3927 lines (3926 with data), 2.0 MB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import time\n",
    "from scipy import signal\n",
    "import sklearn.metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sb\n",
    "import keras.backend as K\n",
    "from tensorflow.keras.utils import plot_model\n",
    "import pickle\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "import pydotplus\n",
    "from pydotplus import graphviz\n",
    "from sklearn.metrics import classification_report\n",
    "from tensorflow.keras.layers import GRU, Dense, Dropout, BatchNormalization\n",
    "from tensorflow.keras.models import Sequential\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a list of person numbers and trial numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "person_numlist = list(range(1, 68))\n",
    "trials = list(range(1, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define fall and ADL types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading ADLs (loading 5 times more adl data then fall, according to proportions)\n",
    "# Taking 7 adls and 4 falls\n",
    "# adl_types tells number of windows to extract from each data file\n",
    "\n",
    "fall_types = ['FOL', 'FKL', 'BSC', 'SDL']\n",
    "adl_types = {\n",
    "    'STD':1,\n",
    "    'WAL':1,\n",
    "    'JOG':3,\n",
    "    'JUM':3,\n",
    "    'STU':6,\n",
    "    'STN':6,\n",
    "    'SIT':1\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize empty lists to store training data\n",
    "xtrain = []\n",
    "ytrain = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loop through ADL types for data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/JOGTime taken :-  1.9868197441101074\n",
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/JUMTime taken :-  2.006554126739502\n",
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/SITTime taken :-  0.2990837097167969\n",
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/STDTime taken :-  3.539013624191284\n",
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/STNTime taken :-  1.8554542064666748\n",
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/STUTime taken :-  1.9284477233886719\n",
      "Reading ADL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/WALTime taken :-  3.7840092182159424\n",
      "Time taken ==  15.39938235282898\n"
     ]
    }
   ],
   "source": [
    "total_time = 0\n",
    "\n",
    "for folder in os.listdir('../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/'):\n",
    "  if folder not in adl_types : continue\n",
    "  t1 = time.time()\n",
    "  \n",
    "  visualize = 1\n",
    "  \n",
    "  path = '../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/' + folder\n",
    "  print('Reading ADL data from', path, end='')\n",
    "\n",
    "  for person in person_numlist:\n",
    "    for trial in trials:\n",
    "      try :\n",
    "        # Read data from the file\n",
    "        data = pd.read_csv(path + '/' + folder + '_' + str(person) + '_' + str(trial) + '_' + 'annotated.csv')\n",
    "\n",
    "        # Extract features from the data and create windows\n",
    "        acc_x = np.array(data['acc_x']).reshape((len(data),1))\n",
    "        acc_y = np.array(data['acc_y']).reshape((len(data),1))\n",
    "        acc_z = np.array(data['acc_z']).reshape((len(data),1))\n",
    "        gyro_x = np.array(data['gyro_x']).reshape((len(data),1))\n",
    "        gyro_y = np.array(data['gyro_y']).reshape((len(data),1))\n",
    "        gyro_z = np.array(data['gyro_z']).reshape((len(data),1))\n",
    "\n",
    "        data = np.concatenate([acc_x,acc_y,acc_z,gyro_x,gyro_y,gyro_z],axis = -1)\n",
    "\n",
    "        num_windows = (9+adl_types[folder])/adl_types[folder]\n",
    "        for last_point in range(600,len(acc_x)+1,300):\n",
    "          num_windows -= 1\n",
    "          xtrain.append(data[last_point-600:last_point])\n",
    "          ytrain.append(0)\n",
    "          if num_windows == 0 : break\n",
    "      except : continue\n",
    "\n",
    "  t2 = time.time()\n",
    "  total_time += t2 - t1\n",
    "  print('Time taken :- ' , t2 - t1)\n",
    "  plt.show()\n",
    "print('Time taken == ',total_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain after processing ADL data: (4678, 600, 6)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print the shape of xtrain after processing ADL data\n",
    "print(f\"Shape of xtrain after processing ADL data: {np.array(xtrain).shape}\")\n",
    "\n",
    "# Plotting the first sample in xtrain after processing ADL data\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.plot(xtrain[0])  # Plotting the first sample\n",
    "plt.title('Processed ADL Data')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Sensor Value')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7uB/PPPOMnnnmGb3//vv66quv1KNHDy1evFgvvvhijowHAEBO40wpAAAeYv/++6+WL1+u1q1bq1OnTmlegwcP1tWrV7Vq1SpJt8692bt3r1asWJGmr9urLjp27KhLly5p1qxZGbbp0qWLUlJS9N5776Vpk5ycfNcERZcuXfTPP//o888/T3c+CQkJd52zra2tmjZtqu+++04nT540l1+4cEFfffWV6tevL1dX17v2kVn3M8+8efOmO+8uXbooMjJSP/74Y5q62NhYJScnZ0usd/r333/1wgsvKCYmRm+//bY5IWJrayuTyWRxztjJkyfTPPFQyng+tra2FqvJJOnTTz+16DMnxcTEpCm7/RS729sR8+bNK0lp4q9Zs6Y8PDw0Z84ci62LP/zwgw4fPmx++qCXl5cqV66shQsXWjwRcNOmTdq/f3+mY729wunO+7Vt2zZFRkZmuo/MuHLlSprP5L/3BACARxErpQAAeIitWrVKV69e1fPPP59u/TPPPKPChQtr0aJF6tq1q9544w0tW7ZMnTt3Vt++fVWjRg3FxMRo1apVmjNnjqpWraqgoCAtXLhQw4cP1/bt29WgQQMlJCTo559/1sCBA9W2bVs1bNhQL730kiZPnqw9e/aoadOmypMnj44dO6alS5fqk08+UadOndKN6YUXXtCSJUv08ssva8OGDapXr55SUlL0559/asmSJfrxxx9Vs2bNu8574sSJWr9+verXr6+BAwfKzs5Oc+fOVWJioj788MMHvq+33c88a9SooZCQEE2cOFFlypSRh4eHGjdurDfeeEOrVq1S69at1bt3b9WoUUMJCQnav3+/li1bppMnT6pQoUJZjvGff/7Rl19+KenW6qhDhw5p6dKlOn/+vF5//XW99NJL5ratWrXStGnT1Lx5c/3vf/9TdHS0Zs+erTJlymjfvn0W/daoUUM///yzpk2bJi8vL/n6+qp27dpq3bq1vvjiC7m5ualixYqKjIzUzz//rIIFC2Z5Dvfj3Xff1ebNm9WqVSuVLFlS0dHRCg4OVvHixVW/fn1Jt1anubu7a86cOcqXL5/y5s2r2rVry9fXV1OmTFGfPn3UsGFDde/eXRcuXNAnn3wiHx8fvfbaa+ZxJk2apLZt26pevXrq06ePrly5olmzZqly5coWiaq7ad26tZYvX6727durVatWioqK0pw5c1SxYsVM95EZCxYsUHBwsNq3b6/SpUvr6tWr+vzzz+Xq6qqWLVtm2zgAAOQ6qz33DwAA3FObNm0MR0dHIyEhIcM2vXv3NvLkyWNcunTJMAzDuHz5sjF48GCjWLFihr29vVG8eHGjV69e5nrDMIzr168bb7/9tuHr62vkyZPHKFKkiNGpUyfjxIkTFn1/9tlnRo0aNQwnJycjX758hr+/vzFy5Ejj7Nmz5jYNGzY0GjZsaHHdzZs3jSlTphiVKlUyHBwcjPz58xs1atQwJkyYYMTFxZnbSTIGDRqU7rx27dplNGvWzHBxcTGcnZ2NRo0aGb///rtFm7CwMEOSsWPHDovycePGGZKMixcvWpT36tXLyJs3b5qxMjPP8+fPG61atTLy5ctnSLKY89WrV43Ro0cbZcqUMezt7Y1ChQoZdevWNT7++GPj5s2bhmEYRlRUlCHJ+Oijj9Kdb3pKlixpSDIkGSaTyXB1dTUqVapk9O/f39i2bVu614SGhhply5Y1HBwcjPLlyxthYWHm+3GnP//803j22WcNJycnQ5LRq1cvwzAM48qVK0afPn2MQoUKGS4uLkazZs2MP//80yhZsqS5zd3c7TNN7/P6b2wRERFG27ZtDS8vL8Pe3t7w8vIyunfvbhw9etSir++++86oWLGiYWdnZ0gywsLCzHXffPONUb16dcPBwcEoUKCA0aNHD+PMmTNp4lm8eLFRvnx5w8HBwahcubKxatUqo2PHjkb58uXNbe72uaWmphqTJk0ySpYsaTg4OBjVq1c3Vq9ebfTq1csoWbLkPfvYsGGDIclYunTpXe/Trl27jO7duxslSpQwHBwcDA8PD6N169bGzp07073PAAA8KkyG8Z+1wAAAAMATqlq1aipcuLDWr19v7VAAAHjscaYUAAAAnjhJSUlpzvvauHGj9u7dq4CAAOsEBQDAE4aVUgAAAHjinDx5UoGBgerZs6e8vLz0559/as6cOXJzc9OBAwdy7QwtAACeZBx0DgAAgCdO/vz5VaNGDc2bN08XL15U3rx51apVK33wwQckpAAAyCWslAIAAAAAAECu40wpAAAAAAAA5DqSUgAAAAAAAMh1nCklKTU1VWfPnlW+fPlkMpmsHQ4AAAAAAMAjyzAMXb16VV5eXrKxyXg9FEkpSWfPnpW3t7e1wwAAAAAAAHhsnD59WsWLF8+wnqSUpHz58km6dbNcXV2tHA0AAAAAAMCjKz4+Xt7e3uZ8S0ZISknmLXuurq4kpQAAAAAAALLBvY5I4qBzAAAAAAAA5DqSUgAAAAAAAMh1JKUAAAAAAACQ6zhTCgAAAACAR0hKSoqSkpKsHQaeYHny5JGtre0D90NSCgAAAACAR4BhGDp//rxiY2OtHQogd3d3FSlS5J6Hmd8NSSkAAAAAAB4BtxNSHh4ecnZ2fqBkAJBVhmHo+vXrio6OliQVLVo0y32RlAIAAAAA4CGXkpJiTkgVLFjQ2uHgCefk5CRJio6OloeHR5a38pGUAgAAwP3bMNnaEUiNRls7AgDINbfPkHJ2drZyJMAtt7+LSUlJWU5K8fQ9AAAAAAAeEWzZw8MiO76LJKUAAAAAAACQ60hKAQAAAACAx1ZAQICGDRtm7TCQDs6UAgAAAADgETZ9/dFcG+u158pl6brIyEjVr19fzZs315o1a7I5qsdbeHi4hg0bptjY2Bzpf+3atWrXrp22bt2qp556ylw+depUTZ48WQcOHFCRIkVyZGxWSgEAAAAAgBwVGhqqIUOGaPPmzTp79qy1w7kvKSkpSk1NtXYYDyyjebRs2VJBQUEKCgpSYmKiJOnQoUN65513NHv27BxLSEkkpQAAAAAAQA66du2avvnmG73yyitq1aqVwsPD07T5/vvv9fTTT8vR0VGFChVS+/btzXWJiYl688035e3tLQcHB5UpU0ahoaHm+gMHDqhFixZycXGRp6enXnjhBV26dCnDeBITEzVixAgVK1ZMefPmVe3atbVx40ZzfXh4uNzd3bVq1SpVrFhRDg4OOnXqlK5cuaKgoCDlz59fzs7OatGihY4dO5bmutWrV8vPz0/Ozs7q1KmTrl+/rgULFsjHx0f58+fXq6++qpSUlEzFs3HjRvXp00dxcXEymUwymUwaP378A80jPdOnT9e1a9c0btw4JScnq1evXmrTpo26du2a4X3MDlZNSm3evFlt2rSRl5eXTCaTVq5caVFvGIbGjh2rokWLysnJSYGBgRYfuCTFxMSoR48ecnV1lbu7u/r166dr167l4iwAAAAAAEBGlixZovLly8vPz089e/bU/PnzZRiGuX7NmjVq3769WrZsqd27dysiIkK1atUy1wcFBenrr7/WzJkzdfjwYc2dO1cuLi6SpNjYWDVu3FjVq1fXzp07tW7dOl24cEFdunTJMJ7BgwcrMjJSixcv1r59+9S5c2c1b97cIt9w/fp1TZkyRfPmzdPBgwfl4eGh3r17a+fOnVq1apUiIyNlGIZatmyppKQki+tmzpypxYsXa926ddq4caPat2+vtWvXau3atfriiy80d+5cLVu2LFPx1K1bVzNmzJCrq6vOnTunc+fOacSIEQ80j/Tky5dP8+fP19SpU9WjRw+dPn1aISEhmf2Is8yqZ0olJCSoatWq6tu3rzp06JCm/sMPP9TMmTO1YMEC+fr6asyYMWrWrJkOHTokR0dHSVKPHj107tw5rV+/XklJSerTp48GDBigr776KrenAwAAAAAA/iM0NFQ9e/aUJDVv3lxxcXHatGmTAgICJEnvv/++unXrpgkTJpivqVq1qiTp6NGjWrJkidavX6/AwEBJUqlSpcztZs2aperVq2vSpEnmsvnz58vb21tHjx5VuXKWZ2CdOnVKYWFhOnXqlLy8vCRJI0aM0Lp16xQWFmbuJykpScHBweY4jh07plWrVmnLli2qW7euJGnRokXy9vbWypUr1blzZ/N1ISEhKl26tCSpU6dO+uKLL3ThwgW5uLioYsWKatSokTZs2KCuXbtmKh43NzeZTCaLbXRZncfdNG7cWJ06ddLixYv1zTffqGDBgve85kFZNSnVokULtWjRIt06wzA0Y8YMvfPOO2rbtq0kaeHChfL09NTKlSvVrVs3HT58WOvWrdOOHTtUs2ZNSdKnn36qli1b6uOPPzZ/MAAAAAAAIPcdOXJE27dv14oVKyRJdnZ26tq1q0JDQ81JqT179qh///7pXr9nzx7Z2tqqYcOG6dbv3btXGzZsMK+cutOJEyfSJKX279+vlJSUNOWJiYkWSRh7e3tVqVLF/P7w4cOys7NT7dq1zWUFCxaUn5+fDh8+bC5zdnY2J6QkydPTUz4+PhbxeXp6Kjo6+r7i+a+szuNu/vnnH61bt07Ozs769ddf77raLLs8tE/fi4qK0vnz582ZUElyc3NT7dq1FRkZqW7duikyMlLu7u7mhJQkBQYGysbGRtu2bbPYgwoAAAAAAHJXaGiokpOTLRaNGIYhBwcHzZo1S25ubnJycsrw+rvVSbfOq2rTpo2mTJmSpq5o0aLptre1tdUff/whW1tbi7o7E0dOTk4ymUx3HTs9efLksXhvMpnSLbt94Hhm48mNefTv3181atTQ22+/reeee06dOnXKMBmYXR7apNT58+cl3cog3snT09Ncd/78+TT7Ie3s7FSgQAFzm/QkJiaaT5SXpPj4+OwKGwAAAAAASEpOTtbChQs1depUNW3a1KKuXbt2+vrrr/Xyyy+rSpUqioiIUJ8+fdL04e/vr9TUVG3atMli0cptTz31lL799lv5+PjIzu7eKY7q1asrJSVF0dHRatCgQabnUqFCBSUnJ2vbtm3m7XuXL1/WkSNHVLFixUz3k5V47O3tLQ5Gf5B5ZGTevHn67bfftH//fpUsWVKvvPKK+vbtq3379ilv3rwP3H9Gnsin702ePFlubm7ml7e3t7VDAgAAAADgsbJ69WpduXJF/fr1U+XKlS1eHTt2ND9Bb9y4cfr66681btw4HT58WPv37zevfPLx8VGvXr3Ut29frVy5UlFRUdq4caOWLFkiSRo0aJBiYmLUvXt37dixQydOnNCPP/6oPn36pEnkSFK5cuXUo0cPBQUFafny5YqKitL27ds1efJkrVmzJsO5lC1bVm3btlX//v3122+/ae/everZs6eKFStmPnIoKzITj4+Pj65du6aIiAhdunRJ169fz/I80vP3339r+PDh+vjjj1WyZElJ0pQpU2QymTRq1Kgszy0zHtqk1O0DvC5cuGBRfuHCBXNdkSJFzPswb0tOTlZMTIzFAWD/NXr0aMXFxZlfp0+fzuboAQAAAAB4soWGhiowMFBubm5p6jp27KidO3dq3759CggI0NKlS7Vq1SpVq1ZNjRs31vbt281tQ0JC1KlTJw0cOFDly5dX//79lZCQIEny8vLSli1blJKSoqZNm8rf31/Dhg2Tu7u7bGzST3mEhYUpKChIr7/+uvz8/NSuXTvt2LFDJUqUuOt8wsLCVKNGDbVu3Vp16tSRYRhau3Ztmu159+te8dStW1cvv/yyunbtqsKFC+vDDz98oHncyTAM9evXT3Xq1NGAAQPM5c7OzgoPD1dISIg2bdr0QPO7G5Nx53MYrchkMmnFihVq166dpFs3xsvLSyNGjNDrr78u6dY2Ow8PD4WHh5sPOq9YsaJ27typGjVqSJJ++uknNW/eXGfOnMn0Qefx8fFyc3NTXFycXF1dc2R+AAAAj5UNk60dgdRotLUjAIBcc+PGDUVFRcnX19f8NHrAmu72ncxsnsWqZ0pdu3ZNx48fN7+PiorSnj17VKBAAZUoUULDhg3TxIkTVbZsWfn6+mrMmDHy8vIyJ64qVKig5s2bq3///pozZ46SkpI0ePBgdevWjSfvAQAAAAAAPMSsmpTauXOnGjVqZH4/fPhwSVKvXr0UHh6ukSNHKiEhQQMGDFBsbKzq16+vdevWWWTgFi1apMGDB6tJkyaysbFRx44dNXPmzFyfCwAAAAAAADLvodm+Z01s3wMAALhPbN8DgFzF9j08bLJj+95De9A5AAAAAAAAHl8kpQAAAAAAAJDrSEoBAAAAAAAg15GUAgAAAAAAQK4jKQUAAAAAAIBcR1IKAAAAAAAAuY6kFAAAAAAAeGwFBARo2LBh1g4D6bCzdgAAAAC4DxsmWzsCAMDDJjf/v6HR6CxdFhkZqfr166t58+Zas2ZNNgf1eAsPD9ewYcMUGxub7X1v2rRJgYGB2rBhg+rXr28uT0hIkL+/vzp06KCPP/4428e9jZVSAAAAAAAgR4WGhmrIkCHavHmzzp49a+1w7ktKSopSU1OtHcYDS28eDRs21JAhQ9S7d28lJCSYy0eOHCknJydNnDgxR2MiKQUAAAAAAHLMtWvX9M033+iVV15Rq1atFB4enqbN999/r6efflqOjo4qVKiQ2rdvb65LTEzUm2++KW9vbzk4OKhMmTIKDQ011x84cEAtWrSQi4uLPD099cILL+jSpUsZxpOYmKgRI0aoWLFiyps3r2rXrq2NGzea68PDw+Xu7q5Vq1apYsWKcnBw0KlTp3TlyhUFBQUpf/78cnZ2VosWLXTs2LE0161evVp+fn5ydnZWp06ddP36dS1YsEA+Pj7Knz+/Xn31VaWkpGQqno0bN6pPnz6Ki4uTyWSSyWTS+PHjH2ge/zVp0iTZ29vrzTfflCRt2LBB8+bN08KFC+Xo6JjhfcwOJKUAAAAAAECOWbJkicqXLy8/Pz/17NlT8+fPl2EY5vo1a9aoffv2atmypXbv3q2IiAjVqlXLXB8UFKSvv/5aM2fO1OHDhzV37ly5uLhIkmJjY9W4cWNVr15dO3fu1Lp163ThwgV16dIlw3gGDx6syMhILV68WPv27VPnzp3VvHlziwTT9evXNWXKFM2bN08HDx6Uh4eHevfurZ07d2rVqlWKjIyUYRhq2bKlkpKSLK6bOXOmFi9erHXr1mnjxo1q37691q5dq7Vr1+qLL77Q3LlztWzZskzFU7duXc2YMUOurq46d+6czp07pxEjRjzQPP7L0dFRCxcu1GeffabvvvtOffv21VtvvaUaNWrcz8ecJZwpBQAAkBmc5QQAQJaEhoaqZ8+ekqTmzZsrLi5OmzZtUkBAgCTp/fffV7du3TRhwgTzNVWrVpUkHT16VEuWLNH69esVGBgoSSpVqpS53axZs1S9enVNmjTJXDZ//nx5e3vr6NGjKleunEUsp06dUlhYmE6dOiUvLy9J0ogRI7Ru3TqFhYWZ+0lKSlJwcLA5jmPHjmnVqlXasmWL6tatK0latGiRvL29tXLlSnXu3Nl8XUhIiEqXLi1J6tSpk7744gtduHBBLi4uqlixoho1aqQNGzaoa9eumYrHzc1NJpNJRYoUeeB5ZKRmzZoaPXq0OnTooOrVq+vtt9++a/vsQlIKAAAAAADkiCNHjmj79u1asWKFJMnOzk5du3ZVaGioOSm1Z88e9e/fP93r9+zZI1tbWzVs2DDd+r1792rDhg3mlVN3OnHiRJqk1P79+5WSkpKmPDExUQULFjS/t7e3V5UqVczvDx8+LDs7O9WuXdtcVrBgQfn5+enw4cPmMmdnZ3NCSpI8PT3l4+NjEZ+np6eio6PvK57/yuo87mbMmDF69913NWrUKNnZ5U66iKQUAAAAAADIEaGhoUpOTjav5pEkwzDk4OCgWbNmyc3NTU5OThlef7c66dZ5VW3atNGUKVPS1BUtWjTd9ra2tvrjjz9ka2trUXdn4sjJyUkmk+muY6cnT548Fu9NJlO6ZbcPHM9sPLkxj9uJqNxKSEkkpQAAAAAAQA5ITk7WwoULNXXqVDVt2tSirl27dvr666/18ssvq0qVKoqIiFCfPn3S9OHv76/U1FRt2rTJvH3vTk899ZS+/fZb+fj4ZCqZUr16daWkpCg6OloNGjTI9FwqVKig5ORkbdu2zbx97/Llyzpy5IgqVqyY6X6yEo+9vb3FwegPMo+HDUkpAADwcOMsJ2QgeE+w1cYeWG2g1cYGgEfF6tWrdeXKFfXr109ubm4WdR07dlRoaKhefvlljRs3Tk2aNFHp0qXVrVs3JScna+3atXrzzTfl4+OjXr16qW/fvpo5c6aqVq2qv//+W9HR0erSpYsGDRqkzz//XN27d9fIkSNVoEABHT9+XIsXL9a8efPSrCIqV66cevTooaCgIE2dOlXVq1fXxYsXFRERoSpVqqhVq1bpzqVs2bJq27at+vfvr7lz5ypfvnwaNWqUihUrprZt22b5HmUmHh8fH127dk0RERGqWrWqnJ2dszyPhw1P3wMAAAAAANkuNDRUgYGBaRJS0q2k1M6dO7Vv3z4FBARo6dKlWrVqlapVq6bGjRtr+/bt5rYhISHq1KmTBg4cqPLly6t///5KSEiQJHl5eWnLli1KSUlR06ZN5e/vr2HDhsnd3V02NumnPMLCwhQUFKTXX39dfn5+ateunXbs2KESJUrcdT5hYWGqUaOGWrdurTp16sgwDK1duzbN9rz7da946tatq5dfflldu3ZV4cKF9eGHHz7QPB4mJuPO5zA+oeLj4+Xm5qa4uDi5urpaOxwAAHAnVkohA8H50/6Sk1tYKQUgt924cUNRUVHy9fWVo6OjtcMB7vqdzGyehZVSAAAAAAAAyHUkpQAAAAAAAJDrSEoBAAAAAAAg15GUAgAAAAAAQK6zs3YAAAAAeHCRf1222th1ShW02tgAAODRxUopAAAAAAAA5DqSUgAAAAAAAMh1JKUAAAAAAACQ6zhTCgAAPBaCY/dZbeyB7lWsNvbDwGrnWRWw3meuK3H/93Oj0daLAwCAR9hDvVIqJSVFY8aMka+vr5ycnFS6dGm99957MgzD3MYwDI0dO1ZFixaVk5OTAgMDdezYMStGDQAAAAAAHhYBAQEaNmyYtcNAOh7qlVJTpkxRSEiIFixYoEqVKmnnzp3q06eP3Nzc9Oqrr0qSPvzwQ82cOVMLFiyQr6+vxowZo2bNmunQoUNydHS08gwAAAAAAMhZwXuCc22sgdUGZum6yMhI1a9fX82bN9eaNWuyOarHW3h4uIYNG6bY2Ngc6T8gIECbNm3KsH7jxo1q2LBhjoz9UCelfv/9d7Vt21atWrWSJPn4+Ojrr7/W9u3bJd1aJTVjxgy98847atu2rSRp4cKF8vT01MqVK9WtWzerxQ4AAAAAAG4JDQ3VkCFDFBoaqrNnz8rLy8vaIWVaSkqKTCaTbGwe6s1m95TRPJYvX66bN29alN28eVOtWrWSo6OjateunWMxPdRJqbp16+qzzz7T0aNHVa5cOe3du1e//fabpk2bJkmKiorS+fPnFRgYaL7Gzc1NtWvXVmRkJEkpAADw2Lt9ltZpm3+tHEnu85aTtUMAAGTCtWvX9M0332jnzp06f/68wsPD9dZbb1m0+f777/Xuu+9q//79cnFxUYMGDbRixQpJUmJiosaOHauvvvpK0dHR8vb21ujRo9WvXz9J0oEDB/TGG2/o119/Vd68edW0aVNNnz5dhQoVSjeexMREvf322/r6668VGxurypUra8qUKQoICJD0fyuTFi5cqFGjRuno0aM6fvy43NzcNHToUH3//fdKTExUw4YNNXPmTJUtW9biui+//FKvv/66Tp8+rZYtW2rhwoVaunSpxo0bp7i4OL3wwguaPn26bG1t7xnPxo0b1adPH0mSyWSSJI0bN07jx4/P8jx8fHws7keBAgXS3KP+/fvr0qVL2rFjR47uQnuo03yjRo1St27dVL58eeXJk0fVq1fXsGHD1KNHD0nS+fPnJUm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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate synthetic accelerometer and gyroscope data\n",
    "num_samples = 1000\n",
    "time = np.arange(num_samples)\n",
    "accel_x = np.random.randn(num_samples)\n",
    "accel_y = np.random.randn(num_samples)\n",
    "accel_z = np.random.randn(num_samples)\n",
    "gyro_x = np.random.randn(num_samples)\n",
    "gyro_y = np.random.randn(num_samples)\n",
    "gyro_z = np.random.randn(num_samples)\n",
    "\n",
    "# Time Series Plots\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(time, accel_x, label='Accelerometer X')\n",
    "plt.plot(time, accel_y, label='Accelerometer Y')\n",
    "plt.plot(time, accel_z, label='Accelerometer Z')\n",
    "plt.title('Accelerometer Data')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Acceleration')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(time, gyro_x, label='Gyroscope X')\n",
    "plt.plot(time, gyro_y, label='Gyroscope Y')\n",
    "plt.plot(time, gyro_z, label='Gyroscope Z')\n",
    "plt.title('Gyroscope Data')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Angular Velocity')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Histograms\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.hist(accel_x, bins=30, alpha=0.5, label='Accelerometer X')\n",
    "plt.hist(accel_y, bins=30, alpha=0.5, label='Accelerometer Y')\n",
    "plt.hist(accel_z, bins=30, alpha=0.5, label='Accelerometer Z')\n",
    "plt.title('Accelerometer Data Histograms')\n",
    "plt.xlabel('Acceleration')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.hist(gyro_x, bins=30, alpha=0.5, label='Gyroscope X')\n",
    "plt.hist(gyro_y, bins=30, alpha=0.5, label='Gyroscope Y')\n",
    "plt.hist(gyro_z, bins=30, alpha=0.5, label='Gyroscope Z')\n",
    "plt.title('Gyroscope Data Histograms')\n",
    "plt.xlabel('Angular Velocity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Scatter Plot\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(accel_x, accel_y, label='Accelerometer XY', alpha=0.5)\n",
    "plt.title('Accelerometer XY Scatter Plot')\n",
    "plt.xlabel('Accelerometer X')\n",
    "plt.ylabel('Accelerometer Y')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loop through Fall types for data loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading FALL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/BSCTime taken: 1.9506537914276123\n",
      "Reading FALL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/FKLTime taken: 2.1013131141662598\n",
      "Reading FALL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/FOLTime taken: 2.1320266723632812\n",
      "Reading FALL data from ../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/SDLTime taken: 2.1810848712921143\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time taken: 8.365078449249268\n"
     ]
    }
   ],
   "source": [
    "# Importing necessary libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "# Initialize variables\n",
    "total_time = 0\n",
    "fall_processing_times = []\n",
    "\n",
    "# Iterate over fall data folders\n",
    "for folder in os.listdir('../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/'):\n",
    "    if folder not in fall_types:\n",
    "        continue\n",
    "    \n",
    "    # Start timing\n",
    "    t1 = time.time()\n",
    "    \n",
    "    # Process fall data\n",
    "    visualize = 1\n",
    "    path = '../MobiAct_Dataset_v2.0/MobiAct_Dataset_v2.0/Annotated Data/' + folder\n",
    "    print('Reading FALL data from', path, end='')\n",
    "    \n",
    "    for person in person_numlist:\n",
    "        for trial in trials:\n",
    "            try:\n",
    "                data = pd.read_csv(path + '/' + folder + '_' + str(person) + '_' + str(trial) + '_' + 'annotated.csv')\n",
    "\n",
    "                acc_x = np.array(data['acc_x']).reshape((len(data), 1))\n",
    "                acc_y = np.array(data['acc_y']).reshape((len(data), 1))\n",
    "                acc_z = np.array(data['acc_z']).reshape((len(data), 1))\n",
    "                gyro_x = np.array(data['gyro_x']).reshape((len(data), 1))\n",
    "                gyro_y = np.array(data['gyro_y']).reshape((len(data), 1))\n",
    "                gyro_z = np.array(data['gyro_z']).reshape((len(data), 1))\n",
    "\n",
    "                data = np.concatenate([acc_x, acc_y, acc_z, gyro_x, gyro_y, gyro_z], axis=-1)\n",
    "\n",
    "                acc_x_sd = ((acc_x - np.sum(acc_x) / len(acc_x)) ** 2)\n",
    "                at = np.argmax(acc_x_sd)\n",
    "                \n",
    "                # Extract data around the detected fall event\n",
    "                if at - 120 >= 0 and at + 480 < len(acc_x):\n",
    "                    xtrain.append(data[at - 120:at + 480])\n",
    "                    ytrain.append(1)\n",
    "\n",
    "                if at - 240 >= 0 and at + 360 < len(acc_x):\n",
    "                    xtrain.append(data[at - 240:at + 360])\n",
    "                    ytrain.append(1)\n",
    "\n",
    "                if at - 360 >= 0 and at + 240 < len(acc_x):\n",
    "                    xtrain.append(data[at - 360:at + 240])\n",
    "                    ytrain.append(1)\n",
    "\n",
    "                if at - 480 >= 0 and at + 120 < len(acc_x):\n",
    "                    xtrain.append(data[at - 480:at + 120])\n",
    "                    ytrain.append(1)\n",
    "                    \n",
    "            except:\n",
    "                continue\n",
    "\n",
    "    # End timing\n",
    "    t2 = time.time()\n",
    "    processing_time = t2 - t1\n",
    "    total_time += processing_time\n",
    "    \n",
    "    # Store the processing time for the current fall type\n",
    "    fall_processing_times.append((folder, processing_time))\n",
    "    \n",
    "    print('Time taken:', processing_time)\n",
    "    \n",
    "# Plotting the processing times for different fall types\n",
    "fall_types, times = zip(*fall_processing_times)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.bar(fall_types, times, color='skyblue')\n",
    "plt.xlabel('Fall Type')\n",
    "plt.ylabel('Processing Time (seconds)')\n",
    "plt.title('Processing Time for Different Fall Types')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('Total time taken:', total_time)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert lists to numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain after conversion: (6729, 600, 6)\n",
      "Shape of ytrain after conversion: (6729,)\n"
     ]
    }
   ],
   "source": [
    "# Convert lists to numpy arrays\n",
    "xtrain = np.array(xtrain)\n",
    "ytrain = np.array(ytrain)\n",
    "\n",
    "# Print the shapes of xtrain and ytrain after conversion\n",
    "print(f\"Shape of xtrain after conversion: {xtrain.shape}\")\n",
    "print(f\"Shape of ytrain after conversion: {ytrain.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the data into training, testing, and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest = train_test_split(xtrain,ytrain,train_size = 0.8)\n",
    "xtest,xval,ytest,yval = train_test_split(xtest,ytest,train_size = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain: (5383, 600, 6), ytrain: (5383,)\n",
      "Shape of xtest: (673, 600, 6), ytest: (673,)\n",
      "Shape of xval: (673, 600, 6), yval: (673,)\n"
     ]
    }
   ],
   "source": [
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape,xval.shape,yval.shape\n",
    "\n",
    "# Print the shapes of the datasets\n",
    "print(f\"Shape of xtrain: {xtrain.shape}, ytrain: {ytrain.shape}\")\n",
    "print(f\"Shape of xtest: {xtest.shape}, ytest: {ytest.shape}\")\n",
    "print(f\"Shape of xval: {xval.shape}, yval: {yval.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize the data using min-max normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Min-max normalization xtrain ,xval and xtest data\n",
    "\n",
    "for i in range(6):\n",
    "    min_ = min([min(j) for j in xtrain[:,:,i]])\n",
    "    max_ = max([max(j) for j in xtrain[:,:,i]])\n",
    "    \n",
    "    xtrain[:,:,i] = 2*(xtrain[:,:,i]-min_)/(max_-min_)-1\n",
    "    \n",
    "for i in range(6):\n",
    "    min_ = min([min(j) for j in xtest[:,:,i]])\n",
    "    max_ = max([max(j) for j in xtest[:,:,i]])\n",
    "    \n",
    "    xtest[:,:,i] = 2*(xtest[:,:,i]-min_)/(max_-min_)-1\n",
    "    \n",
    "for i in range(6):\n",
    "    min_ = min([min(j) for j in xval[:,:,i]])\n",
    "    max_ = max([max(j) for j in xval[:,:,i]])\n",
    "    \n",
    "    xval[:,:,i] = 2*(xval[:,:,i]-min_)/(max_-min_)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of xtrain: (5383, 600, 6), ytrain: (5383,)\n",
      "Shape of xtest: (673, 600, 6), ytest: (673,)\n",
      "Shape of xval: (673, 600, 6), yval: (673,)\n"
     ]
    }
   ],
   "source": [
    "xtrain.shape,ytrain.shape,xtest.shape,ytest.shape,xval.shape,yval.shape\n",
    "\n",
    "# Print the shapes of the datasets\n",
    "print(f\"Shape of xtrain: {xtrain.shape}, ytrain: {ytrain.shape}\")\n",
    "print(f\"Shape of xtest: {xtest.shape}, ytest: {ytest.shape}\")\n",
    "print(f\"Shape of xval: {xval.shape}, yval: {yval.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\lstm.py:148: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 64)                18176     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 18241 (71.25 KB)\n",
      "Trainable params: 18241 (71.25 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "Epoch 1/10\n",
      "WARNING:tensorflow:From c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "169/169 [==============================] - 14s 74ms/step - loss: 0.4750 - accuracy: 0.7620 - val_loss: 0.3710 - val_accuracy: 0.8514\n",
      "Epoch 2/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.2590 - accuracy: 0.8861 - val_loss: 0.2654 - val_accuracy: 0.9183\n",
      "Epoch 3/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.3052 - accuracy: 0.8781 - val_loss: 0.2268 - val_accuracy: 0.9376\n",
      "Epoch 4/10\n",
      "169/169 [==============================] - 12s 70ms/step - loss: 0.3136 - accuracy: 0.8687 - val_loss: 0.2651 - val_accuracy: 0.8722\n",
      "Epoch 5/10\n",
      "169/169 [==============================] - 12s 72ms/step - loss: 0.2563 - accuracy: 0.9084 - val_loss: 0.5647 - val_accuracy: 0.5423\n",
      "Epoch 6/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.4356 - accuracy: 0.7620 - val_loss: 0.4403 - val_accuracy: 0.7548\n",
      "Epoch 7/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.2882 - accuracy: 0.8716 - val_loss: 0.2371 - val_accuracy: 0.8767\n",
      "Epoch 8/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.4257 - accuracy: 0.7958 - val_loss: 0.4557 - val_accuracy: 0.8276\n",
      "Epoch 9/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.3251 - accuracy: 0.8661 - val_loss: 0.2247 - val_accuracy: 0.9391\n",
      "Epoch 10/10\n",
      "169/169 [==============================] - 12s 71ms/step - loss: 0.2379 - accuracy: 0.9164 - val_loss: 0.1865 - val_accuracy: 0.9539\n"
     ]
    }
   ],
   "source": [
    "# Define the LSTM model\n",
    "model = keras.Sequential([\n",
    "    layers.LSTM(64, input_shape=(xtrain.shape[1], xtrain.shape[2])),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Display the model summary\n",
    "model.summary()\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(xtrain, ytrain, epochs=10, batch_size=32, validation_data=(xval, yval))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 1s 27ms/step - loss: 0.0944 - accuracy: 0.9703\n",
      "Test accuracy: 0.9702823162078857\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "test_loss, test_acc = model.evaluate(xtest, ytest)\n",
    "print(f'Test accuracy: {test_acc}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 1s 24ms/step\n",
      "{'Batch Size': 32, 'Accuracy': 0.9390787518573551, 'Precision': 0.8472906403940886, 'Recall': 0.945054945054945, 'F1-Score': 0.8935064935064934, 'Test Loss': 0.18649323284626007}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Make predictions\n",
    "predictions = model.predict(xtest)\n",
    "# Convert predictions to binary labels\n",
    "binary_predictions = (predictions > 0.5).astype(int)\n",
    "\n",
    "# Calculate classification report\n",
    "report = classification_report(ytest, binary_predictions, output_dict=True)\n",
    "\n",
    "# Fill in the table\n",
    "table = {\n",
    "    'Batch Size': 32,\n",
    "    'Accuracy': report['accuracy'],\n",
    "    'Precision': report['1']['precision'],  # Assuming that '1' is the positive class\n",
    "    'Recall': report['1']['recall'],\n",
    "    'F1-Score': report['1']['f1-score'],\n",
    "    'Test Loss': history.history['val_loss'][-1]  # Last validation loss value\n",
    "}\n",
    "\n",
    "# This is the structure of your table filled with values for a batch size of 32\n",
    "print(table)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training history\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.title('Training and Validation Accuracy Over 10 Epochs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation and Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 1s 23ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "predictions = model.predict(xtest)\n",
    "binary_predictions = (predictions > 0.5).astype(int)\n",
    "\n",
    "# Create a confusion matrix\n",
    "cm = confusion_matrix(ytest, binary_predictions)\n",
    "\n",
    "# Plot the confusion matrix using seaborn\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
    "plt.title(\"Confusion Matrix over 10 Epochs \")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiheaded CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN architecture\n",
    "def build_cnn(input_layer):\n",
    "  cnn = layers.Conv1D(9,7,padding=\"same\")(input_layer)\n",
    "  cnn = layers.BatchNormalization()(cnn)\n",
    "  cnn = layers.Dropout(rate = 0.2)(cnn)\n",
    "  \n",
    "  cnn = layers.Conv1D(18,5,padding=\"same\")(cnn)\n",
    "  cnn = layers.BatchNormalization()(cnn)\n",
    "  cnn = layers.Dropout(rate = 0.2)(cnn)\n",
    "  \n",
    "  cnn = layers.Conv1D(36,3,padding=\"same\")(cnn)\n",
    "  cnn = layers.BatchNormalization()(cnn)\n",
    "  cnn = layers.Dropout(rate = 0.2)(cnn)\n",
    "  \n",
    "  return cnn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-LSTM Attentions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Bi-LSTM architecture\n",
    "def build_bilstm(input_layer,last_sequences = False):\n",
    "\n",
    "  lstm = layers.Bidirectional(layers.LSTM(18,return_sequences = True))(input_layer)\n",
    "  lstm = layers.LayerNormalization()(lstm)\n",
    "  lstm = layers.Dropout(rate = 0.2)(lstm)\n",
    "      \n",
    "  lstm = layers.Bidirectional(layers.LSTM(36,return_sequences = True))(lstm)\n",
    "  lstm = layers.LayerNormalization()(lstm)\n",
    "  lstm = layers.Dropout(rate = 0.2)(lstm)\n",
    "  \n",
    "  lstm = layers.Bidirectional(layers.LSTM(72,return_sequences = last_sequences))(lstm)\n",
    "  lstm = layers.LayerNormalization()(lstm)\n",
    "  lstm = layers.Dropout(rate = 0.2)(lstm)\n",
    "  \n",
    "  return lstm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the dense layer architecture\n",
    "def build_dense(input_layer):\n",
    "    \n",
    "  dense = layers.Dense(72,name = 'dense_1')(input_layer)\n",
    "  dense = layers.Dense(1,name = 'dense_3',activation = 'sigmoid')(dense)\n",
    "  \n",
    "  return dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the overall model architecture\n",
    "def build_model():\n",
    "    input_layer1 = keras.Input((xtrain.shape[1],3), name = 'Input1')\n",
    "    input_layer2 = keras.Input((xtrain.shape[1],3), name = 'Input2')\n",
    "    \n",
    "    output1 = build_cnn(input_layer1)\n",
    "    output2 = build_cnn(input_layer2)\n",
    "    \n",
    "    output = layers.concatenate([output1,output2])\n",
    "    \n",
    "    output = build_bilstm(output)\n",
    "\n",
    "    output = build_dense(output)\n",
    "    \n",
    "    return keras.Model([input_layer1,input_layer2],output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\normalization\\layer_normalization.py:328: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
      "\n",
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_6 (Conv1D)           (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_9 (Conv1D)           (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_6 (Bat  (None, 600, 9)               36        ['conv1d_6[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_9 (Bat  (None, 600, 9)               36        ['conv1d_9[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_9 (Dropout)         (None, 600, 9)               0         ['batch_normalization_6[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " dropout_12 (Dropout)        (None, 600, 9)               0         ['batch_normalization_9[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " conv1d_7 (Conv1D)           (None, 600, 18)              828       ['dropout_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv1d_10 (Conv1D)          (None, 600, 18)              828       ['dropout_12[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_7 (Bat  (None, 600, 18)              72        ['conv1d_7[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_10 (Ba  (None, 600, 18)              72        ['conv1d_10[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_10 (Dropout)        (None, 600, 18)              0         ['batch_normalization_7[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " dropout_13 (Dropout)        (None, 600, 18)              0         ['batch_normalization_10[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_8 (Conv1D)           (None, 600, 36)              1980      ['dropout_10[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_11 (Conv1D)          (None, 600, 36)              1980      ['dropout_13[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_8 (Bat  (None, 600, 36)              144       ['conv1d_8[0][0]']            \n",
      " chNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_11 (Ba  (None, 600, 36)              144       ['conv1d_11[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_11 (Dropout)        (None, 600, 36)              0         ['batch_normalization_8[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " dropout_14 (Dropout)        (None, 600, 36)              0         ['batch_normalization_11[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate  (None, 600, 72)              0         ['dropout_11[0][0]',          \n",
      " )                                                                   'dropout_14[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_3 (Bidirecti  (None, 600, 36)              13104     ['concatenate_1[0][0]']       \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_3 (Lay  (None, 600, 36)              72        ['bidirectional_3[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_15 (Dropout)        (None, 600, 36)              0         ['layer_normalization_3[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " bidirectional_4 (Bidirecti  (None, 600, 72)              21024     ['dropout_15[0][0]']          \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_4 (Lay  (None, 600, 72)              144       ['bidirectional_4[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_16 (Dropout)        (None, 600, 72)              0         ['layer_normalization_4[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " bidirectional_5 (Bidirecti  (None, 144)                  83520     ['dropout_16[0][0]']          \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_5 (Lay  (None, 144)                  288       ['bidirectional_5[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_17 (Dropout)        (None, 144)                  0         ['layer_normalization_5[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_17[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 135181 (528.05 KB)\n",
      "Trainable params: 134929 (527.07 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(build_model(),show_shapes = True,dpi=40), build_model().summary()\n",
    "\n",
    "# # Define your model\n",
    "# model = build_model()\n",
    "\n",
    "# # Generate the model plot\n",
    "# plot_model(model, show_shapes=True, dpi=40)\n",
    "# build_model().summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Compilation and Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m===================================== BATCH_SIZE= 128 learning_rate= 0.0001  =====================================\u001b[0m\n",
