CoGen_downloaded.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ec83687-ade9-4c17-ae0e-6dd1f2b42f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "30b5b981-db60-4cc6-9ed4-5b190c964c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('NEDC.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c967864-90e6-482c-8199-22d14a371b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_velocity (km/s)</th>\n",
       "      <th>end_velocity (km/s)</th>\n",
       "      <th>acceleration (m/s2)</th>\n",
       "      <th>duration (seconds)</th>\n",
       "      <th>start_velocity (m/s)</th>\n",
       "      <th>end_velocity (m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.83</td>\n",
       "      <td>5</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   start_velocity (km/s)  end_velocity (km/s)  acceleration (m/s2)  \\\n",
       "0                      0                    0                 0.00   \n",
       "1                      0                   15                 1.04   \n",
       "2                     15                   15                 0.00   \n",
       "3                     15                    0                -0.83   \n",
       "4                      0                    0                 0.00   \n",
       "\n",
       "   duration (seconds)  start_velocity (m/s)  end_velocity (m/s)  \n",
       "0                  11              0.000000            0.000000  \n",
       "1                   4              0.000000            8.333333  \n",
       "2                   8              8.333333            8.333333  \n",
       "3                   5              8.333333            0.000000  \n",
       "4                  21              0.000000            0.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8281d775-059b-4a05-a24f-3c3fc01d3b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('start_velocity (km/s)', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0216e43f-b963-4f48-bd6d-7bcd76424007",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('end_velocity (km/s)',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1449b4b-fce5-4a90-9df6-0dd0de387ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acceleration (m/s2)</th>\n",
       "      <th>duration (seconds)</th>\n",
       "      <th>start_velocity (m/s)</th>\n",
       "      <th>end_velocity (m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.04</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.83</td>\n",
       "      <td>5</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acceleration (m/s2)  duration (seconds)  start_velocity (m/s)  \\\n",
       "0                 0.00                  11              0.000000   \n",
       "1                 1.04                   4              0.000000   \n",
       "2                 0.00                   8              8.333333   \n",
       "3                -0.83                   5              8.333333   \n",
       "4                 0.00                  21              0.000000   \n",
       "\n",
       "   end_velocity (m/s)  \n",
       "0            0.000000  \n",
       "1            8.333333  \n",
       "2            8.333333  \n",
       "3            0.000000  \n",
       "4            0.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "40a231e0-6c4e-4d38-818b-a641d134aee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2181f4102c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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3a8WKFZ4B0vLy8nTw4EHP8kVFRbrxxhs1aNAgXXDBBXK5XPr00081ePBgv8UIIDgdPXjK1DHpWrJ2T7PLwho7sMZLtY7t4I5e7qDLrZKKar/GDCDwUhJiWmwv1u4u1JK1e9Q9vnOX9Xpra5xltDOAmfyd8+1BuxAcfCq0nU6n577of/zjH7r88sv1k5/8RFOnTtXWrVsNDfBYt99+u/bu3auqqiqtX79ep59+umfexx9/rBdffNHz++9//3vPsgUFBXr33XeVnZ3t1/gABCf7UYOnZPfr1uozOBs7MJeXx4N890O5fvXqJh0orjQ8VgDWUVNX32p7sXZ3oWrq6ju1fm9tDY8qAszl75xvD9qF4OBToZ2amqpt27aprq5OK1as0Pjx4yVJFRUV6tq1q6EBAoAZ+v538JQxGSmqqm27kyx118ju5fEgMZFdmnwDDiA0VVTXtTm/0st8bxJi2r7LL97LfADG8nfOtwftQnDwqdCeMmWKrrjiCp188smKiIjQuHHjJEnr16/XwIEDDQ0QAMySlhKvBblZSnfEt7lcoi1KjoRo5WS2/Niv0Rkp2rSvWBKXcAGhztsf3Tr7zN7orl00upVHFY3OSFF0V59O5QD4yN853x60C8HBp0/ht7/9rf74xz/qpptu0tq1az2PFejatatmzJhhaIAAYKa+yXHqk2RrtYjOyXTIkXDk8Rnzc7OaLTc6I0VTRqfrhTV7PNO4hAsIXW390a2xveiM4spqTRmd3uykurGtKankD3mAmfyd8+1BuxAcOnRdwbXXXqtLL71UEyZM0M9+9rNm86+77jrDAgOAQGksomcs3aJVxzw2Y0Fulmckzz7dYvXk5GwdLHHrO2e5YiK7aNO+Yt3x6qYml5aZ8ddtAIHR3vbCVwkxUZr8/HpNHZOuqaPTVVVb36Steef2MZ3dBQAd4O+cbw/aheDQoUI7IyNDc+fO1TXXXKOzzjpLl1xyiS655BL17WvG0+IAwDyNRbSzrNrzDM7Gb7KP1vj73H9sb9LhNjLrr9sAAqe97YUvHAnRGpGW3OQxQo1oX4DA8GfOtwftQnDo0KXjs2bN0oYNG7Rr1y5dfPHFWr58uU444QQNHz5cs2fP1ubNm/0UJgCYLykuWif0TNDQ/sk6oWdCqx1oa5eRm/nXbQCB1d72wpf10r4A1uOvnG/vtmkXrM+nIemOO+443Xrrrbr11ltVWlqq9957T3/72980duxYJSYm6uKLL9Ytt9yik046yeh4AcCSAv3XbQChi/YFwLFoF6yv02O/JyYm6oorrtAVV1yhuro6ffzxx3r77be1bt06Cm0AYSUpzpgOrqSi2vOsbntslBzxdJxAuDOqfQFtLEIH7YIx/NUmGPqQta5du+qcc87ROeecY+RqASBsHCiu1D1Lt2j1MQOszM/NUp9usQGMDACCH20sgKP5s03w6fFehYWFuu222zR48GA5HA517969yQ8AoONKKqqbNfbSkWdxz1i6RSUVPK4DAHxFGwvgaP5uE3z6RvsXv/iFdu/ereuvv16pqamKiIjoVBAAAMlZVt2ssW+0apdTzrJqLhEDAB/RxgI4mr/bBJ8K7dWrV2vNmjU65ZRTfN4wAKApl7umzfmlXuYHGvc9AqEt2HM82NtYIBQFsl3xd5vgU6E9cOBAVVZWdmrDAICm7LaoNucnepkfSNz3CIS2UMjxYG5jgVAU6HbF322CT/doP/PMM7rvvvv0ySefqLCwUC6Xq8kPAKDjHAnRzZ6J2Sgn0yFHgjW/OeK+RyC0hUqOB2sbC4QiK7Qr/m4TfCq0u3XrJpfLpbFjx6pnz55KTk5WcnKyunXrpuTk5E4FBADhKikuWvNzs5o1+jmZDi3IzbLsJZrtuccJQPAKlRwP1jYWCEVWaFf83Sb4dOn41VdfraioKL3yyisMhgYABurTLVZPTs6Ws6xape4aJdqi5Eiw9n2Q3PcIhLZQyvFgbGOBUGSVdsWfbYJPhfZXX32lTZs26cQTT+x0AACAppLiguukj/segdAWajkebG0sEIqs1K74q03w6dLxESNGaN++fUbHAgAIQtz3CIQ2chyA0cKhXfGp0P7Vr36lO++8Uy+++KI2bNigLVu2NPkBAIQP7nsEQhs5DsBo4dCu+HTp+M9//nNJ0tSpUz3TIiIi1NDQoIiICNXV1RkTHQAgKHDfIxDayHEARgv1dsWnQnvPnj1GxwEACHLc9wiENnIcgNFCuV3xqdBOS0szOg4AAAAAAEJCu+/R/uyzz9q90oqKCn399dc+BQQAAAAAQDBrd6H9i1/8QhMmTNAbb7yh8vLyFpfZtm2b7r33Xp1wwgnasGGDYUECAAAAABAs2n3p+LZt2/Tss8/q/vvv11VXXaWf/OQn6tOnj2w2m4qKirRjxw6VlZVp0qRJ+te//qUhQ4b4M24AAAAAACyp3YV2VFSU7rjjDt1xxx364osvtGbNGu3du1eVlZU65ZRTNG3aNJ199tnq3r27P+MFAAAAAMDSfBoMbcSIERoxYoTRsQAAAAAAEPTafY82AAAAAADwjkIbAAAAAAADUWgDAAAAAGAgn+7RDqSnn35ajz32mAoKCnTKKafoySef1Gmnndbq8m+88YYeeOAB/ec//1FmZqYWLFigCy64wMSIAVhRflGFXO5albprlBQbpeiuXVRYXqWEmCgl2iLVNzlOJRXVcpZVy+WuUXx0pCK7Rqi4okrJcTGqqquXq7JGdtuPy7e0flflkfW3tExrDrncKiqvlstdK7stUrHRXeUsdSs+JkoJtkgdZ+C2jnb0/tpjo+SIj1ZSXHSH1xNMmrzXsZFKjotWqt0W6LAMF8jPdn9RhUqPOj5bOoY7y6gc8HV7dlukGiS/xmDG++hNqORLoNu6QG+fnDQGOWmMQOeDP2MIqkL7r3/9q6ZPn67Fixfr9NNP16JFizRhwgTt3LlTPXv2bLb8p59+qsmTJ2vevHm66KKL9Morr2jixInauHGjTj755ADsAQAr2FtYrnuXbdXa3YWeaaMzUjRldLqufeFzjTy+u2ZdfJLuX/6VVu9yNllm1kUn6eF3t+nDHT94po/JSNGcSUOUlhLf6vqPXaY1eYXlmtnCax+46CRd/cfPNLBXomHbOtqB4krds3RLk/3NyXRofm6W+nSLbfd6gklr7/XcSUPUvwPvndUF8rM16vgM9Dba2l5cdFe9cN2pevqjXVrtpxjM3seWhEq+BLqtC/T2yUly0koCnQ/+jiGioaGhwZcXrly5UitXrtT333+v+vr6JvNeeOGFTgXVmtNPP12nnnqqnnrqKUlSfX29+vXrp1/96leaMWNGs+V//vOfq7y8XH//+9890376059q6NChWrx4cbu26XK5lJSUpJKSEtntdmN2BH6zceNGDR8+XOPvW6Lu/U/0aR2H83bq/TlTtGHDBg0bNszgCNFZnc3J/KIK3b10S5OOqdHojBRl90+WJG3KK2pxmTEZKRraP1lPfbi72fQFuVmS1Or6G5dp7S/qh1xuTX99c6uvnTI6Xde/9IXGZKRofm6WIjqxraOVVFTr9lc3NelkGuVkOvTk5OyQ+2bb23u98IqhQfetQEv8/dm2lY/7iyp0TxvH5/zcrE5/+9NWPnckBzqzvdvHZrTZXnQ2BjPeR29CJV8C3daZsX1ykpwMlpwMdD6aEYNP92g/9NBDOvfcc7Vy5Uo5nU4VFRU1+fGH6upqbdiwQePGjfNM69Kli8aNG6d169a1+Jp169Y1WV6SJkyY0OryklRVVSWXy9XkB0DgGJ2TLndtix2TJK3dXajsft2U3a9bq8us+e8yLU13uWvbXH/jMq0pKq9u87U97TGe/5d2cltHc5ZVt9jJSNKqXU45y6rbtZ5g4u29LioPjX02+rPtSD6Wejk+S9t5fLbFqBzozPa8tRedjcGM99GbUMmXQLd1/tg+OUlOthRDMORkoPPRjBh8unR88eLFevHFF/WLX/yiUxvvCKfTqbq6OqWmpjaZnpqaqh07drT4moKCghaXLygoaHU78+bN00MPPdT5gAEYwuicdFXWtDm/qra+zfltLVPqrpG3a4RK3a1v31vnX+auM2xbTbfb9nLtXU8w8fZeG30yGChGf7YdyUdvuWbEcWXGNrxtz1ub0dkYzN7HFmMIkXwJdFvnj+2Tk+SkL/OtIND5aEYMPn2jXV1drVGjRnVqw1Y1c+ZMlZSUeH727dsX6JCAsGZ0Ttpjo9qcHxPZRTGRbTeNrc1PtEV5XX+irfX5dlvbf/tMsHU1bFtNt2vMeoKJt/fa2/xgYfRn25F8NOr4bIsZ2/C2PW/tRWdjMHsfW4whRPIl0G2dP7ZPTpKTvsy3gkDnoxkx+FRo33DDDXrllVc6teGOcjgc6tq1qw4dOtRk+qFDh9SrV68WX9OrV68OLS9JMTExstvtTX4ABI7ROWm3RWpMRkqL80ZnpGjTvmJt2lfc6jJj/rtMS9Pttsg219+4TGuS46PbfO33rirP/xM7ua2jORKilZPpaHFeTqZDjoTQuj9b8v5eJ8eHxj4b/dl2JB8TvRyfiQacCBqVA53Z3qZ9xRrtxxjMeB+9CZV8CXRb54/tk5PkZEsxBENOBjofzYjBp0Lb7Xbr8ccf15lnnqlf/epXmj59epMff4iOjtbw4cO1cuVKz7T6+nqtXLlSI0eObPE1I0eObLK8JL3//vutLg8g9PVNjtOcSUOadVCNo46/sGaPth8o0ZxJQ5o1vqMzUjTr4pO07UBJk+mNI432TY5rdf1HL9OaVLtNc1t57ayLT9I9S7d41nNcJ7d1tKS4aM3PzWq2vzmZDi3IzQq5gdCktt/ruZOGWH4QmfYK5Gd7nJfj04jBgozKgc5s74U1e/SrszN1RkbT99ioGMx4H70JlXwJdFsX6O2Tk+SklQQ6H8yIwadRx88+++zWVxgRoQ8//LBTQbXmr3/9q6677jr94Q9/0GmnnaZFixbp9ddf144dO5Samqprr71Wffv21bx58yQdebzXmWeeqfnz5+vCCy/Ua6+9prlz53bo8V6MOh5cGHU89BmVk0c/R9seG6WY/z5HOz7myPM3j36Odqm7RrHRXRXVtUuz52gn2n5cvrX1t7ZMa45+LmaiLVJx0V1VWOZWbPSR54C29hxtX7Z1tKP3N9EWJUdCmD1H2xap5PjgewZpe/jrs21PPjY+a7Zx2y0dw51lVA74ur2ko57Z668YzHgfvQmVfAl0W+fP7ZOT5GSw5WSg89GfMfh0bcNHH33U6Q374uc//7l++OEHzZo1SwUFBRo6dKhWrFjhGfAsLy9PXbr8+CX9qFGj9Morr+j+++/Xvffeq8zMTC1fvpxnaAM48u3zMdPSeyQ0+T0prqWGNkHt0dL62yvVbmvWUbb1XM7ObOtoLe9vaGvpvQ5FgfxszTjxNCoHOrs9f8Zg9gl8S0IlXwLd1gV6++SkMchJYwQ6H/wZQ6dvIti/f78k6bjjjut0MO1x++236/bbb29x3scff9xs2uWXX67LL7/cz1EBAAAAAHCET/do19fXa/bs2UpKSlJaWprS0tLUrVs3Pfzww6qv9/5oHAAAAAAAQpVP32jfd999+tOf/qT58+dr9OjRkqQ1a9bot7/9rdxut+bMmWNokAAAAAAABAufCu2XXnpJf/zjH3XJJZd4pmVlZalv37669dZbKbQBAAAAAGHLp0vHDx8+rIEDBzabPnDgQB0+fLjTQQEAAAAAEKx8KrRPOeUUPfXUU82mP/XUUzrllFM6HRQAAAAAAMHKp0vHH330UV144YX64IMPNHLkSEnSunXrtG/fPv3jH/8wNEAAAAAAAIKJT99on3nmmfrmm280adIkFRcXq7i4WJdddpl27typM844w+gYAQAAAAAIGj4/R7tPnz4MegYAAAAAwDHaXWhv2bJFJ598srp06aItW7a0uWxWVlanAwMAAAAAIBi1u9AeOnSoCgoK1LNnTw0dOlQRERFqaGhotlxERITq6uoMDRIAAAAAgGDR7kJ7z5496tGjh+f/AAAAAACguXYX2mlpaZ7/7927V6NGjVJkZNOX19bW6tNPP22yLAAAAAAA4cSnUcfPPvtsHT58uNn0kpISnX322Z0OCgAAAACAYOVTod3Q0KCIiIhm0wsLCxUfH9/poAAAAAAACFYderzXZZddJunIgGe//OUvFRMT45lXV1enLVu2aNSoUcZGCAAAAABAEOlQoZ2UlCTpyDfaiYmJio2N9cyLjo7WT3/6U914443GRggAAAAAQBDpUKG9ZMkSSdKAAQP0m9/8hsvEAQAAAAA4RocK7UYPPvig0XEAAAAAABASfCq0JenNN9/U66+/rry8PFVXVzeZt3Hjxk4HBgAAAABAMPJp1PEnnnhCU6ZMUWpqqjZt2qTTTjtNKSkp+u6773T++ecbHSMAAAAAAEHDp0L7mWee0XPPPacnn3xS0dHRuvvuu/X+++/rjjvuUElJidExAgAAAAAQNHwqtPPy8jyP8YqNjVVpaakk6Re/+IVeffVV46IDAAAAACDI+FRo9+rVS4cPH5Yk9e/fX5999pkkac+ePWpoaDAuOgAAAAAAgoxPhfbYsWP19ttvS5KmTJmiadOmafz48fr5z3+uSZMmGRogAAAAAADBxKdRx5977jnV19dLkm677TalpKTo008/1SWXXKL/+Z//MTRAAAAAAACCSYcL7draWs2dO1dTp07VcccdJ0m68sordeWVVxoeHAAAAAAAwabDl45HRkbq0UcfVW1trT/iAQAAAAAgqPl0j/Y555yjTz75xOhYAAAAAAAIej7do33++edrxowZ2rp1q4YPH674+Pgm8y+55BJDggMAAAAAINj4VGjfeuutkqTHH3+82byIiAjV1dV1LioAAAAAAIKUT5eO19fXt/rjryL78OHDuvrqq2W329WtWzddf/31Kisra/M1Z511liIiIpr83HzzzX6JDwAAAAAAycdvtI/mdrtls9mMiKVNV199tQ4ePKj3339fNTU1mjJlim666Sa98sorbb7uxhtv1OzZsz2/x8XF+TtUAAAAAEAY86nQrqur09y5c7V48WIdOnRI33zzjY4//ng98MADGjBggK6//npDg9y+fbtWrFihzz//XCNGjJAkPfnkk7rgggv0u9/9Tn369Gn1tXFxcerVq5eh8QAILflFFXK5a+WqrFFSbJQSbZHqm8wf5QBYB+0U0DHkDALNp0J7zpw5eumll/Too4/qxhtv9Ew/+eSTtWjRIsML7XXr1qlbt26eIluSxo0bpy5dumj9+vWaNGlSq6/9y1/+oj//+c/q1auXLr74Yj3wwAN8qw3AY29hue5dtlVrdxd6po3JSNGcSUOUlhLfxisBwBy0U0DHkDOwAp/u0X755Zf13HPP6eqrr1bXrl0900855RTt2LHDsOAaFRQUqGfPnk2mRUZGqnv37iooKGj1dVdddZX+/Oc/66OPPtLMmTP1f//3f7rmmmva3FZVVZVcLleTHwCB48+czC+qaNYRS9Ka3YW6b9lW5RdVGLYtIBTQR5qPdgptISebI2dgFT4V2vn5+crIyGg2vb6+XjU1Ne1ez4wZM5oNVnbsT2cK95tuukkTJkzQkCFDdPXVV+vll1/WsmXL9O2337b6mnnz5ikpKcnz069fP5+3D6Dz/JmTLndts4640ZrdhXK5aw3bFhAK6CPNRzuFtpCTzZEzsAqfCu3Bgwdr9erVzaa/+eabys7Obvd6fv3rX2v79u1t/hx//PHq1auXvv/++yavra2t1eHDhzt0//Xpp58uSdq9e3ery8ycOVMlJSWen3379rV7/QCM58+cdFW2/YfBUnf7/3AIhAP6SPPRTqEt5GRz5Ayswqd7tGfNmqXrrrtO+fn5qq+v11tvvaWdO3fq5Zdf1t///vd2r6dHjx7q0aOH1+VGjhyp4uJibdiwQcOHD5ckffjhh6qvr/cUz+2xefNmSVLv3r1bXSYmJkYxMTHtXicA//JnTtpjo9qcn2hrez4QbugjzUc7hbaQk82RM7AKn77RvvTSS/XOO+/ogw8+UHx8vGbNmqXt27frnXfe0fjx442OUYMGDdJ5552nG2+8Uf/+97+1du1a3X777bryyis9I47n5+dr4MCB+ve//y1J+vbbb/Xwww9rw4YN+s9//qO3335b1157rXJycpSVlWV4jACCj90WqTEZKS3OG5ORIrut009ABIBOoZ0COoacgVX4VGhL0hlnnKH3339f33//vSoqKrRmzRqde+65RsbWxF/+8hcNHDhQ55xzji644AKNGTNGzz33nGd+TU2Ndu7cqYqKIwMcREdH64MPPtC5556rgQMH6te//rVyc3P1zjvv+C1GAMGlb3Kc5kwa0qxDbhyZlMeAAAg02imgY8gZWEXQ/Emne/fueuWVV1qdP2DAADU0NHh+79evnz755BMzQgMQxNJS4rUgN0sud61K3TVKtEXJzrM2AVgI7RTQMeQMrKDdhXZycrIiIiLatezhw4d9DggAzNY3OU59Ax0EALSBdgroGHIGgdbuQnvRokV+DAMAAAAAgNDQ7kL7uuuu82ccAAAAAACEBJ8HQ/v22291//33a/LkyZ5nXL/33nv6+uuvDQsOAAAAAIBg41Oh/cknn2jIkCFav3693nrrLZWVlUmSvvzySz344IOGBggAAAAAQDDxadTxGTNm6JFHHtH06dOVmJjomT527Fg99dRThgUHAGYoqaiWs6xaLneN7LFRcsRHKykuOmjWDwBmoC0zBu9jaAiVzzFU9sOKfCq0t27d2uKjtnr27Cmn09npoADALAeKK3XP0i1avevHtisn06H5uVnq0y3W8usHADPQlhmD9zE0hMrnGCr7YVU+XTrerVs3HTx4sNn0TZs2qW9fBtIHEBxKKqqbdTCStGqXUzOWblFJRbWl1w8AZqAtMwbvY2gIlc8xVPbDynwqtK+88krdc889KigoUEREhOrr67V27Vr95je/0bXXXmt0jADgF86y6mYdTKNVu5xylnWuk/H3+gHADLRlxuB9DA2h8jmGyn5YmU+F9ty5czVw4ED169dPZWVlGjx4sHJycjRq1Cjdf//9RscIAH7hcte0Ob/Uy/xArx8AzEBbZgzex9AQKp9jqOyHlfl0j3Z0dLSef/55zZo1S1u3blVZWZmys7OVmZlpdHwA4Dd2W1Sb8xO9zA/0+gHADLRlxuB9DA2h8jmGyn5Ymc/P0Zakfv366YILLtAVV1xBkQ0g6DgSopWT6WhxXk6mQ46Ezo266e/1A4AZaMuMwfsYGkLlcwyV/bAynwrt3NxcLViwoNn0Rx99VJdffnmngwIAMyTFRWt+blazjiYn06EFuVmdfryFv9cPAGagLTMG72NoCJXPMVT2w8p8unR81apV+u1vf9ts+vnnn6+FCxd2NiYAME2fbrF6cnK2nGXVKnXXKNEWJUeCcc+Q9Pf6AcAMtGXG4H0MDaHyOYbKfliVT4V2WVmZoqObfwBRUVFyuVydDgoAzJQU599Oxd/rBwAz0JYZg/cxNITK5xgq+2FFPhXaQ4YM0V//+lfNmjWryfTXXntNgwcPNiQwALCS/KIKudy1clXWKCk2Som2SPVNjgt0WAACgPYAwYzjFzCHT4X2Aw88oMsuu0zffvutxo4dK0lauXKlXn31Vb3xxhuGBggAgba3sFz3LtuqtbsLPdPGZKRozqQhSkuJD2BkAMxGe4BgxvELmMenwdAuvvhiLV++XLt379att96qX//619q/f78++OADTZw40eAQAcB3+UUV2n7QpfXfFWrHQZfyiyo6/PpjT0okac3uQt23bGuH1wfAeJ3N845sh/YAwSq/qEL3cfwCpvHpG21JuvDCC3XhhRcaGQsAGMqIv9y73LXNTkoardldKJe7Vn0NiRaAL8z8ho72AMHM5a7VGo5fwDQ+Fdqff/656uvrdfrppzeZvn79enXt2lUjRowwJDgAvsvLy5PT6ezUOhwOh/r3729QROby9s3Tgtysdt2T5qqsaXN+qbvt+QD8x6g8by/aAwSzMndt2/Or2p4PoGN8KrRvu+023X333c0K7fz8fC1YsEDr1683JDgAvsnLy9PAgYNUWdm5y8BiY+O0Y8f2oCy2jfrmyR4b1eb8RFvb8wH4j9nfMNMeIJgl2No+7U+I8flCVwAt8Cmjtm3bpmHDhjWbnp2drW3btnU6KACd43Q6VVlZodOnPih77wE+rcN18D9a/8JDcjqdwVloG/TNk90WqTEZKS1ebjcmI0V2LycuAPzH7G+YaQ8QzCIkjc5IafGPU6MzUhQRYX5MQCjzqUeIiYnRoUOHdPzxxzeZfvDgQUVG0skAVmHvPUDd+58Y6DACwqhvnvomx2nOpCG6b9nWJifXjfeA8kgUIHDM/oaZ9gBBLUKaMjpdkpoU26MzUjzTARjHp6r43HPP1cyZM/W3v/1NSUlJkqTi4mLde++9Gj9+vKEBomM6e19uMN+TCxzNyG+e0lLitSA3Sy53rUrdNUq0RcnOc0eBgAvEN8y0BwhWiTGRenX9XmX3T9bU0emqqq1XTGQXbdpXrFfX79XsS08OdIhASPGpB/rd736nnJwcpaWlKTs7W5K0efNmpaam6v/+7/8MDRDtZ8R9ucF8Ty5wNKO/eeqbHMdorIDFBOobZtoDBKO+yXGadfFJum/ZVj314W7PdK7IAPzDp0K7b9++2rJli/7yl7/oyy+/VGxsrKZMmaLJkycrKoqBQAKls/flBvs9ucCx+OYJCH3kOdB+5AtgHp+vqYqPj9dNN91kZCwwSDjflwsci2+egNBHngPtR74A5ujUzUvbtm1TXl6eqqurm0y/5JJLOhUUAAAAAADByqdC+7vvvtOkSZO0detWRUREqKGhQZIU8d/nAtTV1RkXIQAESH5RhVzuWrkqa5QUG6VELq8D0AraCwQLjlXAHD4V2nfeeafS09O1cuVKpaen69///rcKCwv161//Wr/73e+MjhEATLe3sFz3Ltva5BEojQPGpKXEBzAyAFZDe4FgwbEKmKeLLy9at26dZs+eLYfDoS5duqhLly4aM2aM5s2bpzvuuMPoGCVJc+bM0ahRoxQXF6du3bq16zUNDQ2aNWuWevfurdjYWI0bN067du3yS3wAQkd+UUWzExFJWrO7UPct26r8It9H9gcQWmgvECw4VgFz+VRo19XVKTExUdKR5y4fOHBAkpSWlqadO3caF91Rqqurdfnll+uWW25p92seffRRPfHEE1q8eLHWr1+v+Ph4TZgwQW632y8xAggNLndtsxORRmt2F8rlrjU5IgBWRXuBYMGxCpjLp0vHTz75ZH355ZdKT0/X6aefrkcffVTR0dF67rnndPzxxxsdoyTpoYcekiS9+OKL7Vq+oaFBixYt0v33369LL71UkvTyyy8rNTVVy5cv15VXXumXOAEEP1dlTZvzS91tzwcQPmgvECw4VgFz+fSN9v3336/6+npJ0uzZs7Vnzx6dccYZ+sc//qEnnnjC0AB9tWfPHhUUFGjcuHGeaUlJSTr99NO1bt26AEYGwOrssVFtzk+0tT0fQPigvUCw4FgFzOXTN9oTJkzw/D8jI0M7duzQ4cOHlZyc7Bl5PNAKCgokSampqU2mp6ameua1pKqqSlVVVZ7fXS6XfwIE0C5m5GRJRbWcZdVyuWtk/+8IrOcM7KGVO35otuyYjBTZbZ16MiIQtOgjm7PbIjUmI0VrWrgkl/aiuWPbW0d8tJLiogMdVtDqSE5yrBqLYxneGJZR3bt37/BrZsyYoQULFrS5zPbt2zVw4EBfw+qwefPmeS5TBxB4/s7JA8WVumfpFq3e5fRMy8l06OGJJ0v6ukmx3TgyK49BQbiij2yub3Kc5kwaovuWbW1SwNBeNNdaezs/N0t9usUGMLLg1ZGc5Fg1Dscy2iOgf7r69a9/rV/+8pdtLuPrPd+9evWSJB06dEi9e/f2TD906JCGDh3a6utmzpyp6dOne353uVzq16+fTzEAnZGXlyen0+l9wRZs377d4GgCx585WVJR3ayjlKRVu5x6YPlXmnvZEP1mQq1K3TVKtEXJzrNGEeboI1uWlhKvBblZcrlpL1rTVns7Y+kWPTk5m28DfdDRnORY7TyOZbRXQAvtHj16qEePHn5Zd3p6unr16qWVK1d6CmuXy6X169e3OXJ5TEyMYmJi/BIT0F55eXkaOHCQKis796iNmqpqgyIKHH/mpLOsullH2WjVLqeqauo1qLfdL9sGghF9ZOv6Jsepb6CDsDBv7a2zrJrixAe+5CTHaudwLKO9guZmjLy8PB0+fFh5eXmqq6vT5s2bJR25RzwhIUGSNHDgQM2bN0+TJk1SRESE7rrrLj3yyCPKzMxUenq6HnjgAfXp00cTJ04M3I4A7eB0OlVZWaHTpz4oe+8BHX79wa3r9NXbz6m2lkd1tMXlZYRVRmAFAGPQ3iJUcCyjvYKm0J41a5Zeeuklz+/Z2dmSpI8++khnnXWWJGnnzp0qKSnxLHP33XervLxcN910k4qLizVmzBitWLFCNpvN1NgBX9l7D1D3/id2+HWug/8xPpgQZPcywiojsAKAMWhvESo4ltFePj3eKxBefPFFNTQ0NPtpLLKlI8/OPvqe74iICM2ePVsFBQVyu9364IMP9JOf/MT84AFYkiMhWjmZjhbn5WQ65Ejg0i8AMALtLUIFxzLaK2gKbQAwWlJctObnZjXrMHMyHVqQm8U9VgBgENpbhAqOZbRX0Fw6DgD+0KdbrJ6cnC1nWbVnBFZHAs/CBACj0d4iVHAsoz0otAGEvaQ4OkcAMAPtLUIFxzK84dJxAAAAAAAMRKENAAAAAICBKLQBAAAAADAQhTYAAAAAAAai0AYAAAAAwEAU2gAAAAAAGIhCGwAAAAAAA1FoAwAAAABgIAptAAAAAAAMRKENAAAAAICBIgMdABCK8vLy5HQ6fX799u3bDYwGAAAAgJkotAGD5eXlaeDAQaqsrOj0umqqqg2ICAAAAICZKLQBgzmdTlVWVuj0qQ/K3nuAT+s4uHWdvnr7OdXW1hobHAAAAAC/o9AG/MTee4C69z/Rp9e6Dv7H2GAAAAAAmIZCG4br7P3JDodD/fv3NzAidEZn7hfnswQAAEA4otCGoYy4Pzk2Nk47dmynQAuwypJCSRG65pprfF4HnyUAAADCEYU2DNXZ+5NdB/+j9S88JKfTSXEWYDUVpZIaNPSqe9QjfWCHX89nCQAAgHBFoQ2/6Mz9ybCWhJ79+SwBAACADugS6AAAAAAAAAglfKMNAK0oqaiWs6xaLneN7LFRcsRHKykuOtBhAQhDtEcwCscSYA4KbQBowYHiSt2zdItW7/pxBP2cTIfm52apT7fYAEYGINzQHsEoHEuAebh0HACOUVJR3exERJJW7XJqxtItKqmoDlBkAMIN7RGMwrEEmItCGwCO4SyrbnYi0mjVLqecZZyMADAH7RGMwrEEmItLx9HM9u3bA/JawCpc7po255d6mQ8ARqE9glE4lgBzUWjDo7KkUFKErrnmmk6vq6aKv4oieNltUW3OT/QyHwCMQnsEo3AsAeai0IZHTUWppAYNveoe9Ugf6NM6Dm5dp6/efk61tbXGBgeYyJEQrZxMh1a1cIldTqZDjgRGZwVgDtojGIVjCTAXhTaaSejZX937n+jTa10H/2NsMEAAJMVFa35ulmYs3dLkhCQn06EFuVk8BgWAaWiPYBSOJcBcQVNoz5kzR++++642b96s6OhoFRcXe33NL3/5S7300ktNpk2YMEErVqzwS4x5eXlyOlseZKI9qqqqFBMT4/PruT8aME6fbrF6cnK2nGXVKnXXKNEWJUcCzxoFYD7aIxiFYwkwT9AU2tXV1br88ss1cuRI/elPf2r368477zwtWbLE83tnCtm25OXlaeDAQaqsrPB9JRERUkNDp2Ph/mjAGElxnHwAsAbaIxiFYwkwR9AU2g899JAk6cUXX+zQ62JiYtSrVy8/RNSU0+lUZWWFTp/6oOy9B3T49Y33NnN/NAAAAAAEt6AptH318ccfq2fPnkpOTtbYsWP1yCOPKCUlxW/bs/ce4NP9zY33NnN/NAAAAAAEt5AutM877zxddtllSk9P17fffqt7771X559/vtatW6euXbu2+JqqqipVVVV5fne5XGaFC6AF5CRgHeQjYC3kJGBdAS20Z8yYoQULFrS5zPbt2zVwoG+XUl955ZWe/w8ZMkRZWVk64YQT9PHHH+ucc85p8TXz5s3zXKZ+NG8NV1lZmSSpZP93qq+t63CsZd/nH9lOwX98vo+8s+uwQgylBXslSRs3bvS8px3xzTffSPL9c7BKDKH0WZaVlbWr409MTFRERESz6b7mJIDOaSknyUcgMOgjAWtpLSePFtHQYMDoWz764YcfVFhY2OYyxx9/vKKjfxyw4cUXX9Rdd93VrlHHW9KjRw898sgj+p//+Z8W5x/7l8H8/HwNHjzYp20BaL+SkhLZ7fZm08lJIDBayknyEQgM+kjAWlrLyaMF9BvtHj16qEePHqZtb//+/SosLFTv3r1bXSYmJqbJt3cJCQnat29fu/5qESgul0v9+vXTvn37vH7gaB3vY+d15j1MTExscXow5qQRwvF4DLd9tvr+tpSTvuaj1ffVSOG0r1J47W8g99XoPpLPLTSF075K1szJowXNPdp5eXk6fPiw8vLyVFdXp82bN0uSMjIylJCQIEkaOHCg5s2bp0mTJqmsrEwPPfSQcnNz1atXL3377be6++67lZGRoQkTJrR7u126dNFxxx3nj10ynN1uD4uk8jfex87z53sYTDlphHA8HsNtn4N5fzuaj8G8rx0VTvsqhdf+WnlfycnWsa+hy6r7GzSF9qxZs/TSSy95fs/OzpYkffTRRzrrrLMkSTt37lRJSYkkqWvXrtqyZYteeuklFRcXq0+fPjr33HP18MMP++1Z2gAAAAAABE2h/eKLL3p9hvbRt5vHxsbqn//8p5+jAgAAAACgqS6BDgCdFxMTowcffJBv6juJ97HzeA+NE47vZbjtczjtL/sausJpf0NpX0NpX7xhX0OX1fc3oKOOAwAAAAAQavhGGwAAAAAAA1FoAwAAAABgIAptAAAAAAAMRKENAAAAAICBKLQBAAAAADAQhTYAAAAAAAai0PaioaFBLpdLPAUNsAZyErAO8hGwFnISsA4KbS9KS0uVlJSk0tLSQIcCQOQkYCXkI2At5CRgHRTaAAAAAAAYiEIbAAAAAAADUWgDAAAAAGAgCm0AAAAAAAxEoQ0AAAAAgIEiAx0AYAUlFdVyllXL5a6RPTZKjvhoJcVFBzosAAAk0U8BVkNOwhsKbYS9A8WVumfpFq3e5fRMy8l0aH5ulvp0iw1gZAAA0E8BVkNOoj24dBxhraSiullDKUmrdjk1Y+kWlVRUBygyAADopwCrISfRXhTaCGvOsupmDWWjVbuccpbRWAIAAod+CrAWchLtRaGNsOZy17Q5v9TLfAAA/Il+CrAWchLtRaGNsGa3RbU5P9HLfAAA/Il+CrAWchLtRaGNsOZIiFZOpqPFeTmZDjkSGD0SABA49FOAtZCTaC8KbYS1pLhozc/NatZg5mQ6tCA3i8c0AAACin4KsBZyEu0V0dDQ0BDoIKzM5XIpKSlJJSUlstvtgQ4HftL4LMRSd40SbVFyJPAsRKsiJwHrIB/NQz+F9iAnzUNOwhueow3oyF8naRwBAFZFPwVYCzkJb7h0HAAAAAAAA1FoAwAAAABgIAptAAAAAAAMRKENAAAAAICBKLQBAAAAADAQhTYAAAAAAAai0AYAAAAAwEAU2gAAAAAAGIhCGwAAAAAAA1FoAwAAAABgIAptAAAAAAAMRKENAAAAAICBKLQBAAAAADAQhTYAAAAAAAai0AYAAAAAwEAU2gAAAAAAGIhCGwAAAAAAAwV9oZ2fn69rrrlGKSkpio2N1ZAhQ/TFF1945jc0NGjWrFnq3bu3YmNjNW7cOO3atSuAEQMAAAAAQllQF9pFRUUaPXq0oqKi9N5772nbtm1auHChkpOTPcs8+uijeuKJJ7R48WKtX79e8fHxmjBhgtxudwAjBwAAAACEqshAB9AZCxYsUL9+/bRkyRLPtPT0dM//GxoatGjRIt1///269NJLJUkvv/yyUlNTtXz5cl155ZWmxwwAAAAACG1B/Y3222+/rREjRujyyy9Xz549lZ2dreeff94zf8+ePSooKNC4ceM805KSknT66adr3bp1gQgZAAAAABDigvob7e+++07PPvuspk+frnvvvVeff/657rjjDkVHR+u6665TQUGBJCk1NbXJ61JTUz3zjlVVVaWqqirP7y6Xy387AMArchKwDvIRsBZyErCuoP5Gu76+XsOGDdPcuXOVnZ2tm266STfeeKMWL17s8zrnzZunpKQkz0+/fv0MjBhAR5GTgHWQj4C1kJOAdQV1od27d28NHjy4ybRBgwYpLy9PktSrVy9J0qFDh5osc+jQIc+8Y82cOVMlJSWen3379vkhcgDtRU4C1kE+AtZCTgLWFdSXjo8ePVo7d+5sMu2bb75RWlqapCMDo/Xq1UsrV67U0KFDJR25pGb9+vW65ZZbWlxnTEyMYmJi/Bo3gPYjJwHrIB8BayEnAesK6kJ72rRpGjVqlObOnasrrrhC//73v/Xcc8/pueeekyRFRETorrvu0iOPPKLMzEylp6frgQceUJ8+fTRx4sTABg8AAAAACElBXWifeuqpWrZsmWbOnKnZs2crPT1dixYt0tVXX+1Z5u6771Z5ebluuukmFRcXa8yYMVqxYoVsNlsAIwcAAAAAhKqIhoaGhkAHYWUul0tJSUkqKSmR3W4PdDhA2CMnAesgHwFrIScB6wjqwdAAAAAAALAaCm0AAAAAAAwUkHu0a2pqVFBQoIqKCvXo0UPdu3cPRBgAAAAAABjOtG+0S0tL9eyzz+rMM8+U3W7XgAEDNGjQIPXo0UNpaWm68cYb9fnnn5sVDgAAAAAAfmFKof34449rwIABWrJkicaNG6fly5dr8+bN+uabb7Ru3To9+OCDqq2t1bnnnqvzzjtPu3btMiMsAAAAAAAMZ8ql459//rlWrVqlk046qcX5p512mqZOnarFixdryZIlWr16tTIzM80IDQAAAAAAQ5lSaL/66qvtWi4mJkY333yzn6MBAAAAAMB/Aj7quMvl0vLly7V9+/ZAhwIAAAAAQKeZXmhfccUVeuqppyRJlZWVGjFihK644gplZWVp6dKlZocDAAAAAIChTC+0V61apTPOOEOStGzZMjU0NKi4uFhPPPGEHnnkEbPDAQAAAADAUKYX2iUlJZ7nZq9YsUK5ubmKi4vThRdeyGjjAAAAAICgZ3qh3a9fP61bt07l5eVasWKFzj33XElSUVGRbDab2eEAAAAAAGAoU0YdP9pdd92lq6++WgkJCUpLS9NZZ50l6cgl5UOGDDE7HAAAAAAADGV6oX3rrbfqtNNO0759+zR+/Hh16XLkS/Xjjz+ee7QBAAAAAEHP9EJbkkaMGKERI0Y0mXbhhRcGIhQAAAAAAAxlSqE9ffr0di/7+OOP+zESAAAAAAD8y5RCe9OmTU1+37hxo2pra3XiiSdKkr755ht17dpVw4cPNyMcAAAAAAD8xpRC+6OPPvL8//HHH1diYqJeeuklJScnSzoy4viUKVM8z9cGAAAAACBYmf54r4ULF2revHmeIluSkpOT9cgjj2jhwoVmhwMAAAAAgKFML7RdLpd++OGHZtN/+OEHlZaWmh0OAAAAAACGMr3QnjRpkqZMmaK33npL+/fv1/79+7V06VJdf/31uuyyy8wOBwAAAAAAQ5n+eK/FixfrN7/5ja666irV1NQcCSIyUtdff70ee+wxs8MBJEmHXG4VlVfL5a6VPTZSyXHRSrXbAh0WAACWQV8JWAs5aW2mF9pxcXF65pln9Nhjj+nbb7+VJJ1wwgmKj483OxRAkpRXWK6Zy7Zq7e5Cz7QxGSmaO2mI+qdwXAIAQF8JWAs5aX2mXzreKD4+XllZWcrKyqLIRsAccrmbNVKStGZ3oe5dtlWHXO4ARQYAgDXQVwLWQk4GB9O/0S4vL9f8+fO1cuVKff/996qvr28y/7vvvjM7JISxovLqZo1UozW7C1VUXs0lOACAsEZfCVgLORkcTC+0b7jhBn3yySf6xS9+od69eysiIsLsEAAPl7u2U/MBAAh19JWAtZCTwcH0Qvu9997Tu+++q9GjR5u9aaAZu63tFPA2HwCAUEdfCVgLORkcTL9HOzk5Wd27dzd7s0CLkuOjNSYjpcV5YzJSlBwfbXJEAABYC30lYC3kZHAwvdB++OGHNWvWLFVUVJi9aaCZVLtNcycNadZYNY7ayP0tAIBwR18JWAs5GRwiGhoaGszcYHZ2tr799ls1NDRowIABioqKajJ/48aNZobjlcvlUlJSkkpKSmS32wMdDvykyXMIbZFKjuc5hFZFTgLWQT6GF/pK6yMnwws5aW2mX8A/ceJEszcJeJVqt9EwAQDQBvpKwFrISWszvdB+8MEHzd4kAAAAAACmCdiQdBs2bND27dslSSeddJKys7MDFQoAAAAAAIYxvdD+/vvvdeWVV+rjjz9Wt27dJEnFxcU6++yz9dprr6lHjx5mhwQAAAAAgGFMH3X8V7/6lUpLS/X111/r8OHDOnz4sL766iu5XC7dcccdZocDAAAAAIChTP9Ge8WKFfrggw80aNAgz7TBgwfr6aef1rnnnmt2OAAAAAAAGMr0b7Tr6+ubPdJLkqKiolRfX292OAAAAAAAGMr0Qnvs2LG68847deDAAc+0/Px8TZs2Teecc47Z4QAAAAAAYCjTC+2nnnpKLpdLAwYM0AknnKATTjhB6enpcrlcevLJJ80OBwAAAAAAQ5l+j3a/fv20ceNGffDBB9qxY4ckadCgQRo3bpzZoQAAAAAAYLiAPEc7IiJC48eP1/jx4wOxeQAAAAAA/Mb0S8fvuOMOPfHEE82mP/XUU7rrrrvMDgcAAAAAAEOZXmgvXbpUo0ePbjZ91KhRevPNN80OBwAAAAAAQ5leaBcWFiopKanZdLvdLqfTaXY4AAAAAAAYyvRCOyMjQytWrGg2/b333tPxxx9vdjgAAAAAABjK9MHQpk+frttvv10//PCDxo4dK0lauXKlFi5cqEWLFpkdDgAAAAAAhjK90J46daqqqqo0Z84cPfzww5KkAQMG6Nlnn9W1115rdjgAAAAAABgqII/3uuWWW3TLLbfohx9+UGxsrBISEgIRBgAAAAAAhjP9Hm1Jqq2t1QcffKC33npLDQ0NkqQDBw6orKwsEOEAAAAAAGAY07/R3rt