{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import random\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import sklearn\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy version 1.26.2\n",
      "pandas version 2.1.4\n",
      "sklearn version 1.4.2\n",
      "Tensorflow version 2.17.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Numpy version {np.__version__}\")\n",
    "print(f\"pandas version {pd.__version__}\")\n",
    "print(f\"sklearn version {sklearn.__version__}\")\n",
    "print(f\"Tensorflow version {tf.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>name</th>\n",
       "      <th>email</th>\n",
       "      <th>password</th>\n",
       "      <th>attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Varsha</td>\n",
       "      <td>varsha@gmail.com</td>\n",
       "      <td>varsha6106</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>Varsha</td>\n",
       "      <td>varsha@gmail.com</td>\n",
       "      <td>varsha8052</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Mary</td>\n",
       "      <td>mary@gmail.com</td>\n",
       "      <td>mary8790</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>Mary</td>\n",
       "      <td>mary@gmail.com</td>\n",
       "      <td>mary1453</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>Khushi</td>\n",
       "      <td>khushi@gmail.com</td>\n",
       "      <td>khushi4126</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  Walc health  absences  G1  G2  G3    name             email    password  \\\n",
       "0    1      3         6   5   6   6  Varsha  varsha@gmail.com  varsha6106   \n",
       "1    1      3         4   6   6   7  Varsha  varsha@gmail.com  varsha8052   \n",
       "2    3      3        10   6   7   9    Mary    mary@gmail.com    mary8790   \n",
       "3    1      5         2  16  15  16    Mary    mary@gmail.com    mary1453   \n",
       "4    2      5         4   7  11  11  Khushi  khushi@gmail.com  khushi4126   \n",
       "\n",
       "  attendance  \n",
       "0         64  \n",
       "1         92  \n",
       "2         53  \n",
       "3         96  \n",
       "4         95  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "df = pd.read_csv(\"modified_student_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>name</th>\n",
       "      <th>email</th>\n",
       "      <th>password</th>\n",
       "      <th>attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>20</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Ansh</td>\n",
       "      <td>ansh@gmail.com</td>\n",
       "      <td>ansh9438</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>Ram</td>\n",
       "      <td>ram@gmail.com</td>\n",
       "      <td>ram7188</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>Ram</td>\n",
       "      <td>ram@gmail.com</td>\n",
       "      <td>ram1477</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>Piyush</td>\n",
       "      <td>piyush@gmail.com</td>\n",
       "      <td>piyush9710</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>at_home</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>Karan</td>\n",
       "      <td>karan@gmail.com</td>\n",
       "      <td>karan3025</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  services  services   \n",
       "391     MS   M   17       U     LE3       T     3     1  services  services   \n",
       "392     MS   M   21       R     GT3       T     1     1     other     other   \n",
       "393     MS   M   18       R     LE3       T     3     2  services     other   \n",
       "394     MS   M   19       U     LE3       T     1     1     other   at_home   \n",
       "\n",
       "     ... Walc health  absences  G1  G2  G3    name             email  \\\n",
       "390  ...    5      4        11   9   9   9    Ansh    ansh@gmail.com   \n",
       "391  ...    4      2         3  14  16  16     Ram     ram@gmail.com   \n",
       "392  ...    3      3         3  11   9   8     Ram     ram@gmail.com   \n",
       "393  ...    4      5         0  11  12  10  Piyush  piyush@gmail.com   \n",
       "394  ...    3      5         5   8   9   9   Karan   karan@gmail.com   \n",
       "\n",
       "       password attendance  \n",
       "390    ansh9438         87  \n",
       "391     ram7188         87  \n",
       "392     ram1477         93  \n",
       "393  piyush9710         61  \n",
       "394   karan3025         64  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
       "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
       "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
       "       'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
       "       'Walc', 'health', 'absences', 'G1', 'G2', 'G3', 'name', 'email',\n",
       "       'password', 'attendance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. school: The student's school (binary):\n",
    "\n",
    "- \"GP\" = Gabriel Pereira\n",
    "- \"MS\" = Mousinho da Silveira\n",
    "\n",
    "2. sex: The student's gender (binary):\n",
    "\n",
    "- \"F\" = Female\n",
    "- \"M\" = Male\n",
    "\n",
    "3. age: The student's age (numeric).\n",
    "\n",
    "4. address: The student's home address type (binary):\n",
    "\n",
    "- \"U\" = Urban\n",
    "- \"R\" = Rural\n",
    "\n",
    "5. famsize: Family size (binary):\n",
    "\n",
    "- \"LE3\" = Less than or equal to 3 members\n",
    "- \"GT3\" = Greater than 3 members\n",
    "\n",
    "6. Pstatus: Parent's cohabitation status (binary):\n",
    "\n",
    "- \"T\" = Living together\n",
    "- \"A\" = Apart\n",
    "7. Medu: Mother's education level (numeric):\n",
    "\n",
    "- 0 = None\n",
    "- 1 = Primary education (4th grade)\n",
    "- 2 = 5th to 9th grade\n",
    "- 3 = Secondary education\n",
    "- 4 = Higher education\n",
    "\n",
    "8. Fedu: Father's education level (numeric), same scale as Medu.\n",
    "\n",
    "9. Mjob: Mother's job (categorical):\n",
    "\n",
    "- \"teacher\" = Teacher\n",
    "- \"health\" = Health care related\n",
    "- \"services\" = Civil services (e.g. administrative or police)\n",
    "- \"at_home\" = At home\n",
    "- \"other\" = Other\n",
    "\n",
    "10. Fjob: Father's job (categorical), same categories as Mjob.\n",
    "\n",
    "11. reason: Reason to choose this school (categorical):\n",
    "\n",
    "- \"home\" = Close to home\n",
    "- \"reputation\" = School's reputation\n",
    "- \"course\" = Preference for the course\n",
    "- \"other\" = Other reasons\n",
    "\n",
    "12. guardian: Student's guardian (categorical):\n",
    "\n",
    "- \"mother\" = Mother\n",
    "- \"father\" = Father\n",
    "- \"other\" = Other\n",
    "\n",
    "13. traveltime: Home to school travel time (numeric):\n",
    "\n",
    "- 1 = <15 minutes\n",
    "- 2 = 15 to 30 minutes\n",
    "- 3 = 30 minutes to 1 hour\n",
    "- 4 = >1 hour\n",
    "\n",
    "14. studytime: Weekly study time (numeric):\n",
    "\n",
    "- 1 = <2 hours\n",
    "- 2 = 2 to 5 hours\n",
    "- 3 = 5 to 10 hours\n",
    "4 = >10 hours\n",
    "\n",
    "15. failures: Number of past class failures (numeric).\n",
    "\n",
    "16. schoolsup: Extra educational support (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "17. famsup: Family educational support (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "18. paid: Extra paid classes within the course subject (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "19. activities: Extracurricular activities (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "20. nursery: Attended nursery school (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "21. higher: Wants to take higher education (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "22. internet: Internet access at home (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "23. romantic: In a romantic relationship (binary):\n",
    "\n",
    "- \"yes\" = Yes\n",
    "- \"no\" = No\n",
    "\n",
    "24. famrel: Quality of family relationships (numeric, from 1 - very bad to 5 - excellent).\n",
    "\n",
    "25. freetime: Free time after school (numeric, from 1 - very low to 5 - very high).\n",
    "\n",
    "26. goout: Going out with friends (numeric, from 1 - very low to 5 - very high).\n",
    "\n",
    "27. Dalc: Workday alcohol consumption (numeric, from 1 - very low to 5 - very high).\n",
    "\n",
    "28. Walc: Weekend alcohol consumption (numeric, from 1 - very low to 5 - very high).\n",
    "\n",
    "29. health: Current health status (numeric, from 1 - very bad to 5 - very good).\n",
    "\n",
    "30. absences: Number of school absences (numeric).\n",
    "\n",
    "31. G1: First period grade (numeric, from 0 to 20).\n",
    "\n",
    "32. G2: Second period grade (numeric, from 0 to 20).\n",
    "\n",
    "33. G3: Final grade (numeric, from 0 to 20)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        0\n",
       "sex           0\n",
       "age           0\n",
       "address       0\n",
       "famsize       0\n",
       "Pstatus       0\n",
       "Medu          0\n",
       "Fedu          0\n",
       "Mjob          0\n",
       "Fjob          0\n",
       "reason        0\n",
       "guardian      0\n",
       "traveltime    0\n",
       "studytime     0\n",
       "failures      0\n",
       "schoolsup     0\n",
       "famsup        0\n",
       "paid          0\n",
       "activities    0\n",
       "nursery       0\n",
       "higher        0\n",
       "internet      0\n",
       "romantic      0\n",
       "famrel        0\n",
       "freetime      0\n",
       "goout         0\n",
       "Dalc          0\n",
       "Walc          0\n",
       "health        0\n",
       "absences      0\n",
       "G1            0\n",
       "G2            0\n",
       "G3            0\n",
       "name          0\n",
       "email         0\n",
       "password      0\n",
       "attendance    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### No null value is present in data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(395, 37)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier detection and removal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 168 outliers\n"
     ]
    }
   ],
   "source": [
    "# Function to remove outliers using IQR method\n",
    "def remove_outliers_iqr(df):\n",
    "    \"\"\"\n",
    "    Removes outliers from a dataframe using the IQR method, applied only to numerical columns.\n",
    "    \n",
    "    Args:\n",
    "        df: Pandas DataFrame with numerical columns.\n",
    "    \n",
    "    Returns:\n",
    "        DataFrame with outliers removed.\n",
    "    \"\"\"\n",
    "    # Select only numerical columns\n",
    "    df_numeric = df.select_dtypes(include=[float, int])\n",
    "\n",
    "    # Calculate Q1 (25th percentile) and Q3 (75th percentile) for each numerical column\n",
    "    Q1 = df_numeric.quantile(0.25)\n",
    "    Q3 = df_numeric.quantile(0.75)\n",
    "    \n",
    "    # Calculate the Interquartile Range (IQR)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    # Define the bounds for outliers\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "    \n",
    "    # Filter the data and keep only rows that are within the bounds for numeric columns\n",
    "    df_clean = df[~((df_numeric < lower_bound) | (df_numeric > upper_bound)).any(axis=1)]\n",
    "    \n",
    "    # Print number of outliers removed\n",
    "    num_outliers = df.shape[0] - df_clean.shape[0]\n",
    "    print(f\"Removed {num_outliers} outliers\")\n",
    "    \n",
    "    return df_clean\n",
    "\n",
    "# Remove outliers from the dataset\n",
    "df_clean = remove_outliers_iqr(df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot the distribution of final grades (G3)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(df['G3'], bins=10, kde=True, color='skyblue')\n",
    "plt.title('Distribution of Final Grades (G3)')\n",
    "plt.xlabel('Final Grade (G3)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Insight: This plot shows the distribution of students' final grades. A normal distribution indicates a balanced performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot study time vs final grades (G3)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='studytime', y='G3', hue='sex', palette='coolwarm')\n",
    "plt.title('Study Time vs Final Grades (G3)')\n",
    "plt.xlabel('Study Time')\n",
    "plt.ylabel('Final Grade (G3)')\n",
    "plt.legend(title='Sex')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Insight: This barplot identify if more study time correlates with higher final grades and if there are any differences between genders.\n",
    "- male study hour is more than female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\1359025314.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(data=df, x='famsize', y='G3', palette='pastel')\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ha8GCBWrevLnS0tJcl4Jdjs8++0xxcXFavHixWw1PPfXUZW+7sAYNGqSXXnpJ33zzjb777juFh4erc+fOhd5eTu/v4cOHL7rcgw8+qC1btmjt2rWuXsMcOb2Xdrs9X69bADCjJwkACsjT01M2m83tEq99+/bpyy+/LNT2WrZsqY4dO+qtt9664DDUBfnrvqenZ67lP/3001z3X3Tr1k2HDh1yG7o8LS1Nb731lttyTZo0UY0aNfTiiy8qJSUl1/6OHTsm6fz9T/8OIBEREYqOjr7gsOI5WrRoIUnatGmTW3thjs3x48fzPF7vvPOOJOW6/PL06dP6888/dcMNN1y0xhyHDh3SF1984ZpOTk7WBx98oEaNGrldxufl5aWBAwdq0aJFmjdvnurXr+8WAAsrpwfI/Bw3bNig+Pj4y952YTVo0EANGjTQO++8o88//1y33XabW6/OhaxevTrP/6slS5ZIyn3pp9ncuXM1Z84czZw50zW6ollERITatGmjOXPm5Bm2cl63AJAXepIAoIBuvvlmzZgxQ126dNHtt9+upKQkzZw5UzVr1tTvv/9eqG3Onz9fXbp0Ue/evdW1a1fXZWRHjhzRihUrtHbt2ov2xJh1795dU6ZM0d13360bbrhB27Zt04IFC3LdF3TffffpjTfe0KBBg/Trr78qKipKH374ofz8/NyW8/Dw0DvvvKOuXbuqbt26uvvuu1W5cmX9/fffWr16tQIDA/XNN9/ozJkzqlKlivr27auGDRsqICBAK1as0MaNG/XSSy9dtOa4uDjVq1dPK1as0D333HNZx2b+/PmaPXu2evfurbi4OJ05c8Z1yWGPHj1y3RO0YsUKGYaRr2G5pfP3Hw0dOlQbN25UpUqV9N577+no0aN59iQOGjRIr732mlavXq3nn38+X9u/lO7du2vx4sXq06ePbr75ZiUmJmr27NmqU6dOniG2pAwaNEjjxo2TlP8h2x944AGlpaWpT58+ql27tjIyMrR+/Xp98sknqlatmu6+++481zt+/LhGjBihOnXqyOFwaP78+W7z+/TpI39/f82cOVOtWrVS/fr1dd999ykuLk5Hjx5VfHy8Dh48mOd3hwGAJIYAB1A+5QwBvnHjxjznX2gIcH9//1zLPvXUU8a/f12+++67Rq1atQyHw2HUrl3bmDt3bp7L5WcI8Bxnz541XnnlFaNFixZGYGCg4eXlZURGRhrdu3c3FixYYGRmZrqWzRkC/NNPP821nXPnzhkPP/ywERUVZfj6+hotW7Y04uPjcw3nbBiGsX//fqNnz56Gn5+fERYWZjz00EPG999/7zYEeI7Nmzcbt9xyixEaGmo4HA4jNjbW6N+/v7Fy5UrDMAwjPT3deOSRR4yGDRsaFSpUMPz9/Y2GDRsas2bNytfznzFjhhEQEJBrSPOCHpuNGzca/fr1M6pWrWo4HA7D39/fuPbaa40ZM2a4DXmdY8CAAUarVq3yVWNsbKxx8803G0uXLjUaNGjg+v/P6/8hR926dQ0PDw/j4MGD+dpHzmvzhRdeyHN+dna28eyzzxqxsbGGw+EwGjdubHz77bfG4MGDjdjY2Etu50KvnbzeM/kZAjzH4cOHDU9PT+Oqq67K1/M0DMP47rvvjHvuuceoXbu2ERAQYHh7exs1a9Y0HnjgAePo0aNuy5rfSxcbJl2SkZiY6Frvzz//NAYNGmRERkYadrvdqFy5stG9e3fjs88+y3edAK48NsMoxF3GAAAUsdOnTysuLk7Tp0/X0KFDS2SfR44cUfXq1fXxxx/nuyepoBo3bqyQkBCtXLmyWLZfWhw/flxRUVGaOHHiBUeTA4CygnuSAAClQlBQkMaPH68XXnihUKPwFcYrr7yi+vXrF1tA2rRpk7Zs2aJBgwYVy/ZLk3nz5ikrK6tIBqcAAKvRkwQAQBHbvn27fv31V7300ks6fvy4EhIS5OPjY3VZxWLVqlXauXOnnnzySbVt21aLFy+2uiQAuGz0JAEAUMQ+++wz3X333XI6nfroo4/KbUCSpClTpmjs2LFq1KiRXn/9davLAYAiQU8SAAAAAJjQkwQAAAAAJoQkAAAAADAp918mm52drUOHDqlChQqy2WxWlwMAAADAIoZh6MyZM4qOjpaHx4X7i8p9SDp06JBiYmKsLgMAAABAKXHgwAFVqVLlgvPLfUiqUKGCpPMHIjAw0OJqAGs4nU4tW7ZMnTp1kt1ut7ocAIAFOBcAUnJysmJiYlwZ4ULKfUjKucQuMDCQkIQrltPplJ+fnwIDAzkxAsAVinMB8D+Xug2HgRsAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYGJpSJo2bZqaNWumChUqKCIiQr1799bu3bvdljl37pxGjhyp0NBQBQQE6NZbb9XRo0ctqhgAAABAeWdpSPrhhx80cuRI/fzzz1q+fLmcTqc6deqk1NRU1zJjxozRN998o08//VQ//PCDDh06pFtuucXCqgEAAACUZ15W7vz77793m543b54iIiL066+/6qabbtLp06f17rvvauHChWrXrp0kae7cubrmmmv0888/6/rrr7eibAAAAADlmKUh6d9Onz4tSQoJCZEk/frrr3I6nerQoYNrmdq1a6tq1aqKj4/PMySlp6crPT3dNZ2cnCxJcjqdcjqdxVk+SkBGRoaOHTtmdRllTmZmplJSUrR//355eZWqt32pFx4eLm9vb6vLAIDLlvM5iM9DuJLl9/Vfaj4tZWdna/To0WrZsqXq1asnSTpy5Ii8vb1VsWJFt2UrVaqkI0eO5LmdadOmafLkybnaly1bJj8/vyKvGyUrJSVFmzdvtrqMMotjV3CNGzdWQECA1WUAQJFZvny51SUAlklLS8vXcqUmJI0cOVLbt2/XTz/9dFnbmTBhgsaOHeuaTk5OVkxMjDp16qTAwMDLLRMWy8jIUMuWLa0uo8w5fPiwPv/8c916662KioqyupwyhZ4kAOWF0+nU8uXL1bFjR9ntdqvLASyRc5XZpZSKkDRq1Ch9++23Wrt2rapUqeJqj4yMVEZGhv755x+33qSjR48qMjIyz205HA45HI5c7Xa7nV8I5YDdbpe/v7/VZZRZUVFRqlatmtVlAAAsxGciXMny+9q3dHQ7wzA0atQoffHFF1q1apWqV6/uNr9Jkyay2+1auXKlq2337t3666+/1KJFi5IuFwAAAMAVwNKepJEjR2rhwoX66quvVKFCBdd9RkFBQfL19VVQUJCGDh2qsWPHKiQkRIGBgXrggQfUokULRrYDAAAAUCwsDUlvvvmmJKlNmzZu7XPnztWQIUMkSS+//LI8PDx06623Kj09XZ07d9asWbNKuFIAAAAAVwpLQ5JhGJdcxsfHRzNnztTMmTNLoCIAAAAAVzpL70kCAAAAgNKGkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAICJl9UFAAAAFFRGRoaSkpKsLqNMyczMVEpKiv7++295efERsKAiIiLk7e1tdRkoIbxDAABAmZOUlKRXXnnF6jLKpM2bN1tdQpk0evRoValSxeoyUEIISQAAoMyJiIjQ6NGjrS6jTDl06JAWLVqk/v37Kzo62upyypyIiAirS0AJIiQBAIAyx9vbm7/qF1BmZqak8x/2OXbAxTFwAwAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYWBqS1q5dqx49eig6Olo2m01ffvml2/whQ4bIZrO5Pbp06WJNsQAAAACuCJaGpNTUVDVs2FAzZ8684DJdunTR4cOHXY+PPvqoBCsEAAAAcKXxsnLnXbt2VdeuXS+6jMPhUGRkZAlVBAAAAOBKZ2lIyo81a9YoIiJCwcHBateunaZOnarQ0NALLp+enq709HTXdHJysiTJ6XTK6XQWe71AaZSZmen6l/cBAFyZOBcAyvdrv1SHpC5duuiWW25R9erV9eeff+o///mPunbtqvj4eHl6eua5zrRp0zR58uRc7cuWLZOfn19xlwyUSikpKZKkDRs2aMeOHRZXAwCwAucCQEpLS8vXcqU6JN12222un+vXr68GDRqoRo0aWrNmjdq3b5/nOhMmTNDYsWNd08nJyYqJiVGnTp0UGBhY7DUDpdH+/fu1efNmNW/eXLGxsVaXAwCwAOcC4H9XmV1KqQ5J/xYXF6ewsDDt3bv3giHJ4XDI4XDkarfb7bLb7cVdIlAqeXl5uf7lfQAAVybOBYDy/dovU9+TdPDgQZ04cUJRUVFWlwIAAACgnLK0JyklJUV79+51TScmJmrLli0KCQlRSEiIJk+erFtvvVWRkZH6888/NX78eNWsWVOdO3e2sGoAAAAA5ZmlIWnTpk1q27atazrnXqLBgwfrzTff1O+//673339f//zzj6Kjo9WpUyc9/fTTeV5OBwAAAABFwdKQ1KZNGxmGccH5S5cuLcFqAAAAAKCM3ZMEAAAAAMWNkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAICJl9UFXOlOnTql1NRUq8tAOZeUlOT618uLtz2Kl7+/v4KDg60uo0zhXICSwLkAJamsnwtshmEYVhdRnJKTkxUUFKTTp08rMDDQ6nLcnDp1StOnT5fT6bS6FAAoMna7XePHjy/TJ8eSdP5c8LyczkyrSwGAImO3e2n8+EdL3bkgv9mAPyNYKDU1VU6nUzWbd5dvYKjV5QDAZTubfEJ7N3yr1NTUUndiLK3Onwsy1atuqML87VaXAwCX7XiqU1/tOFGmzwWEpFLANzBU/sGRVpcBALBQmL9dUYEOq8sAAIiBGwAAAADADSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMPEqzEqJiYn68ccftX//fqWlpSk8PFyNGzdWixYt5OPjU9Q1AgAAAECJKVBIWrBggV599VVt2rRJlSpVUnR0tHx9fXXy5En9+eef8vHx0R133KFHH31UsbGxxVUzAAAAABSbfIekxo0by9vbW0OGDNHnn3+umJgYt/np6emKj4/Xxx9/rKZNm2rWrFnq169fkRcMAAAAAMUp3yHpueeeU+fOnS843+FwqE2bNmrTpo2eeeYZ7du3ryjqAwAAAIASle+QdLGA9G+hoaEKDQ0tVEEAAAAAYKVCjW6XlZXlNr1hwwatXbtWTqezSIoCAAAAAKsUKCQdPnxYrVq1ksPhUOvWrXXq1Cl1795dLVq0UJs2bVSvXj0dPny4uGoFAAAAgGJXoJD06KOPyjAMffHFF4qKilL37t2VnJysAwcOaN++fQoPD9czzzxTXLUCAAAAQLEr0BDgK1as0OLFi3X99derZcuWCgsL0/Lly1W5cmVJ0pQpU3TfffcVS6EAAAAAUBIK1JN06tQpVyAKCQmRn5+f2/ch1axZk8vtAAAAAJRpBQpJERERbiFo1KhRCgkJcU2fOnVK/v7+RVcdAAAAAJSwAoWkRo0aKT4+3jX93HPPuYWkn376SQ0aNCi66gAAAACghBXonqSvvvrqovObNWum1q1bX1ZBAAAAAGClAoWkS7nuuuuKcnMAAAAAUOIKFJKysrK0c+dO1a9fX5I0e/ZsZWRkuOZ7enpq+PDh8vAo1HfUAgAAAIDlChSSPvnkE82ePVtr166VJD3yyCOqWLGivLzOb+b48ePy8fHR0KFDi75SAAAAACgBBerymTt3rkaOHOnW9sMPPygxMVGJiYl64YUXNH/+/CItEAAAAABKUoFC0q5du9S0adMLzm/durW2bt162UUBAAAAgFUKdLndsWPH3KYTEhIUGhrqmrbb7UpNTS2aygAAAADAAgXqSapUqZJ2797tmg4PD3cbpOGPP/5QZGRk0VUHAAAAACWsQCGpffv2euaZZ/KcZxiGpk2bpvbt2xdJYQAAAABghQJdbvf444/r2muvVfPmzTVu3DhdddVVkqTdu3frxRdf1O7du/XBBx8US6EAAAAAUBIKFJJq1Kih5cuXa8iQIRowYIBsNpuk871ItWvX1rJly1SzZs1iKRQAAAAASkKBQpIkXXfdddq5c6e2bNmi//73v5KkWrVqqXHjxkVeHAAAAACUtAKHpByNGjVSo0aNirCUK9fZ5BNWlwAARYLfZ4V3PNVpdQkAUCTKw++zfIek5557Tg899JB8fX0vueyGDRt0/Phx3XzzzZdV3JVi74ZvrS4BAGCxr3YQMAGgtMh3SNq5c6eqVq2qfv36qUePHmratKnCw8MlSZmZmdq5c6d++uknzZ8/X4cOHWIAhwKo2by7fANDL70gAJRyZ5NP8IefQupVN1Rh/narywCAy3Y81Vnm//CT75D0wQcfaOvWrXrjjTd0++23Kzk5WZ6ennI4HEpLS5MkNW7cWPfee6+GDBkiHx+fYiu6vPENDJV/MN8vBQBXsjB/u6ICHVaXAQBQAe9Jatiwod5++23NmTNHv//+u/bv36+zZ88qLCxMjRo1UlhYWHHVCQAAAAAlolADN3h4eDBwAwAAAIByycPqAgAAAACgNCEkAQAAAIAJIQkAAAAATAhJAAAAAGByWSFp7969Wrp0qc6ePStJMgyjSIoCAAAAAKsUKiSdOHFCHTp00FVXXaVu3brp8OHDkqShQ4fq4YcfLtICAQAAAKAkFSokjRkzRl5eXvrrr7/k5+fnah8wYIC+//77IisOAAAAAEpaob4nadmyZVq6dKmqVKni1l6rVi3t37+/SAoDAAAAACsUqicpNTXVrQcpx8mTJ+VwOC67KAAAAACwSqFC0o033qgPPvjANW2z2ZSdna3p06erbdu2RVYcAAAAAJS0Ql1uN336dLVv316bNm1SRkaGxo8frx07dujkyZNat25dUdcIAAAAACWmUD1J9erV03//+1+1atVKvXr1Umpqqm655RZt3rxZNWrUyPd21q5dqx49eig6Olo2m01ffvml23zDMDRx4kRFRUXJ19dXHTp00J49ewpTMgAAAADkS6F6kiQpKChIjz/++GXtPDU1VQ0bNtQ999yjW265Jdf86dOn67XXXtP777+v6tWr68knn1Tnzp21c+dO+fj4XNa+AQAAACAv+Q5Jv//+e7432qBBg3wt17VrV3Xt2jXPeYZh6JVXXtETTzyhXr16SZI++OADVapUSV9++aVuu+22fNcDAAAAAPmV75DUqFEj2Ww2GYYhm83majcMQ5Lc2rKysi67sMTERB05ckQdOnRwtQUFBal58+aKj4+/YEhKT09Xenq6azo5OVmS5HQ65XQ6L7uuopSZmWl1CQBQLDIzM0vd79zSinMBgPKqNJ4L8ltPvkNSYmKi6+fNmzdr3LhxeuSRR9SiRQtJUnx8vF566SVNnz69gKXm7ciRI5KkSpUqubVXqlTJNS8v06ZN0+TJk3O1L1u2LM9hy62UkpJidQkAUCzWrVungIAAq8soEzgXACivSuO5IC0tLV/L5TskxcbGun7u16+fXnvtNXXr1s3V1qBBA8XExOjJJ59U7969819pEZswYYLGjh3rmk5OTlZMTIw6deqkwMBAy+rKy99//63NmzdbXQYAFLmWLVuqcuXKVpdRJnAuAFBelcZzQc5VZpdSqIEbtm3bpurVq+dqr169unbu3FmYTeYSGRkpSTp69KiioqJc7UePHlWjRo0uuJ7D4cjzC23tdrvsdnuR1FZUvLwKPW4GAJRqXl5epe53bmnFuQBAeVUazwX5radQQ4Bfc801mjZtmjIyMlxtGRkZmjZtmq655prCbDKX6tWrKzIyUitXrnS1JScna8OGDa5L/AAAAACgqBXqz1ezZ89Wjx49VKVKFddIdr///rtsNpu++eabfG8nJSVFe/fudU0nJiZqy5YtCgkJUdWqVTV69GhNnTpVtWrVcg0BHh0dbenlfAAAAADKt0KFpOuuu04JCQlasGCBdu3aJUkaMGCAbr/9dvn7++d7O5s2bVLbtm1d0zn3Eg0ePFjz5s3T+PHjlZqaqvvvv1///POPWrVqpe+//57vSAIAAABQbAp9IbS/v7/uv//+y9p5mzZtXEOI58Vms2nKlCmaMmXKZe0HAAAAAPLrsu4W3blzp/766y+3e5MkqWfPnpdVFAAAAABYpVAhKSEhQX369NG2bdtcXzAr/e8LZYviy2QBAAAAwAqFGt3uoYceUvXq1ZWUlCQ/Pz/t2LFDa9euVdOmTbVmzZoiLhEAAAAASk6hepLi4+O1atUqhYWFycPDQx4eHmrVqpWmTZumBx98kC/FAwAAAFBmFaonKSsrSxUqVJAkhYWF6dChQ5Kk2NhY7d69u+iqAwAAAIASVqiepHr16mnr1q2qXr26mjdvrunTp8vb21tvvfWW4uLiirpGAAAAACgxhQpJTzzxhFJTUyVJU6ZMUffu3XXjjTcqNDRUn3zySZEWCAAAAAAlqVAhqXPnzq6fa9asqV27dunkyZMKDg52jXAHAAAAAGVRge9Jcjqd8vLy0vbt293aQ0JCCEgAAAAAyrwChyS73a6qVavyXUgAAAAAyqVCjW73+OOP6z//+Y9OnjxZ1PUAAAAAgKUKdU/SG2+8ob179yo6OlqxsbHy9/d3m//bb78VSXEAAAAAUNIKFZJ69+5dxGUAAHBlO57qtLoEACgS5eH3WaFC0lNPPVXUdQAAcEXy9/eX3e6lr3acsLoUACgydrtXrqvNypJChaQcZ86ckWEYrmkPDw8FBARcdlEAAFwpgoODNX78o67vHwSKy6FDh7Ro0SL1799f0dHRVpeDcs7f31/BwcFWl1FoBQpJW7Zs0X/+8x8tWbJEkhQdHa20tDTXfJvNpvj4eDVr1qxoqwQAoBwLDg4u0x8mUDZkZmZKkiIiIlSlShWLqwFKtwKFpNdff12tWrVya/vwww9VuXJlGYah9957T6+99po+/PDDIi0SAAAAAEpKgULS+vXrNWrUKLe266+/XnFxcZIkX19f9e/fv+iqAwAAAIASVqDvSdq/f7/Cw8Nd01OmTFFYWJhrOioqSkePHi266gAAAACghBUoJPn4+Gj//v2u6TFjxigwMNA1feDAAfn5+RVddQAAAABQwgoUkho3bqwvv/zygvMXL16sxo0bX25NAAAAAGCZAt2TNGLECN12222qVq2ahg8fLg+P8xkrKytLs2bN0uuvv66FCxcWS6EAAAAAUBIKFJJuvfVWjR07Vg888ID+85//uAZsSEhIUEpKisaOHau+ffsWS6EAAAAAUBIK/GWyzz//vPr06aOPPvpIe/bskSTddNNNGjhwoK6//voiLxAAAAAASlKBQ5J0fthvAhEAAACA8qhAAzcAAAAAQHlHSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACb5Ht2ucePGstls+Vr2t99+K3RBAAAAAGClfIek3r17F2MZAAAAAFA65DskPfXUU8VZxxXtbPIJq0sAgCLB7zMAQHlQqC+TRdHw9/eX3W7X3g3fWl0KABQZu90uf39/q8sAAKDQChWSsrKy9PLLL2vRokX666+/lJGR4Tb/5MmTRVJceRccHKzx48crNTXV6lJQzh06dEiLFi1S//79FR0dbXU5KOf8/f0VHBxsdRkAABRaoULS5MmT9c477+jhhx/WE088occff1z79u3Tl19+qYkTJxZ1jeVacHAwHyZQ7DIzMyVJERERqlKlisXVAAAAlG6FGgJ8wYIFevvtt/Xwww/Ly8tLAwcO1DvvvKOJEyfq559/LuoaAQAAAKDEFCokHTlyRPXr15ckBQQE6PTp05Kk7t276//9v/9XdNUBAAAAQAkrVEiqUqWKDh8+LEmqUaOGli1bJknauHGjHA5H0VUHAAAAACWsUCGpT58+WrlypSTpgQce0JNPPqlatWpp0KBBuueee4q0QAAAAAAoSYUauOG5555z/TxgwABVrVpV8fHxqlWrlnr06FFkxQEAAABASSuS70lq0aKFWrRoURSbAgAAAABLFTok7dmzR6tXr1ZSUpKys7Pd5jEMOAAAAICyqlAh6e2339bw4cMVFhamyMhI2Ww21zybzUZIAgAAAFBmFSokTZ06Vc8884weffTRoq4HAAAAACxVqNHtTp06pX79+hV1LQAAAABguUKFpH79+rm+GwkAAAAAypNCXW5Xs2ZNPfnkk/r5559Vv3592e12t/kPPvhgkRQHAAAAACWtUCHprbfeUkBAgH744Qf98MMPbvNsNhshCQAAAECZVaiQlJiYWNR1AAAAAECpUKh7kgAAAACgvMp3T9LYsWP19NNPy9/fX2PHjr3osjNmzLjswgAAAADACvkOSZs3b5bT6XT9fCHmL5YFAAAAgLIm3yFp9erVSkhIUFBQkFavXl2cNQEAAACAZQp0T1KtWrV07Ngx1/SAAQN09OjRIi8KAAAAAKxSoJBkGIbb9JIlS5SamlqkBQEAAACAlRjdDgAAAABMChSSbDZbroEZGKgBAAAAQHlSoC+TNQxDQ4YMkcPhkCSdO3dOw4YNk7+/v9tyixcvLroKAQAAAKAEFSgkDR482G36zjvvLNJiAAAAAMBqBQpJc+fOLa46AAAAAKBUYOAGAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgUqpD0qRJk2Sz2dwetWvXtrosAAAAAOWYl9UFXErdunW1YsUK17SXV6kvGQAAAEAZVuoTh5eXlyIjI60uAwAAAMAVotSHpD179ig6Olo+Pj5q0aKFpk2bpqpVq15w+fT0dKWnp7umk5OTJUlOp1NOp7PY6wVKo8zMTNe/vA8AlAcZGRk6duyY1WWUKYcPH3b7FwUTHh4ub29vq8vAZcrv5yCbYRhGMddSaN99951SUlJ09dVX6/Dhw5o8ebL+/vtvbd++XRUqVMhznUmTJmny5Mm52hcuXCg/P7/iLhkolVJSUrR582Y1btxYAQEBVpcDAJct5/caUFI4h5YPaWlpuv3223X69GkFBgZecLlSHZL+7Z9//lFsbKxmzJihoUOH5rlMXj1JMTExOn78+EUPBFCe7d+/X7Nnz9awYcMUGxtrdTkAcNnoSSq4zMxMbdiwQc2bN+ce70KgJ6l8SE5OVlhY2CVDUpl6h1SsWFFXXXWV9u7de8FlHA6HHA5Hrna73S673V6c5QGlVs7J0MvLi/cBgHLBbrfL39/f6jLKFKfTqR07dig2NpZzAa5Y+X3tl+ohwP8tJSVFf/75p6KioqwuBQAAAEA5VapD0rhx4/TDDz9o3759Wr9+vfr06SNPT08NHDjQ6tIAAAAAlFOl+nK7gwcPauDAgTpx4oTCw8PVqlUr/fzzzwoPD7e6NAAAAADlVKkOSR9//LHVJQAAAAC4wpTqy+0AAAAAoKQRkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAlHPZ2dlKSEhQUlKSEhISlJ2dbXVJQKnmZXUBAAAAKD7btm3T119/rVOnTkmSdu/ereDgYPXs2VP169e3uDqgdKInCQAAoJzatm2bPvjgA0VFRWn48OG64YYbNHz4cEVFRemDDz7Qtm3brC4RKJUISQAAAOVQdna2vv76a11zzTUaMmSIqlatKk9PT1WtWlVDhgzRNddco2+++YZL74A8EJIAAADKoYSEBJ06dUrt27eXh4f7Rz4PDw+1a9dOJ0+eVEJCgkUVAqUXIQkAAKAcOnPmjCQpMjIyz/k57TnLAfgfQhIAAEA5VKFCBUnSkSNH8pyf056zHID/ISQBAACUQ3FxcQoODtbKlStz3XeUnZ2tVatWKSQkRHFxcRZVCJRehCQAAIByyMPDQz179tQff/yhefPmaf/+/crMzNT+/fs1b948/fHHH+rRo0eu+5UA8D1JAAAA5Vb9+vU1aNAgff3115o9e7YkKT4+XiEhIRo0aBDfkwRcACEJAACgHKtfv77q1q2rPXv2aO3atbrppptUq1YtepCAi+DdAQAAUM55eHgoLi5OERERiouLIyABl8A7BAAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJoQkAAAAADAhJAEAAACAiZfVBQAAAKB4paSkaNasWTpx4oR2796tESNGKCAgwOqygFKrTPQkzZw5U9WqVZOPj4+aN2+uX375xeqSAAAAyoRJkyZp0qRJSkpKUlZWlpKSklxtAPJW6kPSJ598orFjx+qpp57Sb7/9poYNG6pz585KSkqyujQAAIBSbdKkSUpJSZEkxcTEqG7duoqJiZF0vneJoATkrdSHpBkzZui+++7T3XffrTp16mj27Nny8/PTe++9Z3VpAAAApVZKSoorIE2aNEkjRoxQSEiIRowY4QpH5mUA/E+pvicpIyNDv/76qyZMmOBq8/DwUIcOHRQfH5/nOunp6UpPT3dNJycnS5KcTqecTmfxFgyUUpmZma5/eR8AwJVh1qxZks73IDkcDtfvf6fTKYfDoSpVqujgwYOaNWuWxowZY2WpQInJ7+egUh2Sjh8/rqysLFWqVMmtvVKlStq1a1ee60ybNk2TJ0/O1b5s2TL5+fkVS51AaZfzV8INGzZox44dFlcDACgJJ06ckCQFBgZqyZIlrvbly5e72nOWM88HyrO0tLR8LVeqQ1JhTJgwQWPHjnVNJycnKyYmRp06dXL9MgCuNPv379fmzZvVvHlzxcbGWl0OAKAE7N69W0lJSUpOTtadd94pp9Op5cuXq2PHjrLb7Zo5c6YkKTQ0VN26dbO4WqBk5FxldimlOiSFhYXJ09NTR48edWs/evSoIiMj81zH4XDI4XDkarfb7bLb7cVSJ1DaeXl5uf7lfQAAV4ace48OHDig9PR01+cju92u9PR0HTx40LUc5wZcKfL7Wi/VIcnb21tNmjTRypUr1bt3b0lSdna2Vq5cqVGjRllbHAAAQCkWEBCggIAA1yh2VapUUWBgoGbOnOkKSDnLAHBX6ke3Gzt2rN5++229//77+uOPPzR8+HClpqbq7rvvtro0AACAUm3SpEmuEHTw4EHt3LnTLSAxBDiQt1LdkyRJAwYM0LFjxzRx4kQdOXJEjRo10vfff59rMAcAAADklvNdSbNmzdKJEycUGhqqESNG0IMEXESpD0mSNGrUKC6vAwAAKKSAgACNGTNGS5YsUbdu3bgHCbiEUn+5HQAAAACUJEISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMCEkAAAAAYEJIAgAAAAATQhIAAAAAmBCSAAAAAMCEkAQAAAAAJl5WFwAUREZGhpKSkqwuo8zJOWZJSUny8uJtXxARERHy9va2ugwAAFCC+LSEMiUpKUmvvPKK1WWUWYsWLbK6hDJn9OjRqlKlitVlAACAEkRIQpkSERGh0aNHW11GmZOZmal169apZcuW9CQVUEREhNUlAACAEsanJZQp3t7e/FW/EJxOpwICAlS5cmXZ7XarywEAACjVGLgBAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCQAAAAAMCEkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAwISQBAAAAgAkhCQAAAABMvKwuoLgZhiFJSk5OtrgSwDpOp1NpaWlKTk6W3W63uhwAgAU4FwD/ywQ5GeFCyn1IOnPmjCQpJibG4koAAAAAlAZnzpxRUFDQBefbjEvFqDIuOztbhw4dUoUKFWSz2awuB7BEcnKyYmJidODAAQUGBlpdDgDAApwLgPM9SGfOnFF0dLQ8PC5851G570ny8PBQlSpVrC4DKBUCAwM5MQLAFY5zAa50F+tBysHADQAAAABgQkgCAAAAABNCEnAFcDgceuqpp+RwOKwuBQBgEc4FQP6V+4EbAAAAAKAg6EkCAAAAABNCEgAAAACYEJIAAAAAwISQBAAAAAAmhCSgDDty5Igeeugh1axZUz4+PqpUqZJatmypN998U9ddd51sNtsFH23atJEk/d///Z9q1KghX19fhYeHq1evXtq1a5e1TwwAkG9DhgxR796985xXrVq1PM8Bzz33nCTpxIkT6tKli6Kjo+VwOBQTE6NRo0YpOTm5BJ8BUPp4WV0AgMJJSEhQy5YtVbFiRT377LOqX7++HA6Htm3bprfeekujRo1Sp06dJEkHDhzQddddpxUrVqhu3bqSJG9vb0lSkyZNdMcdd6hq1ao6efKkJk2apE6dOikxMVGenp6WPT8AQNGYMmWK7rvvPre2ChUqSJI8PDzUq1cvTZ06VeHh4dq7d69GjhypkydPauHChVaUC5QKhCSgjBoxYoS8vLy0adMm+fv7u9rj4uLUq1cvGYYhm80mSTp37pwkKTQ0VJGRkW7buf/++10/V6tWTVOnTlXDhg21b98+1ahRowSeCQCgOFWoUCHX7/4cwcHBGj58uGs6NjZWI0aM0AsvvFBS5QGlEpfbAWXQiRMntGzZMo0cOdItIJnlBKSCSE1N1dy5c1W9enXFxMRcbpkAgDLm0KFDWrx4sVq3bm11KYClCElAGbR3714ZhqGrr77arT0sLEwBAQEKCAjQo48+mu/tzZo1y7Xed999p+XLl7suxwMAlG2PPvqo63d8zuPHH390W2bgwIHy8/NT5cqVFRgYqHfeeceiaoHSgZAElCO//PKLtmzZorp16yo9PT3f691xxx3avHmzfvjhB1111VXq37+/6xI9AEDZ9sgjj2jLli1uj6ZNm7ot8/LLL+u3337TV199pT///FNjx461qFqgdOCeJKAMqlmzpmw2m3bv3u3WHhcXJ0ny9fUt0PaCgoIUFBSkWrVq6frrr1dwcLC++OILDRw4sMhqBgBYIywsTDVr1rzoMpGRkYqMjFTt2rUVEhKiG2+8UU8++aSioqJKqEqgdKEnCSiDQkND1bFjR73xxhtKTU0t0m0bhiHDMArUEwUAKD+ys7MlifMArmj0JAFl1KxZs9SyZUs1bdpUkyZNUoMGDeTh4aGNGzdq165datKkySW3kZCQoE8++USdOnVSeHi4Dh48qOeee06+vr7q1q1bCTwLAEBROH36tLZs2eLWFhoaKkk6c+aMjhw54jbPz89PgYGBWrJkiY4ePapmzZopICBAO3bs0COPPKKWLVuqWrVqJVQ9UPoQkoAyqkaNGtq8ebOeffZZTZgwQQcPHpTD4VCdOnU0btw4jRgx4pLb8PHx0Y8//qhXXnlFp06dUqVKlXTTTTdp/fr1ioiIKIFnAQAoCmvWrFHjxo3d2oYOHSpJmjhxoiZOnOg27//+7/80e/Zs+fr66u2339aYMWOUnp6umJgY3XLLLXrsscdKrHagNLIZhmFYXQQAAAAAlBbckwQAAAAAJoQkAAAAADAhJAEAAACACSEJAAAAAEwISQAAAABgQkgCAAAAABNCEgAAAACYEJIAAAAAwISQBAAoF6pVq6ZXXnnFNW2z2fTll18W6z7btGmj0aNHF+s+AAAlj5AEAChyQ4YMkc1my/XYu3dvse1z48aNuv/++4tse1lZWXruuedUu3Zt+fr6KiQkRM2bN9c777zjWmbx4sV6+umni2yfAIDSwcvqAgAA5VOXLl00d+5ct7bw8PBi219Rb3vy5MmaM2eO3njjDTVt2lTJycnatGmTTp065VomJCSkSPcJACgd6EkCABQLh8OhyMhIt4enp6dmzJih+vXry9/fXzExMRoxYoRSUlJc682bN08VK1bUt99+q6uvvlp+fn7q27ev0tLS9P7776tatWoKDg7Wgw8+qKysLNd6/77czqxdu3YaNWqUW9uxY8fk7e2tlStX5rnO119/rREjRqhfv36qXr26GjZsqKFDh2rcuHGuZcyX261ZsybP3rMhQ4a4lv/qq6907bXXysfHR3FxcZo8ebIyMzMLeGQBAMWNkAQAKFEeHh567bXXtGPHDr3//vtatWqVxo8f77ZMWlqaXnvtNX388cf6/vvvtWbNGvXp00dLlizRkiVL9OGHH2rOnDn67LPP8rXPe++9VwsXLlR6erqrbf78+apcubLatWuX5zqRkZFatWqVjh07lq993HDDDTp8+LDrsWrVKvn4+Oimm26SJP34448aNGiQHnroIe3cuVNz5szRvHnz9Mwzz+Rr+wCAkkNIAgAUi2+//VYBAQGuR79+/SRJo0ePVtu2bVWtWjW1a9dOU6dO1aJFi9zWdTqdevPNN9W4cWPddNNN6tu3r3766Se9++67qlOnjrp37662bdtq9erV+arllltukXS+JyfHvHnzXPdO5WXGjBk6duyYIiMj1aBBAw0bNkzffffdBffh7e3t6jGz2+269957dc899+iee+6RdP7yvccee0yDBw9WXFycOnbsqKefflpz5szJ13MAAJQc7kkCABSLtm3b6s0333RN+/v7S5JWrFihadOmadeuXUpOTlZmZqbOnTuntLQ0+fn5SZL8/PxUo0YN17qVKlVStWrVFBAQ4NaWlJSUr1p8fHx011136b333lP//v3122+/afv27fr6668vuE6dOnW0fft2/frrr1q3bp3Wrl2rHj16aMiQIW6DN/yb0+nUrbfeqtjYWL366quu9q1bt2rdunVuPUdZWVm5njsAwHqEJABAsfD391fNmjXd2vbt26fu3btr+PDheuaZZxQSEqKffvpJQ4cOVUZGhiso2O12t/VsNluebdnZ2fmu595771WjRo108OBBzZ07V+3atVNsbOxF1/Hw8FCzZs3UrFkzjR49WvPnz9ddd92lxx9/XNWrV89zneHDh+vAgQP65Zdf5OX1v9NsSkqKJk+e7OrVMvPx8cn38wAAFD9CEgCgxPz666/Kzs7WSy+9JA+P81d8//tSu+JSv359NW3aVG+//bYWLlyoN954o8DbqFOnjiQpNTU1z/kzZszQokWLtH79eoWGhrrNu/baa7V79+5cwREAUPoQkgAAJaZmzZpyOp16/fXX1aNHD61bt06zZ88usf3fe++9GjVqlPz9/dWnT5+LLtu3b1+1bNlSN9xwgyIjI5WYmKgJEyboqquuUu3atXMtv2LFCo0fP14zZ85UWFiYjhw5Ikny9fVVUFCQJk6cqO7du6tq1arq27evPDw8tHXrVm3fvl1Tp04tlucLACgcBm4AAJSYhg0basaMGXr++edVr149LViwQNOmTSux/Q8cOFBeXl4aOHDgJS9x69y5s7755hv16NFDV111lQYPHqzatWtr2bJlbpfR5fjpp5+UlZWlYcOGKSoqyvV46KGHXNv79ttvtWzZMjVr1kzXX3+9Xn755Ute8gcAKHk2wzAMq4sAAKAk7Nu3TzVq1NDGjRt17bXXWl0OAKCUIiQBAMo9p9OpEydOaNy4cUpMTNS6deusLgkAUIpxuR0AoNxbt26doqKitHHjxhK9BwoAUDbRkwQAAAAAJvQkAQAAAIAJIQkAAAAATAhJAAAAAGBCSAIAAAAAE0ISAAAAAJgQkgAAAADAhJAEAAAAACaEJAAAAAAw+f8SG5AH/nyXcwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot final grades (G3) by family size (famsize)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=df, x='famsize', y='G3', palette='pastel')\n",