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_12 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_15 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_12 (Ba  (None, 600, 9)               36        ['conv1d_12[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_15 (Ba  (None, 600, 9)               36        ['conv1d_15[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_18 (Dropout)        (None, 600, 9)               0         ['batch_normalization_12[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_21 (Dropout)        (None, 600, 9)               0         ['batch_normalization_15[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_13 (Conv1D)          (None, 600, 18)              828       ['dropout_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_16 (Conv1D)          (None, 600, 18)              828       ['dropout_21[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (Ba  (None, 600, 18)              72        ['conv1d_13[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_16 (Ba  (None, 600, 18)              72        ['conv1d_16[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_19 (Dropout)        (None, 600, 18)              0         ['batch_normalization_13[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_22 (Dropout)        (None, 600, 18)              0         ['batch_normalization_16[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_14 (Conv1D)          (None, 600, 36)              1980      ['dropout_19[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_17 (Conv1D)          (None, 600, 36)              1980      ['dropout_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_14 (Ba  (None, 600, 36)              144       ['conv1d_14[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_17 (Ba  (None, 600, 36)              144       ['conv1d_17[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_20 (Dropout)        (None, 600, 36)              0         ['batch_normalization_14[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_23 (Dropout)        (None, 600, 36)              0         ['batch_normalization_17[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate  (None, 600, 72)              0         ['dropout_20[0][0]',          \n",
      " )                                                                   'dropout_23[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_6 (Bidirecti  (None, 600, 36)              13104     ['concatenate_2[0][0]']       \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_6 (Lay  (None, 600, 36)              72        ['bidirectional_6[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_24 (Dropout)        (None, 600, 36)              0         ['layer_normalization_6[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " bidirectional_7 (Bidirecti  (None, 600, 72)              21024     ['dropout_24[0][0]']          \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_7 (Lay  (None, 600, 72)              144       ['bidirectional_7[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_25 (Dropout)        (None, 600, 72)              0         ['layer_normalization_7[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " bidirectional_8 (Bidirecti  (None, 144)                  83520     ['dropout_25[0][0]']          \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_8 (Lay  (None, 144)                  288       ['bidirectional_8[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_26 (Dropout)        (None, 144)                  0         ['layer_normalization_8[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_26[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 135181 (528.05 KB)\n",
      "Trainable params: 134929 (527.07 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 181s 4s/step - loss: 0.4992 - accuracy: 0.7585 - val_loss: 0.3373 - val_accuracy: 0.8440\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 179s 4s/step - loss: 0.2279 - accuracy: 0.9118 - val_loss: 0.2350 - val_accuracy: 0.8945\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 187s 4s/step - loss: 0.1551 - accuracy: 0.9411 - val_loss: 0.1830 - val_accuracy: 0.9272\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 189s 4s/step - loss: 0.1162 - accuracy: 0.9569 - val_loss: 0.1424 - val_accuracy: 0.9421\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 189s 4s/step - loss: 0.0986 - accuracy: 0.9656 - val_loss: 0.1016 - val_accuracy: 0.9688\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0873 - accuracy: 0.9695 - val_loss: 0.0919 - val_accuracy: 0.9688\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 196s 5s/step - loss: 0.0850 - accuracy: 0.9695 - val_loss: 0.0735 - val_accuracy: 0.9733\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.0724 - accuracy: 0.9740 - val_loss: 0.0599 - val_accuracy: 0.9762\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.0696 - accuracy: 0.9781 - val_loss: 0.0511 - val_accuracy: 0.9777\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 201s 5s/step - loss: 0.0619 - accuracy: 0.9772 - val_loss: 0.0442 - val_accuracy: 0.9896\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 200s 5s/step - loss: 0.0581 - accuracy: 0.9809 - val_loss: 0.0417 - val_accuracy: 0.9881\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 200s 5s/step - loss: 0.0559 - accuracy: 0.9835 - val_loss: 0.0381 - val_accuracy: 0.9911\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 201s 5s/step - loss: 0.0499 - accuracy: 0.9844 - val_loss: 0.0351 - val_accuracy: 0.9926\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 199s 5s/step - loss: 0.0478 - accuracy: 0.9859 - val_loss: 0.0334 - val_accuracy: 0.9926\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 203s 5s/step - loss: 0.0487 - accuracy: 0.9850 - val_loss: 0.0327 - val_accuracy: 0.9926\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.0446 - accuracy: 0.9855 - val_loss: 0.0311 - val_accuracy: 0.9926\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 196s 5s/step - loss: 0.0439 - accuracy: 0.9872 - val_loss: 0.0308 - val_accuracy: 0.9926\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.0441 - accuracy: 0.9861 - val_loss: 0.0310 - val_accuracy: 0.9926\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.0344 - accuracy: 0.9900 - val_loss: 0.0302 - val_accuracy: 0.9926\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.0409 - accuracy: 0.9866 - val_loss: 0.0303 - val_accuracy: 0.9926\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 190s 4s/step - loss: 0.0382 - accuracy: 0.9902 - val_loss: 0.0291 - val_accuracy: 0.9926\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0373 - accuracy: 0.9890 - val_loss: 0.0283 - val_accuracy: 0.9941\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 190s 4s/step - loss: 0.0364 - accuracy: 0.9879 - val_loss: 0.0281 - val_accuracy: 0.9941\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 193s 4s/step - loss: 0.0355 - accuracy: 0.9881 - val_loss: 0.0283 - val_accuracy: 0.9941\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0311 - accuracy: 0.9903 - val_loss: 0.0276 - val_accuracy: 0.9941\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0367 - accuracy: 0.9892 - val_loss: 0.0269 - val_accuracy: 0.9941\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0340 - accuracy: 0.9900 - val_loss: 0.0262 - val_accuracy: 0.9941\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0326 - accuracy: 0.9898 - val_loss: 0.0256 - val_accuracy: 0.9941\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0349 - accuracy: 0.9898 - val_loss: 0.0261 - val_accuracy: 0.9941\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 190s 4s/step - loss: 0.0319 - accuracy: 0.9896 - val_loss: 0.0259 - val_accuracy: 0.9941\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0315 - accuracy: 0.9926 - val_loss: 0.0256 - val_accuracy: 0.9941\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0310 - accuracy: 0.9903 - val_loss: 0.0256 - val_accuracy: 0.9941\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0291 - accuracy: 0.9903 - val_loss: 0.0259 - val_accuracy: 0.9941\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0304 - accuracy: 0.9909 - val_loss: 0.0261 - val_accuracy: 0.9941\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0299 - accuracy: 0.9911 - val_loss: 0.0257 - val_accuracy: 0.9941\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0274 - accuracy: 0.9909 - val_loss: 0.0253 - val_accuracy: 0.9941\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0285 - accuracy: 0.9907 - val_loss: 0.0250 - val_accuracy: 0.9941\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0303 - accuracy: 0.9900 - val_loss: 0.0246 - val_accuracy: 0.9941\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 193s 4s/step - loss: 0.0253 - accuracy: 0.9928 - val_loss: 0.0247 - val_accuracy: 0.9941\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0250 - accuracy: 0.9933 - val_loss: 0.0244 - val_accuracy: 0.9941\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0248 - accuracy: 0.9909 - val_loss: 0.0244 - val_accuracy: 0.9941\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0230 - accuracy: 0.9942 - val_loss: 0.0243 - val_accuracy: 0.9941\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 193s 4s/step - loss: 0.0244 - accuracy: 0.9928 - val_loss: 0.0247 - val_accuracy: 0.9955\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0272 - accuracy: 0.9916 - val_loss: 0.0250 - val_accuracy: 0.9955\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0265 - accuracy: 0.9924 - val_loss: 0.0247 - val_accuracy: 0.9941\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0257 - accuracy: 0.9918 - val_loss: 0.0243 - val_accuracy: 0.9941\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0246 - accuracy: 0.9920 - val_loss: 0.0243 - val_accuracy: 0.9941\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.0290 - accuracy: 0.9909 - val_loss: 0.0244 - val_accuracy: 0.9941\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 190s 4s/step - loss: 0.0218 - accuracy: 0.9939 - val_loss: 0.0245 - val_accuracy: 0.9941\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 192s 4s/step - loss: 0.0270 - accuracy: 0.9924 - val_loss: 0.0243 - val_accuracy: 0.9941\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 1s 64ms/step - loss: 0.0307 - accuracy: 0.9881\n",
      "Test Loss: 0.030711360275745392, Test Accuracy: 0.9881129264831543\n",
      "22/22 [==============================] - 3s 63ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Model 0\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       491\n",
      "           1       0.97      0.98      0.98       182\n",
      "\n",
      "    accuracy                           0.99       673\n",
      "   macro avg       0.98      0.99      0.98       673\n",
      "weighted avg       0.99      0.99      0.99       673\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
      "image/png": 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nzjhmGj58uBUaGmp5eHhYISEh1h133GG98cYb+Z7z3//+txUXF2dVrVrV8vLysrp165bvYx0sy7ISEhKsZs2aWXa73apUqZLVr18/68cff3RaJzo62vLx8SlwxnPnzlkPPfSQFRgYaEniowlQ5vFddQBQhlz+/rsVK1Y4fY0LgKLBNU4AAACGCCcAAABDhBMAAIAhrnECAAAwxBEnAAAAQ4QTAACAoXL3AZh5eXk6duyY/Pz8+KoAAAAgy7J09uxZVa9e/S+/k7HchdOxY8cUGhrq6jEAAEAJc/ToUV1//fV/uk65C6fLX5Vw9OhR+fv7u3gaAADgapmZmQoNDTX6OqVyF06XT8/5+/sTTgAAwMHkEh4uDgcAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIZeG02effabu3burevXqstlsWr169V9us2XLFjVv3lx2u1116tTRkiVLrvmcAAAAkovDKSsrS02aNNG8efOM1k9JSVG3bt3UsWNHJSUlaeTIkRo8eLA2bNhwjScFAACQKrjyxbt06aIuXboYr79gwQKFh4dr+vTpkqT69etr69atevXVV9WpU6drNSYAAICkUnaNU2JioqKiopyWderUSYmJiVfcJjs7W5mZmU43AACAwihV4ZSWlqbg4GCnZcHBwcrMzNSvv/5a4Dbx8fEKCAhw3EJDQ4tjVAAAUAaVqnAqjLi4OGVkZDhuR48edfVIAACglHLpNU5XKyQkROnp6U7L0tPT5e/vLy8vrwK3sdvtstvtxTEeAAAo40pVOEVGRmrdunVOyz7++GNFRka6aKKChY1d6+oRik3qS91cPQIAAMXGpeF07tw5HTx40HE/JSVFSUlJqlSpkm644QbFxcXpp59+0rJlyyRJjz76qObOnaunnnpKjzzyiDZt2qT33ntPa9eWn1CB6zRa2sjVIxSbXdG7XD0CAJRILg2nr7/+Wh07dnTcj42NlSRFR0dryZIlOn78uI4cOeJ4PDw8XGvXrtWoUaM0a9YsXX/99XrzzTf5KAJXmhTg6gmKT/gNrp4AAOBiLg2nDh06yLKsKz5e0KeCd+jQQd988801nAoAAKBgZf5ddQAAAEWlVF0cDgCAkfJyGcGkDFdPUO5wxAkAAMAQ4QQAAGCIU3UAAJRSfExK8eOIEwAAgCGOOAFAOVGuvtXA09UToKziiBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAy5PJzmzZunsLAweXp6qk2bNvryyy//dP2ZM2eqbt268vLyUmhoqEaNGqULFy4U07QAAKA8c2k4JSQkKDY2VhMnTtTOnTvVpEkTderUSSdOnChw/XfffVdjx47VxIkTtXfvXr311ltKSEjQuHHjinlyAABQHrk0nGbMmKEhQ4Zo4MCBatCggRYsWCBvb28tWrSowPW3b9+utm3b6qGHHlJYWJjuuusuPfjgg395lAoAAKAouCyccnJytGPHDkVFRf3fMG5uioqKUmJiYoHb3HLLLdqxY4cjlA4dOqR169apa9euxTIzAAAo3yq46oVPnTql3NxcBQcHOy0PDg7Wvn37CtzmoYce0qlTp3TrrbfKsixdunRJjz766J+eqsvOzlZ2drbjfmZmZtHsAAAAKHdcfnH41diyZYumTJmi1157TTt37tR//vMfrV27Vi+88MIVt4mPj1dAQIDjFhoaWowTAwCAssRlR5yCgoLk7u6u9PR0p+Xp6ekKCQkpcJvx48fr4Ycf1uDBgyVJjRo1UlZWloYOHapnnnlGbm75OzAuLk6xsbGO+5mZmcQTAAAoFJcdcapYsaJatGihjRs3Opbl5eVp48aNioyMLHCb8+fP54sjd3d3SZJlWQVuY7fb5e/v73QDAAAoDJcdcZKk2NhYRUdHq2XLlmrdurVmzpyprKwsDRw4UJLUv39/1ahRQ/Hx8ZKk7t27a8aMGWrWrJnatGmjgwcPavz48erevbsjoAAAAK4Vl4ZTnz59dPLkSU2YMEFpaWlq2rSp1q9f77hg/MiRI05HmJ599lnZbDY9++yz+umnn1SlShV1795dkydPdtUuAACAcsSl4SRJMTExiomJKfCxLVu2ON2vUKGCJk6cqIkTJxbDZAAAAM5K1bvqAAAAXIlwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGXB5O8+bNU1hYmDw9PdWmTRt9+eWXf7r+mTNnNHz4cFWrVk12u1033nij1q1bV0zTAgCA8qyCK188ISFBsbGxWrBggdq0aaOZM2eqU6dO2r9/v6pWrZpv/ZycHN15552qWrWqVq5cqRo1aujw4cMKDAws/uEBAEC549JwmjFjhoYMGaKBAwdKkhYsWKC1a9dq0aJFGjt2bL71Fy1apNOnT2v79u3y8PCQJIWFhRXnyAAAoBxz2am6nJwc7dixQ1FRUf83jJuboqKilJiYWOA2H3zwgSIjIzV8+HAFBwfrpptu0pQpU5Sbm1tcYwMAgHLMZUecTp06pdzcXAUHBzstDw4O1r59+wrc5tChQ9q0aZP69eundevW6eDBgxo2bJguXryoiRMnFrhNdna2srOzHfczMzOLbicAAEC54vKLw69GXl6eqlatqjfeeEMtWrRQnz599Mwzz2jBggVX3CY+Pl4BAQGOW2hoaDFODAAAyhKXhVNQUJDc3d2Vnp7utDw9PV0hISEFblOtWjXdeOONcnd3dyyrX7++0tLSlJOTU+A2cXFxysjIcNyOHj1adDsBAADKFZeFU8WKFdWiRQtt3LjRsSwvL08bN25UZGRkgdu0bdtWBw8eVF5enmPZDz/8oGrVqqlixYoFbmO32+Xv7+90AwAAKAyXnqqLjY3VwoULtXTpUu3du1ePPfaYsrKyHO+y69+/v+Li4hzrP/bYYzp9+rSeeOIJ/fDDD1q7dq2mTJmi4cOHu2oXAABAOeLSjyPo06ePTp48qQkTJigtLU1NmzbV+vXrHReMHzlyRG5u/9d2oaGh2rBhg0aNGqXGjRurRo0aeuKJJ/T000+7ahcAAEA54tJwkqSYmBjFxMQU+NiWLVvyLYuMjNTnn39+jacCAADIr1S9qw4AAMCVCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAENFEk6ZmZlavXq19u7dWxRPBwAAUCIVKpx69+6tuXPnSpJ+/fVXtWzZUr1791bjxo31/vvvF+mAAAAAJUWhwumzzz5Tu3btJEmrVq2SZVk6c+aMZs+erRdffLFIBwQAACgpChVOGRkZqlSpkiRp/fr1euCBB+Tt7a1u3brpwIEDRTogAABASVGocAoNDVViYqKysrK0fv163XXXXZKkX375RZ6enkU6IAAAQElRoTAbjRw5Uv369ZOvr69uuOEGdejQQdJvp/AaNWpUlPMBAACUGIUKp2HDhql169Y6evSo7rzzTrm5/XbgqlatWlzjBAAAyqxChZMktWzZUo0bN1ZKSopq166tChUqqFu3bkU5GwAAQIlSqGuczp8/r0GDBsnb21sNGzbUkSNHJEmPP/64XnrppSIdEAAAoKQoVDjFxcXp22+/1ZYtW5wuBo+KilJCQkKRDQcAAFCSFOpU3erVq5WQkKCbb75ZNpvNsbxhw4ZKTk4usuEAAABKkkIdcTp58qSqVq2ab3lWVpZTSAEAAJQlhQqnli1bau3atY77l2PpzTffVGRkZNFMBgAAUMIU6lTdlClT1KVLF+3Zs0eXLl3SrFmztGfPHm3fvl2ffvppUc8IAABQIhTqiNOtt96qb7/9VpcuXVKjRo303//+V1WrVlViYqJatGhR1DMCAACUCFd9xOnixYv65z//qfHjx2vhwoXXYiYAAIAS6aqPOHl4eOj999+/FrMAAACUaIU6VdejRw+tXr26iEcBAAAo2Qp1cXhERISef/55bdu2TS1atJCPj4/T4yNGjCiS4QAAAEqSQoXTW2+9pcDAQO3YsUM7duxwesxmsxFOAACgTCpUOKWkpBT1HAAAACVeoa5x+j3LsmRZVlHMAgAAUKIVOpyWLVumRo0aycvLS15eXmrcuLHefvvtopwNAACgRCnUqboZM2Zo/PjxiomJUdu2bSVJW7du1aOPPqpTp05p1KhRRTokAABASVCocJozZ47mz5+v/v37O5bdc889atiwoSZNmkQ4AQCAMqlQp+qOHz+uW265Jd/yW265RcePH//bQwEAAJREhQqnOnXq6L333su3PCEhQREREX97KAAAgJKoUKfqnnvuOfXp00efffaZ4xqnbdu2aePGjQUGFQAAQFlQqCNODzzwgL744gsFBQVp9erVWr16tYKCgvTll1/qvvvuK+oZAQAASoRCHXGSpBYtWuhf//pXUc4CAABQohXqiNO6deu0YcOGfMs3bNigjz766G8PBQAAUBIVKpzGjh2r3NzcfMsty9LYsWP/9lAAAAAlUaHC6cCBA2rQoEG+5fXq1dPBgwf/9lAAAAAlUaHCKSAgQIcOHcq3/ODBg/Lx8fnbQwEAAJREhQqne++9VyNHjlRycrJj2cGDBzV69Gjdc889RTYcAABASVKocHr55Zfl4+OjevXqKTw8XOHh4apXr54qV66sadOmFfWMAAAAJUKhPo4gICBA27dv18cff6xvv/1WXl5eatKkidq1a1fU8wEAAJQYV3XEKTExUWvWrJEk2Ww23XXXXapataqmTZumBx54QEOHDlV2dvY1GRQAAMDVriqcnn/+eX3//feO+7t27dKQIUN05513auzYsfrwww8VHx9f5EMCAACUBFcVTklJSbrjjjsc95cvX67WrVtr4cKFio2N1ezZs/muOgAAUGZdVTj98ssvCg4Odtz/9NNP1aVLF8f9Vq1a6ejRo0U3HQAAQAlyVeEUHByslJQUSVJOTo527typm2++2fH42bNn5eHhUbQTAgAAlBBXFU5du3bV2LFj9b///U9xcXHy9vZ2eifdd999p9q1axf5kAAAACXBVX0cwQsvvKD7779f7du3l6+vr5YuXaqKFSs6Hl+0aJHuuuuuIh8SAACgJLiqcAoKCtJnn32mjIwM+fr6yt3d3enxFStWyNfXt0gHBAAAKCkK/QGYBalUqdLfGgYAAKAkK9RXrgAAAJRHhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABgqEeE0b948hYWFydPTU23atNGXX35ptN3y5ctls9nUo0ePazsgAACASkA4JSQkKDY2VhMnTtTOnTvVpEkTderUSSdOnPjT7VJTU/Xkk0+qXbt2xTQpAAAo71weTjNmzNCQIUM0cOBANWjQQAsWLJC3t7cWLVp0xW1yc3PVr18/Pffcc6pVq1YxTgsAAMozl4ZTTk6OduzYoaioKMcyNzc3RUVFKTEx8YrbPf/886pataoGDRr0l6+RnZ2tzMxMpxsAAEBhuDScTp06pdzcXAUHBzstDw4OVlpaWoHbbN26VW+99ZYWLlxo9Brx8fEKCAhw3EJDQ//23AAAoHxy+am6q3H27Fk9/PDDWrhwoYKCgoy2iYuLU0ZGhuN29OjRazwlAAAoqyq48sWDgoLk7u6u9PR0p+Xp6ekKCQnJt35ycrJSU1PVvXt3x7K8vDxJUoUKFbR//37Vrl3baRu73S673X4NpgcAAOWNS484VaxYUS1atNDGjRsdy/Ly8rRx40ZFRkbmW79evXratWuXkpKSHLd77rlHHTt2VFJSEqfhAADANeXSI06SFBsbq+joaLVs2VKtW7fWzJkzlZWVpYEDB0qS+vfvrxo1aig+Pl6enp666aabnLYPDAyUpHzLAQAAiprLw6lPnz46efKkJkyYoLS0NDVt2lTr1693XDB+5MgRubmVqkuxAABAGeXycJKkmJgYxcTEFPjYli1b/nTbJUuWFP1AAAAABeBQDgAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYKhHhNG/ePIWFhcnT01Nt2rTRl19+ecV1Fy5cqHbt2um6667Tddddp6ioqD9dHwAAoKi4PJwSEhIUGxuriRMnaufOnWrSpIk6deqkEydOFLj+li1b9OCDD2rz5s1KTExUaGio7rrrLv3000/FPDkAAChvXB5OM2bM0JAhQzRw4EA1aNBACxYskLe3txYtWlTg+u+8846GDRumpk2bql69enrzzTeVl5enjRs3FvPkAACgvHFpOOXk5GjHjh2KiopyLHNzc1NUVJQSExONnuP8+fO6ePGiKlWqVODj2dnZyszMdLoBAAAUhkvD6dSpU8rNzVVwcLDT8uDgYKWlpRk9x9NPP63q1as7xdfvxcfHKyAgwHELDQ3923MDAIDyyeWn6v6Ol156ScuXL9eqVavk6elZ4DpxcXHKyMhw3I4ePVrMUwIAgLKigitfPCgoSO7u7kpPT3danp6erpCQkD/ddtq0aXrppZf0ySefqHHjxldcz263y263F8m8AACgfHPpEaeKFSuqRYsWThd2X77QOzIy8orbvfzyy3rhhRe0fv16tWzZsjhGBQAAcO0RJ0mKjY1VdHS0WrZsqdatW2vmzJnKysrSwIEDJUn9+/dXjRo1FB8fL0maOnWqJkyYoHfffVdhYWGOa6F8fX3l6+vrsv0AAABln8vDqU+fPjp58qQmTJigtLQ0NW3aVOvXr3dcMH7kyBG5uf3fgbH58+crJydHPXv2dHqeiRMnatKkScU5OgAAKGdcHk6SFBMTo5iYmAIf27Jli9P91NTUaz8QAABAAUr1u+oAAACKE+EEAABgiHACAAAwRDgBAAAYIpwAAAAMEU4AAACGCCcAAABDhBMAAIAhwgkAAMAQ4QQAAGCIcAIAADBEOAEAABginAAAAAwRTgAAAIYIJwAAAEMVXD0AgJIvNzdXFy9edPUYuAru7u6qUKGCbDabq0cByhTCCcCfOnfunH788UdZluXqUXCVvL29Va1aNVWsWNHVowBlBuEE4Ipyc3P1448/ytvbW1WqVOHoRSlhWZZycnJ08uRJpaSkKCIiQm5uXJkBFAXCCcAVXbx4UZZlqUqVKvLy8nL1OLgKXl5e8vDw0OHDh5WTkyNPT09XjwSUCfwTBMBf4khT6cRRJqDo8V8VAACAIcIJAIqIzWbT6tWri3xdACUH1zgBuGphY9cW6+ulvtTtqrcZMGCAli5dKkny8PDQDTfcoP79+2vcuHGqUOHa/Oo7fvy4rrvuuiJfF0DJQTgBKLM6d+6sxYsXKzs7W+vWrdPw4cPl4eGhuLg4p/VycnKK5C37ISEh12RdACUHp+oAlFl2u10hISGqWbOmHnvsMUVFRemDDz7QgAED1KNHD02ePFnVq1dX3bp1JUlHjx5V7969FRgYqEqVKunee+9Vamqq03MuWrRIDRs2lN1uV7Vq1RQTE+N47Pen33JychQTE6Nq1arJ09NTNWvWVHx8fIHrStKuXbt0++23y8vLS5UrV9bQoUN17tw5x+OXZ542bZqqVaumypUra/jw4XwwKVDMCCcA5YaXl5dycnIkSRs3btT+/fv18ccfa82aNbp48aI6deokPz8//e9//9O2bdvk6+urzp07O7aZP3++hg8frqFDh2rXrl364IMPVKdOnQJfa/bs2frggw/03nvvaf/+/XrnnXcUFhZW4LpZWVnq1KmTrrvuOn311VdasWKFPvnkE6cok6TNmzcrOTlZmzdv1tKlS7VkyRItWbKkyH4+AP4ap+oAlHmWZWnjxo3asGGDHn/8cZ08eVI+Pj568803Hafo/vWvfykvL09vvvmm4+MXFi9erMDAQG3ZskV33XWXXnzxRY0ePVpPPPGE47lbtWpV4GseOXJEERERuvXWW2Wz2VSzZs0rzvfuu+/qwoULWrZsmXx8fCRJc+fOVffu3TV16lQFBwdLkq677jrNnTtX7u7uqlevnrp166aNGzdqyJAhRfJzAvDXOOIEoMxas2aNfH195enpqS5duqhPnz6aNGmSJKlRo0ZO1zV9++23OnjwoPz8/OTr6ytfX19VqlRJFy5cUHJysk6cOKFjx47pjjvuMHrtAQMGKCkpSXXr1tWIESP03//+94rr7t27V02aNHFEkyS1bdtWeXl52r9/v2NZw4YN5e7u7rhfrVo1nThxwvTHAaAIcMQJQJnVsWNHzZ8/XxUrVlT16tWd3k33+0iRfvtOvhYtWuidd97J9zxVqlS56g+TbN68uVJSUvTRRx/pk08+Ue/evRUVFaWVK1cWbmf027sDf89msykvL6/Qzwfg6hFOAMosHx+fK16D9EfNmzdXQkKCqlatKn9//wLXCQsL08aNG9WxY0ej5/T391efPn3Up08f9ezZU507d9bp06dVqVIlp/Xq16+vJUuWKCsryxF027Ztk5ubm+PCdQAlA6fqAEBSv379FBQUpHvvvVf/+9//lJKSoi1btmjEiBH68ccfJUmTJk3S9OnTNXv2bB04cEA7d+7UnDlzCny+GTNm6N///rf27dunH374QStWrFBISIgCAwMLfG1PT09FR0dr9+7d2rx5sx5//HE9/PDDjuubAJQMhBMASPL29tZnn32mG264Qffff7/q16+vQYMG6cKFC44jUNHR0Zo5c6Zee+01NWzYUHfffbcOHDhQ4PP5+fnp5ZdfVsuWLdWqVSulpqZq3bp1BZ7y8/b21oYNG3T69Gm1atVKPXv21B133KG5c+de030GcPVslmVZrh6iOGVmZiogIEAZGRlXPBz/dxX3pyq7UqrnQ64eodg0Cr/B1SMUm13RuyRJFy5cUEpKisLDw+Xp6eniqXC1/vj3x++msqc8/l66Fq6mDTjiBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACgGvEZrNp9erVkqTU1FTZbDYlJSW5dCYAf08FVw8AoBSaFFDMr5dx1ZsMGDBAS5culSRVqFBB119/vXr16qXnn3+er48BUGiEE4Ayq3Pnzlq8eLEuXryoHTt2KDo6WjabTVOnTnX1aABKKU7VASiz7Ha7QkJCFBoaqh49eigqKkoff/yxJCkvL0/x8fEKDw+Xl5eXmjRpopUrVzpt//333+vuu++Wv7+//Pz81K5dOyUnJ0uSvvrqK915550KCgpSQECA2rdvr507dxb7PgIoXoQTgHJh9+7d2r59uypWrChJio+P17Jly7RgwQJ9//33GjVqlP7xj3/o008/lST99NNPuu2222S327Vp0ybt2LFDjzzyiC5duiRJOnv2rKKjo7V161Z9/vnnioiIUNeuXXX27FmX7SOAa49TdQDKrDVr1sjX11eXLl1Sdna23NzcNHfuXGVnZ2vKlCn65JNPFBkZKUmqVauWtm7dqtdff13t27fXvHnzFBAQoOXLl8vDw0OSdOONNzqe+/bbb3d6rTfeeEOBgYH69NNPdffddxffTgIoVoQTgDKrY8eOmj9/vrKysvTqq6+qQoUKeuCBB/T999/r/PnzuvPOO53Wz8nJUbNmzSRJSUlJateunSOa/ig9PV3PPvustmzZohMnTig3N1fnz5/XkSNHrvl+AXAdwglAmeXj46M6depIkhYtWqQmTZrorbfe0k033SRJWrt2rWrUqOG0jd1ulyR5eXn96XNHR0fr559/1qxZs1SzZk3Z7XZFRkYqJyfnGuwJgJKCcAJQLri5uWncuHGKjY3VDz/8ILvdriNHjqh9+/YFrt+4cWMtXbpUFy9eLPCo07Zt2/Taa6+pa9eukqSjR4/q1KlT13QfALgeF4cDKDd69eold3d3vf7663ryySc1atQoLV26VMnJydq5c6fmzJnj+OynmJgYZWZmqm/fvvr666914MABvf3229q/f78kKSIiQm+//bb27t2rL774Qv369fvLo1QASj+OOAEoNypUqKCYmBi9/PLLSklJUZUqVRQfH69Dhw4pMDBQzZs317hx4yRJlStX1qZNmzRmzBi1b99e7u7uatq0qdq2bStJeuuttzR06FA1b95coaGhmjJlip588klX7h6AYmCzLMty9RDFKTMzUwEBAcrIyJC/v/81eY2wsWuvyfOWRKmeD7l6hGLTKPwGV49QbHZF75IkXbhwQSkpKQoPD+fTtkuhP/798bup7CmPv5euhatpA07VAQAAGCKcAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEEAABgiHACAAAwRDgBAAAYIpwAAAAM8V11AK5ao6WNivX1CvNVCwMGDHB8Ye/vHThwQMeOHdMrr7yiHTt26Pjx41q1apV69Ojxl8/57bffavz48fr888+VmZmpkJAQtWnTRnPmzFHVqlWvekYApQ9HnACUWZ07d9bx48edbuHh4crKylKTJk00b9484+c6efKk7rjjDlWqVEkbNmzQ3r17tXjxYlWvXl1ZWVnXbB8uXrx4zZ4bwNUjnACUWXa7XSEhIU43d3d3denSRS+++KLuu+8+4+fatm2bMjIy9Oabb6pZs2YKDw9Xx44d9eqrryo8PNyx3vfff6+7775b/v7+8vPzU7t27ZScnCxJysvL0/PPP6/rr79edrtdTZs21fr16x3bpqamymazKSEhQe3bt5enp6feeecdSdKbb76p+vXry9PTU/Xq1dNrr71WRD8lAFeDU3UAYCAkJESXLl3SqlWr1LNnT9lstnzr/PTTT7rtttvUoUMHbdq0Sf7+/tq2bZsuXbokSZo1a5amT5+u119/Xc2aNdOiRYt0zz336Pvvv1dERITjecaOHavp06erWbNmjniaMGGC5s6dq2bNmumbb77RkCFD5OPjo+jo6GL7GQAgnACUYWvWrJGvr6/jfpcuXbRixYpCPdfNN9+scePG6aGHHtKjjz6q1q1b6/bbb1f//v0VHBwsSZo3b54CAgK0fPlyeXh4SJJuvPFGx3NMmzZNTz/9tPr27StJmjp1qjZv3qyZM2c6nTYcOXKk7r//fsf9iRMnavr06Y5l4eHh2rNnj15//XXCCShmnKoDUGZ17NhRSUlJjtvs2bONtpsyZYp8fX0dtyNHjkiSJk+erLS0NC1YsEANGzbUggULVK9ePe3a9dvF60lJSWrXrp0jmn4vMzNTx44dU9u2bZ2Wt23bVnv37nVa1rJlS8efs7KylJycrEGDBjnN9OKLLzpOAQIoPhxxAlBm+fj4qE6dOle93aOPPqrevXs77levXt3x58qVK6tXr17q1auXpkyZombNmmnatGlaunSpvLy8imzuy86dOydJWrhwodq0aeO0nru7e5G8HgBzhBMA/EGlSpVUqVKlv1yvYsWKql27tuNddY0bN9bSpUt18eLFfEed/P39Vb16dW3btk3t27d3LN+2bZtat259xdcIDg5W9erVdejQIfXr16+QewSgqBBOAMqdc+fO6eDBg477KSkpSkpKUqVKlXTDDTcUuM2aNWu0fPly9e3bVzfeeKMsy9KHH36odevWafHixZKkmJgYzZkzR3379lVcXJwCAgL0+eefq3Xr1qpbt67GjBmjiRMnqnbt2mratKkWL16spKQkxzvnruS5557TiBEjFBAQoM6dOys7O1tff/21fvnlF8XGxhbdDwbAXyKcAJQ7X3/9tTp27Oi4fzk+oqOjtWTJkgK3adCggby9vTV69GgdPXpUdrtdERERevPNN/Xwww9L+u003qZNmzRmzBi1b99e7u7uatq0qeO6phEjRigjI0OjR4/WiRMn1KBBA33wwQdO76gryODBg+Xt7a1XXnlFY8aMkY+Pjxo1aqSRI0f+/R8GgKtisyzLcvUQxSkzM1MBAQHKyMiQv7//NXmNsLFrr8nzlkSpng+5eoRi0yi84CMRZdHlT+q+cOGCUlJSFB4eLk9PTxdPhav1x78/fjeVPeXx99K1cDVtwLvqAAAADBFOAAAAhggnAAAAQ4QTAACAIcIJAADAEOEE4C+Vszfflhn8vQFFj3ACcEWXv9IjJyfHxZOgMM6fPy9JBX53HoDC4QMwAVxRhQoV5O3trZMnT8rDw0NubvxbqzSwLEvnz5/XiRMnFBgYyHfaAUWIcAJwRTabTdWqVVNKSooOHz7s6nFwlQIDAxUSEuLqMYAyhXAC8KcqVqyoiIgITteVMh4eHhxpAq6BEhFO8+bN0yuvvKK0tDQ1adJEc+bM+dNvC1+xYoXGjx+v1NRURUREaOrUqeratWsxTgyUL25ubnzlCgCoBFwcnpCQoNjYWE2cOFE7d+5UkyZN1KlTJ504caLA9bdv364HH3xQgwYN0jfffKMePXqoR48e2r17dzFPDgAAyhuXh9OMGTM0ZMgQDRw4UA0aNNCCBQvk7e2tRYsWFbj+rFmz1LlzZ40ZM0b169fXCy+8oObNm2vu3LnFPDkAAChvXBpOOTk52rFjh6KiohzL3NzcFBUVpcTExAK3SUxMdFpfkjp16nTF9QEAAIqKS69xOnXqlHJzcxUcHOy0PDg4WPv27Stwm7S0tALXT0tLK3D97OxsZWdnO+5nZGRIkjIzM//O6H8qL/v8NXvukibTVn4+YC/311xXj1BsruV/H3AdfjeVPfxeKtrnNvnQ2BJxcfi1FB8fr+eeey7f8tDQUBdMU/YEuHqAYrXX1QMUm4DHytffLMqe8vO/YH4vFaWzZ88qIODPX8el4RQUFCR3d3elp6c7LU9PT7/iZ4+EhIRc1fpxcXGKjY113M/Ly9Pp06dVuXJl2Wy2v7kHKG6ZmZkKDQ3V0aNH5e/v7+pxAEASv5tKO8uydPbsWVWvXv0v13VpOFWsWFEtWrTQxo0b1aNHD0m/hc3GjRsVExNT4DaRkZHauHGjRo4c6Vj28ccfKzIyssD17Xa77Ha707LAwMCiGB8u5O/vzy8nACUOv5tKr7860nSZy0/VxcbGKjo6Wi1btlTr1q01c+ZMZWVlaeDAgZKk/v37q0aNGoqPj5ckPfHEE2rfvr2mT5+ubt26afny5fr666/1xhtvuHI3AABAOeDycOrTp49OnjypCRMmKC0tTU2bNtX69esdF4AfOXLE6fuxbrnlFr377rt69tlnNW7cOEVERGj16tW66aabXLULAACgnLBZJpeQAyVEdna24uPjFRcXl+8ULAC4Cr+byg/CCQAAwJDLPzkcAACgtCCcAAAADBFOAAAAhggnAAAAQ4QTSoW8vDzl5paf72QCAJRMhBNKvD179qh///7q1KmTHnvsMW3fvt3VIwEA/5grpwgnlGj79+/XLbfcotzcXLVq1UqJiYl64oknNHv2bFePBqAc++GHHzRz5kwdP37c1aOgmLn8k8OBK7EsS8uWLVOnTp3073//W5I0btw4zZ49W4sXL9aFCxf01FNPuXhKAOXNwYMHFRkZqV9++UU///yzYmNjFRQU5OqxUEwIJ5RYNptNx44dU1pammOZn5+fRowYIU9PTy1fvlw1atRQv379XDglgPIkKytL8fHxuueee9SqVSvFxMTo0qVLeuqpp4incoJwQolkWZZsNpuaN2+uAwcOaP/+/apbt66k3+LpkUce0f79+/Xaa6/pvvvuk7e3t4snBlAeuLm5qUWLFqpcubL69OmjoKAg9e3bV5KIp3KCr1xBiZacnKybb75Z99xzj2bNmiVfX19HVB09elQ1a9bUunXr1LlzZ1ePCqCcyMrKko+Pj+N+QkKCHnzwQY0ePVpjx45V5cqVlZeXp8OHDys8PNyFk+Ja4IgTSrTatWvrvffeU5cuXeTl5aVJkyY5/kXn4eGhxo0bKyAgwMVTAihPLkdTbm6u3Nzc1KdPH1mWpYceekg2m00jR47UtGnTdPjwYb399tscES9jCCeUeB07dtSKFSvUq1cvHT9+XL1791bjxo21bNkynThxQqGhoa4eEUA55O7uLsuylJeXp759+8pms+nhhx/WBx98oOTkZH311VdEUxnEqTqUGjt37lRsbKxSU1NVoUIFubu7a/ny5WrWrJmrRwNQjl3+v1GbzaY77rhDSUlJ2rJlixo1auTiyXAtEE4oVTIzM3X69GmdPXtW1apV40JMACVCbm6uxowZo5kzZyopKUmNGzd29Ui4RjhVh1LF399f/v7+rh4DAPJp2LChdu7cSTSVcRxxAgCgCFx+xy/KNr5yBQCAIkA0lQ+EEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAMo0m82m1atXu3oMAGUE4QSgVEtLS9Pjjz+uWrVqyW63KzQ0VN27d9fGjRtdPRqAMoivXAFQaqWmpqpt27YKDAzUK6+8okaNGunixYvasGGDhg8frn379rl6RABlDEecAJRaw4YNk81m05dffqkHHnhAN954oxo2bKjY2Fh9/vnnBW7z9NNP68Ybb5S3t7dq1aql8ePH6+LFi47Hv/32W3Xs2FF+fn7y9/dXixYt9PXXX0uSDh8+rO7du+u6666Tj4+PGjZsqHXr1hXLvgIoGTjiBKBUOn36tNavX6/JkyfLx8cn3+OBgYEFbufn56clS5aoevXq2rVrl4YMGSI/Pz899dRTkqR+/fqpWbNmmj9/vtzd3ZWUlCQPDw9J0vDhw5WTk6PPPvtMPj4+2rNnj3x9fa/ZPgIoeQgnAKXSwYMHZVmW6tWrd1XbPfvss44/h4WF6cknn9Ty5csd4XTkyBGNGTPG8bwRERGO9Y8cOaIHHnhAjRo1kiTVqlXr7+4GgFKGU3UASiXLsgq1XUJCgtq2bauQkBD5+vrq2Wef1ZEjRxyPx8bGavDgwYqKitJLL72k5ORkx2MjRozQiy++qLZt22rixIn67rvv/vZ+AChdCCcApVJERIRsNttVXQCemJiofv36qWvXrlqzZo2++eYbPfPMM8rJyXGsM2nSJH3//ffq1q2bNm3apAYNGmjVqlWSpMGDB+vQoUN6+OGHtWvXLrVs2VJz5swp8n0DUHLZrML+sw0AXKxLly7atWuX9u/fn+86pzNnzigwMFA2m02rVq1Sjx49NH36dL322mtOR5EGDx6slStX6syZMwW+xoMPPqisrCx98MEH+R6Li4vT2rVrOfIElCMccQJQas2bN0+5ublq3bq13n//fR04cEB79+7V7NmzFRkZmW/9iIgIHTlyRMuXL1dycrJmz57tOJokSb/++qtiYmK0ZcsWHT58WNu2bdNXX32l+vXrS5JGjhypDRs2KCUlRTt37tTmzZsdjwEoH7g4HECpVatWLe3cuVOTJ0/W6NGjdfz4cVWpUkUtWrTQ/Pnz861/zz33aNSoUYqJiVF2dra6deum8ePHa9KkSZIkd3d3/fzzz+rfv7/S09MVFBSk+++/X88995wkKTc3V8OHD9ePP/4of39/de7cWa+++mpx7jIAF+NUHQAAgCFO1QEAABginAAAAAwRTgAAAIYIJwAAAEOEEwAAgCHCCQAAwBDhBAAAYIhwAgAAMEQ4AQAAGCKcAAAADBFOAAAAhggnAAAAQ/8f39A0Grp2ABEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T7datW7GXjwLG0qVLT7t+aRV3marVajWaNm1qPPfccydd2rlgwQKjdevWRkBAgBEfH2+8+OKLxtSpUw3A2Llzp2e55ORko0+fPkZoaKgBFLkU89ChQ8bIkSON2rVrG1ar1ahTp44xdOhQz2W+hTXNnTu3yL5Le/y++eYbY/jw4UbTpk2NkJAQw2q1Go0aNTLuu+8+IyUlpciyx1+mWrj9U03Hf74dO3YYQ4YMMWJiYgx/f3+jdu3axtVXX2189tlnpz3mTzzxxCn3cfzPVUk/Ayd+BRQUFBiTJ0822rZta4SEhBghISHGpZdeavzwww+nrUfkRCbD8FJ7ooiIiMhR6oMhIiIiXqeAISIiIl6ngCEiIiJep4AhIiIiXqeAISIiIl6ngCEiIiJeV+UG2nK5XOzfv5/Q0NBTDkMsIiIiJzMMg8zMTGJjYzGbS26jqHIBY//+/cTFxfm6DBERkUorMTGROnXqlLhMlQsYhTemSkxMJCwszMfViIiIVB4ZGRnExcUVucnjqVS5gFF4WiQsLEwBQ0RE5AyUpouBOnmKiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjX+TRg/Pjjj/Tt25fY2FhMJhPz588/7TrLli2jXbt22Gw2GjVqxPTp08u9ThERESkbnwaM7Oxs2rRpw5tvvlmq5Xfu3EmfPn249NJL2bBhAw8++CB33HEHixYtKudKRUREpCx8erv23r1707t371Iv/84771C/fn1eeeUVAJo1a8ZPP/3Ea6+9Rq9evcqrTBERESmjStUHY/Xq1fTs2bPIvF69erF69epTrpOfn09GRkaRSURERMqXT1swyio5OZno6Ogi86Kjo8nIyCA3N5fAwMCT1pkwYQJPPfVURZUoIiLnCqcDco9A7mHIOex+7nKUvI7ZAgHVICgSAiMhMAL8rBVS7vmmUgWMMzF27FhGjRrleZ2RkUFcXJwPK5IK53SA0+7rKs4xBthzjvvFW8xjbhoYLl8XWqkYgGGA2eTrSs4dTgPyHU5cLnAZxtHpuOdH5/tZTAT6Wwjwt+BfigPoMiDb7iAr30FWnoPsfAcmRx7hZBFmZBDkzCDAmeWdD2ENhaAId+AIigT/IO9st4ycBuTaHWTbnWTnO8grcGIxm7FaTPhbzPj7mbFazEefm/A3m90/i72eh4h6FV5vpQoYMTExpKSkFJmXkpJCWFhYsa0XADabDZvNVhHlSUVyOiBtNxza4X481ZdkzhHIT/d1tVJFmI5OcowFKI+vYzMQenQ6nQwjiCNGCEcIoeDo157ZZMJiMmExmzCb3Y8Wkwl/k5NAZwbBzgyCXFmYMcCe6Z7S9pTDJyk9CxBydCqLbc1GkqCAUbIuXbrw9ddfF5m3ePFiunTp4qOK5IzlHIYfJ8KBP91NkIXNkSc+BoRDZhIc2n502uF+PLLz9E2dcloZBJNpCiXLEkaOJZw8/3AK/KtREFANl60aVn8rNn8zAX5mbH4W93N/MwF+Fmz+FvwtJlwGGBjgAhdgGIb70QUuDHLyHaTnFhydHGTk2knPdZCeaycjz4HTZQDgZzYR4H9sPzY/91+zNj8zZpOJApcLh9OgwOk6bnK/djgN3O0HJswmEybT0S97kwmzCUxHv/VNppK//t0Bwb2ye71j65sx4cLgSHYBWfn62QPws7i/lPMdJbd0Wcwm/C0m/Mxm/Mwm93pFnpvItTtJyykgt8BZ6v3b/MzUDLNRMzSAmqE2ggIDOWKEcNAVwgFHEEn2IPbnB3Ao18mR7ALScuxk20u/fTMuwsgmwpRFBJlUM2URQRYBJt+1iNr8zESF2KgeYiUiyIrd6SLX7iTH7iDH7jw6OcgrOPZvckdEbZ/U6tOAkZWVxfbt2z2vd+7cyYYNG4iMjKRu3bqMHTuWffv2MWPGDADuvvtupkyZwiOPPMLw4cP54YcfmDNnDgsXLvTVR5Cycjlh3XT44Rn3+dCz4RcIUQ0hIh6Cok4dUoIiwS+g2E04DYP03AIycx1Y/cwEWS0E2Sz4m8ve/9nAIDPfwZGsAg7n2Dmcnc+RbPfzQ1l2juTkk5nnJNBqIdRmIdjmR4jNnxCbHyEBfoQcnZeRV8Cf+zPZkpTBn/vTScst3ZdZoL+FBjWCaVgzmOrBNv45mM3fKZnsT88rdvl8/HFiKfPnLDdOoMDXRZRe9RAbDaoHU796MPVruB8bVA8mPMifAxn5JKXnkZyey/70PJLT89iflktyRh5J6XnYT/OFDO7AZfUzY/MzYy2cLGasfu7Q5W9xhymzyYTZzLHnJo6GLHeYKnJawmUUOTXhPO49wzBwuoou5zTcAS6vwEWe3Umew0mB0zhW5HHf1VHBVupFBVEvKph6UUHERwVT9+hjRJD/acNdoax8B/uO5LIvLYd9R3LZm5bLviO5HMqyExcZSOPoUBrVDKFxdCi1wgNKvd1CDqeL7HwnmfkFntMrmfkOMvPcz3MLnJ4QHWi1uJ8fPXVTeArHBNidLuwOF/kO96Pd6SK/wOmZbzId+zexmI/9uxT+e7lc7s+aWVhD3rF6MvIcZOUXEGLzp+HRn63Cn7MaIbZSfWaH00V6bgFHcgqoHembUzomwzCM0y9WPpYtW8all1560vyhQ4cyffp0hg0bxq5du1i2bFmRdR566CH+/PNP6tSpw/jx4xk2bFip95mRkUF4eDjp6emEhYV54VNIqe35Bb4eDcm/u19Ht4ROd5bQF+AI5KVBSE2IanR0anjseWgs+S6DAxn55NidZNsd5OQfS/KFr7Pt7r+g03IKOJJj50iO+y+ZtJwCMvIKKO7/AKvFTKDVQrDVQpDNj2CrBX+L2fPLo/AXi/uXy7FfKq5y+L/Jz2yicXQoLWuH0bJ2OE2iQzmSU8C2lEy2Hcji75RM/jmYjd156i+tmLAAmsSE0jQm9OhjGGGBfkV+wbp/ybl/sR37Jeeen5Xv/gV4/HIl/aV5/C/TakH+1AoPoFZ4IDHhAcRWCyAmPJDY8ABiwgOoHmIjv8BFZn7B0f0f/0vfXYvDZRz3C97s+UUf4O/+ErD5mTGZOOk8v9NlFPniPB0DA+Poei7D/dxVZH2D2GqBxFcPJizAv+z/mLi/yEvzW9d8jnbkcDhd5DncfzXnFTjJd7iIDrMReobHQyqXsnyH+jRg+IIChg9kpsD3T8LGme7XAeFw6WPQYThYSm5Ec7kMDmTms+dwDomHc0g8ksOewznsPZzLnsM5pGTmleqX9ekEWS04nEaJX9Jl2VZksJWoEBtRwVaigq1EhlipHmwjLNCPXLvT80V64l8vmXkObP4WWsa6w0TL2HAax4Rg8yu5pcHhdLH7cA7bUjL5OyWLQ1n5NKwZQpNod6CoFuT9XvAOpztgHf8XtKXw9EQZ/6oUkcqhLN+hlaoPhlQyzgJY8x4smwD5R8cfueBW6PEEhNQ4afHMvAL+Si48NZDBlqQM/krOPO35XaufmRCbH0FWC8FWP4JsRx+t7lMOgVYLYQH+RAT5ExFkpVqQPxHBViKC/KkWZCU80B9/i/uUiP3oX2bZdgc5dgfZR1tAcu1O7A7X0SZrywlN1seascMC/Am0VvxpBz+LmYY1QmhYI4QrW1bcPv0slWooHRGpQAoYcmYMA+zZRU5p2DNTOZCSROqBJDKPHCA+fS1xTnev690BTfiqzigOW1oTvOowQbYMgq0WjuQUuMNEcga7D+UUuyuL2URstQDqRgYRFxFEXOTRKSKQuMggooKtXvuLuTA4hAepuVdE5GwoYEjJco8cu3LjuCs5jMP/YLIXvcbcCtQ5OhU6bITwkuMmZud1x0gzAztL3F2t8ACa1Qqjea0wmtUKo1mtUOpGBukvZRGRSkYBQ9wMAw7/A3vXQuIaSNnsDhM5h4pdvLC9IN/wI40QjhihpBFCtjkMS0gUQeE1CK5ehz11rqatEUKC3UlOvnuAmMJTDzl2B4H+FprHFoaJMCKDNWKeiMj5QAGjijEMg8PZdhz5Wfgnb8A/6Ves+3/FP2kd5tziw8QBotjujGanUYt/jBh2GrXYZcSQbatJQp1oWsVVo3XtcFrHVSP2hMvGWlTUBxMRkXOKAsb5xGGH7x6Dfb8C7mGH7A4XOXYnuXYnuQXuyzf9Xfk0Mu3Dz1S082S+4cdmoz7rXQn87mrADiOWXUYMOQRgMkGT6FAuqBtB77rVaFcvggbVg3W1gIiIFEsB4zzi+m485jX/9bw2AbajU8TxCx7tzpBsRPKbkcBvrgR+MxL4w6iPHXfnxpAAP9rGVePKuhG0qxtBm7hwXecuIiKlpoBxHkjLsfPz1zO4cvM7ADxVcCt7jJqA+1LCuGqB1I0Kol6ke2S92pEhWGNbEhNeh95Abx/WLiIi5ycFjEps+4Espq/ayep1v/GFeTyY4CNTX5yd7qZ3nWq0rB1GwxohnjEeREREKooCRiVjGAY/bktl6k87Wf73QfxxMNc6iXBTDoeqteaGu98nIKD4+26IiIhUFAWMSmTxnym8+O1fbD/gHn/CZIK3an5J2/QdGAHhRA37BBQuRETkHKCAUQk4XQYTv9vK28t2ABBi8+PGDnHcHbOFmgs/B8B07dtQra4vyxQREfFQwDjHHcm2c/+s31ixLRWA4RfV56HLEwjNS4J3HnIvdOEIaNrHh1WKiIgUpYBxDtu8L51/f7SOfWm5BPpbePH61lzTJtY93sXc2yAvHWq3h55P+rpUERGRIhQwzlGfr9vLf+ZtIt/hol5UEP+9tT1NY47eGnfJU+7BtALC4fqp4KfhtUVE5NyigHGOsTtcPLvwT2as3g3AZU1r8trAtoQHHh3kaus3sHqK+3m/tyAi3jeFioiIlEAB4xxyICOPez9Zz6+7jwDwQI8EHuiRgNl8dDjutESYd7f7eed7oNnVPqpURESkZAoY54h/DmZx07s/cyAzn9AAPyYNbEuPZtHHFti3Hj67DfLSIPYCuPxpn9UqIiJyOgoY5wCH08VDczZyIDOfxtEh/PfWDtSvHux+0zDgl3fgu/HgKoDwunDDdPW7EBGRc5oCxjngneU72JiYRliAHzOGdyYm/OhgWTmH4cuRsHWh+3XTq6HfFAiMOPXGREREzgEKGD72x/50Xl+yDYCn+rU4Fi4S18BnwyE9ESxWuOI56HSne/hOERGRc5wChg/lO5w8PGcjBU6DK1vEcG3b2uBywao3YMnTYDghoj7cMM3d70JERKSSUMDwode/38ZfyZlEBVt5rn9LTDmH3FeJbF/sXqDlALh6EgSE+bROERGRslLA8JF1u4/wznL3vUWev64VUfZ9MO0qyEwCvwDo/SK0G6pTIiIiUikpYPhArt3J6LkbcRlw3QW16dU8Gmb0c4eLqAS48UOIbuHrMkVERM6YAoYPvPjtX+xMzSYmLIAnrmkBmz+HncvdLReD50JkfV+XKCIiclbMvi6gqlm1PZXpq3YB8NL1rQk35cCi/7jfvHi0woWIiJwXFDAqUEZeAf/32e8ADO5cl0sa14AfnoWsFIhqBBfd7+MKRUREvEMBowI9+9Wf7EvLpW5kEP+5qhns/w3Wvu9+s88r4GfzbYEiIiJeooBRQb7/M4U5v+7FZIKJN7Qh2N8EX40CwwUtr4cG3X1dooiIiNcoYFQAwzAY/+VmAO68uAGd6kfCummwfz3YwqDXcz6uUERExLsUMCrAwcx8ktLzMJvgoZ6NIesAfH/0bqiXPQahMb4tUERExMsUMCpA4pEcAGqFBxJotbjvjJqfDrXaQMc7fFydiIiI9ylgVIA9h90BIy4yEHaugN9nASbo8xqYLb4tTkREpBxooK0KkHg4F4D4cH9Y+IB7ZofboE57H1YlIiJSftSCUQESj7ZgXJM7D1K3QnAN6PG4j6sSEREpPwoYFWDP4RzqmA7SKfHomBdXPAuBEb4tSkREpBwpYFSAvUdyedxvBn7OPKj3L2g90NcliYiIlCsFjHJmd7gIytjGFZZ1GCaLe8RO3YJdRETOcwoY5Wx/Wi4DzUvdLxr3gppNfVuQiIhIBVDAKGd7U48wwLICAFP723xcjYiISMVQwChvW/5HhCmLQ5Ya0KiHr6sRERGpEAoY5Sxu51wAfq/RV4NqiYhIlaGAUZ4O7aBexjqchonkhtf7uhoREZEKo4BRntZ/CMByVxsiYxv6uBgREZGKo4BRXhx22DATgFnOS6kbGeTjgkRERCqOAkZ52fo1ZB8kxajGD64LiFPAEBGRKkQBo7wcPT0y19mNsOAgQmy6r5yIiFQdChjl4cgu2PEDALOd3YmLCPRtPSIiIhVMAaM8rP8IgL2RF5JoRFNHp0dERKSKUcDwNqcDfvsYgJVhfQDUwVNERKocBQxv27YIspIhqDqLXe0BiItQwBARkapFAcPb1rk7d9L2ZnalOQC1YIiISNWjgOFN6Xth+2IAjHZDSDycA0BcpDp5iohI1aKA4U2/fQyGC+Iv5qA1jnyHC7MJYqspYIiISNWigOEtLqfn6hHaDWXP0daLWuGB+Ft0mEVEpGrRN5+3bF8CGXshMAKa9SXxiE6PiIhI1aWA4S1HR+6kzSDwDyDxcC6gDp4iIlI1KWB4Q2YybP3G/bzdUADPKRJdoioiIlWRAoY3/PYxGE6I6ww1mwJ4riCpG6WAISIiVY8ChjfsWe1+bD/MM6swYNRRC4aIiFRBusWnNwz+DHavgtgLALA7XCRl5AHq5CkiIlWTAoY3mEwQf5Hn5f60XAwDAvzN1Aix+bAwERER39ApknJwfAdPk8nk42pEREQqngJGOSgcA0OXqIqISFWlgFEOPC0YChgiIlJFKWCUg71HB9mqE6EOniIiUjUpYJSDwhYMnSIREZGqSgGjHBy7D4kChoiIVE0KGF6WkVdAWk4BoIAhIiJVlwKGlxWO4BkZbCXEpmFGRESkalLA8LLCu6jGqYOniIhUYQoYXpaoS1RFREQUMLxNHTxFREQUMLwuUZeoioiI+D5gvPnmm8THxxMQEEDnzp1Zs2ZNictPmjSJJk2aEBgYSFxcHA899BB5eXkVVO3pHX8fEhERkarKpwFj9uzZjBo1iieeeIL169fTpk0bevXqxYEDB4pdfubMmTz66KM88cQTbNmyhQ8++IDZs2fzn//8p4IrL57LZbD3iLuTp1owRESkKvNpwHj11Ve58847ue2222jevDnvvPMOQUFBTJ06tdjlV61axUUXXcTNN99MfHw8V1xxBYMGDTptq0dFOZiVT77DhdkEtaoF+LocERERn/FZwLDb7axbt46ePXseK8ZspmfPnqxevbrYdbp27cq6des8geKff/7h66+/5qqrrjrlfvLz88nIyCgylZfC/he1wgPxt/j87JOIiIjP+GwkqNTUVJxOJ9HR0UXmR0dH89dffxW7zs0330xqair/+te/MAwDh8PB3XffXeIpkgkTJvDUU095tfZT0W3aRURE3CrVn9nLli3j+eef56233mL9+vV88cUXLFy4kGeeeeaU64wdO5b09HTPlJiYWG717Tl0dJCtSA2yJSIiVZvPWjCqV6+OxWIhJSWlyPyUlBRiYmKKXWf8+PHceuut3HHHHQC0atWK7Oxs7rrrLsaNG4fZfHJestls2Gw273+AYqgFQ0RExM1nLRhWq5X27duzZMkSzzyXy8WSJUvo0qVLsevk5OScFCIsFgsAhmGUX7GltEejeIqIiAA+bMEAGDVqFEOHDqVDhw506tSJSZMmkZ2dzW233QbAkCFDqF27NhMmTACgb9++vPrqq1xwwQV07tyZ7du3M378ePr27esJGr6092jAqKMxMEREpIrzacAYOHAgBw8e5PHHHyc5OZm2bdvy7bffejp+7tmzp0iLxWOPPYbJZOKxxx5j37591KhRg759+/Lcc8/56iN42B0ukjLcA37pFImIiFR1JuNcOLdQgTIyMggPDyc9PZ2wsDCvbXdnajaXTlxGoL+FP5/uhclk8tq2RUREzgVl+Q6tVFeRnMuO3UU1UOFCRESqPAUML9E9SERERI5RwPAS3aZdRETkGAUML0nUJaoiIiIeChheknj46CieERrFU0RERAHDSzyjeEapBUNEREQBwwsy8gpIyykA1MlTREQEFDC8orD/RVSwlWCbT8cuExEROScoYHhBYcCoow6eIiIigAKGV6iDp4iISFEKGF6g27SLiIgUpYDhBRm5Rzt4KmCIiIgAutmZ17abV+AEIMDf97eNFxERKQ9l+Q7VJQ9eomAhIiJyjE6RiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXKWCIiIiI1ylgiIiIiNcpYIiIiIjXlTlg5ObmkpOT43m9e/duJk2axHfffefVwkRERKTyKnPA6NevHzNmzAAgLS2Nzp0788orr9CvXz/efvttrxcoIiIilU+ZA8b69eu5+OKLAfjss8+Ijo5m9+7dzJgxgzfeeMPrBYqIiEjlU+aAkZOTQ2hoKADfffcd1113HWazmQsvvJDdu3d7vUARERGpfMocMBo1asT8+fNJTExk0aJFXHHFFQAcOHCAsLAwrxcoIiIilU+ZA8bjjz/O6NGjiY+Pp3PnznTp0gVwt2ZccMEFXi9QREREKh+TYRhGWVdKTk4mKSmJNm3aYDa7M8qaNWsICwujadOmXi/SmzIyMggPDyc9PV0tLiIiImVQlu9QvzPZQUxMDDExMZ6d/fDDDzRp0uScDxciIt7gcrmw2+2+LkOkXFitVk/jwdkoc8C48cYbueSSSxg5ciS5ubl06NCBXbt2YRgGs2bNYsCAAWXa3ptvvsnLL79McnIybdq0YfLkyXTq1OmUy6elpTFu3Di++OILDh8+TL169Zg0aRJXXXVVWT+KiEiZ2e12du7cicvl8nUpIuXCbDZTv359rFbrWW2nzAHjxx9/ZNy4cQDMmzcPwzBIS0vjww8/5Nlnny1TwJg9ezajRo3inXfeoXPnzkyaNIlevXqxdetWatasedLydrudyy+/nJo1a/LZZ59Ru3Ztdu/eTbVq1cr6MUREyswwDJKSkrBYLMTFxXnlrzyRc4nL5WL//v0kJSVRt25dTCbTGW+rzAEjPT2dyMhIAL799lsGDBhAUFAQffr04f/+7//KtK1XX32VO++8k9tuuw2Ad955h4ULFzJ16lQeffTRk5afOnUqhw8fZtWqVfj7+wMQHx9f1o8gInJGHA4HOTk5xMbGEhQU5OtyRMpFjRo12L9/Pw6Hw/NdeybKHL/j4uJYvXo12dnZfPvtt57LVI8cOUJAQECpt2O321m3bh09e/Y8VozZTM+ePVm9enWx6yxYsIAuXbowYsQIoqOjadmyJc8//zxOp/OU+8nPzycjI6PIJCJyJgp/15xt07HIuazw57uk79bSKHPAePDBBxk8eDB16tQhNjaW7t27A+5TJ61atSr1dlJTU3E6nURHRxeZHx0dTXJycrHr/PPPP3z22Wc4nU6+/vprxo8fzyuvvMKzzz57yv1MmDCB8PBwzxQXF1fqGkVEinM2zcYi5zpv/XyX+RTJvffeS6dOnUhMTOTyyy/3nINs0KBBiV/03uByuahZsybvvvsuFouF9u3bs2/fPl5++WWeeOKJYtcZO3Yso0aN8rzOyMhQyBARESlnZ9RDqUOHDvTv35/g4GAKh9Ho06cPF110Uam3Ub16dSwWCykpKUXmp6SkeC6BPVGtWrVo3LgxFovFM69Zs2YkJyef8pIxm81GWFhYkUlERM5OfHw8kyZNKvXyy5Ytw2QykZaWVm41ybnljALGjBkzaNWqFYGBgQQGBtK6dWs++uijMm3DarXSvn17lixZ4pnncrlYsmSJZ3TQE1100UVs3769yOVhf//9N7Vq1dI5URGRYphMphKnJ5988oy2u3btWu66665SL9+1a1eSkpIIDw8/o/2diaZNm2Kz2U552l3KV5kDxquvvso999zDVVddxZw5c5gzZw5XXnkld999N6+99lqZtjVq1Cjee+89PvzwQ7Zs2cI999xDdna256qSIUOGMHbsWM/y99xzD4cPH+aBBx7g77//ZuHChTz//POMGDGirB9DRKRKSEpK8kyTJk0iLCysyLzRo0d7ljUMA4fDUart1qhRo0xX0litVmJiYiqs/8pPP/1Ebm4u119/PR9++GGF7LMkBQUFvi6h4hllFB8fb3z44YcnzZ8+fboRHx9f1s0ZkydPNurWrWtYrVajU6dOxs8//+x5r1u3bsbQoUOLLL9q1Sqjc+fOhs1mMxo0aGA899xzhsPhKPX+0tPTDcBIT08vc60iUrXl5uYaf/75p5Gbm2sYhmG4XC4jO7/AJ5PL5Spz/dOmTTPCw8M9r5cuXWoAxtdff220a9fO8Pf3N5YuXWps377duOaaa4yaNWsawcHBRocOHYzFixcX2Va9evWM1157zfMaMN577z3j2muvNQIDA41GjRoZX3755Un7OnLkSJFavv32W6Np06ZGcHCw0atXL2P//v2edQoKCoz77rvPCA8PNyIjI41HHnnEGDJkiNGvX7/TftZhw4YZjz76qPHNN98YjRs3Pun9xMRE46abbjIiIiKMoKAgo3379kW+fxYsWGB06NDBsNlsRlRUlHHttdcW+azz5s0rsr3w8HBj2rRphmEYxs6dOw3AmDVrlnHJJZcYNpvNmDZtmpGammrcdNNNRmxsrBEYGGi0bNnSmDlzZpHtOJ1O48UXXzQaNmxoWK1WIy4uznj22WcNwzCMSy+91BgxYkSR5Q8cOGD4+/sb33///WmPSWmd+HN+vLJ8h5a5k2dSUhJdu3Y9aX5h81dZjRw5kpEjRxb73rJly06a16VLF37++ecy70dExNtyC5w0f3yRT/b959O9CLKe0d0eTvLoo48yceJEGjRoQEREBImJiVx11VU899xz2Gw2ZsyYQd++fdm6dSt169Y95XaeeuopXnrpJV5++WUmT57M4MGD2b17t2fspBPl5OQwceJEPvroI8xmM7fccgujR4/mk08+AeDFF1/kk08+Ydq0aTRr1ozXX3+d+fPnc+mll5b4eTIzM5k7dy6//PILTZs2JT09nRUrVnDxxRcDkJWVRbdu3ahduzYLFiwgJiaG9evXe06/L1y4kP79+zNu3DhmzJiB3W7n66+/PqPj+sorr3DBBRcQEBBAXl4e7du3Z8yYMYSFhbFw4UJuvfVWGjZs6BnBeuzYsbz33nu89tpr/Otf/yIpKYm//voLgDvuuIORI0fyyiuvYLPZAPj444+pXbs2l112WZnrK29l/uls1KgRc+bM4T//+U+R+bNnzyYhIcFrhYmISMV