3r8477zzl5eWpqqpK48ePV2JiohYsWKCqqiotXrzY7JAAAAAAADCM6d9o33nnnRoxYoSKiooUGxvrmT5p0iStXLnS7HAAAAAAADCU6YX26tWrdf/99ys6OrrJ9AEDBig/P9/n9c6fP18RERG66667PNPcbrduu+02paSkKCEhQbm5uTp06JDP2wAAAAAAwBvTC+36+nrV1dU1m75//34lJib6tM7PP/9cf/jDH5SVldVk+rRp0/TOO+/ojTfe0CeffKIDBw7osssu82kbAAAAAAC0h+mF9rnnntvkedkREREqKyvTgw8+qAsuuKDD6ysrK9PVV1+t559/XsnJyZ7pJSUl+tOf/qTHH39cY8eO1fDhw7VkyRJ9+umn+uyzz4zYFQAAAAAAmjG90F64cKHWrl2rwYMHy+1266qrrvJcNr5gwYIOr++2227ThRdeqHHjxjWZvmHDBtXU1DSZPnDgQPXv31/r1q1rdX1VVVVyuVxNfgAEDjkJWAf5CFgLOQlYl+mF9nHHHacvv/xS9913n6ZNm6bs7GzNnz9fmzZtUs+ePTu0rtdee00bN27UvHnzms0rKChQdHS0unXr1mR6amqqCgoKWl3nvHnzlJSU5Pnp169fh2ICYCxyErAO8hGwFnISsK6IhsYHWQeZffv2acSIEXr//fc992afddZZGjp0qBYtWqRXXnlFU6ZMUVVVVZPXnXbaaTr77LNb/fa8qqqqyWtcLpf69eunkpIS2e12/+0QgBaRk4B1kI+AtZCTgHWZ/o32Sy+9pHfffdfz+913361u3bpp1KhR2rt3b7vXs2HDBn3//fcaNmyYIiMjFRkZqU8++URPPPGEIiMjlZqaqurqahUXFzd53aFDh9SrV69W1xsTEyO73d7kB0DgkJOAdZCPgLWQk4B1mV5oz5071/P87HXr1umpp57So48+KofDoWnTprV7Peecc462bt2qzZs3e35GjBihq6++2vP/qKioJs/m3rlzp/Ly8jRy5EjD9wsAAAAAAEmKNHuD+/btU0ZGhiRp+fLl+tnPfqabbrpJo0eP1llnndXu9SQmJurkk09uMi0+Pl4pKSme6ddff72mT5+u7t27y26361e/+pVGjhypn/70p4btDwAAAAAARzP9G+2EhAQVFhZKkv71r39p/PjxkiSbzabKykpDt/X73/9eF110kXJzc5WTk6NevXrprbfeMnQbAAAAAAAczfRvtMePH68bbrhB2dnZ+uabbzzPzv766681YMCATq37448/bvK7zWbT008/raeffrpT6wUAAAAAoL1M/0b76aef1siRI/XDDz9o6dKlSklJkXRkcLPJkyebHQ4AAAAAAIYK2sd7mcXlcikpKYnHJAAWQU4C1kE+AtZCTgLWYco32nl5eR1aPj8/30+RAAAAAADgX6YU2qeeeqr+53/+R59//nmry5SUlOj555/XySefrKVLl5oRFgAAAAAAhjNlMLRt27Zpzpw5Gj9+vGw2m4YPH64+ffrIZrOpqKhI27Zt09dff61hw4bp0Ucf9QyQBgAAAABAsDH1Hu3Kykq9++67WrNmjfbu3avKyko5HA5lZ2drwoQJzZ6LbQXc64KOKqmolrOsWi53jeyxUXLERyspLjrQYYWMjuRk5sDBOtCOW1H69O2rXTu2GRUiEDboI8ML/Zv1kZMINqHcrpj6eK/Y2Fj97Gc/089+9jMzNwuY5kBxpe5ZukWrdzk903IyHZqfm6U+3WIDGFl4OpCfr4sXrvC63Du/Ps+EaAAgeNG/ATBaqLcrpj/eCwhVJRXVzRoLSVq1y6kZS7eopKI6QJEBAOA7+jcARguHdoVCGzCIs6y6WWPRaNUup5xlwd9gAADCD/0bAKOFQ7tCoQ0YxOWuaXN+qZf5AABYEf0bAKOFQ7tCoQ0YxG6LanN+opf5AABYEf0bAKOFQ7tCoQ0YxJEQrZxMR4vzcjIdciSExgiKAIDwQv8GwGjh0K6YOup4o127dumjjz7S999/r/r6+ibzZs2aFYiQgE5LiovW/NwszVi6RauOGT1xQW5WyDyqAAAQXujfABgtHNoV0wvt559/XrfccoscDod69eqliIgIz7yIiAgKbQS1Pt1i9eTkbDnLqlXqrlGiLUqOhNB5HiAAIDzRvwEwWqi3K6YX2o888ojmzJmje+65x+xNA6ZIigudBgIAgEb0bwCMFsrtiun3aBcVFenyyy83e7MAAAAAAJjC9EL78ssv17/+9S+zNwsAAAAAgClMv3Q8IyNDDzzwgD777DMNGTJEUVFNh26/4447zA4JAAAAAADDmF5oP/fcc0pISNAnn3yiTz75pMm8iIgICm0AAAAAQFAzvdDes2eP2ZsEAAAAAMA0AXmOdqOGhgZJavKILyAQSiqq5SyrlstdI3tslBzxoTsCIgAA4Yw+H7COUM7HgBTaL7/8sh577DHt2rVLkvSTn/xE/+///T/94he/CEQ4CHMHiit1z9ItWr3L6ZmWk+nQ/Nws9ekWG8DIAACAkejzAesI9Xw0fdTxxx9/XLfccosuuOACvf7663r99dd13nnn6eabb9bvf/97s8NBmCupqG6W4JK0apdTM5ZuUUlFdYAig5ncVVWKT0zy+pM5cHCgQwUA+Ig+H7COcMhH07/RfvLJJ/Xss8/q2muv9Uy75JJLdNJJJ+m3v/2tpk2bZnZICGPOsupmCd5o1S6nnGXVIXP5ClrXUF+nixeu8LrcO78+z4RoAAD+QJ8PWEc45KPp32gfPHhQo0aNajZ91KhROnjwoNnhIMy53DVtzi/1Mh8AAAQH+nzAOsIhH00vtDMyMvT66683m/7Xv/5VmZmZZoeDMGe3RbU5P9HLfAAAEBzo8wHrCId8NP3S8Yceekg///nPtWrVKo0ePVqStHbtWq1cubLFAhzwJ0dCtHIyHVrVwqUrOZkOORKC+5IVAABwBH0+YB3hkI+mf6Odm5ur9evXy+FwaPny5Vq+fLkcDof+/e9/a9KkSWaHgzCXFBet+blZysl0NJmek+nQgtysoL83BAAAHEGfD1hHOORjQB7vNXz4cP35z38OxKaBZvp0i9WTk7PlLKtWqbtGibYoORJC5xl+AADgCPp8wDpCPR9NKbRdLpfsdrvn/21pXA4wU1Jc6CQ1AABoHX0+YB2hnI+mFNrJyck6ePCgevbsqW7duikiIqLZMg0NDYqIiFBdXZ0ZIQEAAAAA4BemFNoffvihunfvLkn66KOPzNgkABjOXVWl+MSkdi3bp29f7dqxzc8RAQAAwIpMKbTPPPNMz//T09PVr1+/Zt9qNzQ0aN++fWaEAwA+aaiv08ULV7Rr2Xd+fZ6fowEAAIBVmT7qeHp6un744Ydm0w8fPqz09HSzwwEAAAAAwFCmF9qN92Ifq6ysTDabzexwAAAAAAAwlGmP95o+fbokKSIiQg888IDi4uI88+rq6rR+/XoNHTrUrHAAAAAAAPAL0wrtTZs2STryjfbWrVsVHf3jMO7R0dE65ZRT9Jvf/MascAAAAAAA8AvTCu3G0canTJmi//3f/+V52QAAAACAkGRaod1oyZIlZm8SAAAAAADTmF5oS9IXX3yh119/XXl5eaqurm4y76233gpESAhz+UUVcrlr5aqsUVJslBJtkeqbHOf9hQCAsEOfAVgLOQkrMr3Qfu2113TttddqwoQJ+te//qVzzz1X33zzjQ4dOqRJkyaZHQ6gvYXlunfZVq3dXeiZNiYjRXMmDVFaSnwAIwMAWA19BmAt5CSsyvTHe82dO1e///3v9c477yg6Olr/+7//qx07duiKK65Q//79zQ4HYS6/qKJZ4yxJa3YX6r5lW5VfVBGgyAAAVkOfAVgLOQkrM73Q/vbbb3XhhRdKOjLaeHl5uSIiIjRt2jQ999xzZoeDMOdy1zZrnBut2V0ol7vW5IgAAFZFnwFYCzkJKzO90E5OTlZpaakkqW/fvvrqq68kScXFxaqo4K9OMJersqbN+aXutucDAMIHfQZgLeQkrMz0e7RzcnL0/vvva8iQIbr88st155136sMPP9T777+vc845x+xwEObssVFtzk+0tT0fABA+6DMAayEnYWWmF9pPPfWU3G63JOm+++5TVFSUPv30U+Xm5ur+++83OxyEObstUmMyUrSmhcuOxmSkyG4LyMD8AAALos8ArIWchJWZeul4bW2t/v73v6tr165HNt6li2bMmKG3335bCxcuVHJyspnhAOqbHKc5k4ZoTEZKk+mNo1XyaAgAQCP6DMBayElYmal/5omMjNTNN9+s7du3m7lZoE1pKfFakJsll7tWpe4aJdqiZOf5iwCAFtBnANZCTsKqTL+e4rTTTtPmzZuVlpZm9qaBVjU0/tsgRRz1OwCgc/YXVajUXStXZY2SYqOUYIvUcUF+Atw3OU59Ax0E4CNyEjCH6YX2rbfequnTp2vfvn0aPny44uObPkg+KyvL7JAQ5vYWljd7BmPjJUdpKfFtvBIA0BbaV8BayEnAPKY/3uvKK6/Unj17dMcdd2j06NEaOnSosrOzPf92xLx583TqqacqMTFRPXv21MSJE7Vz584my7jdbt12221KSUlRQkKCcnNzdejQISN3CUFsf1FFsw5HOvLsxfuWbdX+Ih45BwC+oH0FrIWcBMxl+jfae/bsMWxdn3zyiW677Tadeuqpqq2t1b333qtzzz1X27Zt83xTPm3aNL377rt64403lJSUpNtvv12XXXaZ1q5da1gcCF6l7lrtLCjVn64boZ72GJW565Roi9Qhl1v3LN2iUndtp9ZfUlEtZ1m1XO4a2WOj5IiPVlJctEHRA4B1+bt9BXwVrn0zOQm0zF9tgumFtpH3Zq9YsaLJ7y+++KJ69uypDRs2KCcnRyUlJfrTn/6kV155RWPHjpUkLVmyRIMGDdJnn32mn/70p4bFguBUUVWjv9zwU83++9fNLqP6yw0/VXlVtc/rPlBcqXuWbtHqXU7PtJxMh+bnZqlPt9hOxQ0AVufP9hXwVTj3zeQk0Jw/2wTTC+2XX365zfnXXnutz+suKSmRJHXv3l2StGHDBtXU1GjcuHGeZQYOHKj+/ftr3bp1FNqQI9HW6mVUD//9a82ZNMSn9ZZUVDdLWklatcupGUu36MnJ2WHx13MA4ctf7Svgq3Dvm8lJoCl/twmmF9p33nlnk99rampUUVGh6OhoxcXF+Vxo19fX66677tLo0aN18sknS5IKCgoUHR2tbt26NVk2NTVVBQUFLa6nqqpKVVVVnt9dLpdP8SA4VFTXNetwGq3ZXaiK6jqf1ussq26WtI1W7XLKWVYd0p25kchJwDo6ko/+al8BX4Vi30xOAr7zd5tg+mBoRUVFTX7Kysq0c+dOjRkzRq+++qrP673tttv01Vdf6bXXXutUfPPmzVNSUpLnp1+/fp1aH6zN2/1Ivt6v5HLXeFlv2/PxI3ISsI6O5KO/2lfAV6HYN5OTgO/83SaYXmi3JDMzU/Pnz2/2bXd73X777fr73/+ujz76SMcdd5xneq9evVRdXa3i4uImyx86dEi9evVqcV0zZ85USUmJ52ffvn0+xYTgYLe1fVGHt/mtvy6qzfmJXubjR+QkYB0dyUd/ta+Ar0KxbyYnAd/5u02wRKEtSZGRkTpw4ECHXtPQ0KDbb79dy5Yt04cffqj09PQm84cPH66oqCitXLnSM23nzp3Ky8vTyJEjW1xnTEyM7HZ7kx+EruT4aI3JSGlx3piMFCXH+3a5iCMhWjmZjhbn5WQ65EgIrkvTAomcBKyjI/nor/YV8FUo9s3kJOA7f7cJphfab7/9dpOfv/3tb1q8eLGuueYajR49ukPruu222/TnP/9Zr7zyihITE1VQUKCCggJVVlZKkpKSknT99ddr+vTp+uijj7RhwwZNmTJFI0eOZCA0SJJS7TbNnTSkWcczJiNFcycNUard5tN6k+KiNT83q1ny5mQ6tCA3K+juAQOAjvJX+wr4Ktz7ZnISaMrfbYLp14hMnDixye8RERHq0aOHxo4dq4ULF3ZoXc8++6wk6ayzzmoyfcmSJfrlL38pSfr973+vLl26KDc3V1VVVZowYYKeeeYZX8NHCOqfEq+FVwxVUXm1XO5a2W2RSo6P7nSH06dbrJ6cnC1nWbVK3TVKtEXJkRAez+oEAMl/7Svgq3Dvm8lJoCl/tgmmF9r19fWGrauhocHrMjabTU8//bSefvppw7aL0JNqt/mlk0mKC5/OGwBa4q/2FfBVuPfN5CTQlL/aBMvcow0AAAAAQCgw5Rvt6dOnt3vZxx9/3I+RAAAAAADgX6YU2ps2bWry+8aNG1VbW6sTTzxRkvTNN9+oa9euGj58uBnhwAclFdVyllXL5a6RPTZKjvjwvuwKAADAF5xTwUo4Hv3HlEL7o48+8vz/8ccfV2Jiol566SUlJydLkoqKijRlyhSdccYZZoSDDjpQXKl7lm7R6l1Oz7ScTIfm52apT7fYAEYGAAAQPDingpVwPPqX6fdoL1y4UPPmzfMU2ZKUnJysRx55pMOjjsP/SiqqmyWgJK3a5dSMpVtUUlEdoMgAAACCB+dUsBKOR/8zfdRxl8ulH374odn0H374QaWlpWaHAy+cZdXNErDRql1OOcuqQ+LykvyiCrnctXJV1igpNkqJtkj1TY4LdFgIA5kDB+tAfr7X5fr07atdO7aZEBEAX9CPwJtwOaeyCnKybRyP/md6oT1p0iRNmTJFCxcu1GmnnSZJWr9+vf7f//t/uuyyy8wOB1643DVtzi/1Mj8Y7C0s173Ltmrt7kLPtDEZKZozaYjSUuIDGBnCwYH8fF28cIXX5d759XkmRAPAF/QjaI9wOKeyCnLSO45H/zP90vHFixfr/PPP11VXXaW0tDSlpaXpqquu0nnnnadnnnnG7HDghd0W1eb8RC/zrS6/qKJZQyxJa3YX6r5lW5VfVBGgyAAAwYB+BO0V6udUVkFOtg/Ho/+Z/o12XFycnnnmGT322GP69ttvJUknnHCC4uP565IVORKilZPp0KoWLi3JyXTIkRDcl5S43LXNGuJGa3YXyuWuVV8T4mDERwAITmb1I/QTwS/Uz6mswirndmboTLvA8eh/phfajeLj45WVlRWozaOdkuKiNT83SzOWbmmSiDmZDi3IzQr6Tt5VGfjLZhjxEQCClxn9CP1EaAj1cyqrsMK5nRk62y5wPPpfwAptBI8+3WL15ORsOcuqVequUaItSo6E0PhLuj02sJfNeBvx8cnJ2SHxPgNAqPJ3P0I/EVpC+ZzKKgJ9bmcGo9oFjkf/otBGuyTFhWbS2W2RGpORojUtXGI0JiNFdpt/U+ToER/jortq6ph0Zffrpqraetmiuqq4oiYk33cACBX+7kecZdXasLdIt4/NaNI/bMwr0gtr9jAycBAK1XMqqwj0uZ0ZjGwXOB79J/iPNKAT+ibHac6kIbpv2dYmDXLjyJT+fgxE44iPcdFd9cTkbC1Zu0dPfbjbM/+M/16+w6WBAGBN/u5HyqpqWuwfRmek6InJ2SqvCo3LYAGjBPrczgy0C8GBQhthLy0lXgtys+Ry13oum7Gb9KzFxhEfp45J15K1e5oN3rGaSwMBwPL82Y90i43Wo//c2ax/aPx97sQhnd4GEGoCeW5nBtqF4EChDRyloUGKMHF7jSM+Zvfr1uQvkkdbtcvJpYEAYHF9k+P8MpJxdV19qyMor91dqOq6ej9sFQh+/srJRvlFFXK5a+WqrFFSbJQSTSzkaReCA4U2wt7ewvJmz1tsvLwoLcW/j51rHPFx+0FXm8uFygiZAICOKauqbXN+uZf5AIwXyHNHiXYhWHQJdABAIOUXVTRrKKUjz1m8b9lW5RdV+D2GPt1i1b97238BDYURMgEAHRcX3bXN+bFe5gMwlhXOHe1ezgs5b7QGvtEOc4G87MUKXO7aVi+9WbO7UC53rV8vO2rUMzFGOZmOJs8xbJST6ZAjIVr7iypUetRnlWCL1HFh9FkBCE5m9DNmt49m9p1RXbtodEZKi33V6IwURXXlOxN0TLicT/hrP61w7th462Fb540IPArtMBboy16swNsl2WZdst14CfmMpVuaNJo5/x11vKSyRjPD/LMCEHzM6GfM7svM3l5hWZWmjE6XpCbbHJ2Roimj03W4vErH90gwfLsITeFy7ufP/XRVBv7c0dt5I+P6WAOFdpjydtnLgtyssPhm2x7b9qU13uYbqU+3WD05OVvOsmrPCJmOhGiVVdU2K7KlHz+r+blZIfmXaADBzYx+Zr+XbRjdPgai70ywRem6JZ9r6ph0TR2drqraesVEdtGmfcW649VNWnrLKEO3h9Bldr4Eir/309u5oVmXbbd23kiRbR0U2mHKCpe9WEGMl0vyYky+JC8prnkDeaDE3eZnVepmwAsA1mNGP1PqZRtGt4+B6DvttkgN69/ykynGZKTIbuNUDu1jdr4Eir/3026L1JiMlCbP6G5kdk62dN4I66B1NkBJRbWcZdVyuWtkj42SIz4wB31H7hmzwmUvVlBYXqUbxhyvC4f0VqrdpqraetmiuqqgpFK9k2JVWF6ldBMvyWvpWCqzyOXtANARZvQzZvdlrW3PkRCtBblZkqT13xUaej9o3+Q4zZ00RGt3O9XzqH7qUEmlRmc4wuLqs1ATqPNGV2WN51jtaY9RmbtOibZIHXK5dc/SLSFzPuHvdoGcDD3+ykkK7U46UFype5Zu0epj7o+Yn5ulPt1iTYujo/eiWOWyl0BLsEXJ5a7VP7YebPKXyTMyUjRlTLoSTHwfWjuWHrrkJMVFd1VFdV2LrwuXzwpAcDGjnzG7L2tpe46EaP3lhp9q9t+/9tt9r5Fdu+gfWwu0eveP/cMZmQ6deWLPTq8b5grkeWO3+KhWj9W/3PBTKaLBr9s3ixntAjkZOvyZkwxV2QklFdXNPhhJWrXLqRlLt6ikotqUOHx5zEDjZS8tCadL0RJiIvWnNXuaXf6zenehXlizRwkx5rwPbR1Ls97+Wg9cOKjF143JSFFimHxWAIKLGf1MopdtGN0+trRPC3KzmhUu0o998P5OPurH0z/sbto/rDb5XAOdF+jzxvjoyFaP1Yf//rXio0PjfMLf7QI5GTr8nZMU2p3gLKtu9sE0WrXLKWeZOYnWnnvGjtU3OU5zJg1p1hA1/gU+XC57qaqpb/O9q6qpNyWOto6l1bucyk5L1hmtfFahMHAJgNBjRj9znJdtGN0+trRPPe0xfr0f1CrnGui8QH+W4XKPtr/bhUB/jjCOvz/L0PjTVYC4LHLvrK/3oqSlxGtBbpZc7lrPaIX2sHuOtkU+Qy/b2eMs1wVDeuu+iwbLVXnks0oM0edeAggdZvQzaSnxmp+bpdKjtuHP9vHYfaqsavm2nkad7Ues0k+h8wL9WYbT+Dz+bBcC/TnCOP7+LCm0O8Hu5R4Ps+6d7cy9KH2T48JidPHWWOYz9LKd6K5dNHPZVxqTkRI2j14DEBrM6GfM/qPj0fu0/aCrzWU7249YpZ9C5wX6swy38Xn81S7ERXdtc36sl/mwDn/nJIV2JzgSopWT6WjyoPhGOZkOORL8M4LksSPjJdoidc7AHlq544dmyxp1H5wvo/F1ZBT0QAnUZ9iROEZnpGjTvmJJ4fXoNQBor0D2N/HRXdt81E98J0+6rdJPScaMzGuVJ7UEQqA/y0Qvj6UyckwDK5wD+iuGqK5ddP7JqfrZ8H7NRm9/c8M+RZn8aFj4zt85SaHdCUlx0Zqfm6UZS7c0+YByMh1akJvll46jtZHxHp54sqSvmxTbRt0H58tofB0dBT1QkuKi9fDEk3Xfsq1NOp4xGSl6eOLJpnX+rR1LozNSNGV0uu54dZNnGpckAcCPAt3fOMvceuCik/Tw379u1o/MuvgkOcvc6t+JOKzSTxkxMq9VntQSKIE4bzxa473LLR1LRo5pEOic9HcMJRVVuvu8gbp/+VfN1v/wxJNVUlElybxHw8J3/s7JiIaGhtAYy99PXC6XkpKSVFJSIrvd3uIyjX+dbbwHxJHgn7/OllRU6/ZXN7V4035OpkNzLxvS5F4UI+6D87bNJydnN9vX/KIK3b10S4sDbljt0uf8ogo98LevNLhPkrL7dVNVbb1iIrto075ibT9QotmXnmxqrCUV1TpQ4tYeZ7knjhfW7GnyaK/37jxDg3q3fCyGg/bkZKP4xCRdvHCF13W+fmuOrnhmlWHLSdI7vz5P5aUlXpdrb4ztXR9gpo7koz9Yob/ZftClX/xpfZNnEyfYuup7V5XuWbpF/3f96Z1qs63QT/lyLuCPdYQKf543ticn9xdV+G1MAyvkpL9jaKmIP3r9VvtSCd75Kyf5RtsASXHmXPbkbWS8qpp6wwuw9ozGd+y+t2cUdKtc+uxy1+qz7w5rcJ8kz7SIiAhJ0rrvDpsea1JctMqqavXK+r2tXtoVLo9eAxAa/HkJqRX6G7stUgN7Jer6l75oNs+INtvlrtWHO37Qhy3cHiZJv5ng/3305VzAH+sIFWadN7Ym4r//NjQc+X9EWwt3kBVy0t8xVFTXtbn+o78cQXDwV05yxh5EAjHKoS/bDKZRLcuravTE5GwtWbtHT3242zN9dEaKnpicrfIq82Pt6+XSLqtcDYC2uauqFJ+Y5H05d6UJ0QCB4e9LSK3Q3/i7zbbCPhpx/sFIzdYQDjnp7xi8PQYtVB6Ths6j0A4igRit0pdtBtOolinxMfr9B7ua/WWy8fdHLj05EGHx6LUQ0FBf1+7L1oFQlF9U0eLllWt2F+q+ZVsNuYTUKv2NP9tsK+yjEecfgR5xG+GTk/6OwdtVKlx5iEYcCUEkEKNV+rJNu5dRLa3UAFXV1bd6+c/a3YWqqqs3OaIfGflInMb7sRov3Uw45n4sK4wOCiC0uNy12pRXrNvHZnjuLbZFddXGvCK9sGaPIZeQ2v/71I1BR92/3LiN7QdKTO1v/PUYMyv0qUacfwR6xG2Yc1m33RapSUN7a8qY4xXZtYtKK4+MLl9TV68la77z2/F69HlMz8QYnZGRotV+ypnk+Og2czI5nmMZR1in4oFXgRit0pdtBtOlz94uL/I2Pxh4u0zMCqODAgg9Ztya0zc5TrMuPkn3LtvaZBtW7G98ZYU+1Yjzj0CPuA1zLuvumxynu8af2Op5hT+O12PPY+Kiu+qF606VFKHVu3881oyKIdVu09xJQ3RvCzk5d9IQpdptnVo/QgeFdpDp0y1WT07ONmWU885sM1guffZ2+VCwX8q238tlYvNzs/x+GRmA8GTGrTn5RRW6LwzaMCv0qUacfwTiHAY/MuOy7vacdxg1wrnU8uXwFdV1mvrS53rgwkG676JBclUanzP9U+K18IqhKiqvlstdK7stUsnx0RTZaIJCOwgZMTJeRy8V9mWb/rqMzkhWuCTPn0q9XCbmbb6VRogPd+0dXE2S+vTtq107tvk5IqBtZtya43LXtth+S6HXhlmhTzXi/CPQI26HMzPOedpz3mGk1i6Hr6iu08xlX+m9O8/Qaekphm6zUardRmGNNgV3FQGfcKnwj6xwSZ4/eb1MzAKjg6J92ju4mnTkmdtAoLkq/H9rjrc2ijYM+JEZ5zxmjzpuhVHOgdZQaIcZM0acDDZWuCTPX7xeJmaB0UEBhCYz2hdvbZy3+UA4yS+q0Ox3vtbQ/smaMjpdVbX1ionsok37ijX7na81+9KTg27UcSuMcg60hkI7zJgx4mQwssIlef6Q6OUyMW/zfb2MjFHMAbSn/emsmK5dNDojpcV+bXRGimK6dun0NoBQ4XLXauWOH7Ryxw8tzv/NhM6fA5qR90cL9VsAEdzogcIMl9iEl+P+e5nYmIym9yc1Xibmbb4vxfHewnLdvXSLzv/f1fr5c5/pvP9drXuWbtHewvJO7QuA4NKe9qezCsurNGV0ukYfs43RGSmaMjpdheVVnd4GECrMOAc0I++P1tcP5zGAUfgzT5jhEpvwk5YSr/m5WSo96tL4xKOeo23kpfPcmgCgUX5RhRa9v1N3nzdQ9/73ebqJsVGqravXovd36jcTBna6PYiPidK1L3yuqWPSNfWYS2HveHWTlt4yyqC9AYKfGeeAZuT9sUL5FkAENwrtMMMlNuHJ21+Qjbp0nlsTADRyuWu1bPNBLdt8sMX5N52Z0en2wG6L1LD+3Zo8Q7sRfRrQlBnngGbkfUtC9RZABDcuHQ8zXGIDf+LWBACNzGgP6NOA9jMjXzgPAH7En3rDEJfYwF+4NQHBIHPgYB3Iz2/XsjyT3HdmtQf0aUD7+TtfOA8AfkShHaa4xAb+wK0JCAYH8vN5JrkJzGwP6NOA9vNnvnAeAPyIS8cBGIbLOAE0oj0Awg95D/yIPysBMBSXcQJoRHsAhB/yHjiCQhuA4biME0Aj2gMg/JD3AIU2AIQtfwwK1t51MsiYcRjcDQAA66HQBoAw5Y9Bwdq7TgYZMw6DuwEAYD0MhgYAAAAAgIH4RtuLhoYGSZLL5QpwJEBoS0xMVEREhNflOpaTDaqrrWvX9o1ezh/rbP+2Gwx/f4xfZ3vX5w/+2O9A8s/+tCcn6SMBc/injwTgq/bkZERDY0aiRfv371e/fv0CHQYQ8kpKSmS3270uR04C5mhPTpKPgDnoIwFraU9OUmh7UV9frwMHDrT7L4mB4HK51K9fP+3bt69djTBaxvvYeZ15D9ubY8GQk0YIx+Mx3PbZ6vvbnhxrbz5afV+NFE77KoXX/gZyX43uI/ncQlM47atk/Zzk0nEvunTpouOOOy7QYbSL3W4Pi6TyN97HzvPnexhMOWmEcDwew22fg3l/O5qPwbyvHRVO+yqF1/5aeV/Jydaxr6HLqvvLYGgAAAAAABiIQhsAAAAAAANRaIeAmJgYPfjgg4qJiQl0KEGN97HzeA+NE47vZbjtczjtL/sausJpf0NpX0NpX7xhX0OX1feXwdAAAAAAADAQ32gDAAAAAGAgCm0AAAAAAAxEoQ0AAAAAgIEotAEAAAAAMBCFNgAAAAAABqLQBgAAAADAQBTaXjQ0NMjlcomnoAHWQE4C1kE+AtZCTgLWQaHtRWlpqZKSklRaWhroUACInASshHwErIWcBKyDQhsAAAAAAANRaAMAAAAAYCAKbQAAAAAADEShDQAAAACAgSi0AQAAAAAwUGSgA0BwKKmolrOsWi53jeyxUXLERyspLjrQYRkm1PcPvjHquOD4AgAAVsQ5iv/eAwpteHWguFL3LN2i1bucnmk5mQ7Nz81Sn26xAYzMGKG+f/CNUccFxxcAALAizlH8+x5w6TjaVFJR3ezgk6RVu5yasXSLSiqqAxSZMUJ9/+Abo44Lji8AAGBFnKP4/z2g0EabnGXVzQ6+Rqt2OeUsC+4kDPX9g2+MOi44vgAAgBVxjuL/94BLx9Eml7umzfmlXuZbXajvH3xj1HHB8QX8KC8vT05nyyc0RnE4HOrfv79ftwEAoYBzFP+/BxTaaJPdFtXm/EQv860u1PcPvjHquOD4Ao7Iy8vTwIGDVFlZ4dftxMbGaceO7RTbAOAF5yj+fw8otNEmR0K0cjIdWtXCZRU5mQ45EoJ7VMJQ3z/4xqjjguMLOMLpdKqyskKnT31Q9t4D/LIN18H/aP0LD8npdFJoA4AXnKP4/z2g0EabkuKiNT83SzOWbmlyEOZkOrQgNyvoh/8P9f2Db4w6Lji+gKbsvQeoe/8TAx0GAIQ9zlH8/x5QaMOrPt1i9eTkbDnLqlXqrlGiLUqOhNB5xl6o7x98Y9RxwfEFAACsiHMU/74HFNpol6S40E66UN8/+Mao44LjCwAAWBHnKP57D3i8FwAAAAAABqLQBgAAAADAQBTaAAAAAAAYiEIbAAAAAAADUWgDAAAAAGAgCm0AAAAAAAxEoQ0AAAAAgIEotAEAAAAAMBCFNgAAAAAABqLQBgAAAADAQEFfaOfn5+uaa65RSkqKYmNjNWTIEH3xxRee+Q0NDZo1a5Z69+6t2NhYjRs3Trt27QpgxAAAAACAUBbUhXZRUZFGjx6tqKgovffee9q2bZsWLlyo5ORkzzKPPvqonnjiCS1evFjr169XfHy8JkyYILfbHcDIAQAAAAChKjLQAXTGggUL1K9fPy1ZssQzLT093fP/hoYGLVq0SPfff78uvfRSSdLLL7+s1NRULV++XFdeeaXpMQMAAAAAQltQf6P99ttva8SIEbr88svVs2dPZWdn6/nnn/fM37NnjwoKCjRu3DjPtKSkJJ1++ulat25di+usqqqSy+Vq8gMgcMhJwDrIR8BayEnAuoK60P7uu+/07LPPKjMzU//85z91yy236I477tBLL70kSSooKJAkpaamNnldamqqZ96x5s2bp6SkJM9Pv379/LsTANpETgLWQT4C1kJOAtYV1IV2fX29hg0bprlz5yo7O1s33XSTbrzxRi1evNjndc6cOVMlJSWen3379hkYMYCOIicB6yAfAWshJwHrCup7tHv37q3Bgwc3mTZo0CAtXbpUktSrVy9J0qFDh9S7d2/PMocOHdLQoUNbXGdMTIxiYmL8EzCADiMnAesgHwFrIScB6wrqb7RHjx6tnTt3Npn2zTffKC0tTdKRgdF69eqllStXeua7XC6tX79eI0eONDVWAAAAAEB4COpvtKdNm6ZRo0Zp7ty5uuKKK/Tvf/9bzz33nJ577jlJUkREhO666y498sgjyszMVHp6uh544AH16dNHEydODGzwAAAAAICQFNSF9qmnnqply5Zp5syZmj17ttLT07Vo0SJdffXVnmXuvvtulZeX66abblJxcbHGjBmjFStWyGazBTByAAAAAECoCupCW5IuuugiXXTRRa3Oj4iI0OzZszV79mwTowIAAAAAhKugvkcbAAAAAACrodAGAAAAAMBAFNoAAAAAABgo6O/RBoxQUlEtZ1m1XO4a2WOj5IiPVlJctGXXi9AWqOOG4xUAAJjJCuce/oqBQhth70Bxpe5ZukWrdzk903IyHZqfm6U+3WItt16EtkAdNxyvAADATFY49/BnDFw6jrBWUlHdLLkkadUup2Ys3aKSimpLrRehLVDHDccrAAAwkxXOPfwdA4U2wpqzrLpZcjVatcspZ5lvCeav9SK0Beq44XgFAABmssK5h79joNBGWHO5a9qcX+plvtnrRWgL1HHD8QoAAMxkhXMPf8cQkHu0a2pqVFBQoIqKCvXo0UPdu3cPRBiA7LaoNucneplv9noR2gJ13HC8AgAAM1nh3MPfMZj2jXZpaameffZZnXnmmbLb7RowYIAGDRqkHj16KC0tTTfeeKM+//xzs8IBJEmOhGjlZDpanJeT6ZAjwbcRB/21XoS2QB03HK8AAMBMVjj38HcMphTajz/+uAYMGKAlS5Zo3LhxWr58uTZv3qxvvvlG69at04MPPqja2lqde+65Ou+887Rr1y4zwgKUFBet+blZzZIsJ9OhBblZPg/t76/1IrQF6rjheAUAAGaywrmHv2Mw5dLxzz//XKtWrdJJJ53U4vzTTjtNU6dO1eLFi7VkyRKtXr1amZmZZoQGqE+3WD05OVvOsmqVumuUaIuSI6Hzz8/z13oR2gJ13HC8AgAAM1nh3MOfMZhSaL/66qvtWi4mJkY333yzn6MBmkuK809S+2u9CG2BOm44XgEAgJmscO7hrxgCPuq4y+XS8uXLtX379kCHAgAAAABAp5leaF9xxRV66qmnJEmVlZUaMWKErrjiCmVlZWnp0qVmhwMAAAAAgKFML7RXrVqlM844Q5K0bNkyNTQ0qLi4WE888YQeeeQRs8MBAAAAAMBQphfaJSUlnudmr1ixQrm5uYqLi9OFF17IaOMAAAAAgKBneqHdr18/rVu3TuXl5VqxYoXOPfdcSVJRUZFsNpvZ4QAAAAAAYChTRh0/2l133aWrr75aCQkJSktL01lnnSXpyCXlQ4YMMTscAAAAAAAMZXqhfeutt+q0007Tvn37NH78eHXpcuRL9eOPP557tAEAAAAAQc+0QvuMM87QpZdeqksvvVQjRozQiBEjmsy/8MILzQoFAAAAAAC/Me0e7RtvvFHr1q3TsGHDNGjQIN1zzz1au3atGhoazAoBAAAAAAC/M63Qvvbaa7V06VI5nU4tXLhQxcXFuvzyy9WrVy9NnTpVy5cvV2VlpVnhAAAAAADgF6aPOh4TE6MLLrhAf/jDH3TgwAG9/fbb6t27tx544AGlpKTooosu0tq1a80OCwAAAAAAQ5heaB/r9NNP15w5c7R161Zt3bpV55xzjg4ePBjosAAAAAAA8Inpo44fraysTPX19Z7fe/TooWnTpgUwIgAAAAAAOsf0b7T37NmjCy+8UPHx8UpKSlJycrKSk5PVrVs3JScnmx0OAAAAAACGMv0b7WuuuUYNDQ164YUXlJqaqoiICLNDAAAAAADAb0wvtL/88ktt2LBBJ554otmbBgAAAADA70y/dPzUU0/Vvn37zN4sAAAAAACmMP0b7T/+8Y+6+eablZ+fr5NPPllRUVFN5mdlZZkdEgAAAAAAhjG90P7hhx/07bffasqUKZ5pERERamhoUEREhOrq6swOCQAAAAAAw5heaE+dOlXZ2dl69dVXGQwNAAAAABByTC+09+7dq7ffflsZGRlmbxoAAAAAAL8zfTC0sWPH6ssvvzR7swAAAAAAmML0b7QvvvhiTZs2TVu3btWQIUOaDYZ2ySWXmB0SAAAAAACGMb3QvvnmmyVJs2fPbjaPwdDap6SiWs6yarncNbLHRskRH62kuOhAhwUELXIKAAB0BOcO8Mb0Qru+vt7sTYaUA8WVumfpFq3e5fRMy8l0aH5ulvp0iw1gZEBwIqcAAEBHcO6A9jD9Hm34rqSiullSS9KqXU7NWLpFJRXVAYoMCE7kFAAA6AjOHdBephTar732WruX3bdvn9auXevHaIKXs6y6WVI3WrXLKWcZiQ10BDkFAAA6gnMHtJcphfazzz6rQYMG6dFHH9X27dubzS8pKdE//vEPXXXVVRo2bJgKCwvNCCvouNw1bc4v9TIfQFPkFAAA6AjOHdBeptyj/cknn+jtt9/Wk08+qZkzZyo+Pl6pqamy2WwqKipSQUGBHA6HfvnLX+qrr75SamqqGWEFHbstqs35iV7mA2iKnAIAAB3BuQPay7TB0C655BJdcsklcjqdWrNmjfbu3avKyko5HA5lZ2crOztbXbpwy3hbHAnRysl0aFULl6vkZDrkSGCkQ6AjyCkAANARnDugvUwfddzhcGjixIlmbzYkJMVFa35ulmYs3dIkuXMyHVqQm8UjBYAOIqcAAEBHcO6A9jK90Ebn9OkWqycnZ8tZVq1Sd40SbVFyJPDcPsBX5BQAAOgIzh3QHhTaQSgpjkQGjEROAQCAjuDcAd5wUzQAAAAAAAai0AYAAAAAwECmF9offfSR2ZsEAAAAAMA0phfa5513nk444QQ98sgj2rdvn2HrnT9/viIiInTXXXd5prndbt12221KSUlRQkKCcnNzdejQIcO2CQAAAADAsUwvtPPz83X77bfrzTff1PHHH68JEybo9ddfV3V1tc/r/Pzzz/WHP/xBWVlZTaZPmzZN77zzjt544w198sknOnDggC677LLO7gIAAAAAAK0yvdB2OByaNm2aNm/erPXr1+snP/mJbr31VvXp00d33HGHvvzyyw6tr6ysTFdffbWef/55JScne6aXlJToT3/6kx5//HGNHTtWw4cP15IlS/Tpp5/qs88+M3q3AAAAAACQFODB0IYNG6aZM2fq9ttvV1lZmV544QUNHz5cZ5xxhr7++ut2reO2227ThRdeqHHjxjWZvmHDBtXU1DSZPnDgQPXv31/r1q1rdX1VVVVyuVxNfgAEDjkJWAf5CFgLOQlYV0AK7ZqaGr355pu64IILlJaWpn/+85966qmndOjQIe3evVtpaWm6/PLLva7ntdde08aNGzVv3rxm8woKChQdHa1u3bo1mZ6amqqCgoJW1zlv3jwlJSV5fvr169fh/QtFJRXV+vb7Mm3KK9K3P5SppML3S/2t6JDLrR0HXfr3nsPaUeDSIZfb53WF+ntlNjNyks/MGMH4PhqZ++GAPhKwFnIycKzQ54VCH2aF99FfIs3e4K9+9Su9+uqramho0C9+8Qs9+uijOvnkkz3z4+Pj9bvf/U59+vRpcz379u3TnXfeqffff182m82w+GbOnKnp06d7fne5XGHfaB0ortQ9S7do9S6nZ1pOpkPzc7PUp1tsACMzRl5huWYu26q1uws908ZkpGjupCHqnxLfoXWF+nsVCP7OST4zYwTj+2hk7ocL+kjAWsjJwLBCnxcKfZgV3kd/Mv0b7W3btunJJ5/UgQMHtGjRoiZFdiOHw+H1MWAbNmzQ999/r2HDhikyMlKRkZH65JNP9MQTTygyMlKpqamqrq5WcXFxk9cdOnRIvXr1anW9MTExstvtTX7CWUlFdbMEkKRVu5yasXRL0P/V6ZDL3ayRkqQ1uwt177KtHfrLYKi/V4Hiz5zkMzNGML6PRuZ+OKGPBKyFnDSfFfq8UOjDrPA++pvphfaDDz6oyy+/XDExMU2m19bWatWqVZKkyMhInXnmmW2u55xzztHWrVu1efNmz8+IESN09dVXe/4fFRWllStXel6zc+dO5eXlaeTIkcbvWIhyllU3S4BGq3Y55SwL7iQoKq9u1kg1WrO7UEXl7d+/UH+vQhGfmTGC8X00MvcBAOHDCn1eKPRhVngf/c30S8fPPvtsHTx4UD179mwyvaSkRGeffbbq6uratZ7ExMRm34bHx8crJSXFM/3666/X9OnT1b17d9ntdv3qV7/SyJEj9dOf/tSYnQkDLndNm/NLvcy3Ope7tlPzmy4b2u9VKOIzM0Ywvo9G5j4AIHxYoc8LhT7MCu+jv5leaDc0NCgiIqLZ9MLCQsXHG3s/we9//3t16dJFubm5qqqq0oQJE/TMM88Yuo1QZ7dFtTk/0ct8q7Pb2k4Bb/ObLhva71Uo4jMzRjC+j0bmPgAgfFihzwuFPswK76O/mfYpXHbZZZKkiIgI/fKXv2xy6XhdXZ22bNmiUaNGdWobH3/8cZPfbTabnn76aT399NOdWm84cyREKyfToVUtXNqRk+mQIyE6AFEZJzk+WmMyUrSmhctvxmSkKDm+/fsX6u9VKOIzM0Ywvo9G5j4AIHxYoc8LhT7MCu+jv5l2j3bjYwcaGhqUmJjY5FEEvXr10k033aQ///nPZoWDdkqKi9b83CzlZDqaTM/JdGhBbpaS4oI7CVLtNs2dNERjMlKaTG8ctTHV3v4R7UP9vQpFfGbGCMb30cjcBwCEDyv0eaHQh1nhffS3iIaGhgYzN/jQQw/pN7/5jeGXifuLy+VSUlKSSkpKwnokx5KKajnLqlXqrlGiLUqOhOiQSIBGh1xuFZVXy+Wuld0WqeT4aJ8bqVB/rwLNHznJZ2aMYHwfjcz9cNTefNy4caOGDx+u8fctUff+J/ollsN5O/X+nCnasGGDhg0b5pdtAFbHeat5rNDnhUIfZoX30V9Mv4D/wQcfNHuTMEBSXOgc9C1JtdsMa5hC/b0KRXxmxgjG99HI3AcAhA8r9Hmh0IdZ4X30F1MK7WHDhmnlypVKTk5WdnZ2i4OhNdq4caMZIQEAAAAA4BemFNqXXnqpZ/CziRMnmrFJAAAAAAACwpRC++jLxbl0HAAAAAAQykwbdbzR559/rvXr1zebvn79en3xxRdmhwMAAAAAgKFML7Rvu+027du3r9n0/Px83XbbbWaHAwAAAACAoUwvtLdt29biYzeys7O1bds2s8MBAAAAAMBQphfaMTExOnToULPpBw8eVGSk6U8bAwAAAADAUKYX2ueee65mzpypkpISz7Ti4mLde++9Gj9+vNnhAAAAAABgKNO/Qv7d736nnJwcpaWlKTs7W5K0efNmpaam6v/+7//MDgcAAAAAAEOZXmj37dtXW7Zs0V/+8hd9+eWXio2N1ZQpUzR58mRFRUWZHQ4AAAAAAIYKyE3R8fHxuummmwKxaQAAAAAA/Coghfa3336rRYsWafv27ZKkwYMH684779QJJ5wQiHAAAAAAADCM6YOh/fOf/9TgwYP173//W1lZWcrKytL69et10kkn6f333zc7HAAAAAAADGX6N9ozZszQtGnTNH/+/GbT77nnHkYeBwAAAAAENdO/0d6+fbuuv/76ZtOnTp2qbdu2mR0OAAAAAACGMr3Q7tGjhzZv3txs+ubNm9WzZ0+zwwEAAAAAwFCmXzp+44036qabbtJ3332nUaNGSZLWrl2rBQsWaPr06WaHAwAAAACAoUwvtB944AElJiZq4cKFmjlzpiSpT58++u1vf6s77rjD7HAAAAAAADCU6YV2RESEpk2bpmnTpqm0tFSSlJiYaHYYAAAAAAD4RUCeo92IAhsAAAAAEGpMKbSzs7MVERHRrmU3btzo52gAAAAAAPAfUwrtiRMnmrEZAAAAAAACzpRC+8EHHzRjMwAAAAAABFxA7tEuLi7Wm2++qW+//Vb/7//9P3Xv3l0bN25Uamqq+vbtG4iQOqWkolrOsmq53DWyx0bJER+tpLjoQIcVcPlFFXK5a+WqrFFSbJQSbZHqmxwX6LBatL+oQqVHxZpgi9RxJsZaUlGtwvJq1dY3qL6hQRVVtUqKi+ZYMklrx2prud24fJm7Ro6EGFXX1auiuk4JMZGK7tpFxZXVSrDRFsA66KcANAqX9sDf56GBPneUpEMut4rKq+Vy18oeG6nkuGil2m2mxoDWmV5ob9myRePGjVNSUpL+85//6MYbb1T37t311ltvKS8vTy+//LLZIXXKgeJK3bN0i1bvcnqm5WQ6ND83S326xQYwssDaW1iue5dt1drdhZ5pYzJSNGfSEKWlxAcwsuYCHeuB4krN+ttXuvK0/lqydk+TODiW/K+lz/+cgT006+KTdP/yr5rl9sMTT9ZD73ytz747rCcmZ2vRyl1NXjs6I0VTRqdr8vPrNSItmc8PAUc/BaBRuLQH/j63C/S5oyTlFZZrZgsxzJ00RP0tdq4drrqYvcHp06frl7/8pXbt2iWb7ce/uFxwwQVatWqV2eF0SklFdbPGSpJW7XJqxtItKqmoDlBkgZVfVNGs8ZGkNbsLdd+yrcovqghQZM3t9xLrfj/H2ngMDextb1ZkSxxL/tbasTqoT5LuXba1xdy+b9lWDe6TpKlj0lv8zNbuLtSStXs0dUw6nx8Cjn4KQKNwaQ/8fR4a6HNH6cg32ccW2Y0x3Ltsqw653H6PAd6ZXmh//vnn+p//+Z9m0/v27auCggKzw+kUZ1l1s8aq0apdTjnLQqPB6iiXu7ZZ4jdas7tQLnetyRG1rtRLrKV+jrXxGMru163VOML5WPK31o7Vtj6PNbsLld2vW5vLrP3vMhKfHwKLfgpAo3BpD/x9Hhroc0dJKiqvbjOGovLQ+CyDnemXjsfExMjlcjWb/s0336hHjx5mh9MpLndNm/NLvcwPVa7K4HlfAh1r4zFUVVsf0DjCVWufv7fPw9v8Y5fh80Og0E8BaBQu7YG/z+0Cfe4oyesfC6z0pVY4M73QvuSSSzR79my9/vrrkqSIiAjl5eXpnnvuUW5urtnhdIrdFtXm/EQv80OVPTZ43pdAx9p4DMVEtn1xiZXes1DS2ufv7fPwNv/YZfj8ECj0U8bLy8uT09nyt4JGcDgc6t+/v9/Wj/AVLu2Bv8/tAn3uKEl2W9slnLf5MIfpn8LChQv1s5/9TD179lRlZaXOPPNMFRQUaOTIkZozZ47Z4XSKIyFaOZkOrWrhMpycTIccCaE3gmN72G2RGpORojUtXNIyJiPFUsmf6CXWRD/H2ngMbdpXrNEZKS1eBhTOx5K/tXasbtpX3OZxsWlfsSS1+pmNPmoZPj8EEv2UsfLy8jRw4CBVVvrvHszY2Djt2LGdYhuGC5f2wN/noYE+d5Sk5PjoNmNIjg+NzzLYmV7xJCUl6f3339eaNWu0ZcsWlZWVadiwYRo3bpzZoXRaUly05udmacbSLU0arZxMhxbkZoXkoxLao29ynOZMGqL7lm1t0gA0jsZopUd8HeclVn8/pqHxGHrwb19pyuh0SWo26ng4H0v+1tqxuv1AieZMGqIHln/VLLcfnniyZr/ztdb9d9RxSS2OOn7Hq5v4/BBw9FPGcjqdqqys0OlTH5S99wDD1+86+B+tf+EhOZ1OCm0YLlzaA3+fhwb63FGSUu02zZ00RPe2EMPcSUN4xJdFRDQ0NDSYucF9+/apX79+Zm6yU1wul5KSklRSUiK73d7iMo3PIyx11yjRFiVHQmg+j7CjGp9f2Pi+2IPgOdqNsSYG6DnadfUNqqtvUEV1nZJiOZZa0p6c7KjWjtXWcrtx+fKqGnWPj1FNXb0qq+sU/9/naJdUVis+hs8P1uGvfqq9+bhx40YNHz5c4+9bou79T+z0dltyOG+n3p8zRRs2bNCwYcP8sg1/74cZ+4DQxnnrj/x9Hhroc0fpmOdo2yKVHM9ztK3E9G+0BwwYoDFjxuiaa67Rz372MyUnJ5sdguGS4kKzgeqsvslx6hvoINrJ7IbxWBxDgdXasdra5+L92Ob5lbAW2hgAjcKlPfD3eWigzx2lI99sU1hbl+mP9/riiy902mmnafbs2erdu7cmTpyoN998U1VVVWaHAgAAAACA4UwvtLOzs/XYY48pLy9P7733nnr06KGbbrpJqampmjp1qtnhAAAAAABgKNML7UYRERE6++yz9fzzz+uDDz5Qenq6XnrppUCFAwAAAACAIQJWaO/fv1+PPvqohg4dqtNOO00JCQl6+umnAxUOAAAAAACGMH0wtD/84Q965ZVXtHbtWg0cOFBXX321/va3vyktLc3sUAAAAAAAMJzphfYjjzyiyZMn64knntApp5xi9uYBAAAAAPAr0wvtvLw8RUREeF3u1ltv1ezZs+VwOEyICgAAAAAAY5h+j3Z7imxJ+vOf/yyXy+XnaAAAAAAAMFbABkPzpqGhIdAhAAAAAADQYZYttAEAAAAACEYU2gAAAAAAGIhCGwAAAAAAA1FoAwAAAABgINML7by8vBYHOmtoaFBeXp7n92uuuUZ2u73Ndc2bN0+nnnqqEhMT1bNnT02cOFE7d+5ssozb7dZtt92mlJQUJSQkKDc3V4cOHTJmZwAAAAAAOIbphXZ6erp++OGHZtMPHz6s9PR0z+/PPvus12dof/LJJ7rtttv02Wef6f3331dNTY3OPfdclZeXe5aZNm2a3nnnHb3xxhv65JNPdODAAV122WXG7RAAAAAAAEeJNHuDDQ0NLT5Lu6ysTDabrUPrWrFiRZPfX3zxRfXs2VMbNmxQzv9v777Do6jWP4B/l5TdTdkAWZLQUiCRHggdElGa6FWkRFGECwKWiwgiooBIVZr+FEQRbARU2lVEsCG9CggJSA8JLbSAiSSbXs/vD27WLLubbMjsbPt+nofnITOTOe+ZOe+cOdnZM926ITMzE19++SVWr16NHj16AADi4uLQrFkzHDx4EJ07d773itiha7dzocsvhi6vCH5qD/iq3FG/lpetw3II9nLsMnMLkZZdCF1+Ebw93eHupkB6dgF8VTyfcilrC3mFRdD6qJBTWKJvFz4qdzSw8BzYS5siIiIqz1X6J2vX8+rtXGSV239V7hHINcg20J4wYQIAQKFQYNq0afDy+qchlpSU4NChQ2jTpk21ysjMzAQA1K5dGwAQHx+PoqIi9OrVS79N06ZNERwcjAMHDpgcaBcUFKCgoED/s06nq1ZMcrmcnoM3N5zA/uR0/bKYcH/MGdAKIf7eNozM/tnLsbuekYdJ649jb1Kafll0uD9GRIdheNxhtA2u6ZLnU86cLGsLialZWPVcZ0y5x3ZhL22KSGqO2kcSOauq5qSr9E/WrqerHEeqHtkeHT969CiOHj0KIQROnDih//no0aM4e/YsWrdujRUrVtzz/ktLSzF+/HhER0ejZcuWAIDU1FR4enqiZs2aBtsGBgYiNTXV5H7mzZsHPz8//b+GDRvec0xyuXY71yjZAWBfcjqmbjiBa7dzbRSZ/bOXY5eZW2g0yAaA/cnpiNt/ESNjwlz2fMqVk+XbwoLYSMz+6ZTZdnG1gnNgL22KyBocsY8kcmZVyUlX6Z+sXc+rley/onsEci2yfaK9c+dOAMCIESOwePFi+Pr6Srr/MWPG4OTJk9i3b1+19jNlyhT9p+/Anb8M2vuNhC6/2CjZy+xLTocuvxj1ZY7JUdjLsUvLLjQaZJfZn5yOkdFhssdkL+TKyfJtIUCjrLBdZOUXW7QfU7/rauePnIsj9pFEzqwqOekq/ZO165lVyf4rukcg1yLrd7SLiorw9ddf47XXXtN/6iyFl19+GT/99BP27NmDBg0a6JcHBQWhsLAQGRkZBp9q37x5E0FBQSb3pVQqoVQqJYtNDrq8ogrXZ+VXvN6V2cux01VSTkFxqf7/rnY+5crJ8m0hO7+kwm0rOgf20qaIrMER+0giZ1aVnHSV/sna9XSV40jVJ+tA28PDA8HBwSgpqfgm1lJCCIwdOxYbNmzArl27DGYtB4B27drBw8MD27dvR2xsLAAgMTERKSkp6NKliyQx2AON2qPC9b4q8+tdZUIMc6pz7CSNo5JylO7/fMtDrphcTfm24KNyq3BbX5UHrt3ONZkrtmhTrp7HRERUOXu557GG8v2gtevpzMeRpCX7rONTp07Fm2++ia+//lo/adm9GjNmDFavXo2NGzfC19dX/71rPz8/qNVq+Pn5YdSoUZgwYQJq164NjUaDsWPHokuXLk4147hG5Y6YcH/sM/EYS0y4PzQq06eZEzncOXb3h2uxN9n4se37w7Vmj53UtD6e6BahxR4Tj49Hh/vj6JUMABWfT6qe8nl0S1dQYU5dz8jDnP0XTebKvebjvWIeExGRJezlnkdqd/eDL/cIt2o/7FtJP+/roMeRpCf7e7Q//vhj7NmzB/Xq1UOTJk3Qtm1bg39VsXTpUmRmZuLBBx9E3bp19f/WrVun32bhwoV47LHHEBsbi27duiEoKAjff/+91NWyqfq1vDBnQCvEhPsbLC+72Tb1yZarTIhRGQWAMd0bI/quYxcd7o8x3cNh/CI66/Dz8sT82Eh0izB8d3zZrOPL912s8HxS9ZXPo0nrj2PaYy1M5tT0vi0waf1xs7lyL/l4r5jHRERkKXu555GSqX5w+b6LeDY6zGr9cINK+nm+4ovKyP4nl/79+0u2LyFEpduoVCosWbIES5YskaxcexTi740FsZHQ5RcjK78IvioPaCp4fNRVJsSoTGZ+MUauPIKRMWEYGR2GguJSKN1r4OiVDIxceRjrR3dFPZliqVdTjY8GRyEtuxBZ+UVQe7rBw60G/s4pwPrRXSs8nySN8nmUU1CIuQNaIaewBBm5RfBRueGWrgDPfH4QadmFAMznSlXz8V4xj4mIyFL2dM8jFVP9YG5hCcatOYqRMWGY+mhzq/TDIf7emB8biaxy/bwv36NNd5F9oD1jxgy5i3QZ9Wt5WXxTzYkc7tDlFSG3sAQf70g2uV7u4+Dn5Qk/L0+DZY3q+Mgag6u7O48OXUjH4M8Pmt3eXBupSj7eK+YxERFZyt7ueaRgrh8sq2e3CC06hvmb3Ka6OKimysj+6DjZB07kcAePA1XGntuIPcdGRET2xRn7DGesEzkP2QfaJSUl+L//+z907NgRQUFBqF27tsE/kkfZhE2muNKEWzwOVBl7biP2HBsREdkXZ+wznLFO5DxkH2jPmjULH3zwAZ566ilkZmZiwoQJGDhwIGrUqIGZM2fKHY7LknPCJnvG40CVsec2Ys+xERGRfXHGPsMZ60TOQ/Y/86xatQqff/45Hn30UcycORODBw9G48aNERkZiYMHD2LcuHFyh+Sy5Jqwyd7xOFBl7LmN2HNsRERkX5yxz3DGOpFzkH2gnZqailatWgEAfHx8kJmZCQB47LHHMG3aNLnDcXlyTNjkCHgcqDL23EbsOTYiIrIvzthnOGOdyPHJ/uh4gwYNcOPGDQBA48aNsWXLFgDA4cOHoVQq5Q6HiIiIiIiISFKyD7QHDBiA7du3AwDGjh2LadOmISIiAsOGDcPIkSPlDoeIiIiIiIhIUrI/Oj5//nz9/5966imEhITg999/R0REBPr27St3OERERERERESSkn2gvWfPHnTt2hXu7neK7ty5Mzp37ozi4mLs2bMH3bp1kzskIiIiIiIiIsnIPtDu3r07bty4gYCAAIPlmZmZ6N69O0pKSuQOiYiIiMhunDlzxqr712q1CA4OtmoZRESuTvaBthACCoXCaHl6ejq8vb3lDoeIiIjILuRlpgNQYOjQoVYtR632wtmzZzjYJiKyItkG2gMHDgQAKBQKPPvsswYzjJeUlOD48ePo2rWrXOEQERER2ZWi3CwAAm2emYQ6YU2tUobuxiUcWj4LaWlpHGgTEVmRbANtPz8/AHc+0fb19YVardav8/T0ROfOnfH888/LFQ4RERGRXfIJCEbt4Ca2DoOIiKpBtoF2XFwcACA0NBQTJ07kY+JERERERETklGR/j/Ybb7xh8B3ty5cvY9GiRdiyZYvcoRARERERERFJTvaBdr9+/fDVV18BADIyMtCxY0e8//776NevH5YuXSp3OERERERERESSkn2gnZCQgPvvvx8A8N133yEoKAiXL1/GV199hcWLF8sdDhEREREREZGkZB9o5+bmwtfXFwCwZcsWDBw4EDVq1EDnzp1x+fJlucMhIiIiIiIikpTsA+3w8HD88MMPuHLlCn777Tc89NBDAIBbt25Bo9HIHQ4RERERERGRpGQfaE+fPh0TJ05EaGgoOnXqhC5dugC48+l2VFSU3OEQERERERERSUq213uVeeKJJxATE4MbN26gdevW+uU9e/bEgAED5A6HiIiIiIiISFKyD7QBICgoCEFBQQbLOnbsaItQiIiIiIiIiCQl+6PjRERERERERM7MJp9ok+1du50LXX4xdHlF8FN7wFfljvq1vGwdls04wvG4ejsXWeVi9FG5o4FEMTpC/e9VWd1yCorg761EQUkpdHlF0Kj+qacl9bfm8SciIpKLHH2+3PcVd5fnp3JHCcB+m2yKA20XdDk9B29uOIH9yen6ZTHh/pgzoBVC/L1tGJltOMLxsGaMjlD/e1VWt6MpGVg8OAoLtyWZrOfsH09h+9m/jJaX1d+ZjxEREbkOOfozufvMu8vT+nhi7QtdMGPTSfbbZFN8dNzFXLuda3TxA4B9yemYuuEErt3OtVFktuEIx+NqJTFerUaMjlD/e1W+biNjwhC3/6LZejar52dy+bXbuVY9/kRERHKRo8+X+77CVHkLYiONBtnlY2C/TXLhQNvF6PKLjS48ZfYlp0OXXyxzRLblCMcjq5IYs6oRoyPU/16Vr1tUw5oV1jOqYU2Ty3X5xVY9/kRERHKRo8+X+77CVHkBGiX7bbILfHTcxejyiipcn5Vf8Xpn4wjHw5oxOkL971X5uhUUl1a4rbn1WflFEKLichz5GBERkeuQo8+X+77CVHnZ+SWyxkBkDgfaLkaj9qhwva+q4vXOxhGOhzVjdIT636vydVO6V/zwjrn1ltTfkY8RERG5Djn6fLnvK0yV56NykzUGInP46LiL0ajcERPub3JdTLg/NCrX+tuLIxwP30pi9K1GjI5Q/3tVvm5Hr2QguoJ6Hr2SYXK5RuVu1eNPREQkFzn6fLnvK0yVd0tXwH6b7AIH2i6mfi0vzBnQyugCVDYTo7O80slSjnA8GlQSY3VeVeEI9b9X5eu2fN9FjIgOMxpsl9XzzPVMk8vr1/Ky6vEnIiKSixx9vtz3FabKm7T+OGY+3pL9Ntkc/6TjgkL8vbEgNvJ/Ez0VwVflAY0TvTe5qhzheIT4e2N+bCSyysXoK9H7IB2h/veqfN1yCorwTr+W+vdol6/n7H4tMbGP+fpb8/gTERHJRY4+X+77ClPleXvUYL9NNseBtouqX8sL9W0dhB1xhONhzc7BEep/ryypmyXbsHMmIiJnIEefL/d9hTPfx5Dj4qPjRERERERERBLiQJuIiIiIiIhIQhxoExEREREREUmIA20iIiIiIiIiCXGgTURERERERCQhDrSJiIiIiIiIJMSBNhEREREREZGEONAmIiIiIiIikhAH2kREREREREQS4kCbiIiIiIiISEIcaBMRERERERFJyN3WARARERGRvM6cOWPV/Wu1WgQHB1u1DCIie8aBNhEREZGLyMtMB6DA0KFDrVqOWu2Fs2fPcLBNRC6LA20iIiIiF1GUmwVAoM0zk1AnrKlVytDduIRDy2chLS2NA20iclkcaBMRERG5GJ+AYNQObmLrMIiInJZLDLSXLFmC9957D6mpqWjdujU++ugjdOzY0dZhkR25ejsXWfnF0OUVwU/tAR+VOxrU8rJ1WJKRu37XbudCV648X5U76st4PM2VX3YcsvKK4OflAU+3GkjPKYCPsvIY5azTTV0+bucUQpdfDI3KHWpPN6Rl5cNb6Xxtk4iI5CXHPYGz31cRWcLpB9rr1q3DhAkTsGzZMnTq1AmLFi1Cnz59kJiYiICAAFuHR3bgcnoO3txwAvuT0/XLYsL9MWdAK4T4e9swMmnIXT9bH09T5Q9oUxfjezcxWh4d7o8R0WEYtvww2gbXNBujnHVKSc/BFBNlTXusBYZ8cRBNg3ydpm0SEZG85OjPbH0fQGQvnP71Xh988AGef/55jBgxAs2bN8eyZcvg5eWF5cuX2zo0sgNXb+cadQYAsC85HVM3nMDV27k2ikwactfvWiXlXbPy8TRX/oiYRiaX709OR9z+ixgZE2Y2RjnrdFOXbzTILivr7Z9OYUFspNO0TSIikpcc9wTOfl9FVBVOPdAuLCxEfHw8evXqpV9Wo0YN9OrVCwcOHDD5OwUFBdDpdAb/yHll5RcbdQZl9iWnIyu/WOaIpCV3/XSVlKe7h/KqkpPmynd3q2E2rv3J6YhqWNNsjNaokzm3cworLCtAo9T/39HbJjkm9pFE9qUqOSnHPYGz31cRVYVTD7TT0tJQUlKCwMBAg+WBgYFITU01+Tvz5s2Dn5+f/l/Dhg3lCJVsRJdXVOH6rPyK19s7uetnjfKqkpPmys+qJK6C4lKzMcp5DCsbtGfnl1ilXCJLsY8ksi9S9JFlpOhXnP2+iqgqnP472lU1ZcoUTJgwQf+zTqfjjYQT06g9Klzvq6p4vb2Tu37WKK8qOWmufN9K4lK6//M3x7tjlPMYalQVX5J9VG5WKZfIUuwjyV6kpKQgLS3NqmUUFBRAqVRabf9arbbarz+Too8sI0W/4uz3VURV4dQDba1WCzc3N9y8edNg+c2bNxEUFGTyd5RKpVUvqmRffFXuiAn3xz4TjznFhPvDt5KBj72Tu36aSsqrbCBpSlVy0lz5xSWlZuOKDvfH0SsZZmO0Rp3MqeXtWWFZt3QF+v87etskx8Q+kuxBSkoKmjZthrw8K3/fV6EAhLDa7tVqL5w9e6Zag+2q5KQc9wTOfl9FVBVO3do9PT3Rrl07bN++Hf379wcAlJaWYvv27Xj55ZdtGxzZhQa1vDBnQCtM3XDCoFMomx3T0V9FIXf96ldSnrVf8WWu/Lh9F0wuL5t1fNyao2ZjlLNOgRoV5g5ohTdNlDW9bws88/lBp2mbRET3Ki0tDXl5ueg0cgY0dUOtUsaNEwdwctNnaPPMJNQJayr5/nU3LuHQ8llIS0ur9qfalpLjnsDZ76uIqsKpB9oAMGHCBAwfPhzt27dHx44dsWjRIuTk5GDEiBG2Do3sRIi/N+bHRt55v3J+EXxVd96R7Cydgdz1C/H3xoLYSOjKlaeR8T3aFZVf/jho1B5Q/u892utHd60wRjnrFOzvjfcHtdG/R9tX5Q4vTzekZ+fj61GdnKptEhFVh6ZuKGoHN7HKvnU3LgEAfAKCrVaGLchxT+Ds91VElnL6gfZTTz2Fv/76C9OnT0dqairatGmDzZs3G02QRq7N2S/+ctevfi0v1Je1RMvKN3Ucwur4VGuf1hCoUSFQozJYxnePEpGjOXPmjEPt11XIcU/g7PdVRJZw+oE2ALz88st8VJyIiIhIBnmZ6QAUGDp0qFXLKSootOr+iYiqwyUG2kREREQkj6LcLADCat9vLvv+dHEx38lMRPaLA20iIiIikpy1vt9c9v1pIiJ7xoF2JcT/Xuug0+lsHAmRc/P19YVCoah0O+YkkTwsyUlL8zE7OxsAkHn1AkqLS6QJ8C5ZqZcBAAkJCfrypHbu3DkA1qtH9q1rAABd6iWrvUbNGcpwhjqUtdfs7GyL+jP2kUT2xZKcVAhhxRcEOoGrV6+iYcOGtg6DyOllZmZCo9FUuh1zkkgeluQk85FIHuwjieyLJTnJgXYlSktLcf36dYv/kmgLOp0ODRs2xJUrVyy6CJNpPI7VV51jaGmOOUJOSsEV26Or1dne62tJjlmaj/ZeVym5Ul0B16qvLesqdR/J8+acXKmugP3nJB8dr0SNGjXQoEEDW4dhEY1G4xJJZW08jtVnzWPoSDkpBVdsj65WZ0eub1Xz0ZHrWlWuVFfAteprz3VlTprHujove61vDVsHQERERERERORMONAmIiIiIiIikhAH2k5AqVRixowZVpt501XwOFYfj6F0XPFYulqdXam+rKvzcqX6OlNdnakulWFdnZe915eToRERERERERFJiJ9oExEREREREUmIA20iIiIiIiIiCXGgTURERERERCQhDrSJiIiIiIiIJMSBNhEREREREZGEONAmIiIiIiIikhAH2kREREREREQS4kC7EkII6HQ68HXjRPaBOUlkP5iPRPaFOUlkPzjQrkRWVhb8/PyQlZVl61CICMxJInvCfCSyL8xJIvvBgTYRERERERGRhDjQJiIiIiIiIpIQB9pEREREREREEuJAm4iIiIiIiEhCHGgTERERERERScjd1gEQ2YPM3EKkZRdCl18EjdoDWm9P+Hl52u1+SR62On9sN0RE1mPra6ytyycieXCgTS7vekYeJq0/jr1Jafpl3SK0mB8biXo11Xa3X5KHrc4f2w0RkfXY+hpr6/KJSD58dJxcWmZuoVGHBwB7ktIwef1xZOYW2tV+SR62On9sN0RE1mPra6ytyyciefETbXJpadmFRh1emT1JaUjLLrynx7mstV+Sh63OH9sNEZH12Poaa+vy75aSkoK0NNPxSEWr1SI4ONiqZRDZKw60yaXp8osqXJ9VyXq590vysNX5Y7shIrIeW19jbV1+eSkpKWjatBny8nKtWo5a7YWzZ89wsE0uiQNtcmkalUeF630rWS/3fkketjp/bDdERNZj62usrcsvLy0tDXl5ueg0cgY0dUOtUobuxiUcWj4LaWlpHGiTS+JAm1ya1scT3SK02GPiUa5uEVpofe7tES5r7ZfkYavzx3ZDRGQ9tr7G2rp8UzR1Q1E7uIns5RK5Ak6GRi7Nz8sT82Mj0S1Ca7C8W4QWC2Ij7/m7UtbaL8nDVueP7YaIyHpsfY21dflEJC9+ok0ur15NNT4aHIW07EJk5RfBV+UBrU/132lprf2SPGx1/thuiIisx9bXWFuXT0Ty4UCbCHf+ymyNTs5a+yV52Or8sd0QEVmPra+xti6fiOTBR8eJiIiIiIiIJMSBNhEREREREZGEONAmIiIiIiIikhAH2kREREREREQS4kCbiIiIiIiISEIcaBMRERERERFJiANtIiIiIiIiIglxoE1EREREREQkIQ60iYiIiIiIiCTk8APta9euYejQofD394darUarVq1w5MgR/XohBKZPn466detCrVajV69eSEpKsmHERERERERE5MwceqB9+/ZtREdHw8PDA7/++itOnz6N999/H7Vq1dJv8+6772Lx4sVYtmwZDh06BG9vb/Tp0wf5+fk2jJyIiIiIiIiclbutA6iOBQsWoGHDhoiLi9MvCwsL0/9fCIFFixbhrbfeQr9+/QAAX331FQIDA/HDDz/g6aeflj1mIiIiIiIicm4O/Yn2pk2b0L59ezz55JMICAhAVFQUPv/8c/36ixcvIjU1Fb169dIv8/PzQ6dOnXDgwAFbhExEREREREROzqE/0b5w4QKWLl2KCRMm4M0338Thw4cxbtw4eHp6Yvjw4UhNTQUABAYGGvxeYGCgft3dCgoKUFBQoP9Zp9NZrwJEVCnmJJH9YD4S2RfmJJH9cuhPtEtLS9G2bVvMnTsXUVFReOGFF/D8889j2bJl97zPefPmwc/PT/+vYcOGEkZMRFXFnCSyH8xHIvvCnCSyXw490K5bty6aN29usKxZs2ZISUkBAAQFBQEAbt68abDNzZs39evuNmXKFGRmZur/XblyxQqRE5GlmJNE9oP5SGRfmJNE9suhHx2Pjo5GYmKiwbJz584hJCQEwJ2J0YKCgrB9+3a0adMGwJ1Hag4dOoTRo0eb3KdSqYRSqbRq3ERkOeYkkf1gPhLZF+Ykkf1y6IH2q6++iq5du2Lu3LkYNGgQ/vjjD3z22Wf47LPPAAAKhQLjx4/HO++8g4iICISFhWHatGmoV68e+vfvb9vgiYiIiIiIyCk59EC7Q4cO2LBhA6ZMmYLZs2cjLCwMixYtwpAhQ/TbvPHGG8jJycELL7yAjIwMxMTEYPPmzVCpVDaMnIiIiIiIiJyVQw+0AeCxxx7DY489Zna9QqHA7NmzMXv2bBmjIiIiIiIiIlfl0JOhEREREREREdkbDrSJiIiIiIiIJMSBNhEREREREZGEHP472kRSyMwtRFp2IXT5RdCoPaD19oSfl6fN90Ukp+q2XbZ9IufmLDnuLPUgIvvGgTa5vOsZeZi0/jj2JqXpl3WL0GJ+bCTq1VTbbF9Ecqpu22XbJ3JuzpLjzlIPIrJ/fHScXFpmbqFRhwsAe5LSMHn9cWTmFtpkX0Ryqm7bZdsncm7OkuPOUg8icgwcaJNLS8suNOpwy+xJSkNatuWdrpT7IpJTddsu2z6Rc3OWHHeWehCRY+BAm1yaLr+owvVZlay31r6I5FTdtsu2T+TcnCXHnaUeROQYONAml6ZReVS43reS9dbaF5Gcqtt22faJnJuz5Liz1IOIHAMH2uTStD6e6BahNbmuW4QWWh/LZyGVcl9Ecqpu22XbJ3JuzpLjzlIPInIMHGiTS/Pz8sT82EijjrdbhBYLYiOr9LoPKfdFJKfqtl22fSLn5iw57iz1ICLHwNd7kcurV1ONjwZHIS27EFn5RfBVeUDrc2/v1JRyX0Ryqm7bZdsncm7OkuPOUg8isn+yDrQzMjKwYcMG7N27F5cvX0Zubi7q1KmDqKgo9OnTB127dpUzHCI9Py/pOlkp90Ukp+q2XbZ9IufmLDnuLPUgIvsmy6Pj169fx3PPPYe6devinXfeQV5eHtq0aYOePXuiQYMG2LlzJ3r37o3mzZtj3bp1coREREREREREZBWyfKIdFRWF4cOHIz4+Hs2bNze5TV5eHn744QcsWrQIV65cwcSJE+UIjYiIiIiIiEhSsgy0T58+DX9//wq3UavVGDx4MAYPHoz09HQ5wiIiIiIiIiKSnCyPjlc2yK7u9kRERERERET2QvbXe61cuRI///yz/uc33ngDNWvWRNeuXXH58mW5wyEiIiIiIiKSlOwD7blz50KtVgMADhw4gCVLluDdd9+FVqvFq6++Knc4RERERERERJKS/T3aV65cQXh4OADghx9+QGxsLF544QVER0fjwQcflDscIiIiIiIiIknJ/om2j4+PfrKzLVu2oHfv3gAAlUqFvLw8ucMhIiIiIiIikpTsn2j37t0bzz33HKKionDu3Dn861//AgCcOnUKoaGhcodDREREREREJCnZP9FesmQJunTpgr/++gvr16/XzzAeHx+PwYMHyx0OERERERERkaRk+0R7+fLlePzxx6HVavHxxx8brZ81a5ZcoRARERERERFZjWyfaH/zzTdo0KABunbtigULFuDs2bNyFU1EREREREQkG9kG2jt27MCNGzfw0ksvIT4+Hh07dkRERARee+017NmzB6WlpXKFQkRERERERGQ1sn5Hu1atWhg6dCj++9//Ii0tDR999BHy8vIwZMgQBAQEYNiwYfjuu++Qk5MjZ1hEREREREREkpF9MrQynp6eePjhh/HJJ5/gypUr2Lx5M0JDQ/H222/jgw8+sFVYRERERERERNUi++u9zGnfvj3at2+P2bNno6ioyNbhEBEREREREd0T2QfaQgh899132LlzJ27dumXw3WyFQoH169fDw8ND7rCIiIiIiIiIJCH7QHv8+PH49NNP0b17dwQGBkKhUMgdAhEREREREZHVyD7Q/vrrr/H999/jX//6l9xFExEREREREVmd7JOh+fn5oVGjRnIXS0RERERERCQL2QfaM2fOxKxZs5CXlyd30URERERERERWJ/uj44MGDcKaNWsQEBCA0NBQo4nPEhIS5A6JiIiIiIiISDKyD7SHDx+O+Ph4DB06lJOhERERERERkdORfaD9888/47fffkNMTIzcRVM1ZOYWIi27ELr8ImjUHtB6e8LPy9PWYRE5BOYPERGVYZ9A5BpkH2g3bNgQGo1G7mKpGq5n5GHS+uPYm5SmX9YtQov5sZGoV1Ntw8iI7B/zh4iIyrBPIHIdsk+G9v777+ONN97ApUuX5C6a7kFmbqFRhwAAe5LSMHn9cWTmFtooMiL7x/whIqIy7BOIXIvsn2gPHToUubm5aNy4Mby8vIwmQ/v777/lDokqkJZdaNQhlNmTlIa07EI+7kRkBvOHiIjKsE8gci2yD7QXLVokd5FUDbr8ogrXZ1WynsiVMX+IiKgM+wQi12KTWcfJcWhUHhWu961kPZErY/4QEVEZ9glErkWW72jn5ORYdXuyHq2PJ7pFaE2u6xahhdaHjzgRmcP8ISKiMuwTiFyLLAPt8PBwzJ8/Hzdu3DC7jRACW7duxSOPPILFixfLERZZwM/LE/NjI406hm4RWiyIjeR3iYgqwPwhIqIy7BOIXIssj47v2rULb775JmbOnInWrVujffv2qFevHlQqFW7fvo3Tp0/jwIEDcHd3x5QpU/Diiy/KERZZqF5NNT4aHIW07EJk5RfBV+UBrQ/f+UhkCeYPERGVYZ9A5DpkGWg3adIE69evR0pKCr799lvs3bsXv//+O/Ly8qDVahEVFYXPP/8cjzzyCNzc3OQIiarIz4udANG9Yv4QEVEZ9glErkHWydCCg4Px2muv4bXXXpOzWCIiIiIiIiLZyPIdbSIiIiIiIiJXwYE2ERERERERkYScZqA9f/58KBQKjB8/Xr8sPz8fY8aMgb+/P3x8fBAbG4ubN2/aLkgiIiIiIiJyek4x0D58+DA+/fRTREZGGix/9dVX8eOPP+Lbb7/F7t27cf36dQwcONBGURIREREREZErcPiBdnZ2NoYMGYLPP/8ctWrV0i/PzMzEl19+iQ8++AA9evRAu3btEBcXh99//x0HDx60YcRERERERETkzGQfaIeGhmL27NlISUmRZH9jxozBo48+il69ehksj4+PR1FRkcHypk2bIjg4GAcOHJCkbCIiIiIiIqK7yfp6LwAYP348VqxYgdmzZ6N79+4YNWoUBgwYAKVSWeV9rV27FgkJCTh8+LDRutTUVHh6eqJmzZoGywMDA5Gammp2nwUFBSgoKND/rNPpqhwXOZ6bunzczimELr8YGrU7anl5IlCjkq38zNxCpOcUorhUoFQI5BYUw8/LE1pvvmvT3nMyM7cQadmF0OUXwUfpDk+3GsjIK4SPyoPnzwHYOvcdjb3nozOzh7ZqDzGQIeYkkf2yyUB7/PjxSEhIwIoVKzB27Fi89NJLeOaZZzBy5Ei0bdvWov1cuXIFr7zyCrZu3QqVSrqL/Lx58zBr1izJ9kf2LyU9B1M2nMD+5HT9sphwf8wd0ArB/t5WL/96Rh6mbzyJpzsGI27/RYM4ukVoMT82EvVqqq0eh72y55y8npGHSeuPY29Smn5ZdLg/RkSHYfDnh9A+pJbLnz97Zuvcd0T2nI/OzB7aqj3EQMaYk0T2y2bf0W7bti0WL16M69evY8aMGfjiiy/QoUMHtGnTBsuXL4cQosLfj4+Px61bt9C2bVu4u7vD3d0du3fvxuLFi+Hu7o7AwEAUFhYiIyPD4Pdu3ryJoKAgs/udMmUKMjMz9f+uXLkiRXXJTt3U5RvdOADAvuR0vLnhBG7q8q1afmZuISatP46mdTVGg2wA2JOUhsnrjyMzt9Cqcdgze83JsnNXfpANAPuT0xG3/yJGxoTx/NkxW+e+o7LXfHRm9tBW7SEGMo05SWS/ZP9Eu0xRURE2bNiAuLg4bN26FZ07d8aoUaNw9epVvPnmm9i2bRtWr15t9vd79uyJEydOGCwbMWIEmjZtikmTJqFhw4bw8PDA9u3bERsbCwBITExESkoKunTpYna/SqXynh5jJ8d0O6fQ6MahzL7kdNzOKbTqY3Fp2YXYm5SGZ7uG4uMdySa32ZOUhrTsQpd9BNlec7Ls3JmyPzkdI6PDAPD82Stb576jstd8dGb20FbtIQYyjTlJZL9kH2gnJCQgLi4Oa9asQY0aNTBs2DAsXLgQTZs21W8zYMAAdOjQocL9+Pr6omXLlgbLvL294e/vr18+atQoTJgwAbVr14ZGo8HYsWPRpUsXdO7cWfqKkUPS5RdXa331yy8CABQUl1a4Xdb/tiP7oavknJQ/pzx/9sfWuU9kKXtoq/YQAxGRo5F9oN2hQwf07t0bS5cuRf/+/eHh4WG0TVhYGJ5++ulql7Vw4ULUqFEDsbGxKCgoQJ8+ffDJJ59Ue7/kPDSqilOgsvXVL/9O+1e6V/wtDl+VcZ6QbWkqOSflzynPn/2xde4TWcoe2qo9xEBE5GhkvzJeuHABISEhFW7j7e2NuLi4Ku97165dBj+rVCosWbIES5YsqfK+yDXU8vZETLg/9pl4JC4m3B+1vK37uK/WxxPdIrQ4eiUD0eH+Jh/N6xahhdaHjx3bm7Jzt8fE4+PR4f44eiUDAM+fvbJ17hNZyh7aqj3EQETkaGSfDK179+5ITze+UGdkZKBRo0Zyh0MuLlCjwtwBrRAT7m+wvGwmVWt/58zPyxPzYyOReEOHEdFhiL4rjm4RWiyIjeT3e+1Q2bnrFqE1WF426/jyfRd5/uyYrXOfyFL20FbtIQYiIkcj+yfaly5dQklJidHygoICXLt2Te5wiBDs7433B7X5592gKnfU8pbv3aD1aqrxf0+2RnpOIWb2bYGSUoHcwhL4qT2g9eF7mO1ZvZpqfDQ4CmnZhcjKL4L3/96jnZlXiB9fjuH5s3O2zn0iS9lDW7WHGIiIHIlsA+1Nmzbp///bb7/Bz89P/3NJSQm2b9+O0NBQucIhMhCoUdn0ZsHPiwMyR2X63PGdso7C1rlPZCl7aKv2EAMRkaOQbaDdv39/AIBCocDw4cMN1nl4eCA0NBTvv/++XOEQERERERERWYVsA+3S0juvugkLC8Phw4eh1Wor+Q0iIiIiIiIixyP7d7QvXrwod5FEREREREREspFloL148WK88MILUKlUWLx4cYXbjhs3To6QiIiIiIiIiKxCloH2woULMWTIEKhUKixcuNDsdgqFggNtIiIiIiIicmiyDLTLPy7OR8eJiIiIiIjImdWwdQBEREREREREzkT2gXZsbCwWLFhgtPzdd9/Fk08+KXc4RERERERERJKSfaC9Z88e/Otf/zJa/sgjj2DPnj1yh0NEREREREQkKdkH2tnZ2fD09DRa7uHhAZ1OJ3c4RERERERERJKSfaDdqlUrrFu3zmj52rVr0bx5c7nDISIiIiIiIpKULLOOlzdt2jQMHDgQ58+fR48ePQAA27dvx5o1a/Dtt9/KHQ4RERERERGRpGQfaPft2xc//PAD5s6di++++w5qtRqRkZHYtm0bHnjgAbnDISIiIiIiIpKU7ANtAHj00Ufx6KOP2qJoIiIiIiIiIquyyUAbAOLj43HmzBkAQIsWLRAVFWWrUIiIiIiIiIgkI/tA+9atW3j66aexa9cu1KxZEwCQkZGB7t27Y+3atahTp47cIRERERERERFJRvZZx8eOHYusrCycOnUKf//9N/7++2+cPHkSOp0O48aNkzscIiIiIiIiIknJ/on25s2bsW3bNjRr1ky/rHnz5liyZAkeeughucMhIiIiIiIikpTsn2iXlpbCw8PDaLmHhwdKS0vlDoeIiIiIiIhIUrIPtHv06IFXXnkF169f1y+7du0aXn31VfTs2VPucIiIiIiIiIgkJftA++OPP4ZOp0NoaCgaN26Mxo0bIywsDDqdDh999JHc4RARERERERFJSvbvaDds2BAJCQnYtm0bzp49CwBo1qwZevXqJXcoRERERERERJKzyXu0FQoFevfujd69e9uieCIiIiIiIiKrkWWgvXjxYou35Su+KpeZW4i07ELo8ougUXtA6+0JPy9PpytTTldv5yIrvxi6vCL4qT3go3JHg1pessdR/jh7e7rD3U2B9OwC+Ko84KtyR30bxOQKburycTunELr8YmhU7lB7uuHv7HzU9FKisKQUuYUlBu3+2u1c6Mq1F54bsnfOfg23B9buR+yhn5KiHbEtEpGrkGWgvXDhQou2UygUHGhX4npGHiatP469SWn6Zd0itJgfG4l6NdVOU6acLqfn4M0NJ7A/OV2/LCbcH3MGtEKIv7dscZg6ztHh/hgRHYbhcYfRNrim7DG5gpT0HEwxcf7f7t8S724+i19P3tQv7xahxdv9W2LWj6ew4+xfBtvz3JC9cvZruD2wdj9iD/2UFO2IbZGIXIksk6FdvHjRon8XLlyQIxyHlZlbaNRBAcCepDRMXn8cmbmFTlGmnK7ezjW6eQGAfcnpmLrhBK7ezpUlDnPHeX9yOuL2X8TImDB9TNdkiskV3NTlGw2ygTvnf9oPJ/FEu4YGy/ckpWHqhhNoXs/PaHueG7JHzn4NtwfW7kfsoZ+Soh2xLRKRq5F91vEyhYWFSExMRHFxsa1CcDhp2YVGHVSZPUlpSMuWvpOyRZlyysovNrp5KbMvOR1Z+fK0z4qO8/7kdEQ1rKmPSSdTTK7gdk5hhec/QKM0ubzsfNy9nOeG7I2zX8PtgbX7EXvop6RoR2yLRORqZB9o5+bmYtSoUfDy8kKLFi2QkpICABg7dizmz58vdzgORZdfVOH6rErWO0qZctLl2Uf9KjvOBcWl+v87+jG3J5UNjLPzS0wuL38+yuO5IXvj7Ndwe2DtfsQe+ikp2hHbIhG5GtlnHZ8yZQr+/PNP7Nq1Cw8//LB+ea9evTBz5kxMnjxZ7pAchkblUeF630rWO0qZctKo7aN+lR1npfs/fxNz9GNuTzSqii+BPio3k8vLn4/yeG7I3jj7NdweWLsfsYd+Sop2xLZI1pCSkoK0NNNPSkhFq9UiODjYqmWQc5J9oP3DDz9g3bp16Ny5MxQKhX55ixYtcP78ebnDcShaH090i9Bij4lHr7pFaKH1kX7WTluUKSdflTtiwv2xz8RjeTHh/vCtZCAmlYqOc3S4P45eydDHVNngkCxXy9uzwvN/S1dgcnnZ+bh7Oc8N2Rtnv4bbA2v3I/bQT0nRjtgWSWopKSlo2rQZ8vKsO0+BWu2Fs2fPcLBNVSb7XeFff/2FgIAAo+U5OTkGA28y5uflifmxkZi8/rhBR9UtQosFsZFWeT2GLcqUU4NaXpgzoBWmbjhhcBNTNpurXK9OMXecy2YdH7fmqD4mvkZKOoEaFeYOaIU3TZz/t/u3wrubzxhsXzbr+OwfTxks57khe+Xs13B7YO1+xB76KSnaEdsiSS0tLQ15ebnoNHIGNHVDrVKG7sYlHFo+C2lpaRxoU5XJPtBu3749fv75Z4wdOxYA9IPrL774Al26dJE7HIdTr6YaHw2OQlp2IbLyi+Cr8oDWx7rvoLRFmXIK8ffG/NhIZOUX6+vna4P3k959nNWebvBwq4G/cwqwfnRXaPiuZqsI9vfG+4Pa6N+j7atyh5enG27n5OP1Pk0xvtd9yCssMWj3s/u1xMQ+/7QXnhuyZ85+DbcH1u5H7KGfkqIdsS2SNWjqhqJ2cBNbh0FkRPaB9ty5c/HII4/g9OnTKC4uxocffojTp0/j999/x+7du+UOxyH5ecnfKdmiTDnJPag2x9RxblTHx0bRuI5AjQqBGpXBsoreTVu/lhfqWzsoIgk5+zXcHli7H7GHfkqKdsS2SESuQvZZx2NiYnDs2DEUFxejVatW2LJlCwICAnDgwAG0a9dO7nCIiIiIiIiIJGWTmXsaN26Mzz//3BZFExEREREREVmV7J9o9+rVCytWrIBOp5O7aCIiIiIiIiKrk32g3aJFC0yZMgVBQUF48sknsXHjRhQVFckdBhEREREREZFVyD7Q/vDDD3Ht2jX88MMP8Pb2xrBhwxAYGIgXXniBk6ERERERERGRw5N9oA0ANWrUwEMPPYQVK1bg5s2b+PTTT/HHH3+gR48etgiHiIiIiIiISDI2mQytTGpqKtauXYtvvvkGx48fR8eOHW0ZDhEREREREVG1yf6Jtk6nQ1xcHHr37o2GDRti6dKlePzxx5GUlISDBw/KHQ4RERERERGRpGT/RDswMBC1atXCU089hXnz5qF9+/Zyh0BERERERERkNbIPtDdt2oSePXuiRo2KP0zfv38/2rdvD6VSKVNkRERERERERNUn+6PjvXv3rnSQDQCPPPIIrl27JkNERERERERERNKxyazjlhBC2DoEIiIiIiIioiqz24E2ERERERERkSNy6IH2vHnz0KFDB/j6+iIgIAD9+/dHYmKiwTb5+fkYM2YM/P394ePjg9jYWNy8edNGERMREREREZGzc+iB9u7duzFmzBgcPHgQW7duRVFRER566CHk5OTot3n11Vfx448/4ttvv8Xu3btx/fp1DBw40IZRExERERERkTOTfdZxSykUikq32bx5s8HPK1asQEBAAOLj49GtWzdkZmbiyy+/xOrVq9GjRw8AQFxcHJo1a4aDBw+ic+fOVomd7rh2Oxe6/GLo8orgp/aAr8od9Wt52Toskxwh1qu3c5FVLkYflTsa2FmMjsqS81/V4+8IbYrIWdjD9dHaOc9rChGRY7Hbgfa9TIaWmZkJAKhduzYAID4+HkVFRejVq5d+m6ZNmyI4OBgHDhzgQNuKLqfn4M0NJ7A/OV2/LCbcH3MGtEKIv7cNIzPmCLE6QoyOypJjW9Xjz/NFJB97yDdrx2APdSQioqqx20fHs7Ky0KhRI4u3Ly0txfjx4xEdHY2WLVsCAFJTU+Hp6YmaNWsabBsYGIjU1FST+ykoKIBOpzP4R1Vz7Xau0Q0BAOxLTsfUDSdw7XaujSIz5gixXq0kxqt2EKM1WTMnLTn/VT3+jtCmiO6VvfWR9nB9tHbO85pCFbG3nCSif8jyiXZUVJRFj4IDQEJCwj2VMWbMGJw8eRL79u27p98vM2/ePMyaNata+3B1uvxioxuCMvuS06HLL0Z9mWMyxxFizaokxqz8Ypkjkpc1c9KS8w+gSsffEdoU0b2ytz7SHq6P1s55XlOoIvaWk0T0D1k+0e7fvz/69euHfv36oU+fPjh//jyUSiUefPBBPPjgg1CpVDh//jz69OlzT/t/+eWX8dNPP2Hnzp1o0KCBfnlQUBAKCwuRkZFhsP3NmzcRFBRkcl9TpkxBZmam/t+VK1fuKSZXpssrqnB9Vn7F6+XkCLE6QozWZM2ctOTYVvX4u/r5Iudmb32kPeSbtWOwhzqS/bK3nCSif8jyifaMGTP0/3/uuecwbtw4vP3220bbVPXiIITA2LFjsWHDBuzatQthYWEG69u1awcPDw9s374dsbGxAIDExESkpKSgS5cuJvepVCqhVCqrFAcZ0qg9Klzvq6p4vZwcIVY5YrTnSXakzMm76+nnVf1je/c2tmhT9nz+yLnYWx9pq2t4+ZyzdgyO0E+R7dhbThLRP2SfDO3bb7/FkSNHjJYPHToU7du3x/Llyy3e15gxY7B69Wps3LgRvr6++u9d+/n5Qa1Ww8/PD6NGjcKECRNQu3ZtaDQajB07Fl26dOFEaFakUbkjJtwf+0w86hYT7g+Nyn7m4NOo3HF/uBZ7k9OM1t0frrWLWH0rOZ6+1YzRVSbZMVXPeQNamj3/ZW1V/O//lh5/udu/q5w/IlOsfX005e6ce7lHuFVz3pH6VCIi+ofsk6Gp1Wrs37/faPn+/fuhUqmqtK+lS5ciMzMTDz74IOrWrav/t27dOv02CxcuxGOPPYbY2Fh069YNQUFB+P7776tdDzKvfi0vzBnQCjHh/gbLy27+7emTNgWAMd0bI/quWKPD/TGmezgsm1nAuhpUcjyr8wobV5lkx1w93/75DMZ0D8f9FbTVqh5/Odu/q5w/InPcAMx8vIXJfJv5eEu4SVyeqZxbvu8ino0Os1rOO1KfSkRE/5D9z6Djx4/H6NGjkZCQgI4dOwIADh06hOXLl2PatGlV2pclrwBTqVRYsmQJlixZck/x0r0J8ffGgthI6PKLkZVfBF+VBzR2+DhrZn4xRq48gpExYRgZHYaC4lIo3Wvg6JUMjFx5GOtHd0U9WweJO8dzfmwkssodT18J3hPrKpPsmKtnbmEJRq48jJ/HxiC/uNRsW63q8Zer/bvK+SMyJyO/GP/+8hAWxEZi0iNNkZ1fAh+VG27pCvD0Zwfw9ahOqCtheaZyLrewBOPWHMXImDBMfbS5VXLeUfpUIiL6h+wD7cmTJ6NRo0b48MMP8c033wAAmjVrhri4OAwaNEjucMiK6tfysvubfF1eEXILS/DxjmST6+1pkpnqDqpNcZVJdiqqZ25hCf7KLkDHMH+z2wBVP/5ytH9XOX9E5ujyipCWXYhRK42/kgZInwPmcq6sH+kWoa30WnKvHKFPJSKif9jkiz2DBg3ioJrsgqtPMuMq9XfWejprvYgsJXcOMOeIiMhSsn9Hu0xhYSGuXr2KlJQUg39EciqbZMYUV5hkxlXq76z1dNZ6EVlK7hxgzhERkaVkH2gnJSXh/vvvh1qtRkhICMLCwhAWFobQ0FCj13MRWZurTzLjKvV31no6a72ILCV3DjDniIjIUrL/6fXZZ5+Fu7s7fvrpJ9StWxcKhT3M60yuzNUnmXGV+jtrPZ21XkSWkjsHmHNERGQJ2Qfax44dQ3x8PJo2bSp30URmufokM65Sf2etp7PWi8hScucAc46IiCoj+6PjzZs3R1pamtzFEhEREREREclC9oH2ggUL8MYbb2DXrl1IT0+HTqcz+EdERERERETkyGR/dLxXr14AgJ49exosF0JAoVCgpKRE7pCIiIiIiIiIJCP7QHvnzp1yF0lEREREREQkG9kH2g888IDcRRIREREREd2TM2fOWHX/Wq0WwcHBVi2D5Cf7QBsA9u7di08//RQXLlzAt99+i/r16+Prr79GWFgYYmJibBESERERERGRXl5mOgAFhg4datVy1GovnD17hoNtJyP7QHv9+vX497//jSFDhiAhIQEFBQUAgMzMTMydOxe//PKL3CEREREREREZKMrNAiDQ5plJqBNmnVcT625cwqHls5CWlsaBtpORfaD9zjvvYNmyZRg2bBjWrl2rXx4dHY133nlH7nCIiIiIiIjM8gkIRu3gJrYOgxyM7K/3SkxMRLdu3YyW+/n5ISMjQ+5wiIiIiIiIiCQl+0A7KCgIycnJRsv37duHRo0ayR0OERERERERkaRkH2g///zzeOWVV3Do0CEoFApcv34dq1atwsSJEzF69Gi5wyEiIiIiIiKSlOzf0Z48eTJKS0vRs2dP5Obmolu3blAqlZg4cSLGjh0rdzhEREREREREkpJ9oK1QKDB16lS8/vrrSE5ORnZ2Npo3bw4fHx+5QyEiIiIiIiKSnE3eow0Anp6eaN68ua2KJyIiIiIiIrIK2b+jTUREREREROTMONAmIiIiIiIikhAH2kREREREREQS4kCbiIiIiIiISEIcaBMRERERERFJyGazjhPZk2u3c6HLL4Yurwh+ag/4qtxRv5aXrcOSzNXbucgqVz8flTsaWLF+tj6e5so3dxxsHS+RM5Pj+sMcJiIie8OBNrm8y+k5eHPDCexPTtcviwn3x5wBrRDi723DyKQhd/1sfTxNlT+gTV2M793EbFyLtiZiw7EbNomXyJnJcT2w9TWHiIjIFD46Ti7t2u1coxs0ANiXnI6pG07g2u1cG0UmjauV1O+qxPWz9fE0V/6ImEYVxjUippFN4iVyZnJcf2x9zSEiIjKHA21yabr8YqMbtDL7ktOhyy+WOSJpZVVSvyyJ62fr42mufHe3GhXG5e5mfCl0hvNPZEtyXH9sfc0hIiIyhwNtcmm6vKIK12flV7ze3sldP1sfT3PlZ1UWl7nfc/DzT2RLclwPbH3NISIiMocDbXJpGrVHhet9VRWvt3dy18/Wx9Nc+b6VxWXu9xz8/BPZkhzXA1tfc4iIiMzhQJtcmkbljphwf5PrYsL9oVE59nyBvpXUz1fi+tn6eJorv7iktMK4iktKTS539PNPZEtyXH9sfc0hIiIyhwNtcmn1a3lhzoBWRjdqZTPWOvrrYRpUUj+pX7Fj6+Nprvy4fRcqjCtu3wWbxEvkzOS4/tj6mkNERGQO/9RLLi/E3xsLYiOhyy9GVn4RfFUe0DjRO1hD/L0xPzYSWeXq52vF92jb+nhWVL654zCxT1O88EC4U55/IluS4/pj62sOERGRKRxoE+HOpyL1bR2EFVlrUG2OrY+nufLNHQdbx0vkzOS4/jCHiYjI3vDRcSIiIiIiIiIJcaBNREREREREJCEOtImIiIiIiIgkxIE2ERERERERkYQ40CYiIiIiIiKSEAfaRERERERERBLiQJuIiIiIiIhIQhxoExEREREREUmIA20iIiIiIiIiCXGgTURERERERCQhd1sHQERERERE5MrOnDljtX1rtVoEBwdbbf9kGgfaRERERERENpCXmQ5AgaFDh1qtDLXaC2fPnuFgW2YcaBMREREREdlAUW4WAIE2z0xCnbCmku9fd+MSDi2fhbS0NA60ZcaBNhERERERkQ35BASjdnATW4dBEnKJydCWLFmC0NBQqFQqdOrUCX/88YetQyIiIiIiIiIn5fSfaK9btw4TJkzAsmXL0KlTJyxatAh9+vRBYmIiAgICJCkjM7cQadmF0OUXQaP2gNbbE35enpLsuyqu3c6FLr8Yurwi+Kk94KtyR/1aXma3zSooBgQgAGTnF8NX7Q5fpfnfcWZXb+ciq9yx81G5o4ELHgdnZC4vLMmXu9uF2tMNaVn58FZat43c1OXjdk4hdPnF0KjcZSuXyBrkuL5Wpf+zRnkalTsEYNUY7OVeg4iILOP0A+0PPvgAzz//PEaMGAEAWLZsGX7++WcsX74ckydPrvb+r2fkYdL649iblKZf1i1Ci/mxkahXU13t/VvqcnoO3txwAvuT0/XLYsL9MWdAK4T4exttO/vHUxjcKQRx+y9a9DvOrCrHjhyLqXPbs2kdTO/bAlM3nMC+Cs65uXYx7bEWGPLFQTQN8rVKG0lJz8EUG5RLZA1yXF/lvobfXZ6XpxuWD++AJTuTsNdKMdjLvQYREVnOqR8dLywsRHx8PHr16qVfVqNGDfTq1QsHDhyo9v4zcwuNOj4A2JOUhsnrjyMzt7DaZVji2u1co5sMANiXnI6pG07g2u1co22b1fMzGmSb+x1ndrWSY3fVRY6DMzKXF83q+eHNuwbZgGHbr6hdvP3TKSyIjbRKG7mpyzcaZMtRLpE1yHF9rUr/JwVT5Y2MCcNHdw2ypYzBXu41iIioapx6oJ2WloaSkhIEBgYaLA8MDERqaqrJ3ykoKIBOpzP4Z3b/2YVGHV+ZPUlpSMuWp/PT5Rcb3WSU2ZecDl1+sdG2UQ1rWvw7ziyrkmOX5SLHwZ5VJSfLM5cXlrT9ytpFgEap/7+UbeR2TqFNyiWyVFXyUY7ra1X6PymYKs/a/am93GuQfbrXPpKIrM/pHx2vqnnz5mHWrFkWbavLL6pwfVYl66Wiy7M8jrJtC4pLLf4dZ1aVY0e2UZWcLM/cubWk7QtR8b6z80sMtpdKZTfk1iqXyFJV6iNluL7KfQ03VZ61+1N7udcg+3SvfSSR1FJSUpCWZvqPglIpKCiAUqm02v61Wq2kr0Bz6oG2VquFm5sbbt68abD85s2bCAoKMvk7U6ZMwYQJE/Q/63Q6NGzY0OS2GpVHheX7VrJeKhq15XGUbat0r/hhBrlit7WqHDuyjarkZHnmzq0Ubd9H5Val7S2lUVV8SbZWuUSWqlIfKcP1Ve5ruKnyrN2f2su9Btmne+0jiaSUkpKCpk2bIS/Pyl9rUyhQ6ach1aBWe+Hs2TOSDbadeqDt6emJdu3aYfv27ejfvz8AoLS0FNu3b8fLL79s8neUSqXFfynR+niiW4QWe0w80tUtQgutjzyzgWpU7ogJ9zf6zilwZzKW8jfvZdsevZKB6HB/k4+73f07zsy3kmPn6yLHwZ5VJSfLM5cXR69kVJov4n//N7fNLV2B/v9StpFa3p42KZfIUlXJRzmur1Xp/6Rgqjxr96f2cq9B9ule+0giKaWlpSEvLxedRs6Apm6oVcq4ceIATm76DG2emYQ6YU0l37/uxiUcWj4LaWlpHGhbasKECRg+fDjat2+Pjh07YtGiRcjJydHPQl4dfl6emB8bicnrjxt0gN0itFgQGynbazfq1/LCnAGtzM6iXP71ImXbzv7xFEZEhwGAyZlaXeUVXw0qOXZ8jZLjMpcXZ65nWpQv5raZ3rcFnvn8oFXaSKBGhbkDWhlN1mbtcomsQY7ra1X6PymYKm/5votYPrwDakCBvcn/3AtIFYO93GsQEVVGUzcUtYObWGXfuhuXAAA+AcFWK0NqTj/Qfuqpp/DXX39h+vTpSE1NRZs2bbB582ajCdLuVb2aanw0OApp2YXIyi+Cr8oDWh/5320Z4u+NBbGR/5vI6U4cGjPv8Azx98bsfi2RVVCMaY81hxBAdkExfJTuZn/HmYX4e2N+bCSyyh07X76r2ClUlBeV5YupduHl6Yb07Hx8PaqT1dpIsL833h/URv8ebV+VuyzlElmDHNfXqvR/1irPT+WO+bGtrBaDvdxrEJHjOnPmjEPu25E5/UAbAF5++WWzj4pLwc/LPjq7+rW8UL8K29I/OHBxXubywpJ8MdUu5Hh/daBGhUCNSvZyiaxBjutrVfo/a5ZnzRjs5V6DiBxLXmY6AAWGDh1q9bKKCvgWhPJcYqBNRERERETkaopyswAIq323Gfjn+9PFxXz1aHkcaBMRERERETkxa363uez702So4ndSEBEREREREVGV8BPtSoj/vatNp9PZOBIi5+br6wuFQlHpdsxJInlYkpPMRyJ5SN1HZmdnAwAyr15AaXFJ9QM0ISv1MgAgISFBX56Uzp07B8C6dci+dQ0AoEu9ZLXXqFm7DGeogxxllLXX7Oxsi/o0S3JSIYQV3/rtBK5evYqGDRvaOgwip5eZmQmNRlPpdsxJInlYkpPMRyJ5sI8ksi+W5CQH2pUoLS3F9evXLf5Loi3odDo0bNgQV65csegiTKbxOFZfdY6hpTnmCDkpBVdsj65WZ3uvryU5Zmk+2ntdpeRKdQVcq762rKvUfSTPm3NypboC9p+TfHS8EjVq1ECDBg1sHYZFNBqNSySVtfE4Vp81j6Ej5aQUXLE9ulqdHbm+Vc1HR65rVblSXQHXqq8915U5aR7r6rzstb6cDI2IiIiIiIhIQhxoExEREREREUmIA20noFQqMWPGDKvN8ucqeByrj8dQOq54LF2tzq5UX9bVeblSfZ2prs5Ul8qwrs7L3uvLydCIiIiIiIiIJMRPtImIiIiIiIgkxIE2ERERERERkYQ40CYiIiIiIiKSEAfaTmjOnDno2rUrvLy8ULNmTVuH4xCWLFmC0NBQqFQqdOrUCX/88YetQ3Ioe/bsQd++fVGvXj0oFAr88MMPtg7JIcybNw8dOnSAr68vAgIC0L9/fyQmJhpsk5+fjzFjxsDf3x8+Pj6IjY3FzZs3bRSx9ObPnw+FQoHx48frlzljna9du4ahQ4fC398farUarVq1wpEjR/TrhRCYPn066tatC7VajV69eiEpKcmGEUvLGa+xrpy/zp63rpCvzpaTzEfnzUfAcXOSA20nVFhYiCeffBKjR4+2dSgOYd26dZgwYQJmzJiBhIQEtG7dGn369MGtW7dsHZrDyMnJQevWrbFkyRJbh+JQdu/ejTFjxuDgwYPYunUrioqK8NBDDyEnJ0e/zauvvooff/wR3377LXbv3o3r169j4MCBNoxaOocPH8ann36KyMhIg+XOVufbt28jOjoaHh4e+PXXX3H69Gm8//77qFWrln6bd999F4sXL8ayZctw6NAheHt7o0+fPsjPz7dh5NJw1musq+avs+etK+SrM+Yk89E58xFw8JwU5LTi4uKEn5+frcOwex07dhRjxozR/1xSUiLq1asn5s2bZ8OoHBcAsWHDBluH4ZBu3bolAIjdu3cLIYTIyMgQHh4e4ttvv9Vvc+bMGQFAHDhwwFZhSiIrK0tERESIrVu3igceeEC88sorQgjnrPOkSZNETEyM2fWlpaUiKChIvPfee/plGRkZQqlUijVr1sgRolW5yjXWFfLXFfLWFfLVFXKS+eg8dXXknOQn2uTSCgsLER8fj169eumX1ahRA7169cKBAwdsGBm5oszMTABA7dq1AQDx8fEoKioyaJ9NmzZFcHCww7fPMWPG4NFHHzWoG+Ccdd60aRPat2+PJ598EgEBAYiKisLnn3+uX3/x4kWkpqYa1NnPzw+dOnVy2DqXcaVrrCvkryvkrbPnq6vkJPPReerqyDnJgTa5tLS0NJSUlCAwMNBgeWBgIFJTU20UFbmi0tJSjB8/HtHR0WjZsiUAIDU1FZ6enkZzLTh6+1y7di0SEhIwb948o3XOWOcLFy5g6dKliIiIwG+//YbRo0dj3LhxWLlyJQDo6+WM1yFXuca6Qv66St46e766Qk4yH52rro6ck+42LZ0sNnnyZCxYsKDCbc6cOYOmTZvKFBERSWnMmDE4efIk9u3bZ+tQrOrKlSt45ZVXsHXrVqhUKluHI4vS0lK0b98ec+fOBQBERUXh5MmTWLZsGYYPH27j6EgKzp6/rpS3zFfHx3x0Lo6ck/xE20G89tprOHPmTIX/GjVqZOswHY5Wq4Wbm5vRTIw3b95EUFCQjaIiV/Pyyy/jp59+ws6dO9GgQQP98qCgIBQWFiIjI8Nge0dun/Hx8bh16xbatm0Ld3d3uLu7Y/fu3Vi8eDHc3d0RGBjodHWuW7cumjdvbrCsWbNmSElJAQB9vZzxOuQK11hXyF9Xyltnz1dnz0nmo3PlI+DYOcmBtoOoU6cOmjZtWuE/T09PW4fpcDw9PdGuXTts375dv6y0tBTbt29Hly5dbBgZuQIhBF5++WVs2LABO3bsQFhYmMH6du3awcPDw6B9JiYmIiUlxWHbZ8+ePXHixAkcO3ZM/699+/YYMmSI/v/OVufo6Gij18ycO3cOISEhAICwsDAEBQUZ1Fmn0+HQoUMOW+cyznyNdaX8daW8dfZ8ddacZD46Zz4CDp6TNp2Kjazi8uXL4ujRo2LWrFnCx8dHHD16VBw9elRkZWXZOjS7tHbtWqFUKsWKFSvE6dOnxQsvvCBq1qwpUlNTbR2aw8jKytK3MwDigw8+EEePHhWXL1+2dWh2bfTo0cLPz0/s2rVL3LhxQ/8vNzdXv81//vMfERwcLHbs2CGOHDkiunTpIrp06WLDqKVXfrZUIZyvzn/88Ydwd3cXc+bMEUlJSWLVqlXCy8tLfPPNN/pt5s+fL2rWrCk2btwojh8/Lvr16yfCwsJEXl6eDSOXhrNeY109f501b10hX50xJ5mPzpmPQjh2TnKg7YSGDx8uABj927lzp61Ds1sfffSRCA4OFp6enqJjx47i4MGDtg7JoezcudNkmxs+fLitQ7Nrpo4ZABEXF6ffJi8vT7z00kuiVq1awsvLSwwYMEDcuHHDdkFbwd03CM5Y5x9//FG0bNlSKJVK0bRpU/HZZ58ZrC8tLRXTpk0TgYGBQqlUip49e4rExEQbRSs9Z7zGunr+OnPeukK+OltOMh+dNx+FcNycVAghhCwfnRMRERERERG5AH5Hm4iIiIiIiEhCHGgTERERERERSYgDbSIiIiIiIiIJcaBNREREREREJCEOtImIiIiIiIgkxIE2ERERERERkYQ40CYiIiIiIiKSEAfaRERERERERBLiQJsks2LFCtSsWdNm5V+6dAkKhQLHjh2zelmFhYUIDw/H77//bvWyqiItLQ0BAQG4evWqrUOhSjz44IMYP368zcrftWsXFAoFMjIyrF5Weno6AgICcOnSJauXZU1VOWdPP/003n//fesGRHSXmTNnok2bNna7v7t169YNq1evttr+TWFuuh5rt+O7hYaGYtGiRXa7v/ISExMRFBSErKwsq+zfFFe6V+VAmxzSs88+i/79+xssa9iwIW7cuIGWLVtavfxly5YhLCwMXbt2lWR/s2bNwtChQyvcpqioCJMmTUKrVq3g7e2NevXqYdiwYbh+/bp+G61Wi2HDhmHGjBmSxEXOwdQAsWvXrrhx4wb8/PysXv6cOXPQr18/hIaGWr0se/HWW29hzpw5yMzMtHUoZCFT/Up12PqPaVKYOHEitm/frv9ZymO0adMm3Lx5E08//bQk+1u5ciViYmIq3Y65SY7m8OHDeOGFF/Q/KxQK/PDDD5Lse8qUKRg7dix8fX0l2V/37t3xxRdfVLiNK92rcqBNdqWoqOief9fNzQ1BQUFwd3eXMCJjQgh8/PHHGDVqlGT73LhxIx5//PEKt8nNzUVCQgKmTZuGhIQEfP/990hMTDT6vREjRmDVqlX4+++/JYuP7FN18sXT0xNBQUFQKBQSRmQsNzcXX375paT54ghatmyJxo0b45tvvrF1KCSzwsJCW4cgGR8fH/j7+1tl34sXL8aIESNQo4Y0t6KW9KMAc5McT506deDl5SX5flNSUvDTTz/h2WeflWR/f//9N/bv34++fftWuq2r3KtyoO1ENm/ejJiYGNSsWRP+/v547LHHcP78eYNtrl69isGDB6N27drw9vZG+/btcejQIf36H3/8ER06dIBKpYJWq8WAAQP06woKCjBx4kTUr18f3t7e6NSpE3bt2lVhTBs3bkTbtm2hUqnQqFEjzJo1C8XFxfr1CoUCS5cuxeOPPw5vb2/MmTMHJSUlGDVqFMLCwqBWq9GkSRN8+OGH+t+ZOXMmVq5ciY0bN0KhUEChUGDXrl0mHx3fvXs3OnbsCKVSibp162Ly5MkG5T/44IMYN24c3njjDdSuXRtBQUGYOXNmhXWKj4/H+fPn8eijj+qXlZX93//+F/fffz/UajU6dOiAc+fO4fDhw2jfvj18fHzwyCOP4K+//jLY35UrV3Dq1Ck8/PDDEEJg5syZCA4OhlKpRL169TBu3DgAgJ+fH7Zu3YpBgwahSZMm6Ny5Mz7++GPEx8cjJSVFv78WLVqgXr162LBhQ4X1IPnk5ORg2LBh8PHxQd26dU0+tmjqL9Q1a9bEihUrAPzTxtatW4cHHngAKpUKq1atQnp6OgYPHoz69evDy8sLrVq1wpo1a/T7ePbZZ7F79258+OGH+ny5dOmSyUfH169fjxYtWkCpVCI0NNQoztDQUMydOxcjR46Er68vgoOD8dlnn1VY919++QVKpRKdO3fWL7t9+zaGDBmCOnXqQK1WIyIiAnFxcfr1V65cwaBBg1CzZk3Url0b/fr1M3rsfPny5fpY69ati5dfflm/LiUlBf369YOPjw80Gg0GDRqEmzdv6teXPUb49ddfIzQ0FH5+fnj66acNHp2z5Jx98skniIiIgEqlQmBgIJ544gmD9X379sXatWsrPD4kr++++w6tWrWCWq2Gv78/evXqhZycHLP9CgBMmjQJ9913H7y8vNCoUSNMmzbN4I9cZe3piy++QFhYGFQqldm8M6e0tBQNGjTA0qVLDZYfPXoUNWrUwOXLlwEAGRkZeO6551CnTh1oNBr06NEDf/75Z4X7nT17Nho0aAClUok2bdpg8+bNBttUdF9Q/pFbc8eoR48eBvkHAH/99Rc8PT0NPg2/e/2OHTuMbsgVCgU+/fRTPPbYY/Dy8kKzZs1w4MABJCcn48EHH4S3tze6du1qdG+Tn5+PLVu26AfazE3HVFpainnz5unv/1q3bo3vvvtOv76s39q+fTvat28PLy8vdO3aFYmJiQb7mT9/PgIDA+Hr64tRo0YhPz/fovK3bNkClUpl9JWqV155BT169ND/vG/fPv29XsOGDTFu3Djk5OSY3W9lfRJQ8f13+UfHy54MGzBgABQKBUJDQ3Hp0iXUqFEDR44cMdjnokWLEBISgtLSUpNx/fe//0Xr1q1Rv359/bKyr4H+9NNPaNKkCby8vPDEE08gNzcXK1euRGhoKGrVqoVx48ahpKTEYH8///wz2rZti8DAwEr7eZe5VxXkNL777juxfv16kZSUJI4ePSr69u0rWrVqJUpKSoQQQmRlZYlGjRqJ+++/X+zdu1ckJSWJdevWid9//10IIcRPP/0k3NzcxPTp08Xp06fFsWPHxNy5c/X7f+6550TXrl3Fnj17RHJysnjvvfeEUqkU586dE0IIERcXJ/z8/PTb79mzR2g0GrFixQpx/vx5sWXLFhEaGipmzpyp3waACAgIEMuXLxfnz58Xly9fFoWFhWL69Oni8OHD4sKFC+Kbb74RXl5eYt26dfp6DBo0SDz88MPixo0b4saNG6KgoEBcvHhRABBHjx4VQghx9epV4eXlJV566SVx5swZsWHDBqHVasWMGTP05T/wwANCo9GImTNninPnzomVK1cKhUIhtmzZYvY4f/DBB6Jp06YGy8rKbtq0qdi8ebM4ffq06Ny5s2jXrp148MEHxb59+0RCQoIIDw8X//nPfwx+9+OPPxYPPfSQEEKIb7/9Vmg0GvHLL7+Iy5cvi0OHDonPPvvMbCxbt24VCoVCZGZmGix/6qmnxPDhw83+Hslr9OjRIjg4WGzbtk0cP35cPPbYY8LX11e88sor+m0AiA0bNhj8np+fn4iLixNC/NPGQkNDxfr168WFCxfE9evXxdWrV8V7770njh49Ks6fPy8WL14s3NzcxKFDh4QQQmRkZIguXbqI559/Xp8vxcXFYufOnQKAuH37thBCiCNHjogaNWqI2bNni8TERBEXFyfUarW+fCGECAkJEbVr1xZLliwRSUlJYt68eaJGjRri7NmzZus+btw48fDDDxssGzNmjGjTpo04fPiwuHjxoti6davYtGmTEEKIwsJC0axZMzFy5Ehx/Phxcfr0afHMM8+IJk2aiIKCAiGEEJ988olQqVRi0aJFIjExUfzxxx9i4cKFQgghSkpKRJs2bURMTIw4cuSIOHjwoGjXrp144IEH9OXPmDFD+Pj4iIEDB4oTJ06IPXv2iKCgIPHmm29afM4OHz4s3NzcxOrVq8WlS5dEQkKC+PDDDw3q+euvvwpPT0+Rn59v9viQfK5fvy7c3d3FBx98IC5evCiOHz8ulixZIrKyssz2K0II8fbbb4v9+/eLixcvik2bNonAwECxYMEC/X5nzJghvL29xcMPPywSEhLEn3/+aTbvKjJx4kQRExNjsOy1114zWNarVy/Rt29fcfjwYXHu3Dnx2muvCX9/f5Genq6PpXXr1vrtP/jgA6HRaMSaNWvE2bNnxRtvvCE8PDz0/XZl9wXl92fuGK1atUrUqlXLoJ1/8MEHIjQ0VJSWlpqs6/fffy+8vb319ydlAIj69euLdevWicTERNG/f38RGhoqevToYdC33n1N+emnn8R9990nhGBuOrJ33nlHfx91/vx5ERcXJ5RKpdi1a5cQQuj7rU6dOoldu3aJU6dOifvvv1907dpVv49169YJpVIpvvjiC3H27FkxdepU4evra5AX5hQXF4vAwEDxxRdfmF2WnJwsvL29xcKFC8W5c+fE/v37RVRUlHj22Wf1vxMSElKlPqmy++/y+7t165YAIOLi4sSNGzfErVu3hBBC9O7dW7z00ksG9YmMjBTTp083W9/HH3/c6J40Li5OeHh4iN69e4uEhASxe/du4e/vLx566CExaNAgcerUKfHjjz8KT09PsXbtWoPffeKJJ/RxV9TPl3GFe1UOtJ3YX3/9JQCIEydOCCGE+PTTT4Wvr6++Q75bly5dxJAhQ0yuu3z5snBzcxPXrl0zWN6zZ08xZcoUIYTxQLtnz54GFwohhPj6669F3bp19T8DEOPHj6+0LmPGjBGxsbH6n4cPHy769etnsM3dA+0333xTNGnSxKCjX7JkifDx8dF37g888IDRjU2HDh3EpEmTzMbyyiuviB49epgsu/zFec2aNQKA2L59u37ZvHnzRJMmTQx+t3fv3uLjjz8WQgjx/vvvi/vuu08UFhaaLb9MXl6eaNu2rXjmmWeM1r366qviwQcfrHQfZH1ZWVnC09NT/Pe//9UvS09PF2q1+p4G2osWLaq0zEcffVS89tpr+p8feOABg7KEEEYD7WeeeUb07t3bYJvXX39dNG/eXP9zSEiIGDp0qP7n0tJSERAQIJYuXWo2ln79+omRI0caLOvbt68YMWKEye2//vpro7wtKCgQarVa/Pbbb0IIIerVqyemTp1q8ve3bNki3NzcREpKin7ZqVOnBADxxx9/CCHuDB68vLyETqczqGunTp2EEJads/Xr1wuNRmOwj7v9+eefAoC4dOmS2W1IPvHx8RWeD1P9iinvvfeeaNeunf7nGTNmCA8PD/0NbxlTeVeRo0ePCoVCIS5fviyEuHODXr9+fX1+7d27V2g0GqPBYePGjcWnn36qj6X8gKJevXpizpw5Btt36NBBf0Ne2X3B3fszdYzy8vJErVq19H8MF+LODX75P6rfbeHChaJRo0ZGywGIt956S//zgQMHBADx5Zdf6petWbNGqFQqg997/vnnxcSJE4UQzE1HlZ+fL7y8vPR/5CkzatQoMXjwYCHEP/3Wtm3b9Ot//vlnAUDk5eUJIe7cy9494OzUqZNFA20hjO/xfvvtN6FUKvV95ahRo8QLL7xg8Dt79+4VNWrU0MdQfmBsSZ9U0f333fsTwvT9wrp16wz+4BUfHy8UCoW4ePGi2f22bt1azJ4922BZXFycACCSk5P1y1588UXh5eUlsrKy9Mv69OkjXnzxRf3P+fn5wsfHR5w8eVIIUXE/X8YV7lX56LgTSUpKwuDBg9GoUSNoNBr94yVljxUfO3YMUVFRqF27tsnfP3bsGHr27Gly3YkTJ1BSUoL77rsPPj4++n+7d+82eoSrzJ9//onZs2cbbP/888/jxo0byM3N1W/Xvn17o99dsmQJ2rVrhzp16sDHxwefffaZwePRljhz5gy6dOli8B3U6OhoZGdnG8x0GBkZafB7devWxa1bt8zuNy8vDyqVyuS68vsKDAwEALRq1cpgWfl963Q67N69W/+425NPPom8vDw0atQIzz//PDZs2GDwqHuZoqIiDBo0CEIIo0cNAUCtVhscY7Kd8+fPo7CwEJ06ddIvq127Npo0aXJP+7s7X0pKSvD222+jVatWqF27Nnx8fPDbb7/dU75ER0cbLIuOjkZSUpLB42Hl27hCoUBQUFCV82X06NFYu3Yt2rRpgzfeeMNg9v4///wTycnJ8PX11V83ateujfz8fJw/fx63bt3C9evXzV6rzpw5g4YNG6Jhw4b6Zc2bN0fNmjVx5swZ/bLQ0FCDyV/K570l56x3794ICQlBo0aN8O9//xurVq0yyjm1Wg0AzEU70bp1a/Ts2ROtWrXCk08+ic8//xy3b9+u9PfWrVuH6OhoBAUFwcfHB2+99ZZRfoWEhKBOnTrViq9NmzZo1qyZfhbu3bt349atW3jyyScB3MmN7Oxs+Pv7G/SrFy9eNNkP63Q6XL9+3WRel+VCZfcFllCpVPj3v/+N5cuXAwASEhJw8uTJCr/3Wd1+ND8/HzqdDsCdeVN+/PFHfT/K3HRMycnJyM3NRe/evQ3a91dffWXUvsu3kbp16wKA/vp95swZg2s3AHTp0sXiOIYMGYJdu3bpJ5pdtWoVHn30Uf1bdf7880+sWLHCIMY+ffqgtLQUFy9eNNqfJX1SRffflurfvz/c3Nz0j2KvWLEC3bt3r3ASUnN56OXlhcaNG+t/DgwMRGhoKHx8fAyWle/7d+zYgYCAALRo0QJAxf18GVe4V7XurFEkq759+yIkJASff/456tWrh9LSUrRs2VI/MUtZx2JOReuzs7Ph5uaG+Ph4uLm5Gawrn3h3/86sWbMwcOBAo3XlE9vb29tg3dq1azFx4kS8//776NKlC3x9ffHee+8ZfJdcSh4eHgY/KxQKs99nAe7MlnjixIlK91U2wL97Wfl9//rrr2jevLn+AtywYUMkJiZi27Zt2Lp1K1566SW899572L17t34/ZYPsy5cvY8eOHdBoNEZx/P3339W+6SN5KRQKCCEMlpma7OzufHnvvffw4YcfYtGiRfoZ6cePH2+1CZnuJV/uHsw88sgjuHz5Mn755Rds3boVPXv2xJgxY/B///d/yM7ORrt27bBq1SqjfdWpU0eyiZOqWo+7+fr6IiEhAbt27cKWLVswffp0zJw5E4cPH9bfkJVN8sJctA9ubm7YunUrfv/9d2zZsgUfffQRpk6dikOHDiEsLMzk7xw4cABDhgzBrFmz0KdPH/j5+WHt2rVG39m/Oy/v1ZAhQ7B69WpMnjwZq1evxsMPP6yfjCw7Oxt169Y1OTfKvb5as7L7Aks999xzaNOmDa5evYq4uDj06NEDISEhZrc3dV0oY0k/CkCfr3/88QeKi4v1bwFhbjqm7OxsAHe+51v+O8MAoFQqDX6uqD1UV4cOHdC4cWOsXbsWo0ePxoYNG/RzpZTF+eKLL+rnzykvODj4nsqUIg89PT0xbNgwxMXFYeDAgVi9erXB/EammMtDU/1jZX3mpk2bDCYjrKifL+MK96r8RNtJpKenIzExEW+99RZ69uyJZs2aGSVPZGQkjh07ZnaGv8jISLMTl0RFRaGkpAS3bt1CeHi4wb+goCCTv9O2bVskJiYabR8eHl7hzfL+/fvRtWtXvPTSS4iKikJ4eLjRXzM9PT2NJmG4W9kkKuUHL/v374evry8aNGhQ4e9WJCoqCmfPnjUaFN2LjRs3ol+/fgbL1Go1+vbti8WLF2PXrl04cOCAfmBfNshOSkrCtm3bzM4Ge/LkSURFRVU7Pqq+xo0bw8PDw+APRbdv38a5c+cMtqtTpw5u3Lih/zkpKcmiv/Tu378f/fr1w9ChQ9G6dWs0atTIaN+W5sv+/fuN9n3fffcZ/XGtKqKionD69Gmj5XXq1MHw4cPxzTffYNGiRfpJ1dq2bYukpCQEBAQYXTf8/Pzg6+uL0NBQs9eqZs2a4cqVK7hy5Yp+2enTp5GRkYHmzZtbFLOl58zd3R29evXCu+++i+PHj+PSpUvYsWOHfv3JkyfRoEEDaLVai8ol61MoFIiOjsasWbNw9OhReHp66j8BMpUnv//+O0JCQjB16lS0b98eERER+onJKmNJ3t3tmWeewcmTJxEfH4/vvvsOQ4YM0a9r27YtUlNT4e7ubpQbptqYRqNBvXr1TOZ1WS5Udl9gaZ1atWqF9u3b4/PPP8fq1asxcuTICvcTFRWF1NRUi54oqMzGjRvx6KOPGlynmJuOp3nz5lAqlUhJSTFq3+U/Da5Ms2bNjD6YOXjwYJViGTJkCFatWoUff/wRNWrUMJj8tm3btjh9+rTJe1tPT0+T8VTWJ1V0/22Kh4eHyTx87rnnsG3bNnzyyScoLi42+UFXeeb656oqe6rk7vtZc/18GVe4V+VA20nUqlUL/v7++Oyzz5CcnIwdO3ZgwoQJBtsMHjwYQUFB6N+/P/bv348LFy5g/fr1OHDgAABgxowZWLNmDWbMmIEzZ87gxIkTWLBgAQDgvvvuw5AhQzBs2DB8//33uHjxIv744w/MmzcPP//8s8mYpk+fjq+++gqzZs3CqVOncObMGaxduxZvvfVWhXWJiIjAkSNH8Ntvv+HcuXOYNm0aDh8+bLBNaGgojh8/jsTERKSlpZn85O+ll17ClStXMHbsWJw9exYbN27EjBkzMGHChGp9Kta9e3dkZ2fj1KlT97wPACguLsavv/5q8BfAFStW4Msvv8TJkydx4cIFfPPNN1Cr1QgJCUFRURGeeOIJHDlyBKtWrUJJSQlSU1ORmppq8Ollbm4u4uPj8dBDD1UrPpKGj48PRo0ahddffx07duzQP1J5dxvs0aMHPv74Yxw9ehRHjhzBf/7zH6O/IJsSERGh/5TuzJkzePHFF41mMw0NDcWhQ4dw6dIlpKWlmfzL/2uvvYbt27fj7bffxrlz57By5Up8/PHHmDhxYrXq36dPH5w6dcrghnr69OnYuHEjkpOTcerUKfz0009o1qwZgDs3OFqtFv369cPevXtx8eJF7Nq1C+PGjdN/5WPmzJl4//33sXjxYiQlJSEhIQEfffQRAKBXr15o1aoVhgwZgoSEBPzxxx8YNmwYHnjgAZNfUzHFknP2008/YfHixTh27BguX76Mr776CqWlpQaPl+/du5d5aEcOHTqEuXPn4siRI0hJScH333+Pv/76S9/2TPUrERERSElJwdq1a3H+/HksXrzY4llyLck7U7/TtWtXjBo1CiUlJQb9Q69evdClSxf0798fW7ZswaVLl/D7779j6tSpRrMNl3n99dexYMECrFu3DomJiZg8eTKOHTuGV155BUDl9wWm4jPX9z733HOYP38+hBAGMyabEhUVBa1Wa/RHgHtx9ydpzE3H5Ovri4kTJ+LVV1/FypUrcf78ef21feXKlRbv55VXXsHy5csRFxeHc+fOYcaMGVW+XyvrP+bMmYMnnnjC4BP1SZMm4ffff8fLL7+MY8eOISkpCRs3bjSaeb+MJX1SRfffppT9sfnuP1Y1a9YMnTt3xqRJkzB48OBKPynv06cPDhw4UOU/CN4tPj4eubm5Bu+xr6ifB1zoXtWG3w8niW3dulU0a9ZMKJVKERkZKXbt2mU0YcKlS5dEbGys0Gg0wsvLS7Rv314/O7EQdyYRadOmjfD09BRarVYMHDhQv65sNvDQ0FDh4eEh6tatKwYMGCCOHz8uhDCeDE0IITZv3iy6du0q1Gq10Gg0omPHjgazaN8dnxB3JlR49tlnhZ+fn6hZs6YYPXq0mDx5ssFEFrdu3RK9e/cWPj4+AoDYuXOn0WRoQgixa9cu0aFDB+Hp6SmCgoLEpEmTRFFRkX69qclq+vXrV+ksiIMGDRKTJ0/W/2yq7Lsnm7r7GG3btk00aNDAYL8bNmwQnTp1EhqNRnh7e4vOnTvrJ/0oK8PUv507d+r3sXr1aqMJ18i2srKyxNChQ4WXl5cIDAwU7777rlHbu3btmnjooYeEt7e3iIiIEL/88ovJydDKtzEh7kzS1a9fP+Hj4yMCAgLEW2+9JYYNG2YwYVFiYqLo3LmzUKvVAoC4ePGiyfb53XffiebNmwsPDw8RHBws3nvvPYOy7p6QRYg7k6mUn8nflI4dO4ply5bpf3777bdFs2bNhFqtFrVr1xb9+vUTFy5c0K+/ceOGGDZsmNBqtUKpVIpGjRqJ559/3mB2/WXLlokmTZror0Vjx47Vr7t8+bJ4/PHHhbe3t/D19RVPPvmkSE1N1a+/e4InIe5MzhQSEqL/ubJztnfvXvHAAw+IWrVqCbVaLSIjIw0mg8rLyxN+fn7iwIEDFR4bks/p06dFnz59RJ06dYRSqRT33Xef+Oijj/TrTfUrQtyZKM/f31/4+PiIp556SixcuNCgrzPVnoQwnXeW+OSTTwQAMWzYMKN1Op1OjB07VtSrV094eHiIhg0biiFDhugnWro7lpKSEjFz5kxRv3594eHhIVq3bi1+/fVXg31WdF9w9/7MHSMh7uRM2Zs+LPHGG2+Ip59+2mDZ3fcElfWtycnJQqlUiuzsbP165qbjKi0tFYsWLdJf2+vUqSP69Okjdu/eLYQwfV919OhRo/yaM2eO0Gq1wsfHRwwfPly88cYbFk+GVqZjx44CgNixY4fRuj/++EOfB97e3iIyMtJg0sG7+8rK+iQhKr7/vnt/mzZtEuHh4cLd3d2g3xJCiC+//NJgorWKFBUViXr16onNmzfrl5m6lzd1jSs/MeJbb71lNJlbZf28q9yrKoSQ4PlXIhdz/Phx9O7dG+fPnzf7HfXKjBs3DsXFxfjkk08kja1z584YN24cnnnmGUn3S3Svfv75Z7z++us4efKkZN+xtndLly7Fhg0bsGXLFluHQiSLS5cuoXHjxjh8+DDatm1b6fapqalo0aIFEhISKvw+d0U++OADbNu2Db/88ovFv8PcJGf29ttv49tvv8Xx48ct2n7JkiXYtGkTfvvtt3suMzIyEm+99RYGDRpk8e+4yr0qJ0MjugeRkZFYsGABLl68aDAbalW0bNmySjNhWiItLQ0DBw7E4MGDJd0vUXU8+uijSEpKwrVr16r0XTtH5uHhoX+cnciZFRUVIT09HW+99RY6d+5s0SAbAIKCgvDll18iJSXlngfaDRo0wJQpU6r0O8xNckbZ2dm4dOkSPv74Y7zzzjsW/96LL76IjIwMZGVlGbyJw1KFhYWIjY3FI488YvHvuNK9Kj/RJiIiIrKy//znP/jmm29Mrhs6dCiWLVsmc0TS2LVrF7p374777rsP33333T3/8ZlIDhU9hfjrr7/i/vvvlzEa6Tz77LNYs2YN+vfvj9WrV1drElOSDgfaRERERFZ269Yt/buf76bRaBAQECBzRESuJzk52ey6+vXrS/bKOyKAA20iIiIiIiIiSbnGrDREREREREREMuFAm4iIiIiIiEhCHGgTERERERERSYgDbSIiIiIiIiIJcaBNREREREREJCEOtImIiIiIiIgkxIE2ERERERERkYQ40CYiIiIiIiKS0P8Db31G30dtgbsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c63405e-a397-4aab-90f4-025d549caca4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e36d6f75-51b3-4fc7-b10d-556a3fc453c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['acceleration (m/s2)','duration (seconds)', 'start_velocity (m/s)']].values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "674cb912-a997-46a5-a4a5-140408f31fea",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['end_velocity (m/s)'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5393f554-9f48-4e23-982f-df59abece0ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb48397a-ba9d-44f2-b4b5-4dcddd9f96b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 3)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19553e39-23eb-41b9-83e1-da36c0c170d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "03fe06fd-706e-4e77-9f3e-24067b0a0eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11816ab8-aa1c-42ef-83ea-cb9e8ad0aa4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f633c3b3-6bfb-4d98-a9db-26db5c5c07e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38b25944-b8e0-4dde-b84f-9b52aea2f021",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1618665b-8eda-4e18-864f-5b1fb3d3f86a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d3c173b4-7d7e-4ffe-93e8-23e5107a58b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.57201646, 0.12307692, 0.41666667],\n",
       "       [0.57201646, 0.12307692, 0.41666667],\n",
       "       [0.74485597, 0.09230769, 0.29166667],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [0.35802469, 0.06153846, 0.41666667],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.89711934, 0.03076923, 0.125     ],\n",
       "       [0.781893  , 0.10769231, 0.125     ],\n",
       "       [0.17283951, 0.09230769, 0.29166667],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [0.17283951, 0.09230769, 0.29166667],\n",
       "       [0.57201646, 0.30769231, 0.26666667],\n",
       "       [0.28806584, 0.06153846, 0.58333333],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.6872428 , 0.24615385, 0.83333333],\n",
       "       [0.57201646, 0.30769231, 0.26666667],\n",
       "       [0.57201646, 0.04615385, 0.        ],\n",
       "       [0.57201646, 0.12307692, 0.41666667],\n",
       "       [0.89711934, 0.03076923, 0.125     ],\n",
       "       [0.23045267, 0.01538462, 0.125     ],\n",
       "       [0.57201646, 0.04615385, 0.        ],\n",
       "       [0.57201646, 0.06153846, 0.125     ],\n",
       "       [0.23868313, 0.10769231, 0.26666667],\n",
       "       [0.14403292, 0.06153846, 0.66666667],\n",
       "       [0.89711934, 0.03076923, 0.125     ],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.10769231, 0.        ],\n",
       "       [0.74897119, 0.13846154, 0.41666667],\n",
       "       [0.57201646, 0.24615385, 0.        ],\n",
       "       [0.57201646, 0.10769231, 0.        ],\n",
       "       [0.57201646, 0.30769231, 0.26666667],\n",
       "       [0.35802469, 0.06153846, 0.41666667],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.30769231, 0.26666667],\n",
       "       [0.        , 0.09230769, 0.41666667],\n",
       "       [0.57201646, 0.16923077, 0.29166667],\n",
       "       [0.57201646, 0.24615385, 0.        ],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [0.76131687, 0.07692308, 0.29166667],\n",
       "       [0.57201646, 1.        , 0.41666667],\n",
       "       [0.23045267, 0.01538462, 0.125     ],\n",
       "       [0.35802469, 0.06153846, 0.41666667],\n",
       "       [0.57201646, 0.09230769, 1.        ],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [0.57201646, 0.70769231, 0.58333333],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.781893  , 0.10769231, 0.125     ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.17283951, 0.09230769, 0.29166667],\n",
       "       [0.23045267, 0.01538462, 0.125     ],\n",
       "       [0.57201646, 0.06153846, 0.125     ],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.4       , 0.83333333],\n",
       "       [0.781893  , 0.10769231, 0.125     ],\n",
       "       [0.67078189, 0.47692308, 0.58333333],\n",
       "       [0.57201646, 0.06153846, 0.125     ],\n",
       "       [0.89711934, 0.03076923, 0.125     ],\n",
       "       [0.57201646, 0.04615385, 0.        ],\n",
       "       [0.35802469, 0.06153846, 0.41666667],\n",
       "       [0.57201646, 0.16923077, 0.29166667]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35863bea-275b-4ce3-9970-b98453ed3bd1",
   "metadata": {},
   "source": [
    "# Creating the Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c74d3786-a4ff-4ad2-9a2c-9a2d143da4d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "   ------------------------------ --------- 292.1/377.1 MB 1.9 MB/s eta 0:00:44\n",
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      "Installing collected packages: namex, libclang, flatbuffers, wrapt, werkzeug, termcolor, tensorboard-data-server, optree, opt-einsum, ml-dtypes, markdown, h5py, google-pasta, gast, astunparse, absl-py, tensorboard, keras, tensorflow-intel, tensorflow\n",
      "Successfully installed absl-py-2.1.0 astunparse-1.6.3 flatbuffers-24.3.25 gast-0.5.4 google-pasta-0.2.0 h5py-3.11.0 keras-3.3.3 libclang-18.1.1 markdown-3.6 ml-dtypes-0.3.2 namex-0.0.8 opt-einsum-3.3.0 optree-0.11.0 tensorboard-2.16.2 tensorboard-data-server-0.7.2 tensorflow-2.16.1 tensorflow-intel-2.16.1 termcolor-2.4.0 werkzeug-3.0.3 wrapt-1.16.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9936de8-0c3a-4cac-b529-f050c97fdbb0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb45860c-e013-4d73-957c-cfd5381036ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential \n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "14dc800f-1f19-4328-b021-8d2b5376922d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(4,activation='relu'))\n",
    "model.add(Dense(4,activation='relu'))\n",
    "model.add(Dense(4,activation='relu'))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer= 'rmsprop', loss = 'mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b6c31306-5c74-4d5b-958a-aadcf3deee40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 15ms/step - loss: 482.4118\n",
      "Epoch 2/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 471.8615  \n",
      "Epoch 3/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 457.3301\n",
      "Epoch 4/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 504.0562 \n",
      "Epoch 5/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 537us/step - loss: 553.2180\n",
      "Epoch 6/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 506.6021 \n",
      "Epoch 7/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 497.7974 \n",
      "Epoch 8/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 535.2435 \n",
      "Epoch 9/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 478.5672 \n",
      "Epoch 10/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 453.5279 \n",
      "Epoch 11/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 481.5847 \n",
      "Epoch 12/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 483.6704 \n",
      "Epoch 13/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 472.1402 \n",
      "Epoch 14/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 464.4498\n",
      "Epoch 15/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 509.8956 \n",