    "plt.title('Final Grades (G3) by Family Size')\n",
    "plt.xlabel('Family Size')\n",
    "plt.ylabel('Final Grade (G3)')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Insight: This box plot shows how family size impacts final grades. Significant differences between family sizes might suggest social factors affecting academic performance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot final grades (G3) by mother's education (Medu) and father's education (Fedu)\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.scatterplot(data=df, x='Medu', y='G3', hue='Fedu', palette='viridis', style='sex')\n",
    "plt.title('Final Grades (G3) by Mother\\'s Education (Medu) and Father\\'s Education (Fedu)')\n",
    "plt.xlabel('Mother\\'s Education (Medu)')\n",
    "plt.ylabel('Final Grade (G3)')\n",
    "plt.legend(title='Father\\'s Education (Fedu)')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Insight: This scatter plot explores how parental education levels relate to student performance. Differences might highlight the impact of parental involvement or support.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# model using NN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 227 entries, 0 to 394\n",
      "Data columns (total 37 columns):\n",
      " #   Column      Non-Null Count  Dtype\n",
      "---  ------      --------------  -----\n",
      " 0   school      227 non-null    int32\n",
      " 1   sex         227 non-null    int32\n",
      " 2   age         227 non-null    int64\n",
      " 3   address     227 non-null    int32\n",
      " 4   famsize     227 non-null    int32\n",
      " 5   Pstatus     227 non-null    int32\n",
      " 6   Medu        227 non-null    int64\n",
      " 7   Fedu        227 non-null    int64\n",
      " 8   Mjob        227 non-null    int32\n",
      " 9   Fjob        227 non-null    int32\n",
      " 10  reason      227 non-null    int32\n",
      " 11  guardian    227 non-null    int32\n",
      " 12  traveltime  227 non-null    int64\n",
      " 13  studytime   227 non-null    int64\n",
      " 14  failures    227 non-null    int64\n",
      " 15  schoolsup   227 non-null    int32\n",
      " 16  famsup      227 non-null    int32\n",
      " 17  paid        227 non-null    int32\n",
      " 18  activities  227 non-null    int32\n",
      " 19  nursery     227 non-null    int32\n",
      " 20  higher      227 non-null    int32\n",
      " 21  internet    227 non-null    int32\n",
      " 22  romantic    227 non-null    int32\n",
      " 23  famrel      227 non-null    int64\n",
      " 24  freetime    227 non-null    int64\n",
      " 25  goout       227 non-null    int64\n",
      " 26  Dalc        227 non-null    int64\n",
      " 27  Walc        227 non-null    int64\n",
      " 28  health      227 non-null    int64\n",
      " 29  absences    227 non-null    int64\n",
      " 30  G1          227 non-null    int64\n",
      " 31  G2          227 non-null    int64\n",
      " 32  G3          227 non-null    int64\n",
      " 33  name        227 non-null    int32\n",
      " 34  email       227 non-null    int32\n",
      " 35  password    227 non-null    int32\n",
      " 36  attendance  227 non-null    int64\n",
      "dtypes: int32(20), int64(17)\n",
      "memory usage: 49.7 KB\n",
      "None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n",
      "C:\\Users\\arora\\AppData\\Local\\Temp\\ipykernel_46696\\3681618112.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_clean[col] = le.fit_transform(df_clean[col])\n"
     ]
    }
   ],
   "source": [
    "# Encode categorical features\n",
    "label_encoders = {}\n",
    "categorical_columns = df_clean.select_dtypes(include=['object']).columns\n",
    "\n",
    "for col in categorical_columns:\n",
    "    le = LabelEncoder()\n",
    "    df_clean[col] = le.fit_transform(df_clean[col])\n",
    "    label_encoders[col] = le\n",
    "\n",
    "# Check the data types and ensure no missing values remain\n",
    "print(df_clean.info())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
       "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
       "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
       "       'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
       "       'Walc', 'health', 'absences', 'G1', 'G2', 'G3', 'name', 'email',\n",
       "       'password', 'attendance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename G1, G2, G3 to meaningful names\n",
    "df_clean = df_clean.rename(columns={'G1': 'year1_marks', 'G2': 'year2_marks', 'G3': 'final_marks'})\n",
    "\n",
    "# Define the features (columns to use for prediction)\n",
    "X = df_clean[['age', 'year1_marks', 'year2_marks', 'studytime', 'failures']]\n",
    "\n",
    "# Define the target (final year marks)\n",
    "y = df_clean['final_marks']\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_scaled shape: (181, 5), X_test_scaled shape: (46, 5)\n"
     ]
    }
   ],
   "source": [
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit on training data and transform both training and test data\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# Check the shapes of scaled data\n",
    "print(f\"X_train_scaled shape: {X_train_scaled.shape}, X_test_scaled shape: {X_test_scaled.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP - Mean Squared Error: 2.288575149650712\n",
      "MLP - Root Mean Squared Error: 1.5128037379814714\n",
      "MLP - R^2 Score: 0.7013122175623938\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\arora\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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OAgBAhcI15AAAwOPYsWMlnm/ZskXff/+9unXrZk4gAAAqMI6QAwAAj5o1a+ruu+/WFVdcoV27dumtt95Sfn6+1qxZo4YNG5odDwCACoVryAEAgMf111+vzz77TKmpqXI4HEpISNALL7xAGQcA4DLgCDkAAAAAACbgGnIAAAAAAExAIQcAAAAAwAQV/hpyt9utlJQUhYSEyGKxmB0HAAAAAFDBGYahrKwsxcTEyGo9+3HwCl/IU1JSFBsba3YMAAAAAEAls2fPHtWuXfusyyt8IQ8JCZFU9IsIDQ01Oc3ZOZ1OzZs3Tz169JDdbjc7DnBWzFX4CuYqfAVzFb6AeQpf4S1zNTMzU7GxsZ4+ejYVvpAXn6YeGhrq9YU8KChIoaGh/JGDV2OuwlcwV+ErmKvwBcxT+Apvm6vnu2yam7oBAAAAAGACCjkAAAAAACagkAMAAAAAYAIKOQAAAAAAJqCQAwAAAABgAgo5AAAAAAAmoJADAAAAAGACCjkAAAAAACagkAMAAAAAYAIKOQAAAAAAJqCQAwAAAABgAgo5AAAAAAAmoJADAAAAAGACCjkAAAAAACagkAMAAAAAYAIKOQAAAAAAJqCQe4k9R3O1MMVidgwAAAAAQDmhkHuBjGNO3TBxiWbusmnN7nSz4wAAAAAAygGF3AuEBdrVt2VNSdKbi7ebnAYAAAAAUB4o5F7igavrySJDizYf0h/7MsyOAwAAAAC4zCjkXqJuRJD+VN2QJL25aKvJaQAAAAAAlxuF3Isk1nJLkmb/kaqtB7JMTgMAAAAAuJwo5F4kJkjq3iRShiG9uWib2XEAAAAAAJcRhdzL/K1rPUnS12tTtPtwrslpAAAAAACXC4Xcy7SoFaYujWrI5TY05UeOkgMAAABARWVqIXe5XHrqqadUr149BQYGqn79+nruuedkGIZnHcMw9PTTT6tmzZoKDAxUYmKitmzZYmLqy+/BaxpIkr5atVepGXkmpwEAAAAAXA6mFvJx48bprbfe0qRJk7Rx40aNGzdOL730kiZOnOhZ56WXXtKECRM0ZcoULV++XFWqVFHPnj2Vl1dxi2qHetXUIa6aClxuvfMjn0sOAAAAABWRn5lvvmTJEt14443q3bu3JCkuLk6fffaZVqxYIano6Pj48eP15JNP6sYbb5Qkffzxx4qKitLMmTM1aNCg07aZn5+v/Px8z/PMzExJktPplNPpvNw/0kUrzlb89a9d47Ri5xFNXbFL919dVxFV/M2MB3icOlcBb8Vcha9grsIXME/hK7xlrpb2/S3GyeeHl7MXXnhB77zzjubNm6dGjRrpt99+U48ePfTaa6/p9ttv1/bt21W/fn2tWbNGrVu39ryua9euat26td54443TtvnMM89o9OjRp41PnTpVQUFBl/PHKVOGIb26zqY9ORZ1r+VWnzpusyMBAAAAAEohNzdXt912mzIyMhQaGnrW9Uw9Qv74448rMzNT8fHxstlscrlcev7553X77bdLklJTUyVJUVFRJV4XFRXlWXaqUaNGaeTIkZ7nmZmZio2NVY8ePc75izCb0+lUUlKSunfvLrvdLknyr3dAQz9bq6WH/PXi4KsVGmg3OSVw5rkKeCPmKnwFcxW+gHkKX+Etc7X4TO3zMbWQf/nll/r00081depUNWvWTGvXrtXw4cMVExOjwYMHX9Q2HQ6HHA7HaeN2u90n/nicnPP6FjFq/MM2JadlaerKfXrouoYmpwNO8JX/TwHMVfgK5ip8AfMUvsLsuVra9zb1pm6PPvqoHn/8cQ0aNEgtWrTQnXfeqREjRmjs2LGSpOjoaElSWlpaidelpaV5llVkVqtFQ6+pL0l6/5cdyskvNDkRAAAAAKCsmFrIc3NzZbWWjGCz2eR2F10vXa9ePUVHR2vBggWe5ZmZmVq+fLkSEhLKNatZ+rSMUVxEkI7mOjV1+W6z4wAAAAAAyoiphbxv3756/vnn9d1332nnzp2aMWOGXnvtNd10002SJIvFouHDh2vMmDH65ptvtG7dOt11112KiYlRv379zIxebmxWi4Z2K/pc8nd+2q48p8vkRAAAAACAsmDqNeQTJ07UU089paFDh+rAgQOKiYnRAw88oKefftqzzj//+U/l5OTo/vvvV3p6uq666irNmTNHAQEBJiYvX/3a1NL4+ZuVkpGnaav36s6Odc2OBAAAAAC4RKYeIQ8JCdH48eO1a9cuHTt2TNu2bdOYMWPk73/iM7ctFoueffZZpaamKi8vT/Pnz1ejRo1MTF3+/P2seqBr0bXkby3cqvxCjpIDAAAAgK8ztZCj9Aa2j1VUqEMpGXn6ctVes+MAAAAAAC4RhdxHBNhtnmvJ3+QoOQAAAAD4PAq5DxnYPlbRoQHan5GnL1buMTsOAAAAAOASUMh9SIDdpmHHP5d88sKt3HEdAAAAAHwYhdzH3NI+VjXDApSWma/PV/C55AAAAADgqyjkPsbhZ9PQa45fS75oG0fJAQAAAMBHUch90C3taismLEAHsvI1dTlHyQEAAADAF1HIfZDDz6Zh1xYdJX9rMUfJAQAAAMAXUch91M1tY1UrPFAHs/L132W7zI4DAAAAALhAFHIf5e9n1YPHj5JPWbxdxwo4Sg4AAAAAvoRC7sP+0ra2alcN1KFsjpIDAAAAgK+hkPswu82qhzxHybcpt6DQ5EQAAAAAgNKikPu4/n+qrTrVgnQ4p0CfLOUoOQAAAAD4Cgq5j7PbTlxL/vaP25WTz1FyAAAAAPAFFPIKoH+bWqobEaQjOQX6mKPkAAAAAOATKOQVgJ/NqoeubShJeufHbcrmKDkAAAAAeD0KeQXRr3WM6lWvoqO5Tn3w8w6z4wAAAAAAzoNCXkH42awanlh8lHy70nMLTE4EAAAAADgXCnkF0rdljOKjQ5SVX6gpi7ebHQcAAAAAcA4U8grEarXokR6NJUkfLtmhA5l5JicCAAAAAJwNhbyCua5JpP5UJ1x5TrcmLdxqdhwAAAAAwFlQyCsYi8WiR3vGS5I+W7Fbe47kmpwIAAAAAHAmFPIKKKF+hK5uWF1Ol6HX5282Ow4AAAAA4Awo5BVU8bXkM9bs0+a0LJPTAAAAAABORSGvoFrFhqtnsygZhvTaPI6SAwAAAIC3oZBXYP/o0VgWizRnfap+25NudhwAAAAAwEko5BVYo6gQ3dSmliTplXnJJqcBAAAAAJyMQl7BjUhsJLvNop+2HNLSbYfNjgMAAAAAOI5CXsHFVgvSoPZ1JEkvz90kwzBMTgQAAAAAkCjklcJD1zZQgN2qX3en64dNB8yOAwAAAAAQhbxSiAwN0OBOcZKkl+cmy+3mKDkAAAAAmI1CXkn8tUt9hTj8tCk1S7PW7Tc7DgAAAABUehTySqJqFX/d3+UKSdKr85LldLlNTgQAAAAAlRuFvBK556p6qh7sr12Hc/XZit1mxwEAAACASo1CXolUcfjp4esaSpImLNii7PxCkxMBAAAAQOVFIa9kBnWoo7iIIB3KLtC7P243Ow4AAAAAVFoU8krGbrPq0Z7xkqR3f9quA1l5JicCAAAAgMqJQl4J3dAiWq1iw5Vb4NKEBVvMjgMAAAAAlRKFvBKyWCwa1avoKPlnK/Zo+8FskxMBAAAAQOVDIa+kOl4RoWsa15DLbeiVeclmxwEAAACASsfUQh4XFyeLxXLaY9iwYZKkvLw8DRs2TBEREQoODtaAAQOUlpZmZuQK5bFe8bJYpO/XpWrN7qNmxwEAAACASsXUQr5y5Urt37/f80hKSpIk3XzzzZKkESNG6Ntvv9W0adO0ePFipaSkqH///mZGrlDio0M14E+1JUljZ2+SYRgmJwIAAACAysPUQl6jRg1FR0d7HrNmzVL9+vXVtWtXZWRk6L333tNrr72ma6+9Vm3bttUHH3ygJUuWaNmyZWbGrlBGdG8kfz+rVuw4ooXJB8yOAwAAAACVhp/ZAYoVFBTov//9r0aOHCmLxaLVq1fL6XQqMTHRs058fLzq1KmjpUuXqmPHjmfcTn5+vvLz8z3PMzMzJUlOp1NOp/Py/hCXoDhbeWeMrOKnuzrW0X9+3qkXv9+kTvWqyma1lGsG+Baz5ipwoZir8BXMVfgC5il8hbfM1dK+v9cU8pkzZyo9PV133323JCk1NVX+/v4KDw8vsV5UVJRSU1PPup2xY8dq9OjRp43PmzdPQUFBZRn5sig+bb881S+Ugmw2bT6QrWc/nqMrIzl1HednxlwFLgZzFb6CuQpfwDyFrzB7rubm5pZqPa8p5O+995569eqlmJiYS9rOqFGjNHLkSM/zzMxMxcbGqkePHgoNDb3UmJeN0+lUUlKSunfvLrvdXu7vn15tp8bN3awfDgZp1O1XKcBuK/cM8A1mz1WgtJir8BXMVfgC5il8hbfM1eIztc/HKwr5rl27NH/+fE2fPt0zFh0drYKCAqWnp5c4Sp6Wlqbo6OizbsvhcMjhcJw2brfbfeKPh1k5h1x1hT5ZtlspGXn6dOU+/bVr/XLPAN/iK/+fApir8BXMVfgC5il8hdlztbTv7RWfQ/7BBx8oMjJSvXv39oy1bdtWdrtdCxYs8IwlJydr9+7dSkhIMCNmhRZgt2lkj8aSpDcXblV6boHJiQAAAACgYjO9kLvdbn3wwQcaPHiw/PxOHLAPCwvTvffeq5EjR2rhwoVavXq1hgwZooSEhLPe0A2X5qY2tRQfHaLMvEJN+mGr2XEAAAAAoEIzvZDPnz9fu3fv1j333HPastdff119+vTRgAED1KVLF0VHR5c4rR1ly2a16PFe8ZKkj5fu0u7DpbsRAQAAAADgwpleyHv06CHDMNSoUaPTlgUEBGjy5Mk6cuSIcnJyNH369HNeP45L17VRDV3dsLoKXG69NHeT2XEAAAAAoMIyvZDDu1gsFo3q1UQWizTr9/36dfdRsyMBAAAAQIVEIcdpmsaE6i9/qi1JeuG7jTIMPpccAAAAAMoahRxn9I8ejRVgt2rVrqOauz7V7DgAAAAAUOFQyHFG0WEBuu/qKyRJL87epIJCt8mJAAAAAKBioZDjrB7oWl/Vg/2183Cupi7fZXYcAAAAAKhQKOQ4q2CHn0Z0L7r7/RsLtijjmNPkRAAAAABQcVDIcU4D28WqQWSwjuY69eairWbHAQAAAIAKg0KOc/KzWfWvG+IlSR/8slN7juSanAgAAAAAKgYKOc7rmsaR6lQ/QgWFbr0yL9nsOAAAAABQIVDIcV4Wi0X/uqGJJOnrtSn6fW+6uYEAAAAAoAKgkKNUmtcKU/82tSRJz3+3UYZhmJwIAAAAAHwbhRyl9o+ejeXws2r5jiOav/GA2XEAAAAAwKdRyFFqtcIDde9V9SRJY7/fKKfLbXIiAAAAAPBdFHJckL91q6+IKv7afihHny7bZXYcAAAAAPBZFHJckJAAu0Z0byRJGr9gizJynSYnAgAAAADfRCHHBRvUPlaNooKVnuvUxB+2mB0HAAAAAHwShRwXzM9m1RO9m0qSPlq6UzsP5ZicCAAAAAB8D4UcF6Vroxrq2qiGnC5DL87eZHYcAAAAAPA5FHJctCd6N5HVIs1Zn6pl2w+bHQcAAAAAfAqFHBetUVSIbu1QR5I05rsNcrsNkxMBAAAAgO+gkOOSjOjeSCEOP/2xL1Mz1uwzOw4AAAAA+AwKOS5J9WCHhl3bQJL08txk5RYUmpwIAAAAAHwDhRyX7O5OcapdNVCpmXl698cdZscBAAAAAJ9AIcclC7Db9HiveEnSlMXblJqRZ3IiAAAAAPB+FHKUid4taqpt3ao65nTplXnJZscBAAAAAK9HIUeZsFgserJ3E0nS/37dqz/2ZZicCAAAAAC8G4UcZaZNnaq6sXWMDKPoY9AMg49BAwAAAICzoZCjTP3z+ng5/Kxatv2I5m1IMzsOAAAAAHgtCjnKVK3wQN139RWSpLHfb1RBodvkRAAAAADgnSjkKHN/61ZfNUIc2nk4Vx8v3Wl2HAAAAADwShRylLkqDj892qOxJOmNBVt0ODvf5EQAAAAA4H0o5LgsBrStrWYxocrKK9T4+VvMjgMAAAAAXodCjsvCZrXoqT5NJUlTV+zW5rQskxMBAAAAgHehkOOy6XhFhHo2i5LLbWjMdxvNjgMAAAAAXoVCjsvqXzc0kd1m0Y+bD2ph8gGz4wAAAACA16CQ47KqG1FFQzrXkySNmbVBThcfgwYAAAAAEoUc5eDBaxsoooq/th3M0dTlu82OAwAAAABegUKOyy40wK4R3RtJkl6fv1kZuU6TEwEAAACA+SjkKBeD2seqUVSw0nOdemMBH4MGAAAAABRylAs/m9XzMWgfL92p7QezTU4EAAAAAOaikKPcXN2whq6Nj1Sh29AL3/MxaAAAAAAqN9ML+b59+3THHXcoIiJCgYGBatGihVatWuVZbhiGnn76adWsWVOBgYFKTEzUli2c8uyr/nVDE/lZLZq/8YB+2XrI7DgAAAAAYBpTC/nRo0fVuXNn2e12zZ49Wxs2bNCrr76qqlWretZ56aWXNGHCBE2ZMkXLly9XlSpV1LNnT+Xl5ZmYHBerQWSw7uhYV5L03KwNcrkNkxMBAAAAgDn8zHzzcePGKTY2Vh988IFnrF69ep7vDcPQ+PHj9eSTT+rGG2+UJH388ceKiorSzJkzNWjQoHLPjEs3PLGhZqzZp02pWfpi5R7ddmUdsyMBAAAAQLkztZB/88036tmzp26++WYtXrxYtWrV0tChQ3XfffdJknbs2KHU1FQlJiZ6XhMWFqYrr7xSS5cuPWMhz8/PV35+vud5ZmamJMnpdMrp9N6P2yrO5s0Zy0oVu0UPXXOFxnyfrFfmbdL1TasrJMBudiyUUmWaq/BtzFX4CuYqfAHzFL7CW+Zqad/fYhiGaecMBwQESJJGjhypm2++WStXrtTDDz+sKVOmaPDgwVqyZIk6d+6slJQU1axZ0/O6W265RRaLRV988cVp23zmmWc0evTo08anTp2qoKCgy/fD4IK43NKLv9l0IM+i62Lc+nNdt9mRAAAAAKBM5Obm6rbbblNGRoZCQ0PPup6phdzf31/t2rXTkiVLPGN///vftXLlSi1duvSiCvmZjpDHxsbq0KFD5/xFmM3pdCopKUndu3eX3V45jhYvTD6o+/+7RnabRbP/3ll1q7HDxBdUxrkK38Rcha9grsIXME/hK7xlrmZmZqp69ernLeSmnrJes2ZNNW3atMRYkyZN9L///U+SFB0dLUlKS0srUcjT0tLUunXrM27T4XDI4XCcNm63233ij4ev5CwL3ZvV1NUN9+inLYf0yrytmnJnW7Mj4QJUprkK38Zcha9grsIXME/hK8yeq6V9b1Pvst65c2clJyeXGNu8ebPq1i26C3e9evUUHR2tBQsWeJZnZmZq+fLlSkhIKNesKHsWi0VP9m4qq0Wasz5Vy7YfNjsSAAAAAJQbUwv5iBEjtGzZMr3wwgvaunWrpk6dqnfeeUfDhg2TVFTYhg8frjFjxuibb77RunXrdNdddykmJkb9+vUzMzrKSOPoEM9d1vkYNAAAAACViamFvH379poxY4Y+++wzNW/eXM8995zGjx+v22+/3bPOP//5Tz300EO6//771b59e2VnZ2vOnDmeG8LB941IbKSQAD+tT8nU/1bvNTsOAAAAAJQLU68hl6Q+ffqoT58+Z11usVj07LPP6tlnny3HVChPEcEO/f3ahnr++416eV6ybmhZU8EO06cmAAAAAFxWph4hB4oN7hSnuIggHczK11uLtpodBwAAAAAuOwo5vIK/n1X/uqGJJOndn3Zoz5FckxMBAAAAwOVFIYfX6N40SglXRKig0K1xczaZHQcAAAAALisKObyGxWLRU32aymKRZv2+X6t2HjE7EgAAAABcNhRyeJWmMaEa2C5WkvTsrA1y8zFoAAAAACooCjm8zj96NFaww0+/783QjDX7zI4DAAAAAJcFhRxep0aIQ0OvqS9JemnuJuUWFJqcCAAAAADKHoUcXumezvVUu2qg0jLz9fbi7WbHAQAAAIAyRyGHVwqw2zSqV9HHoL394zbtzzhmciIAAAAAKFsUcnitG1pEq31cVeU53Xp5TrLZcQAAAACgTFHI4bUsFoue7N1UkjR9zT6t3ZNubiAAAAAAKEMUcni1VrHh6t+mliRpzKwNMgw+Bg0AAABAxUAhh9d79PrGCrBbtWrXUX23br/ZcQAAAACgTFDI4fVqhgXqr12LPgbtxdmblOd0mZwIAAAAAC4dhRw+4f4uVyg6NEB7jx7T+7/sMDsOAAAAAFwyCjl8QpC/n/55fWNJ0psLt+lgVr7JiQAAAADg0lDI4TP6ta6llrXDlJ1fqNeS+Bg0AAAAAL6NQg6fYbVa9FSfoo9B+2LlHm1IyTQ5EQAAAABcPAo5fEr7uGrq3aKm3IY05js+Bg0AAACA76KQw+c83ite/n5WLdl2WPM3HjA7DgAAAABcFAo5fE5stSDde1U9SdIL329UQaHb5EQAAAAAcOEo5PBJQ7vVV/Vgf+04lKP/LttldhwAAAAAuGAUcvikkAC7/tGj6GPQ3liwRem5BSYnAgAAAIALQyGHz7qlXazio0OUccypCQu2mh0HAAAAAC4IhRw+y2a16IneTSRJHy/dqe0Hs01OBAAAAAClRyGHT7u6YQ1d07iGCt2GXpy9yew4AAAAAFBqFHL4vH/d0EQ2q0XzNqRp6bbDZscBAAAAgFKhkMPnNYwK0W0d6kiSxny3QW63YXIiAAAAADg/CjkqhOGJDRUS4Kf1KZmavmaf2XEAAAAA4Lwo5KgQIoIdeujaBpKkl+duUm5BocmJAAAAAODcKOSoMAZ3ilNstUClZebr7cXbzY4DAAAAAOdEIUeF4fCzaVSvoo9Be/vHbUrNyDM5EQAAAACcHYUcFUqv5tFqV7eq8pxuvTw32ew4AAAAAHBWFHJUKBaLRU/2aSpJ+t+ve7Vub4bJiQAAAADgzCjkqHBax4arX+sYSUUfg2YYfAwaAAAAAO9DIUeF9M/r4+Xws2r5jiOauz7N7DgAAAAAcBoKOSqkmPBA3d/lCknSi7M3qqDQbXIiAAAAACiJQo4K64Gu9VU92KGdh3P132W7zI4DAAAAACVQyFFhBTv89I8ejSRJE37Yooxcp8mJAAAAAOAECjkqtJvb1lajqGCl5zo18YctZscBAAAAAA8KOSo0P5tV/7qhiSTpo6U7tetwjsmJAAAAAKCIqYX8mWeekcViKfGIj4/3LM/Ly9OwYcMUERGh4OBgDRgwQGlp3DEbF6Zb40hd3bC6nC5D4+ZsMjsOAAAAAEjygiPkzZo10/79+z2Pn3/+2bNsxIgR+vbbbzVt2jQtXrxYKSkp6t+/v4lp4aue6N1EVov0/bpUrdp5xOw4AAAAAGB+Iffz81N0dLTnUb16dUlSRkaG3nvvPb322mu69tpr1bZtW33wwQdasmSJli1bZnJq+Jr46FDd0i5WkjTmu40yDMPkRAAAAAAqOz+zA2zZskUxMTEKCAhQQkKCxo4dqzp16mj16tVyOp1KTEz0rBsfH686depo6dKl6tix4xm3l5+fr/z8fM/zzMxMSZLT6ZTT6b132S7O5s0Zfd1D11yhb35L0do96fp6zV71bhFtdiSfxFyFr2CuwlcwV+ELmKfwFd4yV0v7/hbDxEOFs2fPVnZ2tho3bqz9+/dr9OjR2rdvn/744w99++23GjJkSIlyLUkdOnTQNddco3Hjxp1xm88884xGjx592vjUqVMVFBR0WX4O+I45eyyavdemag5D/2rtkt30c0QAAAAAVDS5ubm67bbblJGRodDQ0LOuZ+oR8l69enm+b9mypa688krVrVtXX375pQIDAy9qm6NGjdLIkSM9zzMzMxUbG6sePXqc8xdhNqfTqaSkJHXv3l12u93sOBVWt4JC/Tr+F6Vl5etAeBPdd1U9syP5HOYqfAVzFb6CuQpfwDyFr/CWuVp8pvb5mH7K+snCw8PVqFEjbd26Vd27d1dBQYHS09MVHh7uWSctLU3R0Wc/1djhcMjhcJw2brfbfeKPh6/k9FVhdrse6dlYj371u95avEODOsSpWhV/s2P5JOYqfAVzFb6CuQpfwDyFrzB7rpb2vb3qhN3s7Gxt27ZNNWvWVNu2bWW327VgwQLP8uTkZO3evVsJCQkmpoSvG/Cn2mpaM1RZeYV6Y/5ms+MAAAAAqKRMLeSPPPKIFi9erJ07d2rJkiW66aabZLPZdOuttyosLEz33nuvRo4cqYULF2r16tUaMmSIEhISznpDN6A0rFaLnuzdRJL06fLd2nYw2+REAAAAACojUwv53r17deutt6px48a65ZZbFBERoWXLlqlGjRqSpNdff119+vTRgAED1KVLF0VHR2v69OlmRkYF0alBdV0XH6lCt6Gx328yOw4AAACASsjUa8g///zzcy4PCAjQ5MmTNXny5HJKhMpk1A3xWrT5oOZvTNPSbYeVUD/C7EgAAAAAKhGvuoYcKE8NIkN0a4dYSdIL32+U223aJwACAAAAqIQo5KjUHr6ukar427RuX4a+/T3F7DgAAAAAKhEKOSq1GiEO/a1bfUnSS3OSled0mZwIAAAAQGVBIUeld+9VVyg6NED70o/poyU7zY4DAAAAoJKgkKPSC/S36R89GkmSJi3cqqM5BSYnAgAAAFAZUMgBSf3/VFvx0SHKyivUhB+2mB0HAAAAQCVAIQck2awWPdG7iSTpv8t2aeehHJMTAQAAAKjoKOTAcVc3rKEujWrI6TL00txNZscBAAAAUMFRyIGT/OuGeFkt0vfrUrV611Gz4wAAAACowCjkwEnio0P1l7a1JUkvfL9RhmGYnAgAAABARUUhB04xsntjBdptWr3rqOb8kWp2HAAAAAAVFIUcOEV0WIDuu7qeJGncnE0qKHSbnAgAAABARUQhB87g/q71VT3YoZ2Hc/Xp8l1mxwEAAABQAVHIgTMIdvhpRPeGkqQJC7Yo45jT5EQAAAAAKhoKOXAWA9vFqkFksI7mOvXmoq1mxwEAAABQwVDIgbPws1k1qle8JOmDX3ZqX/oxkxMBAAAAqEgo5MA5XBsfqY5XVFNBoVuvzks2Ow4AAACACoRCDpyDxWLRqF5NJEkz1uzT+pQMkxMBAAAAqCgo5MB5tIoNV99WMTIM6cXZm8yOAwAAAKCCoJADpfBoj8ay2yz6acsh/bj5oNlxAAAAAFQAFHKgFOpEBOnOjnGSpLGzN8ntNswNBAAAAMDnUciBUnro2gYKCfDTxv2Zmrl2n9lxAAAAAPg4CjlQSlWr+GtotwaSpFfmJivP6TI5EQAAAABfRiEHLsCQznGKCQtQSkaePlqy0+w4AAAAAHwYhRy4AAF2m/7Ro7EkadLCrTqaU2ByIgAAAAC+6qIK+Z49e7R3717P8xUrVmj48OF65513yiwY4K36tamlJjVDlZVXqEkLt5odBwAAAICPuqhCftttt2nhwoWSpNTUVHXv3l0rVqzQE088oWeffbZMAwLexma1aFSveEnSx0t3as+RXJMTAQAAAPBFF1XI//jjD3Xo0EGS9OWXX6p58+ZasmSJPv30U3344YdlmQ/wSl0a1dDVDavL6TL08txks+MAAAAA8EEXVcidTqccDockaf78+frzn/8sSYqPj9f+/fvLLh3gxR67Pl4Wi/TNbyn6fW+62XEAAAAA+JiLKuTNmjXTlClT9NNPPykpKUnXX3+9JCklJUURERFlGhDwVs1rhemm1rUkSS98v1GGYZicCAAAAIAvuahCPm7cOL399tvq1q2bbr31VrVq1UqS9M0333hOZQcqg5E9Gsnfz6pl249oUfJBs+MAAAAA8CF+F/Oibt266dChQ8rMzFTVqlU94/fff7+CgoLKLBzg7WpXDdKQTnF6+8ftenH2JnVpVEM2q8XsWAAAAAB8wEUdIT927Jjy8/M9ZXzXrl0aP368kpOTFRkZWaYBAW/3t271FRrgp+S0LE3/de/5XwAAAAAAushCfuONN+rjjz+WJKWnp+vKK6/Uq6++qn79+umtt94q04CAtwsP8tewaxpIkl5L2qw8p8vkRAAAAAB8wUUV8l9//VVXX321JOmrr75SVFSUdu3apY8//lgTJkwo04CALxjcKU4xYQHan5Gnj5bsNDsOAAAAAB9wUYU8NzdXISEhkqR58+apf//+slqt6tixo3bt2lWmAQFfEGC3aWSPxpKkyQu3Kj23wOREAAAAALzdRRXyBg0aaObMmdqzZ4/mzp2rHj16SJIOHDig0NDQMg0I+Iqb2tRSfHSIMvMK9eaibWbHAQAAAODlLqqQP/3003rkkUcUFxenDh06KCEhQVLR0fI2bdqUaUDAV9isFj12fbwk6cMlO7Uv/ZjJiQAAAAB4s4sq5H/5y1+0e/durVq1SnPnzvWMX3fddXr99dfLLBzga7o1rqGOV1RTQaFbr85LNjsOAAAAAC92UYVckqKjo9WmTRulpKRo796ij3rq0KGD4uPjyywc4GssFotG9WoiSZqxZp827s80OREAAAAAb3VRhdztduvZZ59VWFiY6tatq7p16yo8PFzPPfec3G73RQV58cUXZbFYNHz4cM9YXl6ehg0bpoiICAUHB2vAgAFKS0u7qO0D5aVVbLh6t6wpw5DGzdlkdhwAAAAAXuqiCvkTTzyhSZMm6cUXX9SaNWu0Zs0avfDCC5o4caKeeuqpC97eypUr9fbbb6tly5YlxkeMGKFvv/1W06ZN0+LFi5WSkqL+/ftfTGSgXD3ao7H8rBYtSj6oJdsOmR0HAAAAgBe6qEL+0Ucf6T//+Y/+9re/qWXLlmrZsqWGDh2qd999Vx9++OEFbSs7O1u333673n33XVWtWtUznpGRoffee0+vvfaarr32WrVt21YffPCBlixZomXLll1MbKDcxFWvotuurCNJenH2JrndhsmJAAAAAHgbv4t50ZEjR854rXh8fLyOHDlyQdsaNmyYevfurcTERI0ZM8Yzvnr1ajmdTiUmJpbYfp06dbR06VJ17NjxjNvLz89Xfn6+53lmZtE1vE6nU06n84KylafibN6cERdmaJc4/W/1Xv2+N0PfrN2r3i2izY5UJpir8BXMVfgK5ip8AfMUvsJb5mpp3/+iCnmrVq00adIkTZgwocT4pEmTTjvt/Fw+//xz/frrr1q5cuVpy1JTU+Xv76/w8PAS41FRUUpNTT3rNseOHavRo0efNj5v3jwFBQWVOptZkpKSzI6AMtQl0qLZe2167uvf5Nr1q/wu+jaK3oe5Cl/BXIWvYK7CFzBP4SvMnqu5ubmlWu+iCvlLL72k3r17a/78+Z7PIF+6dKn27Nmj77//vlTb2LNnjx5++GElJSUpICDgYmKc0ahRozRy5EjP88zMTMXGxqpHjx4KDQ0ts/cpa06nU0lJSerevbvsdrvZcVBGuuYXauX4n3Uou0Dp1Zvrro51zI50yZir8BXMVfgK5ip8AfMUvsJb5mrxmdrnc1GFvGvXrtq8ebMmT56sTZuK7iLdv39/3X///RozZoyuvvrq825j9erVOnDggP70pz95xlwul3788UdNmjRJc+fOVUFBgdLT00scJU9LS1N09NlP/XU4HHI4HKeN2+12n/jj4Ss5UTrhdrseTmykp2b+oTcXbdfADnUV7Lio/9t5HeYqfAVzFb6CuQpfwDyFrzB7rpb2vS+6GcTExOj5558vMfbbb7/pvffe0zvvvHPe11933XVat25dibEhQ4YoPj5ejz32mGJjY2W327VgwQINGDBAkpScnKzdu3d7jsoDvmBQ+1i9//MO7TiUo3d/3K4R3RuZHQkAAACAFzDtUF1ISIiaN29eYqxKlSqKiIjwjN97770aOXKkqlWrptDQUD300ENKSEg46w3dAG9kt1n1aM/GGvrpr3r3p+26o2Nd1Qg5/SwOAAAAAJWLV99i6vXXX1efPn00YMAAdenSRdHR0Zo+fbrZsYAL1qt5tFrFhiu3wKWJP2wxOw4AAAAAL+BVhXzRokUaP36853lAQIAmT56sI0eOKCcnR9OnTz/n9eOAt7JYLHr8+qKPCpy6fLd2HsoxOREAAAAAs13QKev9+/c/5/L09PRLyQJUaAn1I9StcQ0tSj6oV+Yla9Jtfzr/iwAAAABUWBdUyMPCws67/K677rqkQEBF9s+e8Vq8+aBm/b5f93dJV8va4WZHAgAAAGCSCyrkH3zwweXKAVQKTWNCdVPrWpq+Zp9enL1Jn/7flbJYLGbHAgAAAGACr7qGHKgMRnRvJH+bVUu2HdZPWw6ZHQcAAACASSjkQDmLrRakOxPqSpLGzdkkt9swOREAAAAAM1DIARMMu6aBQhx+Wp+SqW9/TzE7DgAAAAATUMgBE1Sr4q+/dqsvSXplXrIKCt0mJwIAAABQ3ijkgEmGdI5TZIhDe44c09Tlu8yOAwAAAKCcUcgBkwT5++nhxIaSpAk/bFVWntPkRAAAAADKE4UcMNEt7WJ1RfUqOpJToHd/2mF2HAAAAADliEIOmMhus+rRno0lSf/5absOZOWZnAgAAABAeaGQAya7vnm0WseGK7fApYkLtpodBwAAAEA5oZADJrNYLHq8V7wk6bMVu7XzUI7JiQAAAACUBwo54AU6XhGhaxrXUKHb0Cvzks2OAwAAAKAcUMgBL/HP6+NlsUizft+v3/emmx0HAAAAwGVGIQe8RJOaobqpdS1J0rg5m0xOAwAAAOByo5ADXmRE90byt1n1y9bD+mnLQbPjAAAAALiMKOSAF4mtFqQ7E+pKkl6cvUlut2FyIgAAAACXC4Uc8DLDrmmgEIef1qdk6tvfU8yOAwAAAOAyoZADXqZaFX890PUKSdKr8zaroNBtciIAAAAAlwOFHPBC91xVTzVCHNp9JFefrdhtdhwAAAAAlwGFHPBCQf5+evi6hpKkCQu2KDu/0OREAAAAAMoahRzwUgPbx6pe9So6nFOg//y03ew4AAAAAMoYhRzwUnabVY/2bCxJevfH7TqYlW9yIgAAAABliUIOeLFezaPVqnaYcgpcmvTDFrPjAAAAAChDFHLAi1ksFj3WK16S9Ony3dp1OMfkRAAAAADKCoUc8HKd6ldX10Y1VOg29Mq8zWbHAQAAAFBGKOSAD/jn9Y1lsUjf/paidXszzI4DAAAAoAxQyAEf0CwmTP1a15IkjZuzyeQ0AAAAAMoChRzwESO7N5K/zaqftx7Sj5sPmh0HAAAAwCWikAM+IrZakO5MqCtJenH2JrndhsmJAAAAAFwKCjngQ4Zd00AhDj9t2J+pb35LMTsOAAAAgEtAIQd8SLUq/vprt/qSpFfmJSu/0GVyIgAAAAAXi0IO+Jh7OtdTVKhDe48e03+X7TY7DgAAAICLRCEHfEygv00jEhtJkib9sEWZeU6TEwEAAAC4GBRywAf9pW1t1a9RRUdznXp78Taz4wAAAAC4CBRywAf52ax67Pp4SdJ7P+9QWmaeyYkAAAAAXCgKOeCjujeNUtu6VZXndGv8/M1mxwEAAABwgSjkgI+yWCwa1avoKPkXK/do64FskxMBAAAAuBAUcsCHtYurpu5No+Q2pJfnbjI7DgAAAIALQCEHfNxj1zeW1SLNXZ+m1buOmB0HAAAAQCmZWsjfeusttWzZUqGhoQoNDVVCQoJmz57tWZ6Xl6dhw4YpIiJCwcHBGjBggNLS0kxMDHifBpEhuqVdrCTpxdmbZBiGyYkAAAAAlIaphbx27dp68cUXtXr1aq1atUrXXnutbrzxRq1fv16SNGLECH377beaNm2aFi9erJSUFPXv39/MyIBXGp7YSAF2q1buPKqkDey0AgAAAHyBqYW8b9++uuGGG9SwYUM1atRIzz//vIKDg7Vs2TJlZGTovffe02uvvaZrr71Wbdu21QcffKAlS5Zo2bJlZsYGvE50WIDu6VxPkjRuziYVutwmJwIAAABwPn5mByjmcrk0bdo05eTkKCEhQatXr5bT6VRiYqJnnfj4eNWpU0dLly5Vx44dz7id/Px85efne55nZmZKkpxOp5xO5+X9IS5BcTZvzgjv9n+d6+izFbu17WCOPlu+S4Pa174s78Ncha9grsJXMFfhC5in8BXeMldL+/6mF/J169YpISFBeXl5Cg4O1owZM9S0aVOtXbtW/v7+Cg8PL7F+VFSUUlNTz7q9sWPHavTo0aeNz5s3T0FBQWUdv8wlJSWZHQE+7NpIi/6306aXZq+XI/V3OWyX772Yq/AVzFX4CuYqfAHzFL7C7Lmam5tbqvVML+SNGzfW2rVrlZGRoa+++kqDBw/W4sWLL3p7o0aN0siRIz3PMzMzFRsbqx49eig0NLQsIl8WTqdTSUlJ6t69u+x2u9lx4KMSC91aOfEX7T5yTHurNNZD19Yv8/dgrsJXMFfhK5ir8AXMU/gKb5mrxWdqn4/phdzf318NGjSQJLVt21YrV67UG2+8oYEDB6qgoEDp6ekljpKnpaUpOjr6rNtzOBxyOBynjdvtdp/44+ErOeGd7HbpseubaNjUX/WfX3bqjk5xigwJuEzvxVyFb2CuwlcwV+ELmKfwFWbP1dK+t9d9Drnb7VZ+fr7atm0ru92uBQsWeJYlJydr9+7dSkhIMDEh4N1uaBGt1rHhyi1wafz8LWbHAQAAAHAWph4hHzVqlHr16qU6deooKytLU6dO1aJFizR37lyFhYXp3nvv1ciRI1WtWjWFhobqoYceUkJCwllv6AZAslgs+tcNTXTL20v1xco9uqdznBpEhpgdCwAAAMApTC3kBw4c0F133aX9+/crLCxMLVu21Ny5c9W9e3dJ0uuvvy6r1aoBAwYoPz9fPXv21JtvvmlmZMAndKhXTd2bRilpQ5rGzUnWu3e1MzsSAAAAgFOYWsjfe++9cy4PCAjQ5MmTNXny5HJKBFQcj10frx82HVDShjSt2HFEHepVMzsSAAAAgJN43TXkAMpGg8hgDWwfK0l64fuNMgzD5EQAAAAATkYhByqw4YkNFeRv09o96fp+XarZcQAAAACchEIOVGCRIQG6v8sVkqSX5m5SQaHb5EQAAAAAilHIgQruvquvUPVgh3YdztXU5bvMjgMAAADgOAo5UMFVcfhpRPeGkqQ3FmxRZp7T5EQAAAAAJAo5UCkMbBer+jWq6GiuU1MWbTM7DgAAAABRyIFKwc9m1eO9mkiS3vt5h/alHzM5EQAAAAAKOVBJJDaJ1JX1qim/0K1X5iabHQcAAACo9CjkQCVhsVj0ZO+mkqQZa/bptz3p5gYCAAAAKjkKOVCJtKgdpv5takmSnv9uowzDMDkRAAAAUHlRyIFK5pGejRVgt2rFziOauz7V7DgAAABApUUhByqZmPBA3Xf1FZKkF2dvUkGh2+REAAAAQOVEIQcqob92ra8aIQ7tPJyrT5btMjsOAAAAUClRyIFKqIrDT//o3kiSNGHBFqXnFpicCAAAAKh8KORAJXVzu1jFR4co45hTExZsNTsOAAAAUOlQyIFKyma16IneTSRJHy/dqR2HckxOBAAAAFQuFHKgEru6YQ1d07iGCt2GXpy90ew4AAAAQKVCIQcquX/d0EQ2q0Vz16dp2fbDZscBAAAAKg0KOVDJNYwK0aD2sZKk57/bKLfbMDkRAAAAUDlQyAFoRPdGCnb4ad2+DH392z6z4wAAAACVAoUcgKoHOzT0mvqSpJfmJOtYgcvkRAAAAEDFRyEHIEm6p3M91QoP1P6MPL3703az4wAAAAAVHoUcgCQpwG7T473iJUlvLdqm/RnHTE4EAAAAVGwUcgAefVrWVPu4qjrmdOnF2ZvMjgMAAABUaBRyAB4Wi0X/7ttMFov09doUrdp5xOxIAAAAQIVFIQdQQvNaYRrYruhj0EZ/u4GPQQMAAAAuEwo5gNM80rOxQo5/DNpXq/eaHQcAAACokCjkAE5TPdihhxMbSpJemrtJWXlOkxMBAAAAFQ+FHMAZ3ZUQpyuqV9Gh7AJN+mGr2XEAAACACodCDuCM/P2seqpPU0nS+7/s0PaD2SYnAgAAACoWCjmAs7omPlLdGteQ02Xo+e82mh0HAAAAqFAo5ADO6ak+TeVntWjBpgNalHzA7DgAAABAhUEhB3BO9WsE6+5OcZKk52ZtkNPlNjcQAAAAUEFQyAGc10PXNVREFX9tO5ij/y7fY3YcAAAAoEKgkAM4r7BAux7t2ViSNHHhNmXzKWgAAADAJaOQAyiVm9vFqllMqLLyCvXdbv50AAAAAJeK/6oGUCo2q0X/7ttMkrT0gEXr9mWYnAgAAADwbRRyAKXWoV419W0ZLUMWPfPtRrnchtmRAAAAAJ9FIQdwQR6/vrECbIZ+35epz1fuNjsOAAAA4LMo5AAuSGSIQzfEFn302UtzknU4O9/kRAAAAIBvopADuGBXRRtqEh2ijGNOvTh7k9lxAAAAAJ9kaiEfO3as2rdvr5CQEEVGRqpfv35KTk4usU5eXp6GDRumiIgIBQcHa8CAAUpLSzMpMQBJslmk0X2bSJKmrd6rVTuPmJwIAAAA8D2mFvLFixdr2LBhWrZsmZKSkuR0OtWjRw/l5OR41hkxYoS+/fZbTZs2TYsXL1ZKSor69+9vYmoAktSmTrgGtouVJD058w8VutwmJwIAAAB8i5+Zbz5nzpwSzz/88ENFRkZq9erV6tKlizIyMvTee+9p6tSpuvbaayVJH3zwgZo0aaJly5apY8eOZsQGcNxjveI1d0OqNqVm6aOlu3TvVfXMjgQAAAD4DFML+akyMoo+17hatWqSpNWrV8vpdCoxMdGzTnx8vOrUqaOlS5eesZDn5+crP//ETaYyMzMlSU6nU06n83LGvyTF2bw5IyCVnKsh/nY90r2hnvx6g15LSlbPJtUVFRpgckKgCH9X4SuYq/AFzFP4Cm+Zq6V9f4thGF7xQcJut1t//vOflZ6erp9//lmSNHXqVA0ZMqREwZakDh066JprrtG4ceNO284zzzyj0aNHnzY+depUBQUFXZ7wQCXmNqTxf9i0K9uiP0W4NbgRp64DAACgcsvNzdVtt92mjIwMhYaGnnU9rzlCPmzYMP3xxx+eMn6xRo0apZEjR3qeZ2ZmKjY2Vj169DjnL8JsTqdTSUlJ6t69u+x2u9lxgLM601yt1yZT/acs06+HrXq4cXt1qh9hckqAv6vwHcxV+ALmKXyFt8zV4jO1z8crCvmDDz6oWbNm6ccff1Tt2rU949HR0SooKFB6errCw8M942lpaYqOjj7jthwOhxwOx2njdrvdJ/54+EpO4OS52rpuhO7sWFcfLd2l0d9t0uyHr5bDz2ZyQqAIf1fhK5ir8AXMU/gKs+dqad/b1LusG4ahBx98UDNmzNAPP/ygevVK3hCqbdu2stvtWrBggWcsOTlZu3fvVkJCQnnHBXAOI3s0VvVgh7YfzNF/ftphdhwAAADA65layIcNG6b//ve/mjp1qkJCQpSamqrU1FQdO3ZMkhQWFqZ7771XI0eO1MKFC7V69WoNGTJECQkJ3GEd8DJhgXY90TtekjTxhy3aezTX5EQAAACAdzO1kL/11lvKyMhQt27dVLNmTc/jiy++8Kzz+uuvq0+fPhowYIC6dOmi6OhoTZ8+3cTUAM6mX+taurJeNeU53Xpq5h/ykntGAgAAAF7J9FPWz/S4++67PesEBARo8uTJOnLkiHJycjR9+vSzXj8OwFwWi0XP39Rc/jarFiYf1De/pZgdCQAAAPBaphZyABVPg8gQPXRtA0nS6G836EhOgcmJAAAAAO9EIQdQ5h7oWl/x0SE6klOgZ79db3YcAAAAwCtRyAGUOX8/q8YNaCmrRZq5NkULkw+YHQkAAADwOhRyAJdFq9hw3dO56KMMn5i+Ttn5hSYnAgAAALwLhRzAZTOyRyPFVgtUSkaeXp6zyew4AAAAgFehkAO4bIL8/TT2ppaSpI+X7dLqXUdMTgQAAAB4Dwo5gMvqqobVdUu72jIM6bH/rVN+ocvsSAAAAIBXoJADuOyeuKGpaoQ4tPVAtib/sNXsOAAAAIBXoJADuOzCgux69s/NJElvLtqmjfszTU4EAAAAmI9CDqBc9GpRUz2bRanQbejx//0ul9swOxIAAABgKgo5gHLz7I3NFRLgp9/2ZuiDX3aYHQcAAAAwFYUcQLmJCg3QEzc0kSS9Mi9Z2w9mm5wIAAAAMA+FHEC5Gtg+Vlc1qK48p1sjvlgrp8ttdiQAAADAFBRyAOXKYrHo5ZtbKvT4qesTues6AAAAKikKOYByVzMsUM/f1EKSNHnhVv26+6jJiQAAAIDyRyEHYIq+rWLUr3WMXG5DI79Yq5z8QrMjAQAAAOWKQg7ANKNvbK6YsADtPJyrMd9tNDsOAAAAUK4o5ABMExZo1yu3tJLFIn22Yrfmb0gzOxIAAABQbijkAEzVqX51/d9V9SRJj0//XYey801OBAAAAJQPCjkA0z3Ss7Hio0N0KLtAj//vdxmGYXYkAAAA4LKjkAMwncPPpvGDWsvfZtX8jQf0+co9ZkcCAAAALjsKOQCvEB8dqkd7NpYkPTdrg3YeyjE5EQAAAHB5UcgBeI17r6qnhCsilFvg0vAv1qrQ5TY7EgAAAHDZUMgBeA2r1aJXb2mlkAA/rd2TrgkLtpgdCQAAALhsKOQAvEpMeKDG9GsuSZq4cKsWJh8wOREAAABweVDIAXidG1vX0u1X1pFhSCO+WKs9R3LNjgQAAACUOQo5AK/0dN+malU7TOm5Tg399FflOV1mRwIAAADKFIUcgFdy+Nn05h1tVTXIrnX7MjT62w1mRwIAAADKFIUcgNeqFR6o8YPayGKRPluxW9NW8fnkAAAAqDgo5AC8WtdGNTT8ukaSpCdn/qENKZkmJwIAAADKBoUcgNd76NoG6ta4hvIL3frbp6uVccxpdiQAAADgklHIAXg9q9Wi8QNbq1Z4oHYdztU/vvxNbrdhdiwAAADgklDIAfiE8CB/Tbmjrfz9rJq/MU1TftxmdiQAAADgklDIAfiMFrXD9Oyfm0mSXpmbrCVbD5mcCAAAALh4FHIAPmVg+1jd3La23Ib00GdrtOdIrtmRAAAAgItCIQfgUywWi57r11zNYkJ1OKdAgz9YofTcArNjAQAAABeMQg7A5wTYbXpvcHvFhAVo+8Ec/d9Hq5TndJkdCwAAALggFHIAPik6LEAf3tNBIQF+WrXrqEZ+uZY7rwMAAMCnUMgB+KxGUSF658528rdZ9f26VD3//UazIwEAAAClRiEH4NMS6kfo5ZtbSpLe+3mH/vPTdpMTAQAAAKVjaiH/8ccf1bdvX8XExMhisWjmzJkllhuGoaefflo1a9ZUYGCgEhMTtWXLFnPCAvBaN7aupVG94iVJz3+/Ud+v229yIgAAAOD8TC3kOTk5atWqlSZPnnzG5S+99JImTJigKVOmaPny5apSpYp69uypvLy8ck4KwNvd3+UK3ZVQV4YhDf9irVbuPGJ2JAAAAOCcTC3kvXr10pgxY3TTTTedtswwDI0fP15PPvmkbrzxRrVs2VIff/yxUlJSTjuSDgAWi0X/7ttM3ZtGqaDQrf/7aJW2Hsg2OxYAAABwVn5mBzibHTt2KDU1VYmJiZ6xsLAwXXnllVq6dKkGDRp0xtfl5+crPz/f8zwzM1OS5HQ65XQ6L2/oS1CczZszApL3z9VXBzTXXVl5WrsnQ4PfX65p91+pGiEOs2PBBN4+V4FizFX4AuYpfIW3zNXSvr/XFvLU1FRJUlRUVInxqKgoz7IzGTt2rEaPHn3a+Lx58xQUFFS2IS+DpKQksyMApeLNc/XmKGnfQZv2peep/8RFGtrUpTB/s1PBLN48V4GTMVfhC5in8BVmz9Xc3NxSree1hfxijRo1SiNHjvQ8z8zMVGxsrHr06KHQ0FATk52b0+lUUlKSunfvLrvdbnYc4Kx8Za5eeXWubn9vpVIz8/X+zlB9PKSdaoYFmB0L5chX5irAXIUvYJ7CV3jLXC0+U/t8vLaQR0dHS5LS0tJUs2ZNz3haWppat2591tc5HA45HKefnmq3233ij4ev5AS8fa42iArTtAc66bb/LNPOw7m69T8r9dl9HVUnwvvPlEHZ8va5ChRjrsIXME/hK8yeq6V9b6/9HPJ69eopOjpaCxYs8IxlZmZq+fLlSkhIMDEZAF9RJyJIXz6QoHrVq2hf+jHd/PYSbvQGAAAAr2FqIc/OztbatWu1du1aSUU3clu7dq12794ti8Wi4cOHa8yYMfrmm2+0bt063XXXXYqJiVG/fv3MjA3Ah8SEB+qLBzqqUVSw0jLzNeidpdqUWrpTiAAAAIDLydRCvmrVKrVp00Zt2rSRJI0cOVJt2rTR008/LUn65z//qYceekj333+/2rdvr+zsbM2ZM0cBAVwHCqD0IkMC9Pn9CWpaM1SHsgs06J1l+n1vutmxAAAAUMmZWsi7desmwzBOe3z44YeSij5X+Nlnn1Vqaqry8vI0f/58NWrUyMzIAHxUtSr++uy+jmodG670XKduf3e5Vu86YnYsAAAAVGJeew05AJS1sCC7/vt/V6pDvWrKyi/Une+t0JKth8yOBQAAgEqKQg6gUgl2+OmjIR10dcPqyi1w6e4PV+p/q/eaHQsAAACVEIUcQKUT6G/Tu3e1U/emUSoodOsf037TEzPWKb/QZXY0AAAAVCIUcgCVUoDdprfvaKuHr2soi0X6dPlu3fL2Mu1LP2Z2NAAAAFQSFHIAlZbVatGI7o30/t3tFRZo12970tV34s/6eQvXlQMAAODyo5ADqPSuaRypWQ9dpWYxoTqSU6C73l+uyQu3yu02zI4GAACACoxCDgCSYqsF6X9/66Rb2tWW25Benpus+z9ZrYxjTrOjAQAAoIKikAPAcQF2m176Syu92L+F/P2smr8xTX+e9LP+2JdhdjQAAABUQBRyADjFoA519NVfE1QrPFC7Dufqz5N+1tNf/6GMXI6WAwAAoOxQyAHgDFrWDtesh65S75Y15Takj5fu0jWvLtJnK3bLxbXlAAAAKAMUcgA4i6pV/DX5tj9p6v9dqYaRwTqSU6BR09fppjd/0ZrdR82OBwAAAB9HIQeA8+jUoLq+f/hqPdWnqUIcfvp9b4ZuenOJHpn2mw5m5ZsdDwAAAD6KQg4ApWC3WXXvVfX0wyPd9Je2tSVJX63eq2tfWaT//LRduQWFJicEAACAr6GQA8AFqBHi0Cs3t9L//tZJLWqFKSu/UGO+26grn1+gf3/9hzanZZkdEQAAAD7Cz+wAAOCL2tatqpnDOuvLVXs0ZfE27Tqcq4+W7tJHS3epQ71quv3KOrq+ebQcfjazo3oNwzBU4HLrWIFLTpchm9Uim8Uim+34V2vRw2qRLBaL2XEBAAAuOwo5AFwkm9WiWzvU0cB2sfp56yF9unyX5m88oBU7jmjFjiOKqOKvm9vF6vYr6yi2WpDZccuM223oSG6BDmXn61BWgQ5m5+lQVtHzg1n5OpRToJz8QuUWuHSsoPirS7lOV6nvUO9ntSg4wE/hgXaFBfkrLNCu8EC7woPsnrFqVeyKCQtUraqBig4NkJ+Nk74AAIBvoZADwCWyWi3q0qiGujSqof0Zx/T5ij36fOVupWXma8ribXr7x21qExuuDvUidGW9amobV1WhAXazY59VTn6h9qUf076jx7Q3/ZhSjn9fPHYwO/+SP/rNYpGMc2yi0G0oPdep9FyndDj3vNuzWS2KDg1QrfCigl4rPFDRof5Ky5AOZ+crKtyPo+4AAMDrUMgBoAzVDAvUiO6N9NC1DTR/4wF9unyXftpySL/uTtevu9M1ZfE2WSxSk+hQdahXTR3qVVP7uGqqEeIol3yGYehIToGnXO9LP6a9J5XtfenHlHHMWaptVavir+rB/qoe7PA8aoQ4FBHsrxCHnwL9bari8FOg3aYgf5uC/IvGgvxtstuscrsNuQxDLnfRo9BtyH38a6Hbrey8QqUfcyoj16n0Y06l5xYo41hRSc845tTBrHylZBTtMHC6jKKfIf2YtPPklH6auGGxqgbZ1SAyWA0iQ9QwMlgNIoPVMCpY0aEBFHUAAGAaCjkAXAZ+Nquubx6t65tHa+/RXC3ddlgrdxadyr7zcK427M/Uhv2Z+nDJTklS3YggxYQFqkaIQ5EhDkWGOhQZEnDieUiAHHar3EZRYXW5TpTZ4iKbX+jWkZyiU8cPZ+frUHbx98e/5hQoNSNPx5yu8+YPC7SrVnigYsIDVfv4EeeY40efa4YFqFoVf9kv8RRxq9Uiqyyyn+0y+7DSbcftNnQwO/+UHQu52nM4V3/sPqgjBRYdzXVq5c6jWrmz5OfHhzj81KRmqJrGhKp5rTA1iwlVg8jgS/7ZAAAASoNCDgCXWe2qQbq5XZBubhcrSTqQmacVx8v5ih1HlJyWpV2Hc7WrFKdml5XIEIfn1O5aVQNV23Oqd5BqVQ1UsMN3/vVgtVoUFRqgqNAAta1b1TPudDr1/fff65rEntqTka+tB7K1JS276OuBot95Vn5h0T+LnUc8r/P3s6pxVIiaxYSq2fGi3qRmqALOuucAAADg4vjOf3EBQAURGRqgPi1j1KdljCQpPbdAG/Zn6mBWvg5k5utAVl7R98cfB7Pyz3oaefGdyf2OP4pOIy86bbzoq8NzWnlEFX9FhgaoZlhApSqXgf42NYsJU7OYkofcCwrd2n4oW+v3ZWp9SqbWp2RoQ0qmsvILtW5fhtbty/Cs62e1qHF0iFrWDler2mFqUTtMjaJCOJIOAAAuCYUcAEwWHuSvTvWrn3OdPKdLBS63/DwF3MrHg10ifz+r4qNDFR8dqgFti8YMw9CeI8f0R0qG1qdkaH1KptbtzdDhnILjpT1Tn60oWtfhZ1WzmNCikh4bpla1wxUXUUVWK/9MAABA6VDIAcAHBNhtleqotlksFovqRASpTkSQbmhRU1JRSd+Xfkzr9mbot70Z+n1vutbtzVBWfqHnZn3FQgL81Kp2uFrWDlOr2HC1qh2u6LAAk34aAADg7SjkAACcg8ViUe2qQapdNUi9jpd0t9vQzsM5+n1vhn7bm67f92boj30Zysor1M9bD+nnrYc8r48Kdahl7XC1qFV0qnuLWmGqHlw+d9UHAADejUIOAMAFslotuqJGsK6oEax+bWpJkpwutzanZem3PUVH0dfuSdfmtCylZeYraUOakjakeV4fExbgKectjpf1alX8zfpxAACASSjkAACUAbvN6rl53G1X1pEk5RYUan1Kpn7fm6F1e9O1bl+Gth/KUUpGnlIy8jR3/YmSXjMsQE1rhno+hq1pzVDVqRbENekAAFRgFHIAAC6TIH8/tY+rpvZx1TxjWXlOrU/J1B/7MoqK+r4M7TiUo/0ZedqfkacFmw541q3ib1N8zaJyHl8zRI2iQtQwMljhQRxNBwCgIqCQAwBQjkIC7Op4RYQ6XhHhGcvKc2pTapY2pGQWPfZnKjktSzkFLq3edVSrdx0tsY3qwQ41jAxWo6hgNThe0htGBqtaFX/uvA8AgA+hkAMAYLKQAPtpR9ILXW5tP5RzoqCnZmnrgWztSz+mQ9n5OpSdr6XbD5fYTmiAn+KqV1HdiCqqFxGkuhFVFFe96GsEZR0AAK9DIQcAwAv52axqFFV0mnrxjeMkKTu/UFsPZGtLWlFB35yWpS0HsrX36DFl5hXq971Fp8KfKtjhp9hqQaoVHqCY8EDPo/h5ZEiAbFyvDgBAuaKQAwDgQ4IdfmodG67WseElxo8VuLT7SK52HMrRrsM52nk4V7sO52jX4VylZBxTdn6hNu7P1Mb9mWfcrs1qUXRogGqEOE48gh2nPY8I9leg3cbRdgAAygCFHACACiDQ36bG0SFqHB1y2rI8p0t7juRq79FjSsk4ppT0Y0pJz9O+9KLvUzPyVOg2tC/9mPalHzvve/n7WVU1yK7wQH+FB9kVHmRX1SB/hQcVPQ8NsCs4wE/BDpuCHXYFO/wUEuCnYIefggP8ZLdZL8evAAAAn0MhBwCggguw29QwKkQNo04v65Lkchs6lJ2vfenHdDAr3/M4lH38++wTY/mFbhUUupWWma+0zPyLyuPvZ1WQv01BdpsC/G3Hv/cr+t5e9Nxht8nhZ5XDbpXDz6aA418dftbj4zb524q+t9us8vc7/rCV/Gq3WWQ//txus3JaPgDAq1DIAQCo5GxWi6JCAxQVGnDO9QzDUE6BS+m5BUrPdSo916mjuQVKP+ZUek7R16O5BcrOK1R2fqGySnx1Ks/pliQVHC/16XKWx49XgtVS9Jnx/jar7MWF3XaisNv9ip6fGDv+/Hip97OeXPAtnp0Bxev7n7SDwG6zlNxhcNKOA4efVf42m/z9rLLKpXxX0Y387PZy/5UAAExEIQcAAKVisViKTjt3+Kl21Qt/vdPlVk5+UUk/VuDSMadLuQWukt87XTpWUKg8p1v5hS7lO93KO/41v9CtPKdL+YVFywoK3SpwueUsNFTgcnueFxf+QrdbTpdRIoPb0PHXu6WLO8B/mfjpnyvmy2pR0ZkA9qIC7/l6fKzoDIGiMwX8/c48fuK1J51RcIb1AuxFOwVOHbdyFgEAlBsKOQAAKBd2m/X4deb+5faehmHI6TLkdLnldB0v8C5DzuIyX/zc5T5prOh5QeEpy10ndgAUP88/XvyL1jVK7BQ4eZ3inQT5p+w0KHC55XKf2GngNlS0U8LpKrff0amKjuyfKPOnFv/i5yV2GJxU7ovPBChe9+TlJS8tsMjfZpPdz+I5Q8FR4gyDorMMuIEggIqMQg4AACosi8VSVPz8vPdGcnn5Bfr2u9nqdl13GRar5wh+fqHLU+bznK4Thf6U5fnOE2cMnGncs42T1zlpWZ7TpZP2CRzfAVGobC85g8BzecAplwb4WU++nMAiP+uJ7+02q/xsVtmtFvnZTlyG4Ge1yM92fH2btWiZteirX/Fya/Hrj2/TZpHt+FjR16LxU8ds1qLxoteVfI2flR0LAM6MQg4AAGAim9Uif5sUHmSX3aSLyAtdbuV5yn7RJQIFrpLF/bRxl1v5Tpfn+YmvrhJnAHiWnXSmQPHZBsXf5xePFbpL7ByQVLSeSyr6H99ltchT5P2sFtmOF/vi8u4ZL/7eZpGteLnl5LFTn1tls6joq/XEVz+rVVZL0TpWS9G2rae+h9Vy3nWK38sw3Np41KLQrYflsPudWH78Ubwdm+XENqyWkuucvOzUMaCyopADAABUcn42q4JtVslhdpKinQNOl3HSJQXF9wlwqeD45QInX25Q6LmcoOhShJMvISi+j4DT5Vahy5DTffzrSa8tdJ9YXry+q3jMfWKd4te7ir8/abnLbajQbXi+nonbKNq5IJ/er2DTlE2rL8uWi3cG2E7ZMWA9aaeAzXrKslLsTLCe8pozjRW//tSdDDZL0Y6TU9+/eKfHye9/8g4OWyne7+SfxXbKjgvPjgyLRdbinStWedbjbIuKhUIOAAAAr1F0KrkUKJvZUS6KYRglC7rLkMsoKvvFz4uWnSj6J6/v8hT7E0W/eBtu94nXutzybMPlPts2DLmN4vdwy2WcsuyUddzGiW2cNuZy60h6hoKDQ+SWTryPYch1PJ/rlPcvyiwVuk8/8+FUhW5D510JkiSLRSVL/PHSf6LMyzPm2UFgUcmyf7zY204ZtxZvy6KTvi9eX573sxS/j2c7J61z0nsXv2/xOlaLTspV9B7FOU68pmiZxVJyvRMPKTosQG3qXMTdRb0QhRwAAAAoI5bjR039fHN/wlk5nU59//33uuGGThd1aUXxjooSJf3415PHXCftEHCdst65xop3UhSPFe9MOLHspJ0P7hM7ETw7OU76/sSOj5N2Opy6s+Gk7Z26E+LMOyZObNt1Si63ceJnKn79uX+XUqFx9rMxKoNezaP11h1tzY5RJnyikE+ePFkvv/yyUlNT1apVK02cOFEdOnQwOxYAAACAUvDsqPCMVLA9FmXIMIpKust9ylkLJ5X74uJ+cok/eeeA2zOmE9+f/DrPToGT1z0x7jZKvt/JeU5dx23opPWKXmOcvH7xdgx51jn1+6LXnPh5jFPyu09Z1iAy2Ox/TGXG6wv5F198oZEjR2rKlCm68sorNX78ePXs2VPJycmKjIw0Ox4AAAAAlJmTTyVHxee9nwFy3Guvvab77rtPQ4YMUdOmTTVlyhQFBQXp/fffNzsaAAAAAAAXzauPkBcUFGj16tUaNWqUZ8xqtSoxMVFLly4942vy8/OVn3/igzMzMzMlFV334nQ6L2/gS1CczZszAhJzFb6DuQpfwVyFL2Cewld4y1wt7ft7dSE/dOiQXC6XoqKiSoxHRUVp06ZNZ3zN2LFjNXr06NPG582bp6CgoMuSsywlJSWZHQEoFeYqfAVzFb6CuQpfwDyFrzB7rubm5pZqPa8u5Bdj1KhRGjlypOd5ZmamYmNj1aNHD4WGhpqY7NycTqeSkpLUvXv3i7pzJVBemKvwFcxV+ArmKnwB8xS+wlvmavGZ2ufj1YW8evXqstlsSktLKzGelpam6OjoM77G4XDI4XCcNm63233ij4ev5ASYq/AVzFX4CuYqfAHzFL7C7Lla2vf26pu6+fv7q23btlqwYIFnzO12a8GCBUpISDAxGQAAAAAAl8arj5BL0siRIzV48GC1a9dOHTp00Pjx45WTk6MhQ4aYHQ0AAAAAgIvm9YV84MCBOnjwoJ5++mmlpqaqdevWmjNnzmk3egMAAAAAwJd4fSGXpAcffFAPPvig2TEAAAAAACgzXn0NOQAAAAAAFRWFHAAAAAAAE1DIAQAAAAAwAYUcAAAAAAATUMgBAAAAADABhRwAAAAAABNQyAEAAAAAMAGFHAAAAAAAE1DIAQAAAAAwgZ/ZAS43wzAkSZmZmSYnOTen06nc3FxlZmbKbrebHQc4K+YqfAVzFb6CuQpfwDyFr/CWuVrcP4v76NlU+EKelZUlSYqNjTU5CQAAAACgMsnKylJYWNhZl1uM81V2H+d2u5WSkqKQkBBZLBaz45xVZmamYmNjtWfPHoWGhpodBzgr5ip8BXMVvoK5Cl/APIWv8Ja5ahiGsrKyFBMTI6v17FeKV/gj5FarVbVr1zY7RqmFhobyRw4+gbkKX8Fcha9grsIXME/hK7xhrp7ryHgxbuoGAAAAAIAJKOQAAAAAAJiAQu4lHA6H/v3vf8vhcJgdBTgn5ip8BXMVvoK5Cl/APIWv8LW5WuFv6gYAAAAAgDfiCDkAAAAAACagkAMAAAAAYAIKOQAAAAAAJqCQAwAAAABgAgq5l5g8ebLi4uIUEBCgK6+8UitWrDA7EiqxsWPHqn379goJCVFkZKT69eun5OTkEuvk5eVp2LBhioiIUHBwsAYMGKC0tDSTEgNFXnzxRVksFg0fPtwzxlyFt9i3b5/uuOMORUREKDAwUC1atNCqVas8yw3D0NNPP62aNWsqMDBQiYmJ2rJli4mJURm5XC499dRTqlevngIDA1W/fn0999xzOvk+0MxVmOHHH39U3759FRMTI4vFopkzZ5ZYXpp5eeTIEd1+++0KDQ1VeHi47r33XmVnZ5fjT3E6CrkX+OKLLzRy5Ej9+9//1q+//qpWrVqpZ8+eOnDggNnRUEktXrxYw4YN07Jly5SUlCSn06kePXooJyfHs86IESP07bffatq0aVq8eLFSUlLUv39/E1Ojslu5cqXefvtttWzZssQ4cxXe4OjRo+rcubPsdrtmz56tDRs26NVXX1XVqlU967z00kuaMGGCpkyZouXLl6tKlSrq2bOn8vLyTEyOymbcuHF66623NGnSJG3cuFHjxo3TSy+9pIkTJ3rWYa7CDDk5OWrVqpUmT558xuWlmZe333671q9fr6SkJM2aNUs//vij7r///vL6Ec7MgOk6dOhgDBs2zPPc5XIZMTExxtixY01MBZxw4MABQ5KxePFiwzAMIz093bDb7ca0adM862zcuNGQZCxdutSsmKjEsrKyjIYNGxpJSUlG165djYcfftgwDOYqvMdjjz1mXHXVVWdd7na7jejoaOPll1/2jKWnpxsOh8P47LPPyiMiYBiGYfTu3du45557Soz179/fuP322w3DYK7CO0gyZsyY4Xlemnm5YcMGQ5KxcuVKzzqzZ882LBaLsW/fvnLLfiqOkJusoKBAq1evVmJiomfMarUqMTFRS5cuNTEZcEJGRoYkqVq1apKk1atXy+l0lpi38fHxqlOnDvMWphg2bJh69+5dYk5KzFV4j2+++Ubt2rXTzTffrMjISLVp00bvvvuuZ/mOHTuUmppaYq6GhYXpyiuvZK6iXHXq1EkLFizQ5s2bJUm//fabfv75Z/Xq1UsScxXeqTTzcunSpQoPD1e7du086yQmJspqtWr58uXlnrmYn2nvDEnSoUOH5HK5FBUVVWI8KipKmzZtMikVcILb7dbw4cPVuXNnNW/eXJKUmpoqf39/hYeHl1g3KipKqampJqREZfb555/r119/1cqVK09bxlyFt9i+fbveeustjRw5Uv/617+0cuVK/f3vf5e/v78GDx7smY9n+u8B5irK0+OPP67MzEzFx8fLZrPJ5XLp+eef1+233y5JzFV4pdLMy9TUVEVGRpZY7ufnp2rVqpk6dynkAM5p2LBh+uOPP/Tzzz+bHQU4zZ49e/Twww8rKSlJAQEBZscBzsrtdqtdu3Z64YUXJElt2rTRH3/8oSlTpmjw4MEmpwNO+PLLL/Xpp59q6tSpatasmdauXavhw4crJiaGuQpcBpyybrLq1avLZrOddsfftLQ0RUdHm5QKKPLggw9q1qxZWrhwoWrXru0Zj46OVkFBgdLT00usz7xFeVu9erUOHDigP/3pT/Lz85Ofn58WL16sCRMmyM/PT1FRUcxVeIWaNWuqadOmJcaaNGmi3bt3S5JnPvLfAzDbo48+qscff1yDBg1SixYtdOedd2rEiBEaO3asJOYqvFNp5mV0dPRpN80uLCzUkSNHTJ27FHKT+fv7q23btlqwYIFnzO12a8GCBUpISDAxGSozwzD04IMPasaMGfrhhx9Ur169Esvbtm0ru91eYt4mJydr9+7dzFuUq+uuu07r1q3T2rVrPY927drp9ttv93zPXIU36Ny582kfH7l582bVrVtXklSvXj1FR0eXmKuZmZlavnw5cxXlKjc3V1ZryYpgs9nkdrslMVfhnUozLxMSEpSenq7Vq1d71vnhhx/kdrt15ZVXlnvmYpyy7gVGjhypwYMHq127durQoYPGjx+vnJwcDRkyxOxoqKSGDRumqVOn6uuvv1ZISIjnupqwsDAFBgYqLCxM9957r0aOHKlq1aopNDRUDz30kBISEtSxY0eT06MyCQkJ8dzboFiVKlUUERHhGWeuwhuMGDFCnTp10gsvvKBbbrlFK1as0DvvvKN33nlHkmSxWDR8+HCNGTNGDRs2VL169fTUU08pJiZG/fr1Mzc8KpW+ffvq+eefV506ddSsWTOtWbNGr732mu655x5JzFWYJzs7W1u3bvU837Fjh9auXatq1aqpTp06552XTZo00fXXX6/77rtPU6ZMkdPp1IMPPqhBgwYpJibGpJ9KfOyZt5g4caJRp04dw9/f3+jQoYOxbNkysyOhEpN0xscHH3zgWefYsWPG0KFDjapVqxpBQUHGTTfdZOzfv9+80MBxJ3/smWEwV+E9vv32W6N58+aGw+Ew4uPjjXfeeafEcrfbbTz11FNGVFSU4XA4jOuuu85ITk42KS0qq8zMTOPhhx826tSpYwQEBBhXXHGF8cQTTxj5+fmedZirMMPChQvP+N+ngwcPNgyjdPPy8OHDxq233moEBwcboaGhxpAhQ4ysrCwTfpoTLIZhGCbtCwAAAAAAoNLiGnIAAAAAAExAIQcAAAAAwAQUcgAAAAAATEAhBwAAAADABBRyAAAAAABMQCEHAAAAAMAEFHIAAAAAAExAIQcAAAAAwAQUcgAAcEHi4uI0fvx4s2MAAODzKOQAAHixu+++W/369ZMkdevWTcOHDy+39/7www8VHh5+2vjKlSt1//33l1sOAAAqKj+zAwAAgPJVUFAgf3//i359jRo1yjANAACVF0fIAQDwAXfffbcWL16sN954QxaLRRaLRTt37pQk/fHHH+rVq5eCg4MVFRWlO++8U4cOHfK8tlu3bnrwwQc1fPhwVa9eXT179pQkvfbaa2rRooWqVKmi2NhYDR06VNnZ2ZKkRYsWaciQIcrIyPC83zPPPCPp9FPWd+/erRtvvFHBwcEKDQ3VLbfcorS0NM/yZ555Rq1bt9Ynn3yiuLg4hYWFadCgQcrKyvKs89VXX6lFixYKDAxURESEEhMTlZOTc5l+mwAAeAcKOQAAPuCNN95QQkKC7rvvPu3fv1/79+9XbGys0tPTde2116pNmzZatWqV5syZo7S0NN1yyy0lXv/RRx/J399fv/zyi6ZMmSJJslqtmjBhgtavX6+PPvpIP/zwg/75z39Kkjp16qTx48crNDTU836PPPLIabncbrduvPFGHTlyRIsXL1ZSUpK2b9+ugQMHllhv27ZtmjlzpmbNmqVZs2Zp8eLFevHFFyVJ+/fv16233qp77rlHGzdu1KJFi9S/f38ZhnE5fpUAAHgNTlkHAMAHhIWFyd/fX0FBQYqOjvaMT5o0SW3atNELL7zgGXv//fcVGxurzZs3q1GjRpKkhg0b6qWXXiqxzZOvR4+Li9OYMWP017/+VW+++ab8/f0VFhYmi8VS4v1OtWDBAq1bt047duxQbGysJOnjjz9Ws2bNtHLlSrVv315SUXH/8MMPFRISIkm68847tWDBAj3//PPav3+/CgsL1b9/f9WtW1eS1KJFi0v4bQEA4Bs4Qg4AgA/77bfftHDhQgUHB3se8fHxkoqOShdr27btaa+dP3++rrvuOtWqVUshISG68847dfjwYeXm5pb6/Tdu3KjY2FhPGZekpk2bKjw8XBs3bvSMxcXFecq4JNWsWVMHDhyQJLVq1UrXXXedWrRooZtvvlnvvvuujh49WvpfAgAAPopCDgCAD8vOzlbfvn21du3aEo8tW7aoS5cunvWqVKlS4nU7d+5Unz591LJlS/3vf//T6tWrNXnyZElFN30ra3a7vcRzi8Uit9stSbLZbEpKStLs2bPVtGlTTZw4UY0bN9aOHTvKPAcAAN6EQg4AgI/w9/eXy+UqMfanP/1J69evV1xcnBo0aFDicWoJP9nq1avldrv16quvqmPHjmrUqJFSUlLO+36natKkifbs2aM9e/Z4xjZs2KD09HQ1bdq01D+bxWJR586dNXr0aK1Zs0b+/v6aMWNGqV8PAIAvopADAOAj4uLitHz5cu3cuVOHDh2S2+3WsGHDdOTIEd16661auXKltm3bprlz52rIkCHnLNMNGjSQ0+nUxIkTtX37dn3yySeem72d/H7Z2dlasGCBDh06dMZT2RMTE9WiRQvdfvvt+vXXX7VixQrddddd6tq1q9q1a1eqn2v58uV64YUXtGrVKu3evVvTp0/XwYMH1aRJkwv7BQEA4GMo5AAA+IhHHnlENptNTZs2VY0aNbR7927FxMTol19+kcvlUo8ePdSiRQsNHz5c4eHhslrP/q/5Vq1a6bXXXtO4cePUvHlzffrppxo7dmyJdTp16qS//vWvGjhwoGrUqHHaTeGkoiPbX3/9tapWraouXbooMTFRV1xxhb744otS/1yhoaH68ccfdcMNN6hRo0Z68skn9eqrr6pXr16l/+UAAOCDLAafKQIAAAAAQLnjCDkAAAAAACagkAMAAAAAYAIKOQAAAAAAJqCQAwAAAABgAgo5AAAAAAAmoJADAAAAAGACCjkAAAAAACagkAMAAAAAYAIKOQAAAAAAJqCQAwAAAABgAgo5AAAAAAAm+H+8O+RyiQwwgQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Build the MLP model using scikit-learn\n",
    "mlp = MLPRegressor(hidden_layer_sizes=(100,),  # Number of neurons in each hidden layer\n",
    "                   solver='adam',              # Adam optimizer for gradient descent\n",
    "                   learning_rate_init=0.01,    # Initial learning rate\n",
    "                   max_iter=100,              # Maximum number of training iterations\n",
    "                   random_state=42)\n",
    "\n",
    "# Train the model\n",
    "mlp.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Predict the target values for the test set\n",
    "y_pred_mlp = mlp.predict(X_test_scaled)\n",
    "\n",
    "# Calculate performance metrics\n",
    "mse_mlp = mean_squared_error(y_test, y_pred_mlp)\n",
    "rmse_mlp = np.sqrt(mse_mlp)\n",
    "r2_mlp = r2_score(y_test, y_pred_mlp)\n",
    "\n",
    "print(f\"MLP - Mean Squared Error: {mse_mlp}\")\n",
    "print(f\"MLP - Root Mean Squared Error: {rmse_mlp}\")\n",
    "print(f\"MLP - R^2 Score: {r2_mlp}\")\n",
    "\n",
    "# Plot loss curve during training (MLPRegressor has a loss_curve_ attribute)\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(mlp.loss_curve_)\n",
    "plt.title('MLP Training Loss Curve')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Loss')\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of actual vs predicted values (MLP)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred_mlp, color='blue', label='Predicted vs Actual')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "plt.title('MLP - Predicted vs Actual Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\arora\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 69ms/step - loss: 111.6652 - mae: 9.9795 - val_loss: 18.5989 - val_mae: 3.6879\n",
      "Epoch 2/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 32.6829 - mae: 4.3910 - val_loss: 13.5938 - val_mae: 3.1099\n",
      "Epoch 3/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 17.6525 - mae: 3.4456 - val_loss: 19.7190 - val_mae: 3.8694\n",
      "Epoch 4/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 17.8338 - mae: 3.5764 - val_loss: 16.0565 - val_mae: 2.6810\n",
      "Epoch 5/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 14.6920 - mae: 3.0493 - val_loss: 10.2698 - val_mae: 2.2901\n",
      "Epoch 6/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 13.2623 - mae: 2.7868 - val_loss: 8.1602 - val_mae: 2.3264\n",
      "Epoch 7/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 9.1266 - mae: 2.4629 - val_loss: 9.3126 - val_mae: 2.3132\n",
      "Epoch 8/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.7329 - mae: 2.2270 - val_loss: 6.2321 - val_mae: 1.7457\n",
      "Epoch 9/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 7.6208 - mae: 2.1012 - val_loss: 5.9668 - val_mae: 1.8753\n",
      "Epoch 10/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 7.5198 - mae: 2.2017 - val_loss: 7.0394 - val_mae: 1.8905\n",
      "Epoch 11/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 8.1183 - mae: 2.3554 - val_loss: 3.4620 - val_mae: 1.5451\n",
      "Epoch 12/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 5.2414 - mae: 1.7672 - val_loss: 2.4403 - val_mae: 1.2805\n",
      "Epoch 13/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.8295 - mae: 1.7244 - val_loss: 2.9907 - val_mae: 1.3073\n",
      "Epoch 14/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.9831 - mae: 1.8267 - val_loss: 1.8229 - val_mae: 1.0560\n",
      "Epoch 15/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.4928 - mae: 1.6726 - val_loss: 1.6116 - val_mae: 0.9429\n",
      "Epoch 16/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 4.4066 - mae: 1.5960 - val_loss: 1.3596 - val_mae: 0.8850\n",
      "Epoch 17/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.7151 - mae: 1.5827 - val_loss: 1.2397 - val_mae: 0.9411\n",
      "Epoch 18/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.8738 - mae: 1.5679 - val_loss: 1.0528 - val_mae: 0.7826\n",
      "Epoch 19/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.9924 - mae: 1.3425 - val_loss: 1.2834 - val_mae: 0.9352\n",
      "Epoch 20/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.4639 - mae: 1.5120 - val_loss: 0.8515 - val_mae: 0.7325\n",
      "Epoch 21/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.3533 - mae: 1.4185 - val_loss: 1.1845 - val_mae: 0.8758\n",
      "Epoch 22/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.3150 - mae: 1.5721 - val_loss: 1.0248 - val_mae: 0.8213\n",
      "Epoch 23/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.4078 - mae: 1.4931 - val_loss: 0.9764 - val_mae: 0.7921\n",
      "Epoch 24/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.2769 - mae: 1.6062 - val_loss: 1.5740 - val_mae: 1.0861\n",
      "Epoch 25/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.0426 - mae: 1.5061 - val_loss: 1.5692 - val_mae: 1.0598\n",
      "Epoch 26/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 4.0372 - mae: 1.5980 - val_loss: 1.6132 - val_mae: 1.0611\n",
      "Epoch 27/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.6867 - mae: 1.5547 - val_loss: 1.2241 - val_mae: 0.9339\n",
      "Epoch 28/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.1821 - mae: 1.5490 - val_loss: 0.8529 - val_mae: 0.7357\n",
      "Epoch 29/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.8124 - mae: 1.3595 - val_loss: 1.0862 - val_mae: 0.8482\n",
      "Epoch 30/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.7907 - mae: 1.5228 - val_loss: 1.9619 - val_mae: 1.2019\n",
      "Epoch 31/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 3.5146 - mae: 1.5133 - val_loss: 1.5458 - val_mae: 1.0589\n",
      "Epoch 32/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.0288 - mae: 1.3731 - val_loss: 1.5535 - val_mae: 1.0962\n",
      "Epoch 33/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.8824 - mae: 1.6233 - val_loss: 0.6895 - val_mae: 0.6791\n",
      "Epoch 34/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 3.0757 - mae: 1.3569 - val_loss: 0.7645 - val_mae: 0.7118\n",
      "Epoch 35/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.6244 - mae: 1.5397 - val_loss: 0.5530 - val_mae: 0.6122\n",
      "Epoch 36/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.8462 - mae: 1.3682 - val_loss: 0.5564 - val_mae: 0.6280\n",
      "Epoch 37/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.2602 - mae: 1.4239 - val_loss: 0.5858 - val_mae: 0.6248\n",
      "Epoch 38/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.2394 - mae: 1.4311 - val_loss: 0.6916 - val_mae: 0.6627\n",
      "Epoch 39/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7100 - mae: 1.3038 - val_loss: 1.0867 - val_mae: 0.8673\n",
      "Epoch 40/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4776 - mae: 1.2766 - val_loss: 0.5698 - val_mae: 0.6121\n",
      "Epoch 41/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.7233 - mae: 1.2412 - val_loss: 0.7239 - val_mae: 0.7106\n",
      "Epoch 42/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7220 - mae: 1.3762 - val_loss: 0.7574 - val_mae: 0.7495\n",
      "Epoch 43/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7560 - mae: 1.3185 - val_loss: 0.5379 - val_mae: 0.5766\n",
      "Epoch 44/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.8194 - mae: 1.4020 - val_loss: 0.9096 - val_mae: 0.7975\n",
      "Epoch 45/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4867 - mae: 1.2376 - val_loss: 0.5095 - val_mae: 0.5676\n",
      "Epoch 46/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9545 - mae: 1.0931 - val_loss: 0.5604 - val_mae: 0.6106\n",
      "Epoch 47/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.1865 - mae: 1.3516 - val_loss: 0.6877 - val_mae: 0.7024\n",
      "Epoch 48/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.5269 - mae: 1.2353 - val_loss: 1.0901 - val_mae: 0.8163\n",
      "Epoch 49/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3251 - mae: 1.1625 - val_loss: 1.1760 - val_mae: 0.9495\n",
      "Epoch 50/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 2.7981 - mae: 1.1987 - val_loss: 1.1059 - val_mae: 0.9171\n",
      "Epoch 51/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.7011 - mae: 1.4751 - val_loss: 0.8093 - val_mae: 0.6964\n",
      "Epoch 52/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5966 - mae: 1.2655 - val_loss: 0.6160 - val_mae: 0.6623\n",
      "Epoch 53/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.9642 - mae: 1.4017 - val_loss: 0.8412 - val_mae: 0.7972\n",
      "Epoch 54/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.8857 - mae: 1.3106 - val_loss: 0.7420 - val_mae: 0.7257\n",
      "Epoch 55/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.6787 - mae: 1.3383 - val_loss: 0.6352 - val_mae: 0.6772\n",
      "Epoch 56/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.3083 - mae: 1.4215 - val_loss: 0.7685 - val_mae: 0.7023\n",
      "Epoch 57/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.6530 - mae: 1.2725 - val_loss: 0.7076 - val_mae: 0.6944\n",
      "Epoch 58/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.1612 - mae: 1.1511 - val_loss: 1.5888 - val_mae: 1.0482\n",
      "Epoch 59/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.9329 - mae: 1.3639 - val_loss: 2.2693 - val_mae: 1.3230\n",
      "Epoch 60/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 4.0100 - mae: 1.6472 - val_loss: 1.3981 - val_mae: 0.9840\n",
      "Epoch 61/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.7002 - mae: 1.4443 - val_loss: 2.0529 - val_mae: 1.1955\n",
      "Epoch 62/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.2283 - mae: 1.6334 - val_loss: 1.4885 - val_mae: 0.9476\n",
      "Epoch 63/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 5.4781 - mae: 1.8201 - val_loss: 1.1441 - val_mae: 0.8923\n",
      "Epoch 64/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.8306 - mae: 1.3568 - val_loss: 1.1341 - val_mae: 0.8982\n",
      "Epoch 65/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.7600 - mae: 1.3679 - val_loss: 0.9040 - val_mae: 0.7726\n",
      "Epoch 66/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.5987 - mae: 1.2908 - val_loss: 1.2329 - val_mae: 0.9522\n",
      "Epoch 67/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4806 - mae: 1.2323 - val_loss: 1.0345 - val_mae: 0.8583\n",
      "Epoch 68/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.1972 - mae: 1.4534 - val_loss: 1.2555 - val_mae: 0.9177\n",
      "Epoch 69/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.0653 - mae: 1.3363 - val_loss: 0.5295 - val_mae: 0.6195\n",
      "Epoch 70/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4807 - mae: 1.2471 - val_loss: 0.4706 - val_mae: 0.5562\n",
      "Epoch 71/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4066 - mae: 1.2053 - val_loss: 0.5030 - val_mae: 0.5496\n",
      "Epoch 72/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5456 - mae: 1.3293 - val_loss: 0.6391 - val_mae: 0.6337\n",
      "Epoch 73/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.9150 - mae: 1.2998 - val_loss: 0.7474 - val_mae: 0.6655\n",
      "Epoch 74/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.3896 - mae: 1.2356 - val_loss: 0.7734 - val_mae: 0.7421\n",
