBHfddRepqal4eXnxn//8h8cff5xHH30UgNGjR7NixQqSkpKK/FodSS03dhAREUHk/0VCzr3bvMwPkwYVi4hIYbRq1crmeVZWFmPHjqVJkyYEBQXh6+vLihUrOHToUL77adKkifXnnO6v48ePF3ibypUrA1i32bNnD61bt7Ypf/nzkkQtN3YyYcIEZo2fxYm0E8z+cjb9u/Z3dpVERK4bPu4+JI10TiuCj7v9bv9QrpxtC9A777zDe++9x+TJk7nxxhspV64cQ4cOveqd0C8fiGyxWK56g9FLt8m5suvSbS6/2sue3XH25tSWm2nTptGkSRP8/f3x9/enbdu2fP/99/lus27dOlq2bImXlxe1a9fmo48+KqbaXl1FP/OyurC6YU6uiYjI9cVisVDOo5xTHo6cJfnnn3+mZ8+ePPLIIzRt2pTatWuzb98+hx3vSurVq8emTZtslm3ZsqXY61FQTg031apVY/z48WzZsoUtW7Zw66230rNnT/744488y+/fv5/u3bvToUMHfv31V1566SWee+45Fi5cWMw1z5u/pz8ACWkJTq6JiIiUBXXq1GHlypVs2LCB2NhYnn76aeLj44u9HkOGDGHGjBl89tln7Nu3jzfffJPffvutxN7+wqndUj169LB5PnbsWKZNm0Z0dDSNGjXKVf6jjz4iLCyMyZMnA9CgQQO2bNnCpEmTuPfee4ujyvkK8AoAIDEt0ck1ERGRsuCVV15h//793HHHHfj4+PDUU0/Rq1cvEhKK94/oPn368PfffzN8+HBSU1O5//77GTBgQK7WnJKixIy5ycrKYsGCBSQnJ9O2bds8y2zcuJGuXbvaLLvjjjuYMWPGFSc7SktLIy3t4qVMiYmOCx7WlptUtdyIiMiVDRgwgAEDBlif16xZM88xLBUqVGDx4sX57mvt2rU2zw8cOJCrzKW3Wrj8WJ06dcp17GbNmuVa9sorr/DKK69Yn99+++3UqVMn37o5i9PDzc6dO2nbti2pqan4+vqyaNEiGjZsmGfZ+Ph4KlWqZLOsUqVKZGZmcvLkSevo7kuNGzeO1157zSF1v1yAp1puRESk7Dl//jwfffQRd9xxB66urnz55Zf8+OOPrFy50tlVy5PTLwWvV68e27dvJzo6mmeeeYb+/fuza9euK5a/0mjtK/X7jRw5koSEBOvj8OHD9qv8ZXLCjcbciIhIWWKxWFi2bBkdOnSgZcuW/O9//2PhwoXcdtttzq5anpzecuPh4WFt1mrVqhWbN2/m/fff5+OPP85VNjQ0NNdAquPHj+Pm5kZQUFCe+/f09MTT09P+Fc+DuqVERKQs8vb25scff3R2NQrM6S03lzMMw2aMzKXatm2bqwlsxYoVtGrVqlB3RHUU64DidHVLiYiIOItTw81LL73Ezz//zIEDB9i5cycvv/wya9eupU+fPoDZpdSvXz9r+YEDB3Lw4EGGDRtGbGwsM2fOZMaMGTZTUzuTtVtKLTciIiJO49RuqWPHjtG3b1/i4uIICAigSZMmLF++nNtvvx0wbw9/6RTTtWrVYtmyZbzwwgtMnTqVKlWqMGXKlBJxGThonhsREZGSwKnhZsaMGfmunz17dq5lHTt2ZNu2bQ6q0bXRPDciIiLOV+LG3JRm6pYSERFxPoUbO1K3lIiIiPMp3NhRTrfUubRzZBv5331VRESksDp16sTQoUOtz2vWrGm9JdGVWCyWq85yXBD22k9xULixo5xuKQODpPQkJ9dGRERKkh49elxx0ruNGzdisVgKPaZ08+bNPPXUU/aontWrr75Ks2bNci2Pi4ujW7dudj2Woyjc2JGXmxduLuYYbY27ERGRSz3++OOsXr2agwcP5lo3c+ZMmjVrRosWLQq1z+DgYHx8fOxVxXyFhoYW26S410rhxo4sFovuLyUiInm6++67CQkJyXUl8Pnz55k/fz69evXioYceolq1avj4+HDjjTfy5Zdf5rvPy7ul9u3bxy233IKXlxcNGzbM895PUVFRhIeH4+PjQ+3atXnllVfIyMgAzKuUX3vtNXbs2IHFYsFisVjre3m31M6dO7n11lvx9vYmKCiIp556iqSki70WAwYMoFevXkyaNInKlSsTFBTEoEGDrMdyJKfffqGs8ff051TKKQ0qFhEpToYBWeedc2xXH7jC/Q0v5ebmRr9+/Zg9ezajR4+23hNxwYIFpKen88QTT/Dll18SFRWFv78/S5cupW/fvtSuXZuIiIir7j87O5vevXtTsWJFoqOjSUxMtBmfk8PPz4/Zs2dTpUoVdu7cyZNPPomfnx+RkZE88MAD/P777yxfvtx6u4WAgIBc+zh//jx33nknbdq0YfPmzRw/fpwnnniCwYMH24S3NWvWULlyZdasWcOff/7JAw88QLNmzXjyySev+nquhcKNnWmuGxERJ8g6D1/7OufY9yeBW7kCFX3sscd4++23Wbt2LZ07dwbMLqnevXtTtWpVmxn3hwwZwvLly1mwYEGBws2PP/5IbGwsBw4coFq1agC89dZbucbJjBo1yvpzzZo1efHFF5k/fz6RkZF4e3vj6+uLm5sboaGhVzzWF198QUpKCnPmzKFcOfO1f/DBB/To0YMJEyZQqVIlAAIDA/nggw9wdXWlfv363HXXXaxatUrhprTRXDciInIl9evXp127dsycOZPOnTvz119/8fPPP7NixQqysrIYP3488+fP58iRI6SlpZGWlmYND1cTGxtLWFiYNdiAeU/Gy/33v/9l8uTJ/PnnnyQlJZGZmYm/v3+hXkdsbCxNmza1qdvNN99MdnY2e/bssYabRo0a4erqai1TuXJldu7cWahjFYXCjZ1prhsRESdw9TFbUJx17EJ4/PHHGTx4MFOnTmXWrFnUqFGDLl268Pbbb/Pee+8xefJkbrzxRsqVK8fQoUNJT08v0H4Nw8i1zHJZd1l0dDQPPvggr732GnfccQcBAQF89dVXvPPOO4V6DYZh5Np3Xse8/KbWFouF7GzHT5WicGNn6pYSEXECi6XAXUPOdv/99/P8888zb948PvvsM5588kksFgs///wzPXv25JFHHgHMMTT79u2jQYMGBdpvw4YNOXToEEePHqVKlSqAeYn5pdavX0+NGjV4+eWXrcsuv3rLw8ODrKysqx7rs88+Izk52dp6s379elxcXAgPDy9QfR1JV0vZmbqlREQkP76+vjzwwAO89NJLHD16lAEDBgBQp04dVq5cyYYNG4iNjeXpp58mPj6+wPu97bbbqFevHv369WPHjh38/PPPNiEm5xiHDh3iq6++4q+//mLKlCksWrTIpkzNmjXZv38/27dv5+TJk6SlpeU6Vp8+ffDy8qJ///78/vvvrFmzhiFDhtC3b19rl5QzKdzYmbqlRETkah5//HHOnDnDbbfdRlhYGACvvPIKLVq04I477qBTp06EhobSq1evAu/TxcWFRYsWkZaWRuvWrXniiScYO3asTZmePXvywgsvMHjwYJo1a8aGDRt45ZVXbMrce++93HnnnXTu3Jng4OA8L0f38fHhhx9+4PTp09x0003cd999dOnShQ8++KDwb4YDWIy8OunKsMTERAICAkhISCj0AKqCmPDLBEasGkH/pv2Z3Wu23fcvIiKQmprK/v37qVWrFl5eXs6ujthJfue1MN/farmxs5wxN2q5ERERcQ6FGzuzdktpzI2IiIhTKNzYmW6/ICIi4lwKN3ambikRERHnUrixs5xuKbXciIg43nV2TUyZZ6/zqXBjZ5rnRkTE8XKm9C/o7L1SOuScz0tv2VAUmqHYznJabtKy0kjLTMPTzdPJNRIRKXvc3Nzw8fHhxIkTuLu74+Kiv9VLu+zsbE6cOIGPjw9ubtcWTxRu7Cwn3IDZNRXsFuzE2oiIlE0Wi4XKlSuzf//+XLcPkNLLxcWFsLCwK963qqAUbuzM1cUVXw9fktKTSEhLILicwo2IiCN4eHhQt25ddU2VIR4eHnZphVO4cQB/T38z3GjcjYiIQ7m4uGiGYslFnZQOoLluREREnEfhxgE0142IiIjzKNw4gG7BICIi4jwKNw6gbikRERHnUbhxAOtEfuqWEhERKXYKNw6gbikRERHnUbhxgJwBxeqWEhERKX4KNw6gbikRERHnUbhxAN0ZXERExHkUbhxA89yIiIg4j8KNA2hAsYiIiPMo3DiA5rkRERFxHoUbB1C3lIiIiPMo3DhATrfUubRzZBvZTq6NiIjI9UXhxgFyuqUMDJLSk5xcGxERkeuLwo0DeLl54e7iDmhQsYiISHFTuHEAi8Vy8YopjbsREREpVgo3DqJbMIiIiDiHwo2DWG/BoG4pERGRYqVw4yC6BYOIiIhzODXcjBs3jptuugk/Pz9CQkLo1asXe/bsyXebtWvXYrFYcj12795dTLUuGM11IyIi4hxODTfr1q1j0KBBREdHs3LlSjIzM+natSvJyclX3XbPnj3ExcVZH3Xr1i2GGhecbsEgIiLiHG7OPPjy5cttns+aNYuQkBC2bt3KLbfcku+2ISEhlC9f3oG1uza6BYOIiIhzlKgxNwkJZitHhQoVrlq2efPmVK5cmS5durBmzZorlktLSyMxMdHmURysA4rVLSUiIlKsSky4MQyDYcOG0b59exo3bnzFcpUrV2b69OksXLiQb775hnr16tGlSxd++umnPMuPGzeOgIAA66N69eqOegk2NM+NiIiIczi1W+pSgwcP5rfffuOXX37Jt1y9evWoV6+e9Xnbtm05fPgwkyZNyrMra+TIkQwbNsz6PDExsVgCjua5ERERcY4S0XIzZMgQlixZwpo1a6hWrVqht2/Tpg379u3Lc52npyf+/v42j+KgeW5EREScw6ktN4ZhMGTIEBYtWsTatWupVatWkfbz66+/UrlyZTvX7tqoW0pERMQ5nBpuBg0axLx58/j222/x8/MjPj4egICAALy9vQGzW+nIkSPMmTMHgMmTJ1OzZk0aNWpEeno6n3/+OQsXLmThwoVOex15UbeUiIiIczg13EybNg2ATp062SyfNWsWAwYMACAuLo5Dhw5Z16WnpzN8+HCOHDmCt7c3jRo1YunSpXTv3r24ql0g6pYSERFxDothGIazK1GcEhMTCQgIICEhwaHjbw6ePUjN92vi4epB2qg0hx1HRETkelCY7+8SMaC4LMrplkrPSictU+FGRESkuJSYS8HLgpiYGPbu3Ut4eDitbmplXZ6QlkCIW4gTayYiInL9ULixk6ioKCZOnGh9HhkZiW95X5LSk0hMSySknMKNiIhIcVC3lB3ExMQw79OJ/D4B9r5jLps4cSLeFvOKLw0qFhERKT5qubGDvXv3cj4dGl2Yf9DNFTKzwD3bHdBcNyIiIsVJLTd2EB4ezplkyMo2n1f0M/+t4GPeAFRz3YiIiBQfhRs7iIiI4P/+L5JT58znFf3MMThVK1YF1C0lIiJSnBRu7GTChAn4VjRvH/H1nKmMHz9et2AQERFxAoUbO/IJNO823qBWReDiLMXqlhIRESk+Cjf25GmGGtJOAhcn8lO3lIiISPFRuLEnz2Dz37QTgO4MLiIi4gwKN/Z0ecuNuqVERESKncKNPVlbbi7rllLLjYiISLFRuLGnnJabVNtuKbXciIiIFB+FG3u6QreUBhSLiIgUH4Ube/KyHVCsbikREZHip3BjT5e23BiGuqVEREScQOHGnnLCTXY6ZCZZu6XOpZ0j28h2YsVERESuHwo39uTmA64+5s9pJ6wtNwYG59LOObFiIiIi1w+FG3uzXjF1Ei83L9xd3AF1TYmIiBQXhRt7u2RQscVi0aBiERGRYqZwY2+XXQ5uvQWDLgcXEREpFgo39qZbMIiIiDiVwo29eWquGxEREWdSuLE3dUuJiIg4lcKNveUMKL5wfyl1S4mIiBQvhRt7u9L9pdQtJSIiUiwUbuztCt1SarkREREpHgo39qYBxSIiIk6lcGNvOS036WcgO1MDikVERIqZwo29eVQALObPaac0oFhERKSYKdzYm4sreFYwf047qW4pERGRYqZw4wiXDCpWt5SIiEjxUrhxhEsGFatbSkREpHgp3DjCJS036pYSEREpXgo3juB5cZbinG6p9Kx0UjNTnVgpERGR64PCjSNc0nLj5+FnXayuKREREcdTuHGES8KNq4urNeBoULGIiIjjKdw4gpftLMW6BYOIiEjxUbhxhMtvnqlBxSIiIsVG4cYRLr+/VM6dwdUtJSIi4nAKN45wacuNYahbSkREpBgp3DhCTrjJSoXMZHVLiYiIFCOFG0dwKweuXubPaSfx99AtGERERIqLU8PNuHHjuOmmm/Dz8yMkJIRevXqxZ8+eq263bt06WrZsiZeXF7Vr1+ajjz4qhtoWgsWS5yzF6pYSERFxPKeGm3Xr1jFo0CCio6NZuXIlmZmZdO3aleTk5Ctus3//frp3706HDh349ddfeemll3juuedYuHBhMda8APK4v5S6pURERBzPzZkHX758uc3zWbNmERISwtatW7nlllvy3Oajjz4iLCyMyZMnA9CgQQO2bNnCpEmTuPfeex1d5YLL687gCjciIiIOV6LG3CQkmF/+FSpUuGKZjRs30rVrV5tld9xxB1u2bCEjIyNX+bS0NBITE20exSIn3KSeULeUiIhIMSox4cYwDIYNG0b79u1p3LjxFcvFx8dTqVIlm2WVKlUiMzOTkydP5io/btw4AgICrI/q1avbve55snZLndQ8NyIiIsWoxISbwYMH89tvv/Hll19etazFYrF5bhhGnssBRo4cSUJCgvVx+PBh+1T4atQtJSIi4hROHXOTY8iQISxZsoSffvqJatWq5Vs2NDSU+Ph4m2XHjx/Hzc2NoKCgXOU9PT3x9PS0a30L5JL7S6lbSkREpPg4teXGMAwGDx7MN998w+rVq6lVq9ZVt2nbti0rV660WbZixQpatWqFu7u7o6paeJdeCq5uKRERkWLj1HAzaNAgPv/8c+bNm4efnx/x8fHEx8eTkpJiLTNy5Ej69etnfT5w4EAOHjzIsGHDiI2NZebMmcyYMYPhw4c74yVcmTXcnLB2S51LP0e2ke3ESomIiJR9Tg0306ZNIyEhgU6dOlG5cmXrY/78+dYycXFxHDp0yPq8Vq1aLFu2jLVr19KsWTPeeOMNpkyZUrIuAwfbAcUXuqUAzqWdc1KFRERErg9OHXOTMxA4P7Nnz861rGPHjmzbts0BNbIja8vNabxc3PFw9SA9K52EtASbsCMiIiL2VWKulipzPHMGNxuQflp3BhcRESkmCjeO4uIGHoHmzxpULCIiUmwUbhwpj0HFmutGRETEsRRuHCmPQcXqlhIREXEshRtH0lw3IiIixU7hxpEuuXmmuqVERESKh8KNI3nlvnmmuqVEREQcS+HGkS4ZUJwz5kbdUiIiIo6lcONIlwwots5zk66WGxEREUdSuHEkDSgWEREpdgo3jnTJgGJrt5QGFIuIiDiUwo0jeeXRLaUBxSIiIg6lcONIOS03WecJdPME1C0lIiLiaAo3juTmBy4eAFRwzQbULSUiIuJoCjeOZLFYW2/8SQfULSUiIuJoCjeOlhNujDQA0rPSSc1MdWaNREREyjSFG0e7MNeNd1aydZHG3YiIiDiOwo2jXWi5cUk/hZ+HH6CuKREREUdSuHG0S+8vpbluREREHE7hxtEuub+U9c7g6pYSERFxGIUbR8vjFgzqlhIREXEchRtH81S3lIiISHFSuHG0S+4vpVswiIiIOJ7CjaNdOqBYdwYXERFxOIUbR8tpuUk/RXmPCwOK1S0lIiLiMAo3juYRZP5rZBPi7g6oW0pERMSRFG4czdUD3M3uqBB38+1Wy42IiIjjKNwUhwtdUxVdDUBjbkRERBxJ4aY4XLgcvIIlG1C3lIiIiCMp3BSHCy035S0ZgLqlREREHEnhpjh4meHG10gF1C0lIiLiSEUKN4cPH+aff/6xPt+0aRNDhw5l+vTpdqtYmXKhW6pcdgqgbikRERFHKlK4efjhh1mzZg0A8fHx3H777WzatImXXnqJ119/3a4VLBMudEt5ZSUBcC79HFnZWc6skYiISJlVpHDz+++/07p1awC+/vprGjduzIYNG5g3bx6zZ8+2Z/3KhgstNx6ZF1tsktKTnFUbERGRMq1I4SYjIwNPT08AfvzxR+655x4A6tevT1xcnP1qV1ZcaLlxTT+Fh6sHoEHFIiIijlKkcNOoUSM++ugjfv75Z1auXMmdd94JwNGjRwkKCrJrBcuEnFsw6P5SIiIiDlekcDNhwgQ+/vhjOnXqxEMPPUTTpk0BWLJkibW7Si5xyc0zdWdwERERx3IrykadOnXi5MmTJCYmEhgYaF3+1FNP4ePjY7fKlRk5LTeZSQR71eEv1C0lIiLiKEVquUlJSSEtLc0abA4ePMjkyZPZs2cPISEhdq1gmeAeABYzR1b39AbULSUiIuIoRQo3PXv2ZM6cOQCcPXuWiIgI3nnnHXr16sW0adPsWsEywWKxtt5U9TAHFKtbSkRExDGKFG62bdtGhw4dAPjvf/9LpUqVOHjwIHPmzGHKlCl2rWCZcSHcVPZwB9QtJSIi4ihFCjfnz5/Hz88PgBUrVtC7d29cXFxo06YNBw8etGsFy4wLg4pDXM23XN1SIiIijlGkcFOnTh0WL17M4cOH+eGHH+jatSsAx48fx9/f364VLDMutNxUdDWfqltKRETEMYoUbkaPHs3w4cOpWbMmrVu3pm3btoDZitO8eXO7VrDMuDBLcQUX87YL6pYSERFxjCKFm/vuu49Dhw6xZcsWfvjhB+vyLl268N577xV4Pz/99BM9evSgSpUqWCwWFi9enG/5tWvXYrFYcj12795dlJdRvC603ASQASjciIiIOEqR5rkBCA0NJTQ0lH/++QeLxULVqlULPYFfcnIyTZs25dFHH+Xee+8t8HZ79uyx6f4KDg4u1HGd4kK48SMNULeUiIiIoxQp3GRnZ/Pmm2/yzjvvkJRk3gDSz8+PF198kZdffhkXl4I1CHXr1o1u3boV+vghISGUL1++0Ns51YVuqXLZKYAGFIuIiDhKkcLNyy+/zIwZMxg/fjw333wzhmGwfv16Xn31VVJTUxk7dqy962mjefPmpKam0rBhQ0aNGkXnzp2vWDYtLY20tDTr88REJ7WYeJktN15Z58x6qOVGRETEIYoUbj777DM+/fRT693AAZo2bUrVqlV59tlnHRZuKleuzPTp02nZsiVpaWnMnTuXLl26sHbtWm655ZY8txk3bhyvvfaaQ+pTKBdabjwyzVCjMTciIiKOYTEMwyjsRl5eXvz222+Eh4fbLN+zZw/NmjUjJSWl8BWxWFi0aBG9evUq1HY9evTAYrGwZMmSPNfn1XJTvXp1EhISivey9fNHYHE1DIsrrnuzcHNxJ21UGhaLpfjqICIiUkolJiYSEBBQoO/vIl0t1bRpUz744INcyz/44AOaNGlSlF0WWZs2bdi3b98V13t6euLv72/zcIoLA4otRhYBLpCRnUFaVtpVNhIREZHCKlK31MSJE7nrrrv48ccfadu2LRaLhQ0bNnD48GGWLVtm7zrm69dff6Vy5crFeswicfUENz/IPEewK5zNhtUbVtP9lu7OrpmIiEiZUqSWm44dO7J3717+9a9/cfbsWU6fPk3v3r35448/mDVrVoH3k5SUxPbt29m+fTsA+/fvZ/v27Rw6dAiAkSNH0q9fP2v5yZMns3jxYvbt28cff/zByJEjWbhwIYMHDy7Kyyh+ObMUm/P4cVfvu4iKinJihURERMqeIs9zU6VKlVwDh3fs2MFnn33GzJkzC7SPLVu22FzpNGzYMAD69+/P7NmziYuLswYdgPT0dIYPH86RI0fw9vamUaNGLF26lO7dS0frR1KmN75AxewLCzzNVrDevXsTERHhzKqJiIiUGUUON/bQqVMn8hvPPHv2bJvnkZGRREZGOrhWjnM21R1fCwRnY7aZmfceZe/evQo3IiIidlKkbikpGk+/qgBUzLiwIMj85/KrzkRERKToFG6KUXC1Bua/Od1SQRAVFaVWGxERETsqVLdU7969811/9uzZa6lL2XdhQPFtzZvB7u00u60Z458b79w6iYiIlDGFCjcBAQFXXX/p1U1ymQvhpmaALwBx6XHOrI2IiEiZVKhwU5jLvCUPXuYtGPwMcwbnY8nHSEhNIMAr/9AoIiIiBacxN8XpQsuNa8YZKvuaEw/uPbXXmTUSEREpcxRuitOFm2eSeoLwIPMKqT2n9jixQiIiImWPwk1xutByQ+Y5GlWoA6jlRkRExN4UboqTR3mwuAJwY/kqgFpuRERE7E3hpjhZXMDTnLmvgb/ZRaWWGxEREftSuCluF7qmavv4A2a4yTay89tCRERECkHhprhdGFRc2d0VNxc3zmec5+i5o06ulIiISNmhcFPcLrTcuKWfpXZgbQD2nNS4GxEREXtRuCluOVdMpZ2kXlA9QONuRERE7EnhprjlzHWTprluREREHEHhprip5UZERMShFG6Km5dabkRERBxJ4aa4XdJykxNuDpw9QFpmmhMrJSIiUnYo3BS3S8JNqG8ofh5+ZBvZ/H3mb+fWS0REpIxQuClu1gHFJ7GAuqZERETsTOGmuOW03GRnsGXjaupV1KBiERERe1K4KWZRL79KUqr584O9b+Pg1oOAJvITERGxF4WbYhQTE8PEiRM5ec58XiUQ1i9ZD8De02q5ERERsQeFm2K0d68ZYLbuN593aQScMn9Wy42IiIh9KNwUo/Bwc/Dwkm3m83taYA03J86f4EzKGedUTEREpAxRuClGERERREZGsmw7ZGVD85rwVuQzVPGrAmhQsYiIiD0o3BSzCRMm8N3KaE5azFackX0b6TYMIiIidqRw4wQRERFUav6E+eSfJZrrRkRExI4Ubpyl2j3mv8fXcGNgDUAtNyIiIvagcOMs/vXALxyyM2jjmgio5UZERMQeFG6c6ULrTd3UWAD2ndpHtpHtzBqJiIiUego3zlTVDDd+J3/Cy8WNlMwU/kn8x8mVEhERKd0UbpypYlvwDMKSfoZ/h+hycBEREXtQuHEmFzeochcAvf3cAM1ULCIicq0UbpztQtdUe5fTgFpuRERErpXCjbNV7gouHlTMPkt9d10xJSIicq0UbpzN3Q8q3QpAD1+13IiIiFwrhZuS4MIl4feUgwNnD5CamerkComIiJReCjclQdUeALTzgiBXg79O/+XkComIiJReCjclgU81CGyBiwXu8tG4GxERkWuhcFNS5HRNadyNiIjINVG4KSkuXBJ+hw/8dfIPJ1dGRESk9FK4KSkCm3HevQLlXCDg7FZn10ZERKTUUrgpKSwWkkNuA6Bxxn4nV0ZERKT0cmq4+emnn+jRowdVqlTBYrGwePHiq26zbt06WrZsiZeXF7Vr1+ajjz5yfEWLiV+thwG43TOV0+dPOrk2IiIipZNTw01ycjJNmzblgw8+KFD5/fv30717dzp06MCvv/7KSy+9xHPPPcfChQsdXNPi4VX1TpKyLVR1gyMH/+fs6oiIiJRKbs48eLdu3ejWrVuBy3/00UeEhYUxefJkABo0aMCWLVuYNGkS9957r4NqWYxcPdlKRTpyguzDi6HBo86ukYiISKlTqsbcbNy4ka5du9osu+OOO9iyZQsZGRl5bpOWlkZiYqLNoyTbX+5GAELORju5JiIiIqVTqQo38fHxVKpUyWZZpUqVyMzM5OTJvMeojBs3joCAAOujevXqxVHVIksNuZUsAypnHofkg86ujoiISKlTqsINgMVisXluGEaey3OMHDmShIQE6+Pw4cMOr+O1qBHSgl9SLjz5R+NuRERECqtUhZvQ0FDi4+Ntlh0/fhw3NzeCgoLy3MbT0xN/f3+bR0kWHhTOkmTzZ+PIt86tjIiISClUqsJN27ZtWblypc2yFStW0KpVK9zd3Z1UK/uqWb4m36dcGOd9bB2kJzi3QiIiIqWMU8NNUlIS27dvZ/v27YB5qff27ds5dOgQYHYp9evXz1p+4MCBHDx4kGHDhhEbG8vMmTOZMWMGw4cPd0b1HcLVxRX86hKbDhYjA+J+cHaVREREShWnhpstW7bQvHlzmjdvDsCwYcNo3rw5o0ePBiAuLs4adABq1arFsmXLWLt2Lc2aNeONN95gypQpZeMy8EvUq1iPJUnmz3///D4xMTHOrZCIiEgpYjFyRuReJxITEwkICCAhIaHEjr+JWhnFL9smsr46nEmGkGdg2IuRTJgwwdlVExERcYrCfH+XqjE31wv3RHeiU+FEOgSWg5vDYeLEiWrBERERKQCFm5LoNGQD/7swlvj5O8x/9+7d67QqiYiIlBYKNyVQ+/rtAXg7GTKz4F83QaeGEB4e7uSaiYiIlHwKNyXQHe3vwNPwZHcGfLTBXPbFsBAibmrl3IqJiIiUAgo3JZDFYqFZtWYAHGn6BJkuvlTxPg77Zzu1XiIiIqWBwk0JFR5kdkH5NaqFW9PXzIU7XoaMc06slYiISMmncFNC1QuqB8DeU3shfDD41oHUY7BrvJNrJiIiUrIp3JRQOS03e07tAVcPaDHJXBH7DiQdcF7FRERESjiFmxKqXsVLWm4Aqt4DlTpDdhpsH+HEmomIiJRsCjclVJ0KdQA4nXKaH37+ASwWaPEuYIFD8+HEBudWUEREpIRSuCmhXhv1Gpw1f76zz51ERUVBYDO44XFz4dahYGQ7qXYiIiIll8JNCRQTE8PEiRPh1IUFQZfcfqHJG+DmC6c3w4F5Tq2niIhISaRwUwJZb7Nw7MKCupcs9w6FRi+ZC3aMhMzzxV4/ERGRkkzhpgSy3mZh+4UFDYCAS5bXfwHK1YDz/0DsJCfUUEREpORSuCmBIiIiiIyMhOPA34ALRAyOICIiwizg6gXNJpg/75oA5484q6oiIiIljsJNCTVhwgSio6N5vu3zAOzz38f5jEu6oMLuh4rtIOs87HjJSbUUEREpeRRuSrCIiAjeeeodapWvxemU08zbeckAYosFWk42f94/h9/XzWbu3LnmoGMRESmTYmJi9Lu+ABRuSjhXF1cGtx4MwPsx72MYxsWVQTdBzUcAOLv6Ufr160ebNm3My8ZFRKRMiYqK4q7b2jDwSf2uvxqFm1LgseaPUc69HL8f/521B9barPvVuJ/zadC+Hoy4BwLLXXLZuIiIlAkxMTEs+3IiB96H6NfA012/6/OjcFMKlPcqT/+m/QGYsmmKzbrf/z7LxO/Mn8c9AMenweqXwXXfFEj6u7irKiIiDrBv725mPAm+XnBjdRjVy1xunTpEbCjclBI5XVPf7v6W/Wf2W5eHh4cz9lt4+Wv47RC4uULnhtDKZR4suQGWNoYdL8PJGDCy1V8rIlIKdQjZRusbIDXdfB51NzSufskUIWJD4aaUaBDcgK43dMXAYOrmqdblERERDHsxkre+haYjofZQ+N+RLuZNNi2ukPAH/PEWrGhD4hx/dnzShv+8qv5aEZFSI+lvapz9BIDBn8GizeDuBktfqUzETa2cXLmSyWLYjFAt+xITEwkICCAhIQF/f39nV6dQlu1bxl3z7iLAM4B/hv2Dr4evdV1MTAx79+4lPDz84nw46Wfg6Pfwz7dkHf4OV8O8lDw9E3pPhqW/QnR