      "Epoch 16/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 452.6236 \n",
      "Epoch 17/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 435.3142  \n",
      "Epoch 18/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 471.8289 \n",
      "Epoch 19/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 481.8133 \n",
      "Epoch 20/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 490.5463 \n",
      "Epoch 21/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 473.4385 \n",
      "Epoch 22/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 434.8668 \n",
      "Epoch 23/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 505.0863 \n",
      "Epoch 24/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 455.1093\n",
      "Epoch 25/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 510.6070 \n",
      "Epoch 26/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 545.6032  \n",
      "Epoch 27/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 462.1507 \n",
      "Epoch 28/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 446.2622 \n",
      "Epoch 29/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 489.7840 \n",
      "Epoch 30/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 464.3643\n",
      "Epoch 31/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 483.6658 \n",
      "Epoch 32/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 436.4317\n",
      "Epoch 33/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 472.3745\n",
      "Epoch 34/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 510.9120\n",
      "Epoch 35/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 423.9182 \n",
      "Epoch 36/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 487.2783  \n",
      "Epoch 37/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 511.9485 \n",
      "Epoch 38/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 522.7377 \n",
      "Epoch 39/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 518.0522  \n",
      "Epoch 40/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 443.8965\n",
      "Epoch 41/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 568.4355  \n",
      "Epoch 42/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 506.9606 \n",
      "Epoch 43/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 530.0670 \n",
      "Epoch 44/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 472.0807 \n",
      "Epoch 45/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 433.6446 \n",
      "Epoch 46/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 468.8531 \n",
      "Epoch 47/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 520.3542 \n",
      "Epoch 48/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 510.8562 \n",
      "Epoch 49/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 488.1556 \n",
      "Epoch 50/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 484.5617  \n",
      "Epoch 51/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 559us/step - loss: 555.0918\n",
      "Epoch 52/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 499.6570 \n",
      "Epoch 53/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 573.9583 \n",
      "Epoch 54/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 524.3934 \n",
      "Epoch 55/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 467.3966 \n",
      "Epoch 56/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 494.1478\n",
      "Epoch 57/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 473.0617 \n",
      "Epoch 58/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 519.1989  \n",
      "Epoch 59/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 455.1183\n",
      "Epoch 60/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 502.9265 \n",
      "Epoch 61/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 441.1288 \n",
      "Epoch 62/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 511.0181 \n",
      "Epoch 63/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 461.9532 \n",
      "Epoch 64/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 470.0520\n",
      "Epoch 65/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 450.5458 \n",
      "Epoch 66/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 555.4788 \n",
      "Epoch 67/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 521.7513  \n",
      "Epoch 68/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 439.0065  \n",
      "Epoch 69/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 424.7912\n",
      "Epoch 70/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 461.6011  \n",
      "Epoch 71/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 514.2953\n",
      "Epoch 72/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 479.6191 \n",
      "Epoch 73/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 495.7029 \n",
      "Epoch 74/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 436.8827\n",
      "Epoch 75/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 542.7671\n",
      "Epoch 76/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 428.1754 \n",
      "Epoch 77/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 484.0433 \n",
      "Epoch 78/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 493.0658  \n",
      "Epoch 79/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 484.2085\n",
      "Epoch 80/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 486.5346 \n",
      "Epoch 81/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 416.7558  \n",
      "Epoch 82/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 422.5191 \n",
      "Epoch 83/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 467.9924\n",
      "Epoch 84/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 484.4674 \n",
      "Epoch 85/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 449.5030 \n",
      "Epoch 86/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 432.3762 \n",
      "Epoch 87/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 446.4110  \n",
      "Epoch 88/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 431.0465 \n",
      "Epoch 89/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 491.8947\n",
      "Epoch 90/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 502.8960\n",
      "Epoch 91/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 522.5325\n",
      "Epoch 92/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 418.9088 \n",
      "Epoch 93/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 434.2495  \n",
      "Epoch 94/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 453.7952\n",
      "Epoch 95/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 450.9356\n",
      "Epoch 96/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 448.2510\n",
      "Epoch 97/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 482.9655  \n",
      "Epoch 98/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 441.8075 \n",
      "Epoch 99/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 498.5071  \n",
      "Epoch 100/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 419.3366  \n",
      "Epoch 101/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 513.6175  \n",
      "Epoch 102/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 416.6117  \n",
      "Epoch 103/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 442.1405\n",
      "Epoch 104/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 451.3295  \n",
      "Epoch 105/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 434.2224 \n",
      "Epoch 106/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 417.4052  \n",
      "Epoch 107/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 493.6841\n",
      "Epoch 108/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 506.2380 \n",
      "Epoch 109/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 505.9826\n",
      "Epoch 110/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 383.5223 \n",
      "Epoch 111/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 468.2223  \n",
      "Epoch 112/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 471.7737\n",
      "Epoch 113/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 481.3477 \n",
      "Epoch 114/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 432.7058 \n",
      "Epoch 115/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 368.6790\n",
      "Epoch 116/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 436.6446 \n",
      "Epoch 117/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 429.4460\n",
      "Epoch 118/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 467.6519 \n",
      "Epoch 119/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 473.1911 \n",
      "Epoch 120/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 405.2094 \n",
      "Epoch 121/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 434.8691 \n",
      "Epoch 122/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 503.2414 \n",
      "Epoch 123/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 410.8753  \n",
      "Epoch 124/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 434.1877 \n",
      "Epoch 125/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 422.2275  \n",
      "Epoch 126/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 462.6620\n",
      "Epoch 127/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 480.6027\n",
      "Epoch 128/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 484.0487 \n",
      "Epoch 129/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 449.5537\n",
      "Epoch 130/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 458.1728  \n",
      "Epoch 131/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 416.4396 \n",
      "Epoch 132/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 482.5740 \n",
      "Epoch 133/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 393.7670 \n",
      "Epoch 134/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 483.7777 \n",
      "Epoch 135/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 462.4746 \n",
      "Epoch 136/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 364.5398 \n",
      "Epoch 137/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 390.7367 \n",
      "Epoch 138/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 445.2067 \n",
      "Epoch 139/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 404.3237 \n",
      "Epoch 140/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 418.4439 \n",
      "Epoch 141/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 417.2713 \n",
      "Epoch 142/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 454.4910 \n",
      "Epoch 143/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 422.0603 \n",
      "Epoch 144/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 424.1280 \n",
      "Epoch 145/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 403.2616 \n",
      "Epoch 146/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 359.3030  \n",
      "Epoch 147/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 430.1979 \n",
      "Epoch 148/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 363.0194 \n",
      "Epoch 149/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 398.1805 \n",
      "Epoch 150/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 367.7470 \n",
      "Epoch 151/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 429.1198 \n",
      "Epoch 152/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 446.2185 \n",
      "Epoch 153/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 436.3110 \n",
      "Epoch 154/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 452.8840 \n",
      "Epoch 155/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 428.9320   \n",
      "Epoch 156/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 424.6039 \n",
      "Epoch 157/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 456.1916\n",
      "Epoch 158/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 467.5323 \n",
      "Epoch 159/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 395.9458  \n",
      "Epoch 160/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 387.2812 \n",
      "Epoch 161/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 383.6524\n",
      "Epoch 162/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 410.2482 \n",
      "Epoch 163/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 408.8331  \n",
      "Epoch 164/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 393.0383\n",
      "Epoch 165/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 475.4915 \n",
      "Epoch 166/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 463.7928 \n",
      "Epoch 167/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 445.2265 \n",
      "Epoch 168/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 406.5291 \n",
      "Epoch 169/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 372.7587 \n",
      "Epoch 170/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 429.6614 \n",
      "Epoch 171/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - loss: 445.7634 \n",
      "Epoch 172/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 381.2178 \n",
      "Epoch 173/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 354.0959 \n",
      "Epoch 174/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 358.4230 \n",
      "Epoch 175/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 454.8815\n",
      "Epoch 176/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 346.1814 \n",
      "Epoch 177/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 349.7120 \n",
      "Epoch 178/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 452.2276 \n",
      "Epoch 179/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 437.4838 \n",
      "Epoch 180/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 439.2952 \n",
      "Epoch 181/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 430.9179\n",
      "Epoch 182/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 407.9684 \n",
      "Epoch 183/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 442.5483 \n",
      "Epoch 184/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 474.4829\n",
      "Epoch 185/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 381.8482 \n",
      "Epoch 186/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 385.9549 \n",
      "Epoch 187/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 382.0013 \n",
      "Epoch 188/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 471.9781 \n",
      "Epoch 189/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 410.8231 \n",
      "Epoch 190/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 444.7027 \n",
      "Epoch 191/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 387.0463 \n",
      "Epoch 192/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 442.3141 \n",
      "Epoch 193/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 406.5036 \n",
      "Epoch 194/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 430.7124  \n",
      "Epoch 195/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 404.3923  \n",
      "Epoch 196/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 343.3917 \n",
      "Epoch 197/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 346.0935  \n",
      "Epoch 198/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 413.7895 \n",
      "Epoch 199/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 342.1347 \n",
      "Epoch 200/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 372.9329 \n",
      "Epoch 201/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 462.8819 \n",
      "Epoch 202/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 414.8785  \n",
      "Epoch 203/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 344.7811 \n",
      "Epoch 204/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 378.6832 \n",
      "Epoch 205/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 355.6826 \n",
      "Epoch 206/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 380.6570 \n",
      "Epoch 207/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 337.8678 \n",
      "Epoch 208/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 378.8138 \n",
      "Epoch 209/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 361.1212 \n",
      "Epoch 210/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 390.7016 \n",
      "Epoch 211/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 398.7424  \n",
      "Epoch 212/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 408.6142  \n",
      "Epoch 213/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 356.0055 \n",
      "Epoch 214/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 365.2624  \n",
      "Epoch 215/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 332.9061 \n",
      "Epoch 216/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 397.4525 \n",
      "Epoch 217/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 359.5677\n",
      "Epoch 218/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 428.3552  \n",
      "Epoch 219/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 442.8971   \n",
      "Epoch 220/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 408.5736\n",
      "Epoch 221/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 751us/step - loss: 357.2363\n",
      "Epoch 222/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 345.4838  \n",
      "Epoch 223/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 338.6065 \n",
      "Epoch 224/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 351.5732 \n",
      "Epoch 225/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 406.4125 \n",
      "Epoch 226/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 377.2642 \n",
      "Epoch 227/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 370.8834 \n",
      "Epoch 228/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 372.1624 \n",
      "Epoch 229/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 324.2083 \n",
      "Epoch 230/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 353.0269   \n",
      "Epoch 231/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 322.8471\n",
      "Epoch 232/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 387.1612 \n",
      "Epoch 233/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 366.3475 \n",
      "Epoch 234/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 298.6488 \n",
      "Epoch 235/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 333.1269 \n",
      "Epoch 236/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 347.5768 \n",
      "Epoch 237/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 337.6850 \n",
      "Epoch 238/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 332.3815 \n",
      "Epoch 239/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 540us/step - loss: 380.0572\n",
      "Epoch 240/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 327.5128 \n",
      "Epoch 241/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 633us/step - loss: 346.7146\n",
      "Epoch 242/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 377.1045 \n",
      "Epoch 243/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 321.1511  \n",
      "Epoch 244/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 352.4494 \n",
      "Epoch 245/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 309.1376 \n",
      "Epoch 246/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 368.8996 \n",
      "Epoch 247/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 325.7878 \n",
      "Epoch 248/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 354.1998  \n",
      "Epoch 249/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 305.9328 \n",
      "Epoch 250/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 348.9744  \n",
      "Epoch 251/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 373.7505 \n",
      "Epoch 252/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 314.0778 \n",
      "Epoch 253/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 267.5335\n",
      "Epoch 254/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 294.1821\n",
      "Epoch 255/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 311.5284\n",
      "Epoch 256/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 386.6961\n",
      "Epoch 257/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 334.1880 \n",
      "Epoch 258/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 368.3309 \n",
      "Epoch 259/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 322.1647\n",
      "Epoch 260/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 326.6303 \n",
      "Epoch 261/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 311.3050 \n",
      "Epoch 262/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 336.9317 \n",
      "Epoch 263/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 347.2398 \n",
      "Epoch 264/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 259.8050   \n",
      "Epoch 265/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 251.3542  \n",
      "Epoch 266/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 323.4740  \n",
      "Epoch 267/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 314.0108  \n",
      "Epoch 268/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 271.7556\n",
      "Epoch 269/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 293.6613  \n",
      "Epoch 270/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 301.5713  \n",
      "Epoch 271/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 307.7235  \n",
      "Epoch 272/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 271.4282  \n",
      "Epoch 273/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 315.2986  \n",
      "Epoch 274/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 307.0774 \n",
      "Epoch 275/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 276.3463  \n",
      "Epoch 276/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 864us/step - loss: 293.0074\n",
      "Epoch 277/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 313.9582\n",
      "Epoch 278/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 302.4911 \n",
      "Epoch 279/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 283.0091\n",
      "Epoch 280/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 304.0351  \n",
      "Epoch 281/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 312.8785  \n",
      "Epoch 282/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 302.0363 \n",
      "Epoch 283/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 297.4922  \n",
      "Epoch 284/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 255.3325 \n",