      "Epoch 75/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.3280 - mae: 1.1803 - val_loss: 0.6430 - val_mae: 0.6239\n",
      "Epoch 76/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 2.2882 - mae: 1.1816 - val_loss: 0.5209 - val_mae: 0.5693\n",
      "Epoch 77/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4531 - mae: 1.2297 - val_loss: 0.5868 - val_mae: 0.6409\n",
      "Epoch 78/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.0578 - mae: 1.3604 - val_loss: 0.5175 - val_mae: 0.5439\n",
      "Epoch 79/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.4795 - mae: 1.2149 - val_loss: 0.5268 - val_mae: 0.5677\n",
      "Epoch 80/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8715 - mae: 1.0797 - val_loss: 0.6139 - val_mae: 0.6381\n",
      "Epoch 81/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.3671 - mae: 1.2317 - val_loss: 0.6750 - val_mae: 0.6649\n",
      "Epoch 82/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0206 - mae: 1.1282 - val_loss: 0.5857 - val_mae: 0.6156\n",
      "Epoch 83/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.0061 - mae: 1.3459 - val_loss: 0.7102 - val_mae: 0.7096\n",
      "Epoch 84/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0438 - mae: 1.1264 - val_loss: 0.9549 - val_mae: 0.7796\n",
      "Epoch 85/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4458 - mae: 1.2953 - val_loss: 0.8124 - val_mae: 0.7584\n",
      "Epoch 86/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8192 - mae: 1.0286 - val_loss: 0.7566 - val_mae: 0.7232\n",
      "Epoch 87/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.8023 - mae: 1.2997 - val_loss: 1.5673 - val_mae: 1.0468\n",
      "Epoch 88/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.5675 - mae: 1.5142 - val_loss: 1.1259 - val_mae: 0.8757\n",
      "Epoch 89/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.4014 - mae: 1.2098 - val_loss: 1.0863 - val_mae: 0.8553\n",
      "Epoch 90/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 2.5594 - mae: 1.2639 - val_loss: 0.7177 - val_mae: 0.6573\n",
      "Epoch 91/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9472 - mae: 1.1109 - val_loss: 0.8299 - val_mae: 0.7586\n",
      "Epoch 92/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.7538 - mae: 1.3135 - val_loss: 0.8789 - val_mae: 0.7930\n",
      "Epoch 93/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2359 - mae: 1.1258 - val_loss: 1.0772 - val_mae: 0.8834\n",
      "Epoch 94/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.8183 - mae: 1.3117 - val_loss: 0.7284 - val_mae: 0.6958\n",
      "Epoch 95/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.3120 - mae: 1.1640 - val_loss: 0.8442 - val_mae: 0.7914\n",
      "Epoch 96/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4993 - mae: 1.1865 - val_loss: 0.7790 - val_mae: 0.7135\n",
      "Epoch 97/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.7607 - mae: 1.3113 - val_loss: 0.6853 - val_mae: 0.6401\n",
      "Epoch 98/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.5796 - mae: 1.2796 - val_loss: 0.7914 - val_mae: 0.6744\n",
      "Epoch 99/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.8306 - mae: 1.2908 - val_loss: 0.9604 - val_mae: 0.8694\n",
      "Epoch 100/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.8083 - mae: 1.3560 - val_loss: 1.2963 - val_mae: 0.9203\n",
      "Epoch 101/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 3.0646 - mae: 1.2775 - val_loss: 1.1920 - val_mae: 0.9183\n",
      "Epoch 102/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.1607 - mae: 1.3587 - val_loss: 1.4724 - val_mae: 0.9877\n",
      "Epoch 103/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.0552 - mae: 1.4149 - val_loss: 1.2352 - val_mae: 0.9229\n",
      "Epoch 104/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4752 - mae: 1.2611 - val_loss: 0.7879 - val_mae: 0.7026\n",
      "Epoch 105/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2663 - mae: 1.1488 - val_loss: 1.2406 - val_mae: 0.9508\n",
      "Epoch 106/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.5499 - mae: 1.4609 - val_loss: 0.8031 - val_mae: 0.7188\n",
      "Epoch 107/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.0923 - mae: 1.3719 - val_loss: 0.7679 - val_mae: 0.7279\n",
      "Epoch 108/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.8361 - mae: 1.3744 - val_loss: 0.6933 - val_mae: 0.7003\n",
      "Epoch 109/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1604 - mae: 1.0975 - val_loss: 0.6188 - val_mae: 0.6664\n",
      "Epoch 110/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.7904 - mae: 1.2693 - val_loss: 0.9657 - val_mae: 0.8624\n",
      "Epoch 111/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1213 - mae: 1.1108 - val_loss: 1.2358 - val_mae: 0.9186\n",
      "Epoch 112/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.2157 - mae: 1.3655 - val_loss: 0.9741 - val_mae: 0.8463\n",
      "Epoch 113/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3645 - mae: 1.1472 - val_loss: 0.7722 - val_mae: 0.7143\n",
      "Epoch 114/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1863 - mae: 1.1392 - val_loss: 1.1878 - val_mae: 0.9382\n",
      "Epoch 115/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.4819 - mae: 1.2723 - val_loss: 0.6838 - val_mae: 0.6544\n",
      "Epoch 116/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1238 - mae: 1.1482 - val_loss: 0.7410 - val_mae: 0.7174\n",
      "Epoch 117/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9217 - mae: 1.0895 - val_loss: 0.6978 - val_mae: 0.6967\n",
      "Epoch 118/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.1259 - mae: 1.1676 - val_loss: 0.9779 - val_mae: 0.7755\n",
      "Epoch 119/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.1235 - mae: 1.3817 - val_loss: 0.7650 - val_mae: 0.7044\n",
      "Epoch 120/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.6181 - mae: 1.2602 - val_loss: 0.6907 - val_mae: 0.7158\n",
      "Epoch 121/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9455 - mae: 1.1431 - val_loss: 0.6638 - val_mae: 0.7186\n",
      "Epoch 122/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2303 - mae: 1.1517 - val_loss: 0.6546 - val_mae: 0.7201\n",
      "Epoch 123/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.5212 - mae: 1.2373 - val_loss: 0.4894 - val_mae: 0.5725\n",
      "Epoch 124/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3631 - mae: 1.1957 - val_loss: 0.5401 - val_mae: 0.6193\n",
      "Epoch 125/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 2.3997 - mae: 1.1999 - val_loss: 0.9157 - val_mae: 0.8297\n",
      "Epoch 126/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2221 - mae: 1.1500 - val_loss: 1.1611 - val_mae: 0.9566\n",
      "Epoch 127/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2104 - mae: 1.1399 - val_loss: 0.9451 - val_mae: 0.7878\n",
      "Epoch 128/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.3213 - mae: 1.1892 - val_loss: 1.2466 - val_mae: 0.9560\n",
      "Epoch 129/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 2.6361 - mae: 1.2510 - val_loss: 0.7012 - val_mae: 0.6729\n",
      "Epoch 130/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9723 - mae: 1.0895 - val_loss: 0.7493 - val_mae: 0.6967\n",
      "Epoch 131/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8593 - mae: 1.0611 - val_loss: 0.9967 - val_mae: 0.8583\n",
      "Epoch 132/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2284 - mae: 1.1480 - val_loss: 0.8377 - val_mae: 0.7574\n",
      "Epoch 133/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7741 - mae: 1.0589 - val_loss: 1.4499 - val_mae: 1.0426\n",
      "Epoch 134/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2214 - mae: 1.1558 - val_loss: 1.1918 - val_mae: 0.9098\n",
      "Epoch 135/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.4692 - mae: 1.2172 - val_loss: 1.5884 - val_mae: 1.0832\n",
      "Epoch 136/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 3.4387 - mae: 1.5168 - val_loss: 0.9377 - val_mae: 0.8146\n",
      "Epoch 137/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.4753 - mae: 1.4457 - val_loss: 1.8279 - val_mae: 1.1998\n",
      "Epoch 138/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.0584 - mae: 1.5454 - val_loss: 0.7570 - val_mae: 0.7209\n",
      "Epoch 139/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.7968 - mae: 1.3829 - val_loss: 0.8652 - val_mae: 0.8277\n",
      "Epoch 140/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.6764 - mae: 1.3150 - val_loss: 0.6655 - val_mae: 0.6940\n",
      "Epoch 141/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4600 - mae: 1.2261 - val_loss: 0.7741 - val_mae: 0.7494\n",
      "Epoch 142/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.8652 - mae: 1.3695 - val_loss: 0.7733 - val_mae: 0.7454\n",
      "Epoch 143/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1731 - mae: 1.1598 - val_loss: 0.6797 - val_mae: 0.6507\n",
      "Epoch 144/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3675 - mae: 1.2201 - val_loss: 0.9276 - val_mae: 0.8291\n",
      "Epoch 145/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1668 - mae: 1.1459 - val_loss: 0.7569 - val_mae: 0.7252\n",
      "Epoch 146/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.0152 - mae: 1.1708 - val_loss: 0.7203 - val_mae: 0.6654\n",
      "Epoch 147/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2470 - mae: 1.1734 - val_loss: 0.7702 - val_mae: 0.7442\n",
      "Epoch 148/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9325 - mae: 1.0138 - val_loss: 0.7279 - val_mae: 0.7181\n",
      "Epoch 149/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.4174 - mae: 1.2193 - val_loss: 0.6410 - val_mae: 0.6909\n",
      "Epoch 150/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7855 - mae: 1.0378 - val_loss: 0.5434 - val_mae: 0.6182\n",
      "Epoch 151/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5682 - mae: 0.9942 - val_loss: 0.6594 - val_mae: 0.6962\n",
      "Epoch 152/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7372 - mae: 1.0114 - val_loss: 0.7083 - val_mae: 0.6937\n",
      "Epoch 153/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.8008 - mae: 1.0855 - val_loss: 0.6664 - val_mae: 0.6714\n",
      "Epoch 154/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9487 - mae: 1.0966 - val_loss: 0.7334 - val_mae: 0.7137\n",
      "Epoch 155/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.3338 - mae: 1.1543 - val_loss: 0.8086 - val_mae: 0.7510\n",
      "Epoch 156/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0287 - mae: 1.0592 - val_loss: 0.8280 - val_mae: 0.7488\n",
      "Epoch 157/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 1.7949 - mae: 1.0700 - val_loss: 0.9842 - val_mae: 0.8339\n",
      "Epoch 158/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.7670 - mae: 1.2410 - val_loss: 1.2603 - val_mae: 0.9583\n",
      "Epoch 159/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.0950 - mae: 1.0866 - val_loss: 0.8254 - val_mae: 0.6798\n",
      "Epoch 160/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5561 - mae: 1.2804 - val_loss: 0.7757 - val_mae: 0.7142\n",
      "Epoch 161/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.4741 - mae: 1.2525 - val_loss: 0.7438 - val_mae: 0.7313\n",
      "Epoch 162/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9817 - mae: 1.1272 - val_loss: 0.9343 - val_mae: 0.8140\n",
      "Epoch 163/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2212 - mae: 1.1303 - val_loss: 0.7451 - val_mae: 0.6633\n",
      "Epoch 164/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8464 - mae: 1.0671 - val_loss: 0.7464 - val_mae: 0.6896\n",
      "Epoch 165/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.3313 - mae: 1.2222 - val_loss: 0.6451 - val_mae: 0.5947\n",
      "Epoch 166/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.7141 - mae: 1.0085 - val_loss: 0.8384 - val_mae: 0.7862\n",
      "Epoch 167/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 2.7557 - mae: 1.3125 - val_loss: 0.8328 - val_mae: 0.7413\n",
      "Epoch 168/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0214 - mae: 1.1002 - val_loss: 0.8694 - val_mae: 0.7899\n",
      "Epoch 169/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2148 - mae: 1.1793 - val_loss: 0.6825 - val_mae: 0.6635\n",
      "Epoch 170/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3470 - mae: 1.1787 - val_loss: 0.8355 - val_mae: 0.7999\n",
      "Epoch 171/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2814 - mae: 1.1309 - val_loss: 0.7846 - val_mae: 0.6862\n",
      "Epoch 172/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.3912 - mae: 1.2323 - val_loss: 0.8819 - val_mae: 0.7887\n",
      "Epoch 173/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4266 - mae: 1.1879 - val_loss: 0.8146 - val_mae: 0.7438\n",
      "Epoch 174/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.4144 - mae: 1.2691 - val_loss: 0.8200 - val_mae: 0.7273\n",
      "Epoch 175/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0414 - mae: 1.1074 - val_loss: 0.8245 - val_mae: 0.7446\n",
      "Epoch 176/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8093 - mae: 1.0786 - val_loss: 0.7724 - val_mae: 0.7046\n",
      "Epoch 177/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4712 - mae: 1.2188 - val_loss: 0.6370 - val_mae: 0.6242\n",
      "Epoch 178/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2363 - mae: 1.2100 - val_loss: 0.9971 - val_mae: 0.8486\n",
      "Epoch 179/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 2.1081 - mae: 1.1512 - val_loss: 0.7729 - val_mae: 0.7212\n",
      "Epoch 180/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9272 - mae: 1.0555 - val_loss: 1.0587 - val_mae: 0.8551\n",
      "Epoch 181/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4459 - mae: 1.1400 - val_loss: 0.8225 - val_mae: 0.7244\n",
      "Epoch 182/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2916 - mae: 1.2433 - val_loss: 0.7793 - val_mae: 0.7453\n",
      "Epoch 183/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2318 - mae: 1.1711 - val_loss: 0.7169 - val_mae: 0.7192\n",
      "Epoch 184/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6525 - mae: 1.0151 - val_loss: 0.6653 - val_mae: 0.6483\n",
      "Epoch 185/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1825 - mae: 1.1969 - val_loss: 0.7900 - val_mae: 0.7810\n",
      "Epoch 186/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.3504 - mae: 1.1910 - val_loss: 0.6795 - val_mae: 0.6843\n",
      "Epoch 187/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9127 - mae: 1.0226 - val_loss: 0.6272 - val_mae: 0.6452\n",
      "Epoch 188/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7645 - mae: 1.0883 - val_loss: 0.7660 - val_mae: 0.7171\n",
      "Epoch 189/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9330 - mae: 1.1066 - val_loss: 0.8880 - val_mae: 0.7908\n",
      "Epoch 190/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.6864 - mae: 1.0692 - val_loss: 0.6990 - val_mae: 0.6591\n",
      "Epoch 191/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6489 - mae: 0.9915 - val_loss: 0.6917 - val_mae: 0.6564\n",
      "Epoch 192/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6303 - mae: 1.0144 - val_loss: 0.6962 - val_mae: 0.6844\n",
      "Epoch 193/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6317 - mae: 0.9995 - val_loss: 0.6071 - val_mae: 0.6372\n",
      "Epoch 194/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0650 - mae: 1.0874 - val_loss: 0.8154 - val_mae: 0.7757\n",
      "Epoch 195/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.7533 - mae: 1.2600 - val_loss: 0.7289 - val_mae: 0.7642\n",
      "Epoch 196/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7001 - mae: 1.0208 - val_loss: 0.8234 - val_mae: 0.6554\n",
      "Epoch 197/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1587 - mae: 1.1426 - val_loss: 0.8941 - val_mae: 0.8400\n",
      "Epoch 198/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.6104 - mae: 1.2977 - val_loss: 0.6430 - val_mae: 0.6563\n",
      "Epoch 199/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7817 - mae: 1.0468 - val_loss: 0.6613 - val_mae: 0.6689\n",
      "Epoch 200/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5705 - mae: 0.9894 - val_loss: 0.6900 - val_mae: 0.6604\n",
      "Epoch 201/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8527 - mae: 0.9834 - val_loss: 0.7138 - val_mae: 0.7009\n",
      "Epoch 202/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0606 - mae: 1.0788 - val_loss: 0.7750 - val_mae: 0.7615\n",
      "Epoch 203/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1525 - mae: 1.1453 - val_loss: 0.6135 - val_mae: 0.6666\n",
      "Epoch 204/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0269 - mae: 1.0924 - val_loss: 0.7038 - val_mae: 0.6469\n",
      "Epoch 205/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8764 - mae: 1.1207 - val_loss: 0.7003 - val_mae: 0.6994\n",
      "Epoch 206/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.5344 - mae: 1.0132 - val_loss: 0.8838 - val_mae: 0.8326\n",
      "Epoch 207/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8957 - mae: 1.1151 - val_loss: 0.5417 - val_mae: 0.6222\n",
      "Epoch 208/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8935 - mae: 1.1000 - val_loss: 0.5700 - val_mae: 0.6455\n",
      "Epoch 209/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8888 - mae: 1.0622 - val_loss: 0.7193 - val_mae: 0.7079\n",
      "Epoch 210/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7984 - mae: 1.1140 - val_loss: 0.8421 - val_mae: 0.7518\n",
      "Epoch 211/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4949 - mae: 0.9742 - val_loss: 0.8072 - val_mae: 0.7773\n",
      "Epoch 212/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 2.0150 - mae: 1.0597 - val_loss: 0.7290 - val_mae: 0.6701\n",
      "Epoch 213/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9905 - mae: 1.1552 - val_loss: 0.7038 - val_mae: 0.7427\n",
      "Epoch 214/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2544 - mae: 1.1775 - val_loss: 0.8012 - val_mae: 0.7748\n",
      "Epoch 215/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2934 - mae: 1.0780 - val_loss: 0.9105 - val_mae: 0.7917\n",
      "Epoch 216/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7795 - mae: 1.0155 - val_loss: 0.8229 - val_mae: 0.6837\n",
      "Epoch 217/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1043 - mae: 1.1445 - val_loss: 0.8375 - val_mae: 0.7135\n",
      "Epoch 218/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9540 - mae: 1.1076 - val_loss: 0.9959 - val_mae: 0.8585\n",
      "Epoch 219/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.2698 - mae: 1.1614 - val_loss: 0.6997 - val_mae: 0.7153\n",
      "Epoch 220/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8675 - mae: 1.0433 - val_loss: 0.4652 - val_mae: 0.5597\n",
      "Epoch 221/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.6673 - mae: 1.0292 - val_loss: 0.5445 - val_mae: 0.6187\n",
      "Epoch 222/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.0197 - mae: 1.0940 - val_loss: 0.5927 - val_mae: 0.6585\n",
      "Epoch 223/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2634 - mae: 1.1634 - val_loss: 1.1384 - val_mae: 0.9052\n",
      "Epoch 224/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2044 - mae: 1.1786 - val_loss: 0.8995 - val_mae: 0.8155\n",
      "Epoch 225/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7701 - mae: 1.0177 - val_loss: 0.7349 - val_mae: 0.6705\n",
      "Epoch 226/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8642 - mae: 1.0708 - val_loss: 0.6963 - val_mae: 0.6772\n",
      "Epoch 227/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6876 - mae: 1.0224 - val_loss: 0.7935 - val_mae: 0.7353\n",
      "Epoch 228/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9108 - mae: 1.0350 - val_loss: 0.7232 - val_mae: 0.7441\n",
      "Epoch 229/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1337 - mae: 1.1384 - val_loss: 0.6881 - val_mae: 0.7104\n",
      "Epoch 230/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - loss: 1.5391 - mae: 0.9484 - val_loss: 0.8720 - val_mae: 0.7850\n",
      "Epoch 231/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2950 - mae: 1.2115 - val_loss: 0.7950 - val_mae: 0.7052\n",
      "Epoch 232/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7792 - mae: 1.0767 - val_loss: 0.7691 - val_mae: 0.7693\n",
      "Epoch 233/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0255 - mae: 1.0907 - val_loss: 0.6350 - val_mae: 0.6676\n",
      "Epoch 234/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7742 - mae: 1.0374 - val_loss: 0.6463 - val_mae: 0.6621\n",
      "Epoch 235/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5424 - mae: 0.9946 - val_loss: 0.8278 - val_mae: 0.7983\n",
      "Epoch 236/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9065 - mae: 1.0557 - val_loss: 0.6855 - val_mae: 0.6493\n",
      "Epoch 237/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6524 - mae: 1.0178 - val_loss: 0.6512 - val_mae: 0.6523\n",
      "Epoch 238/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4856 - mae: 0.9290 - val_loss: 0.5859 - val_mae: 0.6146\n",
      "Epoch 239/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.1468 - mae: 0.7823 - val_loss: 0.5881 - val_mae: 0.6199\n",
      "Epoch 240/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7220 - mae: 1.0132 - val_loss: 0.8552 - val_mae: 0.7370\n",
      "Epoch 241/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 1.6662 - mae: 0.9928 - val_loss: 0.8625 - val_mae: 0.8168\n",
      "Epoch 242/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.7526 - mae: 1.3026 - val_loss: 0.7859 - val_mae: 0.6833\n",
      "Epoch 243/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5716 - mae: 1.2202 - val_loss: 1.1862 - val_mae: 0.9410\n",
      "Epoch 244/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7775 - mae: 1.3296 - val_loss: 0.7720 - val_mae: 0.7018\n",
      "Epoch 245/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8069 - mae: 1.0679 - val_loss: 0.7820 - val_mae: 0.7356\n",
      "Epoch 246/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.0624 - mae: 1.1323 - val_loss: 0.7160 - val_mae: 0.7180\n",
      "Epoch 247/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.0462 - mae: 1.1342 - val_loss: 0.5508 - val_mae: 0.6171\n",
      "Epoch 248/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9426 - mae: 1.0144 - val_loss: 0.6303 - val_mae: 0.6782\n",
      "Epoch 249/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2178 - mae: 1.1849 - val_loss: 0.6619 - val_mae: 0.7008\n",
      "Epoch 250/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8922 - mae: 1.0626 - val_loss: 0.7349 - val_mae: 0.7174\n",
      "Epoch 251/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6914 - mae: 1.0352 - val_loss: 0.6685 - val_mae: 0.6643\n",
      "Epoch 252/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 2.1005 - mae: 1.1471 - val_loss: 0.6040 - val_mae: 0.6229\n",
      "Epoch 253/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2301 - mae: 1.2062 - val_loss: 0.6776 - val_mae: 0.7061\n",
      "Epoch 254/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7105 - mae: 1.0655 - val_loss: 0.7456 - val_mae: 0.7622\n",
      "Epoch 255/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6754 - mae: 1.0417 - val_loss: 0.6241 - val_mae: 0.6609\n",
      "Epoch 256/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5960 - mae: 0.9789 - val_loss: 0.7362 - val_mae: 0.6987\n",
      "Epoch 257/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7661 - mae: 1.0684 - val_loss: 0.6761 - val_mae: 0.6914\n",
      "Epoch 258/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8144 - mae: 1.0917 - val_loss: 0.6611 - val_mae: 0.6840\n",
      "Epoch 259/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5269 - mae: 0.9513 - val_loss: 0.5858 - val_mae: 0.6364\n",
      "Epoch 260/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7558 - mae: 1.0470 - val_loss: 0.5793 - val_mae: 0.6426\n",
      "Epoch 261/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6326 - mae: 1.0103 - val_loss: 0.6964 - val_mae: 0.7094\n",
      "Epoch 262/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7853 - mae: 1.0198 - val_loss: 0.6594 - val_mae: 0.6577\n",
      "Epoch 263/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2721 - mae: 1.1725 - val_loss: 0.7195 - val_mae: 0.6599\n",
      "Epoch 264/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8593 - mae: 1.0882 - val_loss: 0.6961 - val_mae: 0.6837\n",
      "Epoch 265/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8476 - mae: 1.0546 - val_loss: 0.6542 - val_mae: 0.6939\n",
      "Epoch 266/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9433 - mae: 1.1414 - val_loss: 0.9516 - val_mae: 0.8039\n",
      "Epoch 267/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6838 - mae: 0.9723 - val_loss: 0.6829 - val_mae: 0.6275\n",
      "Epoch 268/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0290 - mae: 1.1456 - val_loss: 0.6917 - val_mae: 0.6783\n",
      "Epoch 269/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5054 - mae: 0.9873 - val_loss: 0.7758 - val_mae: 0.7653\n",
      "Epoch 270/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.3979 - mae: 0.9033 - val_loss: 0.7633 - val_mae: 0.6999\n",
      "Epoch 271/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.8330 - mae: 1.0205 - val_loss: 0.9793 - val_mae: 0.8131\n",
      "Epoch 272/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.0517 - mae: 1.1176 - val_loss: 0.8418 - val_mae: 0.7338\n",
      "Epoch 273/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3817 - mae: 1.2494 - val_loss: 0.6907 - val_mae: 0.7128\n",
      "Epoch 274/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0109 - mae: 1.1592 - val_loss: 0.9958 - val_mae: 0.8836\n",
      "Epoch 275/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6380 - mae: 1.0029 - val_loss: 1.0332 - val_mae: 0.7913\n",
      "Epoch 276/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.7648 - mae: 1.2616 - val_loss: 0.8565 - val_mae: 0.8042\n",
      "Epoch 277/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9270 - mae: 1.0470 - val_loss: 1.0089 - val_mae: 0.8249\n",
      "Epoch 278/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 1.7667 - mae: 1.0574 - val_loss: 1.0040 - val_mae: 0.8893\n",
      "Epoch 279/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7917 - mae: 1.0658 - val_loss: 0.8681 - val_mae: 0.7782\n",
      "Epoch 280/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6846 - mae: 1.0475 - val_loss: 0.8530 - val_mae: 0.8018\n",
      "Epoch 281/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5920 - mae: 1.0273 - val_loss: 0.7842 - val_mae: 0.7361\n",
      "Epoch 282/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6314 - mae: 0.9839 - val_loss: 0.8755 - val_mae: 0.8005\n",