09MXyIiJSshgGrL4djq2CSp2JKfcWh/fF0Mv9Zdyyk6HlFKg3xNm1LBaF+f5Wy00pcmedO6lToQ4JaQnM3THXZl1ERAR9+/a1DSoegVDzYWg/n68yP6DreDPQeLjBwufh9hvVXysiUqL9PcsMNq5e0Ho6EW3acF/f53FreWF2+h0jIfmQc+tYAinclCIuFheGtDYT+pRNUyhMo1ud8Ias3Am93oNvNpsj7Re/AC2rn3NUdUVE5FqkxMG2F82fm7wBfnUurqvzFAS3h8xk2Pys2cIjVgo3pcyAZgPw8/Bj98ndrPx7ZYG3y7mlQ2YWPPgf+O5X8PGEhscj4cQGB9ZYRESKZMsQyDgLFVpCvaG26ywu0Ho6uHjA0aVw6Gtn1LDEUrgpZfw9/Xm02aMATImZcpXStnJu6TBj1hxCeq+D0NvN1L+2G5za7IjqiohIURz+Bg4vBIsbRMwAF7fcZQIaQKMLt9/Z+hyknS7eOpZgGlBcCu07tY/wD8zL//YO3kvdoLpF21HmeVjbHY6vA/fycNsaCGxmt3qKiDhKnhdRlBXpZ+C7hpAaD41ehqZvXrlsVhp83xwSY6H2o9BmZvHVs5hpQHEZVzeoLt3rdgfgg00fFH1Hbj7Q8TvzBpwZZ2H1bXD2d/tUUkTEQaKiorilfRveGlFGp7XYNtwMNv71oPGo/Mu6ekLEp4DFHHwcv7pYqljSKdyUUs9HmHcLn7V9FolpiUXfkbsvdFoGFVpB2ikz4CTusVMtRUTsKyYmhv99MZFf34LYt+GFbmXsNgTxq+DvC60vrT81r5K6muB2UPcZ8+dNT0FmiuPqV0oo3JRSt9e+nfoV63Mu/Ryfbf/s2nbmEQCdf4DyTSH1GKy6le2/LNRMxiJS4mTsm8Xm16FhVfP5uAegUbUyMq1FZrIZTgDqDoKQ9gXfttk48K4KSX/B7685pn4FVBJmwle4KaUsFov1svD/bPoP2Ub2te3QswLcuhICGkLKUQK33ceoYWW0yVdESp/MFIh5ivYuH1POC1buhOU7zGkt5gyEenVrObuGuRT6S/630eY9AX2qm2GlMNz94aYLs9fHToIz2wu3vZ1ERUXRpk0b+vVz7veHwk0p1q9pPwI8A9h3eh/L/1x+7Tv0CmZb+bfZcxRqVIT1Y2BSH9i1ciJbNvx47fsXESmKxH2woi389QlgYWX8zdw5AQZ8DCfPQYta0Nr7B2fX0kZhv+R/XzeL7Nj3zCc3fQTufoU/aLWeUP0+MLJIWv0wn8/9rFhbT2JiYlg0ZyITHoK3HjCXOavLUFdLlXIv/vAi70a/S5uKbXjW/9lrvnJg7ty5jHi+Hz+9AjdUurg827DgEtQCQjpBpU4Q3AE8Asr2FQsi4nyHFkD045B5DjyD4eZ5EHqb9XdPmyqHqXvsZfNeerdvgIqtnV1jYmJi6NKxDT1aQNu6kJUNaRnwSP/HqFa9tjk3jYuHORjYxYMvv/6Gxi5LubE6fP4L7PSLZMKECUU7eEocKf+thbdrGi/MhcnLITLyGvZXEFmpcPgb4te/SaglFoCkVKg8yPx3zpw59O3b95oPU5jvb4WbUu7vM39zw/s3gAX4ADh5bR/kmJgY2rRpg5833NUMOjUwH/WqXFbQ4sI/ycF8teYY62Jh1xG4f8Bwxk14O999KwiJSIFkpcOvw2Hvf8znwR3g5q/A5/JfRsD6h+Hgl+bVRXf+Cm7exVvXHJkpcHQZB3+eQHD6Znw8C7f5iURo8H9wKqno9/2LiYnh05Ft+OQJM1T9vBuWbIN+Ly2gWfv7Cr2/y/dt8zv87B9ma9r+uZBuzrGTlW12F36yxpwsNivbfvcwVLjJR1kLNzExMbSZ3AbqA9uBxebya/kwRUVFMXHiRJvn40cPMefDObYWjq+Fc/tybZeeCVneNfAOvhH86oJ/uPmvX11GvP4BEyZeDD4O/0tCREqUgv5xExMTw5G96+nqMwPftF3mwoZR0OTNvCeyA3PyumWNzdsV1HseWk62/wu4rI7W19KqGcStgEPz4Z9vITPJWm5fPPxvG6Rlmvf06/PgfYQGB0J2GmSnQ1Ya/xz+m9jfd2CxwMTvzLFEUPTWjrlz59K/fz++eBYeanfZSv8GUO0eqHoPBEWAi2uBz0vO94K3B9zfBl57pAo1yh29WMCnOtzwOG/Nj+flsR/ZbDd+/PhCv468KNzko6yFm7lz59JvVD947MKCxcD2a28GvNoH/r9z32fRR0Pp1BDa1oE6lcDL48r7S0m/+B/9PyvgWILuSC5SFhTky/HyP5giIyOZMO5Nc/qJtFPmX/1pp/jvvI/ZHr2cYd2ggi+cz/TCp8sCqHr31Sty9HtzUlKAW1dB6K0Oey3vvTORTg3hwTbwYHtPfNzSLhbwCYMaD/D+t8cY+tocm+3y+pLPaS2/3LW03OTsr2Yw9GgB97SAWxu74kLWxYKewWyOC+Wtz3ay+g9wsUDkC08zcvggyDgHGYlmV2BGIgf//J0ZH08mLAj+HQEBPuYuDFyxVOth3ucqtCu4uFrr4IhWeoWbfJS1cGP9IHcEOgOZwEyI/saxweHy/5AWC1QPguVfT6FBNTdI3Gu27pzbR3biX7hYLv6nSsuAub9A8C3j6Nl3hMPqKCKONWrkC/yzYTIVfc0/bjrf0pYuHduZYzCyUyErldMnjrLhlzV4uUN5HwjyhSA/8L9Kz1HMn3D/f+Dr7wrxu2zTQPjzYzNgdP/NnOaigC4PYCMihzNu1EDr7zHO7ePs4c2cOhBNzWBwveRynHTXinjU6QM1HjRbRCwW8zUUslXk0ufX0tqR5/5eHwFHl8ORJXB0GWQkFHn/fx2DT9dCy/umcF/fIUXeT2Ep3OSjrIUbuPBBfnsiPAjUA79sP/6M/JOQciGOP24B/kPGRK+nT6/2tKoFz99pDrCzqtoDGgw3+9Mv/EIQkRLOyOavH1/Fa88bVK1wLTuygEcgeAZxIjGbmO1/ceoc7DwMU36AjKxCtkJnJMH3Tc3LqQtxK4KYmBi639aGQbfDTbWhbijUDjG7kq7keAL8dxPMj4YnX5rNI337F6yO+dTBnq0d+e4vO4OVX4zm9xXj6dnSfK0AqemAux9efsHmpeVufuDuz8nEdBZ+u5KEFFjxG6zeZd6EvLhb3xVu8lEWww2YH+Ttu7cz7sQ4DiYf5JYat/Bj3x9xd3V3+HEL+5dJu3CYPrQujQL+BC58/IJamyPpqv2LmM1bCtw3rwHKIsXs+C+w7QU4vQWA/cfh5z2QmmE+utzenUZNWpoz67p4sf9wHG+8NYm0DEhIgVPnzAGzXy78gZZtuth0Zdile+b4z/BjR8CAW5ZAtR75l09PYMfXA6iVtjhXa1KW4Y5r+YtjB/8+6cajz41jXzzEnb2GOpYAl77f/t7m0IGMrCu/Fnu3LhVFob6/jetMQkKCARgJCQnOropDxJ6INfze8jN4FeO5Zc85uzo2oqOjjTlz5hjR0dHmgoTdhhHztGF86WkYX2AYX2CcnFneePZ2jJrB5mP8qCcNI3GfYSTsMYyzsYZxdpdhnPndeHf0o0aTMIzK5TEAIzIy0rkvTsSJcv3fcoRzfxvGT/dZ/69mzvMx/u9uDE938/9gziOvOkRGRtqUiYqKyvMQBS13VduGm/VcGGIYKcfzLpORZBi/jzOMBYHW1/TrWxjP3o7RpRFGWEWM6I0bHFfHEqCwr6VYPmf5KMz3t1puyqBvd39Lr/m9APis12f0a9rPuRW6mtTjsPcDMna9j3t24e6TlZ0NCzfDuCUw7avS99dTURRr87WUeCOiIln59dukZcIf/9jnSkSbz0SLBvDHW7D7PfMKH4sL3PAE3Pg6Ua++W+C/5gtztdQ1fx6zUmF5K0j4A6rfC+0XXOz2zkqFfR/BrnHm7x4A/wZ8/ls4/UZ9S843oj1eS2lQml6LuqXycT2EG4Axa8bw+k+v4+XmxS+P/kLLKi2dXaWr+nLup/w850mGdDVnSM42zH5dTy9vPDw8zV9OFhdSU9NJSDyHYUBo+YvbHzUaU+W2/0BIxyKP3ynp/9HzvOrkGr7I7L2/sqhEfiYMA85s52j0O2T++QVhFc3FcWdg5e9w871juOHmgeAdWuhd53wmXCzwWCd4t78Pfu7nzZWht0GLd6H8jdbyJfL9ATi9DX6IACOTP0NeJSYujHaVdlMr+QtIOWKW8a0NN74GNR4q1GXR4hwKN/m4XsJNtpFNz6968t3e76juX52tT20luFyws6uVr4L2uV9arlE1iOoBD7UFN9cLBYLaQKOXoOpd5l+ZBRQVFcW770wk88KFXfl90Tvjl+CmjT/x9tCO3NMSElNgTxzsPgoTPlxM85t75HqtV6xjRhIk7uavX//Hgk9fp2FVM0h+9rN5qf76DaW3Bcze5yUqKpIFs94mOQ2OJzqgVeQqdcxVNnGvOVndwS8hcY+13LkU81LecpffQLr8jeYluqG3Q0gHYrbuzH3s7CxIOw7nj7B3xxomj4ukaiDc3Rya1jCLpLiH4d1uKlS5q3QN/N/5BuwczdlkOJ18ceAsPtWg8WioPQBcHDsuUeynVIWbDz/8kLfffpu4uDgaNWrE5MmT6dChQ55l165dS+fOnXMtj42NpX79+gU63vUSbgDOpp6l9Set2Xd6H51qdmLFIyscPsD4WhV00Nrl5caPepqoe1zgr5nmBFkAAY2h4QhijtVk776/bX+hZ56HxN1ms3XCH5w58AtnDqynZkXIzDa/yI4lwA0N21A+tB54hYBXJfAK4dPP/8cHMxZw6CScSXZwCDIMOLUZ9s8mbe8cPC3JeZdz9TZnZ/WrB/71mbd0K5Omf4enm3n35P69WnFL04qQsAvOH7ri4Q6dhFMV7qP5fVPN11xCFHkulXyCSJ77zM6Cszvg+M+c3vMtGUfXUOnC1cTbD8KKndDjyf/Q4JYnzAGzhVSYOuaUrVYBHmgDQ/9ViWo+xy4WcPHktHc7nhy7hmXbzYDaLhy63ghD/l0f37Q9WAfsAxnZrqzblcXuo1C5PNzUqDJhwa7mxHdG1uWHB+B0Erz2DbTuM5M+fR8t9Ot1tpjo9VhWtqf1Debz+LMw9lvo++o6Wre9xal1k8IrNeFm/vz59O3blw8//JCbb76Zjz/+mE8//ZRdu3YRFhaWq3xOuNmzZ4/NCwsODsbV1TVX+bxcT+EGYNeJXUR8GkFSehJDI4by3p3vObtKV3VNffMp8bBnMuz90JyACvj7OHy8CsqXg3s61qFRVcO8VJRr/+ifTjInJ7yh2R1UrNXOelUFfnWJeuWtAn+R5Xot54/Cgc/h79mQGGstd/gUzNtgfpHVrwz1q0C9qpdNzlUQXpVIoBpzl2xl1xGoVgGe7AzBOf8lXDwg7N9QdxBUbFPov9btMr7CMCA1nmlvD2fjynlUCQQ/L+h0cwtujmhyYaKxc5B5jvOJxzl+5C/8vMDdDY6egUOnoFm7HoTUbGnOe1Iu7MK/1Yl6aQwTJ07E0x1a3wAvPdGBO1v4wIkN1s9NjrQM887TNly9zfusVe4Kle8A//rEbNqU+7VkpkDSX3BuH4f+WMUPC6dSp5L5foM5NX1YjVr4lPM174104XEuKYXtO3bi7QGtal/yluCKpXJXqPmQeZNEd/8r/0GQehKOrYK4FaQd/A7PrONXPmEWF/CqRFJ2eX5cH8uRM+b/m9k/mZ/x0ng1EJiTnL46vB9vPwwb98HUleZVQfa615EUr1ITbiIiImjRogXTpk2zLmvQoAG9evVi3Ljct3vPCTdnzpyhfPnyRTrm9RZuAL6J/YZ7v74XgDE3juGG5BvKfp9y+lkOrx6B14GPL35hX84zCAIaQUAjDpwpx4Chk9h9FNxdoVIAhPjDf94exQ1VfSHlGKQdJ27/do4f+oPQ8lj/or+S4wmw75h5qeyJc+bdi594Nopa9W8yj+1ZETwrMvLVSYyf+A6e7tCzJbzWvzb1Aw6AkW3uyNULqvWG2gMYMXkFEyZOsh4jKiqK8W+9AUn7zW6KxN3s27aUY/vWUa8y1kGmu45Amzsepe0dj5tTsHtWsG6f88Xo4QafvXE3DzY/AacuuYtvYAsIH8Sm4zew589DV/3svDziRT6Z9i4uFjOfDB48iFdeGY15A7QLIcli4dXXXuP99/9DpQCzu+DpPl3oeeuNZhhI+tt8ZKXk/yYX0bEEs3UuPDSP4OLuDxVv5nBaTR56fhpb9puXyt7W2GwVebhzEB5Zp2w2OZvux3/Xn2NfvPlaurQOo06lbDj/j13quy4WvtwIt/afyv19n821/mphcu7cOYyN6s/tN0KVQDP8HTkNTz43hm73PgFeodZbG5SES37txd6z/4pzlYpwk56ejo+PDwsWLOBf//qXdfnzzz/P9u3bWbduXa5tcsJNzZo1SU1NpWHDhowaNSrPrqocaWlppKVdnBo7MTGR6tWrX1fhBmDU6lGM/XksZAAzgbiyP3h07ty5PP1EP57obH4pHT5lftH37DuC2+8bana7XNIiUZBf6pf+svT2MG87UTcU3nv9WcLKp16czTQ1vlB1PZtsznjqd+k8G8E3Q60BZgvKJTOtXu2LrLC/0PPc3+mtsHeqObYjK9VclARfbTTvIdY+ojGtmtQ1ZznNSIB089/stLO4kFGo156fbMPCwRMGfx+Hf06bY43OpUK3Hv+m+U23mEHE3Y/YP/9hwJPPcS4VMrPML/CwIBj78lNUDzTMrrjkQ5B8ELLO2xwj/qw5T0uNmx6h9V0vQsCN1rlX8vxMjBsHCb9D3A8Qt4LsY+twMdKv/CLcA8CvLifTKzB1zgr2xZutStnZ5jn/6MMPaFA/3OwauvDYuyeWl14yZ++O/tMMImCfKfkvVajPRClVlsLa9a5UzHNz5MgRAzDWr19vs3zs2LFGeHh4ntvs3r3bmD59urF161Zjw4YNxjPPPGNYLBZj3bp1VzzOmDFjbK7jz3mU1XlurmT9xvUGD2PwKgYvYlDlynNSlBXR0dF5nvv8XnNB5nEoyNwQmzasMprVwPh3BMaL3THeegDjkycwTi26xTBW3GwY/6tnGP8NMrI/t1jn2DC+wDg0BeONf2MsmjPhml673ebiSD1pHPx+sPHXe9jUsyCPrLnm42rlEj/F2P4WxsKhGL/P7WYYez80jCPLDSNxnxGz8ecCn8MCvebsbGPz+h+MpjUw7myKUafS1T8XV/tMfDHnE6PrjRjv9MH4/FmM1+7F6NseY9mcVwwj5YRhZGcX6bzYez6VsjQ/S2E5e34WsY/CzHPj9HCzYYPtJElvvvmmUa9evQLv5+677zZ69OhxxfWpqalGQkKC9XH48OHrMtzMmTPHwAuDZy4EnJcxaIwxZ84cZ1fNoRz1C91eISh643ojyBejXmWMJmEYLhb7hU57/UKfM2eO4WLBuKs5xnuPmEEtqgdG9Nz+hrH/C8P45zvDOPazYZz5zdj282LD3xvDYskjjGRnX3hkGdEb1xuuLhhurvabAK4wr9men4vChujCnBd7fynrS15Ks1IRbtLS0gxXV1fjm2++sVn+3HPPGbfcckuB9/Pmm28a9evXL3D5sj5D8ZVYfwF7crEF51WM/nP6G1nZWc6unkM58xe6vUKQMxX2y9sRs9E64hzac58l/RyKlAWlZobiiIgIWrZsyYcffmhd1rBhQ3r27JnngOK83HfffZw+fZrVq1cXqPz1OKA4h7Xv2QLcBtxsLu9Zrydz/zUXP08/Z1bvulbSxzgUdtxCsc5GW0KUpdciUhKVigHFcPFS8I8++oi2bdsyffp0PvnkE/744w9q1KjByJEjOXLkCHPmzAFg8uTJ1KxZk0aNGpGens7nn3/O+PHjWbhwIb179y7QMa/ncAO2v4D3eO3hyf89SXpWOjeG3Mi3D35LrcBazq6ilFD68hYRZyrM93c+N3R3vAceeIBTp07x+uuvExcXR+PGjVm2bBk1apjTYsbFxXHo0MUJx9LT0xk+fDhHjhzB29ubRo0asXTpUrp37+6sl1DqREREWL+YIoggPCicXl/1YufxnbT+tDUL71/ILTU0uZXkdulnR0SkJHP6DMXF7XpvucnLP4n/0POrnmyL24abixsfdv+QJplN9Fe6iIiUGKWmW8oZFG7ydj7jPI9++yhf//G1uSAG+AHILvvz4YiISMlXmO/vgt9VUMo0H3cfvrr3K56q+5S5IAJ4BCgHEydOJCYmJr/NRURESgyFG7GyWCy0z2oP84F0oDbwLBAOe/fudW7lRERECkjhRmyEh4dDLPApcAwoBzwMizIWkZx+hTtSi4iIlCAKN2IjIiKCyMhIOA58Amwwly86vIjmHzdn85HNzqyeiIjIVWlAseTp0jlNkoKT6L+4P0fOHcHNxY0xHccwov0I3FycOpOAiIhcR3S1VD4UbormdMppnln6jPVqqpur38zcf83VpH8iIlIsFG7yoXBTdIZh8PlvnzNo2SDOpZ/Dz8OPofWGUiepDvXq1dN8OCIi4jAKN/lQuLl2B84eoO+ivvxy6BdzwR/ADxA5UPPhiIiIY2ieG3GomuVrMqHBBPgRyAIaAc/BxD8msnjtYudWTkRErnsKN1Ikf+37C37BvGT8AOZdym6C+9bdxxNLnuCv0385tX4iInL9UriRIgkPDzd/iANmA7OAvyCLLGb8OoN6H9Sj36J+7Dm5x3mVFBGR65LCjRSJdT6cHAchqkoUGx7bQLc63cgyspj721waTG3AQwsf4vfjvxMTE8PcuXN1KwcREXEoDSiWa3LpfDiXXi215egW3vzpTb7d8+3FwruAHcAhiByiwcciIlJwuloqHwo3xWtH/A6GLR7G6vjVYLlkxQno2bwnvVv1pn1Ye2qVr4XFYha4UmASEZHrl8JNPhRuit/cuXPp92I/aA3UBIJzl6nsW5n2Ye05vf00q2atMu9rlQ2RkWrhERERhZt8KdwUv5iYGNq0aXNxgQ9QHfq81Ie/M/5my9EtZGRn2G6UCGwGtkL06mi14IiIXOc0z42UKLkGH5+HqF5RfN7vczY8voGEEQmsG7CO+wLvg71AKuAPdAFegJHrRxJ7ItY5lRcRkVJHLTdSbK42lsbawuOKOTFgW6DyxfV33HAHL7R5ga43dMVisWhsjojIdUTdUvlQuCnZoqKimDhxovX5wyMf5nzT83y7+1sMzI9qg4oNqH6kOiveXgGZZjmNzRERKdsUbvKhcFPy5dUi8/eZv5kSM4UZv84gKT3JLHge2A78A8TBxmUbbcf25LM/EREpXRRu8qFwU7olpCbw7CfPMu+veRBou87b4k3rsNY0D21O88rNaR7anDnvzmHSxEnWMmrhEREpnRRu8qFwU/rFxMTQpl0bqAfUwRyXE4J5f6vLZQDHgaPAX+Yj+mddfSUiUtroaikp0yIiIogcHgmxwP+A6TA8Yzg7Bu5gds/ZPNf6OTqEdcDL4gXuQFXgJuBBIBKe+ekZZmybwbGkY858GSIi4iBquZFS62pjaTZGb6TdXe3Mlp3qmC09l3RlWbDQplobetbryT317qF+xfps2rRJ43NEREogdUvlQ+Hm+nL51VePjniUWnfWYsneJWw5usWmbGB2IGc2njG7sE7B8/2eZ/KEyXnuV4OURUSKl8JNPhRurj9XCiL/JP7Dd3u/49s937Lqr1VkGBm5tg3yDKJhpYbUrVCXukF1qVOhDkvnLGX2u7PN8TxokLKISHFQuMmHwo3kZfpn03l64tPmAOVgoAJQ7iobncW8B1Y8vPX8W/y7w7+pHVgbF8vFoWxq4RERsQ+Fm3wo3Ehect3/CsALZn47E8/Knuw7tY99p/exce9G/j77N3jnvR9fD19uDLmRZqHN+GvDX6yYs8K8Wis9/xYehSARkfwp3ORD4Uau5PLxOVFRUYwfP96mjDUEeWNefh4KVIL6neqzP3k/aVlpee88GTgLt7a4lRa1W1CzfE1qBdaiZvmafDLpEyZPnGwtqm4uEZHcFG7yoXAj+SlIC8qVQlBmdiZ7Tu5hx7EdzFs1j6Vbl0IlwK8AB04GzmC28hyDD8d8yIOdHiTQOzBXUbXyiMj1SOEmHwo3Yg8FvgkogCdQ3ny88NoLGAEGBxIOcODsAfYe38v57PNXPE51/+o0DW1K00pNaVKpCSvmrmDGxBlcuM2WurpE5LqhcJMPhRspLgXu5urUBgKAIMyWnkpQuXll4lLi8t5xBpAAJJmP+++6n2Y3NCPUN9T6+HTyp3z49ocKQSJSZijc5EPhRorTtXRzJaQm8Nux39hxbAe/HfuNVX+s4u+kv81ZlwsiG0i9+GjVuBVhlcIo71me8l7mY92Kdaxausq8CWkCPD9Ac/uISMmkcJMPhRspiQoSHGJiYmjTto05y7If4Gs++j3bD5cAF+KT4olPimf/if0kZCQU+eYqfm5+1AqqRVhAGDUCahAWEMYvS3/hf5//z2wxSobIF/Mf9KwgJCL2pnCTD4UbKc0K3NXVto05T4834GU+XpvwGhWrVeRs6lnOpp5l887NrI1ea44J8sXsGrvCJe65pECNijWoWbEmlXwrEeITQiXfSlQqV4nvF37PormLrN1mkcPUJSYi107hJh8KN1LaXUtX1+X7yTW3jwfMWzqP8jXKcyjhEIcSDrF2+1o2/LHBDD/+FL5FKAVqVqxJreBa1jFBlX0rs27pOpbOX2p2iaXA808/z3vj38NisRTpNRemnIiUPgo3+VC4keuFQ0KQBbN1pxxMnT2VCmEVOJZ0jGPJxziefJyte7ayfd92s9XIF3ArXJ1dLa4EegcS6BVIBe8KBHoHcnD3QWK3xUIKkArdOnXj6X5PW9dX8K5AoFcgr4561ea1XG2+IAUmkdJF4SYfCjcituwVgnL2ZdMa5AX4wtTPphJYPZC4pDjik+LZ8NsG1u9Yb44d8r7wKGQQyiUDc/B0ysV/7+x8J3Wq1iHQO5DyXuUJ9DL/nT9nPvNnz4c0IB1eGPQC74x/J1er0eWvW1ediTiPwk0+FG5EiqagX95F7hJzgyU/LqFWg1qcSTnD6ZTTLF29lE/mfnIxAHmZ/97Q+AZcyrlwJvUMZ1LOkGVkXfPrc8EFX09ffD188fPww5JhYfdvu80AlAGkm4/+D/enbo26lPMoh6+HL+Xcy7HgywUs+mqRWfY8DH1qKO+Of/eautgKU1atUHI9ULjJh8KNiOM5dFwQEB0dbd2vYRis2bCGLnd3sYafnEHUQyKH4B/iz5mUM5xNO8uZlDPsO7yPP//50yznceHhAB4uHgSXC6aiT0Xrv3t/3cu2X7aZrUsZcPedd/NY38fwdvfG283b5t93J77LtCnTIAswYNgLw5gwbgKuFleb0FTQ1iVHtUIpgElxUbjJh8KNSMlhzy6xInedWQB3+G7Fd4Q3Dudc+jmS0pPY8tsWXhz5onk1mTvWIHT/I/fjV8GP5IxkktKT+OvQX8T+GWuu9wJ8uPYutquwYMHVxRUXXEhPTTcna8zCbGHKhDo16xDkH2QNSynnUli7cu3FFqgLcx+NjhxNq0atzC67C2OdJo2dVOB7ndk7WJWWAFZQzgxqZTEkKtzkQ+FGpPSx95eTPQNTnq1L7rBoxSKqh1fn5PmTnDx/kmU/LWPeonlm+PE2y+AGdRrUwTfQl5SMFFIyU0jJSOFcyjlSs1KLPFfRNcvC7GLLNh8hISF4e3rjYnExQ5XFDFUH9h8wy+Q0JFmgZq2aeHl7YRgG2UY2Kakp/PPPP+b6nBCWCY0bNKZC+Qp4uHrg6epJUmIS61atM9df8nj4wYcJqxqGh6sH7q7ueLh6sHzZctb8uAYygVR44J4HiHwukkCvQAK9A/H39MfFYr55zgpWhdlfQffpiGOXphCkcJMPhRsRAcd+mRS1iy1XWRcuPizww8ofaN6iOVlGFlnZWWzZtoVe/+plrnfFGpje//B9atxQwxqWdv25i0nvTbrYAnWh2y6iYwTZntmcST3D2dSznD5/mmyyr/7mlXAuFhcCPAPwsfhw5K8jZkuVBfM9coMbwm/A1dOVtMw00rLSOJ92nsTkRHN9JtZB6S0btqRmpZrWq/cqeFdg9bLVrPh2hVnOA+7scSfdenbjfMZ5ktOTSc5IZv8/+1m8bLH5frtc2N95eHbAs7So14LgcsEE+1zssnxrzFu8PfFta/0LE8BGvzGa+KR462D96N+jeefjdy5erXhhcP2Qp4bQrF4zm4H1H73/EdPfn24GWaPkX2FYqsLNhx9+yNtvv01cXByNGjVi8uTJdOjQ4Yrl161bx7Bhw/jjjz+oUqUKkZGRDBw4sMDHU7gREUewZxdbYcras1x0dDRtO7Y1w48n1lA1+7PZNGzUkCwji2wjm6zsLH7f9TvPDnr2YqvNhW+SqR9MpVHDRlgsFixY2BW7i4FPXfgdnRMwXGHiOxOpeUNN0rPSSc9KZ/efu5n4zkTr+pzA1qd/H4JDgknPSicjO4PYvbH8suEXa1DJGWcVWDmQVEsqKZkpeb6fJVoW5nxPmZgtYVlQ54Y6BPgG4O7qjpuLGylJKWzdtNVcnzPxpu+Fn6+VgRlw0qB21dpUKl+JAK8A/D39CfA0/41ZF8Mvq34x65gJPbr34LF+j+Hl5oWnq6f5r5snU9+fyszpM81y564emAqj1ISb+fPn07dvXz788ENuvvlmPv74Yz799FN27dpFWFhYrvL79++ncePGPPnkkzz99NOsX7+eZ599li+//JJ77723QMdUuBERZyrpg3VLegC7WgtYamYqZ1LOcCb1DL9s/YWnn3/aDEAXQgOZ8P5779OscTM8XT3xdPNk9x+7eejfD5nr3bAOSn/97depULUCZ1LNq/c2/76ZX7b8cnHqgnQgA1o3b02juo0o516Och7lOH3sNJ9M/cQc45SNdW6o7vd1h3JwIvkEJ86f4OT5kySlJ+X53haGt5s3lf0qU9m3Mh7pHqz53xpzhvBMzPDjDXf0vANXX1dzcH3qWY6eOUpCakLB71VXWEnAJPPHvFoni6JQ39+GE7Vu3doYOHCgzbL69esbI0aMyLN8ZGSkUb9+fZtlTz/9tNGmTZsCHzMhIcEAjISEhMJXWETkOhAdHW3MmTPHiI6OtltZe5aLjIw0MNsbDMCIioq65rIFKRcdHW1TJueRV10Lety169ca+GMQikFVDMIwqInx/nfvG0v3LjUWxy42FvyxwHj9m9cNmmDQHINGF8pVwFj1yyojOzu76K/FDQNfDIIwqILxn6X/Mb7Z9Y0x69dZxvvR7xuvr33d6PZeN4O7MbgPgwcx6INBf4w64+oYzT9qbjSc2tCo/X5tI/CNQINIDEZi8NzF48+ZM+eK56cwCvP97bRwk5aWZri6uhrffPONzfLnnnvOuOWWW/LcpkOHDsZzzz1ns+ybb74x3NzcjPT09Dy3SU1NNRISEqyPw4cPK9yIiJRyzgpghQlWBT2uPQOYI15LQUNdYcJfUZSKcHPkyBEDMNavX2+zfOzYsUZ4eHie29StW9cYO3aszbL169cbgHH06NE8txkzZkyeb7bCjYiIFEVhgpW992nvYzszgBVWYcKN08bcHD16lKpVq7Jhwwbatm1rXT527Fjmzp3L7t27c20THh7Oo48+ysiRI63L1q9fT/v27YmLiyM0NDTXNmlpaaSlpVmfJyYmUr16dY25ERERKYTSdLWUg6eaurKKFSvi6upKfHy8zfLjx49TqVKlPLcJDQ3Ns7ybmxtBQUF5buPp6Ymnpz2Gk4uIiFy/IiIiChRWClrOkZw1RRQeHh60bNmSlStX2ixfuXIl7dq1y3Obtm3b5iq/YsUKWrVqhbu7o4Z8i4iISGnitHADMGzYMD799FNmzpxJbGwsL7zwAocOHbLOWzNy5Ej69etnLT9w4EAOHjzIsGHDiI2NZebMmcyYMYPhw4c76yWIiIhICeO0bimABx54gFOnTvH6668TFxdH48aNWbZsGTVq1AAgLi6OQ4cOWcvXqlWLZcuW8cILLzB16lSqVKnClClTCjzHjYiIiJR9Tp+huLhpEj8REZHSpzDf307tlhIRERGxN4UbERERKVMUbkRERKRMUbgRERGRMkXhRkRERMoUhRsREREpUxRuREREpExx6iR+zpAzrU9iYqKTayIiIiIFlfO9XZDp+a67cHPu3DkAqlev7uSaiIiISGGdO3eOgICAfMtcdzMUZ2dnc/ToUfz8/LBYLAXaJjExkerVq3P48GHNalyC6LyUXDo3JZPOS8mlc3N1hmFw7tw5qlSpgotL/qNqrruWGxcXF6pVq1akbf39/fWhK4F0XkounZuSSeel5NK5yd/VWmxyaECxiIiIlCkKNyIiIlKmKNwUgKenJ2PGjMHT09PZVZFL6LyUXDo3JZPOS8mlc2Nf192AYhERESnb1HIjIiIiZYrCjYiIiJQpCjciIiJSpijciIiISJmicHMVH374IbVq1cLLy4uWLVvy888/O7tK152ffvqJHj16UKVKFSwWC4sXL7ZZbxgGr776KlWqVMHb25tOnTrxxx9/OKey15Fx48Zx00034efnR0hICL169WLPnj02ZXRuit+0adNo0qSJdTK4tm3b8v3331vX65yUDOPGjcNisTB06FDrMp0b+1G4ycf8+fMZOnQoL7/8Mr/++isdOnSgW7duHDp0yNlVu64kJyfTtGlTPvjggzzXT5w4kXfffZcPPviAzZs3Exoayu233269j5g4xrp16xg0aBDR0dGsXLmSzMxMunbtSnJysrWMzk3xq1atGuPHj2fLli1s2bKFW2+9lZ49e1q/JHVOnG/z5s1Mnz6dJk2a2CzXubEjQ66odevWxsCBA22W1a9f3xgxYoSTaiSAsWjRIuvz7OxsIzQ01Bg/frx1WWpqqhEQEGB89NFHTqjh9ev48eMGYKxbt84wDJ2bkiQwMND49NNPdU5KgHPnzhl169Y1Vq5caXTs2NF4/vnnDcPQ/xd7U8vNFaSnp7N161a6du1qs7xr165s2LDBSbWSy+3fv5/4+Hib8+Tp6UnHjh11nopZQkICABUqVAB0bkqCrKwsvvrqK5KTk2nbtq3OSQkwaNAg7rrrLm677Tab5To39nXd3TizoE6ePElWVhaVKlWyWV6pUiXi4+OdVCu5XM65yOs8HTx40BlVui4ZhsGwYcNo3749jRs3BnRunGnnzp20bduW1NRUfH19WbRoEQ0bNrR+SeqcOMdXX33F1q1b2bJlS651+v9iXwo3V2GxWGyeG4aRa5k4n86Tcw0ePJjffvuNX375Jdc6nZviV69ePbZv387Zs2dZuHAh/fv3Z926ddb1OifF7/Dhwzz//POsWLECLy+vK5bTubEPdUtdQcWKFXF1dc3VSnP8+PFcyVqcJzQ0FEDnyYmGDBnCkiVLWLNmDdWqVbMu17lxHg8PD+rUqUOrVq0YN24cTZs25f3339c5caKtW7dy/PhxWrZsiZubG25ubqxbt44pU6bg5uZmff91buxD4eYKPDw8aNmyJStXrrRZvnLlStq1a+ekWsnlatWqRWhoqM15Sk9PZ926dTpPDmYYBoMHD+abb75h9erV1KpVy2a9zk3JYRgGaWlpOidO1KVLF3bu3Mn27dutj1atWtGnTx+2b99O7dq1dW7sSN1S+Rg2bBh9+/alVatWtG3blunTp3Po0CEGDhzo7KpdV5KSkvjzzz+tz/fv38/27dupUKECYWFhDB06lLfeeou6detSt25d3nrrLXx8fHj44YedWOuyb9CgQcybN49vv/0WPz8/61+cAQEBeHt7W+fw0LkpXi+99BLdunWjevXqnDt3jq+++oq1a9eyfPlynRMn8vPzs45Hy1GuXDmCgoKsy3Vu7Mh5F2qVDlOnTjVq1KhheHh4GC1atLBe5irFZ82aNQaQ69G/f3/DMMxLKMeMGWOEhoYanp6exi233GLs3LnTuZW+DuR1TgBj1qxZ1jI6N8Xvscces/7OCg4ONrp06WKsWLHCul7npOS49FJww9C5sSeLYRiGk3KViIiIiN1pzI2IiIiUKQo3IiIiUqYo3IiIiEiZonAjIiIiZYrCjYiIiJQpCjciIiJSpijciIiISJmicCMi1yWLxcLixYudXQ0RcQCFGxEpdgMGDMBiseR63Hnnnc6umoiUAbq3lIg4xZ133smsWbNslnl6ejqpNiJSlqjlRkScwtPTk9DQUJtHYGAgYHYZTZs2jW7duuHt7U2tWrVYsGCBzfY7d+7k1ltvxdvbm6CgIJ566imSkpJsysycOZNGjRrh6elJ5cqVGTx4sM36kydP8q9//QsfHx/q1q3LkiVLrOvOnDlDnz59CA4Oxtvbm7p16+YKYyJSMinciEiJ9Morr3DvvfeyY8cOHnnkER566CFiY2MBOH/+PHfeeSeBgYFs3ryZBQsW8OOPP9qEl2nTpjFo0CCeeuopdu7cyZIlS6hTp47NMV577TXuv/9+fvvtN7p3706fPn04ffq09fi7du3i+++/JzY2lmnTplGxYsXiewNEpOicfedOEbn+9O/f33B1dTXKlStn83j99dcNwzDvOD5w4ECbbSIiIoxnnnnGMAzDmD59uhEYGGgkJSVZ1y9dutRwcXEx4uPjDcMwjCpVqhgvv/zyFesAGKNGjbI+T0pKMiwWi/H9998bhmEYPXr0MB599FH7vGARKVYacyMiTtG5c2emTZtms6xChQrWn9u2bWuzrm3btmzfvh2A2NhYmjZtSrly5azrb775ZrKzs9mzZw8Wi4WjR4/SpUuXfOvQpEkT68/lypXDz8+P48ePA/DMM89w7733sm3bNrp27UqvXr1o165dkV6riBQvhRsRcYpy5crl6ia6GovFAoBhGNaf8yrj7e1doP25u7vn2jY7OxuAbt26cfDgQZYuXcqPP/5Ily5dGDRoEJMmTSpUnUWk+GnMjYiUSNHR0bme169fH4CGDRuyfft2kpOTrevXr1+Pi4sL4eHh+Pn5UbNmTVatWnVNdQgODmbAgAF8/vnnTJ48menTp1/T/kSkeKjlRkScIi0tjfj4eJtlbm5u1kG7CxYsoFWrVrRv354vvviCTZs2MWPGDAD69OnDmDFj6N+/P6+++ionTpxgyJAh9O3bl0qVKgHw6quvMnDgQEJCQujWrRvnzp1j/fr1DBkypED1Gz16NC1btqRRo0akpaXx3Xff0aBBAzu+AyLiKAo3IuIUy5cvp3LlyjbL6tWrx+7duwHzSqavvvqKZ599ltDQUL744gsaNmwIgI+PDz/88APPP/88N910Ez4+Ptx77728++671n3179+f1NRU3nvvPYYPH07FihW57777Clw/Dw8PRo4cyYEDB/D29qZDhw589dVXdnjlIuJoFsMwDGdXQkTkUhaLhUWLFtGrVy9nV0VESiGNuREREZEyReFGREREyhSNuRGREke95SJyLdRyIyIiImWKwo2IiIiUKQo3IiIiUqYo3IiIiEiZonAjIiIiZYrCjYiIiJQpCjciIiJSpijciIiISJmicCMiIiJlyv8DsASbzdo4XMIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs, loss, color = 'green')\n",
    "plt.plot(epochs, valid_loss, color = 'orange')\n",
    "\n",
    "plt.scatter(epochs, loss, color = 'black', sizes = [10])\n",
    "plt.scatter(epochs, valid_loss, color = 'black', sizes = [10])\n",
    "\n",
    "plt.title('Training vs. Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.legend(['Training', 'Validation'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ceaf62",
   "metadata": {},
   "source": [
    "### Creating the Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29765d4c",
   "metadata": {},
   "source": [