      "Epoch 285/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 316.2851 \n",
      "Epoch 286/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 271.0067  \n",
      "Epoch 287/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 248.3322\n",
      "Epoch 288/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 251.3611 \n",
      "Epoch 289/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 246.8673 \n",
      "Epoch 290/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 265.8677  \n",
      "Epoch 291/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 280.1281\n",
      "Epoch 292/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 291.7708\n",
      "Epoch 293/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 298.2268 \n",
      "Epoch 294/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 260.4875  \n",
      "Epoch 295/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 259.0531\n",
      "Epoch 296/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 245.1705 \n",
      "Epoch 297/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 268.6500 \n",
      "Epoch 298/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 287.3518 \n",
      "Epoch 299/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 263.4472 \n",
      "Epoch 300/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 291.5481 \n",
      "Epoch 301/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 258.3755\n",
      "Epoch 302/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 280.8475 \n",
      "Epoch 303/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 258.1825  \n",
      "Epoch 304/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 239.2548\n",
      "Epoch 305/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 318.4893 \n",
      "Epoch 306/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 253.0993\n",
      "Epoch 307/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 270.0583  \n",
      "Epoch 308/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 283.5774 \n",
      "Epoch 309/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 268.3812 \n",
      "Epoch 310/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 274.7206\n",
      "Epoch 311/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 292.1560 \n",
      "Epoch 312/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 270.6288 \n",
      "Epoch 313/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 256.1759 \n",
      "Epoch 314/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 273.4996 \n",
      "Epoch 315/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 230.9597  \n",
      "Epoch 316/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 802us/step - loss: 240.7891\n",
      "Epoch 317/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 243.2491 \n",
      "Epoch 318/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 274.2538 \n",
      "Epoch 319/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 226.9242 \n",
      "Epoch 320/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 269.8991 \n",
      "Epoch 321/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 210.3470\n",
      "Epoch 322/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 233.1198 \n",
      "Epoch 323/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 256.5695  \n",
      "Epoch 324/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 250.1087 \n",
      "Epoch 325/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 248.4696 \n",
      "Epoch 326/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 219.6180 \n",
      "Epoch 327/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 271.6815 \n",
      "Epoch 328/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 195.6753 \n",
      "Epoch 329/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 265.2189\n",
      "Epoch 330/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 193.3895\n",
      "Epoch 331/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 265.6811  \n",
      "Epoch 332/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 217.5189 \n",
      "Epoch 333/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 258.4417 \n",
      "Epoch 334/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 246.5814  \n",
      "Epoch 335/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 196.4229 \n",
      "Epoch 336/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 239.3722 \n",
      "Epoch 337/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 244.0114 \n",
      "Epoch 338/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 219.6526 \n",
      "Epoch 339/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 180.8583\n",
      "Epoch 340/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 247.4716\n",
      "Epoch 341/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 234.8098  \n",
      "Epoch 342/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 233.5570  \n",
      "Epoch 343/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 226.7054 \n",
      "Epoch 344/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 223.4426 \n",
      "Epoch 345/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 270.9471 \n",
      "Epoch 346/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 210.0449 \n",
      "Epoch 347/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 193.3221  \n",
      "Epoch 348/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 209.7645 \n",
      "Epoch 349/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 256.2004 \n",
      "Epoch 350/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 824us/step - loss: 203.4473\n",
      "Epoch 351/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 214.6169 \n",
      "Epoch 352/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 185.0994\n",
      "Epoch 353/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 228.5559   \n",
      "Epoch 354/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 223.3381 \n",
      "Epoch 355/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 245.4148 \n",
      "Epoch 356/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 194.0954 \n",
      "Epoch 357/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 222.8481 \n",
      "Epoch 358/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 229.1297 \n",
      "Epoch 359/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 264.2225  \n",
      "Epoch 360/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 187.9079  \n",
      "Epoch 361/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 233.8057 \n",
      "Epoch 362/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 199.5311 \n",
      "Epoch 363/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 189.4463 \n",
      "Epoch 364/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 250.4937 \n",
      "Epoch 365/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 221.2312\n",
      "Epoch 366/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 207.5781 \n",
      "Epoch 367/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 245.1229 \n",
      "Epoch 368/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 261.2861 \n",
      "Epoch 369/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 226.6460 \n",
      "Epoch 370/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 207.0525\n",
      "Epoch 371/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 235.6033 \n",
      "Epoch 372/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 188.3932\n",
      "Epoch 373/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 222.1614 \n",
      "Epoch 374/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 216.5596 \n",
      "Epoch 375/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 201.2789  \n",
      "Epoch 376/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 210.6818 \n",
      "Epoch 377/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 230.6902 \n",
      "Epoch 378/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 238.6302  \n",
      "Epoch 379/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 215.3872 \n",
      "Epoch 380/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 200.6578  \n",
      "Epoch 381/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 226.2745 \n",
      "Epoch 382/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 208.0708 \n",
      "Epoch 383/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 241.5005 \n",
      "Epoch 384/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 199.9055 \n",
      "Epoch 385/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 257.8994 \n",
      "Epoch 386/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 187.1780 \n",
      "Epoch 387/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 221.7327  \n",
      "Epoch 388/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 255.2646 \n",
      "Epoch 389/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 225.1183\n",
      "Epoch 390/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 228.0730 \n",
      "Epoch 391/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 226.6470  \n",
      "Epoch 392/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 211.0917 \n",
      "Epoch 393/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 215.7025 \n",
      "Epoch 394/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 213.4344 \n",
      "Epoch 395/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 198.3342 \n",
      "Epoch 396/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 212.3844  \n",
      "Epoch 397/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 197.5290\n",
      "Epoch 398/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 213.5356 \n",
      "Epoch 399/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 180.1343 \n",
      "Epoch 400/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 230.0273   \n",
      "Epoch 401/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 237.2752  \n",
      "Epoch 402/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 190.7887 \n",
      "Epoch 403/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 201.7972 \n",
      "Epoch 404/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 203.0571 \n",
      "Epoch 405/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 211.3980 \n",
      "Epoch 406/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 231.4565  \n",
      "Epoch 407/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 209.4787 \n",
      "Epoch 408/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 229.3912 \n",
      "Epoch 409/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 200.2872 \n",
      "Epoch 410/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 206.7698 \n",
      "Epoch 411/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 190.3915 \n",
      "Epoch 412/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - loss: 214.8324 \n",
      "Epoch 413/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 217.7155 \n",
      "Epoch 414/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 206.1837 \n",
      "Epoch 415/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 184.2813 \n",
      "Epoch 416/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 231.3142 \n",
      "Epoch 417/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 240.6681 \n",
      "Epoch 418/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 247.0794\n",
      "Epoch 419/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 243.2942  \n",
      "Epoch 420/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 200.0331 \n",
      "Epoch 421/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 233.4511\n",
      "Epoch 422/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 208.8891  \n",
      "Epoch 423/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 194.8624  \n",
      "Epoch 424/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 224.3271 \n",
      "Epoch 425/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 218.0076\n",
      "Epoch 426/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 199.9956  \n",
      "Epoch 427/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 221.6598 \n",
      "Epoch 428/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 188.0262 \n",
      "Epoch 429/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 219.3484\n",
      "Epoch 430/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 216.2898 \n",
      "Epoch 431/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 189.5681 \n",
      "Epoch 432/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 207.3611 \n",
      "Epoch 433/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 228.4137  \n",
      "Epoch 434/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 209.6264 \n",
      "Epoch 435/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 223.1325 \n",
      "Epoch 436/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 210.3705 \n",
      "Epoch 437/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 195.2105 \n",
      "Epoch 438/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 237.4540 \n",
      "Epoch 439/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 243.7761\n",
      "Epoch 440/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 192.4323 \n",
      "Epoch 441/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 177.7782 \n",
      "Epoch 442/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 239.5615 \n",
      "Epoch 443/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 214.9322 \n",
      "Epoch 444/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 229.0072 \n",
      "Epoch 445/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 225.3151 \n",
      "Epoch 446/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 199.9742 \n",
      "Epoch 447/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 226.0773  \n",
      "Epoch 448/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 218.8706 \n",
      "Epoch 449/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 232.6396 \n",
      "Epoch 450/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 208.8773  \n",
      "Epoch 451/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 206.2815  \n",
      "Epoch 452/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 218.0244  \n",
      "Epoch 453/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 190.0553\n",
      "Epoch 454/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 206.1858\n",
      "Epoch 455/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 234.1416\n",
      "Epoch 456/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 229.0044 \n",
      "Epoch 457/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 194.2347  \n",
      "Epoch 458/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 200.6189 \n",
      "Epoch 459/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 179.6582 \n",
      "Epoch 460/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 198.9985 \n",
      "Epoch 461/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 227.1998 \n",
      "Epoch 462/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 197.2739\n",
      "Epoch 463/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 209.5212 \n",
      "Epoch 464/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 177.9003   \n",
      "Epoch 465/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 173.2170 \n",
      "Epoch 466/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 197.9811 \n",
      "Epoch 467/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 225.1214 \n",
      "Epoch 468/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 190.3060 \n",
      "Epoch 469/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 221.5579 \n",
      "Epoch 470/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 193.0689 \n",
      "Epoch 471/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 206.2663  \n",
      "Epoch 472/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 220.1160  \n",
      "Epoch 473/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 209.7661 \n",
      "Epoch 474/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 198.5381 \n",
      "Epoch 475/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 201.4650\n",
      "Epoch 476/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 208.0224 \n",
      "Epoch 477/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 191.8338  \n",
      "Epoch 478/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 200.9189 \n",
      "Epoch 479/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 196.2737\n",
      "Epoch 480/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 207.7639 \n",
      "Epoch 481/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 187.6962  \n",
      "Epoch 482/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 200.5578  \n",
      "Epoch 483/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 173.8032 \n",
      "Epoch 484/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 199.2048 \n",
      "Epoch 485/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 180.8669 \n",
      "Epoch 486/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 203.4198  \n",
      "Epoch 487/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 186.5437 \n",
      "Epoch 488/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 225.0075\n",
      "Epoch 489/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 200.4162 \n",
      "Epoch 490/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 183.2917 \n",
      "Epoch 491/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 214.5548  \n",
      "Epoch 492/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 225.9785 \n",
      "Epoch 493/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 200.7231  \n",
      "Epoch 494/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 190.6608  \n",
      "Epoch 495/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 217.8237\n",
      "Epoch 496/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 190.2142 \n",
      "Epoch 497/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 199.5184\n",
      "Epoch 498/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 218.7379  \n",
      "Epoch 499/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 187.9838  \n",
      "Epoch 500/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 206.7823  \n",
      "Epoch 501/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 165.3156\n",
      "Epoch 502/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 204.2659 \n",
      "Epoch 503/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 214.5686 \n",
      "Epoch 504/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 190.0991 \n",
      "Epoch 505/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 195.7571 \n",
      "Epoch 506/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 229.6021 \n",
      "Epoch 507/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 196.6941  \n",
      "Epoch 508/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 199.2143  \n",
      "Epoch 509/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 185.9404 \n",
      "Epoch 510/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 193.2176\n",
      "Epoch 511/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 199.3697 \n",
      "Epoch 512/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 188.1054  \n",
      "Epoch 513/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 216.4268 \n",
      "Epoch 514/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 186.4852  \n",
      "Epoch 515/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 182.0190 \n",
      "Epoch 516/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 192.7204 \n",
      "Epoch 517/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 187.3201 \n",
      "Epoch 518/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 197.8789  \n",
      "Epoch 519/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 161.3338\n",
      "Epoch 520/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 227.4946 \n",
      "Epoch 521/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 198.5861  \n",
      "Epoch 522/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 228.8003  \n",
      "Epoch 523/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 194.6635 \n",
      "Epoch 524/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 212.3918  \n",
      "Epoch 525/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 188.9029\n",
      "Epoch 526/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 225.3219  \n",
      "Epoch 527/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 164.3428 \n",
      "Epoch 528/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 196.6359 \n",
      "Epoch 529/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 184.5368 \n",
      "Epoch 530/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 180.1701 \n",
      "Epoch 531/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 198.2669   \n",
      "Epoch 532/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 178.2412 \n",
      "Epoch 533/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 176.6099 \n",
      "Epoch 534/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 199.8119 \n",
      "Epoch 535/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 191.3846\n",
      "Epoch 536/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 203.6390 \n",
      "Epoch 537/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 171.0861  \n",
      "Epoch 538/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 204.0581\n",
      "Epoch 539/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 178.9388 \n",
      "Epoch 540/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 186.2178 \n",
      "Epoch 541/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 192.1413 \n",
      "Epoch 542/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 179.7108 \n",
      "Epoch 543/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 201.5415 \n",
      "Epoch 544/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 162.0093\n",
      "Epoch 545/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 174.0018 \n",
      "Epoch 546/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 192.1391  \n",
      "Epoch 547/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 198.8690\n",
      "Epoch 548/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 205.5041 \n",
      "Epoch 549/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 204.8884 \n",
      "Epoch 550/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 210.7176 \n",
      "Epoch 551/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 191.4404 \n",
      "Epoch 552/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 195.6811  \n",