      "Epoch 283/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5656 - mae: 0.9551 - val_loss: 0.8245 - val_mae: 0.7338\n",
      "Epoch 284/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0564 - mae: 1.1878 - val_loss: 0.9176 - val_mae: 0.8387\n",
      "Epoch 285/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6055 - mae: 1.0457 - val_loss: 0.9287 - val_mae: 0.8190\n",
      "Epoch 286/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8870 - mae: 1.0824 - val_loss: 0.8249 - val_mae: 0.7224\n",
      "Epoch 287/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5958 - mae: 1.0472 - val_loss: 0.6845 - val_mae: 0.7014\n",
      "Epoch 288/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9244 - mae: 1.1500 - val_loss: 0.7552 - val_mae: 0.7239\n",
      "Epoch 289/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.4429 - mae: 0.9350 - val_loss: 0.6357 - val_mae: 0.6776\n",
      "Epoch 290/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6894 - mae: 0.9753 - val_loss: 0.6292 - val_mae: 0.6514\n",
      "Epoch 291/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8898 - mae: 1.0309 - val_loss: 0.6788 - val_mae: 0.6751\n",
      "Epoch 292/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4865 - mae: 0.9689 - val_loss: 0.7149 - val_mae: 0.6853\n",
      "Epoch 293/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5732 - mae: 0.9902 - val_loss: 0.7020 - val_mae: 0.6808\n",
      "Epoch 294/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7967 - mae: 1.0766 - val_loss: 0.9507 - val_mae: 0.8629\n",
      "Epoch 295/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9576 - mae: 1.0614 - val_loss: 0.9511 - val_mae: 0.7662\n",
      "Epoch 296/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4549 - mae: 0.9205 - val_loss: 0.8826 - val_mae: 0.8196\n",
      "Epoch 297/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5885 - mae: 0.9786 - val_loss: 0.7974 - val_mae: 0.7201\n",
      "Epoch 298/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0095 - mae: 1.1340 - val_loss: 0.8141 - val_mae: 0.7254\n",
      "Epoch 299/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6974 - mae: 0.9402 - val_loss: 0.8596 - val_mae: 0.7330\n",
      "Epoch 300/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7195 - mae: 1.0278 - val_loss: 0.7869 - val_mae: 0.7728\n",
      "Epoch 301/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5036 - mae: 0.9841 - val_loss: 0.6287 - val_mae: 0.6593\n",
      "Epoch 302/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.2435 - mae: 0.8559 - val_loss: 0.6331 - val_mae: 0.6310\n",
      "Epoch 303/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.0244 - mae: 0.7865 - val_loss: 0.7271 - val_mae: 0.7214\n",
      "Epoch 304/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3997 - mae: 0.9562 - val_loss: 0.6574 - val_mae: 0.6440\n",
      "Epoch 305/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5203 - mae: 0.9371 - val_loss: 0.5899 - val_mae: 0.6191\n",
      "Epoch 306/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.2410 - mae: 0.8962 - val_loss: 0.7166 - val_mae: 0.6865\n",
      "Epoch 307/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.8247 - mae: 1.0315 - val_loss: 0.6679 - val_mae: 0.6736\n",
      "Epoch 308/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0409 - mae: 1.0707 - val_loss: 0.7152 - val_mae: 0.6712\n",
      "Epoch 309/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6222 - mae: 0.9771 - val_loss: 0.6771 - val_mae: 0.6917\n",
      "Epoch 310/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6303 - mae: 0.9808 - val_loss: 0.5958 - val_mae: 0.6401\n",
      "Epoch 311/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8283 - mae: 1.0858 - val_loss: 0.6549 - val_mae: 0.6746\n",
      "Epoch 312/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4934 - mae: 0.9641 - val_loss: 0.7556 - val_mae: 0.7392\n",
      "Epoch 313/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2765 - mae: 0.8600 - val_loss: 0.6993 - val_mae: 0.6788\n",
      "Epoch 314/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1508 - mae: 1.0829 - val_loss: 0.7225 - val_mae: 0.7321\n",
      "Epoch 315/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4416 - mae: 0.9422 - val_loss: 0.8813 - val_mae: 0.7596\n",
      "Epoch 316/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3637 - mae: 1.1894 - val_loss: 1.3126 - val_mae: 1.0283\n",
      "Epoch 317/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.3006 - mae: 1.2360 - val_loss: 0.7773 - val_mae: 0.6731\n",
      "Epoch 318/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4793 - mae: 0.9466 - val_loss: 0.7344 - val_mae: 0.7377\n",
      "Epoch 319/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4157 - mae: 0.8854 - val_loss: 0.8009 - val_mae: 0.7795\n",
      "Epoch 320/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5981 - mae: 0.9920 - val_loss: 0.8513 - val_mae: 0.7880\n",
      "Epoch 321/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4783 - mae: 0.9469 - val_loss: 0.7493 - val_mae: 0.7040\n",
      "Epoch 322/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6872 - mae: 1.0362 - val_loss: 0.7286 - val_mae: 0.6951\n",
      "Epoch 323/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6558 - mae: 0.9944 - val_loss: 0.9775 - val_mae: 0.8558\n",
      "Epoch 324/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9860 - mae: 1.0722 - val_loss: 0.6963 - val_mae: 0.6845\n",
      "Epoch 325/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6160 - mae: 1.0556 - val_loss: 0.8141 - val_mae: 0.7442\n",
      "Epoch 326/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9141 - mae: 1.0845 - val_loss: 0.7601 - val_mae: 0.7404\n",
      "Epoch 327/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6306 - mae: 0.9809 - val_loss: 0.8297 - val_mae: 0.7133\n",
      "Epoch 328/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.8084 - mae: 1.0686 - val_loss: 0.7166 - val_mae: 0.7150\n",
      "Epoch 329/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5070 - mae: 0.9762 - val_loss: 0.7685 - val_mae: 0.7240\n",
      "Epoch 330/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7253 - mae: 1.0199 - val_loss: 0.7977 - val_mae: 0.7802\n",
      "Epoch 331/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4913 - mae: 0.9341 - val_loss: 0.9147 - val_mae: 0.7997\n",
      "Epoch 332/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6541 - mae: 1.0745 - val_loss: 0.6946 - val_mae: 0.6966\n",
      "Epoch 333/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6224 - mae: 0.9690 - val_loss: 0.6509 - val_mae: 0.6561\n",
      "Epoch 334/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7648 - mae: 1.0614 - val_loss: 0.7253 - val_mae: 0.6799\n",
      "Epoch 335/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6621 - mae: 1.0309 - val_loss: 0.6557 - val_mae: 0.7074\n",
      "Epoch 336/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4032 - mae: 0.9355 - val_loss: 0.6180 - val_mae: 0.6724\n",
      "Epoch 337/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5218 - mae: 0.9672 - val_loss: 0.6910 - val_mae: 0.7313\n",
      "Epoch 338/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7799 - mae: 1.0670 - val_loss: 0.6735 - val_mae: 0.6704\n",
      "Epoch 339/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8396 - mae: 1.0796 - val_loss: 0.7708 - val_mae: 0.7089\n",
      "Epoch 340/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9139 - mae: 1.0786 - val_loss: 0.9575 - val_mae: 0.8717\n",
      "Epoch 341/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7923 - mae: 1.0288 - val_loss: 0.9238 - val_mae: 0.8301\n",
      "Epoch 342/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4136 - mae: 0.9399 - val_loss: 0.7880 - val_mae: 0.7439\n",
      "Epoch 343/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3163 - mae: 0.8477 - val_loss: 0.7369 - val_mae: 0.7231\n",
      "Epoch 344/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3800 - mae: 0.9514 - val_loss: 1.1395 - val_mae: 0.8506\n",
      "Epoch 345/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5663 - mae: 0.9974 - val_loss: 1.0182 - val_mae: 0.8910\n",
      "Epoch 346/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9143 - mae: 1.0767 - val_loss: 1.1308 - val_mae: 0.8812\n",
      "Epoch 347/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6784 - mae: 1.0051 - val_loss: 0.9380 - val_mae: 0.8250\n",
      "Epoch 348/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5387 - mae: 0.9865 - val_loss: 0.9176 - val_mae: 0.7958\n",
      "Epoch 349/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4822 - mae: 0.9491 - val_loss: 0.8593 - val_mae: 0.8022\n",
      "Epoch 350/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9436 - mae: 1.1118 - val_loss: 0.6839 - val_mae: 0.7130\n",
      "Epoch 351/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8445 - mae: 1.0398 - val_loss: 0.6706 - val_mae: 0.6551\n",
      "Epoch 352/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.2823 - mae: 0.8909 - val_loss: 0.7584 - val_mae: 0.7570\n",
      "Epoch 353/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.2906 - mae: 0.8929 - val_loss: 0.8173 - val_mae: 0.7747\n",
      "Epoch 354/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6178 - mae: 0.9801 - val_loss: 0.9343 - val_mae: 0.7957\n",
      "Epoch 355/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4171 - mae: 0.9664 - val_loss: 0.8462 - val_mae: 0.7463\n",
      "Epoch 356/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4807 - mae: 0.9302 - val_loss: 0.7894 - val_mae: 0.7074\n",
      "Epoch 357/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4055 - mae: 0.9339 - val_loss: 0.6832 - val_mae: 0.6390\n",
      "Epoch 358/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.2865 - mae: 1.1437 - val_loss: 0.6083 - val_mae: 0.6461\n",
      "Epoch 359/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6682 - mae: 1.0126 - val_loss: 0.5735 - val_mae: 0.6285\n",
      "Epoch 360/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9177 - mae: 1.0865 - val_loss: 0.5681 - val_mae: 0.5774\n",
      "Epoch 361/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6810 - mae: 1.0300 - val_loss: 0.8112 - val_mae: 0.7195\n",
      "Epoch 362/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2601 - mae: 1.0737 - val_loss: 0.9913 - val_mae: 0.8208\n",
      "Epoch 363/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7473 - mae: 1.0539 - val_loss: 0.9426 - val_mae: 0.8264\n",
      "Epoch 364/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5894 - mae: 0.9859 - val_loss: 0.7283 - val_mae: 0.6823\n",
      "Epoch 365/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.3980 - mae: 0.9018 - val_loss: 0.6307 - val_mae: 0.6331\n",
      "Epoch 366/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9643 - mae: 1.1092 - val_loss: 0.7487 - val_mae: 0.7473\n",
      "Epoch 367/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.4322 - mae: 0.9337 - val_loss: 0.8014 - val_mae: 0.7507\n",
      "Epoch 368/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 1.5447 - mae: 0.9926 - val_loss: 0.8361 - val_mae: 0.7710\n",
      "Epoch 369/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4802 - mae: 0.9200 - val_loss: 0.7610 - val_mae: 0.7189\n",
      "Epoch 370/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9782 - mae: 1.1377 - val_loss: 0.7053 - val_mae: 0.6832\n",
      "Epoch 371/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6093 - mae: 1.0303 - val_loss: 0.7222 - val_mae: 0.7006\n",
      "Epoch 372/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4860 - mae: 0.9277 - val_loss: 0.8237 - val_mae: 0.7227\n",
      "Epoch 373/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.3220 - mae: 0.8305 - val_loss: 0.8226 - val_mae: 0.7668\n",
      "Epoch 374/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.3916 - mae: 0.8928 - val_loss: 0.7493 - val_mae: 0.6839\n",
      "Epoch 375/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.1852 - mae: 0.8332 - val_loss: 0.8015 - val_mae: 0.7057\n",
      "Epoch 376/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1307 - mae: 0.8032 - val_loss: 0.7253 - val_mae: 0.6884\n",
      "Epoch 377/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0793 - mae: 1.0946 - val_loss: 0.7165 - val_mae: 0.6982\n",
      "Epoch 378/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.1954 - mae: 0.8271 - val_loss: 0.6618 - val_mae: 0.6585\n",
      "Epoch 379/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4800 - mae: 0.9084 - val_loss: 0.7760 - val_mae: 0.7152\n",
      "Epoch 380/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6155 - mae: 0.9911 - val_loss: 0.7046 - val_mae: 0.6726\n",
      "Epoch 381/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3227 - mae: 0.8929 - val_loss: 0.7270 - val_mae: 0.6707\n",
      "Epoch 382/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3557 - mae: 0.9523 - val_loss: 0.7552 - val_mae: 0.7291\n",
      "Epoch 383/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3521 - mae: 0.8709 - val_loss: 0.7153 - val_mae: 0.6548\n",
      "Epoch 384/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 2.0499 - mae: 1.0904 - val_loss: 0.8993 - val_mae: 0.7950\n",
      "Epoch 385/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6126 - mae: 0.9954 - val_loss: 0.7758 - val_mae: 0.6810\n",
      "Epoch 386/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6109 - mae: 0.9853 - val_loss: 0.6978 - val_mae: 0.6796\n",
      "Epoch 387/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6923 - mae: 1.0403 - val_loss: 0.7332 - val_mae: 0.6823\n",
      "Epoch 388/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5512 - mae: 0.9885 - val_loss: 0.8557 - val_mae: 0.7664\n",
      "Epoch 389/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6160 - mae: 0.9441 - val_loss: 0.7971 - val_mae: 0.7061\n",
      "Epoch 390/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4269 - mae: 0.9568 - val_loss: 0.8670 - val_mae: 0.6999\n",
      "Epoch 391/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.3564 - mae: 0.8915 - val_loss: 0.8513 - val_mae: 0.7995\n",
      "Epoch 392/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.1902 - mae: 0.8502 - val_loss: 0.7571 - val_mae: 0.7076\n",
      "Epoch 393/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4056 - mae: 0.8696 - val_loss: 0.6799 - val_mae: 0.6662\n",
      "Epoch 394/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6382 - mae: 0.9410 - val_loss: 0.6730 - val_mae: 0.6379\n",
      "Epoch 395/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.3914 - mae: 0.9145 - val_loss: 0.7965 - val_mae: 0.7017\n",
      "Epoch 396/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4807 - mae: 0.9107 - val_loss: 0.8829 - val_mae: 0.7366\n",
      "Epoch 397/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2709 - mae: 0.8475 - val_loss: 0.9393 - val_mae: 0.8232\n",
      "Epoch 398/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6910 - mae: 0.9894 - val_loss: 1.0478 - val_mae: 0.7778\n",
      "Epoch 399/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.2313 - mae: 0.8502 - val_loss: 0.9873 - val_mae: 0.8398\n",
      "Epoch 400/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.3623 - mae: 0.8969 - val_loss: 0.9944 - val_mae: 0.7609\n",
      "Epoch 401/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.4706 - mae: 0.9420 - val_loss: 0.8925 - val_mae: 0.7912\n",
      "Epoch 402/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.3699 - mae: 0.9334 - val_loss: 1.0393 - val_mae: 0.8980\n",
      "Epoch 403/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3558 - mae: 0.9272 - val_loss: 0.8846 - val_mae: 0.7851\n",
      "Epoch 404/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5639 - mae: 0.9454 - val_loss: 0.9702 - val_mae: 0.7674\n",
      "Epoch 405/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7177 - mae: 1.0450 - val_loss: 0.9177 - val_mae: 0.8240\n",
      "Epoch 406/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4845 - mae: 0.9406 - val_loss: 0.9963 - val_mae: 0.8259\n",
      "Epoch 407/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8983 - mae: 1.0219 - val_loss: 1.1819 - val_mae: 0.9507\n",
      "Epoch 408/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6361 - mae: 0.9661 - val_loss: 0.8266 - val_mae: 0.7071\n",
      "Epoch 409/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 1.5186 - mae: 0.9730 - val_loss: 0.9516 - val_mae: 0.8250\n",
      "Epoch 410/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2019 - mae: 1.1139 - val_loss: 1.0308 - val_mae: 0.8302\n",
      "Epoch 411/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8374 - mae: 1.0545 - val_loss: 0.9972 - val_mae: 0.8511\n",
      "Epoch 412/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7468 - mae: 1.0477 - val_loss: 0.7353 - val_mae: 0.6865\n",
      "Epoch 413/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6254 - mae: 0.9787 - val_loss: 0.7537 - val_mae: 0.6693\n",
      "Epoch 414/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6034 - mae: 0.9859 - val_loss: 0.7995 - val_mae: 0.7117\n",
      "Epoch 415/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6175 - mae: 0.9896 - val_loss: 0.7823 - val_mae: 0.7326\n",
      "Epoch 416/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6181 - mae: 1.0488 - val_loss: 0.6337 - val_mae: 0.6544\n",
      "Epoch 417/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3286 - mae: 0.9138 - val_loss: 0.6986 - val_mae: 0.6169\n",
      "Epoch 418/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6244 - mae: 1.0234 - val_loss: 0.8554 - val_mae: 0.7651\n",
      "Epoch 419/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6481 - mae: 0.9548 - val_loss: 0.8275 - val_mae: 0.7048\n",
      "Epoch 420/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3292 - mae: 0.8946 - val_loss: 1.0980 - val_mae: 0.8738\n",
      "Epoch 421/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5262 - mae: 1.0136 - val_loss: 0.9567 - val_mae: 0.7810\n",
      "Epoch 422/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.1088 - mae: 0.8184 - val_loss: 0.8467 - val_mae: 0.7273\n",
      "Epoch 423/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3630 - mae: 0.9503 - val_loss: 0.8655 - val_mae: 0.7184\n",
      "Epoch 424/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7588 - mae: 0.9827 - val_loss: 0.8157 - val_mae: 0.7006\n",
      "Epoch 425/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9429 - mae: 0.9939 - val_loss: 1.1451 - val_mae: 0.8651\n",
      "Epoch 426/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6630 - mae: 0.9593 - val_loss: 1.0225 - val_mae: 0.8976\n",
      "Epoch 427/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6596 - mae: 1.0139 - val_loss: 0.8428 - val_mae: 0.7310\n",
      "Epoch 428/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6439 - mae: 0.9238 - val_loss: 1.1357 - val_mae: 0.8605\n",
      "Epoch 429/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4670 - mae: 0.9504 - val_loss: 0.9297 - val_mae: 0.8120\n",
      "Epoch 430/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6091 - mae: 0.9788 - val_loss: 0.9675 - val_mae: 0.7625\n",
      "Epoch 431/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4505 - mae: 0.9106 - val_loss: 0.8517 - val_mae: 0.7615\n",
      "Epoch 432/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6391 - mae: 0.9717 - val_loss: 0.7805 - val_mae: 0.7188\n",
      "Epoch 433/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8534 - mae: 1.0008 - val_loss: 0.7943 - val_mae: 0.7573\n",
      "Epoch 434/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5223 - mae: 0.9508 - val_loss: 0.8609 - val_mae: 0.7651\n",
      "Epoch 435/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6041 - mae: 0.9393 - val_loss: 0.8452 - val_mae: 0.7406\n",
      "Epoch 436/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5804 - mae: 0.9892 - val_loss: 0.9426 - val_mae: 0.8491\n",
      "Epoch 437/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.5723 - mae: 0.9821 - val_loss: 0.8477 - val_mae: 0.7567\n",
      "Epoch 438/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7957 - mae: 0.9731 - val_loss: 0.7543 - val_mae: 0.7204\n",
      "Epoch 439/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6236 - mae: 1.0211 - val_loss: 0.9175 - val_mae: 0.7701\n",
      "Epoch 440/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 1.5478 - mae: 0.9950 - val_loss: 0.9303 - val_mae: 0.8108\n",
      "Epoch 441/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.5849 - mae: 0.9877 - val_loss: 1.1419 - val_mae: 0.8820\n",
      "Epoch 442/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4692 - mae: 0.9311 - val_loss: 0.9169 - val_mae: 0.8228\n",
      "Epoch 443/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6809 - mae: 0.9692 - val_loss: 0.7185 - val_mae: 0.6497\n",
      "Epoch 444/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9349 - mae: 1.0813 - val_loss: 0.8735 - val_mae: 0.7809\n",
      "Epoch 445/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.4043 - mae: 0.9107 - val_loss: 1.0221 - val_mae: 0.8763\n",
      "Epoch 446/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5712 - mae: 0.9317 - val_loss: 1.0010 - val_mae: 0.8389\n",
      "Epoch 447/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6423 - mae: 0.9985 - val_loss: 0.8244 - val_mae: 0.6768\n",
      "Epoch 448/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9550 - mae: 1.0694 - val_loss: 0.7632 - val_mae: 0.7525\n",
      "Epoch 449/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6492 - mae: 0.9869 - val_loss: 0.7100 - val_mae: 0.6955\n",
      "Epoch 450/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.3393 - mae: 1.0870 - val_loss: 0.6503 - val_mae: 0.6055\n",
      "Epoch 451/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.2800 - mae: 0.8099 - val_loss: 0.7391 - val_mae: 0.7174\n",
      "Epoch 452/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2697 - mae: 0.8999 - val_loss: 1.0176 - val_mae: 0.8960\n",
      "Epoch 453/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4365 - mae: 0.9794 - val_loss: 0.8065 - val_mae: 0.7352\n",
      "Epoch 454/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.3876 - mae: 0.9211 - val_loss: 0.7986 - val_mae: 0.7645\n",
      "Epoch 455/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9006 - mae: 1.0341 - val_loss: 0.7575 - val_mae: 0.6924\n",
      "Epoch 456/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.8661 - mae: 1.0850 - val_loss: 0.8530 - val_mae: 0.7720\n",
      "Epoch 457/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5393 - mae: 0.9893 - val_loss: 1.0342 - val_mae: 0.8621\n",
      "Epoch 458/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.2728 - mae: 0.8350 - val_loss: 0.9734 - val_mae: 0.7684\n",
      "Epoch 459/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5843 - mae: 1.0101 - val_loss: 0.8901 - val_mae: 0.7724\n",
      "Epoch 460/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.2597 - mae: 0.8986 - val_loss: 1.0958 - val_mae: 0.8530\n",
      "Epoch 461/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6393 - mae: 0.9655 - val_loss: 1.3689 - val_mae: 1.0146\n",
      "Epoch 462/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9748 - mae: 1.1073 - val_loss: 0.9046 - val_mae: 0.7355\n",
      "Epoch 463/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4719 - mae: 0.9304 - val_loss: 0.9216 - val_mae: 0.7752\n",
      "Epoch 464/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6802 - mae: 1.0001 - val_loss: 0.8988 - val_mae: 0.7744\n",
      "Epoch 465/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3023 - mae: 0.8958 - val_loss: 0.9432 - val_mae: 0.8097\n",
      "Epoch 466/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.6168 - mae: 0.9889 - val_loss: 0.8031 - val_mae: 0.6931\n",
      "Epoch 467/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4827 - mae: 0.9758 - val_loss: 0.8347 - val_mae: 0.7449\n",
      "Epoch 468/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7753 - mae: 1.0150 - val_loss: 0.8840 - val_mae: 0.7665\n",
      "Epoch 469/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.1203 - mae: 0.8089 - val_loss: 0.9091 - val_mae: 0.8060\n",
      "Epoch 470/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6443 - mae: 1.0169 - val_loss: 0.7061 - val_mae: 0.6946\n",
      "Epoch 471/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4139 - mae: 0.9325 - val_loss: 0.6729 - val_mae: 0.6648\n",
      "Epoch 472/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4669 - mae: 0.9432 - val_loss: 0.7659 - val_mae: 0.7133\n",
      "Epoch 473/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4915 - mae: 0.9035 - val_loss: 0.8236 - val_mae: 0.7263\n",
      "Epoch 474/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6341 - mae: 0.9515 - val_loss: 0.8383 - val_mae: 0.7900\n",
      "Epoch 475/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 1.7085 - mae: 1.0248 - val_loss: 0.7729 - val_mae: 0.7112\n",
      "Epoch 476/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.5487 - mae: 0.9536 - val_loss: 0.8458 - val_mae: 0.7047\n",
      "Epoch 477/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.1040 - mae: 0.8148 - val_loss: 0.9372 - val_mae: 0.8012\n",
      "Epoch 478/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2741 - mae: 0.9017 - val_loss: 0.8791 - val_mae: 0.7511\n",
      "Epoch 479/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.2906 - mae: 0.8643 - val_loss: 0.7399 - val_mae: 0.7206\n",
      "Epoch 480/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.0791 - mae: 0.8253 - val_loss: 0.6738 - val_mae: 0.6806\n",
      "Epoch 481/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6289 - mae: 0.9941 - val_loss: 0.6645 - val_mae: 0.6646\n",
      "Epoch 482/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3046 - mae: 0.9090 - val_loss: 0.7928 - val_mae: 0.7651\n",
      "Epoch 483/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 1.5822 - mae: 1.0658 - val_loss: 0.6845 - val_mae: 0.6590\n",
      "Epoch 484/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.4260 - mae: 0.8777 - val_loss: 0.7117 - val_mae: 0.6925\n",
      "Epoch 485/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.3130 - mae: 0.8635 - val_loss: 0.7713 - val_mae: 0.6965\n",
      "Epoch 486/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4183 - mae: 0.9514 - val_loss: 0.9961 - val_mae: 0.8186\n",
      "Epoch 487/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5098 - mae: 0.9386 - val_loss: 0.9616 - val_mae: 0.7776\n",
      "Epoch 488/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2324 - mae: 0.8430 - val_loss: 1.0267 - val_mae: 0.8019\n",
      "Epoch 489/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7587 - mae: 0.9610 - val_loss: 0.9406 - val_mae: 0.7767\n",
      "Epoch 490/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.3447 - mae: 0.8556 - val_loss: 0.9181 - val_mae: 0.7987\n",
      "Epoch 491/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 1.5448 - mae: 0.9944 - val_loss: 0.7727 - val_mae: 0.7086\n",