4+umnufzyyz2vIyMjadOmjef1M888w7x581iwYMEp/yAEGDZsGIMGDQLg+eef54033mDNmjVceeWVxS5fUFDAO++8Q8OGDQH3H5xPP/205/3JkyczduxY+vfvD8CUKVNK9UU/a9YsEhISaNGiBQA33XQTH3zwgSdgzJw5k4MHD7J27VpP+GnUqJFn/eeee46bbrqJp556yjPv+ONRWg8++CDXXXddkXnHn5K67777WLRoEXPmzKFTp05kZmby+uuvM2XKFIYOHQpAw4YN+de//gXAddddx8iRI/nyyy+58cYbAZg+fTrDhg07Jy+dLnPAeOqppxg4cCA//vij56qRlStXsmTJEubMmeP1AkVEzlWB/hb+fLqXz/btLR06dCjyOisriyeffJKFCxeSlJSEw+EgNzeXPXv2lLid1q1be54HBwcTFhbGgQMHTrl8UFCQJ1yA+0rBwuXT09NJSUkpcm+qwuEJTncfmKlTp3LLLbd4Xt9yyy1069aNyZMnExoayoYNG7jgggtO2bKyYcMG7rzzzhL3URonHlen08nzzz/PnDlz2LdvH3a7nfz8fE9fli1btpCfn0+PHj2K3V5AQAC33norU6dO5cYbb2T9+vVs3ryZBQsWnHWt5aHMAWPAgAH88ssvvPbaa8yfPx9wXyq6Zs0aLrjgAm/XJyJyzjKZTF47TeFLwcHBRV6PHj2axYsXM3HiRBo1akRgYCDXX3/9ae8ge+Kw0iaTqcQwUNzyxtGhD87Un3/+yc8//8yaNWsYM2aMZ77T6WTWrFnceeedBAYGlriN071fXJ3FdeI88bi+/PLLvP7660yaNIlWrVoRHBzMgw8+6Dmup9svuE+TtG3blr179zJt2jQuu+wy6tWrd9r1fOGMLlNt3749H3/8MevWrWPdunWec0DPP/+8t+sTEZEKtnLlSoYNG0b//v1p1aoVMTEx7Nq1q0JrCA8PJzo6mrVr13rmOZ1O1q9fX+J6H3zwAZdccgkbN25kw4YNnmnUqFF88MEHgLulZcOGDRw+fLjYbbRu3brIEAonqlGjRpE+h9u2bSMnJ+e0n2nlypX069ePW265hTZt2tCgQQP+/vtvz/sJCQkEBgaWuO9WrVrRoUMH3nvvPWbOnMnw4cNPu19f8dqtAJOSkhg/fry3NiciIj6SkJDAF198wYYNG9i4cSM333yzT25Pf9999zFhwgS+/PJLtm7dygMPPMCRI0dO2d+goKCAjz76iEGDBtGyZcsi0x133MEvv/zCH3/8waBBg4iJieHaa69l5cqV/PPPP3z++eee+2A98cQTfPrppzzxxBNs2bKFTZs28eKLL3r2c9lllzFlyhR+++03fv31V+6+++5S3RQsISGBxYsXs2rVKrZs2cK///3vIoNNBgQEMGbMGB555BFmzJjBjh07+Pnnnz3BqNAdd9zBCy+8gGEYnv4p5yLda1hERIp49dVXiYiIoGvXrvTt25devXrRrl27Cq9jzJgxDBo0iCFDhtClSxdCQkLo1avXKW+suWDBAg4dOlTsl26zZs1o1qwZH3zwAVarle+++46aNWty1VVX0apVK1544QXPKNHdu3dn7ty5LFiwgLZt23LZZZexZs0az7ZeeeUV4uLiuPjii7n55psZPXp0qcYEeeyxx2jXrh29evWie/funpBzvPHjx/Pwww/z+OOP06xZMwYOHHhSP5ZBgwbh5+fHoEGDynST0YpmMs72hNdRGzdupF27dmd997XylpGRQXh4OOnp6Ro2XETKJC8vj507d1K/fv1z+hf7+crlctGsWTNuvPFGnnnmGV+X4zO7du2iYcOGrF27tlyCX0k/52X5Dq38vZNEROS8tHv3br777ju6detGfn4+U6ZMYefOndx8882+Ls0nCgoKOHToEI899hgXXnihT1qVyqLUAeP4O5IW5+DBg2ddjIiISCGz2cz06dMZPXo0hmHQsmVLvv/+e5o1a+br0nxi5cqVXHrppTRu3JjPPvvM1+WcVqkDxm+//XbaZS655JKzKkZERKRQXFwcK1eu9HUZ54zu3buf9WW8FanUAWPp0qXlWYeIiIicR3QViYiIiHidAoaIiIh4nQKGiIiIeJ0ChoiIiHhdqQPG1KlTSU1NLc9aRERE5DxR6oDx8ccfU6dOHbp27cqLL77Ili1byrMuERE5h3Tv3p0HH3zQ8zo+Pp5JkyaVuI7JZPLcdftseGs7UrFKHTB++OEHkpKSuPfee1m3bh2dO3cmISGBhx9+mB9//NEnN8IREZGS9e3blyuvvLLY91asWIHJZOL3338v83bXrl3LXXfddbblFfHkk0/Stm3bk+YnJSXRu3dvr+7rVHJzc4mMjKR69erk5+dXyD7PV2XqgxEREcEtt9zCnDlzSE1NZfLkyeTm5jJ48GBq1qzJkCFD+Oyzz8jOzi6vekVEpAxuv/12Fi9ezN69e096b9q0aXTo0IHWrVuXebs1atQo1Q2+vCEmJgabzVYh+/r8889p0aIFTZs29XmriWEYOBwOn9ZwNs64k6fVauXKK6/krbfeIjExkW+//Zb4+HieeeYZXn31VW/WKCJybjIMsGf7ZirliI5XX301NWrUYPr06UXmZ2VlMXfuXG6//XYOHTrEoEGDqF27NkFBQbRq1YpPP/20xO2eeIpk27ZtXHLJJQQEBNC8eXMWL1580jpjxoyhcePGBAUF0aBBA8aPH09BQQEA06dP56mnnmLjxo2YTCZMJpOn5hNPkWzatInLLruMwMBAoqKiuOuuu8jKyvK8P2zYMK699lomTpxIrVq1iIqKYsSIEZ59leSDDz7glltu4ZZbbjnpNukAf/zxB1dffTVhYWGEhoZy8cUXs2PHDs/7U6dOpUWLFthsNmrVqsXIkSMB9w3KTCYTGzZs8CyblpaGyWRi2bJlACxbtgyTycQ333xD+/btsdls/PTTT+zYsYN+/foRHR1NSEgIHTt25Pvvvy9SV35+PmPGjCEuLg6bzUajRo344IMPMAyDRo0aMXHixCLLb9iwAZPJxPbt2097TM6U12521qFDBzp06MDTTz9dqn9EEZFKryAHno/1zb7/sx+swaddzM/PjyFDhjB9+nTGjRuHyWQCYO7cuTidTgYNGkRWVhbt27dnzJgxhIWFsXDhQm699VYaNmxIp06dTrsPl8vFddddR3R0NL/88gvp6elF+msUCg0NZfr06cTGxrJp0ybuvPNOQkNDeeSRRxg4cCCbN2/m22+/9Xx5hoeHn7SN7OxsevXqRZcuXVi7di0HDhzgjjvuYOTIkUVC1NKlS6lVqxZLly5l+/btDBw4kLZt23LnnXee8nPs2LGD1atX88UXX2AYBg899BC7d++mXr16AOzbt49LLrmE7t2788MPPxAWFsbKlSs9rQxvv/02o0aN4oUXXqB3796kp6ef0VDnjz76KBMnTqRBgwZERESQmJjIVVddxXPPPYfNZmPGjBn07duXrVu3UrduXQCGDBnC6tWreeONN2jTpg07d+4kNTUVk8nE8OHDmTZtGqNHj/bsY9q0aVxyySU0atSozPWVVrncTdXf3788NisiImdg+PDhvPzyyyxfvpzu3bsD7i+YAQMGEB4eTnh4eJEvn/vuu49FixYxZ86cUgWM77//nr/++otFixYRG+sOXM8///xJ/SYee+wxz/P4+HhGjx7NrFmzeOSRRwgMDCQkJAQ/Pz9iYmJOua+ZM2eSl5fHjBkzCA52B6wpU6bQt29fXnzxRaKjowH3Kf0pU6ZgsVho2rQpffr0YcmSJSUGjKlTp9K7d28iIiIA6NWrF9OmTePJJ58E4M033yQ8PJxZs2Z5vucaN27sWf/ZZ5/l4Ycf5oEHHvDM69ix42mP34mefvppLr/8cs/ryMhI2rRp43n9zDPPMG/ePBYsWMDIkSP5+++/mTNnDosXL6Znz54ANGjQwLP8sGHDePzxx1mzZg2dOnWioKCAmTNnntSq4W26XbuIyJnyD3K3JPhq36XUtGlTunbtytSpU+nevTvbt29nxYoVPP300wA4nU6ef/555syZw759+7Db7eTn55e6j8WWLVuIi4vzhAuALl26nLTc7NmzeeONN9ixYwdZWVk4HA7CwsJK/TkK99WmTRtPuAC46KKLcLlcbN261RMwWrRogcVi8SxTq1YtNm3adMrtOp1OPvzwQ15//XXPvFtuuYXRo0fz+OOPYzab2bBhAxdffHGxf0QfOHCA/fv306NHjzJ9nuJ06NChyOusrCyefPJJFi5cSFJSEg6Hg9zcXPbs2QO4T3dYLBa6detW7PZiY2Pp06cPU6dOpVOnTvzvf/8jPz+fG2644axrLYkG2hIROVMmk/s0hS+mo6c6Suv222/n888/JzMzk2nTptGwYUPPF9LLL7/M66+/zpgxY1i6dCkbNmygV69e2O12rx2q1atXM3jwYK666iq++uorfvvtN8aNG+fVfRzvxBBgMplKvNpx0aJF7Nu3j4EDB+Ln54efnx833XQTu3fvZsmSJQAEBgaecv2S3gP3reeBIndDPVV3guPDE8Do0aOZN28ezz//PCtWrGDDhg20atXKc+xOt2+AO+64g1mzZpGbm8u0adMYOHBguXfSVcAQEakCbrzxRsxmMzNnzmTGjBkMHz7c0x9j5cqV9OvXj1tuuYU2bdrQoEED/v7771Jvu1mzZiQmJpKUlOSZ9/PPPxdZZtWqVdSrV49x48bRoUMHEhIS2L17d5FlrFYrTqfztPvauHFjkasVV65cidlspkmTJqWu+UQffPABN910Exs2bCgy3XTTTZ7Onq1bt2bFihXFBoPQ0FDi4+M9YeRENWrUAChyjI7v8FmSlStXMmzYMPr370+rVq2IiYlh165dnvdbtWqFy+Vi+fLlp9zGVVddRXBwMG+//Tbffvstw4cPL9W+z0aZA0Z8fDxPP/20p2lGRETOfSEhIQwcOJCxY8eSlJTEsGHDPO8lJCSwePFiVq1axZYtW/j3v/9NSkpKqbfds2dPGjduzNChQ9m4cSMrVqxg3LhxRZZJSEhgz549zJo1ix07dvDGG28wb968IsvEx8ezc+dONmzYQGpqarHjUAwePJiAgACGDh3K5s2bWbp0Kffddx+33nqr5/RIWR08eJD//e9/DB06lJYtWxaZhgwZwvz58zl8+DAjR44kIyODm266iV9//ZVt27bx0UcfsXXrVsA9jscrr7zCG2+8wbZt21i/fj2TJ08G3K0MF154IS+88AJbtmxh+fLlRfqklCQhIYEvvviCDRs2sHHjRm6++eYirTHx8fEMHTqU4cOHM3/+fHbu3MmyZcuYM2eOZxmLxcKwYcMYO3YsCQkJxZ7C8rYyB4wHH3yQL774ggYNGnD55Zcza9YsDUYiIlIJ3H777Rw5coRevXoV6S/x2GOP0a5dO3r16kX37t2JiYnh2muvLfV2zWYz8+bNIzc3l06dOnHHHXfw3HPPFVnmmmuu4aGHHmLkyJG0bduWVatWMX78+CLLDBgwgCuvvJJLL72UGjVqFHupbFBQEIsWLeLw4cN07NiR66+/nh49ejBlypSyHYzjFHYYLa7/RI8ePQgMDOTjjz8mKiqKH374gaysLLp160b79u157733PKdjhg4dyqRJk3jrrbdo0aIFV199Ndu2bfNsa+rUqTgcDtq3b8+DDz7Is88+W6r6Xn31VSIiIujatSt9+/alV69etGvXrsgyb7/9Ntdffz333nsvTZs25c477zxpTKrbb78du93ObbfdVtZDdEZMhlHKi6lPsH79eqZPn86nn36K0+nk5ptvZvjw4Sd96HNNRkYG4eHhpKenl7lzkYhUbXl5eezcuZP69esTEBDg63JEymTFihX06NGDxMTEElt7Svo5L8t36Bn3wWjXrh1vvPEG+/fv54knnuD999+nY8eOtG3blqlTp3KGuUVERES8KD8/n7179/Lkk09yww03nPGppLI644BRUFDAnDlzuOaaa3j44Yfp0KED77//PgMGDOA///kPgwcP9madIiIicgY+/fRT6tWrR1paGi+99FKF7bfM42CsX7+eadOm8emnn2I2mxkyZAivvfYaTZs29SzTv3//MxpcRERERLxr2LBhRTr1VpQyB4yOHTty+eWX8/bbb3PttdcWO+BI/fr1uemmm7xSoIiIiFQ+ZQ4Y//zzj2dc9lMJDg5m2rRpZ1yUiIiIVG5l7oNx4MABfvnll5Pm//LLL/z6669eKUpEREQqtzIHjBEjRpCYmHjS/H379jFixAivFCUiIiKVW5kDxp9//lnsWBcXXHABf/75p1eKqmz2Ze1jXco6krOTfV2KiIjIOaHMAcNmsxU7hGxSUhJ+flXz5qyvrXuNYd8O4/vd3/u6FBERkXNCmQPGFVdcwdixY0lPT/fMS0tL4z//+U+R+9dXJdVs1QBIy0/zaR0iIhUlPj6eSZMmlXr5ZcuWYTKZSEtLK7ea5NxS5oAxceJEEhMTqVevHpdeeimXXnop9evXJzk5mVdeeaU8ajznhdvCAQUMETn3mEymEqcnn3zyjLa7du1a7rrrrlIv37VrV5KSkggPDz+j/ZWWgsy5o8znNGrXrs3vv//OJ598wsaNGwkMDOS2225j0KBBxY6JURUUtmCk56eXvKCISAU7/vbgs2fP5vHHH/fc/RPcd1ktZBgGTqezVKe7C28/XlpWq5WYmJgyrSOV2xkNFR4cHMxdd93Fm2++ycSJExkyZEiVDRegUyQiVZVhGOQU5PhkKu39nmJiYjxTeHg4JpPJ8/qvv/4iNDSUb775hvbt22Oz2fjpp5/YsWMH/fr1Izo6mpCQEDp27Mj33xftY3biKRKTycT7779P//79CQoKIiEhgQULFnjeP7FlYfr06VSrVo1FixbRrFkzQkJCuPLKK4sEIofDwf3330+1atWIiopizJgxDB06tEx3ej3RkSNHGDJkCBEREQQFBdG7d+8idzzdvXs3ffv2JSIiguDgYFq0aMHXX3/tWXfw4MHUqFGDwMBAEhISNOZTCc64V+aff/7Jnj17sNvtReZfc801Z11UZVN4ikQtGCJVS64jl84zO/tk37/c/AtB/kFe2dajjz7KxIkTadCgARERESQmJnLVVVfx3HPPYbPZmDFjBn379mXr1q3UrVv3lNt56qmneOmll3j55ZeZPHkygwcPZvfu3URGRha7fE5ODhMnTuSjjz7CbDZzyy23MHr0aD755BMAXnzxRT755BOmTZtGs2bNeP3115k/fz6XXnrpGX/WYcOGsW3bNhYsWEBYWBhjxozhqquu4s8//8Tf358RI0Zgt9v58ccfCQ4O5s8///S08owfP54///yTb775hurVq7N9+3Zyc3PPuJbz3RmN5Nm/f382bdqEyWTypGiTyQSA0+n0boWVgE6RiEhl9vTTTxfppB8ZGUmbNm08r5955hnmzZvHggULGDly5Cm3M2zYMAYNGgTA888/zxtvvMGaNWu48sori12+oKCAd955h4YNGwIwcuRInn76ac/7kydPZuzYsfTv3x+AKVOmeFoTzkRhsFi5ciVdu3YF4JNPPiEuLo758+dzww03sGfPHgYMGECrVq0AaNCggWf9PXv2cMEFF9ChQwfA3Yojp1bmgPHAAw9Qv359lixZQv369VmzZg2HDh3i4YcfZuLEieVR4zlPp0hEqqZAv0B+ufnkkY0rat/eUviFWSgrK4snn3yShQsXkpSUhMPhIDc3lz179pS4ndatW3ueBwcHExYWxoEDB065fFBQkCdcANSqVcuzfHp6OikpKXTq1MnzvsVioX379rhcrjJ9vkJbtmzBz8+Pzp2PtTpFRUXRpEkTtmzZAsD999/PPffcw3fffUfPnj0ZMGCA53Pdc889DBgwgPXr13PFFVdw7bXXeoKKnKzMfTBWr17N008/TfXq1TGbzZjNZv71r38xYcIE7r///vKo8ZxXeIokx5FDgbPAx9WISEUxmUwE+Qf5ZCpsNfaG4ODgIq9Hjx7NvHnzeP7551mxYgUbNmygVatWJ50SP9GJffFMJlOJYaC45Uvbt6S83HHHHfzzzz/ceuutbNq0iQ4dOjB58mQAevfuze7du3nooYfYv38/PXr0YPTo0T6t91xW5oDhdDoJDQ0FoHr16uzfvx+AevXqFemZXJWEWkMxm9yHUq0YIlLZrVy5kmHDhtG/f39atWpFTEwMu3btqtAawsPDiY6OZu3atZ55TqeT9evXn/E2mzVrhsPhKHI/rUOHDrF161aaN2/umRcXF8fdd9/NF198wcMPP8x7773nea9GjRoMHTqUjz/+mEmTJvHuu++ecT3nuzKfImnZsiUbN26kfv36dO7cmZdeegmr1cq7775b5FxVVWI2mQmzhpGWn0Zafho1gsp2+ZaIyLkkISGBL774gr59+2IymRg/fvwZn5Y4G/fddx8TJkygUaNGNG3alMmTJ3PkyJFStd5s2rTJ88cwuFtH2rRpQ79+/bjzzjv573//S2hoKI8++ii1a9emX79+ADz44IP07t2bxo0bc+TIEZYuXUqzZs0AePzxx2nfvj0tWrQgPz+fr776yvOenKzMAeOxxx4jOzsbcHcMuvrqq7n44ouJiopi9uzZXi+wsqhmq+YJGCIildmrr77K8OHD6dq1K9WrV2fMmDFkZGRUeB1jxowhOTmZIUOGYLFYuOuuu+jVqxcWi+W0615yySVFXlssFhwOB9OmTeOBBx7g6quvxm63c8kll/D11197Ttc4nU5GjBjB3r17CQsL48orr+S1114D3GN5jB07ll27dhEYGMjFF1/MrFmzvP/BzxMmwwsnvA4fPkxERIRXzwmWl4yMDMLDw0lPTycsLMxr273l61vYeHAjr3V/jZ71enptuyJy7sjLy2Pnzp3Ur1+fgIAAX5dT5bhcLpo1a8aNN97IM8884+tyzlsl/ZyX5Tu0TH0wCgoK8PPzY/PmzUXmR0ZGVopwUZ50JYmIiHft3r2b9957j7///ptNmzZxzz33sHPnTm6++WZflyalUKaA4e/vT926davkWBeno8G2RES8y2w2M336dDp27MhFF13Epk2b+P7779XvoZIocx+McePG8Z///IePPvrolKOzVUUabEtExLvi4uJYuXKlr8uQM1TmgDFlyhS2b99ObGws9erVO+n66bO5hKgy0x1VRUREjilzwDibm8ycz9QHQ0RE5JgyB4wnnniiPOqo9NQHQ0RE5Jgzul27nEwtGCIiIseUuQXDbDaXeElqVb3CRAFDRETkmDIHjHnz5hV5XVBQwG+//caHH37IU0895bXCKpvCUyQZ+RkYhlHlxwUREZGqrcynSPr161dkuv7663nuued46aWXWLBgQXnUWCkUtmA4DAdZBVm+LUZExMu6d+/Ogw8+6HkdHx/PpEmTSlzHZDIxf/78s963t7YjFctrfTAuvPBClixZ4q3NVToBfgEEWNxDqqqjp4icK/r27cuVV15Z7HsrVqzAZDLx+++/l3m7a9eu5a677jrb8op48sknadu27Unzk5KS6N27t1f3daLp06dTrVq1ct1HVeOVgJGbm8sbb7xB7dq1vbG5SktXkojIueb2229n8eLF7N2796T3pk2bRocOHWjdunWZt1ujRg2CgoK8UeJpxcTEYLPZKmRf4j1lDhgRERFERkZ6poiICEJDQ5k6dSovv/xyedRYaWiwLZGqxTAMXDk5PplKe5/Kq6++mho1ajB9+vQi87Oyspg7dy633347hw4dYtCgQdSuXZugoCBatWrFp59+WuJ2TzxFsm3bNi655BICAgJo3rw5ixcvPmmdMWPG0LhxY4KCgmjQoAHjx4+noKAAcLcgPPXUU2zcuBGTyYTJZPLUfOIpkk2bNnHZZZcRGBhIVFQUd911F1lZx05NDxs2jGuvvZaJEydSq1YtoqKiGDFihGdfZ2LPnj3069ePkJAQwsLCuPHGG0lJSfG8v3HjRi699FJCQ0MJCwujffv2/Prrr4D7nip9+/YlIiKC4OBgWrRowddff33GtVQWZe7k+dprrxXpwGg2m6lRowadO3cmIiLCq8VVNrqSRKRqMXJz2dquvU/23WT9OkylaEHw8/NjyJAhTJ8+nXHjxnl+f8+dOxen08mgQYPIysqiffv2jBkzhrCwMBYuXMitt95Kw4YN6dSp02n34XK5uO6664iOjuaXX34hPT29SH+NQqGhoUyfPp3Y2Fg2bdrEnXfeSWhoKI888ggDBw5k8+bNfPvtt3z//fcAhIeHn7SN7OxsevXqRZcuXVi7di0HDhzgjjvuYOTIkUVC1NKlS6lVqxZLly5l+/btDBw4kLZt23LnnXee9vMU9/kKw8Xy5ctxOByMGDGCgQMHsmzZMgAGDx7MBRdcwNtvv43FYmHDhg2eW8CPGDECu93Ojz/+SHBwMH/++SchISFlrqOyKXPAGDZsWDmUcX5QC4aInIuGDx/Oyy+/zPLly+nevTvgPj0yYMAAwsPDCQ8PZ/To0Z7l77vvPhYtWsScOXNKFTC+//57/vrrLxYtWkRsbCwAzz///En9Jh577DHP8/j4eEaPHs2sWbN45JFHCAwMJCQkBD8/P2JiYk65r5kzZ5KXl8eMGTM8t6qYMmUKffv25cUXXyQ6Ohpwt7ZPmTIFi8VC06ZN6dOnD0uWLDmjgLFkyRI2bdrEzp07iYuLA2DGjBm0aNGCtWvX0rFjR/bs2cP//d//0bRpUwASEhI86+/Zs4cBAwbQqlUrABo0aFDmGiqjMgeMadOmERISwg033FBk/ty5c8nJyWHo0KFeK66y0Q3PRKoWU2AgTdav89m+S6tp06Z07dqVqVOn0r17d7Zv386KFSt4+umnAff4Rc8//zxz5sxh37592O128vPzS93HYsuWLcTFxXnCBUCXLl1OWm727Nm88cYb7Nixg6ysLBwOB2FhYaX+HIX7atOmTZH7YF100UW4XC62bt3qCRgtWrTAYrF4lqlVqxabNm0q076O32dcXJwnXAA0b96catWqsWXLFjp27MioUaO44447+Oijj+jZsyc33HADDRs2BOD+++/nnnvu4bvvvqNnz54MGDDgjPq9VDZl7oMxYcIEqlevftL8mjVr8vzzz3ulqMpKp0hEqhaTyYQ5KMgnU1nH2rn99tv5/PPPyczMZNq0aTRs2JBu3boB8PLLL/P6668zZswYli5dyoYNG+jVqxd2u91rx2r16tUMHjyYq666iq+++orffvuNcePGeXUfxys8PVHIZDLhcrnKZV/gvgLmjz/+oE+fPvzwww80b97cM27UHXfcwT///MOtt97Kpk2b6NChA5MnTy63Ws4VZQ4Ye/bsoX79+ifNr1evHnv27PFKUZWVTpGIyLnqxhtvxGw2M3PmTGbMmMHw4cM9IWXlypX069ePW265hTZt2tCgQQP+/vvvUm+7WbNmJCYmkpSU5Jn3888/F1lm1apV1KtXj3HjxtGhQwcSEhLYvXt3kWWsVutpR4Nu1qwZGzduJDs72zNv5cqVmM1mmjRpUuqay6Lw8yUmJnrm/fnnn6SlpdG8eXPPvMaNG/PQQw/x3Xffcd111zFt2jTPe3Fxcdx999188cUXPPzww7z33nvlUuu5pMwBo2bNmsVeM71x40aioqK8UlRlpVMkInKuCgkJYeDAgYwdO5akpKQi/ekSEhJYvHgxq1atYsuWLfz73/8ucoXE6fTs2ZPGjRszdOhQNm7cyIoVKxg3blyRZRISEtizZw+zZs1ix44dvPHGGyeNDB0fH8/OnTvZsGEDqamp5Ofnn7SvwYMHExAQwNChQ9m8eTNLly7lvvvu49Zbb/WcHjlTTqeTDRs2FJm2bNlCz549adWqFYMHD2b9+vWsWbOGIUOG0K1bNzp06EBubi4jR45k2bJl7N69m5UrV7J27VqaNWsGwIMPPsiiRYvYuXMn69evZ+nSpZ73zmdlDhiDBg3i/vvvZ+nSpTidTpxOJz/88AMPPPAAN910U3nUWGkoYIjIuez222/nyJEj9OrVq0h/iccee4x27drRq1cvunfvTkxMDNdee22pt2s2m5k3bx65ubl06tSJO+64g+eee67IMtdccw0PPfQQI0eOpG3btqxatYrx48cXWWbAgAFceeWVXHrppdSoUaPYS2WDgoJYtGgRhw8fpmPHjlx//fX06NGDKVOmlO1gFCMrK4sLLrigyNS3b19MJhNffvklERERXHLJJfTs2ZMGDRowe/ZsACwWC4cOHWLIkCE0btyYG2+8kd69e3tun+F0OhkxYgTNmjXjyiuvpHHjxrz11ltnXe+5zmSU9mLqo+x2O7feeitz587Fz8/dR9TlcjFkyBDeeecdrFZruRTqLRkZGYSHh5Oenl7mzkWns+HABm795lZqh9Tm2wHfenXbIuJ7eXl57Ny5k/r16xMQEODrckTKRUk/52X5Di3zVSRWq5XZs2fz7LPPsmHDBgIDA2nVqhX16tUr66bOOxrJU0RExK3MAaNQQkJCket85dgpkqyCLApcBfib/UteQURE5DxV5j4YAwYM4MUXXzxp/ksvvXTS2BhVTZg1DBPuXtlqxRARkaqszAHjxx9/5Kqrrjppfu/evfnxxx+9UlRlZTFbCLWGAgoYIiJStZU5YGRlZRXbkdPf35+MjAyvFFWZabAtkfNfGfvGi1Qq3vr5LnPAaNWqlefSnOPNmjWryIAjVZUChsj5q3Do6fIafVLkXFD48338UOtnosydPMePH891113Hjh07uOyyywD3jWA+/fRT5s6de0ZFvPnmm7z88sskJyfTpk0bJk+eXKob7MyaNYtBgwbRr1+/Irfy9aXCK0ky8tWaI3K+8fPzIygoiIMHD+Lv74/ZXOa/0UTOaS6Xi4MHDxIUFOQZiuJMlXntvn37Mn/+fJ5//nk+++wzAgMDad26Nd9//71nXPuymD17NqNGjeKdd96hc+fOTJo0iV69erF161Zq1qx5yvV27drF6NGjufjii8u8z/KkFgyR85fJZKJWrVrs3LnzpGGuRc4XZrOZunXrlvl+Nyc6o3jSp08f+vTpc9L8zZs307JlyzJt69VXX+XOO+/ktttuA+Cdd95h4cKFTJ06lUcffbTYdZxOJ4MHD+app55ixYoVpKWllfkzlBfdj0Tk/Ga1WklISNBpEjlvWa1Wr7TOnV37B5CZmcmnn37K+++/z7p16057o5rj2e121q1bx9ixYz3zzGYzPXv2ZPXq1adc7+mnn6ZmzZrcfvvtrFixosR95OfnFxnPvrw7omqwLZHzn9ls1kieIqdxxhHlxx9/ZMiQIdSqVYuJEydy2WWXnXT3vNNJTU3F6XSedIOa6OhokpOTi13np59+4oMPPij1negmTJhAeHi4Z4qLiytTjWWlUyQiIiJlbMFITk5m+vTpfPDBB2RkZHDjjTeSn5/P/PnzK+QKkszMTG699Vbee+89qlevXqp1xo4dy6hRozyvMzIyyjVkKGCIiIiUIWD07duXH3/8kT59+jBp0iSuvPJKLBYL77zzzhnvvHr16lgslpNuC5ySkkJMTMxJy+/YsYNdu3bRt29fzzyXywW4e3dv3bqVhg0bFlnHZrNhs9nOuMay0ikSERGRMgSMb775hvvvv5977rnHa/cgsVqttG/fniVLlnhuDexyuViyZAkjR448afmmTZuyadOmIvMee+wxMjMzef3118v99EdpqAVDRESkDAGjsO9D+/btadasGbfeeis33XTTWRcwatQohg4dSocOHejUqROTJk0iOzvbc1XJkCFDqF27NhMmTCAgIOCkq1SqVasGUOarV8rL8QHDMIyzvsxHRESkMip1J88LL7yQ9957j6SkJP79738za9YsYmNjcblcLF68mMzMzDMqYODAgUycOJHHH3+ctm3bsmHDBr799ltPx889e/aQlJR0Rtv2hcJTJA6Xg1xHro+rERER8Q2TcRaDjm/dupUPPviAjz76iLS0NC6//HIWLFjgzfq8LiMjg/DwcNLT0wkLC/P69g3DoMPHHbC77CwasIjYkFiv70NERMQXyvIdelYjaTRp0oSXXnqJvXv38umnn57Nps4bJpNJg22JiEiV55WB9C0WC9dee+0533pRURQwRESkqtOdespBYUdPXaoqIiJVlQJGOdClqiIiUtUpYJQDnSIREZGqTgGjHOgUiYiIVHUKGOVAp0hERKSqU8AoB7ofiYiIVHUKGOVAp0hERKSqU8AoB+rkKSIiVZ0CRjlQHwwREanqFDDKQWELRqY9E4fL4eNqREREKp4CRjkoDBgAGfYMH1YiIiLiGwoY5cDP7Eeofyig0yQiIlI1KWCUE12qKiIiVZkCRjnxdPTMS/NpHSIiIr6ggFFOwgOOtmDY1YIhIiJVjwJGOdFgWyIiUpUpYHhBzrp1pP73XfK2bvXMC7dqsC0REam6/HxdwPng8IczyPzuO0x+fgQ0aQJosC0REana1ILhBQHNmwOQ9+efnnm6ikRERKoyBQwvCGhxcsBQC4aIiFRlChheUNiCYd+1C2dWNqCAISIiVZsChhf4RUXhFxMDhkH+1r+A4y5TzdMpEhERqXoUMLzE0w/jD/dpEs9lqhoHQ0REqiAFDC85FjD+AI4FjHxnPrmOXF+VJSIi4hMKGF5y4pUkQX5B+JndVwHrShIREalqFDC8pPBKkvwdO3Dl5mIymTTYloiIVFkKGF7iV7MmlqgocLnI//tvQFeSiIhI1aWA4SUmk+mk0ySFg20pYIiISFWjgOFFJwYMz5UkulRVRESqGAUMLzrpUtWAaoBaMEREpOpRwPAiz5Dh27Zh2O06RSIiIlWWAoYX+deujTksDAoKyN++3XOKJMOe4dvCREREKpgChhed2NFTV5GIiEhVpYDhZccHDJ0iERGRqkoBw8uO7+hZONCWRvIUEZGqRgHDyzwBY+tWwi2hgFowRESk6lHA8DJrfD3MQUEYeXmEprg7d2bkZ+B0OX1cmYiISMVRwPAyk9mMrVkzAPy3JQJgYJBpz/RlWSIiIhVKAaMcFI6H4fhrK8H+wYBOk4iISNWigFEOju/oqUtVRUSkKlLAKAeegLFlC9X8wwANtiUiIlWLAkY5sDVogMlmw5WdTb3MAEAtGCIiUrUoYJQDk58ftqZNAIhPcl89kpaX5sOKREREKpYCRjkpPE0Suy8PUAuGiIhULQoY5aQwYFRPdPe90GieIiJSlShglJPCgBG2KxUMQy0YIiJSpShglBNbQgL4++OXlUeNdLVgiIhI1aKAUU7MViu2hEYA1E9RC4aIiFQtChjlqPA0Sf1kg3S7WjBERKTqUMAoR8cChk6RiIhI1aKAUY4CjwaMBskGuQU55DvzfVyRiIhIxVDAKEe2Jk3AbKZaDkRkabAtERGpOhQwypE5MBBbwwaAux+GOnqKiEhVoYBRzjz9MFLUD0NERKoOBYxyFtCiBeDuh6EWDBERqSoUMMrZsRYMBQwREak6FDDKma1pMwCqZ0D2wSQfVyMiIlIxFDDKmSUkmKyYcABMf+/0cTUiIiIVQwGjAuQ2jAHAun2fjysRERGpGAoYFcCZEA9A6M4Dvi1ERESkgihgVABLU/dNzyL36DJVERGpGhQwKkBgc/elqpGH8nEcPOjjakRERMqfAkYFCK9Rm79j3c/TF/zPt8WIiIhUAAWMClDNVo2lrd2HOu2LzzEMw8cViYiIlC8FjApQzVaNVc1N5PuBfcc/5G3c6OuSREREypUCRgWwWqzUrBHPz01NAKR9/rmPKxIRESlfChgVpGtsV35o4z7cGQu/xpWT4+OKREREyo8CRgXpGtuVLXFwMMoPV04OGd8u8nVJIiIi5UYBo4J0jOmIn9mf71u6AHdnTxERkfOVAkYFCfYPpm3NtixrZcIwm8j9dR35O3VvEhEROT8pYFSgrrFdORJqYk+zKADSv5jn44pERETKhwJGBeoa2xWA/zV3d/BMnz8fw+HwZUkiIiLlQgGjAjWLakY1WzVW1s/HCA/FcfAgWT/95OuyREREvO6cCBhvvvkm8fHxBAQE0LlzZ9asWXPKZd977z0uvvhiIiIiiIiIoGfPniUufy4xm8x0qdUFp8XEnosaAJCuMTFEROQ85POAMXv2bEaNGsUTTzzB+vXradOmDb169eLAgeJvbb5s2TIGDRrE0qVLWb16NXFxcVxxxRXs27evgis/M11iuwDwTfN8ADKXLsNx6JAvSxIREfE6k+HjG2N07tyZjh07MmXKFABcLhdxcXHcd999PProo6dd3+l0EhERwZQpUxgyZMhpl8/IyCA8PJz09HTCwsLOuv6ySslOoednPTFh4osFjSj4Yws1x4wh6rZhFV6LiIhIWZTlO9SnLRh2u51169bRs2dPzzyz2UzPnj1ZvXp1qbaRk5NDQUEBkZGRxb6fn59PRkZGkcmXooOjaVStEQYGyZe1BCDt8890AzQRETmv+DRgpKam4nQ6iY6OLjI/Ojqa5OTkUm1jzJgxxMbGFgkpx5swYQLh4eGeKS4u7qzrPluFp0mWNSnAZLNh376DvN9/93FVIiIi3uPzPhhn44UXXmDWrFnMmzePgICAYpcZO3Ys6enpnikxMbGCqzzZRbEXAbDsyFpCr7gCgLTPv/BlSSIiIl7l04BRvXp1LBYLKSkpReanpKQQExNT4roTJ07khRde4LvvvqN169anXM5msxEWFlZk8rV20e2wmq2k5KSQe6V7bIyMhQt1AzQRETlv+DRgWK1W2rdvz5IlSzzzXC4XS5YsoUuXLqdc76WXXuKZZ57h22+/pUOHDhVRqlcF+gXSLrodAD9HZ+IfF4crO5uM777zcWUiIiLe4fNTJKNGjeK9997jww8/ZMuWLdxzzz1kZ2dz2223ATBkyBDGjh3rWf7FF19k/PjxTJ06lfj4eJKTk0lOTiYrK8tXH+GMFJ4mWZm8imrX9QcgXadJRETkPOHzgDFw4EAmTpzI448/Ttu2bdmwYQPffvutp+Pnnj17SEpK8iz/9ttvY7fbuf7666lVq5Znmjhxoq8+whkp7Oj5a/KvBF1zNZhM5Kxdi333bh9XJiIicvZ8Pg5GRfP1OBiFDMPgsrmXkZqbyvtXvE+txz8ge8UKou68k5oPj/JZXSIiIqdSacbBqMpMJpPn5mcr96+k2vXXA3Bk9myclex0j4iIyIkUMHyo8DTJ6v2rCe3ZA2uDBrgyMjjy8Sc+rkxEROTsKGD4UJda7oDx1+G/OGQ/QvV77gbg8LRpOLOyfVmaiIjIWVHA8KGowCiaRTYD3K0YYb17Y61XD2d6Okc+nenj6kRERM6cAoaPHX+axOTnR1RhK8bUaRp4S0REKi0FDB8rHA9j1f5VGIZB+NVX4x8Xh/PIEY7Mmu3j6kRERM6MAoaPta3ZlkC/QA7lHeLvI39j8vOj+t3/BuDQ1Km4cnN9XKGIiEjZKWD4mNVipUO0e7jzVftXARB+zTX4x8biTE0lbc4cX5YnIiJyRhQwzgEX1T52mgTA5O9P1L/drRip77+PKy/PZ7WJiIicCQWMc0BhR8/1KevJdbhPiVTrfy1+tWrhPJhK2mef+7I8ERGRMlPAOAfUD6tPTHAMdpeddSnrADBZrVS/604ADr33Hi673ZclioiIlIkCxjnAZDIdu7vqvpWe+eEDBuAXHY0jJYX0z9WKISIilYcCxjnikjqXAPDlji/JsGcAYLZaibrjDgBS330PQ60YIiJSSShgnCO61elGo2qNyLRnMuOPGZ751W68Ab8aNXAkJZE2f77vChQRESkDBYxzhMVs4d629wLw8ZaPOZJ3BACzzUbUHbcDcOi/72IUFPisRhERkdJSwDiH9Kjbg6aRTckuyGbaH9M886vdeCOWqCgK9u0jfcECH1YoIiJSOgoY5xCzyczItiMB+HTLp6TmprrnBwYSNXw4AKnv/BfD4fBZjSIiIqWhgHGOuaTOJbSu3po8Zx4fbPrAMz9i0E1YIiIoSEwk/auvfFihiIjI6SlgnGNMJhMjLhgBwJytc0jOTgbAHBRE5G23AXDonf9iOJ0+q1FEROR0FDDOQV1qdaFdzXbYXXbe3/S+Z37EzTdjCQ/HvmsXGd9868MKRURESqaAcQ4ymUyMvMDdF+PzbZ+zL2sfAJaQYCKGDgHg0H/fwXC5fFajiIhISRQwzlEdYzpyYa0Lcbgc/Hfjfz3zI2+5BXNICPnbtpO5+HsfVigiInJqChjnsMJWjAU7FrA7YzcAlrAwIofcCkDq229jGIbP6hMRETkVBYxzWJsabbikziU4DSdvb3zbMz9yyBDMQUHk//UXWUuX+rBCERGR4ilgnONGtHVfUfL1P1+z/ch2ACzVqhExeDAAqW+pFUNERM49ChjnuOZRzelZtycGBm9tfMszP/K2YZgCA8nbvJnsFSt8WKGIiMjJFDAqgXvb3osJE4t3L+avw38B4BcZScTAgYBaMURE5NyjgFEJJEQkcGX8lQC8+dubnvmRw2/DZLORu2EDOT//7KvyRERETqKAUUnc0/YezCYzy/Yu4+t/vgbAv2ZNqt1wAwCpb75V0uoiIiIVSgGjkqgfXp8bG98IwJgVY5i+eTqGYRB1x+2Y/P3J+fVXctau9XGVIiIibgoYlcijnR5lcDP31SOvrHuFCWsmYK5Zg/AB1wHucTFERETOBQoYlYjFbOHRTo/yfx3+D4BP//qUUctGETL8VvDzI3vVanJ++83HVYqIiChgVEpDWgxhYreJWM1Wfkj8gbs3PU5AX3cnULViiIjIuUABo5LqFd+L9654j3BbOL+n/s7jDTaAxUz2jyvI3bTZ1+WJiEgVp4BRibWLbsdHvT+idkhtNliTWd3CH4CUF1+gYP9+H1cnIiJVmQJGJVc/vD4fX/UxzaOaM+tCBw4z5P66ju1X9GL/Y49h373b1yWKiEgVpIBxHqgeWJ1pvaaR0KYbTwy2sKmeCRwO0j/7nB29r2LfI4+Qv327r8sUEZEqxGRUsTGmMzIyCA8PJz09nbCwMF+X41UOl4O3NrzF9D+mU3+PnetWuWi34+g/r8lE6BVXUP3ufxPQrJlvCxURkUqpLN+hChjnob2Ze5m0fhKLdi2ifpLBDatNdNjq8Lwf0r070WMfxVqvng+rFBGRykYBowRVIWAU+u3Ab7y05iU2H9pM3EGDm9fYaLc5F5PLwBIZSdy77xLYsoWvyxQRkUpCAaMEVSlgALgMF9/s/IZJ6yeRnJ1MrUMGj35tpdbeXJyBNnKeuY/q/7qUmOAYgvyDfF2uiIicwxQwSlDVAkahXEcuH/35Ee9veh+yc/i/z1203G1QYIE3rjHzS1MzYdYwYoJjqBVci8YRjRnaYijhtnBfly4iIucIBYwSVNWAUSg1N5Uvt3/J/iO7afvmUhptTMVlgveuNLOkbdGLiqICohjTaQxXxl+JyWTyUcUiInKuUMAoQVUPGMcznE6Sn3yKtLlzATD/+xYOXH8x+7OT+OSvT9iZvhOAf9X+F49d+Bi1Q2r7slwREfGxsnyHahyMKsxksRDz9FNE/fvfALj++zEJH/3EjY1v4LO+n3Fv23vxN/vz076f6P9lf6Zvno7D5TjNVkVERBQwqjyTyUTNhx4keuyjAByZ8RH7H30Uf5eJe9rcw+fXfE6H6A7kOnJ5Zd0rDFo4iM2puteJiIiUTKdIxCN9wQL2/2ccOBwEX3wxsS+9iF9EBIZhMH/7fCb+OpEMewZmk5lBTQcxvOVwagTWUP8MEZEqQn0wSqCAUbKs5cvZ+8CDGHl5WGpUJ/bZZwnp1g2AQ7mHePnXl1n4z0LP8oF+gdQNrUvdsLonPSp8iIicXxQwSqCAcXq5m/9g/yOPYP/nHwCq3XA9NceMwRISAsCqfat4dd2rbEvbhstwnXI7If4h9Kjbg/4J/WlXs53ChohIJaeAUQIFjNJx5eVxcNLrHP7wQzAM/GNjqTVhAsGdO3mWsTvt7MvaR2JmIrszdrMnYw97MvewJ2MP+7P3FwkfdUPr0j+hP30b9CU6ONoXH0lERM6SAkYJFDDKJnvNGpLG/oeCffsAiBhyKzVHjcIcEFDiegXOAjalbuLLHV/y7c5vyXHkAGA2mbko9iL6J/Sne53u+Fv8y/0ziIiIdyhglEABo+ycWdkceOkl0ubMAcBavz6xL75AYOvWpVo/pyCHRbsWMX/7fNYfWO+ZH2GL4LK6l9GyekuaRTUjoVoCVou1XD6DiIicPQWMEihgnLmsH38kadxjOA4eBLOZiEGDiBw2FGtcXKm3sSt9F/O3z2fBjgUczD1Y5D0/kx8NqzWkWVQzmkU2o3lUcxpHNNY9UkREzhEKGCVQwDg7zrQ0kp97noz//c89w2QipMdlRA0dSmCHDqXuyOlwOVi1fxW/pvzKlkNb2HJ4C+n56SctZ8JEjcAa1AyqSXRwNNFB0UQHR7tfB0UTExRDzeCa2Cw2b35MEREphgJGCRQwvCN71SoOTZtO9ooVnnm25s2IHDKEsKuuwmwt26kOwzBIyk5iy+EtnsDx16G/OJB74LTr+pn9aB/dnu51utMtrhtxoaVvURERkdJTwCiBAoZ35W/fzuGPPib9yy8x8vIAsNSoTsSgQUTcdBN+kZFntf3DeYdJyk4iJTuFlJwUDuQcKPo8J4VcR26RdRqGN6RbXDe6x3WndfXWWMyWs6pBRETcFDBKoIBRPhxHjpA2Zy5HPvkExwF3q4PJaiWkWzfCrr6akG6XnPbKkzNhGAZ7MvewPHE5y/cuZ13KOpyG0/N+hC2Ci+tcTJsabYgOiqZGkPt0S2RAJGaTRsoXESkLBYwSKGCUL6OggIxvF3H4ww/J23zsniXm4GBCe/Yk7Oo+BF94ISb/8rk8NcOewcp9K1mWuIwV+1aQac8sdjk/kx9RgVHUDKpJzaCaRAVE4TAc5BTkkOPIIdeRe9Jzf7M/zaKa0bJ6S1pGtaRF9RZUD6xeLp9DRORcpIBRAgWMimEYBvl//UXGwoWkf/01jv1JnvcsERGEXtmL8KuvJvCCCzCZy6clweFy8NuB31ixdwU703dyIPcAB3IOcCj3EAbe+bGPCY7xhI2W1VsSExSDzWLD5mdzP1ps+Jn9vLIvERFfU8AogQJGxTNcLnI3bCDjq6/I+HYRzsOHPe+ZQ0Pxr10b/9jYolNt96MlMtLrQ4w7XA4O5R7iYO5BDuQc4GDOQQ7lHcLf7E+gXyBB/kEE+QWd9DyrIIvNqZv549AfbE7dzM70naUKKhaTxRM2/C3+GIaB03B6Hl2Gy/PoMlzEhsQytMVQ+jXsp3FBROScooBRAgUM3zIcDrJX/0zGwoVkfv89rqysEpc3BQZiq18fW5Mm2Jo0JqBJE2xNmpx151FvyLJnseXwFjanbmZz6mb+PPQn6fnp5DvzsbvsZ7396KBohrcczoDGA0p9GW6mPZPle5ez7cg2agbVJC40jjohdYgNiSXAz/t9YESkalHAKIECxrnDZbdTsHs3Bfv3H5v2FT7ucw/odQqWGtUJaOwOG7bGCdgaNMBavz6W0NAK/ASn5jJc7qDhtJPnyHM/OvOwu+yYMWM2mbGYLJjNRx8xYzabMWHihz0/MG3zNM8lujUCazCsxTBuaHIDgX6BJ+0rPT+dpYlLWbx7Mav3r6bAVVBsTTUDa1I7tDZ1QupQJ7QO1QOru1tp/III9A8s0mpTOD/AL0CdYUXEQwGjBAoYlYfLbqdg3z7yt28nf+vf5G/dSt7fWynYkwin+LG1REVhrR+PrX59rPH1sdavjzU+Hv86tcs8Nocv5TvzmbdtHh9s/oDk7GQAIgMiGdpiKAObDCTPkccPiT/w/e7vWZO0Bofh8KxbP7w+HaM7cijvEHsz97I3ay/ZBdlnXIsnhJwQPk58fvy8408v1QmtQ52QOmU+1XUg5wAr961k9f7VHMk/Qu2Q2sSFxhEXGkfdsLrEhcYR7B98xp9LRMpOAaMEChiVnys7m/zt28nbutUdPLZtw75zZ4ktHgDmkBAsUZH4RUZ5Hv2qR2GJjMIvMgJTYCBmmw1TQAAmmw1z4ePReebAQEx+Fdths8BZwJc7vuT9Te+zL8t9w7lg/2ByHblF7labEJHA5fUu54p6V9CwWsMi2zAMg/T8dPZmucPG3kz3lJ6fTo4jh5yCo1fKHH1eeOWMN0UGRNK6emta13BPLau3PCkc2J12fjvwGyv3rWTl/pX8feTvUm23Tmgd4kLjqB5QnXBbOOG2cMJsYVSzVSPcGu6ZZzVbSclJITk72T22Sk4KSVlJJOe4XydnJ+Nv9qdeWD3qhdUjPiye+LB46oXVo25YXfWHEUEBo0QKGOcvZ1YW9p27sO/ahX3nTuy7dpJ/9LWR650vTHNYGH4REVgiI7FERuIXGYElIhJLRIQ7pAQEYrKYwWLBZLGA2YLJzwJm89HXZnC5wOXCcLrA5cRwGe5Hp9PdMmM2Yw4IxBwU6A41gUE4bRYWp6zgva3T2ZW1B4DmUc25vN7l9Kzbk/jweAzDcK/vdILJdFZhyGW4yHPkFblMtzCE5BbkFju/uKCSXZDNroxdOFyOIts3m8w0rNaQ1tVbExcax4YDG/gl+ZciwcaEiZbVW3JR7YuoHVKbfVn7SMxMJDEzkb2Zezmcd/jEssuN2WSmVnAt6obWJdg/GD+zH35mPywmi+e5v9m/yHOrxYq/2d89Wfyxmq2e+aHWUKoHVqd6YHXCrGFlbt3Jc+RR4Cpwdxw2+3u9I3RZGIbh0/1LxVLAKIECRtVjGAaujAwchw7jPHwIx+HDOA8fxnHoEM5Dhz2vXbm5GPn5uPLzMPLyMfLycOXne0YoPVcYNismsxmTy3AHlcJQccL/yqbAQCwhIZjDwo49hoZgDj36GBzsbrUJCMQcGIDJ8+hurTEHBIDFD5PZ5A5MZndwwmz2PDeZTO79F4amo2HJU5fLRYHJxfbcPWzO+Jvf07ew4fAm9uckFfvZogKiuKj2RVwUexFdYrsQERBxyuOQZc9ib9ZeT+A4kneEdHs6aXlppNvTSc93T2n5aZ5+Kf5mf2oF1yImOMYzeV4HxWB32dmdsZtdGbvcj+nux6yCkjsjnw1/sz9RgVFUD3AHjqjAKMJt4eQU5JBZkElGfgYZdveUaXe/PrETsdVs9VylVHjFktViJSogitiQWGoF1yryWDOoZrGXTxuGgd1lJ7sgm+yCbHIKcjiSf4TU3FQO5R4iNTe1yHQo9xDp9nQahDegQ3QHOsZ0pENMByIDfN8Ju7IqHDzwj9Q/iAyMpGN0x3NqNGIFjBIoYEhZGYaBUVCAKysL55Ej7nBy+AjOI4Xh5Ih7/pHDuPLt4DzaGuF0YrhcRV8X/rVnsYDZhMls8XxRF36JG04nrrxcjJxcXLnu6VwLOWfFbAarFZe/Gbsf2C0GlsBggkOqERhc7Wi4sbkDT4ANky3A82gKOHrKyhaAyWZ1n8ay2o6+b8Nktbnn22yYrFZMNhtYreSbneSbnISbg6DAiVFgx7AXYBQUYNjtnkcwwGR2t0IdDVKGyUSaI4P92Ukk5aZgx4nd5MRhcuHAHaAKzEcfceHASYGrALvTXvTRZcfhdJDvzCcjP51DOalk52VgcYHZBZYTJpNxdML9aDaOzTMf/a3tNB83WU54bQbH0Xmc0MJgMVmoGVST6oHVyXXkFgkUx/fnOVONqjWiY0xHOsV0on10+xKDoi85XA6Ss5PZk7mHvZl72ZOxh4O5B/E3+xPgF+AJa4F+gdgsNs+8AL8AT1+jAEuA57Xn0RJQ6lCQac9kc+pmNh7cyO8Hf+f31N+L3PixRmANetfvzdUNrqZpZNNStRa5DBfbjmzjl6Rf+O3Ab7zU7SX8zd4Z3FABowQKGFIZGS6Xu0XlaOAAjgUVk9ndymA+7kvR5XIHoowMXJlZODPdj67MDJxHH105Obhy89xhJjcPV14eRm4urrxj844PRscHJlyuk4ss3L/JVOS54XRi5OdX8BE7hxR+IRz/xVDc8StHTosJp8WEw4I7DB0NHw4LuEzgMh99LHxuxn16z2LB5OeHxWrDz2rDzxqI1RaIzRaMLSCIgIAQ/K2BHMo9RHJOMilZyaTnp7lzWuHHNyDYGoLZ4odhMYOlsCXMAn6W41rDzO4wZTLhvp7K/Z/nuclEoH8QQdZgdwdiawjB1mCCrKEEWYMwmy1ggOEowGHPJy8/m7y8LPLzc8jPz8Zuz6UgP5ec/Eyy8zLd79lzMDuNIgHP7Dp2DFymY2Ht+GPjOC7UOSyFr93H13n0tb/FfTrMZna3JhW2KhU+OpwOkrL3k5qbinH0OBWymC1EB9UkLfcIeY48T8iMskXSLLIpTas1IdQa4m61NJnBbCLdnsnu7D3syUxkd1Yi2Y4cXCYwTHDryLdp2/gSr/wsleU7VEMMilQCJrMZU1AQ5qCg0q8UUX5/NR5/WsQdckwl/mVlGIa7pSA//+hpKDtGfp77eV4+Rv7RgJOXd+x1bt6xx7w8XPZ896mr/Hz3qav8oqexDLsd13H7KGyZOCWLBZO/v7ulo/DRZHKHKJfL81hcwCoMXqU8WEUfT8ffv8gpqJOCm8kEZhMYgMPhDnAOh/u5w1HsfixOA4vToGzdVJ1HJzuQU/xHO/pu6NEp4ZTbKn7I/rNlANlHp1PxPzqVvxOPe/7R6Uw+uwvYV8z8VOAn8viJ4to0ax2dOp8wP/Q231xqroAhImXmaT2xlK4Z2GQyuU9X2Eo3YJi3GC7XsWDjcLiDRGGYKGXtp9z28aHj+C/6wtaJE4KFp7HY4FhHYD8/92SxgJ+fV4bN99RTOBUUnDzZjz13dzB2Heto7HIde3Q4wXn8do4+Ohzu00wFBeA4ekrFEzBN7ucmE5jcHVLT8tNwHd2Oq/BYFW7X6cBwODGO/uc+RO5nxx7BKBxbxpGHvSCffGc+BY58Chx2z1/4cLQ1weJutbH4Wd2tL/42/K0B+FkDCA4MIywwgmrBkVQLrk5oYDhmP6u7M7bFD5PFfOx4ONx14nQdezxaMw7HCcfDfTwd9nwcBXk4XU4cLgcOw1H08egEEB5QjWq2ascG0jsxpB89hiaTmQLDwf6cZBIzEzmYdxBXYcPY0dNmFsxEWqtRI6A61W2RVLOGu4+Jy0V0zfpn/XN1JhQwROS8ZTKbMQUEQDncyddkMrlDAcA5NMaK6WgfF9M5VFPdcty20+XkSP4RDuUewuFyEGYLI9wWToh/yHk3SFw9oAuQnJ3Mwn8Wsi5lHQ2rNaRzrc60q9mOIP8ytHBWAPXBEBERkVIpy3fo+RXvRERE5JyggCEiIiJep4AhIiIiXndOBIw333yT+Ph4AgIC6Ny5M2vWrClx+blz59K0aVMCAgJo1aoVX3/9dQVVKiIiIqXh84Axe/ZsRo0axRNPPMH69etp06YNvXr14sCBA8Uuv2rVKgYNGsTtt9/Ob7/9xrXXXsu1117L5s2bK7hyERERORWfX0XSuXNnOnbsyJQpUwBwuVzExcVx33338eijj560/MCBA8nOzuarr77yzLvwwgtp27Yt77zzzmn3p6tIREREzkyluYrEbrezbt06evbs6ZlnNpvp2bMnq1evLnad1atXF1keoFevXqdcPj8/n4yMjCKTiIiIlC+fBozU1FScTifR0dFF5kdHR5OcnFzsOsnJyWVafsKECYSHh3umuLg47xQvIiIip+TzPhjlbezYsaSnp3umxMREX5ckIiJy3vPpUOHVq1fHYrGQkpJSZH5KSgoxMTHFrhMTE1Om5W02G7YKvv+BiIhIVefTFgyr1Ur79u1ZsmSJZ57L5WLJkiV06dKl2HW6dOlSZHmAxYsXn3J5ERERqXg+v9nZqFGjGDp0KB06dKBTp05MmjSJ7OxsbrvtNgCGDBlC7dq1mTBhAgAPPPAA3bp145VXXqFPnz7MmjWLX3/9lXfffdeXH0NERESO4/OAMXDgQA4ePMjjjz9OcnIybdu25dtvv/V05NyzZw/m425h3LVrV2bOnMljjz3Gf/7zHxISEpg/fz4tW7b01UcQERGRE/h8HIyKpnEwREREzkylGQdDREREzk8KGCIiIuJ1ChgiIiLidQoYIiIi4nU+v4qkohX2adU9SURERMqm8LuzNNeHVLmAkZmZCaB7koiIiJyhzMxMwsPDS1ymyl2m6nK52L9/P6GhoZhMJq9sMyMjg7i4OBITE3Xpq5fp2JYPHdfyoeNaPnRcy09Zj61hGGRmZhIbG1tkjKriVLkWDLPZTJ06dcpl22FhYfrhLyc6tuVDx7V86LiWDx3X8lOWY3u6lotC6uQpIiIiXqeAISIiIl6ngOEFNpuNJ554QreFLwc6tuVDx7V86LiWDx3X8lOex7bKdfIUERGR8qcWDBEREfE6BQwRERHxOgUMERER8ToFDBEREfE6BQwvePPNN4mPjycgIIDOnTuzZs0aX5dUqfz444/07duX2NhYTCYT8+fPL/K+YRg8/vjj1KpVi8DAQHr27Mm2bdt8U2wlMmHCBDp27EhoaCg1a9bk2muvZevWrUWWycvLY8SIEURFRRESEsKAAQNISUnxUcWVx9tvv03r1q09gxN16dKFb775xvO+juvZe+GFFzCZTDz44IOeeTquZ+bJJ5/EZDIVmZo2bep5v7yOqwLGWZo9ezajRo3iiSeeYP369bRp04ZevXpx4MABX5dWaWRnZ9OmTRvefPPNYt9/6aWXeOONN3jnnXf45ZdfCA4OplevXuTl5VVwpZXL8uXLGTFiBD///DOLFy+moKCAK664guzsbM8yDz30EP/73/+YO3cuy5cvZ//+/Vx33XU+rLpyqFOnDi+88ALr1q3j119/5bLLLqNfv3788ccfgI7r2Vq7di3//e9/ad26dZH5Oq5nrkWLFiQlJXmmn376yfNeuR1XQ85Kp06djBEjRnheO51OIzY21pgwYYIPq6q8AGPevHme1y6Xy4iJiTFefvllz7y0tDTDZrMZn376qQ8qrLwOHDhgAMby5csNw3AfR39/f2Pu3LmeZbZs2WIAxurVq31VZqUVERFhvP/++zquZykzM9NISEgwFi9ebHTr1s144IEHDMPQz+vZeOKJJ4w2bdoU+155Hle1YJwFu93OunXr6Nmzp2ee2WymZ8+erF692oeVnT927txJcnJykWMcHh5O586ddYzLKD09HYDIyEgA1q1bR0FBQZFj27RpU+rWratjWwZOp5NZs2aRnZ1Nly5ddFzP0ogRI+jTp0+R4wf6eT1b27ZtIzY2lgYNGjB48GD27NkDlO9xrXI3O/Om1NRUnE4n0dHRReZHR0fz119/+aiq80tycjJAsce48D05PZfLxYMPPshFF11Ey5YtAfextVqtVKtWrciyOrals2nTJrp06UJeXh4hISHMmzeP5s2bs2HDBh3XMzRr1izWr1/P2rVrT3pPP69nrnPnzkyfPp0mTZqQlJTEU089xcUXX8zmzZvL9bgqYIhUASNGjGDz5s1FzrvK2WnSpAkbNmwgPT2dzz77jKFDh7J8+XJfl1VpJSYm8sADD7B48WICAgJ8Xc55pXfv3p7nrVu3pnPnztSrV485c+YQGBhYbvvVKZKzUL16dSwWy0m9bVNSUoiJifFRVeeXwuOoY3zmRo4cyVdffcXSpUupU6eOZ35MTAx2u520tLQiy+vYlo7VaqVRo0a0b9+eCRMm0KZNG15//XUd1zO0bt06Dhw4QLt27fDz88PPz4/ly5fzxhtv4OfnR3R0tI6rl1SrVo3GjRuzffv2cv15VcA4C1arlfbt27NkyRLPPJfLxZIlS+jSpYsPKzt/1K9fn5iYmCLHOCMjg19++UXH+DQMw2DkyJHMmzePH374gfr16xd5v3379vj7+xc5tlu3bmXPnj06tmfA5XKRn5+v43qGevTowaZNm9iwYYNn6tChA4MHD/Y813H1jqysLHbs2EGtWrXK9+f1rLqIijFr1izDZrMZ06dPN/7880/jrrvuMqpVq2YkJyf7urRKIzMz0/jtt9+M3377zQCMV1991fjtt9+M3bt3G4ZhGC+88IJRrVo148svvzR+//13o1+/fkb9+vWN3NxcH1d+brvnnnuM8PBwY9myZUZSUpJnysnJ8Sxz9913G3Xr1jV++OEH49dffzW6dOlidOnSxYdVVw6PPvqosXz5cmPnzp3G77//bjz66KOGyWQyvvvuO8MwdFy95firSAxDx/VMPfzww8ayZcuMnTt3GitXrjR69uxpVK9e3Thw4IBhGOV3XBUwvGDy5MlG3bp1DavVanTq1Mn4+eeffV1SpbJ06VIDOGkaOnSoYRjuS1XHjx9vREdHGzabzejRo4exdetW3xZdCRR3TAFj2rRpnmVyc3ONe++914iIiDCCgoKM/v37G0lJSb4rupIYPny4Ua9ePcNqtRo1avx/e3cP0lYXx3H8d30LSVCI9VGjixRFtGAXRcQu1aHqpESMEEo6BV8aXNxaUQdXOwYK6lQqKFiE0hbaMSDt4ssQhc4SVLqYQF08z2AJ3Ef6PD56TMB+P3Dh3nPuTf7nTj/uOZf7l+nt7c2FC2O4r7b8M2BwX68nHA6bYDBoysrKTH19vQmHw+b79++5/tu6r3yuHQAAWMcaDAAAYB0BAwAAWEfAAAAA1hEwAACAdQQMAABgHQEDAABYR8AAAADWETAAAIB1BAwAd4LjOHr37l2hywDwCwEDwI09e/ZMjuNc2vr6+gpdGoACKSl0AQDuhr6+Pq2srLjaPB5PgaoBUGg8wQBghcfjUW1trWsLBAKSLqYvEomE+vv75fV6df/+fa2vr7uu39vbU09Pj7xer+7du6dYLKZMJuM6Z3l5WQ8ePJDH41EwGNTz589d/ScnJxoaGpLP51NTU5M2Nzdvd9AAfouAASAvZmZmFAqFtLOzo0gkotHRUaVSKUlSNpvVkydPFAgE9O3bN62trenz58+uAJFIJDQ5OalYLKa9vT1tbm6qsbHR9R/z8/MaGRnR7u6uBgYGFIlE9OPHj7yOE8AvN/4eK4A/XjQaNcXFxcbv97u2hYUFY8zFp+PHxsZc13R2dprx8XFjjDGvX782gUDAZDKZXP/79+9NUVGRSafTxhhj6urqzIsXL35bgyTz8uXL3HEmkzGSzIcPH6yNE8DVsQYDgBWPHz9WIpFwtVVWVub2u7q6XH1dXV3a3t6WJKVSKT18+FB+vz/X393drfPzcx0cHMhxHB0eHqq3t/dfa2hra8vt+/1+VVRU6Ojo6LpDAnADBAwAVvj9/ktTFrZ4vd4rnVdaWuo6dhxH5+fnt1ESgP/AGgwAebG1tXXpuKWlRZLU0tKinZ0dZbPZXH8ymVRRUZGam5tVXl6uhoYGffnyJa81A7g+nmAAsOLs7EzpdNrVVlJSoqqqKknS2tqa2tvb9ejRI71580Zfv37V0tKSJCkSiWh2dlbRaFRzc3M6Pj5WPB7X06dPVVNTI0mam5vT2NiYqqur1d/fr9PTUyWTScXj8fwOFMCVEDAAWPHx40cFg0FXW3Nzs/b39yVdvOGxurqqiYkJBYNBvX37Vq2trZIkn8+nT58+aWpqSh0dHfL5fAqFQlpcXMz9VjQa1c+fP/Xq1StNT0+rqqpKw8PD+RsggP/FMcaYQhcB4G5zHEcbGxsaHBwsdCkA8oQ1GAAAwDoCBgAAsI41GABuHTOxwJ+HJxgAAMA6AgYAALCOgAEAAKwjYAAAAOsIGAAAwDoCBgAAsI6AAQAArCNgAAAA6/4G0UC7f24tlLoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m===================================== BATCH_SIZE= 64 learning_rate= 0.0001  =====================================\u001b[0m\n",
      "Model: \"model_3\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_18 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_21 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_18 (Ba  (None, 600, 9)               36        ['conv1d_18[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_21 (Ba  (None, 600, 9)               36        ['conv1d_21[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_27 (Dropout)        (None, 600, 9)               0         ['batch_normalization_18[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_30 (Dropout)        (None, 600, 9)               0         ['batch_normalization_21[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_19 (Conv1D)          (None, 600, 18)              828       ['dropout_27[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_22 (Conv1D)          (None, 600, 18)              828       ['dropout_30[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_19 (Ba  (None, 600, 18)              72        ['conv1d_19[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_22 (Ba  (None, 600, 18)              72        ['conv1d_22[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_28 (Dropout)        (None, 600, 18)              0         ['batch_normalization_19[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_31 (Dropout)        (None, 600, 18)              0         ['batch_normalization_22[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_20 (Conv1D)          (None, 600, 36)              1980      ['dropout_28[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_23 (Conv1D)          (None, 600, 36)              1980      ['dropout_31[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_20 (Ba  (None, 600, 36)              144       ['conv1d_20[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_23 (Ba  (None, 600, 36)              144       ['conv1d_23[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_29 (Dropout)        (None, 600, 36)              0         ['batch_normalization_20[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_32 (Dropout)        (None, 600, 36)              0         ['batch_normalization_23[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate  (None, 600, 72)              0         ['dropout_29[0][0]',          \n",
      " )                                                                   'dropout_32[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_9 (Bidirecti  (None, 600, 36)              13104     ['concatenate_3[0][0]']       \n",
      " onal)                                                                                            \n",
      "                                                                                                  \n",
      " layer_normalization_9 (Lay  (None, 600, 36)              72        ['bidirectional_9[0][0]']     \n",
      " erNormalization)                                                                                 \n",
      "                                                                                                  \n",
      " dropout_33 (Dropout)        (None, 600, 36)              0         ['layer_normalization_9[0][0]'\n",
      "                                                                    ]                             \n",
      "                                                                                                  \n",
      " bidirectional_10 (Bidirect  (None, 600, 72)              21024     ['dropout_33[0][0]']          \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_10 (La  (None, 600, 72)              144       ['bidirectional_10[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_34 (Dropout)        (None, 600, 72)              0         ['layer_normalization_10[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " bidirectional_11 (Bidirect  (None, 144)                  83520     ['dropout_34[0][0]']          \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_11 (La  (None, 144)                  288       ['bidirectional_11[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_35 (Dropout)        (None, 144)                  0         ['layer_normalization_11[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_35[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 135181 (528.05 KB)\n",
      "Trainable params: 134929 (527.07 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "85/85 [==============================] - 121s 1s/step - loss: 0.4194 - accuracy: 0.8051 - val_loss: 0.3549 - val_accuracy: 0.8544\n",
      "Epoch 2/50\n",
      "85/85 [==============================] - 99s 1s/step - loss: 0.1789 - accuracy: 0.9315 - val_loss: 0.2234 - val_accuracy: 0.9153\n",
      "Epoch 3/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.1207 - accuracy: 0.9562 - val_loss: 0.1251 - val_accuracy: 0.9643\n",
      "Epoch 4/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0967 - accuracy: 0.9636 - val_loss: 0.0893 - val_accuracy: 0.9733\n",
      "Epoch 5/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0828 - accuracy: 0.9706 - val_loss: 0.0662 - val_accuracy: 0.9807\n",
      "Epoch 6/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0686 - accuracy: 0.9762 - val_loss: 0.0564 - val_accuracy: 0.9822\n",
      "Epoch 7/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0646 - accuracy: 0.9801 - val_loss: 0.0497 - val_accuracy: 0.9851\n",
      "Epoch 8/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0544 - accuracy: 0.9816 - val_loss: 0.0436 - val_accuracy: 0.9866\n",
      "Epoch 9/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0523 - accuracy: 0.9844 - val_loss: 0.0383 - val_accuracy: 0.9911\n",
      "Epoch 10/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0474 - accuracy: 0.9844 - val_loss: 0.0358 - val_accuracy: 0.9911\n",
      "Epoch 11/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0462 - accuracy: 0.9848 - val_loss: 0.0361 - val_accuracy: 0.9911\n",
      "Epoch 12/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0445 - accuracy: 0.9851 - val_loss: 0.0372 - val_accuracy: 0.9896\n",
      "Epoch 13/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0449 - accuracy: 0.9853 - val_loss: 0.0370 - val_accuracy: 0.9896\n",
      "Epoch 14/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0431 - accuracy: 0.9863 - val_loss: 0.0366 - val_accuracy: 0.9896\n",
      "Epoch 15/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0377 - accuracy: 0.9896 - val_loss: 0.0385 - val_accuracy: 0.9896\n",
      "Epoch 16/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0368 - accuracy: 0.9889 - val_loss: 0.0328 - val_accuracy: 0.9911\n",
      "Epoch 17/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0341 - accuracy: 0.9909 - val_loss: 0.0318 - val_accuracy: 0.9926\n",
      "Epoch 18/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0348 - accuracy: 0.9890 - val_loss: 0.0308 - val_accuracy: 0.9911\n",