      "Epoch 553/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 190.3064 \n",
      "Epoch 554/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 189.7403 \n",
      "Epoch 555/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 177.1149 \n",
      "Epoch 556/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 196.6711 \n",
      "Epoch 557/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 172.8526 \n",
      "Epoch 558/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 191.6416 \n",
      "Epoch 559/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 194.7664\n",
      "Epoch 560/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 186.0408  \n",
      "Epoch 561/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 190.9601 \n",
      "Epoch 562/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 196.5827 \n",
      "Epoch 563/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 207.6258 \n",
      "Epoch 564/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 203.5598 \n",
      "Epoch 565/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 182.2331 \n",
      "Epoch 566/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 155.6154\n",
      "Epoch 567/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 206.9623  \n",
      "Epoch 568/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 198.2400 \n",
      "Epoch 569/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 213.9196 \n",
      "Epoch 570/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 185.7181 \n",
      "Epoch 571/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 177.6296 \n",
      "Epoch 572/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 202.8371 \n",
      "Epoch 573/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 192.9827 \n",
      "Epoch 574/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 185.4291 \n",
      "Epoch 575/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 195.5651 \n",
      "Epoch 576/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 152.7114 \n",
      "Epoch 577/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 192.9281\n",
      "Epoch 578/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 212.6605  \n",
      "Epoch 579/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 171.6166 \n",
      "Epoch 580/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 172.5233 \n",
      "Epoch 581/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 659us/step - loss: 168.8286\n",
      "Epoch 582/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 188.0584   \n",
      "Epoch 583/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 207.8262 \n",
      "Epoch 584/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 176.7982 \n",
      "Epoch 585/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 189.7511 \n",
      "Epoch 586/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 192.5383 \n",
      "Epoch 587/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 199.2063 \n",
      "Epoch 588/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 182.1339\n",
      "Epoch 589/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 191.0867 \n",
      "Epoch 590/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 178.0518  \n",
      "Epoch 591/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 182.6175 \n",
      "Epoch 592/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 823us/step - loss: 186.1061\n",
      "Epoch 593/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 194.8309 \n",
      "Epoch 594/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 181.1487 \n",
      "Epoch 595/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 189.1905 \n",
      "Epoch 596/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 208.0713  \n",
      "Epoch 597/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 198.5103  \n",
      "Epoch 598/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 160.8594 \n",
      "Epoch 599/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 193.2992 \n",
      "Epoch 600/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 176.3109 \n",
      "Epoch 601/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 188.1220 \n",
      "Epoch 602/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 835us/step - loss: 195.3569\n",
      "Epoch 603/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 171.8586 \n",
      "Epoch 604/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 184.8675\n",
      "Epoch 605/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 152.7269\n",
      "Epoch 606/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 201.1818 \n",
      "Epoch 607/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 194.5811 \n",
      "Epoch 608/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 182.4797 \n",
      "Epoch 609/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 189.3598 \n",
      "Epoch 610/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 177.1783 \n",
      "Epoch 611/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 205.4016 \n",
      "Epoch 612/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 197.9419 \n",
      "Epoch 613/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 181.1671 \n",
      "Epoch 614/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 181.0388 \n",
      "Epoch 615/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 179.8342 \n",
      "Epoch 616/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 195.0229 \n",
      "Epoch 617/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 175.4912 \n",
      "Epoch 618/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 162.8793 \n",
      "Epoch 619/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 165.5303 \n",
      "Epoch 620/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 202.8067 \n",
      "Epoch 621/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 204.3708  \n",
      "Epoch 622/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 168.3416 \n",
      "Epoch 623/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 177.3759  \n",
      "Epoch 624/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 185.3358 \n",
      "Epoch 625/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 179.9006 \n",
      "Epoch 626/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 172.1222 \n",
      "Epoch 627/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 148.1528\n",
      "Epoch 628/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 178.2007  \n",
      "Epoch 629/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 167.3640 \n",
      "Epoch 630/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 185.8010 \n",
      "Epoch 631/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 158.9512 \n",
      "Epoch 632/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 181.5121 \n",
      "Epoch 633/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 191.2327 \n",
      "Epoch 634/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 178.7468 \n",
      "Epoch 635/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 157.8814 \n",
      "Epoch 636/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 162.5370 \n",
      "Epoch 637/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 174.4264  \n",
      "Epoch 638/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 169.6754 \n",
      "Epoch 639/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 152.8692 \n",
      "Epoch 640/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 167.5715 \n",
      "Epoch 641/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 167.8169  \n",
      "Epoch 642/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 149.7916 \n",
      "Epoch 643/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 186.4160 \n",
      "Epoch 644/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 156.3793 \n",
      "Epoch 645/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 181.9103 \n",
      "Epoch 646/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 166.0694 \n",
      "Epoch 647/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 142.6850\n",
      "Epoch 648/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 152.8822  \n",
      "Epoch 649/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 187.3584  \n",
      "Epoch 650/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 189.3426 \n",
      "Epoch 651/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 153.5874 \n",
      "Epoch 652/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 150.3415  \n",
      "Epoch 653/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 184.3075 \n",
      "Epoch 654/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 161.9259  \n",
      "Epoch 655/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 151.5679  \n",
      "Epoch 656/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 174.3501 \n",
      "Epoch 657/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 166.3264 \n",
      "Epoch 658/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 162.3200 \n",
      "Epoch 659/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 169.2839 \n",
      "Epoch 660/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 183.6184  \n",
      "Epoch 661/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 162.5173 \n",
      "Epoch 662/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 164.9646  \n",
      "Epoch 663/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 168.0167   \n",
      "Epoch 664/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 154.1846 \n",
      "Epoch 665/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 149.6510  \n",
      "Epoch 666/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 153.9553 \n",
      "Epoch 667/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 171.4483 \n",
      "Epoch 668/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 164.9214 \n",
      "Epoch 669/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 162.9520 \n",
      "Epoch 670/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 160.8857\n",
      "Epoch 671/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 158.7882 \n",
      "Epoch 672/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 169.3148  \n",
      "Epoch 673/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 180.8742  \n",
      "Epoch 674/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 160.7365  \n",
      "Epoch 675/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 151.6392 \n",
      "Epoch 676/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 176.4140 \n",
      "Epoch 677/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 178.0889 \n",
      "Epoch 678/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 177.6340  \n",
      "Epoch 679/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 178.3159\n",
      "Epoch 680/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 177.1354  \n",
      "Epoch 681/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 167.1935 \n",
      "Epoch 682/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 154.6281  \n",
      "Epoch 683/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 163.1302 \n",
      "Epoch 684/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 157.3308  \n",
      "Epoch 685/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 180.3941 \n",
      "Epoch 686/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 183.5191 \n",
      "Epoch 687/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 173.9870 \n",
      "Epoch 688/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 181.0538 \n",
      "Epoch 689/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 168.8435  \n",
      "Epoch 690/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 169.1000 \n",
      "Epoch 691/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 160.7199 \n",
      "Epoch 692/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 170.9233  \n",
      "Epoch 693/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 163.5575 \n",
      "Epoch 694/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 163.6561  \n",
      "Epoch 695/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 167.8604 \n",
      "Epoch 696/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 149.9049 \n",
      "Epoch 697/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 163.9994 \n",
      "Epoch 698/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 155.5146 \n",
      "Epoch 699/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 151.8838  \n",
      "Epoch 700/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 129.8387 \n",
      "Epoch 701/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 146.7182 \n",
      "Epoch 702/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 166.0529  \n",
      "Epoch 703/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 156.4406 \n",
      "Epoch 704/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 163.4663 \n",
      "Epoch 705/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 150.1549  \n",
      "Epoch 706/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 152.6434  \n",
      "Epoch 707/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 148.3759 \n",
      "Epoch 708/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 141.7705 \n",
      "Epoch 709/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 159.1590 \n",
      "Epoch 710/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 149.0470 \n",
      "Epoch 711/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 155.5018 \n",
      "Epoch 712/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 141.2404 \n",
      "Epoch 713/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 150.6412 \n",
      "Epoch 714/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 141.7482 \n",
      "Epoch 715/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 152.2050 \n",
      "Epoch 716/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 160.0150 \n",
      "Epoch 717/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 152.8684 \n",
      "Epoch 718/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 167.0082 \n",
      "Epoch 719/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 143.3320\n",
      "Epoch 720/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 164.0719 \n",
      "Epoch 721/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 142.9568 \n",
      "Epoch 722/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 153.7450  \n",
      "Epoch 723/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 160.0831  \n",
      "Epoch 724/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 139.2654 \n",
      "Epoch 725/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 147.2184 \n",
      "Epoch 726/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 132.5787\n",
      "Epoch 727/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 143.0631 \n",
      "Epoch 728/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 164.5374\n",
      "Epoch 729/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 155.9680   \n",
      "Epoch 730/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 140.1909 \n",
      "Epoch 731/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 132.6188 \n",
      "Epoch 732/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 159.8982  \n",
      "Epoch 733/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 147.9106 \n",
      "Epoch 734/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 141.5591  \n",
      "Epoch 735/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 139.4975 \n",
      "Epoch 736/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 140.0726 \n",
      "Epoch 737/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 145.8071\n",
      "Epoch 738/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 129.4680\n",
      "Epoch 739/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 145.4794 \n",
      "Epoch 740/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 145.5448 \n",
      "Epoch 741/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 155.2656\n",
      "Epoch 742/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 157.4886  \n",
      "Epoch 743/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 152.4649  \n",
      "Epoch 744/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 143.2197 \n",
      "Epoch 745/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 140.7606\n",
      "Epoch 746/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 150.3925  \n",
      "Epoch 747/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 146.9076\n",
      "Epoch 748/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 135.5593  \n",
      "Epoch 749/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 158.1921 \n",
      "Epoch 750/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 148.5378  \n",
      "Epoch 751/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 134.5877  \n",
      "Epoch 752/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 140.6605  \n",
      "Epoch 753/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 123.7282\n",
      "Epoch 754/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 143.1351  \n",
      "Epoch 755/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 132.6995  \n",
      "Epoch 756/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 159.0823 \n",
      "Epoch 757/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 149.2375  \n",
      "Epoch 758/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 138.2133 \n",
      "Epoch 759/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 139.7307 \n",
      "Epoch 760/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 137.6863 \n",
      "Epoch 761/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 141.1799 \n",
      "Epoch 762/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 120.3421\n",
      "Epoch 763/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 147.0970 \n",
      "Epoch 764/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 152.0046  \n",
      "Epoch 765/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 119.9701 \n",
      "Epoch 766/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 119.2426\n",
      "Epoch 767/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 138.1816 \n",
      "Epoch 768/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 135.6788 \n",
      "Epoch 769/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 909us/step - loss: 130.1418\n",
      "Epoch 770/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 152.9666  \n",
      "Epoch 771/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 128.9640 \n",
      "Epoch 772/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 142.5664  \n",
      "Epoch 773/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 146.7327  \n",
      "Epoch 774/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 142.5056  \n",
      "Epoch 775/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 111.6515\n",
      "Epoch 776/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 151.1423\n",
      "Epoch 777/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 128.6424 \n",
      "Epoch 778/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 140.5434 \n",
      "Epoch 779/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 137.0262 \n",
      "Epoch 780/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 120.3267 \n",
      "Epoch 781/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 130.1610 \n",
      "Epoch 782/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 143.7532  \n",
      "Epoch 783/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 128.5452 \n",
      "Epoch 784/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 136.6236 \n",
      "Epoch 785/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 118.5039\n",
      "Epoch 786/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 148.6741\n",
      "Epoch 787/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 128.3040  \n",
      "Epoch 788/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 122.7946 \n",
      "Epoch 789/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 114.7505\n",
      "Epoch 790/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 125.7841  \n",
      "Epoch 791/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 138.2809 \n",
      "Epoch 792/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 117.0378\n",
      "Epoch 793/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 147.7323 \n",
      "Epoch 794/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 145.2847 \n",
      "Epoch 795/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 120.1509  \n",
      "Epoch 796/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 126.0308 \n",
      "Epoch 797/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 135.2996  \n",
      "Epoch 798/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 120.8659\n",
      "Epoch 799/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 125.5525 \n",
      "Epoch 800/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 120.4350  \n",
      "Epoch 801/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 124.7961 \n",
      "Epoch 802/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 114.3279\n",
      "Epoch 803/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 117.6753 \n",
      "Epoch 804/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 140.2150 \n",
      "Epoch 805/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 125.3584  \n",
      "Epoch 806/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 128.8079  \n",
      "Epoch 807/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 120.5365 \n",
      "Epoch 808/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 137.7598  \n",
      "Epoch 809/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 110.4418\n",
      "Epoch 810/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 110.7194 \n",
      "Epoch 811/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 142.5282  \n",
      "Epoch 812/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 128.0150 \n",
      "Epoch 813/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 127.2746 \n",
      "Epoch 814/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 122.7610 \n",
      "Epoch 815/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 112.9870 \n",
      "Epoch 816/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 135.2915\n",
      "Epoch 817/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 129.0117  \n",
      "Epoch 818/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 102.5935 \n",
      "Epoch 819/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 110.3195 \n",