      "Epoch 492/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.0210 - mae: 0.7766 - val_loss: 0.7799 - val_mae: 0.7049\n",
      "Epoch 493/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6540 - mae: 0.9299 - val_loss: 0.8376 - val_mae: 0.7549\n",
      "Epoch 494/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.0300 - mae: 0.7686 - val_loss: 0.9637 - val_mae: 0.7727\n",
      "Epoch 495/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.1372 - mae: 0.8354 - val_loss: 0.9825 - val_mae: 0.8077\n",
      "Epoch 496/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2687 - mae: 0.8768 - val_loss: 1.1082 - val_mae: 0.8303\n",
      "Epoch 497/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.3996 - mae: 0.9021 - val_loss: 0.9175 - val_mae: 0.7885\n",
      "Epoch 498/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6694 - mae: 0.9675 - val_loss: 0.9274 - val_mae: 0.8084\n",
      "Epoch 499/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.4417 - mae: 0.9087 - val_loss: 0.9607 - val_mae: 0.8353\n",
      "Epoch 500/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5745 - mae: 0.9626 - val_loss: 0.9151 - val_mae: 0.8038\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step\n",
      "ANN - Mean Squared Error: 1.0139725169793248\n",
      "ANN - Root Mean Squared Error: 1.0069620236033356\n",
      "ANN - Mean Absolute Error: 0.8039977654166843\n",
      "ANN - R-squared: 0.867663859499892\n"
     ]
    }
   ],
   "source": [
    "# Ensure reproducibility at the TensorFlow level\n",
    "tf.keras.utils.set_random_seed(42)\n",
    "\n",
    "# Define the model (Artificial Neural Network)\n",
    "ann = tf.keras.models.Sequential([\n",
    "    Dense(40, activation='relu', input_shape=(X_train_scaled.shape[1],)),  # Input layer\n",
    "    Dropout(0.2),  # Dropout for regularization\n",
    "    Dense(100, activation='relu'),  # Hidden layer with 100 units\n",
    "    Dropout(0.2),\n",
    "    Dense(60, activation='relu'),  # Another hidden layer\n",
    "    Dropout(0.2),\n",
    "    Dense(1)  # Output layer with 1 unit for regression\n",
    "])\n",
    "\n",
    "# Create Adam optimizer with a learning rate of 0.01\n",
    "optimizer = Adam(learning_rate=0.01)\n",
    "\n",
    "# Compile the model using Mean Squared Error as the loss function\n",
    "ann.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['mae'])\n",
    "\n",
    "# Train the model\n",
    "history = ann.fit(X_train_scaled, y_train, batch_size=32, epochs=500, verbose=1, validation_split=0.2)\n",
    "\n",
    "# Predict the target values for the test set\n",
    "y_pred_ann = ann.predict(X_test_scaled)\n",
    "\n",
    "# Calculate MSE, RMSE, MAE, and R-squared\n",
    "mse_ann = mean_squared_error(y_test, y_pred_ann)\n",
    "rmse_ann = np.sqrt(mse_ann)\n",
    "mae_ann = mean_absolute_error(y_test, y_pred_ann)\n",
    "r2_ann = r2_score(y_test, y_pred_ann)\n",
    "\n",
    "# Print performance metrics\n",
    "print(f\"ANN - Mean Squared Error: {mse_ann}\")\n",
    "print(f\"ANN - Root Mean Squared Error: {rmse_ann}\")\n",
    "print(f\"ANN - Mean Absolute Error: {mae_ann}\")\n",
    "print(f\"ANN - R-squared: {r2_ann}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\arora\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 74ms/step - loss: 59.2047 - mae: 6.3457 - val_loss: 21.3069 - val_mae: 4.2388\n",
      "Epoch 2/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 30.0345 - mae: 4.5878 - val_loss: 6.3519 - val_mae: 2.0987\n",
      "Epoch 3/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 10.6046 - mae: 2.6405 - val_loss: 1.1304 - val_mae: 0.8746\n",
      "Epoch 4/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 10.3065 - mae: 2.6223 - val_loss: 10.4185 - val_mae: 2.8524\n",
      "Epoch 5/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 10.5201 - mae: 2.5332 - val_loss: 0.7351 - val_mae: 0.7301\n",
      "Epoch 6/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 11.1919 - mae: 2.7435 - val_loss: 7.2813 - val_mae: 2.4170\n",
      "Epoch 7/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 8.6389 - mae: 2.3284 - val_loss: 2.8633 - val_mae: 1.4875\n",
      "Epoch 8/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 7.0686 - mae: 2.0978 - val_loss: 9.5031 - val_mae: 2.9076\n",
      "Epoch 9/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 6.2556 - mae: 1.9614 - val_loss: 1.3075 - val_mae: 1.0051\n",
      "Epoch 10/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 7.4686 - mae: 2.2253 - val_loss: 6.0443 - val_mae: 2.3203\n",
      "Epoch 11/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 8.2266 - mae: 2.2997 - val_loss: 1.7323 - val_mae: 1.1523\n",
      "Epoch 12/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 6.2713 - mae: 2.0315 - val_loss: 7.6086 - val_mae: 2.5981\n",
      "Epoch 13/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 8.4131 - mae: 2.3009 - val_loss: 3.6208 - val_mae: 1.7689\n",
      "Epoch 14/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 5.6030 - mae: 1.9232 - val_loss: 9.6140 - val_mae: 2.9839\n",
      "Epoch 15/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 6.1710 - mae: 2.0224 - val_loss: 2.8012 - val_mae: 1.5229\n",
      "Epoch 16/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 6.7047 - mae: 2.0708 - val_loss: 4.0690 - val_mae: 1.8846\n",
      "Epoch 17/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.8834 - mae: 1.6063 - val_loss: 4.2242 - val_mae: 1.9030\n",
      "Epoch 18/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.2646 - mae: 1.5704 - val_loss: 4.3815 - val_mae: 1.9307\n",
      "Epoch 19/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.9009 - mae: 1.5386 - val_loss: 5.3983 - val_mae: 2.1426\n",
      "Epoch 20/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.8937 - mae: 1.7456 - val_loss: 3.9243 - val_mae: 1.7850\n",
      "Epoch 21/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 6.2127 - mae: 1.9646 - val_loss: 6.3856 - val_mae: 2.3926\n",
      "Epoch 22/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 4.6882 - mae: 1.6900 - val_loss: 7.6882 - val_mae: 2.6604\n",
      "Epoch 23/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 4.0450 - mae: 1.6374 - val_loss: 4.9199 - val_mae: 2.0837\n",
      "Epoch 24/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 4.9672 - mae: 1.7105 - val_loss: 6.6689 - val_mae: 2.4035\n",
      "Epoch 25/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 5.4410 - mae: 1.8778 - val_loss: 2.9342 - val_mae: 1.5563\n",
      "Epoch 26/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 4.4332 - mae: 1.6722 - val_loss: 9.2098 - val_mae: 2.9519\n",
      "Epoch 27/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 5.3418 - mae: 1.7008 - val_loss: 8.6729 - val_mae: 2.8466\n",
      "Epoch 28/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 5.4024 - mae: 1.8707 - val_loss: 1.4996 - val_mae: 1.0614\n",
      "Epoch 29/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 5.3378 - mae: 1.9011 - val_loss: 9.5991 - val_mae: 2.9371\n",
      "Epoch 30/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 4.0725 - mae: 1.5873 - val_loss: 3.6821 - val_mae: 1.7281\n",
      "Epoch 31/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 5.3987 - mae: 1.8545 - val_loss: 2.9961 - val_mae: 1.5565\n",
      "Epoch 32/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 4.1965 - mae: 1.5788 - val_loss: 5.4711 - val_mae: 2.2028\n",
      "Epoch 33/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 4.5106 - mae: 1.6576 - val_loss: 2.4263 - val_mae: 1.3968\n",
      "Epoch 34/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.4410 - mae: 1.4028 - val_loss: 5.8044 - val_mae: 2.2568\n",
      "Epoch 35/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.3760 - mae: 1.6914 - val_loss: 3.8085 - val_mae: 1.7837\n",
      "Epoch 36/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.2082 - mae: 1.3989 - val_loss: 8.1205 - val_mae: 2.7111\n",
      "Epoch 37/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 4.4411 - mae: 1.6672 - val_loss: 2.5900 - val_mae: 1.4565\n",
      "Epoch 38/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 6.5375 - mae: 2.0301 - val_loss: 4.0853 - val_mae: 1.9047\n",
      "Epoch 39/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.9637 - mae: 1.5813 - val_loss: 7.4773 - val_mae: 2.5919\n",
      "Epoch 40/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.2961 - mae: 1.4746 - val_loss: 2.5445 - val_mae: 1.4171\n",
      "Epoch 41/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.7725 - mae: 1.7038 - val_loss: 1.7548 - val_mae: 1.1814\n",
      "Epoch 42/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 4.6226 - mae: 1.7061 - val_loss: 8.8690 - val_mae: 2.8754\n",
      "Epoch 43/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 4.1585 - mae: 1.6076 - val_loss: 6.3740 - val_mae: 2.3701\n",
      "Epoch 44/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.0964 - mae: 1.6163 - val_loss: 1.2726 - val_mae: 0.9510\n",
      "Epoch 45/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 4.3618 - mae: 1.6659 - val_loss: 11.7135 - val_mae: 3.2268\n",
      "Epoch 46/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 4.2419 - mae: 1.6602 - val_loss: 0.9076 - val_mae: 0.8363\n",
      "Epoch 47/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 3.8971 - mae: 1.5892 - val_loss: 6.2183 - val_mae: 2.3604\n",
      "Epoch 48/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.5848 - mae: 1.5199 - val_loss: 2.9701 - val_mae: 1.5585\n",
      "Epoch 49/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.0710 - mae: 1.4627 - val_loss: 3.2955 - val_mae: 1.6770\n",
      "Epoch 50/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 4.1888 - mae: 1.5660 - val_loss: 10.5698 - val_mae: 3.1404\n",
      "Epoch 51/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 4.6292 - mae: 1.6876 - val_loss: 2.0689 - val_mae: 1.2694\n",
      "Epoch 52/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.9184 - mae: 1.5596 - val_loss: 3.4289 - val_mae: 1.6799\n",
      "Epoch 53/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 4.5088 - mae: 1.6321 - val_loss: 5.3654 - val_mae: 2.2059\n",
      "Epoch 54/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 3.3958 - mae: 1.5176 - val_loss: 4.8508 - val_mae: 2.0833\n",
      "Epoch 55/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.3501 - mae: 1.4630 - val_loss: 6.2951 - val_mae: 2.3623\n",
      "Epoch 56/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.7639 - mae: 1.5062 - val_loss: 4.3528 - val_mae: 1.9420\n",
      "Epoch 57/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.1023 - mae: 1.6268 - val_loss: 1.4779 - val_mae: 1.0929\n",
      "Epoch 58/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.9448 - mae: 1.7614 - val_loss: 5.1936 - val_mae: 2.1462\n",
      "Epoch 59/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 3.7883 - mae: 1.5430 - val_loss: 6.4459 - val_mae: 2.3957\n",
      "Epoch 60/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 4.9427 - mae: 1.7284 - val_loss: 0.6961 - val_mae: 0.7135\n",
      "Epoch 61/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 4.6319 - mae: 1.7691 - val_loss: 7.9303 - val_mae: 2.6780\n",
      "Epoch 62/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 4.9549 - mae: 1.7114 - val_loss: 3.7789 - val_mae: 1.7849\n",
      "Epoch 63/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 4.5801 - mae: 1.6400 - val_loss: 2.9992 - val_mae: 1.5384\n",
      "Epoch 64/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.6627 - mae: 1.5019 - val_loss: 6.9425 - val_mae: 2.4429\n",
      "Epoch 65/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.8338 - mae: 1.5570 - val_loss: 1.9829 - val_mae: 1.2094\n",
      "Epoch 66/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.3682 - mae: 1.4713 - val_loss: 3.6676 - val_mae: 1.7035\n",
      "Epoch 67/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.5697 - mae: 1.4443 - val_loss: 3.7828 - val_mae: 1.7511\n",
      "Epoch 68/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.4337 - mae: 1.4530 - val_loss: 2.5917 - val_mae: 1.4602\n",
      "Epoch 69/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 5.2174 - mae: 1.6898 - val_loss: 4.6937 - val_mae: 1.9880\n",
      "Epoch 70/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.2421 - mae: 1.3798 - val_loss: 2.3675 - val_mae: 1.3858\n",
      "Epoch 71/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 3.6889 - mae: 1.5175 - val_loss: 4.3991 - val_mae: 1.9586\n",
      "Epoch 72/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.2505 - mae: 1.4725 - val_loss: 3.9503 - val_mae: 1.8113\n",
      "Epoch 73/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.2942 - mae: 1.4302 - val_loss: 3.0732 - val_mae: 1.5874\n",
      "Epoch 74/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.1817 - mae: 1.3791 - val_loss: 1.8193 - val_mae: 1.2112\n",
      "Epoch 75/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.3268 - mae: 1.5337 - val_loss: 7.0856 - val_mae: 2.5548\n",
      "Epoch 76/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.2860 - mae: 1.3936 - val_loss: 2.1125 - val_mae: 1.3157\n",
      "Epoch 77/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.9915 - mae: 1.3985 - val_loss: 3.2784 - val_mae: 1.6363\n",
      "Epoch 78/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.0495 - mae: 1.3364 - val_loss: 3.2928 - val_mae: 1.6317\n",
      "Epoch 79/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.8160 - mae: 1.5039 - val_loss: 2.9700 - val_mae: 1.5542\n",
      "Epoch 80/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5797 - mae: 1.3173 - val_loss: 3.0132 - val_mae: 1.5565\n",
      "Epoch 81/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.8687 - mae: 1.4167 - val_loss: 2.7392 - val_mae: 1.5086\n",
      "Epoch 82/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7945 - mae: 1.2952 - val_loss: 1.8465 - val_mae: 1.2278\n",
      "Epoch 83/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.7377 - mae: 1.4343 - val_loss: 5.7827 - val_mae: 2.2696\n",
      "Epoch 84/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.9182 - mae: 1.3562 - val_loss: 0.8329 - val_mae: 0.8028\n",
      "Epoch 85/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.3923 - mae: 1.7090 - val_loss: 4.4489 - val_mae: 1.9519\n",
      "Epoch 86/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4948 - mae: 1.2189 - val_loss: 3.5578 - val_mae: 1.7379\n",
      "Epoch 87/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.7420 - mae: 1.3216 - val_loss: 4.3543 - val_mae: 1.9318\n",
      "Epoch 88/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.7408 - mae: 1.4700 - val_loss: 2.5829 - val_mae: 1.4282\n",
      "Epoch 89/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.1141 - mae: 1.4038 - val_loss: 2.8229 - val_mae: 1.5041\n",
      "Epoch 90/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.9791 - mae: 1.3552 - val_loss: 4.0504 - val_mae: 1.8273\n",
      "Epoch 91/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.7724 - mae: 1.3168 - val_loss: 3.0839 - val_mae: 1.5875\n",
      "Epoch 92/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.1880 - mae: 1.4504 - val_loss: 5.3501 - val_mae: 2.1444\n",
      "Epoch 93/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 3.4699 - mae: 1.5190 - val_loss: 1.4290 - val_mae: 1.0621\n",
      "Epoch 94/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.0258 - mae: 1.4027 - val_loss: 3.3297 - val_mae: 1.6469\n",
      "Epoch 95/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.2993 - mae: 1.3730 - val_loss: 3.4654 - val_mae: 1.7037\n",
      "Epoch 96/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.5596 - mae: 1.4478 - val_loss: 1.8254 - val_mae: 1.2204\n",
      "Epoch 97/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.0414 - mae: 1.4229 - val_loss: 5.2634 - val_mae: 2.1090\n",
      "Epoch 98/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.1383 - mae: 1.4180 - val_loss: 0.8496 - val_mae: 0.7940\n",
      "Epoch 99/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.0851 - mae: 1.4058 - val_loss: 4.8985 - val_mae: 2.0256\n",
      "Epoch 100/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.2187 - mae: 1.4057 - val_loss: 0.9398 - val_mae: 0.8716\n",
      "Epoch 101/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.2211 - mae: 1.3870 - val_loss: 2.5359 - val_mae: 1.4208\n",
      "Epoch 102/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.2339 - mae: 1.4141 - val_loss: 2.6059 - val_mae: 1.4484\n",
      "Epoch 103/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.3613 - mae: 1.4160 - val_loss: 2.1036 - val_mae: 1.3069\n",
      "Epoch 104/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3013 - mae: 1.2114 - val_loss: 3.8648 - val_mae: 1.8053\n",
      "Epoch 105/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3627 - mae: 1.1769 - val_loss: 3.0554 - val_mae: 1.5914\n",
      "Epoch 106/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.6041 - mae: 1.3144 - val_loss: 1.2530 - val_mae: 1.0169\n",
      "Epoch 107/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 4.5201 - mae: 1.7290 - val_loss: 3.6268 - val_mae: 1.7490\n",
      "Epoch 108/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.8536 - mae: 1.3664 - val_loss: 2.8012 - val_mae: 1.5194\n",
      "Epoch 109/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.8173 - mae: 1.2950 - val_loss: 3.0123 - val_mae: 1.5542\n",
      "Epoch 110/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4652 - mae: 1.2281 - val_loss: 1.7277 - val_mae: 1.1177\n",
      "Epoch 111/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.9951 - mae: 1.2929 - val_loss: 1.2819 - val_mae: 0.9800\n",
      "Epoch 112/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.4306 - mae: 1.4844 - val_loss: 4.0311 - val_mae: 1.8269\n",
      "Epoch 113/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.8395 - mae: 1.3309 - val_loss: 0.6190 - val_mae: 0.5756\n",
      "Epoch 114/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.7703 - mae: 1.5211 - val_loss: 7.1694 - val_mae: 2.4180\n",
      "Epoch 115/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.5330 - mae: 1.4806 - val_loss: 1.9793 - val_mae: 1.1913\n",
      "Epoch 116/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.9052 - mae: 1.3407 - val_loss: 2.7794 - val_mae: 1.4764\n",
      "Epoch 117/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.2362 - mae: 1.1641 - val_loss: 2.1242 - val_mae: 1.2824\n",
      "Epoch 118/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.0637 - mae: 1.3326 - val_loss: 3.8457 - val_mae: 1.7209\n",
      "Epoch 119/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.3812 - mae: 1.4986 - val_loss: 2.4972 - val_mae: 1.3253\n",
      "Epoch 120/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5079 - mae: 1.2422 - val_loss: 1.7885 - val_mae: 1.1017\n",
      "Epoch 121/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3415 - mae: 1.2221 - val_loss: 3.4343 - val_mae: 1.6294\n",
      "Epoch 122/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.1042 - mae: 1.3302 - val_loss: 2.6108 - val_mae: 1.4453\n",
      "Epoch 123/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5413 - mae: 1.2606 - val_loss: 1.5576 - val_mae: 1.0987\n",
      "Epoch 124/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 4.0824 - mae: 1.5336 - val_loss: 2.3042 - val_mae: 1.3015\n",
      "Epoch 125/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 3.7640 - mae: 1.4957 - val_loss: 3.0406 - val_mae: 1.4736\n",
      "Epoch 126/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.9522 - mae: 1.3795 - val_loss: 1.9825 - val_mae: 1.1799\n",
      "Epoch 127/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.2631 - mae: 1.4099 - val_loss: 1.5817 - val_mae: 1.0705\n",
      "Epoch 128/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3019 - mae: 1.1776 - val_loss: 2.7144 - val_mae: 1.4465\n",
      "Epoch 129/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2666 - mae: 1.1745 - val_loss: 3.7908 - val_mae: 1.7624\n",
      "Epoch 130/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4695 - mae: 1.2264 - val_loss: 1.6565 - val_mae: 1.1222\n",
      "Epoch 131/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.4240 - mae: 1.2541 - val_loss: 1.7487 - val_mae: 1.0968\n",
      "Epoch 132/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 2.5464 - mae: 1.2731 - val_loss: 2.4202 - val_mae: 1.3099\n",
      "Epoch 133/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5454 - mae: 1.2564 - val_loss: 3.7867 - val_mae: 1.7175\n",
      "Epoch 134/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.7318 - mae: 1.2549 - val_loss: 0.7323 - val_mae: 0.6817\n",
      "Epoch 135/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 3.0119 - mae: 1.3279 - val_loss: 4.1721 - val_mae: 1.8421\n",
      "Epoch 136/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.2545 - mae: 1.4411 - val_loss: 1.1471 - val_mae: 0.8799\n",
      "Epoch 137/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.7222 - mae: 1.3160 - val_loss: 3.9777 - val_mae: 1.7286\n",
      "Epoch 138/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.7767 - mae: 1.2572 - val_loss: 0.6973 - val_mae: 0.6538\n",
      "Epoch 139/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 3.7941 - mae: 1.4518 - val_loss: 4.1185 - val_mae: 1.8500\n",
      "Epoch 140/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3842 - mae: 1.1793 - val_loss: 1.7186 - val_mae: 1.1497\n",
      "Epoch 141/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.5323 - mae: 1.2031 - val_loss: 1.8342 - val_mae: 1.1353\n",
      "Epoch 142/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.8773 - mae: 1.3647 - val_loss: 3.1109 - val_mae: 1.4668\n",
      "Epoch 143/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.1580 - mae: 1.3344 - val_loss: 0.9354 - val_mae: 0.7407\n",
      "Epoch 144/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.0410 - mae: 1.3580 - val_loss: 2.7831 - val_mae: 1.4422\n",
      "Epoch 145/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2001 - mae: 1.1733 - val_loss: 1.0739 - val_mae: 0.8480\n",
      "Epoch 146/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4424 - mae: 1.2348 - val_loss: 1.7655 - val_mae: 1.0797\n",
      "Epoch 147/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.9387 - mae: 1.3358 - val_loss: 0.7089 - val_mae: 0.6650\n",
      "Epoch 148/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.1078 - mae: 1.2976 - val_loss: 3.1982 - val_mae: 1.5852\n",
      "Epoch 149/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.4403 - mae: 1.2549 - val_loss: 1.1588 - val_mae: 0.9081\n",
      "Epoch 150/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9057 - mae: 1.1077 - val_loss: 1.9771 - val_mae: 1.1632\n",
      "Epoch 151/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 2.0223 - mae: 1.0826 - val_loss: 2.5164 - val_mae: 1.3474\n",
      "Epoch 152/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2169 - mae: 1.1734 - val_loss: 1.4967 - val_mae: 1.0226\n",
      "Epoch 153/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.6066 - mae: 1.2992 - val_loss: 2.3936 - val_mae: 1.2952\n",
      "Epoch 154/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1715 - mae: 1.1539 - val_loss: 2.6039 - val_mae: 1.3449\n",
      "Epoch 155/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.9174 - mae: 1.2786 - val_loss: 1.1321 - val_mae: 0.8646\n",
      "Epoch 156/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5941 - mae: 1.3057 - val_loss: 1.3644 - val_mae: 1.0144\n",
      "Epoch 157/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3286 - mae: 1.2444 - val_loss: 0.7709 - val_mae: 0.7454\n",
      "Epoch 158/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.1026 - mae: 1.3161 - val_loss: 4.1495 - val_mae: 1.7608\n",
      "Epoch 159/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.3549 - mae: 1.2119 - val_loss: 0.8621 - val_mae: 0.6520\n",
      "Epoch 160/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.2018 - mae: 1.4524 - val_loss: 2.4928 - val_mae: 1.2647\n",
      "Epoch 161/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.5462 - mae: 1.2209 - val_loss: 0.9824 - val_mae: 0.7751\n",
      "Epoch 162/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.6727 - mae: 1.3148 - val_loss: 3.4067 - val_mae: 1.5961\n",
      "Epoch 163/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5425 - mae: 1.2754 - val_loss: 0.7816 - val_mae: 0.6713\n",
      "Epoch 164/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4992 - mae: 1.2520 - val_loss: 2.6928 - val_mae: 1.3100\n",
      "Epoch 165/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.0412 - mae: 1.3011 - val_loss: 1.4283 - val_mae: 0.9005\n",
      "Epoch 166/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5477 - mae: 1.2825 - val_loss: 1.2455 - val_mae: 0.8726\n",
      "Epoch 167/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.6591 - mae: 1.2614 - val_loss: 1.3148 - val_mae: 0.9715\n",
      "Epoch 168/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2109 - mae: 1.1725 - val_loss: 2.2280 - val_mae: 1.3068\n",
      "Epoch 169/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3075 - mae: 1.2444 - val_loss: 1.0694 - val_mae: 0.8020\n",
      "Epoch 170/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.5598 - mae: 1.3130 - val_loss: 2.3504 - val_mae: 1.1769\n",
      "Epoch 171/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.7381 - mae: 1.2228 - val_loss: 1.0864 - val_mae: 0.7704\n",
      "Epoch 172/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.7721 - mae: 1.3484 - val_loss: 2.1156 - val_mae: 1.2039\n",
      "Epoch 173/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.5108 - mae: 1.2560 - val_loss: 1.8025 - val_mae: 1.1021\n",
      "Epoch 174/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.3059 - mae: 1.4501 - val_loss: 0.7345 - val_mae: 0.6426\n",
      "Epoch 175/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5608 - mae: 1.2644 - val_loss: 3.5700 - val_mae: 1.6391\n",
      "Epoch 176/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 1.9957 - mae: 1.1349 - val_loss: 0.7120 - val_mae: 0.6170\n",
      "Epoch 177/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1841 - mae: 1.1879 - val_loss: 3.3158 - val_mae: 1.5375\n",
      "Epoch 178/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 3.0655 - mae: 1.3399 - val_loss: 1.0950 - val_mae: 0.7637\n",
      "Epoch 179/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.9650 - mae: 1.3287 - val_loss: 4.0632 - val_mae: 1.7033\n",
      "Epoch 180/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 3.1576 - mae: 1.3968 - val_loss: 0.9552 - val_mae: 0.7342\n",
      "Epoch 181/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 3.7940 - mae: 1.5532 - val_loss: 2.2523 - val_mae: 1.2034\n",
      "Epoch 182/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4973 - mae: 1.2809 - val_loss: 1.7191 - val_mae: 1.0396\n",
      "Epoch 183/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.0530 - mae: 1.1061 - val_loss: 2.1566 - val_mae: 1.2040\n",