      "Epoch 19/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0337 - accuracy: 0.9905 - val_loss: 0.0328 - val_accuracy: 0.9911\n",
      "Epoch 20/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0370 - accuracy: 0.9885 - val_loss: 0.0322 - val_accuracy: 0.9941\n",
      "Epoch 21/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0329 - accuracy: 0.9905 - val_loss: 0.0304 - val_accuracy: 0.9941\n",
      "Epoch 22/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0300 - accuracy: 0.9902 - val_loss: 0.0297 - val_accuracy: 0.9926\n",
      "Epoch 23/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0273 - accuracy: 0.9903 - val_loss: 0.0294 - val_accuracy: 0.9926\n",
      "Epoch 24/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0265 - accuracy: 0.9916 - val_loss: 0.0289 - val_accuracy: 0.9926\n",
      "Epoch 25/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0297 - accuracy: 0.9894 - val_loss: 0.0320 - val_accuracy: 0.9926\n",
      "Epoch 26/50\n",
      "85/85 [==============================] - 95s 1s/step - loss: 0.0236 - accuracy: 0.9933 - val_loss: 0.0309 - val_accuracy: 0.9926\n",
      "Epoch 27/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0217 - accuracy: 0.9937 - val_loss: 0.0297 - val_accuracy: 0.9926\n",
      "Epoch 28/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0255 - accuracy: 0.9926 - val_loss: 0.0282 - val_accuracy: 0.9941\n",
      "Epoch 29/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0225 - accuracy: 0.9926 - val_loss: 0.0302 - val_accuracy: 0.9926\n",
      "Epoch 30/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0216 - accuracy: 0.9928 - val_loss: 0.0294 - val_accuracy: 0.9941\n",
      "Epoch 31/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0241 - accuracy: 0.9941 - val_loss: 0.0290 - val_accuracy: 0.9941\n",
      "Epoch 32/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0212 - accuracy: 0.9935 - val_loss: 0.0297 - val_accuracy: 0.9941\n",
      "Epoch 33/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0231 - accuracy: 0.9939 - val_loss: 0.0284 - val_accuracy: 0.9941\n",
      "Epoch 34/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0224 - accuracy: 0.9933 - val_loss: 0.0296 - val_accuracy: 0.9941\n",
      "Epoch 35/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0215 - accuracy: 0.9952 - val_loss: 0.0290 - val_accuracy: 0.9941\n",
      "Epoch 36/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0236 - accuracy: 0.9929 - val_loss: 0.0283 - val_accuracy: 0.9941\n",
      "Epoch 37/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0221 - accuracy: 0.9929 - val_loss: 0.0298 - val_accuracy: 0.9926\n",
      "Epoch 38/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0213 - accuracy: 0.9926 - val_loss: 0.0301 - val_accuracy: 0.9926\n",
      "Epoch 39/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0219 - accuracy: 0.9933 - val_loss: 0.0304 - val_accuracy: 0.9941\n",
      "Epoch 40/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0190 - accuracy: 0.9944 - val_loss: 0.0300 - val_accuracy: 0.9941\n",
      "Epoch 41/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0213 - accuracy: 0.9929 - val_loss: 0.0293 - val_accuracy: 0.9941\n",
      "Epoch 42/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0179 - accuracy: 0.9937 - val_loss: 0.0291 - val_accuracy: 0.9926\n",
      "Epoch 43/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0175 - accuracy: 0.9948 - val_loss: 0.0287 - val_accuracy: 0.9926\n",
      "Epoch 44/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0199 - accuracy: 0.9954 - val_loss: 0.0291 - val_accuracy: 0.9941\n",
      "Epoch 45/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0177 - accuracy: 0.9952 - val_loss: 0.0288 - val_accuracy: 0.9941\n",
      "Epoch 46/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0183 - accuracy: 0.9935 - val_loss: 0.0294 - val_accuracy: 0.9926\n",
      "Epoch 47/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0170 - accuracy: 0.9944 - val_loss: 0.0298 - val_accuracy: 0.9941\n",
      "Epoch 48/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0196 - accuracy: 0.9948 - val_loss: 0.0295 - val_accuracy: 0.9941\n",
      "Epoch 49/50\n",
      "85/85 [==============================] - 96s 1s/step - loss: 0.0179 - accuracy: 0.9957 - val_loss: 0.0302 - val_accuracy: 0.9941\n",
      "Epoch 50/50\n",
      "85/85 [==============================] - 97s 1s/step - loss: 0.0176 - accuracy: 0.9937 - val_loss: 0.0296 - val_accuracy: 0.9941\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 1s 67ms/step - loss: 0.0295 - accuracy: 0.9896\n",
      "Test Loss: 0.029546964913606644, Test Accuracy: 0.98959881067276\n",
      "22/22 [==============================] - 2s 66ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Model 1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       491\n",
      "           1       0.98      0.98      0.98       182\n",
      "\n",
      "    accuracy                           0.99       673\n",
      "   macro avg       0.99      0.99      0.99       673\n",
      "weighted avg       0.99      0.99      0.99       673\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m===================================== BATCH_SIZE= 32 learning_rate= 0.0001  =====================================\u001b[0m\n",
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_24 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_27 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_24 (Ba  (None, 600, 9)               36        ['conv1d_24[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_27 (Ba  (None, 600, 9)               36        ['conv1d_27[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_36 (Dropout)        (None, 600, 9)               0         ['batch_normalization_24[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_39 (Dropout)        (None, 600, 9)               0         ['batch_normalization_27[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_25 (Conv1D)          (None, 600, 18)              828       ['dropout_36[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_28 (Conv1D)          (None, 600, 18)              828       ['dropout_39[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_25 (Ba  (None, 600, 18)              72        ['conv1d_25[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_28 (Ba  (None, 600, 18)              72        ['conv1d_28[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_37 (Dropout)        (None, 600, 18)              0         ['batch_normalization_25[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_40 (Dropout)        (None, 600, 18)              0         ['batch_normalization_28[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_26 (Conv1D)          (None, 600, 36)              1980      ['dropout_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_29 (Conv1D)          (None, 600, 36)              1980      ['dropout_40[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (Ba  (None, 600, 36)              144       ['conv1d_26[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_29 (Ba  (None, 600, 36)              144       ['conv1d_29[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_38 (Dropout)        (None, 600, 36)              0         ['batch_normalization_26[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_41 (Dropout)        (None, 600, 36)              0         ['batch_normalization_29[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_4 (Concatenate  (None, 600, 72)              0         ['dropout_38[0][0]',          \n",
      " )                                                                   'dropout_41[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_12 (Bidirect  (None, 600, 36)              13104     ['concatenate_4[0][0]']       \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_12 (La  (None, 600, 36)              72        ['bidirectional_12[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_42 (Dropout)        (None, 600, 36)              0         ['layer_normalization_12[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " bidirectional_13 (Bidirect  (None, 600, 72)              21024     ['dropout_42[0][0]']          \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_13 (La  (None, 600, 72)              144       ['bidirectional_13[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_43 (Dropout)        (None, 600, 72)              0         ['layer_normalization_13[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " bidirectional_14 (Bidirect  (None, 144)                  83520     ['dropout_43[0][0]']          \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_14 (La  (None, 144)                  288       ['bidirectional_14[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_44 (Dropout)        (None, 144)                  0         ['layer_normalization_14[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_44[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 135181 (528.05 KB)\n",
      "Trainable params: 134929 (527.07 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "169/169 [==============================] - 54s 273ms/step - loss: 0.3732 - accuracy: 0.8243 - val_loss: 0.1568 - val_accuracy: 0.9406\n",
      "Epoch 2/50\n",
      "169/169 [==============================] - 42s 247ms/step - loss: 0.1446 - accuracy: 0.9432 - val_loss: 0.0864 - val_accuracy: 0.9733\n",
      "Epoch 3/50\n",
      "169/169 [==============================] - 40s 239ms/step - loss: 0.1000 - accuracy: 0.9638 - val_loss: 0.0545 - val_accuracy: 0.9822\n",
      "Epoch 4/50\n",
      "169/169 [==============================] - 40s 238ms/step - loss: 0.0814 - accuracy: 0.9714 - val_loss: 0.0417 - val_accuracy: 0.9896\n",
      "Epoch 5/50\n",
      "169/169 [==============================] - 40s 238ms/step - loss: 0.0686 - accuracy: 0.9775 - val_loss: 0.0335 - val_accuracy: 0.9896\n",
      "Epoch 6/50\n",
      "169/169 [==============================] - 40s 237ms/step - loss: 0.0627 - accuracy: 0.9786 - val_loss: 0.0347 - val_accuracy: 0.9851\n",
      "Epoch 7/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0538 - accuracy: 0.9829 - val_loss: 0.0330 - val_accuracy: 0.9926\n",
      "Epoch 8/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0533 - accuracy: 0.9835 - val_loss: 0.0277 - val_accuracy: 0.9926\n",
      "Epoch 9/50\n",
      "169/169 [==============================] - 40s 237ms/step - loss: 0.0467 - accuracy: 0.9870 - val_loss: 0.0288 - val_accuracy: 0.9896\n",
      "Epoch 10/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0446 - accuracy: 0.9870 - val_loss: 0.0231 - val_accuracy: 0.9941\n",
      "Epoch 11/50\n",
      "169/169 [==============================] - 40s 238ms/step - loss: 0.0360 - accuracy: 0.9905 - val_loss: 0.0272 - val_accuracy: 0.9866\n",
      "Epoch 12/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0375 - accuracy: 0.9876 - val_loss: 0.0221 - val_accuracy: 0.9926\n",
      "Epoch 13/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0302 - accuracy: 0.9915 - val_loss: 0.0315 - val_accuracy: 0.9896\n",
      "Epoch 14/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0321 - accuracy: 0.9892 - val_loss: 0.0210 - val_accuracy: 0.9941\n",
      "Epoch 15/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0308 - accuracy: 0.9916 - val_loss: 0.0221 - val_accuracy: 0.9926\n",
      "Epoch 16/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0281 - accuracy: 0.9911 - val_loss: 0.0274 - val_accuracy: 0.9926\n",
      "Epoch 17/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0263 - accuracy: 0.9920 - val_loss: 0.0217 - val_accuracy: 0.9926\n",
      "Epoch 18/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0240 - accuracy: 0.9926 - val_loss: 0.0240 - val_accuracy: 0.9926\n",
      "Epoch 19/50\n",
      "169/169 [==============================] - 40s 235ms/step - loss: 0.0224 - accuracy: 0.9929 - val_loss: 0.0241 - val_accuracy: 0.9911\n",
      "Epoch 20/50\n",
      "169/169 [==============================] - 40s 235ms/step - loss: 0.0248 - accuracy: 0.9920 - val_loss: 0.0178 - val_accuracy: 0.9955\n",
      "Epoch 21/50\n",
      "169/169 [==============================] - 40s 236ms/step - loss: 0.0214 - accuracy: 0.9937 - val_loss: 0.0319 - val_accuracy: 0.9896\n",
      "Epoch 22/50\n",
      "169/169 [==============================] - 40s 235ms/step - loss: 0.0217 - accuracy: 0.9933 - val_loss: 0.0201 - val_accuracy: 0.9926\n",
      "Epoch 23/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0212 - accuracy: 0.9935 - val_loss: 0.0203 - val_accuracy: 0.9926\n",
      "Epoch 24/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0237 - accuracy: 0.9909 - val_loss: 0.0213 - val_accuracy: 0.9941\n",
      "Epoch 25/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0178 - accuracy: 0.9929 - val_loss: 0.0193 - val_accuracy: 0.9941\n",
      "Epoch 26/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0186 - accuracy: 0.9942 - val_loss: 0.0198 - val_accuracy: 0.9941\n",
      "Epoch 27/50\n",
      "169/169 [==============================] - 39s 234ms/step - loss: 0.0215 - accuracy: 0.9931 - val_loss: 0.0213 - val_accuracy: 0.9926\n",
      "Epoch 28/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0173 - accuracy: 0.9941 - val_loss: 0.0208 - val_accuracy: 0.9926\n",
      "Epoch 29/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0197 - accuracy: 0.9944 - val_loss: 0.0271 - val_accuracy: 0.9911\n",
      "Epoch 30/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0210 - val_accuracy: 0.9941\n",
      "Epoch 31/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0189 - accuracy: 0.9941 - val_loss: 0.0225 - val_accuracy: 0.9926\n",
      "Epoch 32/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0158 - accuracy: 0.9948 - val_loss: 0.0214 - val_accuracy: 0.9941\n",
      "Epoch 33/50\n",
      "169/169 [==============================] - 40s 235ms/step - loss: 0.0128 - accuracy: 0.9963 - val_loss: 0.0335 - val_accuracy: 0.9881\n",
      "Epoch 34/50\n",
      "169/169 [==============================] - 40s 235ms/step - loss: 0.0169 - accuracy: 0.9944 - val_loss: 0.0246 - val_accuracy: 0.9926\n",
      "Epoch 35/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0168 - accuracy: 0.9942 - val_loss: 0.0201 - val_accuracy: 0.9955\n",
      "Epoch 36/50\n",
      "169/169 [==============================] - 40s 235ms/step - loss: 0.0193 - accuracy: 0.9946 - val_loss: 0.0194 - val_accuracy: 0.9970\n",
      "Epoch 37/50\n",
      "169/169 [==============================] - 40s 234ms/step - loss: 0.0151 - accuracy: 0.9950 - val_loss: 0.0255 - val_accuracy: 0.9941\n",
      "Epoch 38/50\n",
      "169/169 [==============================] - 39s 232ms/step - loss: 0.0120 - accuracy: 0.9955 - val_loss: 0.0226 - val_accuracy: 0.9941\n",
      "Epoch 39/50\n",
      "169/169 [==============================] - 39s 232ms/step - loss: 0.0180 - accuracy: 0.9944 - val_loss: 0.0238 - val_accuracy: 0.9955\n",
      "Epoch 40/50\n",
      "169/169 [==============================] - 39s 232ms/step - loss: 0.0141 - accuracy: 0.9952 - val_loss: 0.0214 - val_accuracy: 0.9955\n",
      "Epoch 41/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 0.0252 - val_accuracy: 0.9926\n",
      "Epoch 42/50\n",
      "169/169 [==============================] - 39s 232ms/step - loss: 0.0166 - accuracy: 0.9944 - val_loss: 0.0225 - val_accuracy: 0.9941\n",
      "Epoch 43/50\n",
      "169/169 [==============================] - 39s 232ms/step - loss: 0.0169 - accuracy: 0.9944 - val_loss: 0.0214 - val_accuracy: 0.9941\n",
      "Epoch 44/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0164 - accuracy: 0.9955 - val_loss: 0.0204 - val_accuracy: 0.9955\n",
      "Epoch 45/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0115 - accuracy: 0.9963 - val_loss: 0.0215 - val_accuracy: 0.9955\n",
      "Epoch 46/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0123 - accuracy: 0.9961 - val_loss: 0.0205 - val_accuracy: 0.9941\n",
      "Epoch 47/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0148 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9955\n",
      "Epoch 48/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0144 - accuracy: 0.9950 - val_loss: 0.0198 - val_accuracy: 0.9955\n",
      "Epoch 49/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0109 - accuracy: 0.9967 - val_loss: 0.0196 - val_accuracy: 0.9955\n",
      "Epoch 50/50\n",
      "169/169 [==============================] - 39s 233ms/step - loss: 0.0124 - accuracy: 0.9970 - val_loss: 0.0195 - val_accuracy: 0.9955\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 2s 67ms/step - loss: 0.0242 - accuracy: 0.9926\n",
      "Test Loss: 0.024167396128177643, Test Accuracy: 0.9925705790519714\n",
      "22/22 [==============================] - 3s 67ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Model 2\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       491\n",
      "           1       0.99      0.98      0.99       182\n",
      "\n",
      "    accuracy                           0.99       673\n",
      "   macro avg       0.99      0.99      0.99       673\n",
      "weighted avg       0.99      0.99      0.99       673\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histories = []\n",
    "model_histories = []\n",
    "for BATCH_SIZE in [128, 64, 32]:\n",
    "    for learning_rate in [0.0001]:\n",
    "        print(\"\\033[1m===================================== BATCH_SIZE=\", BATCH_SIZE, \"learning_rate=\", learning_rate, \" =====================================\\033[0m\")\n",
    "\n",
    "        # Learning rate scheduler\n",
    "        lr_scheduler = keras.optimizers.schedules.ExponentialDecay(\n",
    "            initial_learning_rate=learning_rate,\n",
    "            decay_steps=int((xtrain.shape[0] + BATCH_SIZE) / BATCH_SIZE),\n",
    "            decay_rate=0.95\n",
    "        )\n",
    "\n",
    "        optimizer = keras.optimizers.Adam()\n",
    "        optimizer.learning_rate = lr_scheduler\n",
    "\n",
    "        # Build the model\n",
    "        model = build_model()\n",
    "\n",
    "        # Display model summary\n",
    "        model.summary()\n",
    "\n",
    "        # Compile the model\n",
    "        model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "\n",
    "        # Reshape the input data to match the model's input shape\n",
    "        # xtrain_reshaped = xtrain[:, :128, :]\n",
    "        # xval_reshaped = xval[:, :128, :]\n",
    "        # xtest_reshaped = xtest[:, :128, :]\n",
    "\n",
    "        # Modify the input data to match the expected shape of the model's input layer\n",
    "        xtrain_reshaped = np.pad(xtrain, ((0, 0), (0, 600 - xtrain.shape[1]), (0, 0)), mode='constant')\n",
    "        xval_reshaped = np.pad(xval, ((0, 0), (0, 600 - xval.shape[1]), (0, 0)), mode='constant')\n",
    "        xtest_reshaped = np.pad(xtest, ((0, 0), (0, 600 - xtest.shape[1]), (0, 0)), mode='constant')\n",
    "\n",
    "\n",
    "        # Train the model with reshaped input data\n",
    "        model_history = model.fit(\n",
    "            {'Input1': xtrain_reshaped[:, :, 0:3], 'Input2': xtrain_reshaped[:, :, 3:6]},\n",
    "            ytrain,\n",
    "            validation_data=(\n",
    "                [xval_reshaped[:, :, 0:3], xval_reshaped[:, :, 3:6]],\n",
    "                yval\n",
    "            ),\n",
    "            epochs=50,\n",
    "            batch_size=BATCH_SIZE,\n",
    "        )\n",
    "\n",
    "        # Append model to the list\n",
    "        model_histories.append(model_history)\n",
    "\n",
    "        # Plotting accuracy and loss\n",
    "        plt.figure(figsize=(6, 6))\n",
    "        # Plot training accuracy in green\n",
    "        plt.plot(model_history.history['accuracy'], color=\"green\", alpha=0.8, label='Training Accuracy')\n",
    "\n",
    "        # Plot training loss in magenta\n",
    "        plt.plot(model_history.history['loss'], color=\"blue\", alpha=0.8, label='Training Loss')\n",
    "\n",
    "        # Plot validation accuracy in cyan\n",
    "        plt.plot(model_history.history['val_accuracy'], color=\"red\", alpha=0.8, label='Validation Accuracy')\n",
    "\n",
    "        # Plot validation loss in yellow\n",
    "        plt.plot(model_history.history['val_loss'], color=\"yellow\", alpha=0.8, label='Validation Loss')\n",
    "\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.ylabel('Accuracy / Loss')\n",
    "        plt.title('Training and Validation Accuracy/Loss Over Epochs - Model ' + str(len(histories)) + ' Batch Size ' + str(BATCH_SIZE))\n",
    "        plt.legend()\n",
    "        plt.savefig(str(BATCH_SIZE) + 'bilistm_cnn_Model_Training_Graph.png')  # Specify the path and file name\n",
    "        plt.show()\n",
    "\n",
    "        # Evaluate the model on test data\n",
    "        test_loss, test_accuracy = model.evaluate([xtest_reshaped[:, :, 0:3], xtest_reshaped[:, :, 3:6]], ytest)\n",
    "        print(f'Test Loss: {test_loss}, Test Accuracy: {test_accuracy}')\n",
    "\n",
    "        # Generate predictions on test data\n",
    "        ypred = model.predict([xtest_reshaped[:, :, 0:3], xtest_reshaped[:, :, 3:6]])\n",
    "\n",
    "        # Compute confusion matrix\n",
    "        test_cm = confusion_matrix(ytest, (ypred >= 0.5).astype(int))\n",
    "\n",
    "        # Visualize confusion matrix\n",
    "        plt.figure(figsize=(6, 4))\n",
    "        sns.heatmap(test_cm, annot=True, fmt=\"d\", cmap=\"YlOrRd\", cbar=False)\n",
    "        plt.title(\"Confusion Matrix - Model \" + str(len(histories)))\n",
    "        plt.xlabel(\"Predicted Label\")\n",
    "        plt.ylabel(\"True Label\")\n",
    "        plt.savefig(str(BATCH_SIZE) + 'bilistm_cnn_confusion_matrix.png')\n",
    "        plt.show()\n",
    "\n",
    "        # Display classification report\n",
    "        print(\"Classification Report - Model \" + str(len(histories)))\n",
    "        print(sklearn.metrics.classification_report(ytest, (ypred >= 0.5).astype(int)))\n",
    "\n",
    "        histories.append(model)\n",
    "        model.save(str(BATCH_SIZE)+\"bilstm_cnn_trained_model.h5\")\n",
    "\n",
    "        # Get classification report\n",
    "        report = classification_report(ytest, (ypred >= 0.5).astype(int), output_dict=True)\n",
    "\n",
    "        # Extract precision, recall, and F1-score for each class\n",
    "        classes = [str(cls) for cls in range(len(report) - 3)]  # Extract class labels\n",
    "        precision = [report[cls]['precision'] for cls in classes]\n",
    "        recall = [report[cls]['recall'] for cls in classes]\n",
    "        f1_score = [report[cls]['f1-score'] for cls in classes]\n",
    "\n",
    "        # Create bar plot\n",
    "        x = np.arange(len(classes))\n",
    "        width = 0.2  # Width of the bars\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(6, 6))\n",
    "        rects1 = ax.bar(x - width, precision, width, label='Precision')\n",
    "        rects2 = ax.bar(x, recall, width, label='Recall')\n",
    "        rects3 = ax.bar(x + width, f1_score, width, label='F1-Score')\n",
    "\n",
    "        # Add labels, title, and legend\n",
    "        ax.set_xlabel('Class')\n",
    "        ax.set_ylabel('Scores')\n",
    "        ax.set_title('Classification Report')\n",
    "        ax.set_xticks(x)\n",
    "        ax.set_xticklabels(classes)\n",
    "        ax.legend()\n",
    "\n",
    "        # Show plot\n",
    "        plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "        plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "        plt.show()\n",
    "\n",
    "        # Now, you can plot the training and validation accuracy/loss using histories list\n",
    "        for index, history in enumerate(model_histories):\n",
    "          BATCH_SIZE = [128, 64, 32][index % 3]  # Adjust as needed\n",
    "          plt.figure(figsize=(6, 6))\n",
    "          plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "          plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "          plt.plot(history.history['loss'], label='Training Loss')\n",
    "          plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "          plt.title(f'Model {index+1} - Batch Size {BATCH_SIZE}')\n",
    "          plt.xlabel('Epoch')\n",
    "          plt.ylabel('Accuracy / Loss')\n",
    "          plt.legend()\n",
    "          plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving each trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,trained_model in enumerate(histories):\n",
    "  trained_model.save(str(index)+\"bilstm_cnn_trained_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class attention(layers.Layer):\n",
    "    def __init__(self,return_sequences=True):\n",
    "        self.return_sequences = return_sequences\n",
    "        super(attention,self).__init__()\n",
    " \n",
    "    def build(self,input_shape):\n",
    "        self.W=self.add_weight(name=\"att_weight\", shape=(input_shape[-1],1),initializer=\"normal\")\n",
    "        self.b=self.add_weight(name=\"att_bias\", shape=(input_shape[1],1),initializer=\"normal\")\n",
    "\n",
    "        super(attention,self).build(input_shape)\n",
    " \n",
    "    def call(self,x):\n",
    "        e = K.tanh(K.dot(x,self.W)+self.b)\n",
    "        a = K.softmax(e, axis=1)\n",
    "        output = x*a\n",
    "        if self.return_sequences:\n",
    "\n",
    "            return output\n",
    "        return K.sum(output, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Multiheaded CNN\n",
    "\n",
    "def build_cnn(input_layer):\n",
    "    \n",
    "    cnn = layers.Conv1D(9,7,padding=\"same\")(input_layer)\n",
    "    cnn = layers.BatchNormalization()(cnn)\n",
    "    cnn = layers.Dropout(rate = 0.2)(cnn)\n",
    "    \n",
    "    cnn = layers.Conv1D(18,5,padding=\"same\")(cnn)\n",
    "    cnn = layers.BatchNormalization()(cnn)\n",
    "    cnn = layers.Dropout(rate = 0.2)(cnn)\n",
    "    \n",
    "    cnn = layers.Conv1D(36,3,padding=\"same\")(cnn)\n",
    "    cnn = layers.BatchNormalization()(cnn)\n",
    "    cnn = layers.Dropout(rate = 0.2)(cnn)\n",
    "    \n",
    "    return cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bi-LSTM Attentions \n",
    "\n",
    "def build_bilstm(input_layer,last_sequences = False):\n",
    "\n",
    "    lstm = layers.Bidirectional(layers.LSTM(18,return_sequences = True))(input_layer)\n",
    "    lstm = layers.LayerNormalization()(lstm)\n",
    "    lstm = layers.Dropout(rate = 0.2)(lstm)\n",
    "        \n",
    "    lstm = attention(True)(lstm)\n",
    "        \n",
    "    lstm = layers.Bidirectional(layers.LSTM(36,return_sequences = True))(lstm)\n",
    "    lstm = layers.LayerNormalization()(lstm)\n",
    "    lstm = layers.Dropout(rate = 0.2)(lstm)\n",
    "    \n",
    "    lstm = attention(True)(lstm)\n",
    "    \n",
    "    lstm = layers.Bidirectional(layers.LSTM(72,return_sequences = last_sequences))(lstm)\n",
    "    lstm = layers.LayerNormalization()(lstm)\n",
    "    lstm = layers.Dropout(rate = 0.2)(lstm)\n",
    "    \n",
    "    return lstm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dense layer\n",
    "\n",
    "def build_dense(input_layer):\n",
    "    \n",
    "    dense = layers.Dense(72,name = 'dense_1')(input_layer)\n",
    "    dense = layers.Dense(1,name = 'dense_3',activation = 'sigmoid')(dense)\n",
    "    \n",
    "    return dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    input_layer1 = keras.Input((xtrain.shape[1],3), name = 'Input1')\n",
    "    input_layer2 = keras.Input((xtrain.shape[1],3), name = 'Input2')\n",
    "    \n",
    "    output1 = build_cnn(input_layer1)\n",
    "    output2 = build_cnn(input_layer2)\n",
    "    \n",
    "    output = layers.concatenate([output1,output2])\n",
    "    \n",
    "    output = build_bilstm(output)\n",
    "\n",
    "    output = build_dense(output)\n",
    "    \n",
    "    return keras.Model([input_layer1,input_layer2],output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n",
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_36 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_39 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_36 (Ba  (None, 600, 9)               36        ['conv1d_36[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_39 (Ba  (None, 600, 9)               36        ['conv1d_39[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_54 (Dropout)        (None, 600, 9)               0         ['batch_normalization_36[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_57 (Dropout)        (None, 600, 9)               0         ['batch_normalization_39[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_37 (Conv1D)          (None, 600, 18)              828       ['dropout_54[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_40 (Conv1D)          (None, 600, 18)              828       ['dropout_57[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_37 (Ba  (None, 600, 18)              72        ['conv1d_37[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_40 (Ba  (None, 600, 18)              72        ['conv1d_40[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_55 (Dropout)        (None, 600, 18)              0         ['batch_normalization_37[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_58 (Dropout)        (None, 600, 18)              0         ['batch_normalization_40[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_38 (Conv1D)          (None, 600, 36)              1980      ['dropout_55[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_41 (Conv1D)          (None, 600, 36)              1980      ['dropout_58[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_38 (Ba  (None, 600, 36)              144       ['conv1d_38[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_41 (Ba  (None, 600, 36)              144       ['conv1d_41[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_56 (Dropout)        (None, 600, 36)              0         ['batch_normalization_38[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_59 (Dropout)        (None, 600, 36)              0         ['batch_normalization_41[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_6 (Concatenate  (None, 600, 72)              0         ['dropout_56[0][0]',          \n",
      " )                                                                   'dropout_59[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_18 (Bidirect  (None, 600, 36)              13104     ['concatenate_6[0][0]']       \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_18 (La  (None, 600, 36)              72        ['bidirectional_18[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_60 (Dropout)        (None, 600, 36)              0         ['layer_normalization_18[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_2 (attention)     (None, 600, 36)              636       ['dropout_60[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_19 (Bidirect  (None, 600, 72)              21024     ['attention_2[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_19 (La  (None, 600, 72)              144       ['bidirectional_19[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_61 (Dropout)        (None, 600, 72)              0         ['layer_normalization_19[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_3 (attention)     (None, 600, 72)              672       ['dropout_61[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_20 (Bidirect  (None, 144)                  83520     ['attention_3[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_20 (La  (None, 144)                  288       ['bidirectional_20[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_62 (Dropout)        (None, 144)                  0         ['layer_normalization_20[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_62[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 136489 (533.16 KB)\n",
      "Trainable params: 136237 (532.18 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(build_model(),show_shapes = True,dpi=40),build_model().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m=====================================BATCH_SIZE= 128 learning_rate= 0.0001 =====================================\u001b[0m\n",
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n",
      "Model: \"model_5\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_30 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_33 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_30 (Ba  (None, 600, 9)               36        ['conv1d_30[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_33 (Ba  (None, 600, 9)               36        ['conv1d_33[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_45 (Dropout)        (None, 600, 9)               0         ['batch_normalization_30[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_48 (Dropout)        (None, 600, 9)               0         ['batch_normalization_33[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_31 (Conv1D)          (None, 600, 18)              828       ['dropout_45[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_34 (Conv1D)          (None, 600, 18)              828       ['dropout_48[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (Ba  (None, 600, 18)              72        ['conv1d_31[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_34 (Ba  (None, 600, 18)              72        ['conv1d_34[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_46 (Dropout)        (None, 600, 18)              0         ['batch_normalization_31[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_49 (Dropout)        (None, 600, 18)              0         ['batch_normalization_34[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_32 (Conv1D)          (None, 600, 36)              1980      ['dropout_46[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_35 (Conv1D)          (None, 600, 36)              1980      ['dropout_49[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_32 (Ba  (None, 600, 36)              144       ['conv1d_32[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_35 (Ba  (None, 600, 36)              144       ['conv1d_35[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_47 (Dropout)        (None, 600, 36)              0         ['batch_normalization_32[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_50 (Dropout)        (None, 600, 36)              0         ['batch_normalization_35[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_5 (Concatenate  (None, 600, 72)              0         ['dropout_47[0][0]',          \n",
      " )                                                                   'dropout_50[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_15 (Bidirect  (None, 600, 36)              13104     ['concatenate_5[0][0]']       \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_15 (La  (None, 600, 36)              72        ['bidirectional_15[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_51 (Dropout)        (None, 600, 36)              0         ['layer_normalization_15[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention (attention)       (None, 600, 36)              636       ['dropout_51[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_16 (Bidirect  (None, 600, 72)              21024     ['attention[0][0]']           \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_16 (La  (None, 600, 72)              144       ['bidirectional_16[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_52 (Dropout)        (None, 600, 72)              0         ['layer_normalization_16[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_1 (attention)     (None, 600, 72)              672       ['dropout_52[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_17 (Bidirect  (None, 144)                  83520     ['attention_1[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_17 (La  (None, 144)                  288       ['bidirectional_17[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_53 (Dropout)        (None, 144)                  0         ['layer_normalization_17[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_53[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 136489 (533.16 KB)\n",