    "Creating a confusion matrix, which is a table that **contains cases where the network\n",
    "guessed correctly and incorrectly**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3b3dd4",
   "metadata": {},
   "source": [
    "#### Extracting the Predicted Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "0f3513c1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "395/395 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_x = cnn_model.predict(X_test_gray_norm)\n",
    "classes_x = np.argmax(predicted_x, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc226f94",
   "metadata": {},
   "source": [
    "> The .predict method of the Sequential class allows us to extract the predicted values and store them in predicted_x\n",
    "\n",
    "> The predicted_x stores the probability distribution of each image, so we use the .argmax to get the highest proabability the network thinks the label should be\n",
    "\n",
    "> The .argmax method from the numpy class will return the index of the maximum value in the array. The axis class is just specifying the dimensions of the array (ours in a 1D array)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "cd892e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_true = y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d69d2da",
   "metadata": {},
   "source": [
    "> Just creating a copy of the testing data labels and storing them in y_true"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565a8227",
   "metadata": {},
   "source": [
    "#### Building the Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "id": "62b31a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33e3d494",
   "metadata": {},
   "source": [
    "> Importing the confusion_matrix function from sci-kit learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "31b59954",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_true, classes_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "501e628d",
   "metadata": {},
   "source": [
    "> Initiate the confusion matrix with the function confusion_matrix and give the correct labels and the predicted labels as parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "8b99277d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2500x2500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (25, 25))\n",
    "sns.heatmap(cm, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec23a681",
   "metadata": {},
   "source": [
    "> Configure the size of the **figure to be larger** so we can see the individual labels and evaluate our data\n",
    "\n",
    "> Use the .heatmap method from Seaborn to produce a heatmap of the confusion matrix. The heatmap shows the **classes on the y-axis** and the individual **numbers on the x-axis are the number of samples it misclassified**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d390b28",
   "metadata": {},
   "source": [
    "### Plotting the Images and Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "b9405978",
   "metadata": {},
   "outputs": [],
   "source": [
    "length = 5\n",
    "width = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fed1d9f",
   "metadata": {},
   "source": [
    "> Creating a 5 x 5 matrix with 25 images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "b5e61d59",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(length, width, figsize = (15, 15))\n",
    "axes = axes.ravel()\n",
    "\n",
    "for i in np.arange(0, length * width):\n",
    "    axes[i].imshow(X_test[i])\n",
    "    axes[i].set_title('Predictions = {}, True = {}'.format(classes_x[i], y_true[i]))\n",
    "    axes[i].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47bd3609",
   "metadata": {},
   "source": [
    "> We are creating a 5 x 5 figure with 25 subplots (images) inside. The size of the figure is set to be larger and then the .ravel method from numpy is used to flatten the axes.\n",
    "\n",
    "> The .arrange method from numpy is similar to a **range except it returns an array instead of a list**. This means that np.arrange(0, 25) creates an array with integers [0, 1, 2, ... 24]. This means the **for loop will run 25 times**.\n",
    "\n",
    "> The axes array stores an image of index i, from the testing image dataset, at the index i\n",
    "\n",
    "> The title for each subplot is the predicted classes and the true label"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  },
  "vscode": {
   "interpreter": {
    "hash": "c261aea317cc0286b3b3261fbba9abdec21eaa57589985bb7a274bf54d6cc0a7"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}