      "Epoch 184/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.6494 - mae: 1.0041 - val_loss: 0.6892 - val_mae: 0.6224\n",
      "Epoch 185/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.4633 - mae: 1.5210 - val_loss: 1.7734 - val_mae: 1.0977\n",
      "Epoch 186/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.2321 - mae: 1.2063 - val_loss: 1.3520 - val_mae: 0.9485\n",
      "Epoch 187/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.3785 - mae: 1.2666 - val_loss: 1.1509 - val_mae: 0.8408\n",
      "Epoch 188/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3691 - mae: 1.1676 - val_loss: 2.5551 - val_mae: 1.3682\n",
      "Epoch 189/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.1797 - mae: 1.4407 - val_loss: 0.8956 - val_mae: 0.7240\n",
      "Epoch 190/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2341 - mae: 1.1980 - val_loss: 2.0307 - val_mae: 1.1980\n",
      "Epoch 191/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 2.2230 - mae: 1.1532 - val_loss: 0.7770 - val_mae: 0.6653\n",
      "Epoch 192/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.6021 - mae: 1.3414 - val_loss: 3.2789 - val_mae: 1.4804\n",
      "Epoch 193/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3510 - mae: 1.2328 - val_loss: 1.0501 - val_mae: 0.7908\n",
      "Epoch 194/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1589 - mae: 1.1754 - val_loss: 1.5734 - val_mae: 1.0050\n",
      "Epoch 195/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2680 - mae: 1.1456 - val_loss: 1.5203 - val_mae: 0.9639\n",
      "Epoch 196/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9447 - mae: 1.1434 - val_loss: 1.8861 - val_mae: 1.1046\n",
      "Epoch 197/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0223 - mae: 1.0965 - val_loss: 1.4291 - val_mae: 0.9303\n",
      "Epoch 198/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3304 - mae: 1.1708 - val_loss: 2.1047 - val_mae: 1.1451\n",
      "Epoch 199/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.0877 - mae: 1.1337 - val_loss: 1.0159 - val_mae: 0.7146\n",
      "Epoch 200/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - loss: 2.1789 - mae: 1.2287 - val_loss: 1.6853 - val_mae: 1.0481\n",
      "Epoch 201/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1477 - mae: 1.1481 - val_loss: 1.6364 - val_mae: 0.9746\n",
      "Epoch 202/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 2.6962 - mae: 1.2980 - val_loss: 1.3621 - val_mae: 0.9235\n",
      "Epoch 203/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9619 - mae: 1.1174 - val_loss: 1.0296 - val_mae: 0.7976\n",
      "Epoch 204/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.8528 - mae: 1.3779 - val_loss: 0.8826 - val_mae: 0.7278\n",
      "Epoch 205/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4122 - mae: 1.2825 - val_loss: 0.9538 - val_mae: 0.7911\n",
      "Epoch 206/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.8445 - mae: 1.0559 - val_loss: 1.1091 - val_mae: 0.8548\n",
      "Epoch 207/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3887 - mae: 1.2003 - val_loss: 1.1254 - val_mae: 0.8423\n",
      "Epoch 208/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2178 - mae: 1.1387 - val_loss: 1.8724 - val_mae: 1.0857\n",
      "Epoch 209/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.6332 - mae: 1.2278 - val_loss: 1.5684 - val_mae: 1.0417\n",
      "Epoch 210/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3349 - mae: 1.2302 - val_loss: 0.7460 - val_mae: 0.6911\n",
      "Epoch 211/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3839 - mae: 1.2411 - val_loss: 2.0204 - val_mae: 1.1727\n",
      "Epoch 212/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.2398 - mae: 1.1545 - val_loss: 0.8118 - val_mae: 0.6601\n",
      "Epoch 213/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.8213 - mae: 1.1223 - val_loss: 1.6776 - val_mae: 1.0556\n",
      "Epoch 214/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.3208 - mae: 1.1817 - val_loss: 1.1523 - val_mae: 0.8310\n",
      "Epoch 215/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1602 - mae: 1.0923 - val_loss: 1.8696 - val_mae: 1.0322\n",
      "Epoch 216/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.2392 - mae: 1.1451 - val_loss: 1.1140 - val_mae: 0.8353\n",
      "Epoch 217/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.5936 - mae: 1.3372 - val_loss: 1.6388 - val_mae: 1.0417\n",
      "Epoch 218/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9555 - mae: 1.0637 - val_loss: 1.5573 - val_mae: 1.0010\n",
      "Epoch 219/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5139 - mae: 1.1945 - val_loss: 1.3669 - val_mae: 0.9028\n",
      "Epoch 220/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2604 - mae: 1.1962 - val_loss: 0.9214 - val_mae: 0.6946\n",
      "Epoch 221/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.4487 - mae: 1.2938 - val_loss: 3.8675 - val_mae: 1.7058\n",
      "Epoch 222/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3079 - mae: 1.1958 - val_loss: 0.8946 - val_mae: 0.7474\n",
      "Epoch 223/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.9234 - mae: 1.3655 - val_loss: 1.4311 - val_mae: 0.9264\n",
      "Epoch 224/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9790 - mae: 1.0793 - val_loss: 1.6279 - val_mae: 1.0215\n",
      "Epoch 225/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8083 - mae: 1.0787 - val_loss: 1.1627 - val_mae: 0.8421\n",
      "Epoch 226/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8573 - mae: 1.0407 - val_loss: 1.4636 - val_mae: 0.9781\n",
      "Epoch 227/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7589 - mae: 1.0412 - val_loss: 0.9949 - val_mae: 0.7792\n",
      "Epoch 228/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 3.0590 - mae: 1.1922 - val_loss: 1.9630 - val_mae: 1.1675\n",
      "Epoch 229/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4073 - mae: 1.1700 - val_loss: 2.1453 - val_mae: 1.1979\n",
      "Epoch 230/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3591 - mae: 1.1699 - val_loss: 1.2171 - val_mae: 0.8349\n",
      "Epoch 231/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2629 - mae: 1.1656 - val_loss: 2.0116 - val_mae: 1.1138\n",
      "Epoch 232/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.1809 - mae: 1.1592 - val_loss: 1.1791 - val_mae: 0.8151\n",
      "Epoch 233/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.1970 - mae: 1.1190 - val_loss: 2.3179 - val_mae: 1.2018\n",
      "Epoch 234/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3099 - mae: 1.1981 - val_loss: 1.0305 - val_mae: 0.7591\n",
      "Epoch 235/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.5630 - mae: 1.2455 - val_loss: 2.2190 - val_mae: 1.1952\n",
      "Epoch 236/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1253 - mae: 1.0766 - val_loss: 0.9205 - val_mae: 0.6894\n",
      "Epoch 237/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 2.3097 - mae: 1.2587 - val_loss: 1.2226 - val_mae: 0.8691\n",
      "Epoch 238/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6376 - mae: 0.9746 - val_loss: 1.0343 - val_mae: 0.8168\n",
      "Epoch 239/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5956 - mae: 0.9916 - val_loss: 1.1999 - val_mae: 0.9046\n",
      "Epoch 240/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7277 - mae: 0.9778 - val_loss: 1.0238 - val_mae: 0.7932\n",
      "Epoch 241/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7537 - mae: 1.0326 - val_loss: 1.8885 - val_mae: 1.0606\n",
      "Epoch 242/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2126 - mae: 1.1624 - val_loss: 1.0205 - val_mae: 0.7527\n",
      "Epoch 243/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5577 - mae: 1.2304 - val_loss: 2.6202 - val_mae: 1.3140\n",
      "Epoch 244/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9236 - mae: 1.0845 - val_loss: 1.3699 - val_mae: 0.9049\n",
      "Epoch 245/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8221 - mae: 1.0590 - val_loss: 1.8582 - val_mae: 1.0912\n",
      "Epoch 246/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7655 - mae: 1.0262 - val_loss: 0.8890 - val_mae: 0.7324\n",
      "Epoch 247/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0119 - mae: 1.1106 - val_loss: 1.9498 - val_mae: 1.0995\n",
      "Epoch 248/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.3051 - mae: 1.0909 - val_loss: 0.7308 - val_mae: 0.6531\n",
      "Epoch 249/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9995 - mae: 1.1149 - val_loss: 3.4325 - val_mae: 1.5694\n",
      "Epoch 250/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5543 - mae: 1.2196 - val_loss: 0.7146 - val_mae: 0.6312\n",
      "Epoch 251/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.5558 - mae: 1.2898 - val_loss: 2.4939 - val_mae: 1.2531\n",
      "Epoch 252/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2562 - mae: 1.1624 - val_loss: 0.8282 - val_mae: 0.6872\n",
      "Epoch 253/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4678 - mae: 1.2855 - val_loss: 1.9465 - val_mae: 1.0912\n",
      "Epoch 254/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.7213 - mae: 1.1874 - val_loss: 0.9251 - val_mae: 0.6783\n",
      "Epoch 255/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1116 - mae: 1.0912 - val_loss: 1.7950 - val_mae: 1.0613\n",
      "Epoch 256/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2277 - mae: 1.1341 - val_loss: 0.7125 - val_mae: 0.6378\n",
      "Epoch 257/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.4001 - mae: 1.2501 - val_loss: 2.6898 - val_mae: 1.3971\n",
      "Epoch 258/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.9377 - mae: 1.3384 - val_loss: 0.6723 - val_mae: 0.5912\n",
      "Epoch 259/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.6405 - mae: 1.3128 - val_loss: 1.7461 - val_mae: 1.0047\n",
      "Epoch 260/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3154 - mae: 1.2170 - val_loss: 1.0828 - val_mae: 0.7835\n",
      "Epoch 261/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0785 - mae: 1.1564 - val_loss: 1.4696 - val_mae: 0.9511\n",
      "Epoch 262/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.6599 - mae: 1.0214 - val_loss: 1.4211 - val_mae: 0.8772\n",
      "Epoch 263/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.3564 - mae: 1.1615 - val_loss: 0.9921 - val_mae: 0.7013\n",
      "Epoch 264/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4294 - mae: 1.1896 - val_loss: 1.9879 - val_mae: 1.1316\n",
      "Epoch 265/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8609 - mae: 1.0838 - val_loss: 1.1590 - val_mae: 0.8502\n",
      "Epoch 266/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2146 - mae: 1.1930 - val_loss: 1.7858 - val_mae: 1.0807\n",
      "Epoch 267/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4874 - mae: 0.9341 - val_loss: 1.2740 - val_mae: 0.8402\n",
      "Epoch 268/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8747 - mae: 1.1317 - val_loss: 1.3837 - val_mae: 0.8875\n",
      "Epoch 269/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.3757 - mae: 0.9423 - val_loss: 1.8342 - val_mae: 1.0983\n",
      "Epoch 270/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.2606 - mae: 0.8630 - val_loss: 0.8941 - val_mae: 0.7090\n",
      "Epoch 271/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0141 - mae: 1.1010 - val_loss: 1.9092 - val_mae: 1.0670\n",
      "Epoch 272/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8238 - mae: 1.0773 - val_loss: 0.9635 - val_mae: 0.7321\n",
      "Epoch 273/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.7147 - mae: 1.2681 - val_loss: 1.7980 - val_mae: 1.0729\n",
      "Epoch 274/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3279 - mae: 1.2103 - val_loss: 1.7568 - val_mae: 1.0666\n",
      "Epoch 275/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4973 - mae: 0.9720 - val_loss: 0.8203 - val_mae: 0.6792\n",
      "Epoch 276/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3589 - mae: 1.2125 - val_loss: 1.0794 - val_mae: 0.7648\n",
      "Epoch 277/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9828 - mae: 1.1343 - val_loss: 2.0381 - val_mae: 1.1367\n",
      "Epoch 278/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 2.2111 - mae: 1.2068 - val_loss: 1.0006 - val_mae: 0.7765\n",
      "Epoch 279/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.4194 - mae: 0.9161 - val_loss: 1.8322 - val_mae: 1.0852\n",
      "Epoch 280/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9880 - mae: 1.1051 - val_loss: 0.6957 - val_mae: 0.6161\n",
      "Epoch 281/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3283 - mae: 1.1982 - val_loss: 1.6396 - val_mae: 1.0178\n",
      "Epoch 282/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2391 - mae: 1.1184 - val_loss: 1.1966 - val_mae: 0.8383\n",
      "Epoch 283/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6274 - mae: 0.9813 - val_loss: 1.9027 - val_mae: 1.0436\n",
      "Epoch 284/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3316 - mae: 1.2129 - val_loss: 1.0231 - val_mae: 0.7785\n",
      "Epoch 285/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.3779 - mae: 1.2220 - val_loss: 1.9510 - val_mae: 1.1255\n",
      "Epoch 286/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3116 - mae: 1.1714 - val_loss: 0.8314 - val_mae: 0.6760\n",
      "Epoch 287/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1520 - mae: 1.1976 - val_loss: 1.5115 - val_mae: 0.9098\n",
      "Epoch 288/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 2.4100 - mae: 1.2413 - val_loss: 1.2046 - val_mae: 0.8041\n",
      "Epoch 289/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9838 - mae: 1.0832 - val_loss: 1.2678 - val_mae: 0.8690\n",
      "Epoch 290/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4518 - mae: 0.9578 - val_loss: 1.4479 - val_mae: 0.9058\n",
      "Epoch 291/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7857 - mae: 1.0523 - val_loss: 1.0596 - val_mae: 0.7376\n",
      "Epoch 292/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.5985 - mae: 1.2818 - val_loss: 0.8848 - val_mae: 0.7372\n",
      "Epoch 293/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2632 - mae: 1.2312 - val_loss: 1.3894 - val_mae: 0.9332\n",
      "Epoch 294/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1072 - mae: 1.1579 - val_loss: 1.0787 - val_mae: 0.7810\n",
      "Epoch 295/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.0324 - mae: 1.1489 - val_loss: 1.4762 - val_mae: 0.9203\n",
      "Epoch 296/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.1197 - mae: 1.0322 - val_loss: 0.9093 - val_mae: 0.7054\n",
      "Epoch 297/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8979 - mae: 1.1043 - val_loss: 1.3836 - val_mae: 0.9042\n",
      "Epoch 298/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.8897 - mae: 1.1064 - val_loss: 0.8184 - val_mae: 0.6815\n",
      "Epoch 299/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9604 - mae: 1.1376 - val_loss: 1.6803 - val_mae: 1.0098\n",
      "Epoch 300/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7086 - mae: 1.0088 - val_loss: 1.0044 - val_mae: 0.7687\n",
      "Epoch 301/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7144 - mae: 1.0351 - val_loss: 2.1124 - val_mae: 1.1786\n",
      "Epoch 302/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7157 - mae: 0.9659 - val_loss: 0.9010 - val_mae: 0.7386\n",
      "Epoch 303/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 2.0847 - mae: 1.1019 - val_loss: 1.9492 - val_mae: 1.1237\n",
      "Epoch 304/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5032 - mae: 0.9581 - val_loss: 1.2142 - val_mae: 0.8172\n",
      "Epoch 305/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9676 - mae: 1.1122 - val_loss: 1.7313 - val_mae: 0.9852\n",
      "Epoch 306/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7436 - mae: 1.0484 - val_loss: 0.9243 - val_mae: 0.7143\n",
      "Epoch 307/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.6339 - mae: 1.1832 - val_loss: 1.6258 - val_mae: 0.9386\n",
      "Epoch 308/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1854 - mae: 1.1770 - val_loss: 1.2383 - val_mae: 0.8357\n",
      "Epoch 309/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 1.7241 - mae: 1.0480 - val_loss: 1.0091 - val_mae: 0.7581\n",
      "Epoch 310/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8806 - mae: 1.1100 - val_loss: 1.4359 - val_mae: 0.9098\n",
      "Epoch 311/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7809 - mae: 1.0806 - val_loss: 1.1308 - val_mae: 0.7854\n",
      "Epoch 312/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.4254 - mae: 1.2430 - val_loss: 1.8093 - val_mae: 1.0400\n",
      "Epoch 313/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5069 - mae: 0.9550 - val_loss: 0.9825 - val_mae: 0.7558\n",
      "Epoch 314/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.3300 - mae: 1.2509 - val_loss: 1.6534 - val_mae: 1.0323\n",
      "Epoch 315/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7791 - mae: 1.0418 - val_loss: 1.1820 - val_mae: 0.8243\n",
      "Epoch 316/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.0792 - mae: 1.0514 - val_loss: 1.8331 - val_mae: 1.0223\n",
      "Epoch 317/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.7653 - mae: 1.0448 - val_loss: 1.1328 - val_mae: 0.7674\n",
      "Epoch 318/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.0425 - mae: 1.1676 - val_loss: 1.7729 - val_mae: 1.0449\n",
      "Epoch 319/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 1.4751 - mae: 0.9261 - val_loss: 0.9050 - val_mae: 0.7175\n",
      "Epoch 320/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7565 - mae: 1.0868 - val_loss: 1.7630 - val_mae: 1.0626\n",
      "Epoch 321/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7057 - mae: 1.0071 - val_loss: 1.1205 - val_mae: 0.8224\n",
      "Epoch 322/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8432 - mae: 1.0824 - val_loss: 1.0609 - val_mae: 0.8106\n",
      "Epoch 323/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9263 - mae: 1.0722 - val_loss: 2.5932 - val_mae: 1.3483\n",
      "Epoch 324/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2062 - mae: 1.1485 - val_loss: 0.7507 - val_mae: 0.6426\n",
      "Epoch 325/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7686 - mae: 1.0835 - val_loss: 1.8256 - val_mae: 1.0486\n",
      "Epoch 326/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.4860 - mae: 1.2258 - val_loss: 0.8593 - val_mae: 0.6836\n",
      "Epoch 327/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.2256 - mae: 1.1668 - val_loss: 1.6167 - val_mae: 0.9612\n",
      "Epoch 328/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - loss: 1.7089 - mae: 1.0659 - val_loss: 0.7216 - val_mae: 0.6227\n",
      "Epoch 329/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 1.7924 - mae: 1.0727 - val_loss: 1.7667 - val_mae: 1.0629\n",
      "Epoch 330/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0656 - mae: 1.0755 - val_loss: 0.9697 - val_mae: 0.7729\n",
      "Epoch 331/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8851 - mae: 1.0407 - val_loss: 1.4344 - val_mae: 0.9268\n",
      "Epoch 332/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4526 - mae: 0.9120 - val_loss: 1.0538 - val_mae: 0.7858\n",
      "Epoch 333/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6372 - mae: 1.0445 - val_loss: 1.0657 - val_mae: 0.7963\n",
      "Epoch 334/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5310 - mae: 0.9854 - val_loss: 1.2768 - val_mae: 0.8789\n",
      "Epoch 335/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9166 - mae: 1.1351 - val_loss: 1.3049 - val_mae: 0.8944\n",
      "Epoch 336/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6989 - mae: 1.0508 - val_loss: 1.2367 - val_mae: 0.8739\n",
      "Epoch 337/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.6148 - mae: 0.9728 - val_loss: 1.2235 - val_mae: 0.8555\n",
      "Epoch 338/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5860 - mae: 1.0031 - val_loss: 1.4113 - val_mae: 0.9171\n",
      "Epoch 339/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 1.8702 - mae: 1.0871 - val_loss: 0.8391 - val_mae: 0.6700\n",
      "Epoch 340/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2076 - mae: 1.1360 - val_loss: 2.2508 - val_mae: 1.1696\n",
      "Epoch 341/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1309 - mae: 1.1174 - val_loss: 0.8089 - val_mae: 0.6670\n",
      "Epoch 342/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.0004 - mae: 1.1207 - val_loss: 2.7590 - val_mae: 1.3125\n",
      "Epoch 343/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1528 - mae: 1.1118 - val_loss: 0.7017 - val_mae: 0.6401\n",
      "Epoch 344/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0151 - mae: 1.1180 - val_loss: 1.7369 - val_mae: 1.0035\n",
      "Epoch 345/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.8476 - mae: 1.0520 - val_loss: 0.8094 - val_mae: 0.6576\n",
      "Epoch 346/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.0073 - mae: 1.1212 - val_loss: 1.8180 - val_mae: 1.0621\n",
      "Epoch 347/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7763 - mae: 1.0332 - val_loss: 1.3399 - val_mae: 0.8915\n",
      "Epoch 348/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 2.0942 - mae: 1.0932 - val_loss: 1.5451 - val_mae: 0.9517\n",
      "Epoch 349/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.0540 - mae: 1.1388 - val_loss: 1.1573 - val_mae: 0.7870\n",
      "Epoch 350/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7960 - mae: 1.0506 - val_loss: 1.2022 - val_mae: 0.8333\n",
      "Epoch 351/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.7606 - mae: 1.0587 - val_loss: 1.1068 - val_mae: 0.7980\n",
      "Epoch 352/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7172 - mae: 0.9991 - val_loss: 1.4414 - val_mae: 0.8936\n",
      "Epoch 353/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4058 - mae: 0.9164 - val_loss: 1.1010 - val_mae: 0.7671\n",
      "Epoch 354/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8877 - mae: 1.1185 - val_loss: 1.4164 - val_mae: 0.9057\n",
      "Epoch 355/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.1109 - mae: 1.1205 - val_loss: 1.2243 - val_mae: 0.8407\n",
      "Epoch 356/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.5955 - mae: 0.9846 - val_loss: 1.6649 - val_mae: 0.9229\n",
      "Epoch 357/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 2.0398 - mae: 1.0700 - val_loss: 0.9444 - val_mae: 0.7137\n",
      "Epoch 358/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.8152 - mae: 1.0877 - val_loss: 1.9118 - val_mae: 1.0670\n",
      "Epoch 359/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.7873 - mae: 1.0504 - val_loss: 0.9302 - val_mae: 0.7090\n",
      "Epoch 360/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9268 - mae: 1.1433 - val_loss: 2.2415 - val_mae: 1.1553\n",
      "Epoch 361/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1375 - mae: 1.1646 - val_loss: 0.6794 - val_mae: 0.6121\n",
      "Epoch 362/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 2.4839 - mae: 1.2257 - val_loss: 3.1142 - val_mae: 1.4428\n",
      "Epoch 363/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.3598 - mae: 1.2028 - val_loss: 0.7341 - val_mae: 0.6372\n",
      "Epoch 364/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.0134 - mae: 1.0902 - val_loss: 2.6064 - val_mae: 1.2712\n",
      "Epoch 365/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.2131 - mae: 1.1093 - val_loss: 0.6992 - val_mae: 0.6056\n",
      "Epoch 366/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 2.6619 - mae: 1.3146 - val_loss: 2.1756 - val_mae: 1.2227\n",
      "Epoch 367/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7630 - mae: 1.0382 - val_loss: 0.9931 - val_mae: 0.7611\n",
      "Epoch 368/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9205 - mae: 1.1025 - val_loss: 1.8440 - val_mae: 1.0369\n",
      "Epoch 369/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6590 - mae: 1.0010 - val_loss: 0.9678 - val_mae: 0.7177\n",
      "Epoch 370/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8357 - mae: 1.0845 - val_loss: 1.0884 - val_mae: 0.7800\n",
      "Epoch 371/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.2152 - mae: 1.2345 - val_loss: 1.5269 - val_mae: 0.9506\n",
      "Epoch 372/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5943 - mae: 0.9795 - val_loss: 1.2431 - val_mae: 0.8411\n",
      "Epoch 373/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9264 - mae: 1.0637 - val_loss: 1.1447 - val_mae: 0.8236\n",
      "Epoch 374/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7091 - mae: 1.0328 - val_loss: 2.6835 - val_mae: 1.3164\n",
      "Epoch 375/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 2.1477 - mae: 1.1230 - val_loss: 0.6757 - val_mae: 0.6071\n",
      "Epoch 376/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.4905 - mae: 1.2329 - val_loss: 2.6884 - val_mae: 1.2400\n",
      "Epoch 377/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6768 - mae: 1.0229 - val_loss: 0.8394 - val_mae: 0.6708\n",
      "Epoch 378/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9618 - mae: 1.1102 - val_loss: 2.1663 - val_mae: 1.1767\n",
      "Epoch 379/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.4734 - mae: 0.9477 - val_loss: 0.6367 - val_mae: 0.5970\n",
      "Epoch 380/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.2449 - mae: 1.1669 - val_loss: 2.0560 - val_mae: 1.1223\n",
      "Epoch 381/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.5978 - mae: 0.9835 - val_loss: 0.8180 - val_mae: 0.6782\n",
      "Epoch 382/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8734 - mae: 1.0934 - val_loss: 1.7845 - val_mae: 1.0684\n",
      "Epoch 383/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6575 - mae: 0.9791 - val_loss: 1.0598 - val_mae: 0.7444\n",
      "Epoch 384/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 1.8273 - mae: 1.0495 - val_loss: 2.6404 - val_mae: 1.2548\n",
      "Epoch 385/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 2.6608 - mae: 1.2125 - val_loss: 1.1061 - val_mae: 0.7543\n",
      "Epoch 386/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.3173 - mae: 1.1918 - val_loss: 0.7617 - val_mae: 0.6606\n",
      "Epoch 387/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.8582 - mae: 1.0980 - val_loss: 1.5006 - val_mae: 0.9891\n",
      "Epoch 388/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8543 - mae: 1.0472 - val_loss: 0.7194 - val_mae: 0.6149\n",
      "Epoch 389/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8059 - mae: 1.0568 - val_loss: 2.4964 - val_mae: 1.2038\n",
      "Epoch 390/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9123 - mae: 1.0813 - val_loss: 0.9080 - val_mae: 0.7374\n",
      "Epoch 391/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.3138 - mae: 1.2007 - val_loss: 2.9695 - val_mae: 1.3355\n",