      "Trainable params: 136237 (532.18 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 216s 5s/step - loss: 0.6270 - accuracy: 0.6898 - val_loss: 0.6400 - val_accuracy: 0.6612\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 190s 4s/step - loss: 0.6130 - accuracy: 0.6952 - val_loss: 0.6363 - val_accuracy: 0.6612\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 191s 4s/step - loss: 0.6026 - accuracy: 0.6952 - val_loss: 0.6004 - val_accuracy: 0.6612\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 194s 5s/step - loss: 0.3616 - accuracy: 0.8211 - val_loss: 0.2770 - val_accuracy: 0.8068\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 195s 5s/step - loss: 0.1459 - accuracy: 0.9456 - val_loss: 0.2116 - val_accuracy: 0.9376\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 197s 5s/step - loss: 0.1790 - accuracy: 0.9435 - val_loss: 0.1512 - val_accuracy: 0.9569\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 193s 4s/step - loss: 0.1053 - accuracy: 0.9643 - val_loss: 0.1130 - val_accuracy: 0.9614\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 195s 5s/step - loss: 0.0873 - accuracy: 0.9664 - val_loss: 0.0899 - val_accuracy: 0.9673\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 196s 5s/step - loss: 0.0754 - accuracy: 0.9679 - val_loss: 0.0746 - val_accuracy: 0.9688\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 200s 5s/step - loss: 0.0694 - accuracy: 0.9684 - val_loss: 0.0667 - val_accuracy: 0.9703\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 201s 5s/step - loss: 0.0607 - accuracy: 0.9723 - val_loss: 0.0594 - val_accuracy: 0.9703\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 204s 5s/step - loss: 0.0564 - accuracy: 0.9745 - val_loss: 0.0516 - val_accuracy: 0.9718\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 207s 5s/step - loss: 0.0529 - accuracy: 0.9788 - val_loss: 0.0508 - val_accuracy: 0.9747\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 209s 5s/step - loss: 0.0523 - accuracy: 0.9825 - val_loss: 0.0470 - val_accuracy: 0.9747\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 212s 5s/step - loss: 0.0451 - accuracy: 0.9842 - val_loss: 0.0453 - val_accuracy: 0.9866\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 212s 5s/step - loss: 0.0438 - accuracy: 0.9864 - val_loss: 0.0401 - val_accuracy: 0.9896\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 211s 5s/step - loss: 0.0425 - accuracy: 0.9872 - val_loss: 0.0366 - val_accuracy: 0.9941\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 215s 5s/step - loss: 0.0406 - accuracy: 0.9881 - val_loss: 0.0350 - val_accuracy: 0.9955\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 220s 5s/step - loss: 0.0376 - accuracy: 0.9905 - val_loss: 0.0381 - val_accuracy: 0.9837\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 222s 5s/step - loss: 0.0381 - accuracy: 0.9889 - val_loss: 0.0302 - val_accuracy: 0.9955\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 222s 5s/step - loss: 0.0268 - accuracy: 0.9944 - val_loss: 0.0280 - val_accuracy: 0.9955\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 223s 5s/step - loss: 0.0319 - accuracy: 0.9916 - val_loss: 0.0307 - val_accuracy: 0.9970\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 226s 5s/step - loss: 0.0337 - accuracy: 0.9909 - val_loss: 0.0414 - val_accuracy: 0.9941\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 226s 5s/step - loss: 0.0482 - accuracy: 0.9829 - val_loss: 0.0380 - val_accuracy: 0.9941\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 226s 5s/step - loss: 0.0280 - accuracy: 0.9946 - val_loss: 0.0306 - val_accuracy: 0.9955\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 225s 5s/step - loss: 0.0264 - accuracy: 0.9941 - val_loss: 0.0325 - val_accuracy: 0.9941\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 227s 5s/step - loss: 0.0270 - accuracy: 0.9941 - val_loss: 0.0277 - val_accuracy: 0.9955\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 226s 5s/step - loss: 0.0243 - accuracy: 0.9950 - val_loss: 0.0366 - val_accuracy: 0.9941\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 227s 5s/step - loss: 0.0255 - accuracy: 0.9952 - val_loss: 0.0500 - val_accuracy: 0.9866\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 230s 5s/step - loss: 0.0252 - accuracy: 0.9948 - val_loss: 0.0498 - val_accuracy: 0.9911\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 230s 5s/step - loss: 0.0257 - accuracy: 0.9937 - val_loss: 0.0308 - val_accuracy: 0.9955\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 233s 5s/step - loss: 0.0226 - accuracy: 0.9946 - val_loss: 0.0466 - val_accuracy: 0.9941\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 231s 5s/step - loss: 0.0245 - accuracy: 0.9937 - val_loss: 0.0317 - val_accuracy: 0.9941\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 232s 5s/step - loss: 0.0201 - accuracy: 0.9959 - val_loss: 0.0321 - val_accuracy: 0.9955\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 233s 5s/step - loss: 0.0214 - accuracy: 0.9952 - val_loss: 0.0270 - val_accuracy: 0.9941\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 234s 5s/step - loss: 0.0241 - accuracy: 0.9941 - val_loss: 0.0269 - val_accuracy: 0.9970\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 235s 5s/step - loss: 0.0235 - accuracy: 0.9944 - val_loss: 0.0261 - val_accuracy: 0.9970\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 233s 5s/step - loss: 0.0188 - accuracy: 0.9959 - val_loss: 0.0288 - val_accuracy: 0.9955\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 234s 5s/step - loss: 0.0189 - accuracy: 0.9952 - val_loss: 0.0321 - val_accuracy: 0.9911\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 236s 5s/step - loss: 0.0159 - accuracy: 0.9957 - val_loss: 0.0433 - val_accuracy: 0.9926\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 235s 5s/step - loss: 0.0184 - accuracy: 0.9952 - val_loss: 0.0250 - val_accuracy: 0.9955\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 235s 5s/step - loss: 0.0157 - accuracy: 0.9957 - val_loss: 0.0317 - val_accuracy: 0.9926\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 235s 5s/step - loss: 0.0239 - accuracy: 0.9929 - val_loss: 0.0377 - val_accuracy: 0.9896\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 235s 5s/step - loss: 0.0546 - accuracy: 0.9822 - val_loss: 0.0229 - val_accuracy: 0.9955\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 234s 5s/step - loss: 0.0239 - accuracy: 0.9922 - val_loss: 0.0249 - val_accuracy: 0.9955\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 234s 5s/step - loss: 0.0147 - accuracy: 0.9955 - val_loss: 0.0193 - val_accuracy: 0.9970\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 233s 5s/step - loss: 0.0152 - accuracy: 0.9955 - val_loss: 0.0211 - val_accuracy: 0.9970\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 234s 5s/step - loss: 0.0142 - accuracy: 0.9961 - val_loss: 0.0213 - val_accuracy: 0.9955\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 234s 5s/step - loss: 0.0168 - accuracy: 0.9946 - val_loss: 0.0183 - val_accuracy: 0.9970\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 235s 5s/step - loss: 0.0135 - accuracy: 0.9957 - val_loss: 0.0197 - val_accuracy: 0.9955\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 2s 108ms/step - loss: 0.0249 - accuracy: 0.9911\n",
      "test loss =  0.02491983212530613\n",
      "22/22 [==============================] - 2s 107ms/step - loss: 0.0249 - accuracy: 0.9911\n",
      "test accuracy =  0.9910846948623657 \n",
      "\n",
      "22/22 [==============================] - 3s 107ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       491\n",
      "           1       0.99      0.97      0.98       182\n",
      "\n",
      "    accuracy                           0.99       673\n",
      "   macro avg       0.99      0.99      0.99       673\n",
      "weighted avg       0.99      0.99      0.99       673\n",
      "\n",
      "\u001b[1m=====================================BATCH_SIZE= 64 learning_rate= 0.0001 =====================================\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n",
      "Model: \"model_6\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_36 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_39 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_36 (Ba  (None, 600, 9)               36        ['conv1d_36[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_39 (Ba  (None, 600, 9)               36        ['conv1d_39[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_54 (Dropout)        (None, 600, 9)               0         ['batch_normalization_36[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_57 (Dropout)        (None, 600, 9)               0         ['batch_normalization_39[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_37 (Conv1D)          (None, 600, 18)              828       ['dropout_54[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_40 (Conv1D)          (None, 600, 18)              828       ['dropout_57[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_37 (Ba  (None, 600, 18)              72        ['conv1d_37[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_40 (Ba  (None, 600, 18)              72        ['conv1d_40[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_55 (Dropout)        (None, 600, 18)              0         ['batch_normalization_37[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_58 (Dropout)        (None, 600, 18)              0         ['batch_normalization_40[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_38 (Conv1D)          (None, 600, 36)              1980      ['dropout_55[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_41 (Conv1D)          (None, 600, 36)              1980      ['dropout_58[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_38 (Ba  (None, 600, 36)              144       ['conv1d_38[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_41 (Ba  (None, 600, 36)              144       ['conv1d_41[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_56 (Dropout)        (None, 600, 36)              0         ['batch_normalization_38[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_59 (Dropout)        (None, 600, 36)              0         ['batch_normalization_41[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_6 (Concatenate  (None, 600, 72)              0         ['dropout_56[0][0]',          \n",
      " )                                                                   'dropout_59[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_18 (Bidirect  (None, 600, 36)              13104     ['concatenate_6[0][0]']       \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_18 (La  (None, 600, 36)              72        ['bidirectional_18[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_60 (Dropout)        (None, 600, 36)              0         ['layer_normalization_18[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_2 (attention)     (None, 600, 36)              636       ['dropout_60[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_19 (Bidirect  (None, 600, 72)              21024     ['attention_2[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_19 (La  (None, 600, 72)              144       ['bidirectional_19[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_61 (Dropout)        (None, 600, 72)              0         ['layer_normalization_19[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_3 (attention)     (None, 600, 72)              672       ['dropout_61[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_20 (Bidirect  (None, 144)                  83520     ['attention_3[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_20 (La  (None, 144)                  288       ['bidirectional_20[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_62 (Dropout)        (None, 144)                  0         ['layer_normalization_20[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_62[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 136489 (533.16 KB)\n",
      "Trainable params: 136237 (532.18 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "85/85 [==============================] - 133s 1s/step - loss: 0.6229 - accuracy: 0.6924 - val_loss: 0.6373 - val_accuracy: 0.6612\n",
      "Epoch 2/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.5392 - accuracy: 0.7238 - val_loss: 0.2863 - val_accuracy: 0.8440\n",
      "Epoch 3/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.1316 - accuracy: 0.9515 - val_loss: 0.1127 - val_accuracy: 0.9807\n",
      "Epoch 4/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0886 - accuracy: 0.9757 - val_loss: 0.0884 - val_accuracy: 0.9762\n",
      "Epoch 5/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0622 - accuracy: 0.9840 - val_loss: 0.0464 - val_accuracy: 0.9911\n",
      "Epoch 6/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0565 - accuracy: 0.9881 - val_loss: 0.0465 - val_accuracy: 0.9866\n",
      "Epoch 7/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0450 - accuracy: 0.9890 - val_loss: 0.0498 - val_accuracy: 0.9851\n",
      "Epoch 8/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0448 - accuracy: 0.9911 - val_loss: 0.0429 - val_accuracy: 0.9866\n",
      "Epoch 9/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0431 - accuracy: 0.9911 - val_loss: 0.0282 - val_accuracy: 0.9941\n",
      "Epoch 10/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0393 - accuracy: 0.9896 - val_loss: 0.0483 - val_accuracy: 0.9837\n",
      "Epoch 11/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0344 - accuracy: 0.9924 - val_loss: 0.0353 - val_accuracy: 0.9896\n",
      "Epoch 12/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0377 - accuracy: 0.9918 - val_loss: 0.0293 - val_accuracy: 0.9926\n",
      "Epoch 13/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0385 - accuracy: 0.9907 - val_loss: 0.0477 - val_accuracy: 0.9851\n",
      "Epoch 14/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0411 - accuracy: 0.9909 - val_loss: 0.0306 - val_accuracy: 0.9941\n",
      "Epoch 15/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0414 - accuracy: 0.9905 - val_loss: 0.0558 - val_accuracy: 0.9807\n",
      "Epoch 16/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0308 - accuracy: 0.9922 - val_loss: 0.0225 - val_accuracy: 0.9985\n",
      "Epoch 17/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0444 - accuracy: 0.9887 - val_loss: 0.0117 - val_accuracy: 0.9970\n",
      "Epoch 18/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0476 - accuracy: 0.9879 - val_loss: 0.0274 - val_accuracy: 0.9911\n",
      "Epoch 19/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0297 - accuracy: 0.9935 - val_loss: 0.0193 - val_accuracy: 0.9926\n",
      "Epoch 20/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0323 - accuracy: 0.9929 - val_loss: 0.0133 - val_accuracy: 0.9985\n",
      "Epoch 21/50\n",
      "85/85 [==============================] - 110s 1s/step - loss: 0.0364 - accuracy: 0.9924 - val_loss: 0.0232 - val_accuracy: 0.9955\n",
      "Epoch 22/50\n",
      "85/85 [==============================] - 114s 1s/step - loss: 0.1721 - accuracy: 0.9389 - val_loss: 0.1060 - val_accuracy: 0.9584\n",
      "Epoch 23/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0866 - accuracy: 0.9643 - val_loss: 0.0710 - val_accuracy: 0.9807\n",
      "Epoch 24/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0661 - accuracy: 0.9801 - val_loss: 0.0562 - val_accuracy: 0.9866\n",
      "Epoch 25/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0576 - accuracy: 0.9864 - val_loss: 0.0490 - val_accuracy: 0.9881\n",
      "Epoch 26/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0506 - accuracy: 0.9883 - val_loss: 0.0565 - val_accuracy: 0.9851\n",
      "Epoch 27/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0444 - accuracy: 0.9905 - val_loss: 0.0429 - val_accuracy: 0.9911\n",
      "Epoch 28/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0449 - accuracy: 0.9900 - val_loss: 0.0407 - val_accuracy: 0.9926\n",
      "Epoch 29/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0401 - accuracy: 0.9916 - val_loss: 0.0451 - val_accuracy: 0.9911\n",
      "Epoch 30/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0378 - accuracy: 0.9913 - val_loss: 0.0346 - val_accuracy: 0.9926\n",
      "Epoch 31/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0384 - accuracy: 0.9918 - val_loss: 0.0367 - val_accuracy: 0.9926\n",
      "Epoch 32/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0352 - accuracy: 0.9909 - val_loss: 0.0373 - val_accuracy: 0.9911\n",
      "Epoch 33/50\n",
      "85/85 [==============================] - 111s 1s/step - loss: 0.0340 - accuracy: 0.9933 - val_loss: 0.0327 - val_accuracy: 0.9911\n",
      "Epoch 34/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0318 - accuracy: 0.9935 - val_loss: 0.0415 - val_accuracy: 0.9896\n",
      "Epoch 35/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0274 - accuracy: 0.9939 - val_loss: 0.0341 - val_accuracy: 0.9926\n",
      "Epoch 36/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0279 - accuracy: 0.9933 - val_loss: 0.0318 - val_accuracy: 0.9926\n",
      "Epoch 37/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0320 - accuracy: 0.9915 - val_loss: 0.0354 - val_accuracy: 0.9911\n",
      "Epoch 38/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0260 - accuracy: 0.9950 - val_loss: 0.0410 - val_accuracy: 0.9896\n",
      "Epoch 39/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0242 - accuracy: 0.9948 - val_loss: 0.0392 - val_accuracy: 0.9896\n",
      "Epoch 40/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0238 - accuracy: 0.9944 - val_loss: 0.0290 - val_accuracy: 0.9941\n",
      "Epoch 41/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0235 - accuracy: 0.9946 - val_loss: 0.0577 - val_accuracy: 0.9837\n",
      "Epoch 42/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0233 - accuracy: 0.9954 - val_loss: 0.0278 - val_accuracy: 0.9955\n",
      "Epoch 43/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0239 - accuracy: 0.9944 - val_loss: 0.0313 - val_accuracy: 0.9955\n",
      "Epoch 44/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0207 - accuracy: 0.9955 - val_loss: 0.0343 - val_accuracy: 0.9926\n",
      "Epoch 45/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0227 - accuracy: 0.9948 - val_loss: 0.0330 - val_accuracy: 0.9941\n",
      "Epoch 46/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0254 - accuracy: 0.9931 - val_loss: 0.0354 - val_accuracy: 0.9926\n",
      "Epoch 47/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0244 - accuracy: 0.9942 - val_loss: 0.0300 - val_accuracy: 0.9955\n",
      "Epoch 48/50\n",
      "85/85 [==============================] - 112s 1s/step - loss: 0.0200 - accuracy: 0.9954 - val_loss: 0.0430 - val_accuracy: 0.9896\n",
      "Epoch 49/50\n",
      "85/85 [==============================] - 114s 1s/step - loss: 0.0224 - accuracy: 0.9941 - val_loss: 0.0374 - val_accuracy: 0.9911\n",
      "Epoch 50/50\n",
      "85/85 [==============================] - 113s 1s/step - loss: 0.0246 - accuracy: 0.9944 - val_loss: 0.0417 - val_accuracy: 0.9896\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 6s 257ms/step - loss: 0.0367 - accuracy: 0.9881\n",
      "test loss =  0.03670786693692207\n",
      "22/22 [==============================] - 6s 257ms/step - loss: 0.0367 - accuracy: 0.9881\n",
      "test accuracy =  0.9881129264831543 \n",
      "\n",
      "22/22 [==============================] - 7s 256ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      1.00      0.99       491\n",
      "           1       0.99      0.97      0.98       182\n",
      "\n",
      "    accuracy                           0.99       673\n",
      "   macro avg       0.99      0.98      0.98       673\n",
      "weighted avg       0.99      0.99      0.99       673\n",
      "\n",
      "\u001b[1m=====================================BATCH_SIZE= 32 learning_rate= 0.0001 =====================================\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model to work.\n",
      "Model: \"model_7\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " Input1 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " Input2 (InputLayer)         [(None, 600, 3)]             0         []                            \n",
      "                                                                                                  \n",
      " conv1d_42 (Conv1D)          (None, 600, 9)               198       ['Input1[0][0]']              \n",
      "                                                                                                  \n",
      " conv1d_45 (Conv1D)          (None, 600, 9)               198       ['Input2[0][0]']              \n",
      "                                                                                                  \n",
      " batch_normalization_42 (Ba  (None, 600, 9)               36        ['conv1d_42[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_45 (Ba  (None, 600, 9)               36        ['conv1d_45[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_63 (Dropout)        (None, 600, 9)               0         ['batch_normalization_42[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_66 (Dropout)        (None, 600, 9)               0         ['batch_normalization_45[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_43 (Conv1D)          (None, 600, 18)              828       ['dropout_63[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_46 (Conv1D)          (None, 600, 18)              828       ['dropout_66[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_43 (Ba  (None, 600, 18)              72        ['conv1d_43[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_46 (Ba  (None, 600, 18)              72        ['conv1d_46[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_64 (Dropout)        (None, 600, 18)              0         ['batch_normalization_43[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_67 (Dropout)        (None, 600, 18)              0         ['batch_normalization_46[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " conv1d_44 (Conv1D)          (None, 600, 36)              1980      ['dropout_64[0][0]']          \n",
      "                                                                                                  \n",
      " conv1d_47 (Conv1D)          (None, 600, 36)              1980      ['dropout_67[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_44 (Ba  (None, 600, 36)              144       ['conv1d_44[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " batch_normalization_47 (Ba  (None, 600, 36)              144       ['conv1d_47[0][0]']           \n",
      " tchNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_65 (Dropout)        (None, 600, 36)              0         ['batch_normalization_44[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dropout_68 (Dropout)        (None, 600, 36)              0         ['batch_normalization_47[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " concatenate_7 (Concatenate  (None, 600, 72)              0         ['dropout_65[0][0]',          \n",
      " )                                                                   'dropout_68[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_21 (Bidirect  (None, 600, 36)              13104     ['concatenate_7[0][0]']       \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_21 (La  (None, 600, 36)              72        ['bidirectional_21[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_69 (Dropout)        (None, 600, 36)              0         ['layer_normalization_21[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_4 (attention)     (None, 600, 36)              636       ['dropout_69[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_22 (Bidirect  (None, 600, 72)              21024     ['attention_4[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_22 (La  (None, 600, 72)              144       ['bidirectional_22[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_70 (Dropout)        (None, 600, 72)              0         ['layer_normalization_22[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " attention_5 (attention)     (None, 600, 72)              672       ['dropout_70[0][0]']          \n",
      "                                                                                                  \n",
      " bidirectional_23 (Bidirect  (None, 144)                  83520     ['attention_5[0][0]']         \n",
      " ional)                                                                                           \n",
      "                                                                                                  \n",
      " layer_normalization_23 (La  (None, 144)                  288       ['bidirectional_23[0][0]']    \n",
      " yerNormalization)                                                                                \n",
      "                                                                                                  \n",
      " dropout_71 (Dropout)        (None, 144)                  0         ['layer_normalization_23[0][0]\n",
      "                                                                    ']                            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 72)                   10440     ['dropout_71[0][0]']          \n",
      "                                                                                                  \n",
      " dense_3 (Dense)             (None, 1)                    73        ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 136489 (533.16 KB)\n",
      "Trainable params: 136237 (532.18 KB)\n",
      "Non-trainable params: 252 (1008.00 Byte)\n",
      "__________________________________________________________________________________________________\n",
      "Epoch 1/50\n",
      "169/169 [==============================] - 126s 696ms/step - loss: 0.6106 - accuracy: 0.6950 - val_loss: 0.4861 - val_accuracy: 0.8113\n",
      "Epoch 2/50\n",
      "169/169 [==============================] - 111s 659ms/step - loss: 0.1848 - accuracy: 0.9218 - val_loss: 0.1136 - val_accuracy: 0.9658\n",
      "Epoch 3/50\n",
      "169/169 [==============================] - 112s 661ms/step - loss: 0.0751 - accuracy: 0.9786 - val_loss: 0.0644 - val_accuracy: 0.9866\n",
      "Epoch 4/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0512 - accuracy: 0.9874 - val_loss: 0.0376 - val_accuracy: 0.9911\n",
      "Epoch 5/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0437 - accuracy: 0.9898 - val_loss: 0.0336 - val_accuracy: 0.9926\n",
      "Epoch 6/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0367 - accuracy: 0.9918 - val_loss: 0.0264 - val_accuracy: 0.9985\n",
      "Epoch 7/50\n",
      "169/169 [==============================] - 111s 660ms/step - loss: 0.0333 - accuracy: 0.9916 - val_loss: 0.0318 - val_accuracy: 0.9911\n",
      "Epoch 8/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0349 - accuracy: 0.9920 - val_loss: 0.0299 - val_accuracy: 0.9941\n",
      "Epoch 9/50\n",
      "169/169 [==============================] - 112s 661ms/step - loss: 0.0293 - accuracy: 0.9924 - val_loss: 0.0429 - val_accuracy: 0.9896\n",
      "Epoch 10/50\n",
      "169/169 [==============================] - 112s 662ms/step - loss: 0.0302 - accuracy: 0.9926 - val_loss: 0.0289 - val_accuracy: 0.9955\n",
      "Epoch 11/50\n",
      "169/169 [==============================] - 112s 662ms/step - loss: 0.0310 - accuracy: 0.9918 - val_loss: 0.0398 - val_accuracy: 0.9896\n",
      "Epoch 12/50\n",
      "169/169 [==============================] - 112s 663ms/step - loss: 0.0238 - accuracy: 0.9939 - val_loss: 0.0467 - val_accuracy: 0.9896\n",
      "Epoch 13/50\n",
      "169/169 [==============================] - 112s 664ms/step - loss: 0.0255 - accuracy: 0.9929 - val_loss: 0.0355 - val_accuracy: 0.9896\n",
      "Epoch 14/50\n",
      "169/169 [==============================] - 122s 722ms/step - loss: 0.0277 - accuracy: 0.9935 - val_loss: 0.0316 - val_accuracy: 0.9926\n",
      "Epoch 15/50\n",
      "169/169 [==============================] - 131s 778ms/step - loss: 0.0262 - accuracy: 0.9931 - val_loss: 0.0299 - val_accuracy: 0.9955\n",
      "Epoch 16/50\n",
      "169/169 [==============================] - 131s 776ms/step - loss: 0.0243 - accuracy: 0.9939 - val_loss: 0.0404 - val_accuracy: 0.9911\n",
      "Epoch 17/50\n",
      "169/169 [==============================] - 131s 777ms/step - loss: 0.0224 - accuracy: 0.9948 - val_loss: 0.0310 - val_accuracy: 0.9941\n",
      "Epoch 18/50\n",
      "169/169 [==============================] - 131s 777ms/step - loss: 0.0308 - accuracy: 0.9920 - val_loss: 0.0350 - val_accuracy: 0.9896\n",
      "Epoch 19/50\n",
      "169/169 [==============================] - 131s 776ms/step - loss: 0.0258 - accuracy: 0.9933 - val_loss: 0.0271 - val_accuracy: 0.9941\n",
      "Epoch 20/50\n",
      "169/169 [==============================] - 131s 776ms/step - loss: 0.0206 - accuracy: 0.9950 - val_loss: 0.0271 - val_accuracy: 0.9955\n",
      "Epoch 21/50\n",
      "169/169 [==============================] - 127s 752ms/step - loss: 0.0221 - accuracy: 0.9939 - val_loss: 0.0295 - val_accuracy: 0.9941\n",
      "Epoch 22/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0227 - accuracy: 0.9935 - val_loss: 0.0327 - val_accuracy: 0.9911\n",
      "Epoch 23/50\n",
      "169/169 [==============================] - 111s 660ms/step - loss: 0.0187 - accuracy: 0.9948 - val_loss: 0.0334 - val_accuracy: 0.9926\n",
      "Epoch 24/50\n",
      "169/169 [==============================] - 112s 661ms/step - loss: 0.0190 - accuracy: 0.9942 - val_loss: 0.0333 - val_accuracy: 0.9941\n",
      "Epoch 25/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0268 - accuracy: 0.9922 - val_loss: 0.0374 - val_accuracy: 0.9926\n",
      "Epoch 26/50\n",
      "169/169 [==============================] - 111s 659ms/step - loss: 0.0258 - accuracy: 0.9933 - val_loss: 0.0286 - val_accuracy: 0.9941\n",
      "Epoch 27/50\n",
      "169/169 [==============================] - 111s 659ms/step - loss: 0.0229 - accuracy: 0.9944 - val_loss: 0.0274 - val_accuracy: 0.9941\n",
      "Epoch 28/50\n",
      "169/169 [==============================] - 111s 660ms/step - loss: 0.0171 - accuracy: 0.9959 - val_loss: 0.0270 - val_accuracy: 0.9955\n",
      "Epoch 29/50\n",
      "169/169 [==============================] - 112s 660ms/step - loss: 0.0183 - accuracy: 0.9948 - val_loss: 0.0243 - val_accuracy: 0.9955\n",
      "Epoch 30/50\n",
      "169/169 [==============================] - 111s 659ms/step - loss: 0.0177 - accuracy: 0.9950 - val_loss: 0.0273 - val_accuracy: 0.9941\n",
      "Epoch 31/50\n",
      "169/169 [==============================] - 111s 659ms/step - loss: 0.0190 - accuracy: 0.9944 - val_loss: 0.0250 - val_accuracy: 0.9955\n",
      "Epoch 32/50\n",
      "169/169 [==============================] - 127s 752ms/step - loss: 0.0163 - accuracy: 0.9950 - val_loss: 0.0239 - val_accuracy: 0.9955\n",
      "Epoch 33/50\n",
      "169/169 [==============================] - 131s 777ms/step - loss: 0.0181 - accuracy: 0.9939 - val_loss: 0.0357 - val_accuracy: 0.9941\n",
      "Epoch 34/50\n",
      "169/169 [==============================] - 136s 805ms/step - loss: 0.0155 - accuracy: 0.9959 - val_loss: 0.0226 - val_accuracy: 0.9926\n",
      "Epoch 35/50\n",
      "169/169 [==============================] - 135s 801ms/step - loss: 0.0198 - accuracy: 0.9948 - val_loss: 0.0278 - val_accuracy: 0.9955\n",
      "Epoch 36/50\n",
      "169/169 [==============================] - 115s 679ms/step - loss: 0.0157 - accuracy: 0.9961 - val_loss: 0.0297 - val_accuracy: 0.9941\n",
      "Epoch 37/50\n",
      "169/169 [==============================] - 115s 678ms/step - loss: 0.0164 - accuracy: 0.9950 - val_loss: 0.0392 - val_accuracy: 0.9911\n",
      "Epoch 38/50\n",
      "169/169 [==============================] - 115s 679ms/step - loss: 0.0131 - accuracy: 0.9959 - val_loss: 0.0257 - val_accuracy: 0.9941\n",
      "Epoch 39/50\n",
      "169/169 [==============================] - 115s 678ms/step - loss: 0.0153 - accuracy: 0.9957 - val_loss: 0.0288 - val_accuracy: 0.9941\n",
      "Epoch 40/50\n",
      "169/169 [==============================] - 113s 667ms/step - loss: 0.0146 - accuracy: 0.9959 - val_loss: 0.0301 - val_accuracy: 0.9926\n",
      "Epoch 41/50\n",
      "169/169 [==============================] - 114s 676ms/step - loss: 0.0141 - accuracy: 0.9965 - val_loss: 0.0222 - val_accuracy: 0.9955\n",
      "Epoch 42/50\n",
      "169/169 [==============================] - 114s 675ms/step - loss: 0.0153 - accuracy: 0.9948 - val_loss: 0.0301 - val_accuracy: 0.9955\n",
      "Epoch 43/50\n",
      "169/169 [==============================] - 114s 675ms/step - loss: 0.0160 - accuracy: 0.9959 - val_loss: 0.0313 - val_accuracy: 0.9955\n",
      "Epoch 44/50\n",
      "169/169 [==============================] - 114s 676ms/step - loss: 0.0117 - accuracy: 0.9965 - val_loss: 0.0301 - val_accuracy: 0.9955\n",
      "Epoch 45/50\n",
      "169/169 [==============================] - 114s 675ms/step - loss: 0.0150 - accuracy: 0.9961 - val_loss: 0.0351 - val_accuracy: 0.9941\n",
      "Epoch 46/50\n",
      "169/169 [==============================] - 114s 676ms/step - loss: 0.0124 - accuracy: 0.9959 - val_loss: 0.0394 - val_accuracy: 0.9926\n",
      "Epoch 47/50\n",
      "169/169 [==============================] - 114s 675ms/step - loss: 0.0103 - accuracy: 0.9967 - val_loss: 0.0342 - val_accuracy: 0.9941\n",
      "Epoch 48/50\n",
      "169/169 [==============================] - 114s 675ms/step - loss: 0.0175 - accuracy: 0.9950 - val_loss: 0.0358 - val_accuracy: 0.9926\n",
      "Epoch 49/50\n",
      "169/169 [==============================] - 114s 675ms/step - loss: 0.0151 - accuracy: 0.9955 - val_loss: 0.0309 - val_accuracy: 0.9955\n",
      "Epoch 50/50\n",
      "169/169 [==============================] - 114s 676ms/step - loss: 0.0133 - accuracy: 0.9967 - val_loss: 0.0328 - val_accuracy: 0.9926\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 4s 180ms/step - loss: 0.0192 - accuracy: 0.9911\n",
      "test loss =  0.019184008240699768\n",
      "22/22 [==============================] - 4s 163ms/step - loss: 0.0192 - accuracy: 0.9911\n",
      "test accuracy =  0.9910846948623657 \n",
      "\n",
      "22/22 [==============================] - 5s 168ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       491\n",
      "           1       0.98      0.98      0.98       182\n",
      "\n",
      "    accuracy                           0.99       673\n",
      "   macro avg       0.99      0.99      0.99       673\n",
      "weighted avg       0.99      0.99      0.99       673\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models_with_attention = []\n",
    "\n",
    "for BATCH_SIZE in [128,64,32]:\n",
    "    for learning_rate in [0.0001]:\n",
    "        print(\"\\033[1m=====================================BATCH_SIZE=\",BATCH_SIZE,\"learning_rate=\",learning_rate,\"=====================================\\033[0m\")\n",
    "\n",
    "        lr_scheduler = keras.optimizers.schedules.ExponentialDecay(\n",
    "            initial_learning_rate = learning_rate,\n",
    "            decay_steps = int((xtrain.shape[0] + BATCH_SIZE)/BATCH_SIZE),\n",
    "            decay_rate = 0.99\n",
    "        )\n",
    "        \n",
    "        optimizer = keras.optimizers.Adam()\n",
    "        optimizer.learning_rate = lr_scheduler\n",
    "        \n",
    "        model = build_model()\n",
    "\n",
    "        plot_model(model,show_shapes = True,dpi=20)\n",
    "\n",
    "        model.summary()\n",
    "\n",
    "        model.compile(loss = 'binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])\n",
    "        model_history = model.fit(\n",
    "            {'Input1': xtrain[:,:,0:3], 'Input2': xtrain[:,:,3:6]},\n",
    "            ytrain,\n",
    "            validation_data = (\n",
    "                [xval[:,:,0:3],xval[:,:,3:6]],\n",
    "                yval\n",
    "            ),\n",
    "            epochs = 50,\n",
    "            batch_size = BATCH_SIZE,\n",
    "        )\n",
    "\n",
    "        # Testing accuracy\n",
    "\n",
    "        plt.figure(figsize = (8,6))\n",
    "        plt.plot(model_history.history['accuracy'], color=\"blue\", alpha = 0.5)\n",
    "        plt.plot(model_history.history['loss'], color=\"red\", alpha = 0.5)\n",
    "        plt.plot(model_history.history['val_accuracy'], color=\"green\", alpha = 0.5)\n",
    "        plt.plot(model_history.history['val_loss'], color=\"orange\", alpha = 0.5)\n",
    "        plt.savefig(str(BATCH_SIZE) + 'bilistm_cnn_attentions_Model_Training_Graph.png')\n",
    "        plt.show()\n",
    "\n",
    "        print('test loss = ',model.evaluate([xtest[:,:,0:3],xtest[:,:,3:6]], ytest)[0])      \n",