      "Epoch 820/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 126.0916\n",
      "Epoch 821/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 121.7366 \n",
      "Epoch 822/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 125.3947  \n",
      "Epoch 823/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 124.0392\n",
      "Epoch 824/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 116.4148  \n",
      "Epoch 825/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 130.7464 \n",
      "Epoch 826/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 117.8132 \n",
      "Epoch 827/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 118.9058\n",
      "Epoch 828/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 116.3683 \n",
      "Epoch 829/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 101.2444\n",
      "Epoch 830/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 123.2127 \n",
      "Epoch 831/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 114.1460\n",
      "Epoch 832/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 131.8330 \n",
      "Epoch 833/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 118.4972 \n",
      "Epoch 834/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 118.7819 \n",
      "Epoch 835/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 114.4699\n",
      "Epoch 836/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 116.8866 \n",
      "Epoch 837/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 107.6138 \n",
      "Epoch 838/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 121.6361 \n",
      "Epoch 839/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0s/step - loss: 114.4085  \n",
      "Epoch 840/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 106.2705\n",
      "Epoch 841/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 104.2394 \n",
      "Epoch 842/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 103.6155 \n",
      "Epoch 843/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 105.7144\n",
      "Epoch 844/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 101.3757\n",
      "Epoch 845/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 97.0693 \n",
      "Epoch 846/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 100.1356\n",
      "Epoch 847/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 96.8146 \n",
      "Epoch 848/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 126.2241 \n",
      "Epoch 849/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 106.4156 \n",
      "Epoch 850/850\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 100.4605\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x2182b9cafc0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train, y=y_train, epochs=850,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "abf79f1a-d1ac-4a55-8412-fb39fc8eabb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_df = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8060a8ec-53bd-4080-9076-b6185702135f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "38bb05b6-417b-4f71-b3e6-357558e93380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.02722930908203"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test,y_test,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "54f82a51-a979-4d4e-89db-0256f78ba947",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "108.8110580444336"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_train,y_train,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5f1237e8-1ba2-4ec8-8773-7d86168ac0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_IDC = pd.read_csv('IDC_NN_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "84ab7f69-2e63-4ef3-9c8d-f5a9290d5f39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acceleration (m/s2)</th>\n",
       "      <th>start_velocity (m/s)</th>\n",
       "      <th>duration (seconds)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.648148</td>\n",
       "      <td>0.180041</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.154321</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.625000</td>\n",
       "      <td>-0.173611</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   acceleration (m/s2)  start_velocity (m/s)  duration (seconds)\n",
       "0             0.000000              0.000000                   0\n",
       "1             0.000000              0.000000                   0\n",
       "2             0.648148              0.180041                   6\n",
       "3             0.555556              0.154321                   4\n",
       "4            -0.625000             -0.173611                   4"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_IDC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e28f04d2-3441-4e1d-8f4c-d7ea31f315ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df_IDC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2ae43ba2-e210-4185-ad93-c4f62598dc07",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_IDC = scaler.transform(df_IDC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a2ac0329-39fa-4385-9077-e73f32260e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.49090909, 0.49090909, 0.        ],\n",
       "       [0.49090909, 0.49090909, 0.        ],\n",
       "       [1.        , 1.        , 0.66666667],\n",
       "       [0.92727273, 0.92727273, 0.44444444],\n",
       "       [0.        , 0.        , 0.44444444],\n",
       "       [0.49090909, 0.49090909, 0.22222222],\n",
       "       [0.92727273, 0.92727273, 0.55555556],\n",
       "       [0.84      , 0.84      , 0.55555556],\n",
       "       [0.05454545, 0.05454545, 0.33333333],\n",
       "       [0.49090909, 0.49090909, 0.44444444],\n",
       "       [0.05454545, 0.05454545, 0.22222222],\n",
       "       [0.84545455, 0.84545454, 0.88888889],\n",
       "       [0.74025974, 0.74025974, 0.77777778],\n",
       "       [0.12727273, 0.12727273, 0.33333333],\n",
       "       [0.49090909, 0.49090909, 0.77777778],\n",
       "       [0.16363636, 0.16363636, 0.22222222],\n",
       "       [0.74025974, 0.74025974, 0.77777778],\n",
       "       [0.12727273, 0.12727273, 1.        ],\n",
       "       [0.08571429, 0.08571429, 0.77777778],\n",
       "       [0.05454545, 0.05454545, 0.77777778]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_IDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b091b281-5364-4d52-a50c-9d439c68c4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.57201646, 0.26153846, 0.        ],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [1.        , 0.        , 0.        ],\n",
       "       [0.17283951, 0.09230769, 0.29166667],\n",
       "       [0.57201646, 0.10769231, 0.        ],\n",
       "       [0.23868313, 0.10769231, 0.26666667],\n",
       "       [0.23045267, 0.01538462, 0.125     ],\n",
       "       [0.781893  , 0.10769231, 0.125     ],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.23868313, 0.10769231, 0.26666667],\n",
       "       [0.57201646, 0.70769231, 0.58333333],\n",
       "       [0.57201646, 0.04615385, 0.        ],\n",
       "       [0.57201646, 0.06153846, 0.125     ],\n",
       "       [0.28806584, 0.18461538, 1.        ],\n",
       "       [0.76131687, 0.07692308, 0.29166667],\n",
       "       [0.57201646, 0.26153846, 0.        ],\n",
       "       [0.57201646, 0.10769231, 0.        ],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.12307692, 0.41666667],\n",
       "       [0.23868313, 0.10769231, 0.26666667],\n",
       "       [0.85596708, 0.03076923, 0.        ],\n",
       "       [0.57201646, 0.16923077, 0.29166667],\n",
       "       [0.73662551, 0.15384615, 0.41666667],\n",
       "       [0.76131687, 0.07692308, 0.29166667],\n",
       "       [0.57201646, 0.16923077, 0.29166667],\n",
       "       [0.781893  , 0.10769231, 0.125     ],\n",
       "       [0.76131687, 0.07692308, 0.29166667]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cc646631-5fd6-43f0-b6f0-9abe9ac7fee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step\n"
     ]
    }
   ],
   "source": [
    "test_predictions = model.predict(df_IDC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ac2bf09d-98e8-4607-9a12-0f3048c8702c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.456976],\n",
       "       [16.456976],\n",
       "       [47.54125 ],\n",
       "       [40.061653],\n",
       "       [12.504187],\n",
       "       [21.775208],\n",
       "       [42.720768],\n",
       "       [40.12712 ],\n",
       "       [11.466102],\n",
       "       [27.09344 ],\n",
       "       [ 8.806986],\n",
       "       [48.26657 ],\n",
       "       [42.48119 ],\n",
       "       [13.627471],\n",
       "       [35.070778],\n",
       "       [12.049041],\n",
       "       [42.48119 ],\n",
       "       [29.355982],\n",
       "       [22.923328],\n",
       "       [21.956478]], dtype=float32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ced699e8-bbf4-4c46-a97d-ebc3a24eec07",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = pd.Series(test_predictions)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "942c9978-1040-4cd4-b9b5-c2999e90ca89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([16.456976, 16.456976, 47.54125 , 40.061653, 12.504187, 21.775208,\n",
       "       42.720768, 40.12712 , 11.466102, 27.09344 ,  8.806986, 48.26657 ,\n",
       "       42.48119 , 13.627471, 35.070778, 12.049041, 42.48119 , 29.355982,\n",
       "       22.923328, 21.956478], dtype=float32)"
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     "execution_count": 56,
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   "cell_type": "code",
   "execution_count": 39,
   "id": "cfa0fdac-4df6-465c-a454-802dbf530f61",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame(y_test, columns=['Test True Y'])"
   ]
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   "execution_count": 60,
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       "    Test True Y  Model Predictions\n",
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       "1      0.000000          16.456976\n",
       "2      8.333333          47.541248\n",
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       "5      0.000000          21.775208\n",
       "6      0.000000          42.720768\n",
       "7     19.444444          40.127121\n",
       "8      8.333333          11.466102\n",
       "9      0.000000          27.093439\n",
       "10    38.888889           8.806986\n",
       "11     0.000000          48.266571\n",
       "12     8.333333          42.481190\n",
       "13    44.444444          13.627471\n",
       "14    27.777778          35.070778\n",
       "15     0.000000          12.049041\n",
       "16     0.000000          42.481190\n",
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       "18    27.777778          22.923328\n",
       "19     0.000000          21.956478\n",
       "20     8.333333                NaN\n",
       "21    19.444444                NaN\n",
       "22    38.888889                NaN\n",
       "23    27.777778                NaN\n",
       "24    19.444444                NaN\n",
       "25    19.444444                NaN\n",
       "26    27.777778                NaN"
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   "execution_count": 61,
   "id": "5129d39f-b860-42d8-98a7-514aee68f63f",
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    {
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       "      <th>21</th>\n",
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       "      <th>26</th>\n",
       "      <td>27.777778</td>\n",
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      "text/plain": [
       "    Test True Y  Model Predictions\n",
       "0      0.000000          16.456976\n",
       "1      0.000000          16.456976\n",
       "2      8.333333          47.541248\n",
       "3      0.000000          40.061653\n",
       "4      0.000000          12.504187\n",
       "5      0.000000          21.775208\n",
       "6      0.000000          42.720768\n",
       "7     19.444444          40.127121\n",
       "8      8.333333          11.466102\n",
       "9      0.000000          27.093439\n",
       "10    38.888889           8.806986\n",
       "11     0.000000          48.266571\n",
       "12     8.333333          42.481190\n",
       "13    44.444444          13.627471\n",
       "14    27.777778          35.070778\n",
       "15     0.000000          12.049041\n",
       "16     0.000000          42.481190\n",
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       "18    27.777778          22.923328\n",
       "19     0.000000          21.956478\n",
       "20     8.333333                NaN\n",
       "21    19.444444                NaN\n",
       "22    38.888889                NaN\n",
       "23    27.777778                NaN\n",
       "24    19.444444                NaN\n",
       "25    19.444444                NaN\n",
       "26    27.777778                NaN"
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     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "pred_df"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8f2763dd-42c9-4901-8ca2-abe6d5e850d9",
   "metadata": {},
   "outputs": [],
   "source": [
    " pred_df = pd.concat([pred_df, test_predictions],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ee9bdfdb-cc96-43f9-8c3d-0cfffe5b023a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df.columns = ['Test True Y', 'Model Predictions']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "369c1ab5-f9ac-41f6-90f5-028c932902fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "ram = pred_df.iloc[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e6b23978-c3e0-4c7f-8f6c-b23347aabf73",
   "metadata": {},
   "outputs": [
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       "    Test True Y  Model Predictions\n",
       "0      0.000000          16.456976\n",
       "1      0.000000          16.456976\n",
       "2      8.333333          47.541248\n",
       "3      0.000000          40.061653\n",
       "4      0.000000          12.504187\n",
       "5      0.000000          21.775208\n",
       "6      0.000000          42.720768\n",
       "7     19.444444          40.127121\n",
       "8      8.333333          11.466102\n",
       "9      0.000000          27.093439\n",
       "10    38.888889           8.806986\n",
       "11     0.000000          48.266571\n",
       "12     8.333333          42.481190\n",
       "13    44.444444          13.627471\n",
       "14    27.777778          35.070778\n",
       "15     0.000000          12.049041\n",
       "16     0.000000          42.481190\n",
       "17     8.333333          29.355982\n",
       "18    27.777778          22.923328\n",
       "19     0.000000          21.956478"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7ad6cb9d-1051-4710-a753-043ad13c0e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Test True Y', ylabel='Model Predictions'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='Test True Y', y='Model Predictions', data=pred_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "006510ce-a8f4-4e25-b35b-a320919395a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "28ff4a08-2574-4c64-bfe3-84ef429eff92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.65313383220603"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(ram['Test True Y'],ram['Model Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ce710224-ef69-46ba-83c5-99f84cb56f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>acceleration (m/s2)</th>\n",
       "      <th>duration (seconds)</th>\n",
       "      <th>start_velocity (m/s)</th>\n",
       "      <th>end_velocity (m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.04</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.83</td>\n",
       "      <td>5</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>-0.69</td>\n",
       "      <td>16</td>\n",
       "      <td>66.666667</td>\n",
       "      <td>44.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>-1.04</td>\n",
       "      <td>8</td>\n",
       "      <td>44.444444</td>\n",
       "      <td>27.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>-1.39</td>\n",
       "      <td>10</td>\n",
       "      <td>27.777778</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.00</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    acceleration (m/s2)  duration (seconds)  start_velocity (m/s)  \\\n",
       "0                  0.00                  11              0.000000   \n",
       "1                  1.04                   4              0.000000   \n",
       "2                  0.00                   8              8.333333   \n",
       "3                 -0.83                   5              8.333333   \n",
       "4                  0.00                  21              0.000000   \n",
       "..                  ...                 ...                   ...   \n",
       "85                 0.00                  10             66.666667   \n",
       "86                -0.69                  16             66.666667   \n",
       "87                -1.04                   8             44.444444   \n",
       "88                -1.39                  10             27.777778   \n",
       "89                 0.00                  20              0.000000   \n",
       "\n",
       "    end_velocity (m/s)  \n",
       "0             0.000000  \n",
       "1             8.333333  \n",
       "2             8.333333  \n",
       "3             0.000000  \n",
       "4             0.000000  \n",
       "..                 ...  \n",
       "85           66.666667  \n",
       "86           44.444444  \n",
       "87           27.777778  \n",
       "88            0.000000  \n",
       "89            0.000000  \n",
       "\n",
       "[90 rows x 4 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b8f55054-d23c-4231-9b28-4e4d9c935988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>acceleration (m/s2)</th>\n",
       "      <td>90.0</td>\n",
       "      <td>0.037333</td>\n",
       "      <td>0.560673</td>\n",
       "      <td>-1.39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>1.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration (seconds)</th>\n",
       "      <td>90.0</td>\n",
       "      <td>13.111111</td>\n",
       "      <td>10.390623</td>\n",
       "      <td>4.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_velocity (m/s)</th>\n",
       "      <td>90.0</td>\n",
       "      <td>14.604938</td>\n",
       "      <td>15.640761</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>19.444444</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_velocity (m/s)</th>\n",
       "      <td>90.0</td>\n",
       "      <td>14.604938</td>\n",
       "      <td>15.640761</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>19.444444</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count       mean        std   min  25%        50%  \\\n",
       "acceleration (m/s2)    90.0   0.037333   0.560673 -1.39  0.0   0.000000   \n",
       "duration (seconds)     90.0  13.111111  10.390623  4.00  7.0  10.000000   \n",
       "start_velocity (m/s)   90.0  14.604938  15.640761  0.00  0.0   8.333333   \n",
       "end_velocity (m/s)     90.0  14.604938  15.640761  0.00  0.0   8.333333   \n",
       "\n",
       "                            75%        max  \n",
       "acceleration (m/s2)    0.460000   1.040000  \n",
       "duration (seconds)    15.000000  69.000000  \n",
       "start_velocity (m/s)  19.444444  66.666667  \n",
       "end_velocity (m/s)    19.444444  66.666667  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c0e775d6-9b6c-456c-8b5f-66e028e4e1f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "780.602435513874"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(ram['Test True Y'],ram['Model Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "3fe785c7-2c27-4f83-9242-5b0994feb532",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(ram['Test True Y'],ram['Model Predictions'])**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "086a22f5-f6a0-4e58-be1b-aea954eddbaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.939263331624797"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "abf7b841-c216-4224-b0b5-899f30d5a608",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_drive = [[0.51,15,11]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5dfb46a8-4ce9-4216-a51c-a14ef93e4c77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gjgau\\pythonanaconda\\envs\\notebook\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.89149091, 42.90545459,  1.22222222]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.transform(new_drive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "11cb9566-e03a-4d7a-a0ed-dc55caea1f13",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gjgau\\pythonanaconda\\envs\\notebook\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "new_drive = scaler.transform(new_drive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "09e8fa84-9c29-4423-86c0-a8d4eb85c5b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[789.8156]], dtype=float32)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(new_drive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b1cb0665-b310-421e-bb3d-f9a291425c8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_acc = model.evaluate(X_train,y_train,verbose = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "aaf6b095-6cbe-47d8-955a-9d695392f6c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the model is -8.811058044433594. Therefore the model is performing as per expectations.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The accuracy of the model is {100-test_acc}. Therefore the model is performing as per expectations.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "28fa4e53-2566-49c1-aed8-ee14e208adb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-3.055158859484737"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2 = r2_score(ram['Test True Y'],ram['Model Predictions'])\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137c766e-ad4e-45e2-a4f9-45d6e17b8aad",
   "metadata": {},
   "source": [
    "# Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8b830f9-b490-4031-8cfa-3fadd7ac4265",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13cfa8af-d908-470f-9eba-ff78dd93974d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f24231f0-008f-4998-b7f4-0f63f75838a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('my_gem_model_1.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab46882d-3928-4f58-82d4-aefbd7d6baae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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