      "Epoch 392/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7004 - mae: 1.0255 - val_loss: 1.0341 - val_mae: 0.7342\n",
      "Epoch 393/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 2.3307 - mae: 1.1853 - val_loss: 1.3186 - val_mae: 0.9263\n",
      "Epoch 394/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.3890 - mae: 0.9377 - val_loss: 1.6717 - val_mae: 1.0991\n",
      "Epoch 395/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.5291 - mae: 0.9646 - val_loss: 1.1300 - val_mae: 0.8510\n",
      "Epoch 396/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.6356 - mae: 1.0152 - val_loss: 1.1337 - val_mae: 0.7604\n",
      "Epoch 397/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7745 - mae: 1.0495 - val_loss: 1.5254 - val_mae: 0.8916\n",
      "Epoch 398/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 1.7428 - mae: 1.0388 - val_loss: 1.0903 - val_mae: 0.8046\n",
      "Epoch 399/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.6267 - mae: 0.9335 - val_loss: 1.0329 - val_mae: 0.8006\n",
      "Epoch 400/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6181 - mae: 0.9696 - val_loss: 1.1531 - val_mae: 0.7994\n",
      "Epoch 401/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.2097 - mae: 0.8846 - val_loss: 1.4822 - val_mae: 0.9492\n",
      "Epoch 402/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.1273 - mae: 1.1509 - val_loss: 1.2760 - val_mae: 0.8737\n",
      "Epoch 403/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4717 - mae: 0.9645 - val_loss: 1.9964 - val_mae: 1.0883\n",
      "Epoch 404/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.9348 - mae: 1.0599 - val_loss: 0.7202 - val_mae: 0.6256\n",
      "Epoch 405/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.4460 - mae: 1.2843 - val_loss: 3.4581 - val_mae: 1.4780\n",
      "Epoch 406/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.5678 - mae: 1.1867 - val_loss: 0.7278 - val_mae: 0.6494\n",
      "Epoch 407/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 3.0748 - mae: 1.3662 - val_loss: 3.7292 - val_mae: 1.5433\n",
      "Epoch 408/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7730 - mae: 1.0029 - val_loss: 1.0737 - val_mae: 0.7254\n",
      "Epoch 409/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0022 - mae: 1.1313 - val_loss: 2.4038 - val_mae: 1.1940\n",
      "Epoch 410/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 2.1833 - mae: 1.1200 - val_loss: 0.6549 - val_mae: 0.6063\n",
      "Epoch 411/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.1146 - mae: 1.1527 - val_loss: 2.2424 - val_mae: 1.3067\n",
      "Epoch 412/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8906 - mae: 1.0526 - val_loss: 1.2664 - val_mae: 0.8662\n",
      "Epoch 413/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8995 - mae: 1.1079 - val_loss: 1.3857 - val_mae: 0.8398\n",
      "Epoch 414/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9567 - mae: 1.1127 - val_loss: 1.3182 - val_mae: 0.8049\n",
      "Epoch 415/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9951 - mae: 1.1113 - val_loss: 1.4906 - val_mae: 0.9417\n",
      "Epoch 416/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9099 - mae: 1.0930 - val_loss: 1.7333 - val_mae: 1.0713\n",
      "Epoch 417/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.8918 - mae: 1.0701 - val_loss: 0.9319 - val_mae: 0.7475\n",
      "Epoch 418/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.9623 - mae: 1.0861 - val_loss: 2.3563 - val_mae: 1.2486\n",
      "Epoch 419/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7654 - mae: 1.0250 - val_loss: 0.7998 - val_mae: 0.6637\n",
      "Epoch 420/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9938 - mae: 1.1183 - val_loss: 2.1969 - val_mae: 1.1251\n",
      "Epoch 421/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9271 - mae: 1.0840 - val_loss: 0.7028 - val_mae: 0.5875\n",
      "Epoch 422/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7408 - mae: 1.0342 - val_loss: 2.4298 - val_mae: 1.1972\n",
      "Epoch 423/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0399 - mae: 1.0452 - val_loss: 1.3559 - val_mae: 0.8757\n",
      "Epoch 424/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.3775 - mae: 1.1974 - val_loss: 1.4248 - val_mae: 0.8905\n",
      "Epoch 425/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1528 - mae: 1.0380 - val_loss: 1.2973 - val_mae: 0.8499\n",
      "Epoch 426/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.8985 - mae: 1.0812 - val_loss: 1.4873 - val_mae: 0.9230\n",
      "Epoch 427/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - loss: 1.7906 - mae: 1.1265 - val_loss: 1.4771 - val_mae: 0.9292\n",
      "Epoch 428/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5233 - mae: 0.9400 - val_loss: 0.7517 - val_mae: 0.6591\n",
      "Epoch 429/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5798 - mae: 0.9823 - val_loss: 2.8246 - val_mae: 1.3491\n",
      "Epoch 430/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9529 - mae: 1.1298 - val_loss: 1.0891 - val_mae: 0.7603\n",
      "Epoch 431/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.6599 - mae: 0.9901 - val_loss: 1.6762 - val_mae: 0.9571\n",
      "Epoch 432/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.3856 - mae: 0.9123 - val_loss: 1.0817 - val_mae: 0.7600\n",
      "Epoch 433/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 2.1291 - mae: 1.1383 - val_loss: 1.8258 - val_mae: 1.0285\n",
      "Epoch 434/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.5130 - mae: 0.9401 - val_loss: 1.8772 - val_mae: 1.0824\n",
      "Epoch 435/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7044 - mae: 0.9896 - val_loss: 0.9280 - val_mae: 0.7532\n",
      "Epoch 436/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.7048 - mae: 1.0218 - val_loss: 2.2862 - val_mae: 1.2195\n",
      "Epoch 437/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7720 - mae: 1.0480 - val_loss: 0.9481 - val_mae: 0.7133\n",
      "Epoch 438/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.2038 - mae: 1.1699 - val_loss: 2.2956 - val_mae: 1.1432\n",
      "Epoch 439/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.2366 - mae: 1.1783 - val_loss: 0.8081 - val_mae: 0.6904\n",
      "Epoch 440/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step - loss: 1.9706 - mae: 1.1095 - val_loss: 2.6273 - val_mae: 1.2751\n",
      "Epoch 441/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9006 - mae: 1.0241 - val_loss: 0.7075 - val_mae: 0.6269\n",
      "Epoch 442/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7113 - mae: 1.0303 - val_loss: 1.7819 - val_mae: 1.0483\n",
      "Epoch 443/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.8512 - mae: 1.0147 - val_loss: 0.7211 - val_mae: 0.6598\n",
      "Epoch 444/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - loss: 1.9934 - mae: 1.0999 - val_loss: 1.7416 - val_mae: 1.0275\n",
      "Epoch 445/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.4326 - mae: 0.9482 - val_loss: 0.8928 - val_mae: 0.7128\n",
      "Epoch 446/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.7771 - mae: 1.2483 - val_loss: 2.1341 - val_mae: 1.1106\n",
      "Epoch 447/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7308 - mae: 1.0188 - val_loss: 1.0639 - val_mae: 0.7451\n",
      "Epoch 448/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - loss: 1.9520 - mae: 1.1306 - val_loss: 1.6949 - val_mae: 1.0334\n",
      "Epoch 449/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4243 - mae: 0.9247 - val_loss: 1.4501 - val_mae: 0.9482\n",
      "Epoch 450/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8869 - mae: 1.0583 - val_loss: 0.9975 - val_mae: 0.7640\n",
      "Epoch 451/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - loss: 1.7775 - mae: 0.9853 - val_loss: 1.2804 - val_mae: 0.8775\n",
      "Epoch 452/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.2769 - mae: 0.8898 - val_loss: 1.4599 - val_mae: 0.9505\n",
      "Epoch 453/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.5870 - mae: 0.9654 - val_loss: 0.7189 - val_mae: 0.6626\n",
      "Epoch 454/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.8515 - mae: 1.0061 - val_loss: 1.6690 - val_mae: 1.0359\n",
      "Epoch 455/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.6897 - mae: 1.0273 - val_loss: 0.8218 - val_mae: 0.7072\n",
      "Epoch 456/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.0694 - mae: 1.1291 - val_loss: 1.7793 - val_mae: 1.0556\n",
      "Epoch 457/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.0924 - mae: 1.1138 - val_loss: 1.1601 - val_mae: 0.8077\n",
      "Epoch 458/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7652 - mae: 1.0486 - val_loss: 1.2344 - val_mae: 0.8462\n",
      "Epoch 459/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step - loss: 1.7376 - mae: 0.9898 - val_loss: 1.5020 - val_mae: 0.9156\n",
      "Epoch 460/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9091 - mae: 1.1075 - val_loss: 0.7563 - val_mae: 0.6383\n",
      "Epoch 461/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.1558 - mae: 1.1661 - val_loss: 3.8589 - val_mae: 1.6644\n",
      "Epoch 462/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.6766 - mae: 1.3023 - val_loss: 0.6814 - val_mae: 0.6282\n",
      "Epoch 463/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.1622 - mae: 1.1581 - val_loss: 2.6223 - val_mae: 1.2790\n",
      "Epoch 464/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.9946 - mae: 1.0930 - val_loss: 0.8084 - val_mae: 0.6758\n",
      "Epoch 465/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 2.9577 - mae: 1.3423 - val_loss: 1.9916 - val_mae: 1.1167\n",
      "Epoch 466/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.1519 - mae: 1.1279 - val_loss: 0.9631 - val_mae: 0.7074\n",
      "Epoch 467/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - loss: 2.0878 - mae: 1.1319 - val_loss: 1.2569 - val_mae: 0.8170\n",
      "Epoch 468/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.7096 - mae: 1.0073 - val_loss: 1.2876 - val_mae: 0.8666\n",
      "Epoch 469/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7844 - mae: 1.0401 - val_loss: 0.7144 - val_mae: 0.6593\n",
      "Epoch 470/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9360 - mae: 1.1183 - val_loss: 1.2996 - val_mae: 0.8907\n",
      "Epoch 471/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7003 - mae: 1.0362 - val_loss: 1.0913 - val_mae: 0.7848\n",
      "Epoch 472/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7190 - mae: 1.0210 - val_loss: 0.8872 - val_mae: 0.6892\n",
      "Epoch 473/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.7286 - mae: 1.0298 - val_loss: 1.1547 - val_mae: 0.8132\n",
      "Epoch 474/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4312 - mae: 0.9007 - val_loss: 1.1485 - val_mae: 0.8019\n",
      "Epoch 475/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step - loss: 1.9532 - mae: 1.0853 - val_loss: 2.0165 - val_mae: 1.1175\n",
      "Epoch 476/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.9769 - mae: 1.1476 - val_loss: 0.9175 - val_mae: 0.7172\n",
      "Epoch 477/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6076 - mae: 0.9596 - val_loss: 1.8349 - val_mae: 1.0177\n",
      "Epoch 478/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.5929 - mae: 0.9818 - val_loss: 1.2836 - val_mae: 0.8151\n",
      "Epoch 479/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4036 - mae: 0.9392 - val_loss: 1.1573 - val_mae: 0.7564\n",
      "Epoch 480/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9582 - mae: 1.0786 - val_loss: 1.0040 - val_mae: 0.7997\n",
      "Epoch 481/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7401 - mae: 1.0734 - val_loss: 2.3256 - val_mae: 1.2929\n",
      "Epoch 482/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 2.0660 - mae: 1.1615 - val_loss: 0.7189 - val_mae: 0.6289\n",
      "Epoch 483/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.9263 - mae: 1.1040 - val_loss: 2.1341 - val_mae: 1.1723\n",
      "Epoch 484/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 1.6279 - mae: 1.0172 - val_loss: 0.7544 - val_mae: 0.6252\n",
      "Epoch 485/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4700 - mae: 0.9533 - val_loss: 2.0464 - val_mae: 1.1100\n",
      "Epoch 486/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.9654 - mae: 1.0797 - val_loss: 0.7745 - val_mae: 0.6688\n",
      "Epoch 487/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 2.1748 - mae: 1.1500 - val_loss: 1.5266 - val_mae: 0.9660\n",
      "Epoch 488/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/step - loss: 1.6041 - mae: 0.9807 - val_loss: 1.1716 - val_mae: 0.8383\n",
      "Epoch 489/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.7887 - mae: 1.0549 - val_loss: 1.3061 - val_mae: 0.8792\n",
      "Epoch 490/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - loss: 1.4571 - mae: 0.9241 - val_loss: 1.0740 - val_mae: 0.7757\n",
      "Epoch 491/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.6322 - mae: 1.0237 - val_loss: 1.7287 - val_mae: 1.0094\n",
      "Epoch 492/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 2.0471 - mae: 0.9826 - val_loss: 0.9371 - val_mae: 0.7494\n",
      "Epoch 493/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.2594 - mae: 1.1176 - val_loss: 1.9754 - val_mae: 1.0713\n",
      "Epoch 494/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 1.7175 - mae: 1.0246 - val_loss: 0.6990 - val_mae: 0.6061\n",
      "Epoch 495/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 2.2500 - mae: 1.1728 - val_loss: 1.3109 - val_mae: 0.8998\n",
      "Epoch 496/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 1.4661 - mae: 0.9362 - val_loss: 1.1779 - val_mae: 0.8271\n",
      "Epoch 497/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - loss: 1.8753 - mae: 1.0187 - val_loss: 1.0852 - val_mae: 0.7956\n",
      "Epoch 498/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 2.0408 - mae: 1.0733 - val_loss: 1.4251 - val_mae: 0.9223\n",
      "Epoch 499/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - loss: 1.4419 - mae: 0.9306 - val_loss: 1.2334 - val_mae: 0.8430\n",
      "Epoch 500/500\n",
      "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - loss: 1.6341 - mae: 1.0077 - val_loss: 1.3798 - val_mae: 0.9132\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step\n",
      "ANN - Mean Squared Error: 1.7894398727100067\n",
      "ANN - Root Mean Squared Error: 1.337699470251075\n",
      "ANN - Mean Absolute Error: 1.1416759594627048\n",
      "ANN - R-squared: 0.7664556361774888\n"
     ]
    }
   ],
   "source": [
    "\"\"\"import tensorflow as tf\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "# Ensure reproducibility at the TensorFlow level\n",
    "os.environ['PYTHONHASHSEED'] = '42'\n",
    "tf.keras.utils.set_random_seed(42)\n",
    "tf.config.experimental.enable_op_determinism()\n",
    "\n",
    "# Define the model\n",
    "ann = tf.keras.models.Sequential([\n",
    "    Dense(40, activation='relu', input_shape=(X_train.shape[1],)),\n",
    "    Dropout(0.2),\n",
    "    Dense(100, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(60, activation='relu'),\n",
    "    Dropout(0.2),\n",
    "    Dense(1)  # No activation for regression\n",
    "])\n",
    "\n",
    "# Create Adam optimizer with learning rate 0.01\n",
    "optimizer = Adam(learning_rate=0.01)\n",
    "\n",
    "# Compile the model with the custom optimizer\n",
    "ann.compile(optimizer=optimizer, loss='mean_squared_error', metrics=['mae'])\n",
    "\n",
    "# Train the model\n",
    "history = ann.fit(X_train, y_train, batch_size=32, epochs=500, verbose=1, validation_split=0.2)\n",
    "\n",
    "# Predict the target values for the test set\n",
    "y_pred_ann = ann.predict(X_test)\n",
    "\n",
    "# Calculate MSE, RMSE, MAE, and R-squared\n",
    "mse_ann = mean_squared_error(y_test, y_pred_ann)\n",
    "rmse_ann = np.sqrt(mse_ann)\n",
    "mae_ann = mean_absolute_error(y_test, y_pred_ann)\n",
    "r2_ann = r2_score(y_test, y_pred_ann)\n",
    "\n",
    "print(f\"ANN - Mean Squared Error: {mse_ann}\")\n",
    "print(f\"ANN - Root Mean Squared Error: {rmse_ann}\")\n",
    "print(f\"ANN - Mean Absolute Error: {mae_ann}\")\n",
    "print(f\"ANN - R-squared: {r2_ann}\")\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss curve during training\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('ANN Training and Validation Loss Curve')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Scatter plot of actual vs predicted values (ANN)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred_ann, color='green', label='Predicted vs Actual')\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "plt.title('ANN - Predicted vs Actual Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comments and Explanation of Parameters:\n",
    "Hidden Layer Size (MLP): Determines the number of neurons in each hidden layer. Larger values can increase model capacity but may lead to overfitting if the dataset is small.\n",
    "\n",
    "Increasing neurons → Higher complexity, potentially better accuracy (but can overfit).\n",
    "Decreasing neurons → Simpler model, lower risk of overfitting but might underperform.\n",
    "Dropout (ANN): Randomly drops neurons during training to prevent overfitting.\n",
    "\n",
    "Increasing dropout → Forces the network to be less dependent on individual neurons, reducing overfitting.\n",
    "Decreasing dropout → Can lead to overfitting, as the model becomes too reliant on specific neurons.\n",
    "Learning Rate: Controls how fast the model updates weights during optimization.\n",
    "\n",
    "Higher learning rate → Faster convergence, but may overshoot the optimal point.\n",
    "Lower learning rate → More precise but slower training.\n",
    "Batch Size: Determines how many samples the model processes before updating weights.\n",
    "\n",
    "Smaller batch size → Noisier updates but potentially more generalization.\n",
    "Larger batch size → More stable updates but could get stuck in local minima.\n",
    "Max Iterations (MLP): Limits the number of optimization steps for MLP.\n",
    "\n",
    "More iterations → More chances for the model to learn, but may overfit after a certain point.\n",
    "Fewer iterations → Prevents overfitting but may lead to underfitting.\n",
    "Epochs (ANN): Controls how many passes the model makes through the entire dataset.\n",
    "\n",
    "More epochs → Longer training, potentially better performance but higher risk of overfitting.\n",
    "Fewer epochs → May stop training prematurely, resulting in underfitting.\n",
    "Additional Graphs and Data:\n",
    "You could also compare the loss (training/validation) curves, which show how well each model is learning over time. Including scatter plots of actual vs. predicted values helps to visualize how well the models predict on unseen data. The lower the spread around the identity line (y=x), the better the model's performance.\n",
    "\n",
    "By using different visualizations and analyzing metrics such as MSE, RMSE, and R-squared, you can provide strong evidence of which model performs better and why. Based on your R-squared scores, ANN appears to perform better than MLP in your case, making it a stronger candidate for predictive tasks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"MLP - Mean Squared Error: 2.288575149650712\n",
    "MLP - Root Mean Squared Error: 1.5128037379814714\n",
    "MLP - R^2 Score: 0.7013122175623938\n",
    "\"\"\"\n",
    "# Scores for MLP\n",
    "mlp_mse = 2.2885\n",
    "mlp_rmse = 1.5128\n",
    "mlp_r2 = 0.7013\n",
    "\n",
    "\"\"\"\n",
    "ANN - Mean Squared Error: 1.0139725169793248\n",
    "ANN - Root Mean Squared Error: 1.0069620236033356\n",
    "ANN - Mean Absolute Error: 0.8039977654166843\n",
    "ANN - R-squared: 0.867663859499892\n",
    "\n",
    "\"\"\"\n",
    "# Scores for ANN\n",
    "ann_mse = 1.0139\n",
    "ann_rmse = 1.0069\n",
    "ann_mae = 0.8039\n",
    "ann_r2 = 0.8676\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# MSE comparison\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.bar(['MLP', 'ANN'], [mlp_mse, ann_mse], color=['lightblue', 'lightgreen'])\n",
    "plt.title('Mean Squared Error (MSE)')\n",
    "plt.ylabel('MSE')\n",
    "plt.grid(True)\n",
    "\n",
    "# RMSE comparison\n",
    "\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.bar(['MLP', 'ANN'], [mlp_rmse, ann_rmse], color=['lightblue', 'lightgreen'])\n",
    "plt.title('Root Mean Squared Error (RMSE)')\n",
    "plt.ylabel('RMSE')\n",
    "plt.grid(True)\n",
    "\n",
    "# R² comparison\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.bar(['MLP', 'ANN'], [mlp_r2, ann_r2], color=['lightblue', 'lightgreen'])\n",
    "plt.title('R² Score')\n",
    "plt.ylabel('R² Score')\n",
    "plt.ylim(0, 1)\n",
    "plt.grid(True)\n",
    "\n",
    "# Show the plot\n",
    "plt.suptitle('Comparison of MLP vs ANN Model Performance')\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.96])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Best Model: The Artificial Neural Network (ANN) clearly outperforms the Multi-Layer Perceptron (MLP) model in all metrics: MSE, RMSE, and R² score.\n",
    "\n",
    "- Reason: The ANN shows a lower MSE and RMSE, meaning its predictions are closer to the actual values, and a higher R² score indicates that it better captures the underlying relationships in the data. The inclusion of multiple hidden layers and dropout regularization in the ANN might be contributing to its superior performance, as it can model more complex patterns compared to MLP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "Mean Squared Error: 1.0139725169793248\n",
      "Root Mean Squared Error: 1.0069620236033356\n",
      "Mean Absolute Error: 0.8039977654166843\n",
      "R-squared: 0.867663859499892\n",
      "\n",
      "Comparison of Actual vs Predicted Values:\n",
      "     Actual Marks  Predicted Marks\n",
      "11             13        12.618332\n",
      "241            11         9.499202\n",
      "17             10         9.788215\n",
      "208             9         8.766021\n",
      "261             8         8.435254\n",
      "116            14        12.653849\n",
      "212            14        12.879122\n",
      "163             9         9.243729\n",
      "62             10        10.128393\n",
      "32             16        15.687671\n"
     ]
    }
   ],
   "source": [
    "# Define a function to test predictions\n",
    "def test_predictions(model, X_test, y_test):\n",
    "    \"\"\"\n",
    "    This function takes the trained model, test data (X_test), and true target values (y_test),\n",
    "    then predicts the target values and shows a comparison between the actual and predicted values.\n",
    "\n",
    "    Args:\n",
    "        model: Trained model used for prediction.\n",
    "        X_test: Test data features.\n",
    "        y_test: Actual target values (true final year marks).\n",
    "\n",
    "    Returns:\n",
    "        A DataFrame showing the actual vs predicted values and error metrics.\n",
    "    \"\"\"\n",
    "    # Predict the target values for the test set\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # Flatten y_pred to match dimensions with y_test\n",
    "    y_pred = y_pred.flatten()\n",
    "\n",
    "    # Create a DataFrame to compare actual and predicted values\n",
    "    results_df = pd.DataFrame({\n",
    "        'Actual Marks': y_test,\n",
    "        'Predicted Marks': y_pred\n",
    "    })\n",
    "\n",
    "    # Calculate the error metrics\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    # Print error metrics\n",
    "    print(f\"Mean Squared Error: {mse}\")\n",
    "    print(f\"Root Mean Squared Error: {rmse}\")\n",
    "    print(f\"Mean Absolute Error: {mae}\")\n",
    "    print(f\"R-squared: {r2}\")\n",
    "\n",
    "    # Show the first 10 predictions vs actual values\n",
    "    print(\"\\nComparison of Actual vs Predicted Values:\")\n",
    "    print(results_df.head(10))\n",
    "\n",
    "    return results_df\n",
    "\n",
    "# Call the function to test predictions\n",
    "results = test_predictions(ann, X_test_scaled, y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_new_input(model, scaler, age, year1_marks, year2_marks, studytime, failures):\n",
    "    \"\"\"\n",
    "    Function to take new input data and predict final marks using the trained model.\n",
    "    \n",
    "    Args:\n",
    "        model: Trained Keras model used for prediction.\n",
    "        scaler: Fitted StandardScaler used for scaling input data.\n",
    "        age: Age of the student.\n",
    "        year1_marks: Marks in year 1 (G1).\n",
    "        year2_marks: Marks in year 2 (G2).\n",
    "        studytime: Time spent studying.\n",
    "        failures: Number of failures.\n",
    "    \n",
    "    Returns:\n",
    "        Predicted final marks.\n",
    "    \"\"\"\n",
    "    # Create a DataFrame for the new input (to match the structure of the original input)\n",
    "    new_input = pd.DataFrame({\n",
    "        'age': [age],\n",
    "        'year1_marks': [year1_marks],\n",
    "        'year2_marks': [year2_marks],\n",
    "        'studytime': [studytime],\n",
    "        'failures': [failures]\n",
    "    })\n",
    "\n",
    "    # Scale the new input data using the fitted scaler\n",
    "    new_input_scaled = scaler.transform(new_input)\n",
    "\n",
    "    # Predict final marks using the trained model\n",
    "    predicted_marks = model.predict(new_input_scaled)\n",
    "    \n",
    "    # Return the predicted final marks\n",
    "    return predicted_marks[0][0]  # Since it's a single prediction, return just the value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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 64ms/step\n",
      "Predicted final marks: 161.92\n"
     ]
    }
   ],
   "source": [
    "# Example: Predict for a student with the following details:\n",
    "age = 18\n",
    "year1_marks = 100\n",
    "year2_marks = 100\n",
    "studytime = 100\n",
    "failures = 0\n",
    "\n",
    "# Call the prediction function with trained model and input data\n",
    "predicted_final_marks = predict_new_input(ann, scaler, age, year1_marks, year2_marks, studytime, failures)\n",
    "\n",
    "# Print the predicted final marks\n",
    "print(f\"Predicted final marks: {predicted_final_marks:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# export the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Save the trained model to an HDF5 file (this saves the architecture, weights, and optimizer state)\n",
    "ann.save(\"final_marks_predictor_model.h5\")\n",
    "\n",
    "# Save the scaler as well (using pickle since it is necessary for preprocessing)\n",
    "import pickle\n",
    "with open(\"scaler.pkl\", \"wb\") as f:\n",
    "    pickle.dump(scaler, f)\n"
   ]
  }
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