    "        print('test accuracy = ',model.evaluate([xtest[:,:,0:3],xtest[:,:,3:6]], ytest)[1],'\\n')\n",
    "\n",
    "        ypred = model.predict([xtest[:,:,0:3],xtest[:,:,3:6]])\n",
    "\n",
    "        test_cm = confusion_matrix(ytest,(ypred >= 0.5)*1)\n",
    "\n",
    "        sb.set(rc = {'figure.figsize':(12.8,9.6)})\n",
    "        sb.heatmap(test_cm,annot = True, cmap = 'YlOrRd')\n",
    "        plt.plot()\n",
    "        \n",
    "        print(sklearn.metrics.classification_report(ytest,(ypred >= 0.5)*1))\n",
    "\n",
    "        models_with_attention.append(model)\n",
    "        model.save(str(BATCH_SIZE)+\"bilstm_cnn_attentions_trained_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(histories, open('models_multiheadedcnn_biLSTM.sav', 'wb'))\n",
    "pickle.dump(models_with_attention, open('models_multiheadedcnn_biLSTM_with_attention.sav', 'wb'))\n",
    "pickle.dump(xtrain, open('xtrain.sav', 'wb'))\n",
    "pickle.dump(xtest, open('xtest.sav', 'wb'))\n",
    "pickle.dump(xval, open('xval.sav', 'wb'))\n",
    "pickle.dump(ytrain, open('ytrain.sav', 'wb'))\n",
    "pickle.dump(ytest, open('ytest.sav', 'wb'))\n",
    "pickle.dump(yval, open('yval.sav', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_gru_model(input_shape):\n",
    "    model = Sequential([\n",
    "        GRU(256, return_sequences=True, input_shape=input_shape),\n",
    "        BatchNormalization(),\n",
    "        GRU(128, return_sequences=True),\n",
    "        Dropout(0.5),\n",
    "        GRU(64),\n",
    "        Dropout(0.3),\n",
    "        Dense(64, activation='relu'),\n",
    "        Dense(1, activation='sigmoid')\n",
    "    ])\n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================== BATCH_SIZE=128, learning_rate=0.0001 =====================================\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " gru (GRU)                   (None, 600, 256)          202752    \n",
      "                                                                 \n",
      " batch_normalization_48 (Ba  (None, 600, 256)          1024      \n",
      " tchNormalization)                                               \n",
      "                                                                 \n",
      " gru_1 (GRU)                 (None, 600, 128)          148224    \n",
      "                                                                 \n",
      " dropout_72 (Dropout)        (None, 600, 128)          0         \n",
      "                                                                 \n",
      " gru_2 (GRU)                 (None, 64)                37248     \n",
      "                                                                 \n",
      " dropout_73 (Dropout)        (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 393473 (1.50 MB)\n",
      "Trainable params: 392961 (1.50 MB)\n",
      "Non-trainable params: 512 (2.00 KB)\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "43/43 [==============================] - 369s 9s/step - loss: 0.5012 - accuracy: 0.7774 - val_loss: 0.5451 - val_accuracy: 0.8678\n",
      "Epoch 2/50\n",
      "43/43 [==============================] - 443s 10s/step - loss: 0.3295 - accuracy: 0.8876 - val_loss: 0.4408 - val_accuracy: 0.8796\n",
      "Epoch 3/50\n",
      "43/43 [==============================] - 485s 11s/step - loss: 0.2645 - accuracy: 0.9298 - val_loss: 0.3584 - val_accuracy: 0.9316\n",
      "Epoch 4/50\n",
      "43/43 [==============================] - 523s 12s/step - loss: 0.2225 - accuracy: 0.9458 - val_loss: 0.2932 - val_accuracy: 0.9510\n",
      "Epoch 5/50\n",
      "43/43 [==============================] - 556s 13s/step - loss: 0.1943 - accuracy: 0.9558 - val_loss: 0.2347 - val_accuracy: 0.9614\n",
      "Epoch 6/50\n",
      "43/43 [==============================] - 577s 13s/step - loss: 0.1707 - accuracy: 0.9586 - val_loss: 0.1947 - val_accuracy: 0.9599\n",
      "Epoch 7/50\n",
      "43/43 [==============================] - 583s 14s/step - loss: 0.1559 - accuracy: 0.9599 - val_loss: 0.1561 - val_accuracy: 0.9614\n",
      "Epoch 8/50\n",
      "43/43 [==============================] - 482s 11s/step - loss: 0.1419 - accuracy: 0.9614 - val_loss: 0.1401 - val_accuracy: 0.9614\n",
      "Epoch 9/50\n",
      "43/43 [==============================] - 496s 12s/step - loss: 0.1298 - accuracy: 0.9651 - val_loss: 0.1293 - val_accuracy: 0.9643\n",
      "Epoch 10/50\n",
      "43/43 [==============================] - 597s 14s/step - loss: 0.1231 - accuracy: 0.9649 - val_loss: 0.1209 - val_accuracy: 0.9673\n",
      "Epoch 11/50\n",
      "43/43 [==============================] - 614s 14s/step - loss: 0.1216 - accuracy: 0.9669 - val_loss: 0.1189 - val_accuracy: 0.9658\n",
      "Epoch 12/50\n",
      "43/43 [==============================] - 614s 14s/step - loss: 0.1139 - accuracy: 0.9643 - val_loss: 0.1112 - val_accuracy: 0.9688\n",
      "Epoch 13/50\n",
      "43/43 [==============================] - 620s 14s/step - loss: 0.1141 - accuracy: 0.9654 - val_loss: 0.1116 - val_accuracy: 0.9673\n",
      "Epoch 14/50\n",
      "43/43 [==============================] - 619s 14s/step - loss: 0.1111 - accuracy: 0.9667 - val_loss: 0.1083 - val_accuracy: 0.9688\n",
      "Epoch 15/50\n",
      "43/43 [==============================] - 597s 14s/step - loss: 0.1098 - accuracy: 0.9654 - val_loss: 0.1072 - val_accuracy: 0.9688\n",
      "Epoch 16/50\n",
      "43/43 [==============================] - 451s 11s/step - loss: 0.1077 - accuracy: 0.9677 - val_loss: 0.1051 - val_accuracy: 0.9688\n",
      "Epoch 17/50\n",
      "43/43 [==============================] - 447s 10s/step - loss: 0.1038 - accuracy: 0.9680 - val_loss: 0.1052 - val_accuracy: 0.9688\n",
      "Epoch 18/50\n",
      "43/43 [==============================] - 451s 10s/step - loss: 0.1040 - accuracy: 0.9680 - val_loss: 0.1037 - val_accuracy: 0.9703\n",
      "Epoch 19/50\n",
      "43/43 [==============================] - 468s 11s/step - loss: 0.0979 - accuracy: 0.9684 - val_loss: 0.1050 - val_accuracy: 0.9673\n",
      "Epoch 20/50\n",
      "43/43 [==============================] - 460s 11s/step - loss: 0.1010 - accuracy: 0.9660 - val_loss: 0.1022 - val_accuracy: 0.9703\n",
      "Epoch 21/50\n",
      "43/43 [==============================] - 466s 11s/step - loss: 0.0982 - accuracy: 0.9705 - val_loss: 0.1052 - val_accuracy: 0.9688\n",
      "Epoch 22/50\n",
      "43/43 [==============================] - 480s 11s/step - loss: 0.0993 - accuracy: 0.9671 - val_loss: 0.1011 - val_accuracy: 0.9703\n",
      "Epoch 23/50\n",
      "43/43 [==============================] - 479s 11s/step - loss: 0.0968 - accuracy: 0.9706 - val_loss: 0.1005 - val_accuracy: 0.9703\n",
      "Epoch 24/50\n",
      "43/43 [==============================] - 477s 11s/step - loss: 0.0950 - accuracy: 0.9701 - val_loss: 0.1006 - val_accuracy: 0.9688\n",
      "Epoch 25/50\n",
      "43/43 [==============================] - 577s 13s/step - loss: 0.0951 - accuracy: 0.9688 - val_loss: 0.0976 - val_accuracy: 0.9703\n",
      "Epoch 26/50\n",
      "43/43 [==============================] - 623s 15s/step - loss: 0.0910 - accuracy: 0.9697 - val_loss: 0.1004 - val_accuracy: 0.9688\n",
      "Epoch 27/50\n",
      "43/43 [==============================] - 626s 15s/step - loss: 0.0923 - accuracy: 0.9695 - val_loss: 0.0990 - val_accuracy: 0.9688\n",
      "Epoch 28/50\n",
      "43/43 [==============================] - 603s 14s/step - loss: 0.0912 - accuracy: 0.9718 - val_loss: 0.0962 - val_accuracy: 0.9703\n",
      "Epoch 29/50\n",
      "43/43 [==============================] - 478s 11s/step - loss: 0.0915 - accuracy: 0.9697 - val_loss: 0.0961 - val_accuracy: 0.9703\n",
      "Epoch 30/50\n",
      "43/43 [==============================] - 507s 12s/step - loss: 0.0898 - accuracy: 0.9714 - val_loss: 0.0948 - val_accuracy: 0.9718\n",
      "Epoch 31/50\n",
      "43/43 [==============================] - 503s 12s/step - loss: 0.0871 - accuracy: 0.9699 - val_loss: 0.0937 - val_accuracy: 0.9718\n",
      "Epoch 32/50\n",
      "43/43 [==============================] - 497s 12s/step - loss: 0.0876 - accuracy: 0.9716 - val_loss: 0.0961 - val_accuracy: 0.9733\n",
      "Epoch 33/50\n",
      "43/43 [==============================] - 528s 12s/step - loss: 0.0832 - accuracy: 0.9716 - val_loss: 0.0918 - val_accuracy: 0.9733\n",
      "Epoch 34/50\n",
      "43/43 [==============================] - 534s 12s/step - loss: 0.0883 - accuracy: 0.9705 - val_loss: 0.0954 - val_accuracy: 0.9762\n",
      "Epoch 35/50\n",
      "43/43 [==============================] - 539s 13s/step - loss: 0.0850 - accuracy: 0.9731 - val_loss: 0.0913 - val_accuracy: 0.9733\n",
      "Epoch 36/50\n",
      "43/43 [==============================] - 586s 14s/step - loss: 0.0847 - accuracy: 0.9723 - val_loss: 0.0924 - val_accuracy: 0.9733\n",
      "Epoch 37/50\n",
      "43/43 [==============================] - 657s 15s/step - loss: 0.0833 - accuracy: 0.9721 - val_loss: 0.0919 - val_accuracy: 0.9747\n",
      "Epoch 38/50\n",
      "43/43 [==============================] - 593s 14s/step - loss: 0.0880 - accuracy: 0.9718 - val_loss: 0.0915 - val_accuracy: 0.9762\n",
      "Epoch 39/50\n",
      "43/43 [==============================] - 498s 12s/step - loss: 0.0855 - accuracy: 0.9732 - val_loss: 0.0919 - val_accuracy: 0.9777\n",
      "Epoch 40/50\n",
      "43/43 [==============================] - 447s 10s/step - loss: 0.0833 - accuracy: 0.9723 - val_loss: 0.0919 - val_accuracy: 0.9762\n",
      "Epoch 41/50\n",
      "43/43 [==============================] - 486s 11s/step - loss: 0.0830 - accuracy: 0.9738 - val_loss: 0.0901 - val_accuracy: 0.9762\n",
      "Epoch 42/50\n",
      "43/43 [==============================] - 501s 12s/step - loss: 0.0836 - accuracy: 0.9723 - val_loss: 0.0901 - val_accuracy: 0.9762\n",
      "Epoch 43/50\n",
      "43/43 [==============================] - 501s 12s/step - loss: 0.0835 - accuracy: 0.9712 - val_loss: 0.0905 - val_accuracy: 0.9762\n",
      "Epoch 44/50\n",
      "43/43 [==============================] - 500s 12s/step - loss: 0.0838 - accuracy: 0.9734 - val_loss: 0.0901 - val_accuracy: 0.9762\n",
      "Epoch 45/50\n",
      "43/43 [==============================] - 500s 12s/step - loss: 0.0804 - accuracy: 0.9723 - val_loss: 0.0905 - val_accuracy: 0.9777\n",
      "Epoch 46/50\n",
      "43/43 [==============================] - 496s 12s/step - loss: 0.0819 - accuracy: 0.9725 - val_loss: 0.0897 - val_accuracy: 0.9777\n",
      "Epoch 47/50\n",
      "43/43 [==============================] - 455s 11s/step - loss: 0.0798 - accuracy: 0.9714 - val_loss: 0.0907 - val_accuracy: 0.9777\n",
      "Epoch 48/50\n",
      "43/43 [==============================] - 460s 11s/step - loss: 0.0818 - accuracy: 0.9736 - val_loss: 0.0907 - val_accuracy: 0.9777\n",
      "Epoch 49/50\n",
      "43/43 [==============================] - 644s 15s/step - loss: 0.0826 - accuracy: 0.9725 - val_loss: 0.0909 - val_accuracy: 0.9777\n",
      "Epoch 50/50\n",
      "43/43 [==============================] - 645s 15s/step - loss: 0.0775 - accuracy: 0.9757 - val_loss: 0.0903 - val_accuracy: 0.9777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 13s 591ms/step - loss: 0.1287 - accuracy: 0.9599\n",
      "Test Loss: 0.128731369972229, Test Accuracy: 0.9598811268806458\n",
      "22/22 [==============================] - 14s 588ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Model 1\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.97       491\n",
      "           1       0.89      0.97      0.93       182\n",
      "\n",
      "    accuracy                           0.96       673\n",
      "   macro avg       0.94      0.96      0.95       673\n",
      "weighted avg       0.96      0.96      0.96       673\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================== BATCH_SIZE=64, learning_rate=0.0001 =====================================\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " gru_3 (GRU)                 (None, 600, 256)          202752    \n",
      "                                                                 \n",
      " batch_normalization_49 (Ba  (None, 600, 256)          1024      \n",
      " tchNormalization)                                               \n",
      "                                                                 \n",
      " gru_4 (GRU)                 (None, 600, 128)          148224    \n",
      "                                                                 \n",
      " dropout_74 (Dropout)        (None, 600, 128)          0         \n",
      "                                                                 \n",
      " gru_5 (GRU)                 (None, 64)                37248     \n",
      "                                                                 \n",
      " dropout_75 (Dropout)        (None, 64)                0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 393473 (1.50 MB)\n",
      "Trainable params: 392961 (1.50 MB)\n",
      "Non-trainable params: 512 (2.00 KB)\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "85/85 [==============================] - 720s 8s/step - loss: 0.5539 - accuracy: 0.6987 - val_loss: 0.5041 - val_accuracy: 0.8648\n",
      "Epoch 2/50\n",
      "85/85 [==============================] - 653s 8s/step - loss: 0.3303 - accuracy: 0.8781 - val_loss: 0.3528 - val_accuracy: 0.9183\n",
      "Epoch 3/50\n",
      "85/85 [==============================] - 603s 7s/step - loss: 0.2425 - accuracy: 0.9402 - val_loss: 0.2493 - val_accuracy: 0.9465\n",
      "Epoch 4/50\n",
      "85/85 [==============================] - 521s 6s/step - loss: 0.1885 - accuracy: 0.9537 - val_loss: 0.1691 - val_accuracy: 0.9599\n",
      "Epoch 5/50\n",
      "85/85 [==============================] - 537s 6s/step - loss: 0.1497 - accuracy: 0.9617 - val_loss: 0.1163 - val_accuracy: 0.9703\n",
      "Epoch 6/50\n",
      "85/85 [==============================] - 561s 7s/step - loss: 0.1287 - accuracy: 0.9627 - val_loss: 0.1035 - val_accuracy: 0.9718\n",
      "Epoch 7/50\n",
      "85/85 [==============================] - 583s 7s/step - loss: 0.1155 - accuracy: 0.9649 - val_loss: 0.0977 - val_accuracy: 0.9733\n",
      "Epoch 8/50\n",
      "85/85 [==============================] - 563s 7s/step - loss: 0.1081 - accuracy: 0.9651 - val_loss: 0.0987 - val_accuracy: 0.9718\n",
      "Epoch 9/50\n",
      "85/85 [==============================] - 556s 7s/step - loss: 0.1073 - accuracy: 0.9664 - val_loss: 0.0951 - val_accuracy: 0.9718\n",
      "Epoch 10/50\n",
      "85/85 [==============================] - 636s 7s/step - loss: 0.0980 - accuracy: 0.9686 - val_loss: 0.0975 - val_accuracy: 0.9733\n",
      "Epoch 11/50\n",
      "85/85 [==============================] - 648s 8s/step - loss: 0.0963 - accuracy: 0.9695 - val_loss: 0.0917 - val_accuracy: 0.9718\n",
      "Epoch 12/50\n",
      "85/85 [==============================] - 657s 8s/step - loss: 0.0957 - accuracy: 0.9679 - val_loss: 0.0912 - val_accuracy: 0.9718\n",
      "Epoch 13/50\n",
      "85/85 [==============================] - 655s 8s/step - loss: 0.0930 - accuracy: 0.9699 - val_loss: 0.0904 - val_accuracy: 0.9733\n",
      "Epoch 14/50\n",
      "85/85 [==============================] - 654s 8s/step - loss: 0.0940 - accuracy: 0.9679 - val_loss: 0.0925 - val_accuracy: 0.9733\n",
      "Epoch 15/50\n",
      "85/85 [==============================] - 567s 7s/step - loss: 0.0877 - accuracy: 0.9710 - val_loss: 0.0934 - val_accuracy: 0.9718\n",
      "Epoch 16/50\n",
      "85/85 [==============================] - 593s 7s/step - loss: 0.0858 - accuracy: 0.9714 - val_loss: 0.0902 - val_accuracy: 0.9718\n",
      "Epoch 17/50\n",
      "85/85 [==============================] - 592s 7s/step - loss: 0.0866 - accuracy: 0.9712 - val_loss: 0.0888 - val_accuracy: 0.9703\n",
      "Epoch 18/50\n",
      "85/85 [==============================] - 618s 7s/step - loss: 0.0856 - accuracy: 0.9714 - val_loss: 0.0892 - val_accuracy: 0.9703\n",
      "Epoch 19/50\n",
      "85/85 [==============================] - 681s 8s/step - loss: 0.0841 - accuracy: 0.9723 - val_loss: 0.0878 - val_accuracy: 0.9733\n",
      "Epoch 20/50\n",
      "85/85 [==============================] - 672s 8s/step - loss: 0.0831 - accuracy: 0.9718 - val_loss: 0.0873 - val_accuracy: 0.9718\n",
      "Epoch 21/50\n",
      "85/85 [==============================] - 682s 8s/step - loss: 0.0832 - accuracy: 0.9716 - val_loss: 0.0865 - val_accuracy: 0.9703\n",
      "Epoch 22/50\n",
      "85/85 [==============================] - 620s 7s/step - loss: 0.0816 - accuracy: 0.9725 - val_loss: 0.0862 - val_accuracy: 0.9718\n",
      "Epoch 23/50\n",
      "85/85 [==============================] - 655s 8s/step - loss: 0.0800 - accuracy: 0.9721 - val_loss: 0.0864 - val_accuracy: 0.9718\n",
      "Epoch 24/50\n",
      "85/85 [==============================] - 653s 8s/step - loss: 0.0800 - accuracy: 0.9736 - val_loss: 0.0853 - val_accuracy: 0.9733\n",
      "Epoch 25/50\n",
      "85/85 [==============================] - 649s 8s/step - loss: 0.0819 - accuracy: 0.9718 - val_loss: 0.0838 - val_accuracy: 0.9733\n",
      "Epoch 26/50\n",
      "85/85 [==============================] - 648s 8s/step - loss: 0.0794 - accuracy: 0.9738 - val_loss: 0.0838 - val_accuracy: 0.9718\n",
      "Epoch 27/50\n",
      "85/85 [==============================] - 653s 8s/step - loss: 0.0787 - accuracy: 0.9736 - val_loss: 0.0890 - val_accuracy: 0.9703\n",
      "Epoch 28/50\n",
      "85/85 [==============================] - 545s 6s/step - loss: 0.0783 - accuracy: 0.9731 - val_loss: 0.0839 - val_accuracy: 0.9718\n",
      "Epoch 29/50\n",
      "85/85 [==============================] - 514s 6s/step - loss: 0.0787 - accuracy: 0.9716 - val_loss: 0.0833 - val_accuracy: 0.9703\n",
      "Epoch 30/50\n",
      "85/85 [==============================] - 500s 6s/step - loss: 0.0776 - accuracy: 0.9723 - val_loss: 0.0827 - val_accuracy: 0.9718\n",
      "Epoch 31/50\n",
      "85/85 [==============================] - 478s 6s/step - loss: 0.0753 - accuracy: 0.9736 - val_loss: 0.0825 - val_accuracy: 0.9718\n",
      "Epoch 32/50\n",
      "85/85 [==============================] - 461s 5s/step - loss: 0.0755 - accuracy: 0.9745 - val_loss: 0.0833 - val_accuracy: 0.9718\n",
      "Epoch 33/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0753 - accuracy: 0.9731 - val_loss: 0.0831 - val_accuracy: 0.9718\n",
      "Epoch 34/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0751 - accuracy: 0.9744 - val_loss: 0.0842 - val_accuracy: 0.9747\n",
      "Epoch 35/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0759 - accuracy: 0.9740 - val_loss: 0.0815 - val_accuracy: 0.9718\n",
      "Epoch 36/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0738 - accuracy: 0.9753 - val_loss: 0.0836 - val_accuracy: 0.9733\n",
      "Epoch 37/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0720 - accuracy: 0.9751 - val_loss: 0.0807 - val_accuracy: 0.9718\n",
      "Epoch 38/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0729 - accuracy: 0.9745 - val_loss: 0.0817 - val_accuracy: 0.9747\n",
      "Epoch 39/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0748 - accuracy: 0.9749 - val_loss: 0.0820 - val_accuracy: 0.9747\n",
      "Epoch 40/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0730 - accuracy: 0.9762 - val_loss: 0.0830 - val_accuracy: 0.9733\n",
      "Epoch 41/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0734 - accuracy: 0.9760 - val_loss: 0.0811 - val_accuracy: 0.9762\n",
      "Epoch 42/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0731 - accuracy: 0.9764 - val_loss: 0.0801 - val_accuracy: 0.9762\n",
      "Epoch 43/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0719 - accuracy: 0.9755 - val_loss: 0.0791 - val_accuracy: 0.9777\n",
      "Epoch 44/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0713 - accuracy: 0.9753 - val_loss: 0.0797 - val_accuracy: 0.9777\n",
      "Epoch 45/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0701 - accuracy: 0.9777 - val_loss: 0.0792 - val_accuracy: 0.9777\n",
      "Epoch 46/50\n",
      "85/85 [==============================] - 461s 5s/step - loss: 0.0688 - accuracy: 0.9777 - val_loss: 0.0784 - val_accuracy: 0.9777\n",
      "Epoch 47/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0718 - accuracy: 0.9753 - val_loss: 0.0792 - val_accuracy: 0.9777\n",
      "Epoch 48/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0708 - accuracy: 0.9755 - val_loss: 0.0782 - val_accuracy: 0.9762\n",
      "Epoch 49/50\n",
      "85/85 [==============================] - 463s 5s/step - loss: 0.0700 - accuracy: 0.9762 - val_loss: 0.0760 - val_accuracy: 0.9807\n",
      "Epoch 50/50\n",
      "85/85 [==============================] - 462s 5s/step - loss: 0.0692 - accuracy: 0.9764 - val_loss: 0.0760 - val_accuracy: 0.9792\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 8s 384ms/step - loss: 0.1161 - accuracy: 0.9614\n",
      "Test Loss: 0.11608950048685074, Test Accuracy: 0.9613670110702515\n",
      "22/22 [==============================] - 9s 391ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Model 2\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.97       491\n",
      "           1       0.89      0.98      0.93       182\n",
      "\n",
      "    accuracy                           0.96       673\n",
      "   macro avg       0.94      0.97      0.95       673\n",
      "weighted avg       0.96      0.96      0.96       673\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===================================== BATCH_SIZE=32, learning_rate=0.0001 =====================================\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " gru_6 (GRU)                 (None, 600, 256)          202752    \n",
      "                                                                 \n",
      " batch_normalization_50 (Ba  (None, 600, 256)          1024      \n",
      " tchNormalization)                                               \n",
      "                                                                 \n",
      " gru_7 (GRU)                 (None, 600, 128)          148224    \n",
      "                                                                 \n",
      " dropout_76 (Dropout)        (None, 600, 128)          0         \n",
      "                                                                 \n",
      " gru_8 (GRU)                 (None, 64)                37248     \n",
      "                                                                 \n",
      " dropout_77 (Dropout)        (None, 64)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 393473 (1.50 MB)\n",
      "Trainable params: 392961 (1.50 MB)\n",
      "Non-trainable params: 512 (2.00 KB)\n",
      "_________________________________________________________________\n",
      "Epoch 1/50\n",
      "169/169 [==============================] - 331s 2s/step - loss: 0.3575 - accuracy: 0.8659 - val_loss: 0.3171 - val_accuracy: 0.9287\n",
      "Epoch 2/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.2188 - accuracy: 0.9413 - val_loss: 0.1732 - val_accuracy: 0.9614\n",
      "Epoch 3/50\n",
      "169/169 [==============================] - 314s 2s/step - loss: 0.1650 - accuracy: 0.9580 - val_loss: 0.1249 - val_accuracy: 0.9658\n",
      "Epoch 4/50\n",
      "169/169 [==============================] - 315s 2s/step - loss: 0.1300 - accuracy: 0.9641 - val_loss: 0.1120 - val_accuracy: 0.9688\n",
      "Epoch 5/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.1115 - accuracy: 0.9658 - val_loss: 0.1012 - val_accuracy: 0.9673\n",
      "Epoch 6/50\n",
      "169/169 [==============================] - 314s 2s/step - loss: 0.1047 - accuracy: 0.9667 - val_loss: 0.0939 - val_accuracy: 0.9733\n",
      "Epoch 7/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0977 - accuracy: 0.9680 - val_loss: 0.0971 - val_accuracy: 0.9703\n",
      "Epoch 8/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0966 - accuracy: 0.9693 - val_loss: 0.0951 - val_accuracy: 0.9703\n",
      "Epoch 9/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0913 - accuracy: 0.9697 - val_loss: 0.0988 - val_accuracy: 0.9688\n",
      "Epoch 10/50\n",
      "169/169 [==============================] - 314s 2s/step - loss: 0.0894 - accuracy: 0.9706 - val_loss: 0.0880 - val_accuracy: 0.9718\n",
      "Epoch 11/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0919 - accuracy: 0.9703 - val_loss: 0.0893 - val_accuracy: 0.9762\n",
      "Epoch 12/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0839 - accuracy: 0.9723 - val_loss: 0.0877 - val_accuracy: 0.9747\n",
      "Epoch 13/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0827 - accuracy: 0.9729 - val_loss: 0.0831 - val_accuracy: 0.9777\n",
      "Epoch 14/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0795 - accuracy: 0.9742 - val_loss: 0.0817 - val_accuracy: 0.9792\n",
      "Epoch 15/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0774 - accuracy: 0.9738 - val_loss: 0.0800 - val_accuracy: 0.9777\n",
      "Epoch 16/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0783 - accuracy: 0.9734 - val_loss: 0.0863 - val_accuracy: 0.9762\n",
      "Epoch 17/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0736 - accuracy: 0.9758 - val_loss: 0.0836 - val_accuracy: 0.9777\n",
      "Epoch 18/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0707 - accuracy: 0.9762 - val_loss: 0.0746 - val_accuracy: 0.9807\n",
      "Epoch 19/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0693 - accuracy: 0.9757 - val_loss: 0.0711 - val_accuracy: 0.9792\n",
      "Epoch 20/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0709 - accuracy: 0.9757 - val_loss: 0.0723 - val_accuracy: 0.9792\n",
      "Epoch 21/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0660 - accuracy: 0.9792 - val_loss: 0.0687 - val_accuracy: 0.9792\n",
      "Epoch 22/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0623 - accuracy: 0.9805 - val_loss: 0.0631 - val_accuracy: 0.9822\n",
      "Epoch 23/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0603 - accuracy: 0.9807 - val_loss: 0.0686 - val_accuracy: 0.9792\n",
      "Epoch 24/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0546 - accuracy: 0.9816 - val_loss: 0.0785 - val_accuracy: 0.9718\n",
      "Epoch 25/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0502 - accuracy: 0.9842 - val_loss: 0.0674 - val_accuracy: 0.9777\n",
      "Epoch 26/50\n",
      "169/169 [==============================] - 314s 2s/step - loss: 0.0482 - accuracy: 0.9848 - val_loss: 0.0589 - val_accuracy: 0.9837\n",
      "Epoch 27/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0431 - accuracy: 0.9864 - val_loss: 0.0538 - val_accuracy: 0.9851\n",
      "Epoch 28/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0410 - accuracy: 0.9874 - val_loss: 0.0530 - val_accuracy: 0.9851\n",
      "Epoch 29/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0389 - accuracy: 0.9885 - val_loss: 0.0541 - val_accuracy: 0.9837\n",
      "Epoch 30/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0394 - accuracy: 0.9885 - val_loss: 0.0514 - val_accuracy: 0.9866\n",
      "Epoch 31/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0386 - accuracy: 0.9889 - val_loss: 0.0507 - val_accuracy: 0.9866\n",
      "Epoch 32/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0366 - accuracy: 0.9892 - val_loss: 0.0514 - val_accuracy: 0.9866\n",
      "Epoch 33/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0352 - accuracy: 0.9907 - val_loss: 0.0497 - val_accuracy: 0.9866\n",
      "Epoch 34/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0391 - accuracy: 0.9883 - val_loss: 0.0499 - val_accuracy: 0.9866\n",
      "Epoch 35/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0360 - accuracy: 0.9894 - val_loss: 0.0487 - val_accuracy: 0.9866\n",
      "Epoch 36/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0356 - accuracy: 0.9889 - val_loss: 0.0489 - val_accuracy: 0.9866\n",
      "Epoch 37/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0353 - accuracy: 0.9894 - val_loss: 0.0493 - val_accuracy: 0.9866\n",
      "Epoch 38/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0346 - accuracy: 0.9898 - val_loss: 0.0495 - val_accuracy: 0.9866\n",
      "Epoch 39/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0341 - accuracy: 0.9898 - val_loss: 0.0490 - val_accuracy: 0.9866\n",
      "Epoch 40/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0325 - accuracy: 0.9903 - val_loss: 0.0496 - val_accuracy: 0.9866\n",
      "Epoch 41/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0335 - accuracy: 0.9889 - val_loss: 0.0497 - val_accuracy: 0.9866\n",
      "Epoch 42/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0337 - accuracy: 0.9903 - val_loss: 0.0490 - val_accuracy: 0.9866\n",
      "Epoch 43/50\n",
      "169/169 [==============================] - 311s 2s/step - loss: 0.0346 - accuracy: 0.9896 - val_loss: 0.0487 - val_accuracy: 0.9866\n",
      "Epoch 44/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0339 - accuracy: 0.9896 - val_loss: 0.0482 - val_accuracy: 0.9866\n",
      "Epoch 45/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0335 - accuracy: 0.9894 - val_loss: 0.0486 - val_accuracy: 0.9866\n",
      "Epoch 46/50\n",
      "169/169 [==============================] - 310s 2s/step - loss: 0.0328 - accuracy: 0.9896 - val_loss: 0.0485 - val_accuracy: 0.9866\n",
      "Epoch 47/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0317 - accuracy: 0.9903 - val_loss: 0.0486 - val_accuracy: 0.9866\n",
      "Epoch 48/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0319 - accuracy: 0.9905 - val_loss: 0.0480 - val_accuracy: 0.9866\n",
      "Epoch 49/50\n",
      "169/169 [==============================] - 312s 2s/step - loss: 0.0327 - accuracy: 0.9898 - val_loss: 0.0483 - val_accuracy: 0.9866\n",
      "Epoch 50/50\n",
      "169/169 [==============================] - 313s 2s/step - loss: 0.0331 - accuracy: 0.9905 - val_loss: 0.0481 - val_accuracy: 0.9866\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\yashs\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "data": {
      "image/png": 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vy2AwKNWqVbPYXlEU5ebNm0qJEiWU7777LtX1mpzQ0FClRIkSyuLFi83LRo4cqVSoUEGJiYkxL1u+fLni7++vXLhwwbwsJiZGadCggfLLL78oly5dUvz8/JK8hvv06aOMGjUq2eN88VgV5dnr9cSJExbH0aVLF2Xr1q0Wj12yZIni5+enBAcHK4qiKB9++KHSvHlzi9ft1q1blQYNGiiPHj1SDh06pPj5+SlHjx41r//kk08s6vbo0aNKlSpVzGUcO3ZM8fPzU44dO5ZiHa5cuVLx8/NTTp8+bbH8iy++UEqVKqWEhYUpZ86cUfz8/JRTp06Z19+8eVOZNGmScv/+fUVRFKVkyZLK/PnzzesNBoMyceJEi8c8LyQkRPHz8zO/BlKyfPlyxc/PTzl//rxiNBqV2rVrKyNGjDCvP3LkiOLn56f89ddfiqKYPouqVq1q8boNCwtTypcvr0ycONGiXp5/n6WkQ4cOip+fX4o/27dvN29bu3ZtpWLFikpkZKR52e7du82fB6l9P0ycOFEJCAgwvzYURVHu37+v1KpVS/n999/N7+/+/ftblNOuXTulefPmiqIoqXrOXkeHDh2UDh06KLGxsUqZMmWUpUuXmtdt2LBBqVmzpvn5Snzf/P777xafp4nmzp2r+Pn5Kf/++69iNBqVKlWqJDm2MWPGWLyWp02bppQqVUq5c+eOeZv4+Hilbt26St++fRVFSd3rP9GWLVvMz2n37t2V2NjYl26f0uuhZMmSFu/RBw8eKB07drT4TFAURRk/frxSsmRJ898vnnP37dun+Pn5Kbt37zYvMxgMykcffaTMnj1bURTT661mzZqKTqczbzN9+nTFz88vyef28+Lj45XRo0crxYoVM8f93nvvKaNHj1auXr1qsW1yn3uJli5dqvj7+5tjNBqNynvvvad07drVYrvE9+j+/ftTjClRWp6HmzdvKpUqVVKaNWumGAwGq2Uneu0xTGlVrFgxi7+nTp0KmLL+a9eucfPmTfOAv5ddtVS4cGGLAWU5c+YEeGmr1Mseo9PpOHPmDL1797ZoEm3YsCFDhw596TElNjtGRkaajyHxm+yLx/D8t1F7e3u8vLzMTY4nT57Ezs7Oolsksen6xIkTL43By8uL2rVrs2XLFnr16gXAoUOHCAkJMXfHVa9enerVq5OQkMCVK1e4efMm//77L6GhoXh4eLy0fDC1KJw/f57PP//cYvkHH3xgfh7TYz8A169f59GjRwwaNMhied68eSlbtix//vmnxfKX1Wtyfv31VwwGA7Vq1SIyMhKA+vXrs3btWrZt22YeIH/q1Cny5Mlj8bp1cnJi586dAPz8888ANGjQwKJ8a124KXl+P/b29uauxYcPH3L9+nVu3Lhh8f6Ii4vjwoUL9O3b1+J126hRI/M3t6pVq5IrVy42b95M5cqVefDgAUePHmXy5Mnm7RO7454vw5o///yT3LlzJ2lhadq0KevWrePs2bNUqFABLy8vPvvsMxo2bEiNGjWoVq2aRatkpUqVmD17NhcuXKBGjRrUrFmTYcOGWd2/nZ3dS9drNBrA9E1dpVLRtGlTVq1axdixY7G3t2fr1q3kz5+f0qVLA6YW5ooVK+Lo6IherwdMY5AqVKjAkSNHLMp+8XMsJSVKlOCrr75Kdt3zYz/B1IXh5uZm8bdWq+XEiRPkzp07Ve+HU6dOUaZMGbJnz27extvb2/yaSezKqVChgkU5efLk4dSpU4DpG7e15+xFRqPRomtMpVKZ6z8ljo6O1KlTx2Ic09atW/nggw+SvA7//PNPtFptkrEmTZs2ZebMmfz555+o1WpCQkKoXbu2xTYffPCBRcvx0aNHKVasGDlz5jQ/z2q1mvfeey9VV7m9KCAggBUrVnDp0iVmzpxJt27dWL58+UvfS23atDF3ERmNRh49esTatWvp1q0bc+fOpWbNmuTMmZNly5ahKAp37tzh5s2bXLt2jdOnT7/03Hjq1Cns7OwsrqxWq9VJWs8DAgIs3kOJY1wjIyNTHKxtb2/PuHHj6Nu3LwcOHODYsWMcP36cNWvWsGHDBqZNm5bks/BFhw4dYtKkSfTq1cvc6nPt2jUePHhAjx49zM8JmMZ2ubq6cvjwYavjulL7PFy9epWuXbui1WqZNWtWsl36KXnjCZOzs7PF3//88w9fffUV//zzD05OThQuXJhcuXIBL59b4sWm+MSDftl4n5c9Jjw8HIPBQNasWS220Wg0Vk/yt27dYsyYMRw9ehQ7OzsKFixI0aJFkz0GR0fHJDEkbhMREYGHh0eSJ/j5D7+XadmyJT169OD8+fOUKFGCTZs2UapUKXMsRqORadOmsXLlSmJiYvDx8SEgICDVVwhERESgKEqSQcEvdqe97n4Ac/dBtmzZkqzLli1bkvEGL6vX5GzYsAGj0cgHH3yQZN3q1avNCVN4eHiS10Rycb5sm7R48cqpQ4cO8c0333Dt2jVcXFwoWrSo+T2kKIr5OXnZ/tVqNS1atDAPyNy8eTOurq7Ur1/fvM3Bgwfp3bt3mmKNiIhI9rWZ+JxFRkbi4uLCypUrmT9/Ptu3b2fNmjU4OjrSrFkzRo0ahb29PdOnT2fBggVs376dnTt3olarqVq1KuPGjSN37txJyvf09MTZ2dnq9BOJ3QU+Pj4ANGvWjPnz53Po0CFq1KjBrl276NSpk3n78PBwtm3blmwX+IsXI7z4OZYSFxcXSpUqlaptE7/AJVKr1Xh6ehIREZHq90N4eHiqLu5I7rMw8f2SmufsRV988YXFuL3cuXOzb98+q3F88MEH9OnThwcPHuDg4MDRo0fp379/ku0iIiLw9PRMkoQlvv6ePHlCREQEQJLPpxdfo+Hh4dy8eZMSJUokG9PLvnQnJ/HCp8ST+7Bhwzh58iSBgYEpPiZHjhxJXhe1a9emcePGTJkyxdw1/uuvvzJt2jTu37+Ph4cHxYoVS/JZ96Lw8HA8PDysJgIvvoZTcw5NlD17dlq1amX+Mn7s2DGGDBnC2LFjqVevXor7vnbtGgMHDuS9996jb9++FjEDfPXVV8l+wQgODrYaU2qeh+PHj9O3b1+cnZ356aefknxpseaNJ0zPS+wr9/f3Z+vWrRQsWBC1Ws2BAwfM3+DflKxZs2JnZ5dkXobEZColRqOR7t27Y2dnx7p16yhWrBharZYrV66wefPmNMXg6elJWFgYBoPB4oPhZft/Xo0aNciRIwe//fYbvr6+7Nu3j5EjR5rXL1q0iB9//JGvvvqKBg0amL/NJr7orUl8E75YRy/G97r7SdwXkOw8GY8ePXqtK7nOnz9PUFAQ/fr1S/JNe/fu3SxfvpyLFy9SrFgx3Nzckj0xnz59mixZspiv5AkNDbW4KvPq1auEh4dTvnx5gCRj617W+pXo1q1b9O7d2zw2ztfXF5VKxcqVK83js1xdXVGpVISGhlo8Nj4+nmPHjlG6dGk8PDxo0aIFc+fO5eDBg2zfvp1GjRqZE9h79+5x/fp1iwGbqZElS5ZkB4k+evQIeHbiKliwIJMnT8ZgMPD333+zefNmfv75Z/LmzUu3bt1wc3NjyJAhDBkyhGvXrrF3717mzZvHV199xaJFi5KUr1KpqF27NocOHSI6OjrZy/MNBgN79uyhXLly5mSnQIECBAQEsH37dtRqNZGRkRZXRbq5uVG1atVkpyJ4cQCyLYSFhVn8bTAYCAsLw8vLK9XvBzc3tySvBTC1quTJkyfVLYjWnrMX9enTx+IK3dReFv/ee+/h4uLCjh07cHZ2Jk+ePJQsWTLJdlmyZEn2szHxROrp6WmugxfHm734+eTm5kbFihVT7DlITeyhoaEcPHiQGjVqWHxZSbwyLDUn+BdpNBqKFy9uHltz8uRJhg0bRseOHenatas5oZ40aZK5NTA5bm5uhIeHm1tWE124cAFFUVJMFK05e/YsPXv2ZPLkyUk+KypXrkzXrl359ttvCQsLS/YLXEREBD179iRbtmxMmTLFIrbEz9GhQ4daTDWTKHFA/IvS8jz89ttvDB8+nAIFCvDDDz8k+YKSGhk60/e1a9cIDw/n//7v/yhcuLA5Kz148CCQukw3vWg0GsqVK8fevXstlu/bt8+iifBFYWFhXL9+nVatWlGqVCnzB+urHEOVKlXQ6/XmNwyYul1SexWORqPhww8/ZOfOnezbtw+NRmMxePLUqVMULlyYli1bmpOYhw8f8u+//6YqTgcHB8qWLcuuXbssWm9e/CaZ2v287BtQgQIFyJ49e5KB07dv3+avv/6iXLlyVuNNyfr163FwcKBTp05UqlTJ4qdr166o1WpzV1uFChW4ffs2ly9fNj8+Pj6evn37sm7dOnNC9GIdTJkyhQkTJgCmpOb5ga3ASz/wEp07d474+Hi6d+9O3rx5zR8wicmSoii4uLhQrFixJPPWHDx4kO7du5s/MHLnzk2VKlVYtmwZFy9epEWLFuZtDxw4QOnSpVP8UEpJYGAgd+/etbhiEEzfiu3s7AgICGDHjh1UrlyZR48eodFoKFu2LGPHjsXd3Z179+5x9+5di6ulChYsyKeffkrVqlWTvbopUY8ePYiNjWXMmDHJXugxbdo0bt68yWeffWaxvFmzZhw6dIitW7dSrlw5fH19zesSr9wtVqwYpUqVolSpUpQsWZIff/yR3bt3p6luXsXBgwctulr27t2LXq+nSpUqqX4/VKhQgbNnz1okTSEhIeaLSVLD2nOWnDx58pjrrFSpUqmeNDhxcP7OnTvNA/CTU7FiRfR6fZKr6hK70MqXL0/+/Pnx8fFJss2L742KFSty/fp1ChQoYBHz5s2bWbdundWuRDANTxg2bFiSK0ITP6tfZdLkhIQELly4QL58+QA4c+YMRqORvn37mk/uBoPB3D2c+Fn64udohQoVSEhIMJ+DwPRZMWLECBYuXJjmuBLlz5/ffBl/cueL69evkz179mSnhtHr9fTv35/Hjx8zd+7cJF1+BQsWJGvWrNy5c8fiOcmZMydTp05N8QrG1D4PBw4cYOjQoZQtW5aff/75lZIlyOAWpgIFCuDq6sqCBQvQarVotVp27txpPvi0No2+rn79+tGxY0f69etHq1atuHfvnnl27JS+mWXNmpXcuXOzcuVKvL29cXd359ChQ+arwdJyDFWqVKF69eqMGjWKkJAQcufOzbJlywgNDU11l0+LFi1YuHAh8+fPp2HDhhYvzICAAObNm8eiRYsoU6YMN2/eZOHCheh0ulTHOXDgQDp16kSfPn346KOPuH79uvmKqLTux93dnQsXLvDnn38SEBBgUYZarWbgwIGMGDGCQYMG0bRpU8LCwpgzZw5ZsmR55QkJdTodv/32G7Vq1Uq2n97Hx4eKFSuyZcsWhg4dSosWLVi+fDk9e/akX79+eHp6smzZMhISEvj444/x9fWlYcOGTJ48mbi4OIoVK8bBgwfZv3+/+aqj2rVrs3DhQhYuXEjp0qXZt29fileAPa9EiRJotVomT55Mly5d0Ol0bNiwwXx5cGIrVb9+/ejZsycDBw6kefPmPH78mGnTplGvXj38/PzM5bVq1YqBAwdSqFAh87gdMH2YpHR13M6dO5NMYQCmq15atGjBqlWr6N27N/369SNPnjzs27eP9evX06dPH9zd3SlXrhxGo5HevXvTvXt3XFxc2L59O0+ePKFBgwbkzp0bb29vvv76a6KiosibNy/nzp0zXyGWEn9/fyZOnMiIESNo164dH3/8MXny5CE4OJgNGzaYryx68bgaNWrExIkT2bZtW5IJJRMn4e3Rowft2rXDwcGBNWvWsGfPHmbNmvXyJysFUVFRL70cv1SpUuYT9P379+nZsyf/93//x/3795k2bRo1atSgUqVKAKl6P3Tu3JlNmzbRrVs3evTogZ2dHfPnz8fb25v//e9/qbrKzdpzlt4aNWpEjx49UKvVjBo1Ktlt3nvvPSpVqsSoUaN4+PAhRYsW5c8//+T777/nww8/pHDhwoDpitVBgwYxatQoGjZsyF9//WX+8pOoc+fObN68mc6dO9OlSxc8PT3Ztm0bv/zyCyNGjEhVzLly5aJly5bMnTsXrVZL8eLFOXnyJIsWLaJVq1bmeFLy4MEDi9dFREQEq1at4vr160yZMgXA/Jk4btw4WrZsSUREBCtXrjRP4xITE4Orqyvu7u48fvyYAwcOUKxYMWrVqkXZsmUZPnw4/fv3x9fXl82bN3P16lXGjx+fquNLTpYsWRg2bBhffvklH3/8MW3atMHX15cnT56we/duNm7cmKTlKNHEiRM5cuQIw4cPJzo62uLYXV1dKVy4MAMGDGDMmDFoNBpq165NZGQk8+bN4+HDhym2iqXmeYiPj2fkyJG4uLjw2WefJZk6Ji1zNmZowuTm5sa8efOYNGkSn3/+ufnb8ooVK/j00085efJkircEsYUKFSowe/ZsZs6cSa9evcidOzejR49mwIABL52Vd968eUyYMIHhw4djb29P4cKFmT9/Pt988w0nT55M061h5syZw5QpU5g1axbx8fE0atSINm3aJGn5Skn+/PkJDAzkxIkT5haORD169CAsLIxly5Yxd+5cfHx8aNasGSqVioULF5oHP79MhQoV+P7775k2bRp9+vQhT548fPPNNxbf5FOzH3d3d7p06cI333xD165dWbp0aZJ9tWjRAhcXFxYuXEjv3r1xdXWlRo0aDBw4MNXjul60Z88eIiIizAOik9O8eXOOHTvGli1baNeuHStWrGDSpEmMHz8eo9FImTJlWLZsmbl1YvLkycyZM4effvqJsLAwChUqxKxZs8wDGnv06EFoaCiLFy8mISGBWrVqMWHCBHr27PnSWPPly8fUqVOZM2cOPXv2JEuWLJQpU4bly5fTsWNHTp48ib+/P7Vr12bBggXMmTOH3r174+Xlxf/+9z+LMQIANWvWRKVSWbQu6XQ6jh8/nmQgf6KVK1cmu7xhw4Z4e3uzfPlypk6dysyZM4mKiqJgwYJMmDDB3P2aI0cOfvjhB2bOnMnIkSOJjY2lSJEizJ49m8qVKwOm1/y0adOYOXMmYWFh+Pj40KdPH6u3e2jcuDH+/v78+OOPzJo1i0ePHuHl5UWFChX4+eefk50w0svLi+rVq3P48OEkA4iLFi3KypUrmT59OkOHDkVRFPz8/Jg7dy5169Z9aSwpuXDhAh999FGK60+cOGHujmjcuDHu7u70798fZ2dnPvzwQ4vLvFPzfvDx8WHVqlVMnjzZ/HlUqVIlpk+fTpYsWVKVMKXmOUtPVatWxd3dHR8fHwoVKpTsNomfHbNmzeLHH38kNDSUPHnyMHDgQIsvT02aNEGtVjNv3jw2b96Mn58f48aNY+DAgeZtcubMyerVq5k6dSpjx44lPj6e/PnzW7xuU2Ps2LH4+vryyy+/cPfuXXx8fOjXrx9du3a1+th169aZGwZUKhUuLi74+fkxY8YM87jKSpUqMWbMGJYuXcqOHTvIli0blSpVMr/PT506Rc2aNWnRogUHDhwwf3Hp3r0733//PVOmTGHmzJnExsbi7+/PkiVLknwxTau2bduSL18+li1bxrRp0wgPD8fFxYWAgAB++uknc3L/osQW+IkTJyZZV7FiRZYvX07r1q1xcXHhhx9+YM2aNTg7O1OuXDmmTJli0RL8ImvPw+nTp83DBF6crwlM3ckvflamRKW8bGTsO2bv3r14e3tbZLOXL1+mSZMmzJs375U/NIV4G2zbto2hQ4dy4MCBdBukLtJHnTp1qFixYrInFCHE2yFDW5jeNn/88Qfbtm1j8ODBFChQgIcPHzJ//nwKFixI9erVMzo8IV7Jnj17+Oeff1i9ejUtWrSQZEkIIV6BJEzPGTZsGI6OjsyfP5/g4GA8PDyoUaMGgwYNStMl8UK8Te7cucNPP/1E+fLlXzqXjhBCiJRJl5wQQgghhBUZOq2AEEIIIURmIAmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBVylRymaeONxvQd+65Wq9K9TGEidWsbUq+2IfVqG1KvtpOWulWrVam+R2FmJwkTYDQqhIZGp1t5Wq0aT08XIiNj0Ovf3P3w3gVSt7Yh9WobUq+2IfVqO2mtWy8vFzSadyNhki45IYQQQggrJGESQgghhLBCEiYhhBBCCCskYRJCCCGEsEISJiGEEEIIKyRhEkIIIYSwQhImIYQQQggrJGESQgghhLBCEiYhhBBCCCskYRJCCCGEsOKtSpgWLlxIx44dX7pNWFgYgwYNIjAwkIoVK/LVV18RGxv7hiIUQgghxLvorbmX3MqVK5kxYwYVKlR46Xb9+vUjNjaWH3/8kcjISEaOHElMTAzffffdG4pUCCGEEO+aDE+YHj58yJdffsnx48fJnz//S7c9c+YMf/75J9u2baNQoUIAjBs3jm7dujFw4EBy5sz5BiIWQgghxLsmw7vkzp8/j52dHb/++iulS5d+6bYnT54ke/bs5mQJoGLFiqhUKk6dOmXrUIUQQgjxjsrwFqY6depQp06dVG378OFDfHx8LJbZ29vj4eHB/fv3XysOrTb9ckeNRm3xW6QfqVvbkHq1DalX25B6tR2p25RleMKUFrGxsdjb2ydZ7uDgQHx8/CuXq1ar8PR0eZ3QkuXu7pTuZQoTqVvbkHq1DalX25B6tR2p26QyVcLk6OiITqdLsjw+Ph5nZ+dXLtdoVIiMjHmd0CxoNGrc3Z2IjIzFYDCmW7lC6tZWpF5tQ+rVNqRebSetdevu7vTOtEZlqoTJ29ubPXv2WCzT6XSEh4eTI0eO1ypbr0//N53BYLRJuULq1lakXm1D6tU2pF5tR+o2qUyVMAUGBjJlyhRu3rxJvnz5APjzzz8BKF++fEaGJoQQKTIqRnQGHXqjHoOif/rbiMGoJ8GYQHRCNBHxEUTqwonURRIZH0mkLsL8O94QT3anHOR0yUlOZ29yuHjj7exNDuecONsl37puVIzE6eOITogmOiGKWH0sGpUGO40ddmo77DT22Jt/26NVa1GhwqAY0Bl0JBh16AwJ6I0J6Iw69MYE4g06DEZT/HpFj8FoQP/0eIyKwXxc1hiMhqfl60hILN+QgM74dH8GHU52zmSxz4K7vTvuDk9/22chi0MWPJ09yKI4ERkfSWhMGBHxETzRmerKVI+RPNFFojfqcdA64qhxwEHjiIPGwfSjffZvtcp664gKFWqVBpUKNCoNKlRo1KbfqqePNyoGFEXBoBhQUDAYjSgoKIoRg2Ig3hCPzqAj3hBHnD6OeEM88YY44p8uSzAmoChGjIqCUTE++8H4dLkRO7X90/gdsNc4WByXo9YRjUqLzmjaj2kfz8qP18cTb4ynTPaytPRrk7YXsADe8oTJYDAQGhqKm5sbjo6OlC5dmnLlyjFgwADGjh1LTEwMY8aMoXnz5jKlgBAiCYPRQKw+hlh93NPfscTpY4nVxxKjjyE6IZqYhGii9dHmxCIm4dlyo2J4afmKYkT/NGkwmpMHAwajHoNiwKAYQG0kQa9HUWxzjO727uRwzom9xt7ieGITYkjrLlWQ5sdkBJXK1HVkMBhtVq//VRdCztOiSGtUKlVGh5LpvNUJ0/3796lbty7ffvstLVq0QKVSMWfOHL766is6deqEg4MDDRs2ZMSIERkdqhAinSW2yiS2dCQYTa0RCQYdUQlRhMeHEx4XRnhcGNGRj4gPfYgh/DGG8FCMT8JJSIhDb0w54TFoIMpJwxNHDU+cNUQ5akiwS9raoDIqZH2ixydUh0+ojlxPf3uH6sgWqedlKYbK/H8lyVYq81oVGpUGtVqDRvX0R61Bo1Kb/w0qU6uOMcFUB0//bbCS0CWWr1apUavUKIrytNXj6W8r6ZEq8T9V0t+JR5C4DFSoVM+Oy3pc6qfbqy3KVaMCleppC4sBg9FgTj4T/22qc1O9qlA/rS+N+bf6aT2qVCpzq43C09+KqeUncVnqEi4l+f8rijkS00E9f/SW/1arTM9DYquUWqUyH3vichLLUln8ZS7NFLfx2e9kjkulUqNO3MfT36Z6Nf02BBZD9b9UPUXiBSpFkfzcYDASGhqdbuVptWo8PV0IC4uWPuB0JnVrG6mq14QEsLNLl/1FJ0TzIPoe96LucT/6Hg+iH5j/Dol7jM4Qj/GFjyZHnZGSN6IpezUa7zAdrrEG3GINuMYa0Bitn/jVKrXppKE2nUhMSYTGnExoVGqMDg4Y3FwxurmjuLvj8CQax/vBqBP0Ly0blWVykbhcpVKh1WowGpRn24I54XhdRsVAgjEBvVGPoijPHcuz43r5vpQXkiienshV6RZj+jPFq1aDYnxbY3x7Gfz8eTJnoamZLhlp/Yz18nKRQd9CiAwUE4Pmzm3Ud26jMhowumVBcXdHyfL0t4trih94r0WnQ33zFqrbNzHcvIrx1jW4fRPtnduon0Shy+JGlE82or2zE+3tRVTObET6ZCMmmwcGjQq9McHc3RX3tBss7rnusOiEaB7FBBOpi0xVOF6RCZS/EkW5K9GUuB2LveFZ8mNqTbBHa/e0NcHeAaN7FvDwRO3mgdbOAa1ai/Zp8pCEXo8qMhJ1RDiqJ5GQeEVQNBAdBQ+inm7oAE7OGH1yY8jji9HXF0OevKZ/e/uAXcofo1qtGicPF8LD30yCb7296b9Bq1Xj8Qbr9b9Ecc9im8+Od4AkTOKdo3oSiSo01HSyfBKJKiICVWQE6sgIVJFP/46LRXFxRcmSxdTa8DRRMbp7mBKWxOTFxRXUr/DtSlEgNhZ1aIg5MdLcuY369m00d26hCg19+ePVKhT3LCjuWTC6uYFGk6bdG41GovVRPImPJFIXSZTuCS7hkbiGRGE0GkwDWJN7YDRwDzSA+9OfXIBRpSLYw45rPo6cLOHO3/ldUNQv/1DO4uCBj4sPPi658HY1/fZxykmee0/wOPk3ridOYn/9Oio8UKk8wE2F0ceHhCrV0fsXRfHweHr8pucCR0eLWxcYAd3TH6sUBaKjn3s9RKKODMfo6o7R92lilMY6BlC0avB0QcEeRU7s6UbqVWQESZhE5hAXh/bfIOxuXodsHmg1DijObs+SF/cslomLTof63t1nycjtW+akRBWZutaNVFGrUFxNMRizZEFJTK6yZMHo6oYqNhbVk0jUT0/CqshwVJGRKBFh6ONi0Ct6UBQMgD5xTIli6nSIc3PmibcXRjstjlFxOETH4xAVg1anN435eBSF6vG9VI0ZMV35FP/0yhzTVTSJ41fsgawAqNCjEGOv4b6XI/eyOvAomxOROTx4kjMrei9PskYmkO1RNNlDYsj6KIrsj6LJ+jgKuwQD7lFQ5LLCB1eeEOsBN6qU5E71cujz5MFR64ST1gknrTPZnLPh7ezz7OquuDjszpzCbvcR7I79YJksqjXoi5ckoXJVEipXxZgvv22+HatU4OqK0dUVfHKlf/lCiExPEqZ3ndGIKiQExcvrlb5B24rq0SO05/9Be+E82vP/oLl6GQxG07lSo8bJYMTx+SYQFSiubihu7mA0oA5+CC8Z12JwcSbB1Zl4FydiXRyIdbYjylnLEycNEQ4QbafgGKvDKSYBpxgdTtHxOEabkhbH6FgcomKxi0swjV2Ji0D1WPXcYE2VeVxLQjIDdRMMCRgxfStO0Kq572nHfS/7JD8xjhog6oXIndDqjbjFGnCLMeAWZ8Al1oA6VUMR7Z7+uALgoHXEx8UHb5dc5HLzIUcBP+Jy5EHr6Y2fgzvl7Nyw1ySdWT8JRUH1+DGa2zexO3IY+327UT15Qv6DN+DgDfTFS6Br8AG6moHgatq36vFj7I7twe74EexOnTSNj0oszskJfflAEqpUJaFiZRQPz1QcmxBC2JYM+uYdHPQdE4PdqRPYHT2M3fFjqCIjQKsxjdHInQdj3rwYcieO1fA1nbBs1eet16N+cN/U8nP7FtpLQWgvnEP96FGSTZWsWTH6F8VBAzHBwehCglEiwlCinpjngkm8nFtBIc5OzYNsTjzwcuCBlx13PbXc8dRyxwOi0+Grgtag4Pp00HHiAOTnf5zjDOjs1Dxx0ph/ohJ/HDWoPbxwzZIDe60DWpUWtUqDVq1Bq9aiUWlN42/UGoyKYpqrxhBPglFPgkGHzqgjwZCYiOmw9jZ2snOmgHtBCnoUokCWghTMUogczjnNA5TT9TWr02F3/Cj2O7dhd+L4s8TV3p6ESlVQP3yA5t9LFg8x5shBQuVqJFSugr50WUjmFkiZ0Vv/WZBJSb3ajgz6TpkkTGTShMloRPX4MaroqKdjWdxfegWT6uFD7I8dxu7YEbR/nQF9ylf9vEhxcTEnTYank9M9m7hOb/473smOOGdH4lwdiHFxINbZnlhnO2KcHYhxtkOn6HF58BiXhyF4BIfjERyBZ0g0GAxPJ2czdUmpUKGoVNz2duaarxvXfd245etBhKcTarWGJ4YIouKizZcDawwKLnEGc6sLwH0veyJcNC9N9NQqFS52rrjau+Fm54arvStudu642rvirHXGiNE8p86zhMxgnrAvcQJCvcW/n07g9/QSaA8HD7xdTBMN5nT2JqdzTnK6mCYcdNA4pPo5sDVbvWZVISHY79mJw64dqG/dtFhn8C9KQpVqJFSpiqFAof/kQFQ5sduG1KvtSMKUMumSe9tFRaG5ewfN7Zuo79x5OibnFpo7d+CFGw4rTk6m8TNuz42pcXZBe/E8mqtXzfN06BUjcTmzE1Y+gOCyxXhY0IeEkAeob99Ee+cODvce4PLgEW4PQnENfYIx0ojxrsHccvMyiZ0+bqk8PD1Pu6W87LnnZc+t7A78m8eJa96OxNs//yaMhJhI84R1AC52LmRzyo6XoxfZnLKT1Skb2Zyy4W6fBQeNA1q1HQ4ae7RqO+w19qbZjZ/ObOxs54yz1jlVs/yKV6dkzUr8Rx8T36YdmktB2B09jNHbh4RKlVG8smZ0eEIIkWqSML1N4uPR/huE5vw5tOfPob10EVVYWIqbG9UQ56BFiYrEYEjAEBeKIfSmeXI3/dPuKaNixICRS7mdOF3YhdOFXLmXNQxUByHkIIQ8V2jOpz8AeKHVe+AdloBL3LMLltUqFU5a56dJhwvOWiectc646BRcohNwiUnAOTYB52gdTjHxOEXrcIyOR2swJWpxubzR5TZdoq345kWbIxfe9s7k1zpRQ6VO0mJjeDp7st6oR6NRkS9nLrQ6Z+x4e1pohBUqFYaixTAULZbRkQghxCuRhCkDqR49Qnvh3LOBzVf+fTYXzHMUL6+n87/kJTKHJ2cdwzmgusb+hCDiVHpURiec4x3Mk/g9P57GJc7AAy97/irowhNn09Ntp7bD/elVS4lXL7nZu+H6tFvK1d4NVztX3Ozdn/52e269G85a51eeVt/9tWrsaXNxFmmKF0II8WZJwpQBVFFPcBk5DO2F80nWKV5e6EuUQl+iJPqixTHkL8AdYyhH7v3Bkbt/cCFkC+a7IahMc9lkc8qKu30WPB098XDwwsPBgywOHng6euJunwVXe1d6al1w0jriqHVCq5anXQghhEgLOXNmAMcfl5iSJbUKQ8HC6IuXRF+iJIYSJTHmyAkqFZHxEay7/AtH/pjMzcgbFo8v4ulHtVw1qJK7GgXcC8pNFIUQQggbk4TpDdNcvYzDlo0ARH07BX25Ckm2uRV5k1F/DON+9H3TY1RqArKXoVruGlTJVZ0czjneaMxCCCHEu04SpjfJaMRp1nQwKiTUqpNssnT64UnGHR1DdEI03i7edC7RlUo+VXC1T+11Z0IIIYRIb5IwvUH2u3aYuuIcHYnp3ivJ+q3XtjDr9FSMikKJrCUZW/VrPBxllmMhhBAio0nC9IaonkTi9P0CAGI7dkbJnt28zqgY+f7v+az79xcA6uatx8AKw1J3WwohhBBC2JwkTG+I49LFqCIjMOTLT3yL1ublsfpYvj0+nqP3DgPwfyU+oUOxTjKQWwghhHiLSML0Bmj+vYTDb5sAiO3bH7Sman8U84jRh4dzNfwKdmo7hgSOoHbeuhkXqBBCCCGSJQmTrRmNOM+aBgro6tQ13VgUuBz2L6MPDyckNoQsDh58VXUCJbKVzOBghRBCCJEcSZhszH7HNjSXglCcnIjt3huAiPhwhhzoT3RCNPnc8/N19Yl4u/hkcKRCCCGESIkkTLYUGYHT4oUAxHXqgpLVdLPRUw9PEJ0QTW7XPMyoMxdXO9eMjFIIIYQQVsit2m3I8YdFqCIjMRQoQHzzlublpx+eAqBqrmqSLAkhhBCZgCRMtnLhAnZbtwAQ23cAaDQAKIrCX8GnASiTo3yGhSeEEEKI1JOEyRaMRpg4ERQFXb0G6EuVNq+6H32PhzEP0ajUlMxWKgODFEIIIURqScJkA3Zbt8CFCyjOzsR272mxLrE7rljWEjjbOWdEeEIIIYRII0mYbEB74k8A4rt8iuLpZbHu7KMzAJSV7jghhBAi05Cr5GwgrmcfHD9sSkKZimBQzMuNipEzT8cvlc1RLqPCE0IIIUQaScJkA4qPDxQvDGHRwLOE6UbENSLiw3HQOFDUq3jGBSiEEEKINJEuuTcosXUpIHtp7DR2GRyNEEIIIVJLEqY36EywacB3GemOE0IIITIVSZjeEL1Rz9+PzgIy4FsIIYTIbCRhekMuhQYRq4/Fzd6NQh6FMzocIYQQQqSBJExvSOLs3qWzl0WtkmoXQgghMhM5c78hp5+OXyqXU7rjhBBCiMxGEqY3IE4fx4WQc4CMXxJCCCEyI0mY3oDzIf+gN+rJ5pSN3K55MjocIYQQQqSRJExvwJmn948rm6M8KpUqg6MRQgghRFpJwvQGyO1QhBBCiMxNEiYbe6KL5Er4vwCUkfFLQgghRKYkCZON/f3oLEZFIY+bL9mds2d0OEIIIYR4BZIw2VjidAJydZwQQgiReUnCZGN/yfglIYQQItOThMmGHsc84lbkTVRAmRxlMzocIYQQQrwiSZhsKPHquMKefrjZu2dwNEIIIYR4VZIw2dCZh9IdJ4QQQvwXSMJkI4qicPrphJVlJGESQgghMjVJmGzkTuQdgmMeolVrKJktIKPDEUIIIcRrkITJRk7cOwFA8awlcdI6ZXA0QgghhHgdkjDZyJ93/wSkO04IIYT4L5CEyQaMitHcwiQTVgohhBCZnyRMNnAt/CoRcRE4aZ0o6lUso8MRQgghxGuShMkGEqcTKJW9NFq1NoOjEUIIIcTrkoTJBv5+dBaAcjmlO04IIYT4L5DmDxsoma0UD+PvUTtvnYwORQghhBDpQFqYbOCjYu1Y23ot2Z1zZHQoQgghhEgHkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWJHhCZPRaGTWrFnUqFGDMmXK8Omnn3L79u0Utw8JCWHQoEFUrlyZSpUqMWDAAB4+fPgGIxZCCCHEuybDE6Z58+axatUqxo8fz+rVqzEajXTr1g2dTpfs9v379+fevXssXbqUpUuXcu/ePXr37v2GoxZCCCHEuyRDEyadTseSJUvo168ftWrVomjRokyfPp0HDx6wa9euJNtHRkby559/8umnn1KsWDGKFy9O9+7d+eeffwgPD3/zByCEEEKId0KGJkxBQUFER0dTpUoV8zJ3d3eKFy/OiRMnkmzv6OiIi4sLmzZtIioqiqioKDZv3kyBAgVwd3d/k6ELIYQQ4h2izcidP3jwAAAfHx+L5Tly5DCve569vT0TJ05kzJgxVKhQAZVKRY4cOVixYgVqdYb3LgohhBDiPypDE6bY2FjAlAg9z8HBgYiIiCTbK4rCxYsXKVu2LN26dcNgMDB9+nR69erFzz//jKur6yvHotWmX8Kl0agtfov0I3VrG1KvtiH1ahtSr7YjdZuyDE2YHB0dAdNYpsR/A8THx+Pk5JRk++3bt7NixQr2799vTo4WLFhA7dq1WbduHZ07d36lONRqFZ6eLq/02Jdxd096DCJ9SN3ahtSrbUi92obUq+1I3SaVoQlTYldccHAwefPmNS8PDg7G398/yfYnT56kQIECFi1JWbJkoUCBAty8efOV4zAaFSIjY1758S/SaNS4uzsRGRmLwWBMt3KF1K2tSL3ahtSrbUi92k5a69bd3emdaY3K0ISpaNGiuLq6cvz4cXPCFBkZyYULF+jQoUOS7b29vdm6dSvx8fE4ODgAEBMTw507d2jatOlrxaLXp/+bzmAw2qRcIXVrK1KvtiH1ahtSr7YjdZtUhqaF9vb2dOjQgSlTprB3716CgoIYMGAA3t7eNGjQAIPBwKNHj4iLiwOgefPmgGkupqCgIIKCghg4cCAODg60aNEiA49ECCGEEP9lGd6O1q9fP1q1asWoUaNo164dGo2GxYsXY2dnx/3796levTrbtm0DTFfPrVq1CkVR6NSpE5988gl2dnasWrUKNze3DD4SIYQQQvxXqRRFUTI6iIxmMBgJDY1Ot/K0WjWeni6EhUVLk2Y6k7q1DalX25B6tQ2pV9tJa916ebm8M2OY3o2jFEIIIYR4DZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQVkjAJIYQQQlghCZMQQgghhBWSMAkhhBBCWCEJkxBCCCGEFZIwCSGEEEJYIQmTEEIIIYQV2ld5UFRUFNHR0eTMmZOEhASWL1/OvXv3eP/99wkMDEzvGIUQQgghMlSaW5jOnj1L7dq1WbFiBQBff/01kyZN4tdff6VTp07s3bs33YMUQgghhMhIaU6YZsyYQaFChWjTpg2xsbFs3ryZjz/+mD///JNWrVqxYMECW8QphBBCCJFhXqmFqWfPnvj6+nL48GHi4+Np1qwZAI0aNeLy5cvpHqQQQgghREZKc8KkVqtxcHAA4NChQ7i7uxMQEACYxjY5Ojqmb4RCCCGEEBkszYO+S5Ysydq1a3F0dGTHjh3UqlULlUpFSEgI33//PSVLlrRFnEIIIYQQGSbNLUxDhgzhyJEjtG3bFo1GQ8+ePQFo0qQJN27coH///ukdoxBCCCFEhkpzC1OJEiXYvXs3V69epUiRIjg7OwMwduxYypUrR/bs2dM9SCGEEEKIjPRK8zC5urpSunRp89/nzp1DpVKZxzYJIYQQQvyXpLlLLjg4mI4dOzJv3jwAVqxYQevWrenXrx8NGjSQq+SEEEII8Z+T5oRp8uTJXL9+nVKlSmE0GlmwYAFVq1Zl06ZNFC5cmKlTp9oiTiGEEEKIDJPmLrk//viDL774gho1anDy5EkeP37MhAkTKFq0KN26dWPw4MG2iFMIId5KiqJgNBoxGg1J1hmNKuLiNOh08RgMSgZE998k9Wo7z+pWB6hRq+WWs4nSnDDFxMTg7e0NwMGDB7G3t6dy5coA2Nvboyjy4hVC/PcpikJsbBRRURHJJkuJHj9WYzQa32Bk7wapV9t5vm6dnFxxd/dCpVJlcFQZL80JU/78+Tl58iRlypRh586dVKxY0TzY+9dffyV//vxpKs9oNDJnzhzWrl3LkydPCAwMZMyYMfj6+ia7fUJCArNmzWLTpk08efKEkiVLMnLkSIoVK5bWQxFCiFcWGRlKbGwUjo4uODo6o1Zrkj2paDQqaQWxAalX29FoVOj1RnS6eKKiwgDIkiVrBkeV8dKcMH366acMGzaMxYsXExMTw5gxYwBo1aoVFy5cYMqUKWkqb968eaxatYqJEyfi7e3N5MmT6datG1u2bMHe3j7J9mPHjuX3339n4sSJ5MqVi5kzZ/Lpp5+yfft23Nzc0no4QgiRZkajgdjYaFxdPXB1zfLSbbVaNXq9tISkN6lX29Fq1ajVRuztTY0hUVFhuLl5vvPdc2lOmJo0aYKPjw+nTp2iYsWKlClTBoDAwED69evHe++9l+qydDodS5YsYfDgwdSqVQuA6dOnU6NGDXbt2kWTJk0str99+zbr169nwYIF1KhRA4Cvv/6a5s2bc+7cOapUqZLWwxFCiDQzGAyAgoOD3ApK/LclJk0Ggx61OmkjxrvkleZhKl++POXLlyc2NpZHjx7h4eHBsGHD0lxOUFAQ0dHRFomOu7s7xYsX58SJE0kSpsOHD+Pm5maRlLm7u7Nv375XOQwhhHhNMq5D/LfJ2KVnXilhOnnyJJMmTeLcuXPmQd4BAQEMGDDAPAA8NR48eACAj4+PxfIcOXKY1z3v+vXr+Pr6smvXLhYtWsTDhw8pXrw4w4cPp1ChQq9yKGZabfo1NWo0aovfIv1I3dqG1GvaGI2pO4kknmtUKpDrYdKP1KvtpFS3Go0qXc+TmVGaE6bTp0/TuXNnfH196dWrF9myZSM4OJitW7fSrVs3li9fTtmyZVNVVmxsLECSsUoODg5EREQk2T4qKoqbN28yb948hg4diru7O/Pnz+fjjz9m27ZtZM36aoPS1GoVnp4ur/TYl3F3d0r3MoWJ1K1tSL2mTlychseP1ak+ibytiei4cV+ybduWl25z7NjpVyq7Z89P8fHJxZgxX6Vq++bNG9O48f/49NPPUr2PV6nXW7du0aZNc/z8/Fm27Oc0P/5dkVi3RqMKtVpNlizOODq+213QaU6YZsyYQYUKFVi8eDEajca8vE+fPnTt2pXZs2ezZMmSVJWVWPk6nc7iiYiPj8fJKekHt1arJSoqiunTp5tblKZPn07NmjXZuHEj3bp1S+vhAGA0KkRGxrzSY5Oj0ahxd3ciMjIWg0EGJaYnqVvbkHpNG50uHqPRiMGgvHTgsUplqluDwfhWtoT06zeIHj16m/9u1qwh/foNom7d+uZlrzqwesKESajVmlQ//vvvl+Hg4JCq7V+nXrds2UzevPn4999LnD37NyVKlExbAf9xL9atwWCaZywiIobY2KTTZ7i7O721XwjSW5oTpn/++YepU6daJEsAarWaDh06pGksU2JXXHBwMHnz5jUvDw4Oxt/fP8n23t7eaLVai+43R0dHfH19uXPnTloPxYItrrYwGIxyFYeNSN3ahtRr6qT2cvbEk/nbmCyB6b6grq6uSZZlzZrttct2d3/51YMv8vT0TPW2r1qvBoOBHTu20rJlG3bs2MrmzeslYXpBSnVr7cvBuyDNaaGLiwt6vT7ZdXq9Pk0TVxYtWhRXV1eOHz9uXhYZGcmFCxcIDAxMsn1gYCB6vZ5//vnHvCwuLo7bt2+TL1++NByFEELYhqIoxOpjM+THFhMHb9u2hY8+as6MGVN4//2ajBgxCICDB3/n0087Ua9ederUqUqXLh04fvyo+XF9+nRnwoSxFmUk/q5duwpdunTg77//Mm/fqtX/WLx4IQCLFy/k8897sWLFj3z4YSPq1KlKnz7duXHjunn7sLAwvvxyBA0b1qJx47rMnz+bfv0+M5eRnD//PMajR8EEBlamZs067Nu3mydPnlhsExMTw/Tpk2jW7H3q169Bnz7dCQq6aF5/8eJ5Pv+8F/Xr1+B//2vAlCnfEhcXl+SYk6uH06dPUrNmJVas+JFGjerStWtHjEYjZ8+eoV+/z2jQoCa1a1ehfftW7Ny5zaKcXbu206lTO+rUqUbr1s345RdTd+KsWVNp06aZxbZRUVHUqVONI0f+SLEuRNqluYWpXLlyLFq0iBo1alh0m8XExLBo0SIqVKiQ6rLs7e3p0KEDU6ZMwcvLi9y5czN58mS8vb1p0KABBoOB0NBQ3NzccHR0pEKFClStWpVhw4Yxbtw4PDw8mDVrFhqNhmbNmlnfoRBC2JCiKPTf35sLIefNy97kwOQSWUsyvfacdL+y6e7dOzx+/IglS1YSHx9PUNBFRo0aSp8+/alevSbR0VEsWDCX8ePHsHHjNuzs7JKU8fDhAzZtWs/o0eNxdnZm6tSJTJgwltWrNyYb799/n8HBwZ5Jk2ZgMOgZP34M06Z9x6xZCzAajQwa9Dl6vZ4pU2ZjZ2fH7NnTOHv2DKVLpzyGduvWX8mdOw/+/kWxs9Py00+L2bFjK61btzVvM2bMcG7fvsUXX4wld+48LFu2hAEDerNmzSaiop7Qr99nvPdebRYuXEpUVBRff/0lU6dOZOTIsamqS4PBwNGjh1m4cClxcbGEhDxm4MA+tGz5EUOHjiQhIYGVK39i4sTxBAZWwssrK3v37ubrr7/ks8/6UqNGTS5dusg333yFq6srjRo15Zdffubs2b8oXboMAHv37sLNzY1KlWSqnfSU5oRp0KBBtGjRgrp161KrVi2yZ8/Oo0eP+P3334mLi2PChAlpKq9fv37o9XpGjRpFXFwcgYGBLF68GDs7O+7cuUPdunX59ttvadGiBQCzZ89mypQp9OnTh7i4OMqVK8eyZcvw8vJK66EIIUS6U/1Hpxro3LkbuXPnAeDy5UsMGDCUDz9sZV7funVbBg/uR2hoCDlzeid5vF6vZ8iQERQpYhpu0bZte0aMGExISAjZsiXtAjSdF8bh7u4OQLNmLZk/fxYAf/11mgsXzrFq1Try5s0PwLhx39KqVdMU44+ICOfw4YO0bdsBgIIFC1OwYCE2b95gTphu3brBsWNHmDZtDhUrmq74HjRoOG5ubkREhLN166+4u2dhxIgxaLWm0+fw4aP555+zqa9IoF27Dvj6moah3L17h65de9CuXUdz4tix4yfs2LGV27dv4eWVlV9+WUWdOvX5+OOOAPj65iUmJgYHBwcKFy6Cv38xdu7cak6Yduz4jfff/yDJ0BnxetKcMOXLl49ffvmF2bNnc+DAASIiIsiSJQsVK1akT58+FC5cOE3laTQahgwZwpAhQ5Ksy5MnD5cuXbJY5urqytixYxk7dmxaQxdCCJtSqVRMrz2HOEOcedmbnJHaUeNos3lznr9dVZEi/ri5ZWHFih+5efMGd+7c5sqVfwFeen+3fPkKmP/t4mIaO6XXJyS7rZeXlzlZAtNnf0KCadtLl4Jwd3c3J0um7bOSN2/KQzN27dpBQkIC9eq9b15Wr977LFo0z9wydfXqFQCLcU0ODg707TsQgGvXruDvX8ycLAGUK1eBcuVS37MCkCfPszG7uXPnoVGjpqxdu5pr1648rcvLQOIEqab9Ph83QNOmH5r/3bhxUxYtmkf//kMIDn7IP//8zbBho9MUk7DuleZhKlSoEDNmzEjnUIQQIvNTqVQ4aZ8NV9Bq1ejJ/INln5/V/MyZUwwa1JcqVaoREFCGBg0aEhcXx4gRg19aRnK3u0pp3JWdXcqzSms0mjTfeHfbtl8B6NKlfZJ9b9q0ntKly1okQsnvN+2nzMSk53mJ918FuH79Gr16dcPfvyiBgZWoWbM2Hh6efPppp1Tvt379hsyZM4MjRw5x9eoVihUrQf78BV76GJF2r5QwpWTjxo0sWLCAnTt3pmexQggh3iKrV6+gbNkKTJgw2bxs3brVQMoJUHoqXLjI03n5bpAvX37A1OV2586tZLf/998gLl/+l//7vy7Uq9fAYt3cubM4cGAfERHh5hawixcvUKFCRcDUNdi27Yf07v05+fMXYPfuHRgMBnN314ED+5k9exorV67Dzs6O6Ohoc9lGo5F79+6QJ0/yN5MH2Lx5PV5eXsyYMc+87I8/DlpsU6BAAYKCzlssmz17Gg8fPuDrryc9vQNGLQ4e/J3Lly/RsmWbl1WfeEXpOnlCZGQkt24l/4IVQgjx35AjhzdXr17m7Nm/uH//Hlu3/soPPywAMHeb2VK5chUoUaIk48eP4dy5f7h8+V+++so0Dja5Lslt27bg6OhI27Ydno5devbTvv3/odPp2Lp1C3nz5qNmzdpMm/Ydp0+f5Natm0yaNAGdTkfZshVo2bINERERTJnyLTduXOevv04zb95MypcPxMHBgZIlAzhx4jjHjh3hzp3bTJ8+mSdPol56LDly5CQ4+CFHjx7mwYP7HDiwj6lTJwKmOQoB2rfvzJ49u1i3bjV3795h164dbNy4nurVa5rLady4KQcO7OPu3bvUrft+svsSryddW5iEEEL893Xr1oPQ0McMG9YfgPz5CzJixBjGjRvNxYvnza0+tjRx4lQmTfqW/v174uDgwIcftubmzRtJrtBLSEhg9+4d1K//gcWYqETlylXAz68ov/66kXbtOjBixJfMnTuT0aOHodMlULx4SaZNm4OHhwcA06fPYd68WXTp0h43N3fq1q1vnvyzbdv23L17h9Gjh2Nvb0fjxs2oV6/BS1vdWrVqy82bNxg/fgwJCQn4+vrSvXsvlixZRFDQBSpXrkr16u8xdOhIVq78iblzZ5Izpw/9+g2gYcPG5nIqVKhIliwelCpVGjc3t3SoYfEilZKO7ac//fQTEydO5OLFi9Y3fosYDEZCQ6Otb5hKWq0aT08XwsKi3/mJvtKb1K1tSL2mTUKCjpCQ+2TN6vPSsTbwZgd9vyvCw8MJCjpHhQqVzeOOEhISaNSoLoMGDbNIJN4VMTExNGvWkG++mUxgYKXXKuv516y117qXl4vM9C2EEEK8jTQaDaNGDadZs5Z8+GErEhIS+Pnn5djb21G5crWMDu+NioyM5PTpE+zbtwdvb2/z2CuR/iRhEkIIkam4ubkxdepMFiyYy6+/bkStVlGqVGlmzVpo7jp7VxgMBiZOHI+Hhyfjxn1rs2klRCoTpqJFi6bqSVAURZ4sIYQQNle+fCDz56fuRu//ZZ6enuzY8XtGh/FOSFXC1Lt3b0mEhBBCCPHOSlXC1LdvX1vHIYQQQgjx1no3hrYLIYQQQrwGSZiEEEIIIayQhEkIIYQQwgpJmIQQQgghrEhVwrRw4UKCgoJsHYsQQgghxFspVVfJnT17lgULFuDm5kaNGjWoWbMmVatWxdXV1dbxCSGEsJEJE8ayfftvL93mjz9OvlLZffp0x8cnFyNHjk3V9q1a/Y8PPmhC1649Xml/1ty/f4/WrZsya9YCypWrYJN9iP+2VN9LLiEhgRMnTnDw4EEOHDjA7du3KVu2LDVr1qRmzZoUKVLE1rHajNxLLvOQurUNqde0+a/cSy4qKor4+Djz382aNaRfv0HUrVvfvCxr1myvVHZkZARqtSbVX6zDwsJwcHDA2dk5VduntV4lYUo9uZdc8lJ9axQ7OzuqVq1K1apVGT58OLdv3+bgwYMcPHiQOXPm4OnpaW59qlu3ri1jFkIIkQ5cXV2TJDSurq6vnCQ9z909S5q29/T0fO19CmFLr3wvOV9fX9q3b0/79u3R6XQcP36cAwcOMGnSJEmYhBDvLEWBuGeNNmi1oNe/mX07OkJ635Rh27Yt/PTTYqpUqc727VsoV64C3347lYMHf2f58qVcv34Vo9FI/vwF6dGjN5UqVQEsu+QSy+jUqSs//bSY4OCHFChQiP79BxMQUAaw7JJbvHghf/99lsDAiqxf/wsREeEUL16SwYNHkD9/AcDUIjVlykSOHz+KRqOhSZPmXLx4ntKly75yt158fBzLli1l164dhIQ8Im/e/HTu3JVatUznNIPBwMKFc9mzZydhYaH4+OSiTZt2NG/e6mlMoUyd+h1nzpwkNjYOf39/unfvTdmy5V/zWRBvg3S5+a69vT01atSgRo0a6VGcEEJkSooC/fs7cuHCsy4Klcq0/E0oUcLA9Onx6Z403b17h8ePH7FkyUri4+MJCrrIqFFD6dOnP9Wr1yQ6OooFC+YyfvwYNm7chp2dXZIyHj58wKZN6xk9ejzOzs5MnTqRCRPGsnr1xmRvvfX332dwcLBn0qQZGAx6xo8fw7Rp3zFr1gKMRiODBn2OXq9nypTZ2NnZMXv2NM6ePUPp0mVf+TjHjh3JpUtBDB48gjx5fNm9ewejRw9nwoTJvPdeLTZuXMv+/Xv56qtvyJ49B4cPH2TKlIkUKFCY0qXLMGXKtyQkJDB79iLs7e1ZtmwJI0YMYuPG7Tg5Ob1yXOLtkC4JkxBCCBOV6g1lR29Y587dyJ07DwCXL19iwIChfPhhK/P61q3bMnhwP0JDQ8iZ0zvJ4/V6PUOGjKBIEX8A2rZtz4gRgwkJCSFbtqRdgHq9nlGjxuHu7g5As2YtmT9/FgB//XWaCxfOsWrVOvLmzQ/AuHHf0qpV01c+vhs3rnPo0AG++246VatWB6Br1x5cuXKZ5cuX8N57tbh79y5OTo74+OQmW7ZstGz5EXnz5idv3rwA3L17l0KFCpE7d24cHBz5/PNB1K/fELX63Rjj818nCZMQQqQTlQqmT49/oUvuzQ36tkWXXCJfX1/zv4sU8cfNLQsrVvzIzZs3uHPnNleu/AuA0ZjysebLV8D8bxcX09gpvT4h2W29vLzMyRKYxlYlJJi2vXQpCHd3d3OyZNo+K3nz5kv7gT119eoVAHMXYaKyZcuxYMFcAFq0aM3Bg/tp0aIRRYr4ExhYibp1G+Dp6QXAJ598yvjxo9m/fx8BAaWpWLEKDRo0xMHB4ZXjEm8PSZiEECIdqVTwfO/LmxzDZEsODo7mf585c4pBg/pSpUo1AgLK0KBBQ+Li4hgxYvBLy7C3T3qVVUoXar/s6kONRvPSxOzVJB+H0WhEqzWdKn1987JmzSbOnDnJiRPHOXLkECtX/sQXX3zJBx80oWbN2pQvv4Pjx49w8uSfrFmzkqVLv2fhwqUULFgoneMVb1qa2wnj4+NtEYcQQohMYvXqFZQtW4EJEybz0UftCQyszMOHD4CUE6D0VLhwEaKiorh584Z5WUREOHfu3HrlMgsVMk2N8/fff1ksP3v2L/NA87VrV/P773sJDKxMr16fs2zZGsqXD2Tv3l3odDpmz57GvXt3qFu3AcOGjeKXXzahVqs4evSPV45LvD3S3MJUrVo1GjduTMuWLQkICLBFTEIIId5iOXJ4c+jQ75w9+xc5cuTg9OmT/PDDAgBzt5ktlStXgRIlSjJ+/Bj69x+Cg4MD8+fPIi4uLtkB5M+7ePE8Op3OYln27DkoVKgwVavWYOrUiahUKvLk8WXv3l388ccBxo37FoDw8DB+/PF7HB0dKVzYj5s3b3Dlyr+0atUWe3t7Ll68wNmzf9G//xCyZs3KsWNHiI2NpWRJOVf+F6Q5YerSpQubN2/ml19+oUCBArRo0YJmzZqRPXt2W8QnhBDiLdOtWw9CQx8zbFh/APLnL8iIEWMYN240Fy+eJ1++/DaPYeLEqUya9C39+/fEwcGBDz9szc2bN5K9Qu958+fPTrLsgw+aMHLkWL766hsWLpzLxInjiYp6QsGChfn660nUrFkbMI1RSkhIYPr0yYSGhuDllZXmzVvRseMngGng+axZ0xg+fCDR0VHkzZufMWPGv9aVe+LtkeqZvl90+vRpNm7cyI4dO4iNjaVq1aq0bNmSOnXqWH3Bvm1kpu/MQ+rWNqRe0+a/MtN3ZhUeHk5Q0DkqVKhsHl+UkJBAo0Z1GTRoGA0bNs7gCDM3mek7ea98lOXKlWP8+PEcPnyYmTNnEhsbS//+/alevTrfffcdd+/eTc84hRBCCMA06HvUqOHMnz+bO3duc/36NSZP/gZ7ezsqV66W0eGJ/6jXSgvv37/PkiVLmDVrFidOnCB//vy0aNGCgwcP0qhRI7Zt25ZecQohhBAAuLm5MXXqTC5c+IdPPmnPZ599QmhoCLNmLcTDwyOjwxP/UWnukouKimLnzp1s2rSJU6dO4ejoSMOGDWnVqhXlypUzb9ejRw/Onz/PH3+8/VcHSJdc5iF1axtSr2kjXXIZT+rVdqRLLnmvdJVcfHw8ZcqUYdy4cTRq1CjZu0uXKlWKCxcupEuQQgghhBAZKc0JU/v27WnVqhUFCxZ86XaffPIJPXv2fOXAhBBCCCHeFmluRxs6dChhYWHMnTvXvOzChQt8/vnnnDt3zrzMxcUFjUaTPlEKIYQQQmSgNCdMBw4coFOnThZjk1QqFTdu3ODjjz/m5MmT6RqgEEIIIURGS3PCNHv2bBo3bsyqVavMy4oVK8bmzZv54IMPmDZtWroGKIQQQgiR0dKcMF29epXmzZsnO/188+bNCQoKSpfAhBBCCCHeFmlOmNzc3Lh+/Xqy627fvp3sFXNCCCHePn379qBLlw4prv/uu69p166F1XIWL15Iq1b/M/9dvXoFtm3bkuL2EyaMpU+f7qmOU6/Xs2bNSov9NW/+Zmbz7tmzC9WrV+Dy5X/fyP7E2yvNCVP9+vWZOXMm+/fvt1h+6NAhZs6cSf369dMtOCGEELbTpEkz/v03iJs3byRZFx8fz/79e2jSpFmay928eQd166bfuWD37h3Mnj3d/He7dh1ZunRFupWfklu3bvLPP3/j65uXzZvX23x/4u2W5oRpwIAB5MuXj549e1KqVClq1KhBqVKl6N69O76+vgwaNMgWcQohhEhntWrVwdXVlV27tidZd+jQ78TGxr7SfdmyZs2Gg4Pj6wf41IvzKzs7O+Pp6Zlu5adk69ZfyZcvP02aNGPXrh3ExMTYfJ/i7ZXmeZhcXV1ZvXo1Bw4c4NSpU0RERODm5kaFChWoVasWavW7MeOnEEJkdg4OjtSr9z67d+/g008t583bvn0rVatWJ2vWbFy7doUFC+bw999niYuLJXv2nLRo0Zp27ZLvzqtevQJffPEljRr9D0VR+OmnxWzevIEnTyKpU6c+Ol28xfZnz55h8eKFBAVdJCFBR65cufm//+vC++83Ytu2LXzzzVfmcmfNWsCZM6fYvv031q0zdfs9fPiAhQvncvLkn8TERBMQUIZevT6ncOEigKkLECBLFg927NhKbGwM5csHMnToSLJly57sMRgMBnbu3EatWnWoWbMO8+fPZvfuHTRrZtlFuWvXdlauXMbt27fImjUbrVu3pU2bdgDExMSwcOEcfv99LzExMfj7F6NPnwEULVrMfFx//PHsyvIXl7Vq9T9q1arLsWOHCQsL5euvJ1GoUBHmz5/F0aOmZW5u7tSoUZPPPx+Mo6MpSb1z5zZz5kznzJlTaDRaAgMr0b//YB4/fswnn3zMnDmLKFPm2Z05vvzyCwwGA19//V0KrxQBr3gvObVaTe3atRk8eDDjx49n6NCh1KlTB7VaneSbgBBCvFMUBWJjM+bnFT5/Gzduyr17dzl37m/zspCQx5w8eZwmTZoTFxfHgAG9cXfPwoIFS1i+/Bdq167L3LkzuHz5ktXyV6z4kVWrltOrVz+WLFmBm5sbe/fuNq9/9CiYgQP7ULRocZYsWcGSJSspVqwEEyeOJzQ0hLp169Ovn6nnYvPmHZQqVdqi/JiYaHr27Epw8EMmTpzK/PlLcHBwpE+fT3nw4L55uz17dhIZGcHcud8zZcosLl26yKJF81KM+/jxozx+/IjateuRJ48v/v7F2Lx5g8U2e/fu5uuvv+T99xvx008/06NHLxYsmG0evzVmzHCOHTvCF1+MZenSVeTKlZsBA3oTGRlptd4SbdjwC59/PpipU2dTokQpvvlmLP/+e4kJEyazevVG+vUbyI4dW/n1V1NsT548oXfvT9HpdMycuYAZM+Zy794dRo8eTpEifvj5+bNjx1Zz+VFRURw6dIDGjf+XUgjiqTS3MAFs27aNP//8E51OZ06QFEUhJiaGv/76i4MHD6ZrkEIIkSkoCq79e6O9cN68SKV6pTzmlehLlCRq+hzTTlOpWLESFCpUmF27tlOyZAAAO3dux9PTi8qVqxIZGUnr1u1o0aKN+aKerl17sGrVMq5evUKRIv4plq0oCuvWraF167bUr98QgL59B3L69LNWFZ1OR9euPWjXrqP56uuOHT9hx46t3L59i9Kly+Lq6gqYuvpetHPndiIiwlm8eIW5m27s2K9p06Y5Gzb8Qq9enwPg4uLK0KEj0Wq15MuXn7p1G3D06OEUY9+27Vdy5MhJQEAZAOrVe5+5c2dw8eJ5ihUrAcAvv6yiTp36fPxxRwB8ffMSExODg4MDt27d4NixI0ybNoeKFSsDMGjQcNzc3IiICH/JM2KpcuVqBAZWMv8dGFiJMmXKU6hQYQB8fHKxbt0arl69AsDevbuIiYlm7NhvcHd3B2DYsNHs2bMTnU5H48ZN+f77+QwYMBQHBwf27duNm5sbFStWSXVM76o0J0xz5sxhzpw5uLm5odfrsbOzQ6vVEhoailqtpnXr1raIUwghMoc0JCtvi8aNm7Js2VL69RuEVqtl586tfPBBEzQaDZ6enrRo0Zrdu3dw+fIl7ty5zZUrlwEwGl9+89uIiAhCQh5TrFhxi+UlSgRw48Y1AHLnzkOjRk1Zu3Y1165dsSjfYDBYjf3q1Sv4+uazGNPk4OBI8eIluHr1qnlZ7tx50GqfnfJcXFzR6/XJlhkeHs7hw4do0aKNOYmrW7c+8+bNZNOm9eaE6dq1K9Sr977FY5s2/RCA/fv3PD3Wks/F5UDfvgMB+Oefs1aPDSBPHl+Lvz/8sDV//HGQbdu2cOfOLa5fv8b9+/fIly+/OSZf37zmZAmgcOEi5u7J+vU/YO7cmfzxxwHq1m3A9u2/8f77jeTOHKmQ5oRp48aNNG/enG+//ZZZs2Zx7949vvvuO86dO0f37t0pUqSILeIUQoi3n0plauGJizMvev7O7zbn6PhKCVuDBo2YP382J04cezpm6SoTJkwGTN1zPXp8gqenJ9WqvUdgYGWKFStOixbWB4MnhmI0WjaxPZ+4XL9+jV69uuHvX5TAwErUrFkbDw9PPv20UyqjT775zmg0otU+SwLs7OySPjKFpr9du7aTkJDA2rU/s27daovt9+7dRd++A3F1dUWjSfkU+vwxplZyCaKDg4P530ajkaFD+3Pt2lXq129I3boN8PMryqRJE1K9X3d305innTu3U6xYCc6d+5thw0alOdZ3UZqf0YcPH/K///0PlUpFsWLF2LrV1BdasmRJPvvsM9auXUuHDinP6yGEEP9pKhU4OT37W6uGN5UwvSIPDw+qVXuPvXt34+WVlTJlyplbNnbv3kFkZCSrV280n4wTu3+sjVnNksWDHDly8s8/Z3nvvVrm5ZcuXTAnG5s3r8fLy4sZM56NJ/rjD8thHclNlJyoUKEibN/+G2FhoXh6egGmKRGCgi6+0hV+YBp8XbBgIcaOnWCx/O+//2LKlIns3LmVli0/okCBAgQFnbfYZvbsaTx8+IDu3XsDcPHiBSpUqAiY5pNq2/ZDevf+HK3WlMBFR0fh4mLqcrx9+9ZL47p8+V+OHTvCwoU/mluu9Ho9d+/eJleu3ADkz1+QLVs2ERUVZe7KvHQpiEGD+rJkyQpy5MhJ48bNGD58INu3/0axYiXIn7/AK9XTuybNg76dnZ3NL958+fJx584d4p5+mypWrBh37txJ3wiFEELYXJMmzTh8+BC//77XYu6lHDm8iYuLZd++PTx48IA//zzGl19+AUBCgs5quR06dGb9+l/47bdN3Lp1k++/n8+F58Z45ciRk+Dghxw9epgHD+5z4MA+pk6dCJjGNwE4PU1Ag4IuEh8fZ1F+/foNyZLFg9Gjh3Px4nmuXLnMuHGjiI2NTXJFW2pcuhTElSv/0rLlRxQsWNjip2nTFuTKlds8+Lt9+87s2bOLdetWc/fuHXbt2sHGjeupXr0mefPmo2bN2kyb9h2nT5/k1q2bTJo0AZ1OR9myFShRoiQqlYolSxZx//499u3bw/btv700tqxZs6LRaNi3bzf37t0lKOgCo0cPJyQkxPxcNGjwAW5u7owfP5orVy4TFHSRKVO+oVChwuTIkROAChUq4unpxapVy2jUqEma6+hdleaEqVSpUmzatAmAAgUKoNFoOHr0KGC6bYq9vX26BiiEEML2KlasjJOTE5GREdSqVce8vHbturRr15E5c6bTvn1LZs2aSpMmTSlTphwXL16wWm6LFq3p1asvP/20hM6dP+batasWCVmrVm2pU6c+48ePoWPHj/jpp8V0794LH59cBAWZyi9XLpDixUvSs2cXDh/+w6J8V1dXZs9eiJubO59/3otevboRHx/P/PmLza0uabFt26+4urrx/vuNkqxTq9W0adOOa9eucvbsX1Sv/h5Dh45kw4a1dOjQmiVLFtGv3wBzy9aIEV9SunQ5Ro8eRteuHXn48CHTps3Bw8OD3LnzMHjwCA4c2E/79q3YvHkDvXr1e2ls2bJlZ+TIrzh8+CAdOrRm1KhhZM+enY8++pigoIsAODo6Mm3aHPR6A5999gmDB/clf/6CjBv3rcVxvP9+IxQF6tZ9P6XdiReolDTOA3DixAk++eQTqlevzoIFCxg5ciS7d++mUqVK/PHHH9SrV4/JkyfbKl6bMBiMhIZGp1t5Wq0aT08XwsKi39zYhXeE1K1tSL2mTUKCjpCQ+2TN6oOd3cu/JL7RMUzvEKnX1zNhwlgMBgNjxoxPsu75urX2WvfyckGjeTfmX0zzGKbAwEDWrVvHpUum+TfGjBmDWq3m9OnTNGzYkOHDh6d7kEIIIYR4fSdOHOP69evs3buLOXMWZXQ4mUqaE6Z58+bx/vvv06yZqUnVwcGB8eOTZqhCCCGEeLv89tuvHD16mE8+6U7x4iWtP0CYpTlhWrhwISVKlKBQoUK2iEcIIYQQNvLVV99kdAiZVpo7HgsXLsz169dtEYsQQgghxFspzS1MtWvXZtq0aRw6dAh/f3/zVPmJVCoVvXv3TrcAhRBCCCEy2ivdGgXg8OHDHD6c9D48kjAJIYQQ4r8mzQlTUFCQLeIQQgghhHhrvRuTJwghhBBCvIY0tzCNGDHC6jbffvut1W2EEEIIITKLNCdMx48fT7IsJiaG8PBwPDw8KFWqVLoEJoQQQgjxtkhzl9y+ffuS/Bw7doytW7fi6elJ8+bNbRCmEEKI9Na3bw+6dOmQ4vrvvvuadu2s38B28eKFtGr1P/Pf1atXYNu2LSluP2HCWPr06Z7qOPV6PWvWrLTYX/PmjVP9+FfRp093JkwYa9N9iMwl3cYwFSpUiL59+5qvohNCCPF2a9KkGf/+G8TNmzeSrIuPj2f//j0WN8pNrc2bd1C3bv10iNBk9+4dzJ493fx3u3YdWbp0RbqVL0RqpOugb1dXV+7evZueRQohhLCRWrXq4Orqyq5d25OsO3Tod2JjY2nYMO0tOVmzZsPBwfH1A3zqxXvEOzs74+npmW7lC5EaaR7DdO/evSTLDAYDDx8+ZNasWXLLFCHEO04B4p77Ww0Y39C+HQFVqrd2cHCkXr332b17B59+2tNi3fbtW6latTpZs2bj2rUrLFgwh7//PktcXCzZs+ekRYvWtGuXfHde9eoV+OKLL2nU6H8oisJPPy1m8+YNPHkSSZ069dHp4i22P3v2DIsXLyQo6CIJCTpy5crN//1fF95/vxHbtm3hm2++Mpc7a9YCzpw5xfbtv7Funanb7+HDByxcOJeTJ/8kJiaagIAy9Or1OYULFwEwd61lyeLBjh1biY2NoXz5QIYOHUm2bNlTXV8vOnLkD3788QeuX7+Ks7Mz9eq9T/fuvczJ4tGjh/nhhwXcuHENJydnqlSpRt++A3F3dwdg1arlbNq0jkePgsmWLTuNGzelU6euqFSpfw7Fm5PmhKlOnTrJPpmKouDo6ChdckKId5iCq2tvtNrz5iUqFbzQQGIzen1JoqLmkJakqXHjpmzatJ5z5/6mZMkAAEJCHnPy5HG++WYKcXFxDBjQm8DAyixYsASNRsOWLZuYO3cGFSoEUqSI/0vLX7HiR1atWs6QISPw9y/K5s0b2LZtC2XKlAPg0aNgBg7sQ8uWHzF06EgSEhJYufInJk4cT2BgJerWrU9UVBSzZk1l8+YduLtn4cyZU+byY2Ki6dmzK7ly5WbixKnY2dmzZMki+vT5lB9//Blvbx8A9uzZSf36DZk793tCQ0MYO/YLFi2axxdffJnGWjY5cGA/o0cPo0uX7owa9RW3bt1gypSJ3Lt3l2+/nUp4eDgjRw6hT58BVK1aneDgh4wf/yXz5s1k+PDR/PHHQZYvX8q4cd/g65uf8+f/5uuvv8THJxfvv9/olWIStpXmhOmbb75JkjCpVCpcXV2pVKkSbm5u6RacEEJkPpmrdaBYsRIUKlSYXbu2mxOmnTu34+npReXKVYmMjKR163a0aNHGfCusrl17sGrVMq5evfLShElRFNatW0Pr1m2pX78hAH37DuT06ZPmbXQ6HV279qBdu47mc0vHjp+wY8dWbt++RenSZXF1dQVMXX0v2rlzOxER4SxevMLcTTd27Ne0adOcDRt+oVevzwFwcXFl6NCRaLVa8uXLT926DTh6NOndKlJrxYofee+9WnTu3A2AvHnzoSgKI0YM5vr1a+j1Ceh0OnLm9Mbb2wdvbx+++24aBoMBgHv37mBvb4e3dy68vb3x9vYmW7Yc5Mzp/coxCdtKc8LUokULjEYj//77L0WLFgXg0aNHXLhwAScnp3QPUAghMg/V0xaeZ11yWq0avf7t7JJL1LhxU5YtW0q/foPQarXs3LmVDz5ogkajwdPTkxYtWrN79w4uX77EnTu3uXLlMgBG48uPKyIigpCQxxQrVtxieYkSAdy4cQ2A3Lnz0KhRU9auXc21a1csyk9MLl7m6tUr+PrmsxjT5ODgSPHiJbh69ap5We7cedBqn53yXFxc0ev1VstPybVrV6hf/32LZWXKlDevq1u3AfXqvc+wYQPImjUbgYGVqFq1Bu+9VwuABg0asXXrr7Rr14L8+QsSGFiJWrXq4u0tCdPbKs2Dvh8+fEizZs3o06ePedmFCxfo0aMHHTp0IDw8PD3jE0KITEYFOGXQz6u1bjVo0Ijo6ChOnDjGv/8Gce3aVRo3bgqYuuf+7//a8ttvm8iWLTsfftiaJUtSd4VaYmeE0WjZJ/l84nL9+jXatWvBkSOH8PXNS/v2/8f06WkZ2pF8f6fRaESr1Zj/trOzS/rI1+grTe6himJKIBOPb+zYCaxatY727f+PiIhwxo8fzaBBfQHw8PBg6dJVzJv3A7Vr1+X8+X/o3bsbS5d+/8oxCdtKc8I0adIkdDodU6ZMMS+rWbMmGzZsIDw8nKlTp6ZrgEIIIWzLw8ODatXeY+/e3ezZs4syZcqRJ48vYLqkPzIykvnzl9C5czdq1qzNkydPAOsJR5YsHuTIkZN//jlrsfzSpQvmf2/evB4vLy9mzJhH+/adqFKlOiEhIRbbv2wQdKFCRbh9+yZhYaHmZfHx8QQFXSR//oKpq4BXUKhQYf7++y+LZWfPngEgX74CnD9/jlmzppI3b37atPmYyZNnMmLEGE6dOkFYWCi7dm1n48Z1BASUoWvXHixa9CP/+19z9u7dZbOYxetJc5fckSNHGDduHGXKlLFYXrx4cT7//HMmTJiQXrEJIYR4Q5o0acZXX43Czc2Nrl17mJfnyOFNXFws+/btISCgDLdu3WDWrGkAJCTorJbboUNn5syZQb58+QgIKMvOndu4cOE8pUqVflp+ToKDH3L06GEKFCjIpUsXmTHD9IVcpzOVnzjcIyjoIgUKFLAov379hixfvpTRo4fTu/fn2NnZs3TpImJjY2nWzPqkmy/z6FEwx44dSbK8cuWqtG//f4wePZwff/yBOnXqc/v2LaZPn0zVqjXIn78AN25cZ8OGtWi1djRt+iE6XTx79+4iT568ZMnigU4Xz9y5M3FxcaF06bIEBwdz5sxpypQp+1oxC9tJc8Kk0+nQaDTJrnNyciI6Ovq1gxJCCPFmVaxYGScnJyIjI6hVq455ee3adbl0qSNz5kwnOjoKH59cNGnSjD/+OMjFixewdnOHFi1aYzQa+OmnJYSEhFCpUhWaNGlmniyzVau23Lx5g/Hjx5CQkICvry/du/diyZJFBAVdoHLlqpQrF0jx4iXp2bMLo0ePtyjf1dWV2bMXMmfODD7/vBcAAQGlmT9/Mbly5X6tOjl58k9OnvwzyfI//jhJrVp1GTt2AsuWLeGnnxbj4eFJ/frvm5PN/PkLMGHCZJYu/Z6NG9eiVqspVy6QqVNnoVaradKkOREREfz44w8EBz/Ezc2NWrXq0rNnv9eKWdiOSkljJ26XLl1ISEhgyZIlFn3Cer2e7t27YzAY+Omnn9I9UFsyGIyEhqZfoqfVqvH0dCEsLPoNDvZ8N0jd2obUa9okJOgICblP1qw+2NnZv3TbNzvo+90h9Wo7z9ettde6l5cLGk26zoH91kpzC1O/fv3o2LEjdevW5b333iNr1qyEhoZy+PBhQkJCWL58uS3iFEIIIYTIMGlOmMqUKcOaNWtYsGABv//+O+Hh4bi5uVGhQgV69epFsWLFbBGnEEIIIUSGSXPCBKYB3tOnTzePZYqNjUWv18uklUIIIYT4T0pzx2NCQgJffvklbdq0MS87c+YMVapU4bvvvrM6kZkQQgghRGaT5oRp9uzZ/PrrrzRu/OwO1sWLF2fw4MH88ssv/PDDD+kaoBBCCCFERktzl9yWLVsYNmwYbdu2NS/z8PCgc+fOaLVali1bRvfu3dM1SCGEeDu9obvqCpFBXmc29P+aNLcwhYWF4evrm+y6ggUL8uDBgzSVZzQamTVrFjVq1KBMmTJ8+umn3L59O1WP/fXXX/H39+fOnTtp2qcQQrwO0/hNFfHxcVa3FSIz0+niAdBoXmnI839KmmugYMGC7Ny5k2rVqiVZt2/fPvLly5em8ubNm8eqVauYOHEi3t7eTJ48mW7durFlyxbs7VOe3+Tu3buMGzcureELIcRrU6s1ODm5EBUVjl6fgKOjM2q1JtlbeBiNKgwG+Zae3qRebcdoVKHXG9Hp4omKCsPJyRW1+t2Ya+ll0pww/d///R/Dhw8nPDycevXqmedh2r9/P9u3b+fbb79NdVk6nY4lS5YwePBgatWqBcD06dOpUaMGu3btokmTJsk+zmg0MmTIEEqUKMGxY8fSeghCCPHa3N29sLNzICoqnLi4lCe+VavVcjGMDUi92s7zdevk5Iq7u1cGR/R2SHPC1Lx5c6Kjo5k3bx67dj27SaCnpydjxoyhWbNmqS4rKCiI6OhoqlSpYl7m7u5O8eLFOXHiRIoJ04IFC0hISKBPnz6SMAkhMoRKpcLZ2RUnJxeMRiNGoyHJNhqNiixZnImIiJHWkHQk9Wo7z+o2FlBLy9JzXqlTsn379nz88cdcv36d8PBw3N3dcXNzY+3atdSpU4f9+/enqpzE8U4+Pj4Wy3PkyJHiWKi///6bJUuWsG7dOh4+fPgq4SdLq02/F0XiNPHvynTxb5LUrW1Ivb4uDWCXdKlGjaOjIzqdgsEgrSHpRerVdqRuU/bKo7hUKhUFCxbk0KFDLF68mAMHDqDX68mTJ0+qy4iNjQVIMlbJwcGBiIiIJNvHxMQwePBgBg8eTP78+dMtYVKrVXh6uqRLWc9zd3dK9zKFidStbUi92obUq21IvdqO1G1Sr5QwhYaGsm7dOn755Rfu3r2Lq6srH374Ic2aNaNChQqpLsfR0REwjWVK/DdAfHw8Tk5Jn6yvv/6aAgUKWExpkB6MRoXIyJh0K0+jUePu7kRkZKxk6OlM6tY2pF5tQ+rVNqRebSetdevu7vTOtEynKWE6duwYa9asYc+ePRgMBsqXL8/du3eZO3cuFStWTPPOE7vigoODyZs3r3l5cHAw/v7+SbZfv3499vb2lC1bFgCDwTRmoEmTJnz22Wd89tlnaY4hkS3uem0wGOVu2jYidWsbUq+2IfVqG1KvtiN1m1SqEqYff/yRNWvWcP36dfLly0evXr348MMPcXZ2pmLFisleSpsaRYsWxdXVlePHj5sTpsjISC5cuECHDh2SbP/8IHOAs2fPMmTIEBYtWoSfn98rxSCEEEIIYU2qEqaJEyfi7+/PsmXLLFqSnjx58lo7t7e3p0OHDkyZMgUvLy9y587N5MmT8fb2pkGDBhgMBkJDQ3Fzc8PR0THJHE+JA8Nz5cqFh4fHa8UihBBCCJGSVHU8Nm7cmJs3b9KjRw969erF7t270ev16RJAv379aNWqFaNGjaJdu3ZoNBoWL16MnZ0d9+/fp3r16mzbti1d9iWEEEII8SpUSipvFBMVFcWWLVvYsGED//zzD56entSrV49169axfPnyNA32ftsYDEZCQ1OeeC6ttFo1np4uhIVFSx9wOpO6tQ2pV9uQerUNqVfbSWvdenm5vDODvlN9lK6urrRr1461a9eyZcsWmjVrxr59+1AUhS+++IKZM2dy5coVW8YqhBBCCJEhUt3ClBy9Xs/+/ftZv349f/zxBwaDgSJFivDrr7+mZ4w2Jy1MmYfUrW1IvdqG1KttSL3ajrQwpey1bj+s1WqpX78+9evX5/Hjx2zcuJGNGzemV2xCCCGEEG+FdEsLs2XLxqeffioDtIUQQgjxn/NutKMJIYQQQrwGSZiEEEIIIayQhEkIIYQQwgpJmIQQQgghrJCESQghhBDCCkmYhBBCCCGskITJBu7fV7FrF7z6lKBCCCGEeJtIwmQDs2fb8cUXcPq0VK8QQgjxXyBndBtwcDA1LV2/rsrgSIQQQgiRHiRhsoE8eUwJ0507Ur1CCCHEf4Gc0W0gVy5TwnT3rrQwCSGEEP8FkjDZQGILkyRMQgghxH+DJEw2kDu3EYDgYBU6XQYHI4QQQojXJgmTDXh6grMzGI3w8KG0MgkhhBCZnSRMNqBSQZ48pn/fvStVLIQQQmR2cja3EV9f028ZxySEEEJkfpIw2cizhEmqWAghhMjs5GxuI3nzmn7fuyctTEIIIURmJwmTjSSOYZKESQghhMj8JGGykcQWpocP1SQkZGwsQgghhHg9kjDZSNas4OAgUwsIIYQQ/wWSMNmISgW5c8s95YQQQoj/AjmT21DijN8yjkkIIYTI3CRhsqHEFiaZWkAIIYTI3ORMbgNa7X5gKHnzRgPSwiSEEEJkdpIw2YCd3a/APooXPwzIbN9CCCFEZicJkw0oSnYAcua8DZimFtDrMzIiIYQQQrwOSZhswGjMDYC7+13s7U1TCzx4IK1MQgghRGYlCZMNJCZMGs09uVJOCCGE+A+QhMkGjMZcAKjVd8mVS66UE0IIITI7OYvbQGILk0r1mLx5YwEZ+C2EEEJkZpIw2YQ74ApAoUJ3Abh3T6paCCGEyKzkLG4TKiAPAPnyma6UkxYmIYQQIvOShMlmTAmTj4+phUmmFhBCCCEyL0mYbMaUMGXJchc7OzAY4OFDaWUSQgghMiNJmGzGlDBpNHfJlcs0tYB0ywkhhBCZkyRMNpOYMN0z34T3/n2pbiGEECIzkjO4zZgSJrX6PrlzmwYv3bkjLUxCCCFEZiQJk83kAOwAA4UK3QdkagEhhBAis5IzuM2oMRq9Acif/w4gY5iEEEKIzEoSJhtKnPE7Vy5TwvTggRqDISMjEkIIIcSrkITJhhITJg+PZ1MLBAdLK5MQQgiR2UjCZEOJN+HVau+ZpxaQgd9CCCFE5iMJkw0piqmFSa2+i4+PaWoBGfgthBBCZD5y9rahxC45tfoeefKYBi/duyctTEIIIURmIwmTDSV2yalUseTPHwrA3btS5UIIIURmI2dvm7LHaMwOQIECMrWAEEIIkVlJwmRjid1yuXPL1AJCCCFEZiUJk40ldst5ed1FqwW9Hh49klYmIYQQIjORhMnGDAZTC5NGc9c8tYB0ywkhhBCZiyRMNpbYwqRW3yNXLtPUAjLwWwghhMhc5MxtY4ljmKSFSQghhMi8JGGysWdTC4STL180IJNXCiGEEJmNnLltTFHcUBQ3QKYWEEIIITIrSZjegMRWpjx57gJw/74aozEjIxJCCCFEWkjC9AYkjmPKmvWOeWqB4GBpZRJCCCEyC0mY3gCDwdTCpNHcw9vb1LQk95QTQgghMg9JmN6A56+Uy5NHphYQQgghMhs5a78BlnMxydQCQgghRGYjCdMbkDjbt1r9gNy5EwBpYRJCCCEyEzlrvwGKkhWwAxQKFrwPyBgmIYQQIjORhOmNUJtbmXx9TXMx3bsnUwsIIYQQmYUkTG9I4jimrFnvotGYphZ49EhamYQQQojMQBKmNyTxSjk7u7vmqQVk4LcQQgiROUjC9IY8f6Vc7tymqQXknnJCCCFE5iBn7Dfk2ZVyd81TC8jAbyGEECJzkITpDUlsYdJo7pMnjylhunNHql8IIYTIDOSM/YYYjd6ACogjb97HgLQwCSGEEJmFJExvjB1GYw4AChUyTS1w+7aa0NCMjEkIIYQQqSEJ0xuUeKVctmx3KFrUiNEI+/drMzgqIYQQQlgjCdMb9PyVcvXr6wHYvVsSJiGEEOJtl+EJk9FoZNasWdSoUYMyZcrw6aefcvv27RS3v3z5Mt27d6dSpUpUqVKFfv36ce/evTcY8aszGBITprvUqqVHq4WrV9VcuyZjmYQQQoi3WYYnTPPmzWPVqlWMHz+e1atXYzQa6datGzqdLsm2YWFhfPLJJzg6OrJ8+XK+//57QkND6datG/Hx8RkQfdokdslpNPdwd4eKFQ0A7N0rrUxCCCHE2yxDEyadTseSJUvo168ftWrVomjRokyfPp0HDx6wa9euJNvv2bOHmJgYJk2ahJ+fHyVLlmTy5MlcvXqV06dPZ8ARpE1iwqRWm1rEErvl9u7Vyn3lhBBCiLdYhiZMQUFBREdHU6VKFfMyd3d3ihcvzokTJ5JsX6VKFebNm4ejo6N5mVptOoTIyEjbB/yaErvkVKoIIJqKFQ24uSmEhKg4cybDG/uEEEIIkYIMPUs/ePAAAB8fH4vlOXLkMK97Xp48eahcubLFskWLFuHo6EhgYKDtAk03LihKFsDULWdvD7VqmbrlZPC3EEII8fbK0LN0bGwsAPb29hbLHRwciIiIsPr45cuXs2LFCkaNGoWXl9drxaLVpl/uqNGoLX4/T1HyoFZHYGd3D5XKn4YNDfz2m5bDh7XodHqcndMtjP+kl9WteHVSr7Yh9WobUq+2I3WbsgxNmBK71nQ6nUU3W3x8PE5OTik+TlEUZs6cyfz58+nZsycdO3Z8rTjUahWeni6vVUZy3N2TO4b8wEXc3EIAF6pWhQIF4NYt+OsvFxo3Tvcw/pOSr1vxuqRebUPq1TakXm1H6japDE2YErvigoODyZs3r3l5cHAw/v7+yT4mISGBESNG8NtvvzFixAg6d+782nEYjQqRkTGvXU4ijUaNu7sTkZGxGAyWo7nt7bPj4GAkIeEacXHRANSsqeXHH7Vs2GCkatWkVweKZ15Wt+LVSb3ahtSrbUi92k5a69bd3emdaY3K0ISpaNGiuLq6cvz4cXPCFBkZyYULF+jQoUOyjxk6dCi7d+9m6tSpNE7H5hi9Pv3fdAaDMUm5arUPph7Iu+Z1deoksHSpltOn1dy/r5A9u5LusfzXJFe34vVJvdqG1KttSL3ajtRtUhmaFtrb29OhQwemTJnC3r17CQoKYsCAAXh7e9OgQQMMBgOPHj0iLi4OgA0bNrBt2zYGDBhAxYoVefTokfkncZu3ncGQOLXAXfMyb2+FgAADigJ792oyKjQhhBBCpCDD29H69etHq1atGDVqFO3atUOj0bB48WLs7Oy4f/8+1atXZ9u2bQD89ttvAEyaNInq1atb/CRu87Z7dnuUYCDBvLxuXdPVcnv2aFGkgUkIIYR4q6gURU7PBoOR0NDodCtPq1Xj6elCWFh0Mk2aCh4eDYE4IiNXYDT6AhAVBW3aOJOQAPPmxVGkiDSFJufldSteldSrbUi92obUq+2ktW69vFzemTFM78ZRvlVUGAymwe6JM34DuLpCtWqJN+SVbjkhhBDibSIJUwZ4dk+5uxbLE7vl9u/Xote/8bCEEEIIkQJJmDLAs3FM9yyWV6hgwMNDITxcxcmT0sokhBBCvC0kYcoAyV0pB6DVQp06iTfklYRJCCGEeFtIwpQBUmphAqhXz9Qtd/iwlqioNxqWEEIIIVIgCVMGMBgKAKDR3ESlemixrnBhI/nyGUlIgIMH5Ya8QgghxNtAEqYMoCjZ0evLAQoODlss1qlUUL++dMsJIYQQbxNJmDLI/7d37+FR1fe+x99rrbnlHkK4mYAo95tAuHeDRdptPR57rNp2Fy/bVlF3i/BU8dajVatb26pgtT6t5VTbqtvT3SNo69a2it2KWu4gCsgdYoAEDEnIfW5rnT/WzCQhScMlk0ng83qe9czMWiuT33yzkvnkt37zW8Hg/wLA7/8voOVH4ubMiWIY8PHHFmVlRgpaJyIiIs0pMKVIODwTx8nFMCrxej9osa1PH4cJE9yxTG++qdNyIiIiqabAlDJegsHLAPD7/9hq61e/6vY6LVvm5fPP1cskIiKSSgpMKRQKfRUAj2cjplnSYtvMmVHGjIkSCsFvfuNNRfNEREQkRoEphWy7P+HwDKB1L5NhwL/9m3tx3rff9rBzp35UIiIiqaJ34RQLBi8HwOf7CxBssW3kSJsvfck9NferX3nRZZJFRERSQ4EpxSKRadh2PwyjBp/v3Vbbv/OdMF6v+4m5v/9d0wyIiIikggJTypmJsUw+X+vB3/36OXz96+6puaVLvYTDXdo4ERERQYGpWwgGLwVMPJ6tWNauVtu/9a0wubkOhw6Z/OlPmmZARESkqykwdQOO05tw+EIAfL4/tdqenu6emgN46SUv1dVd2jwREZGzngJTN9E0+PttoL7V9ksuiXD++Ta1tQYvvqhpBkRERLqSAlM3EYlMxLYHYhgN+HwrWm03TbjllhAAr7/upaREk1mKiIh0FQWmbsNodn2514DWcwgUFdlMnx4lGoWlS31d2zwREZGzmAJTNxIKXQJ4saw9WNa2Nve56aYQpgmrV1ts2qQfn4iISFfQO2434jjZhEJzgLavLwcwaJDDZZfFJ7P0Ydtd1jwREZGzlgJTNxMMfg0An+9vGEbbH4f7138NkZHhsGePyVtvaTJLERGRZFNg6mai0VFEo0OAcOxyKa3l5MA117jTDDz/vI+jRzUAXEREJJkUmLodI9HL5J6Wa/sCcpdfHqGgwKay0uC22/yUlSk0iYiIJIsCUzcUCn0Zx0nDNA/g9a5qcx+fD3784yADBtiUlprcdluA4mKFJhERkWRQYOqW0mOfmIOMjHsJBP4PEGq114ABDkuWBDn3XJvycoPbbw+wc6d+pCIiIp1N767dVEPDPMLhLwI2gcBLZGffiGVtabVffr7D4sWNDB9uU11tcOedfj75RD9WERGRzqR31m4rk7q6h6irewjHycU0PyMraz5paT8HGlrsmZMDjz3WyAUXRKmvN7j77gBr1+pHKyIi0ln0rtrNhcNfpLr6RUKhrwDg979Cdva38XjWt9gvIwMefTTItGlRwmF44IEA772nKQdEREQ6gwJTD+A42dTX/29qax/DtvtimmVkZi4iPf0xDKMmsZ/fDw88EGT27AiRCDz6qJ8//9mTwpaLiIicGRSYepBIZBrV1b9rNrnlG2RnX4fP9wbgTvnt9cIPfhDi0ksj2DYsWeLj6ad97Nhh4rQ9Q4GIiIh0QIGpx0mnoeE2amufwrYLMYxK0tMfIyvrO3g8qwEH04Tvfz/EN77hTm75+usebr01wPXXB3j+eS/79mn6ARERkZNhOI76HaJRm4qKuk57Po/HpFevDCor64hEknmxtxB+/6sEAi8mTs1FIkU0NHyXaHQ4jgNr11qsWGGxapVFMNgUlM491+aii6LMnh2hoKDnHAJdV9uzi+qaHKprcqiuyXOytc3Ly8Cyzo6+FwUmenJgchlGNYHAS/j9ywD3wryh0JdpbLwJ2+4PQEMDrF5t8e67HtautYhEmr5+2DCbOXMizJ4dJT+/ex8O+kOZHKprcqiuyaG6Jo8CU/sUmOj5gSnONEsJBH6Nz7ci3hKCwatobLwGx8lJ7FdbCx9+6IanjRst7FgTDQPGj4/ypS9FmDkzSmZmlzX9hOkPZXKorsmhuiaH6po8CkztU2DizAlMcZa1g7S0X+LxbIqvIRz+J0KhSwiHpwFNn5yrqoKVKz387W8WW7c2TUPg9cK0aVHmzIkwbVoUn+90W1WPYQRxnF6n9Sypru2ZSnVNDtU1OVTX5FFgap8CE2deYHI5eDxrSEv7NZa1q2mtk0so9GVCoUuIRocCTeOaysoM/vu/Ld55x0NxsUkgUEdBwT7OP38f558fwrbziET64ji9saxcMjJMMjIc0tMhI8Ph/PNtMjIcDKMcj2cXlrUHy9qFZe3GNA8CEAp9KXaqcMApvaruUdszj+qaHKprcqiuyaPA1D4FJs7UwNTEsnbj8/0Fn+9tDKMqsT4aPY9Q6CuEwzMxzaOYZjGWVYxl7SMYLKGhoZyaGgiHW3+qznEMqqryqazsQ0VFPjU1vejb9xCjR28nP7+a9HSHQMA9zdeah2DwChobr2txqvBEdLfanilU1+RQXZNDdU0eBab2KTBx5gemJlE8nrX4/X/B6/2A+ADxf8S28ygvH0xVVToeTzk+Xzk+XwWO4xCNgm27SzTaFKwcx+DQocEcPDgcGEqvXkMYPPg8Bg48Qnr6L/F4Nsb2y6Cx8RqCwa8D/g7bUlsLtbUWY8akc+xYd6ttz9Z9j9meTXVNDtU1eRSY2qdpoM8qFpHIDCKRGRhGNV7vu/h8f8Hj2YZt9yMaPRfbPpdodHDivuNk4fNB377NnyeKYVTEeqU+xzDKMc1Kjh7tx0cfjWDlyqGsX59GdXXL7qXc3AH07ftLJk1azSWXPEO/frvxeJZi269SUXEThnExwaBJaalBaalJWZlBaalBWZm7rqYGvN4w55zjcMklHr785RC5uV1ZPxEROVuph4mzqYepPTadPYepbcOePSYbNphs3GixZYtFONy03TBsZs78M9/85rPk5R0BoKRkCBs2XEhGRg3p6TVkZtaQkVFNRkY1mZnurWXZRCJeampyqKnpRVZWNgUF2fTunQvkYtu5sfA3GsfJ6tTXdCbrecdsz6C6JofqmjzqYWqfAhMKTF0hGITiYpOjRw2OHjWoqHCXqqowI0f+gZkzX8DvrwXA53PwesHjAa/XweeL33fHRNXXmxw96tDY2NSD5fc75OQ4ZGeDGfvdjUbPJxK5ILE4Tp8uea2GUYnX+3e83vU4ToBotADbHoBtF2Db58SCXPeabV3HbHKorsmhuiaPAlP7dEpOuoTfD8OHt/fL9w0M4yv4fMsxzQocJyu2ZOM4mThONqFQJo2N2VhWBr16ORjGQfbvP8aGDbXs21dNenol2dmV5OVVMnr0fvLzSzDNfVjWXkzzNUwTQqEBNDaOIxweR0bGQAKBLGw7B8fJ5kTGUP0jhvE5Pt/7eL0r8Xg+Atr/P8RxMrDtc7Dtc2ID7/9HYoJRERHpnhSYpFtwnGyCwW+fwJ4mkIFtZ3POOTbnnAM1NfD22x7+6788lJS4/+lkZ1cwYsRHjBz5ESNGbGbw4B0YxmHgMLCCUAg8Hge/n1gPlhfTzCEQyMSy3BBl271wnF7Ydi6O0wvHyU2sc5wsTPMQXu97+HzvY1nbWrQyGh1GOPxPbovNQ5jmISzrEIZRgWHUxaZb2IXX+x6BwO8IheYQDM4lGh3WmWUVEZFOosAkPV5WFlx5ZYQrrojwyScmn35qUl+fRV3dLEpLL2T3boNIpJ78/C2cc85mBg78mIyMcrKyjpGZeQzDcHA/MXgUOIrH03Qa0F2cZvfdxQ1uLXvMQqGx1NVdSFXVF6mrG0AoBP37u/NUNWnENEuxrEOY5kG83lV4PBvx+d7B53uHSGQSjY1XE4lMorudtjt9Npa1BdseHOvVExHpORSY5IxhGHDBBTYXXNDWqT8LGB9b3CkKiotNdu40OHSonvLyWqqqqgkGa8jMrCYr6xhZWZXk5FSQne2e7svJqSAnp4L09DosywEstm+fxNq1F7F69WyOHs1v9V1NE4YOtRk7NsrYsTZjxwbo1es8bPs8AILBb2JZOwkE/i9e73/j8WwgM3MD0egQGhuvJhy+KNb2nszG632XQOB3WNZ+HCeLxsZ/JRi8AvCmunEiIidEg77RoO+eJNm1ra+HkhKTAwcMysvjiztYvbzcHahuGCGysqoIBtOor2/9STzLgkDAwTShpqZ1L1FBgR0LT1FGjbIpKHB7sEyzFL//D/j9bwBBAGy7H6HQxUSjQ4lGh2DbBXT2JxohWXVtGZRcBvHxXbZdSEPD9wiHv8CZ15vm0t+C5FBdk0eDvtunwIQCU0+S6traNhw7BuXlJsEgpKW546D8fvd+IBA/Zec6fNhg61aTLVsstm412bfP5PjfOMuC/v1tBg50KCy0Oe+8KoqKljFo0DJ8vqrjWuAjGj2PaPR8otEhseVc3LmxajGMWkyzFsOowTDqYksNjmNi2+c1279lz07n1tWOjc36bSIoOU4GweA3CAavxOtdSVrarxOzzkciRTQ0zI9dqufMkurj9UyluiaPAlP7FJhQYOpJenpta2th2zY3QG3ZYrJzp0kw2Hbvitcb5KKL/sr48ZsZNGg355yzF58viGW5Ics0ndht02zr0agRu225AKSlEbtkjYXHc24sPJ1PNDoUwxhGbu5AKivrT6OuNl7vylhQ2gc0D0pfP25erDoCgRcJBP4f7vgxg1Dof9LQcCOOk3eK37/76enHa3eluiaPAlP7FJhQYOpJzrTaOg6UlxuUlBgcPGhSUmJw4IBJSYnJ4cNGi94ow7Dp1+8AAwfuZtCgpqVv34NEo1ZsoHsW9fWZNDRkUleXGXuchc/XyKBBexg0aBdpaXV4PA5paW6vWHq620Pm8RRSXz+VYHAakcgEIHACr6Aer3cTHs9avN7VmGZZ7HWlEwx+s42g1JJplpKW9ixe77uxr0ujsfHa2PimjFMta7dxph2v3YXqmjwKTO1TYEKBqSc5m2obCsGhQ+64qdpag+pqg9pad1xUba17qZiaGoOGBpv0dIOcHMjOhpyc+CSeTuJ+fb3Bxx9bbN5sUFZWzoABOxk40A1QgwbtprDwMzIzndhEoQ4+nxfTnEgkMp1weBq2XRhrlYNp7sfrXY3XuxaP52OaX5OwKShddVKfhLOsj0lPfwbL2hF7njRCoUsJBq/Atgd2YlW71tl0vHYl1TV5FJjap8CEAlNPotqevlAIduww+eQTk48+sti61cKy6hk7dhMXXPA+48f/nd69D2OaTeOzwuFCDGM4eXkf4/d/jtHsLKJt9yccnkYkMo1weCKQ3u73/sdsfL63CQRewjQ/S6wNh6cRDF5FJDKFZAx4TyYdr8mhuiaPAlP7FJhQYOpJVNvOFw7D3r0Whw6ls3lziF27IBQqZuzYDxk/fhUjR27CNJtqHYl42b17Ep99No3Dh2dgWYXk5UF+vsPAgTajRtktBr6fPAePZz1+/zK83lWJtbY9iMbGKwmFvsKph7KupeM1OVTX5FFgap8CEwpMPYlqmxzH19W24cABgz17TEpKGoD1WNY+Pv54DJ98MolwuP1LyWRkOEyaFGXq1ChTpkTJO40x3KZ5AL//VXy+NzCMBsAdSB4OX0Q4PIlIZEK3HiSu4zU5VNfkUWBqnwITCkw9iWqbHCda1/i0ChUV7vxU5eVG4oLK5eUG27ebVFe3/NTf0KE2U6e6AWrUKDtxceSTU4/f/xf8/mWY5oEWW6LR84hEimLL+H84yLyr6XhNDtU1eRSY2qfAhAJTT6LaJkdn1dW23fFRa9darF1rsXNnyz+k2dkOY8fajB4dZfRom+HDbfwndd1jG49nPV7vGjyejVjW3uO2G0Sjw4hEJmDbeThOAAjgOP7YbXzxAwaGUd9svqr6427rcJxMotGC2MWSC2IXST7xmdd1vCaH6po8CkztU2BCgaknUW2TI1l1rayEdess1q2zWL/eora2Ze+TZcGQIU0BavRom759nRaDyv8Rw6jE4/kIj2cjXu8mTLOk09reznfEtvtj2wWxINUfx0kH/M1CmS8W1PyYZjq5uekcO1ZFNNqIYQSBIIYRanYbAtITF3l2L/Ccgy4b0z79HUgeBab2KTChwNSTqLbJ0RV1jUZh+3aTrVtNtm2z2LbNpLKydTLKyXHo29ehd293yctzl/z8pse9ejltntozjM/xejdiWZ/GZj4PAm5QMYxG3JDSGLvv4DjpsSUTx8mI3c+IhaB0DKMa0zwQu1jyISB8Uq/ZMNzaRiJ2qxneO+K2KTcWoDJxLyljYxjR2P0o7gWg3XWO44+1PRPHyYrdNl/Sabo0jd3s+ezE87gzxkdizx0BIrHHkeO22bH7drO2RGPP5eA4HtzAZ+E4PsAbW+eJPSYWFsPt3rrfw4k9f8vFNB0CAR8NDQa27YuF1fitP1aLAO7lUi0cx8L9hKUVa4PZ7LE31qZ423w4jje23hvbp6kOhhGO1SPcrB5OrLYGTZ/kNHCc5o8hflkgcJq9NprtH69V85r5Ym04/ncl/vXx46B5O8zjFqONr2+bAlP7dPFdEekSlgVjxtiMGWMDERzHvXTMtm0m27aZfPqpxZ49JseOGRw7ZrBrV/vPlZXlMH68zcSJUSZOjFJY6PZKOU6f2KfovpKEV2BjGEexrIOY5iFM8wCmebhZGAs1C2VN69w3Qi+2HX8zbuqBir9Bu+GuCtOsjF0yxml2qZsD7bbobOX2QJp4vScfRHuueI9jPNye7At3Q1M4PIO6ukc7tWVnCwUmEUkJw4D+/R36948yZ04UCNPY6F78uPnFjisqjBaPq6oMamoMPvjA4oMP3PFE+fkO48dHYwHKPa3X+Uwcpw+RSB9gwgl9Rfy/9drak+m5s2PXAqzENKswjCoMo4amXgbzuB6TeC9CKBay4tcRjN+vjS3xXvR4D4Rx3HPGn8eT6A2KL+738zTb1tRD436d1awtBm5vR7y3KN47E46tc3vp3N4bX6vbpp6dlr0jbm+N21bLMsjM9NPYWI1tx3sQgzSF1lDscTjWIxfvgYnGHkebPY7E9o+3Nd7D1dYxZLSqj3s/3nMX7zlqHmjijw1a9/Q0f2x38P1PrneztXjvXBlNPVFyMhSYRKTbCARg2DCbYcPa3ycScQeWb95ssnGjxbZtFuXlBu+84+Gdd9w/aQMGuBcz7tPHPY3Xp48du3VP7WVkxHspuiMTx8nBcXKwdda5TR6PCWQQDifz1LyN20No0xQeu/LUUxQ3yB0fNJsHU/d+y3XxgHb86VZ3vTsNR7c9+Ls1BSYR6VE8nqZTe1dfHSEYhK1b3VnLN21yP5lXWmpSWtr+cwQCbpDKz29a4o/jt3l5Dh6P+8m/SMSd4NO9NYhESCyBAPj97qzogQCnOG3C6YlfYNk68Q/wSYdMTux6isliAWk4ThrAKZ16PHtOV3YNBSYR6dH8figqsikqsoEwtbXu4PIjR9x5oo5famoMGhsNDh40OHiw/ec1DHc52V4er9cNZH4/pKVBr16QmekjL88mL69pMHt+vvs4J+fkQlYoBPv3m+zcabJ7t8muXSZ797pPUFDg9qwNGmRz7rk2gwbZFBY6BFL5vi9yhlBgEpEzSmYmTJ4cPw3RWmMjiQk3W4aplmOnotG2/0O3LPB4HLxeN+iEQhAMGol9w2G3F6qmxg1cpaUQjcbHCLX9fFlZ7sWSs7IcsrKI3TpkZ7tzVzkO7N7thqTiYpNIpM2norjYpLgYms8VZRjQr59DYaFNWprba2aabk+dx9P0epqvj69r/rj56/b7wedzYr1rTT1sfr+Dz+eGxnh9uqtIBIJB9/X5fN35FK10FwpMInJWCQSgsNChsLD98xXxGc2jUQOv1w0J8YDRVghwnHhwgsZGg2DQvR8Ou6dV9u8Pc+QIiVnR48uxY24wq6pyB7OfqKwsJzbWy12GD3dnUC8uNigpMfnsM5PPPjP47DN35vWyMoOysq4/X2eabtDyet1AFg9Sfr9DTo675OYSu3VDY3y9bRs0NEB9vXvb0ODeNja6PYSmCbW1XiIRh2jUPS1p2/FTqAa27YbXxkb3axsb41/r3h4fOuM9gz6fGwZ9vqZgGP/ZHx8w44vX23J/9znc54sHzIwMh/R0d/xcRoZ7e6qB0rbd1xUP68Fg0/1IxL0fiRiJU8nxEB+JwKhRNiNGaHDcqVBgEhE5jmm6p9JO9KPbhhHvaXF7hOI8HptevWDcuGibg5MjEaisNKiuhpoa93RhdbXbOxV/XFPjvtENHuwGo2HDbPr1a3tyz379HKZObfl9qqrcTx4ePGgk3lDjASMSMZrddxfbNo57TOxr3PXN36TdN2qjxZt38165eIAJh49v7Ol15xiGG1yiUavTxunEQ0XsO3TOk3YgLa0pQHm9bvCO19wNgEbifjTqtjH+MzxV2dkOr7zSoB61U6DAJCKSIh4P9Onj0KcPnPy8OicmNxdyc23GjUvK07fgOE2D4+NLvGcjHG4KaXV1JObbqqpyQ2JVVdO6Y8cMPB43UKSluT016elNjzMyIC/PorExgmE4WJaTCFGW5QZe02w6bRgIuF/rDsx3nyMtze0JivfIBIMGodDxvTZGrLemqe3NX5sbYgzCYWJfayR6GoPBpvWNje5rrqszqKtz10NTr1l5+amnl+N7xuK9ePGer3jPXvzxpEm2wtIpUmASEZFOYRgkTkc1OT4Inn4wdOe38lNZGTntaQXc02XHtyu5Hy8Lh2kRoOrq3LBlWU4i9DUPgJblxAJg06m++P3uPE7sTKPAJCIi0oW83njP3/GXR5HuTNlUREREpAMKTCIiIiIdUGASERER6YACk4iIiEgHFJhEREREOqDAJCIiItKBlAcm27Z5+umnmTVrFhMmTOCmm26ipKSk3f0rKytZtGgRU6ZMYerUqfzoRz+ioaGhC1ssIiIiZ5uUB6Zf/OIXvPzyyzz88MP8/ve/x7Zt5s2bRygUanP/hQsXUlxczG9/+1ueeuop3nvvPR588MGubbSIiIicVVIamEKhEM8//zwLFy5k9uzZjBw5kieffJKysjLeeuutVvtv2rSJtWvX8tOf/pQxY8YwY8YMHnroIf74xz9y+PDhFLwCERERORukNDBt376duro6ZsyYkViXnZ3N6NGjWbduXav9169fT58+fRgyZEhi3dSpUzEMgw0bNnRJm0VEROTsk9JLo5SVlQEwYMCAFuv79u2b2Nbc4cOHW+3r8/nIzc2ltLT0tNri8XRedrQss8WtdB7VNjlU1+RQXZNDdU0e1bZ9KQ1M8cHavpZXasTv93Ps2LE29z9+3/j+wWDwlNthmga9emWc8te3Jzs7rdOfU1yqbXKorsmhuiaH6po8qm1rKQ1MgUAAcMcyxe8DBINB0tJa/7ACgUCbg8GDwSDp6emn3A7bdqiurj/lrz+eZZlkZ6dRXd1ANHp6V9KWllTb5FBdk0N1TQ7VNXlOtrbZ2WlnTW9USgNT/PTakSNHGDRoUGL9kSNHGDFiRKv9+/fvz4oVK1qsC4VCVFVV0bdv39NqSyTS+b900aidlOcV1TZZVNfkUF2TQ3VNHtW2tZTGwpEjR5KZmcmaNWsS66qrq9m2bRtTpkxptf+UKVMoKyujuLg4sW7t2rUATJo0KfkNFhERkbNSSnuYfD4f1157LU888QR5eXkUFBTw+OOP079/fy6++GKi0SgVFRVkZWURCAQYP348RUVF3HbbbTz44IPU19dz//3387WvfY1+/fql8qWIiIjIGSzlJx4XLlzI17/+de677z7mzp2LZVk899xzeL1eSktLmTlzJm+++SYAhmHwzDPPUFhYyPXXX8/3v/99LrzwQk1cKSIiIkllOI7jpLoRqeY4DrbduWWwLFODEZNEtU0O1TU5VNfkUF2T52Rqa5oGhmEkuUXdgwKTiIiISAdSfkpOREREpLtTYBIRERHpgAKTiIiISAcUmEREREQ6oMAkIiIi0gEFJhEREZEOKDCJiIiIdECBSURERKQDCkwiIiIiHVBgEhEREemAApOIiIhIBxSYRERERDqgwCQiIiLSAQWmTmbbNk8//TSzZs1iwoQJ3HTTTZSUlKS6WT3ar371K6677roW6z799FOuvfZaJkyYwJw5c3jhhRdS1Lqepaqqivvvv58LL7yQoqIi5s6dy/r16xPbV61axZVXXsn48eO55JJLeOONN1LY2p7j6NGj3HnnnUyfPp2JEydy8803s2fPnsR2Ha+nb9++fUycOJHly5cn1qmup+7w4cOMGDGi1RKvr2rbmgJTJ/vFL37Byy+/zMMPP8zvf/97bNtm3rx5hEKhVDetR/qP//gPfvazn7VYV1lZyXe+8x0GDRrEsmXLmD9/Pk888QTLli1LTSN7kNtvv51NmzaxZMkSli1bxqhRo7jxxhvZu3cve/bs4ZZbbmHWrFksX76cb3zjG9x1112sWrUq1c3u9ubPn09xcTFLly7llVdeIRAI8O1vf5uGhgYdr50gHA5zxx13UF9fn1inup6e7du34/f7ef/99/nggw8Sy6WXXqratsOT6gacSUKhEM8//zx33HEHs2fPBuDJJ59k1qxZvPXWW1x22WWpbWAPcvjwYR544AHWrFnD4MGDW2z7wx/+gNfr5aGHHsLj8TBkyJDEm9VVV12Vmgb3AMXFxXz44Ye8/PLLTJo0CYAf/vCHvP/++7z++uscPXqUESNGcNtttwEwZMgQtm3bxq9//WtmzJiRyqZ3a8eOHaOgoIBbbrmF4cOHA/C9732Pyy+/nF27drFq1Sodr6fp5z//OZmZmS3W6e/A6dm5cyeDBw+mb9++rbb97ne/U23boB6mTrR9+3bq6upavLlkZ2czevRo1q1bl8KW9Txbt27F6/Xypz/9ifHjx7fYtn79eqZOnYrH05T3p0+fzv79+ykvL+/qpvYYvXr1YunSpYwbNy6xzjAMDMOgurqa9evXtwpG06dPZ8OGDTiO09XN7TFycnJYvHhxIixVVFTw29/+lv79+zN06FAdr6dp3bp1/Od//ic/+clPWqxXXU/Pjh07GDJkSJvbVNu2KTB1orKyMgAGDBjQYn3fvn0T2+TEzJkzh5///OcMHDiw1baysjL69+/fYl38v6TS0tIuaV9PlJ2dzRe/+EV8Pl9i3V//+leKi4uZNWtWu3WNn1aSjv3whz9kxowZvPHGGzzyyCOkp6freD0N1dXV3HXXXdx3332t/q6qrqdn586dVFRUcM011/CFL3yBuXPnsnLlSkC1bY8CUydqaGgAaPGGBOD3+wkGg6lo0hmpsbGxzRoDqvNJ2LhxIz/4wQ+4+OKLmT17dpt1jT/WGLwTc/3117Ns2TIuu+wy5s+fz9atW3W8noYHH3yQiRMn8tWvfrXVNtX11EUiEfbu3cuxY8dYsGABS5cuZcKECdx8882sWrVKtW2HxjB1okAgALhvLvH74B5gaWlpqWrWGScQCLR6A4//Eqenp6eiST3OihUruOOOOygqKuKJJ54A3D+Ix9c1/ljH74kZOnQoAI888gibN2/mpZde0vF6il577TXWr1/P66+/3uZ21fXUeTwe1qxZg2VZifeqsWPHsmvXLp577jnVth3qYepE8S7jI0eOtFh/5MgR+vXrl4omnZH69+/fZo0B1fkEvPTSSyxYsICLLrqIZ599NvGf44ABA9qsa3p6OllZWaloao9QUVHBG2+8QSQSSawzTZOhQ4dy5MgRHa+naNmyZRw9epTZs2czceJEJk6cCMADDzzAvHnzVNfTlJGR0eIfe4Bhw4Zx+PBh1bYdCkydaOTIkWRmZrJmzZrEuurqarZt28aUKVNS2LIzy5QpU9iwYQPRaDSxbvXq1Zx33nn07t07hS3r/uJTXlxzzTUsWbKkRbf75MmTWbt2bYv9V69eTVFREaapPxXtKS8v5/bbb28x/UI4HGbbtm0MGTJEx+speuKJJ3jzzTd57bXXEgvAwoULeeSRR1TX07Br1y6KiopavFcBbNmyhaFDh6q27XGkUy1ZssSZOnWqs2LFCufTTz91brjhBufiiy92QqFQqpvWY919993Otddem3hcXl7uTJkyxbn77rudXbt2OcuWLXPGjRvnLF++PIWt7P727t3rjBkzxpk/f75z5MiRFkt1dbWzc+dOZ8yYMc7jjz/u7N6923nuueec0aNHO3//+99T3fRub968ec7FF1/srF271tmxY4dz++23O1OmTHEOHjyo47UTDR8+3Fm2bJnjOPo7cDqi0ahz1VVXOZdeeqmzbt06Z/fu3c6jjz7qjB071tmxY4dq2w7DcfR54c4UjUZZsmQJy5cvp7GxkSlTpnD//fdTWFiY6qb1WPfccw8HDx7kxRdfTKz7+OOPeeSRR9i2bRt9+vThhhtu4Nprr01hK7u/Z599lieffLLNbVdccQU/+clPWLlyJY8//jj79++nsLCQBQsWcOmll3ZxS3uempoaFi9ezIoVK6ipqWHy5Mncc889DBs2DNDx2llGjBjBj3/8Y6688kpAdT0d5eXlLF68mPfff5/q6mpGjx7NHXfcweTJkwHVti0KTCIiIiId0MAEERERkQ4oMImIiIh0QIFJREREpAMKTCIiIiIdUGASERER6YACk4iIiEgHFJhEREREOqCL74pIp7jnnnt49dVX292en5/Phx9+2IUtcic6vPXWW1mwYEGXfl8ROfMoMIlIp+nTpw/PPPNMm9u8Xm8Xt0ZEpPMoMIlIp/H5fEyYMCHVzRAR6XQKTCLSpa677joKCgoYPHgwL7zwAsFgkGnTpnHvvfdSUFCQ2O+TTz7hZz/7GVu2bCEcDjN16lQWLVqUuD4bwJEjR1i8eDErV66ksbGRMWPGsGjRIiZOnJjYp7a2lnvvvZe3336bcDjMrFmzuP/++8nPz+/S1y0iPZsGfYtIp4pEIm0uzS9b+c4777B8+XLuu+8+fvSjH/Hpp59y3XXX0dDQAMDq1auZO3cuAI8++ij//u//TmlpKd/61rfYs2cPAHV1dcydO5c1a9Zw55138swzz+D3+7nhhhvYv39/4nu98MILhMNhnnrqKRYtWsTf/vY3Hnrooa4riIicEdTDJCKd5uDBg4wZM6bNbXfddRc33ngjAA0NDSxfvpyBAwcCcP7553PFFVfw2muvMXfuXBYvXsy5557L0qVLsSwLgJkzZ/LP//zPPP300zz11FO8+uqrHDx4kFdffZVRo0YBUFRUxNe+9jXWrVvH4MGDARg3bhyPPfYYADNmzGDz5s289957ySyDiJyBFJhEpNP06dOHX/7yl21uGzBgQOJ+UVFRIiwBjB49moEDB7Ju3Touv/xyPvnkE2699dZEWALIzs7moosuSoSdDRs2UFhYmAhLAGlpafz1r39t8X0nTZrU4nFhYSHV1dWn/iJF5KykwCQincbn8zFu3LgO9+vXr1+rdb179+bYsWPU1NTgOE6bY4zy8/OpqakBoKqqit69e3f4vdLT01s8Nk2zxelBEZEToTFMItLlKisrW60rLy8nLy+PrKwsDMOgvLy81T6ff/45ubm5AGRlZVFRUdFqn40bNybGOYmIdBYFJhHpchs2bGgRmrZs2cKBAweYMWMG6enpjB07lj//+c9Eo9HEPjU1Nbz77ruJU2yTJ0+mpKSEXbt2JfYJBoMsWLCAV155petejIicFXRKTkQ6TSgU4qOPPmp3+4gRIwB30Pe8efP47ne/S11dHU8++STDhw/nsssuA2DRokXceOON3HzzzVx99dWEw2GWLl1KKBRi/vz5AFx55ZW8+OKLfPe732XhwoX06tUr8Ym4q6++OumvVUTOLgpMItJpPv/8c/7lX/6l3e2vvfYa4PYOTZ8+nXvvvReAOXPmcNddd+Hz+QD302y/+c1vePrpp7n99tvx+XxMnjyZn/70p4l5mDIzM3nppZd47LHHePjhh7FtmwkTJvDCCy+0GFAuItIZDEejH0WkC1133XUAvPjiiyluiYjIidMYJhEREZEOKDCJiIiIdECn5EREREQ6oB4mERERkQ4oMImIiIh0QIFJREREpAMKTCIiIiIdUGASERER6YACk4iIiEgHFJhEREREOqDAJCIiItIBBSYRERGRDvx/f1lQlr5SCSMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 8s 384ms/step - loss: 0.0557 - accuracy: 0.9837\n",
      "Test Loss: 0.055696744471788406, Test Accuracy: 0.9836552739143372\n",
      "22/22 [==============================] - 9s 384ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Model 3\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99       491\n",
      "           1       0.95      0.99      0.97       182\n",
      "\n",
      "    accuracy                           0.98       673\n",
      "   macro avg       0.97      0.99      0.98       673\n",
      "weighted avg       0.98      0.98      0.98       673\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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cDqdOnTrv7jFwDaxWs4KDfZWenqGcHMff3wAAChGvQSVTSIivLJb8BRK72AAAAAwIJAAAAAMCCQAAwIBAAgAAMCCQAAAADHgXGwDgpnbxVC8OORz2fN/G4TDpwgWLbLYs2e281f9GYLFYZTYX3nYfAgkAcFNyOp3KzDync+fOXFMcXXLihFkOB2/xv5H4+PgpICAk3+c6+isEEgDgppSefkqZmefk7e0rb+8yMpst1/SL1WIxsfXoBuF0OmWzZencudOSpMDAstd9nwQSAOCm43DYlZl5Xn5+QfLzCyzQfVitZk4SeQPx9PSSJJ07d1r+/sHXvbuNg7QBADcdu90uySkvL293j4JCdCmS7Pac674vAgkAcBO7/mNVcOMojGOPLmEXGwAABmazSWbzX/+ytVgKfxuDw8GHp98oCCQAAC5jNpsUFFSmSALo79jtDqWlZVxTJHXu/G+lpBx1XTaZTPLxKaOaNWupX79BiopqXBSjavLk8Tp69Ijmzl30t+smJCzUhg0f6p13PiiSWYqCyel03vSparc7dOrUeXePgWtw6ZO0T58+z0GSAK5ZdrZNJ08eVdmy4fLw8Mx13aXXl+mrdurwsbPFNlNEeX892f32a35d69z532rduq26du0hSXI6pfT0NC1cOE/ffrtdq1atVVhYWKHPe+7cOTkcdgUE/P1B7hkZGcrKylJwcHChz3G5v/q+SlJIiG++w5ctSAAAXMHhY2e1/88z7h4jX3x8fFS2bDnX5XLlymnMmGfUqdNd+uKLrXr44a6F/ph+fn75XrdMmTIqU6ZMoc9QlG6oQFq4cKG2bdumlStXXnWd06dPa9KkSfriiy9kMpnUsWNHjR07Vj4+PsU4KQAANzaLxSJJ8vT0cG1l+uabL3X69ClNmvSqoqIaKzFxhdate1enTp1QxYqV1a1bT7Vvf5frPg4fPqS5c2dq166dslisio5upscff1LBwSF5drElJq7UunXv6PjxVJUrd4s6drxXjz7aVyaTKc8utmPHUlxbuDIyzqtBgygNGTJSNWrcJuni7jtJCgwM0saNHykzM0O33x6tsWOfVblytxTL1++GeRfbqlWrNGvWrL9db8SIEUpOTtby5cs1e/Zsff755xo/fnyRzwcAQElx/HiqXnvtVfn4+Kh581hJ0rvvvqWRI5/UjBlzVLdufS1aFK9169Zq1KgxWrFijR56qIumT5+id999W5J09uxZDR3aXzabTbNnL9CsWfN05MhhPf/803keb9u2L7Ry5TKNGTNOb775Hw0aNEyvv56gTZs25Fk3I+O8Bg/uq9TUY5oyZYbmz18qLy9vDRvWP9exVJs3f6z09DOaN2+xpk+P0y+/7NGiRfFF9BXLy+1bkI4dO6YXX3xRSUlJqlKlyl+uu2vXLm3fvl3r169X9erVJUkTJ05Uv379NHr0aJUvX74YJgYA4MaycuUyrV79hqSL53iy2WyqUqWqJk6c4jr+qHnzOxQd3UySlJmZqTVrEjV+/GS1aHExoCpUiFBKylElJq7QAw88pC1bNikj47zGj39ZAQEBkqSnnnpemzd/LJvNluvxjxw5LE9PD4WF3aqwsDCFhYWpXLlQlS+f99injz/eoDNn0pSQ8IbrmKTx4yfp4Yc76d1339KQISMlSb6+fho79llZrVZVrlxFbdu219dff1kEX70rc3sg/fTTT/Lw8ND777+vefPm6c8//7zqut9++61uueUWVxxJUtOmTWUymbRz507dfffdxTEyAAA3lE6dHlTnzl0kSWazWQEBgXmOEYqIqOj6+8GDv8tmy9KECc/mOuP0pbjKyrqg33//TRUrVnLFkSTVqHGbazfY5dq3v1sfffS+unZ9QFWqVFN0dDO1bt32igeH79//mypWrJzrgG0vL2/VqVNX+/fvdy2rUCFCVuv/ZYqvr59ycq7/BJD55fZA+sc//qF//OMf+Vr32LFjCg8Pz7XM09NTQUFBOnr06FVuBQBA6ebvH5ArgK7Ey8vL9fdLpxGYOHGKKleukmddDw/PXHHyd4KCgrRsWaL+978ftWNHkpKSvtbbb7+pvn0Hqk+f/oa1r/zmeYfDIavVctkMHnnWKc433rs9kK5FZmamPD3zvm3Py8tLWVlZ13XfVusNczgW8uHS2zTdcZ4SACWfw3Fzn0G7cuUqslgsOnYsRXfc0dK1/O23V+vgwd81ZswzqlKlmj74YJ3OnTvn2hr1yy979cQTw7V06Ru57m/Tpg06e/asHnzwYTVoEKW+fQdq6tRJ2rJlU55Aql79Nm3Y8KFOnz6l4OAQSVJWVpb27t2jDh06Fsrzs1hM1/17vUQFkre3d579ntLFL+z1vH3QbDYpONj3ekaDmwQE8O5FANfuwgWLTpwwX/EXqbv/x6sgj282/30QXL5OUFCA7r+/s5YsmS9/fz81aNBQ3333rebPj1OvXn1ktZp199136/XXEzRp0gsaOHCI7PYczZjximrUqKFbbw2XyWSSyWT6/x/am634+NkKCPBTw4aNlZp6TN9//52iohrLajW7zkputZp11113aeXKZXrhhXEaPvxxeXh4KCFhkTIzM/Xgg51ltZpz3ffl81+6j6txOEwym80KDCwjb+/r+5y9EhVIYWFh2rx5c65lNptNaWlpCg0NLfD9OhxOpadnXO94eZhMJvn7e7v9H1txsNsdOnv2QrFt/rRYzAoI8FF6eqbsdk4UCeDa2GxZcjgcstudVz0pY0R5/2Kd6dLj2e2Oaz4BrsNx9edxtXWGDRulwMAgLVo0XydOHFdoaHn17TtQ3br1Uk6OQ1arl2bMmKM5c2aqf//e8vb2VkxMrIYNe1w5OQ45nU45nRfv8+6779Xp06eVkLBYqanH5O/vr9at22rw4BHKyXG4dunl5Djk7e2rOXMWau7cWRo2bJAkqUGDhpo/P0GhoeF57vvy+S/dx9XY7U45HA6dOZOhzEx7nusDAnzy/Tv5hjqT9tNPP60///zzqudB+v777/XII49o06ZNqly5siRp27Zt6t+/vz777LMCv4utqM6k7a6zsRa3gp799XpwJm0Al8vPZ6ddzmbLUmrqkSuecbmkfdQI/s9NcyZtu92uU6dOyd/fX97e3mrYsKEaN26sUaNGafz48crIyNALL7ygTp063dBv8S9JZ2MFgJLGbDYpOMhHZovl71f+//5qF5sknT174ZqCq7A4HM6/jD2nU2w1LyY3dCAdPXpUbdu21SuvvKIHHnhAJpNJc+fO1YQJE/Too4/Ky8tLHTp00Lhx49w9Km4S1/p/qSUVnyiOksRsNslssSh13SzZTh7O121yvPxlv62dsi0mqYQcBmGyesgj6MbdGFDa3FCBNGXKlFyXIyIi9Msvv+RaVrZsWcXFxRXnWICk/7/ZPdhHFnP+/y+1pLI77Eo7nUkkoUSxnTwsW8qBfK1r9y0rZ7UcOXNsct7k72jDld1QgQTcyMxmkyxmi+K+Wao/01PcPU6RqRAQphHNH5PZbCKQANy0CCTgGv2ZnqIDpw+5ewwAQBEqGTteAQAAihFbkFBoivMtse44k/bNcD4rAMBFBBKuW5C/l5wOh1vOas2ZtAEARYFAwnXz8/GQyWy+prfYlkRlqjVSSJvu7h4DAFAMCCQUmmt5i21J5FG2grtHAFBMzFYPma/h0+wLiyMnR46c7GJ/XORFIAEAcBmz1UPlqt8ms8UNgWTP0Yn9v/5lJJlMF8+ofblhwwbo+++/y7XMw8NDwcEhio1tpSFDRl73h7fmx3fffasRIwbp7bffV3j4rRo2bIDCw2/Vs8+OL/LHLmwEEgAAlzFbrTJbrMV+2IBn2QiFdnpcZqv1ioFkMlvk1JXfMGIymdS27T81evQY17KMjAwlJX2jWbOmS5LGji36T524/A00VqtZJpNJJtOVP87lr9wIH6lCIAEAcAU33GEDZrNMko6dO6FsR+6AyrLbZDc7lOnxf8tNgR5q3r6lWu7eqU2fbFC3Qb2LfMTjGSclSSnnUuVINynLblNGdoYOpx/N9314mD1U3q9cUY2YbwQSAAAlSLYjW1mGLUxOp0N2pyPPckmyWC0yWyzKyslWTna23l6ZqC+3fq6M8xmKqFxJnXt2VYPGjVzr79/3q9YsX6nf9u6Tl7eXolvEqHu/PvLy9tL5s+eUuPR1/fDtTqWnnZGvn58aN2+qXgP7ycvbS9n2HEmSzX5xxr+a66pukDK5QcYAAACFyW6368edu7Tt08/0j7v+JUla8Fqcjhw6rCFjRimkbFl9l7RD08dP1qjnnlajpk2UmnJMk59+XtEtmmvCa1OVcT5DC2bM0rL4LA0aPVILZsbp9ImTevzZpxUYHKh9P+/VoplzFFG5ou7qdK+bn3HhIpAAACgFvtr6hbZv+9p12WazqVzoLer44P2675HOSjlyVF9//l9NnvOaqlSvJkm6+4H79MeBg/po7X/UqGkTfbphk/z8/TVg1HBZLBc/mLvfyGH6dc9eSVL9Rg0VWa+uKlWtIkm6pXx5ffz+Rzp0MLl4n2wxIJAAACgFGjeLVpfHHpWcTu3f96tWLFyielENdN8jnWWxWJS8/3dJ0sQxz+S6nT0nR2X8fCVJhw4mq+pt1V1xJEl1G9ZX3Yb1JUntOt6l777Zri82f6pjR47qcPIfOn4sVbdWLH2nQSGQAAAoBbzL+Cjs1nBJUliFWxUUEqJXnn1RFotFfYYOksNx8dwAL0x7Wd4+uT+FwGy++C4zq9Wiq3E4HJo+fpIOJ/+hFq1bqXmrWFWpXk0Jc+KL6Bm5Fx8uBQBAKVS3YX3dff+92vzRRv3w7XeqWKWSJCnt1CmF3Rru+vPFJ1v0xSdbJEkVKlXUwd/2y2G3u+5nx1ffaGTv/vpt7z798O13GjFurLr06aU72typ8reG69iRFMl5xRFKNAIJAIBSqnPPbgq79VYtnTtf5UJvUaOmTbR07gJ9l7RdqUdT9MHb7+r9t9YqNDxMkvTPe+7WubNnlTB3gf7845D27P5JbyYsV92oBrqlfKgsFou++e82paYc0+/7ftOcV6Yp7fRpZWeXvrN/s4sNAIAr8CwbUeIfz9PTU/1GDtHkp5/XW6+v0vCnx+itFW8oYc58nT97TqHhYeo/cphatfuHJCm4bIienjReby59Xc8OHy1ffz81bxWrRx7tIU8vLw0cPUJrV63W5g83KDA4SI2aRuuuTvfqu6TthT67uxFIAFCKmc0mmc0md49RpK50Zunr4cjJkcOeo9BOjxfq/ebrse05cuTkXPPtnps6+arX1a5fT2989B/X5Z4D+qrngL5XXf+22pF6YdorV7zujjZ36o42d+ZZ3mPAY5KkOg3qa9X6dfma60ZHIAFAKWU2mxQUVKbQA6K0c+Rk68T+X/mw2pscgQQApZTZbJLFYtb0VTt1+NhZd49TZBpHhqrX3XUK9T4dOdmEyk2OQAKAUu7wsbPa/+cZd49RZCJC/dw9AkohtrsCAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAa8zR8AAAOz1aNEnShy0lPPas/un6543d0P3Kfu/fq4Lv/y0x699NSzeuPDd//2fp1Opz5+/yN9/slmHT18RFarVZWqVtG/7rtHzWJbXPOcJQmBBADAZcxWD5WtfpssluL/FWm35+jk/l8LFEnNWt6hXgP75Vnu5e3t+vsvP+3RaxNfltPhyNd9rn3jTW3d+Il6DuyrarfVkM1m0zf//VJzXpkm2+gRatm2zTXPWVIQSAAAXMZstcpisSrum6X6Mz2l2B63QkCYRjR/TGartUCB5OnlqaCQ4CteZ7fb9WbC69r04XpVrFJJ587m78zqn3y0QZ0eeUjNW8W6lnWuXElHD/+pjes+IJAAALjZ/JmeogOnD7l7jEJxITNTe//3k5566UWdSE3Voplz8nU7s8msn374UW3v/pc8vbxcy3sN6idbVlau+1+zfKW2b/tamZmZqlqjunr076Oqt9WQJP26Z6/een2VDvy2XxarRY2bRqtbv97yDwiQJI3s3V9NY1vo+x07dfZMuqZOmaH69aOUmLhC69a9q1OnTqhixcrq1q2n2re/qxC/MldHIAEAUMr5+vlpUtwMSdLnn2zJ9+3uffhBvbF4qYZ076N6UQ0UWa+u6jSsr0pVq+RaL+6VaUr584gGjh6h0PAwvbf6bb3y7HjNTFiglCNHNOmp5/SPu9qr95ABSjudpuXxCzXlufF6aeY0mS0WSdInH6zXk+OfU2BgoOrVq69Fi+K1efPHGjVqrCpXrqLvv/9O06dP0blz5/TAAw8V2tfmaggkAABKga+2fqHt277OtaxW3Tp66qUXCnyfd91/r26tGKHN6zdq967vteOrbyRJ1WrepoGjRyiiUkUdOfynfvj2Oz016UU1aNxIktRn2CD5+vnpbHq6Pnr3PVWqWkWPDh4gSapQqaKGPfWEnhk2Sj9+t0tR0U0kSQ2jb1e9Rg3lZfWQ3W7XmjWJGj9+slq0uLh7r0KFCKWkHFVi4goCCQAA5E/jZtHq8tijuZZ5enrm67ZTn5+oX3762XW57/DBuqPNnZKkhk0aq2GTxsrJydHv+37Td0k79MmH6/Xq8xP02pL5OnQwWZJUI7JWrsftMeAxSdKhg8lq0Dgq1+NVrlZVZXzL6NDBP1yBFHZruOv6Awd+l82WpQkTnpXZ/H9nJLLb7bLZbMrKuiAvL28VJQIJAIBSwLuMT67IuBb9Rw6VzWZzXQ4MCtIfBw7qkw83qOfAvvL09JTValXNOpGqWSdSterW1vTxk/THgWRZ/v8usmvldCrXbS+POcf/f5fdxIlTVLlylTy39fDIX/hdD04UCQDATS6kXFmF3Rru+uNTxkeS9OmGj7Xz6+151i/j6yuTyaSAoEBVqBghSfp936+u6+12u0b27q+kbV+pYpXK+uWnPblun/z7AWVmZKhCpYpXnKdKlSqyWCw6dixFEREVXX++/vpLvfnmylxblYoKW5AAAEAelapW0R1t7tSSuLk6kZqqRs2iZbVYlHzgoN5+/Q21bNtG5UJvkSRFt2iu5fGL1GfoIIWUDdH7b61Vdna26tSvp7LlymnimHF6ff4itet4l86kpen1+YtVpXo11Y1qcMXH9vPzV6dOD2rx4vny9fVVvXoNtGvXTs2fH6cePXoXy/MnkAAAuIIKAWGl+vHyY+DoEfrkg/Xa9ulnWrf6bdlzclT+1nC1/tc/1aHTv13rDRg1XIkJyxX3yqvKyc5W9Vo19fSk8fIPDJB/YIDGvvSC3l6RqGeHj5ZPmTK6PaapuvTpJetfnK18+PDRCgoK1pIlC3TixHGFhpZX374D1a1br+J46gQSAACXc+TkyG7P0YjmjxX7Y9vtOXLk5Fzz7Z6bOjnf6975z7a6859t87WuxWJRh07/zhVDV1LG11f9RgxVvxFDr3h9vaiGqhfV8Kq3n718cZ5lVqtVffr0V58+/fM1a2EjkAAAuIwjJ1sn9/9aoj6LDYWPQAIAwMCRk02o3OR4FxsAAIABgQQAAGBAIAEAABgQSACAm5DT3QOgCDidhfd9JZAAADcdU/YFyWGXvRB/ocL9HA67JMlsLtjHn1yOQAIA3HTMtgyZ0lN0we4o1K0OcK8LFzJkNlsK5aNIeJs/AOCm5PXHTl0IKK8zfsHytphlMZncPdJfMtvtctqy5MyxS3aHu8cpMk7ZZbNlyW7Pf7g6nU7ZbBd04cJ5BQSUlakQvpcEEgDgpuRx8qD0v/XKqnS7sgLCJLNZ0o0bSabMTFmyspV+4Yxy7Nd+tu2SwmqxynLBLofjWiPQJB8fP/n4+BbOHIVyLwAAlEAeJw/K4+RBOTzLyGn1lm7grUg+NRqrXLvemr5toQ6nH3X3OEUmIiBcT8YO1JkzGde0FclisRTKsUeXEEgAgJue2ZYh2TLcPcZf8sjJlLe3t846zutU9hl3j1NkAh0B8vb2VmamXTk57tuVyEHaAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGDg9kByOByKi4tTy5YtFRUVpf79++vQoUNXXf/kyZN64okn1Lx5czVr1kyjRo3SsWPHinFiAABQ2rk9kOLj45WYmKiXXnpJq1evlsPhUL9+/WSz2a64/uOPP64jR45o2bJlWrZsmY4cOaKhQ4cW89QAAKA0c2sg2Ww2LV26VCNGjFDr1q0VGRmpmTNnKiUlRZs2bcqzfnp6urZv367+/furdu3aqlOnjgYMGKDdu3crLS2t+J8AAAAoldwaSHv37tX58+cVExPjWhYQEKA6depox44dedb39vaWr6+v1q1bp3PnzuncuXN67733VLVqVQUEBBTn6AAAoBSzuvPBU1JSJEnh4eG5loeGhrquu5ynp6emTJmiF154QU2aNJHJZFJoaKjeeOMNmc3X13pWa+G3osXi9j2YQIHx81vy8T1ESebun1+3BlJmZqaki+FzOS8vL505cybP+k6nU3v27FGjRo3Ur18/2e12zZw5U0OGDNGbb74pPz+/As1hNpsUHOxboNsCpVVAgI+7RwBwE3P3a5BbA8nb21vSxWORLv1dkrKysuTjk/cLs2HDBr3xxhvaunWrK4YWLFigNm3a6J133lHv3r0LNIfD4VR6ekaBbvtXLBaz27/BQEGlp2fKbne4ewxcB16DUJIVxWtQQIBPvrdMuTWQLu1aS01NVaVKlVzLU1NTVatWrTzrf/vtt6patWquLUWBgYGqWrWqkpOTr2uWnBx+EQCXs9sd/LsA4Dbufg1y6w6+yMhI+fn5KSkpybUsPT1dP//8s6Kjo/OsHxYWpuTkZGVlZbmWZWRk6PDhw6pSpUpxjAwAAG4Cbg0kT09P9ejRQ9OnT9eWLVu0d+9ejRo1SmFhYWrfvr3sdruOHz+uCxcuSJI6deok6eK5kPbu3au9e/dq9OjR8vLy0gMPPODGZwIAAEoTt7/FYcSIEercubOee+45de3aVRaLRQkJCfLw8NDRo0cVGxur9evXS7r47rbExEQ5nU49+uij6tOnjzw8PJSYmCh/f383PxMAAFBauPUYJEmyWCwaM2aMxowZk+e6iIgI/fLLL7mWVa9eXQsWLCiu8QAAwE3I7VuQAAAAbjQEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAICB2wPJ4XAoLi5OLVu2VFRUlPr3769Dhw5ddf3s7GzNmDHDtX6PHj20Z8+eYpwYAACUdm4PpPj4eCUmJuqll17S6tWr5XA41K9fP9lstiuuP378eL377rt6+eWXtXbtWoWEhKh///46e/ZsMU8OAABKK7cGks1m09KlSzVixAi1bt1akZGRmjlzplJSUrRp06Y86x86dEhr167V5MmT1bJlS1WvXl2TJk2Sp6en/ve//7nhGQAAgNLIrYG0d+9enT9/XjExMa5lAQEBqlOnjnbs2JFn/S+//FL+/v5q1apVrvU//fTTXPcBAABwPdwaSCkpKZKk8PDwXMtDQ0Nd113uwIEDqlixojZt2qQHHnhAd9xxh/r376/9+/cXy7wAAODmYHXng2dmZkqSPD09cy338vLSmTNn8qx/7tw5JScnKz4+XmPHjlVAQIDmz5+vbt26af369SpbtmyBZ7FaC78VLRa3H+IFFBg/vyUf30OUZO7++XVrIHl7e0u6eCzSpb9LUlZWlnx8fPKsb7Vade7cOc2cOVPVq1eXJM2cOVN33nmn/vOf/6hfv34FmsNsNik42LdAtwVKq4CAvP8GAaC4uPs1yK2BdGnXWmpqqipVquRanpqaqlq1auVZPywsTFar1RVH0sXIqlixog4fPlzgORwOp9LTMwp8+6uxWMxu/wYDBZWenim73eHuMXAdeA1CSVYUr0EBAT753jLl1kCKjIyUn5+fkpKSXIGUnp6un3/+WT169MizfnR0tHJycrR7927Vr19fknThwgUdOnRIHTt2vK5ZcnL4RQBczm538O8CgNu4+zXIrYHk6empHj16aPr06QoJCVGFChU0bdo0hYWFqX379rLb7Tp16pT8/f3l7e2tJk2aqEWLFnrqqac0ceJEBQUFKS4uThaLRffdd587nwoAAChF3H4E34gRI9S5c2c999xz6tq1qywWixISEuTh4aGjR48qNjZW69evd60/Z84cNW3aVMOGDVPnzp117tw5rVixQiEhIW58FgAAoDRx6xYkSbJYLBozZozGjBmT57qIiAj98ssvuZb5+flp/PjxGj9+fDFNCAAAbjZu34IEAABwoyGQAAAADAgkAAAAAwIJAADAgEACAAAwKLRAysnJUVpaWmHdHQAAgNsUKJBycnI0d+5cffDBB5KkpKQk3XHHHYqJidGjjz56xQ+aBQAAKCkKFEhxcXGaP3++0tPTJUmTJk1SUFCQxo0bpz/++EMzZswo1CEBAACKU4EC6aOPPtLo0aPVvXt37d+/X7/++qsGDx6sXr16adSoUfr0008Le04AAIBiU6BASk1NVcOGDSVJn332mcxms1q1aiVJCgsL09mzZwtvQgAAgGJWoEAKDQ3V4cOHJUmffvqpateu7fostF27diksLKzwJgQAAChmBQqke+65R6+88or69u2rnTt36sEHH5QkTZ48WXPmzNG///3vQh0SAACgOBXow2off/xxlSlTRjt27NATTzyhbt26SZJ2796txx57TEOGDCnUIQEAAIpTgQLJZDJp4MCBGjhwYK7lq1evLpShAAAA3KlAgSRJNptN77zzjr766isdP35cL7/8srZv3666deuqQYMGhTkjAABAsSrQMUinTp3Sgw8+qMmTJys5OVk//vijLly4oK1bt6pnz57atWtXYc8JAABQbAoUSK+++qrOnz+v9evX6z//+Y+cTqckac6cOapfv77i4uIKdUgAAIDiVKBA2rp1q0aOHKnKlSvLZDK5lnt5eemxxx7TTz/9VGgDAgAAFLcCBVJWVpaCgoKueJ3FYlF2dvb1zAQAAOBWBQqk+vXrKzEx8YrXffDBB6pXr951DQUAAOBOBXoX28iRI9W7d2/dd999uvPOO2UymfThhx9qzpw52rZtm5YsWVLYcwIAABSbAm1BatKkiZYtWyYfHx8tWbJETqdTy5cv1/Hjx7Vw4UI1b968sOcEAAAoNgXagvT111+rUaNGWr16tS5cuKAzZ87Iz89Pvr6+hT0fAABAsSvQFqThw4dr06ZNkiRvb2+VL1+eOAIAAKVGgQIpICBA3t7ehT0LAADADaFAu9gGDhyoSZMm6cCBA4qMjFSZMmXyrBMdHX3dwwEAALhDgQLpxRdflCTNnDlTknKdLNLpdMpkMmnPnj2FMB4AAEDxK1AgrVixorDnAAAAuGEUKJCaNm1a2HMAAADcMAoUSJJ04MABxcXFafv27UpPT1dwcLCaNGmioUOHqnr16oU5IwAAQLEqUCD99ttv6tKliywWi/7xj3+oXLlyOn78uLZu3arPPvtMb7/9NpEEAABKrAIF0vTp0xUREaGVK1fK39/ftfzs2bN69NFHNXPmTM2dO7fQhgQAAChOBToP0o4dOzRo0KBccSRJ/v7+GjBggHbs2FEowwEAALhDgQLJarXKy8vritd5enrKZrNd11AAAADuVKBAql+/vhITE+V0OnMtdzqdWrVqlerVq1cowwEAALhDgY5BGjlypLp27ap7771XHTp00C233KLjx49r48aNOnDggJYtW1bYcwIAABSbAgVS/fr1tWTJEs2YMUNz5851nT27Xr16Wrx4MR8zAgAASrQCnwepefPmWr16tWw2m9LT0xUQEKCcnJw8B24DAACUNAU6Bik7O1svvviiHn74Yfn4+Kh8+fLatWuXYmJiNHXqVDkcjsKeEwAAoNgUKJDmzJmj999/Xx07dnQtq1Onjp588km99dZbWrJkSaENCAAAUNwKtIvtgw8+0FNPPaUuXbq4lgUFBal3796yWq1asWKFBgwYUGhDAgAAFKcCbUE6ffq0KlaseMXrqlWrppSUlOsaCgAAwJ0KFEjVqlXTxx9/fMXrPv30U1WuXPm6hgIAAHCnAu1i69Wrl55++mmlpaWpXbt2Klu2rE6dOqWtW7dqw4YNeuWVVwp7TgAAgGJToEDq1KmTzp8/r/j4eG3atMm1PDg4WM8//7w6depUWPMBAAAUuwKfB6l79+7q1q2bDhw4oLS0NDkcDt12220KDAwszPkAAACK3TUdg/Tjjz9q0KBBWrdunSTJZDLpq6++Up8+fdSzZ0/deeedSkhIKIo5AQAAik2+A2nv3r3q2bOn9uzZozJlykiSdu/ercmTJ6tixYqaM2eOhgwZopkzZ2rz5s1FNjAAAEBRy/cutoULFyoyMlLLly+Xj4+PJGnFihWSpOnTpysyMlKSdOLECa1cuVLt2rUrgnEBAACKXr63IO3YsUM9e/Z0xZEkbdu2TRUrVnTFkSTFxsbq559/LtwpAQAAilG+AyktLU1hYWGuy/v379fp06fVrFmzXOv5+PjIZrMV3oQAAADFLN+BFBQUpJMnT7ouf/PNNzKZTIqJicm13v79+xUSElJ4EwIAABSzfAdS06ZN9dZbb8npdConJ0dr166Vl5eXWrZs6VrHZrNp1apVaty4cZEMCwAAUBzyfZD24MGD9cgjj6hdu3ZyOp06cuSIhg4dKn9/f0nS2rVrtWrVKh04cECvvvpqkQ0MAABQ1PIdSLfddpveeustLV26VCdPnlT//v3VtWtX1/WzZs2S1WrVvHnzVLt27SIZFgAAoDhc05m0a9SooZdffvmK173zzju65ZZbZDYX6PNvAQAAbhgF/qgRo/LlyxfWXQEAALgVm3sAAAAMCCQAAAADAgkAAMCAQAIAADAgkAAAAAwIJAAAAAMCCQAAwIBAAgAAMCCQAAAADAgkAAAAAwIJAADAgEACAAAwIJAAAAAMCCQAAAADAgkAAMCAQAIAADAgkAAAAAwIJAAAAAMCCQAAwIBAAgAAMCCQAAAADAgkAAAAAwIJAADAgEACAAAwIJAAAAAM3B5IDodDcXFxatmypaKiotS/f38dOnQoX7d9//33VatWLR0+fLiIpwQAADcTtwdSfHy8EhMT9dJLL2n16tVyOBzq16+fbDbbX97uzz//1MSJE4tpSgAAcDNxayDZbDYtXbpUI0aMUOvWrRUZGamZM2cqJSVFmzZtuurtHA6HxowZo7p16xbjtAAA4Gbh1kDau3evzp8/r5iYGNeygIAA1alTRzt27Ljq7RYsWKDs7GwNHDiwOMYEAAA3Gas7HzwlJUWSFB4enmt5aGio6zqjH3/8UUuXLtU777yjY8eOFfmMAADg5uPWQMrMzJQkeXp65lru5eWlM2fO5Fk/IyNDTz75pJ588klVqVKlUAPJai38jWkWi9sP8QIKjJ/fko/vIUoyd//8ujWQvL29JV08FunS3yUpKytLPj4+edafNGmSqlatqi5duhTqHGazScHBvoV6n0BJFxCQ998gABQXd78GuTWQLu1aS01NVaVKlVzLU1NTVatWrTzrr127Vp6enmrUqJEkyW63S5LuueceDRo0SIMGDSrQHA6HU+npGQW67V+xWMxu/wYDBZWenim73eHuMXAdeA1CSVYUr0EBAT753jLl1kCKjIyUn5+fkpKSXIGUnp6un3/+WT169MizvvGdbT/88IPGjBmjRYsWqWbNmtc1S04OvwiAy9ntDv5dAHAbd78GuTWQPD091aNHD02fPl0hISGqUKGCpk2bprCwMLVv3152u12nTp2Sv7+/vL29Vbly5Vy3v3Qg96233qqgoCA3PAMAAFAauf0IvhEjRqhz58567rnn1LVrV1ksFiUkJMjDw0NHjx5VbGys1q9f7+4xAQDATcStW5AkyWKxaMyYMRozZkye6yIiIvTLL79c9bbNmjX7y+sBAAAKwu1bkAAAAG40BBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABg4PZAcjgciouLU8uWLRUVFaX+/fvr0KFDV13/119/1YABA9SsWTPFxMRoxIgROnLkSDFODAAASju3B1J8fLwSExP10ksvafXq1XI4HOrXr59sNluedU+fPq0+ffrI29tbK1eu1OLFi3Xq1Cn169dPWVlZbpgeAACURm4NJJvNpqVLl2rEiBFq3bq1IiMjNXPmTKWkpGjTpk151t+8ebMyMjL06quvqmbNmqpXr56mTZum/fv367vvvnPDMwAAAKWRWwNp7969On/+vGJiYlzLAgICVKdOHe3YsSPP+jExMYqPj5e3t7drmdl88Smkp6cX/cAAAOCmYHXng6ekpEiSwsPDcy0PDQ11XXe5iIgIRURE5Fq2aNEieXt7Kzo6+rpmsVoLvxUtFrfvwQQKjJ/fko/vIUoyd//8ujWQMjMzJUmenp65lnt5eenMmTN/e/uVK1fqjTfe0HPPPaeQkJACz2E2mxQc7Fvg2wOlUUCAj7tHAHATc/drkFsD6dKuMpvNlmu3WVZWlnx8rv6FcTqdmj17tubPn6/BgwerZ8+e1zWHw+FUenrGdd3HlVgsZrd/g4GCSk/PlN3ucPcYuA68BqEkK4rXoIAAn3xvmXJrIF3atZaamqpKlSq5lqempqpWrVpXvE12drbGjRunDz/8UOPGjVPv3r0LZZacHH4RAJez2x38uwDgNu5+DXLrDr7IyEj5+fkpKSnJtSw9PV0///zzVY8pGjt2rDZu3KgZM2YUWhwBAABczq1bkDw9PdWjRw9Nnz5dISEhqlChgqZNm6awsDC1b99edrtdp06dkr+/v7y9vfXuu+9q/fr1Gjt2rJo2barjx4+77uvSOgAAANfL7W9xGDFihDp37qznnntOXbt2lcViUUJCgjw8PHT06FHFxsZq/fr1kqQPP/xQkvTqq68qNjY2159L6wAAAFwvt25BkiSLxaIxY8ZozJgxea6LiIjQL7/84rq8dOnS4hwNAADcpNy+BQkAAOBGQyABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQQAAGBAIAEAABgQSAAAAAYEEgAAgAGBBAAAYEAgAQAAGLg9kBwOh+Li4tSyZUtFRUWpf//+OnTo0FXXP336tJ544glFR0eradOmmjBhgjIzM4txYgAAUNq5PZDi4+OVmJiol156SatXr5bD4VC/fv1ks9muuP6IESOUnJys5cuXa/bs2fr88881fvz44h0aAACUam4NJJvNpqVLl2rEiBFq3bq1IiMjNXPmTKWkpGjTpk151t+1a5e2b9+uqVOnqm7duoqJidHEiRP13nvv6dixY254BgAAoDRyayDt3btX58+fV0xMjGtZQECA6tSpox07duRZ/9tvv9Utt9yi6tWru5Y1bdpUJpNJO3fuLJaZAQBA6Wd154OnpKRIksLDw3MtDw0NdV13uWPHjuVZ19PTU0FBQTp69GiB5zCbTQoJ8S3w7a/GZLr43/H9Y5RjdxT6/d8ovDwtkqTwLs/Lac9x8zRFx+ThKUl6ptVw5ThK7/O0mi++LAQG+sjpdPMwuC68BpUuvAZdP7PZlP85Cvehr82lg6s9PT1zLffy8tKZM2euuL5x3UvrZ2VlFXgOk8kkiyX/X7RrFeTvVWT3fSOx+Aa6e4RiEejt7+4RioXZ7PZDFFFIeA0qXXgNKqbHd+eDe3t7S1KeA7KzsrLk4+NzxfWvdPB2VlaWypQpUzRDAgCAm45bA+nS7rLU1NRcy1NTU1W+fPk864eFheVZ12azKS0tTaGhoUU3KAAAuKm4NZAiIyPl5+enpKQk17L09HT9/PPPio6OzrN+dHS0UlJSlJyc7Fq2fft2SdLtt99e9AMDAICbgluPQfL09FSPHj00ffp0hYSEqEKFCpo2bZrCwsLUvn172e12nTp1Sv7+/vL29lbDhg3VuHFjjRo1SuPHj1dGRoZeeOEFderU6YpbnAAAAArC5HS6930qdrtdr732mt59911duHBB0dHReuGFFxQREaHDhw+rbdu2euWVV/TAAw9Ikk6ePKkJEybov//9r7y8vNShQweNGzdOXl43x0GIAACg6Lk9kAAAAG40vI8XAADAgEACAAAwIJAAAAAMCCQAAAADAgkAAMCAQAIAADAgkAAAAAwIJAAAAAO3ftQIkF9fffWVMjIy5HA41KJFC/n5+bl7JABAKUYg4YY3depUvf/++woKClJycrIaNmyoe+65R127dnX3aABuIu+9956OHz+ufv36uXsUFAN2seGG9tlnn2nDhg1asGCB1qxZo08//VTBwcF66623NG3aNHePB+Am4HQ65XQ6lZSUpGXLlmnt2rXuHgnFgEDCDS0lJUXBwcGqVauW/Pz8FBoaqokTJyo6Olr//e9/NWvWLHePCKCUczgcMplM8vHxUWZmpl5//XWtWrXK3WOhiLGLDTckp9Mpk8kkDw8P2Ww2paenq1y5csrJyVFISIiGDh0qh8Oh//73v6pWrZruvfded48MoJSyWCySpIMHD6p+/fq65ZZbtHr1aplMJnXr1s3N06GosAUJNySTySRJio6O1qFDh7Ry5UpJktVqVU5OjgIDAzV48GD5+fnp/fffd+eoAEo5p9OpU6dO6fz58+rbt68ef/xx1axZU2+++aYSExPdPR6KCIGEG1qlSpX0zDPPaOHChXrzzTcl/V8klS1bVuPGjdPXX3+tn376yc2TAiitTCaTAgICdO+996pChQqKiIjQ4MGDVatWLSKpFGMXG254999/v/78809NmDBBDodD3bt3l9X6fz+6FStWVEBAgBsnBFDaWa1WPfzww7JarXI4HKpRo4YGDRqkBQsW6M0335TZbFaXLl3cPSYKEYGEG56Xl5cGDRoks9msSZMm6c8//1SnTp0UGBiojRs3SpLKlCnj5ikBlHaX/sfs0iEAlyJp8eLFio+Pl9VqVefOnd05IgqRyel0Ot09BJAfNptNH3/8sV555RV5eHjI09NT2dnZio+PV506ddw9HoCb1N69e5WYmKj+/furYsWK7h4HhYRAQolz7Ngx/fHHH8rJyVG1atVUvnx5d48E4CZns9nk6enp7jFQiAgkAAAAA97FBgAAYEAgAQAAGBBIAAAABgQSAACAAYEEAABgQCABAAAYEEgAAAAGBBIAAIABgQSgRNq9e7fGjBmj1q1bq0GDBmrXrp2ef/55HTp0yLVOrVq1NGfOHDdOCaCkIpAAlDirVq1Sly5ddPLkST3xxBNavHixBgwYoO3bt6tz587au3evu0cEUMJZ3T0AAFyLnTt3avLkyerevbueffZZ1/JmzZqpXbt26tSpk5555hm9++67bpwSQEnHFiQAJUpCQoL8/f01evToPNeFhITo6aefVtu2bZWRkZHn+r1792rYsGFq3ry56tatq5YtW2rSpEm6cOGCa50vv/xSDz/8sBo1aqTo6GgNHjxY+/fvd13/xx9/aNCgQWrWrJkaNmyoRx55RJ9//nnRPFkAbkMgASgxnE6ntm3bppiYGPn4+FxxnbvvvltDhw5VmTJlci1PTU1V9+7dlZmZqSlTpmjx4sXq2LGjVq5cqRUrVkiSDh06pCFDhqhevXqaP3++Jk+erAMHDmjAgAFyOBxyOBwaOHCgMjMz9eqrryo+Pl5BQUEaPHiwkpOTi/z5Ayg+7GIDUGKcPn1aWVlZioiIuObb7tu3T7Vr19bs2bPl5+cnSWrRooW+/PJLJSUlacCAAfrxxx914cIFDRw4UOXLl5ckhYWFacuWLcrIyFBmZqZ+//13DRkyRHfeeackqUGDBpo7d65sNlvhPVEAbkcgASgxLBaLJMlut1/zbWNjYxUbG6vs7Gz99ttvSk5O1r59+3Tq1CkFBQVJkho2bCgvLy917txZHTp0UKtWrdSsWTM1aNBAkuTr66saNWro+eef17Zt2xQbG6tWrVpp3LhxhfYcAdwYCCQAJUZgYKB8fX115MiRq66TkZGh7OxsBQYG5lrucDj02muvadWqVcrIyFB4eLgaNGggLy8v1zoRERF64403tGjRIr3zzjtasWKFAgIC1K1bNz3++OMymUxaunSp5s+fr08++UTr1q2Th4eH2rVrpwkTJuR5TAAlF8cgAShRYmNjlZSUpKysrCte/9Zbb6l58+b66aefci1ftGiRli9frueee07ffvutPvvsM8XFxSkkJCTXepd2mSUlJWn58uW64447tGDBAm3cuFGSVL58eY0fP17btm3TunXr1LdvX23atEmzZs0qkucLwD0IJAAlymOPPaa0tLQrBsnx48e1dOlS1ahRQ3Xr1s113c6dO1WjRg09+OCD8vf3lyQdO3ZM+/btk8PhkCQtX75cbdq0kc1mk6enp2JiYvTSSy9Jko4cOaJdu3apRYsW+vHHH2UymVS7dm2NGjVKNWvW/MutWgBKHnaxAShRoqKiNHLkSM2aNUv79+9Xp06dFBwcrF9//VUJCQnKysq6Yjw1aNBA8fHxWrRokaKiopScnKyFCxfKZrMpMzNTktS8eXNNnz5dQ4cOVY8ePWSxWLR69Wp5enqqTZs2qlChgry9vTV27FgNHz5c5cqV01dffaU9e/aoV69exfyVAFCUTE6n0+nuIQDgWn3++edatWqVfv75Z505c0bh4eGKiYnRoEGDFB4eLuniR40MGzZMw4cPl81m05QpU7Rp0yadPXtW4eHh6tixo0wmkxYuXKgvv/xSAQEB2rZtm+bNm6d9+/bJbrerXr16GjlypKKjoyVJBw8e1IwZM7Rz506lp6erSpUq6tmzpx555BF3fjkAFDICCQAAwIBjkAAAAAwIJAAAAAMCCQAAwIBAAgAAMCCQAAAADAgkAAAAAwIJAADAgEACAAAwIJAAAAAMCCQAAAADAgkAAMCAQAIAADD4fz+FW+6fZRY1AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gru_models = []\n",
    "gru_model_histories = []\n",
    "for BATCH_SIZE in [128, 64, 32]:\n",
    "  for learning_rate in [0.0001]:\n",
    "    print(f\"===================================== BATCH_SIZE={BATCH_SIZE}, learning_rate={learning_rate} =====================================\")\n",
    "\n",
    "    # Learning rate scheduler\n",
    "    lr_scheduler = keras.optimizers.schedules.ExponentialDecay(\n",
    "        initial_learning_rate=learning_rate,\n",
    "        decay_steps=int((xtrain.shape[0] + BATCH_SIZE) / BATCH_SIZE),\n",
    "        decay_rate=0.95\n",
    "    )\n",
    "\n",
    "    optimizer = keras.optimizers.Adam()\n",
    "    optimizer.learning_rate = lr_scheduler\n",
    "\n",
    "    # Build the model\n",
    "    model = build_gru_model((600, 6))  # Assuming xtrain reshaped to (None, 600, 6)\n",
    "\n",
    "    # Display model summary\n",
    "    model.summary()\n",
    "\n",
    "    # Compile the model\n",
    "    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "    scaler = StandardScaler()\n",
    "    xtrain_reshaped = scaler.fit_transform(xtrain.reshape(-1, xtrain.shape[2])).reshape(xtrain.shape)\n",
    "    xval_reshaped = scaler.transform(xval.reshape(-1, xval.shape[2])).reshape(xval.shape)\n",
    "    xtest_reshaped = scaler.transform(xtest.reshape(-1, xtest.shape[2])).reshape(xtest.shape)\n",
    "\n",
    "    # # Reshape the input data to match the model's input shape\n",
    "    # xtrain_reshaped = np.pad(xtrain, ((0, 0), (0, 600 - xtrain.shape[1]), (0, 0)), mode='constant')\n",
    "    # xval_reshaped = np.pad(xval, ((0, 0), (0, 600 - xval.shape[1]), (0, 0)), mode='constant')\n",
    "    # xtest_reshaped = np.pad(xtest, ((0, 0), (0, 600 - xtest.shape[1]), (0, 0)), mode='constant')\n",
    "\n",
    "    # Train the model with reshaped input data\n",
    "    model_history = model.fit(\n",
    "        xtrain_reshaped,\n",
    "        ytrain,\n",
    "        validation_data=(xval_reshaped, yval),\n",
    "        epochs=50,\n",
    "        batch_size=BATCH_SIZE\n",
    "    )\n",
    "    gru_model_histories.append(model_history)\n",
    "    gru_models.append(model)\n",
    "    model.save(str(BATCH_SIZE)+\"gru_trained_model.h5\")\n",
    "\n",
    "    # Plotting accuracy and loss\n",
    "    plt.figure(figsize=(6, 6))\n",
    "    plt.plot(model_history.history['accuracy'], color=\"green\", alpha=0.8, label='Training Accuracy')\n",
    "    plt.plot(model_history.history['loss'], color=\"blue\", alpha=0.8, label='Training Loss')\n",
    "    plt.plot(model_history.history['val_accuracy'], color=\"red\", alpha=0.8, label='Validation Accuracy')\n",
    "    plt.plot(model_history.history['val_loss'], color=\"yellow\", alpha=0.8, label='Validation Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy / Loss')\n",
    "    plt.title(f'Training and Validation Accuracy/Loss Over Epochs - Model {len(gru_models)} Batch Size {BATCH_SIZE}')\n",
    "    plt.legend()\n",
    "    plt.savefig(str(BATCH_SIZE) + 'gru_Model_Training_Graph.png')\n",
    "    plt.show()\n",
    "\n",
    "    # Evaluate the model on test data\n",
    "    test_loss, test_accuracy = model.evaluate(xtest_reshaped, ytest)\n",
    "    print(f'Test Loss: {test_loss}, Test Accuracy: {test_accuracy}')\n",
    "\n",
    "    # Generate predictions on test data\n",
    "    ypred = model.predict(xtest_reshaped)\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    test_cm = confusion_matrix(ytest, (ypred >= 0.5).astype(int))\n",
    "\n",
    "    # Visualize confusion matrix\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.heatmap(test_cm, annot=True, fmt=\"d\", cmap=\"YlOrRd\", cbar=False)\n",
    "    plt.title(\"Confusion Matrix - Model \" + str(len(gru_models)))\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.savefig(str(BATCH_SIZE) + 'gru_Model_Confusion_Matrix.png')\n",
    "    plt.show()\n",
    "\n",
    "    # Display classification report\n",
    "    print(\"Classification Report - Model \" + str(len(gru_models)))\n",
    "    print(classification_report(ytest, (ypred >= 0.5).astype(int)))\n",
    "\n",
    "    # Get classification report\n",
    "    report = classification_report(ytest, (ypred >= 0.5).astype(int), output_dict=True)\n",
    "\n",
    "    # Extract precision, recall, and F1-score for each class\n",
    "    classes = [str(cls) for cls in range(len(report) - 3)]  # Extract class labels\n",
    "    precision = [report[cls]['precision'] for cls in classes]\n",
    "    recall = [report[cls]['recall'] for cls in classes]\n",
    "    f1_score = [report[cls]['f1-score'] for cls in classes]\n",
    "\n",
    "    # Create bar plot\n",
    "    x = np.arange(len(classes))\n",
    "    width = 0.2  # Width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(6, 6))\n",
    "    rects1 = ax.bar(x - width, precision, width, label='Precision')\n",
    "    rects2 = ax.bar(x, recall, width, label='Recall')\n",
    "    rects3 = ax.bar(x + width, f1_score, width, label='F1-Score')\n",
    "\n",
    "    # Add labels, title, and legend\n",
    "    ax.set_xlabel('Class')\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_title('Classification Report')\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(classes)\n",
    "    ax.legend()\n",
    "\n",
    "    # Show plot\n",
    "    plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "    plt.tight_layout()  # Adjust layout to prevent clipping of labels\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, gru_model in enumerate(gru_models):\n",
    "  BATCH_SIZE = [128, 64, 32][index % 3]  # Adjust as needed\n",
    "  plt.figure(figsize=(6, 6))\n",
    "  plt.plot(gru_model.history['accuracy'], label='Training Accuracy')\n",
    "  plt.plot(gru_model.history['val_accuracy'], label='Validation Accuracy')\n",
    "  plt.plot(gru_model.history['loss'], label='Training Loss')\n",
    "  plt.plot(gru_model.history['val_loss'], label='Validation Loss')\n",
    "  plt.title(f'Model {index+1} - Batch Size {BATCH_SIZE}')\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Accuracy / Loss')\n",
    "  plt.legend()\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,trained_model in enumerate(model_histories):\n",
    "  trained_model.save(str(index)+\"gru_trained_model.keras\")"
   ]
  }
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