{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MOVIE SUCCESS IN RELATION TO ITS FILM DISTRIBUTION COMPANY\n",
    "\n",
    "Author: [Alexander Nguyen]\n",
    "\n",
    "Course Project, UC Irvine, Math 10, S24\n",
    "\n",
    "I would like to post my notebook on the course's website. [Yes]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INTRODUCTION\n",
    "\n",
    "The movie industry is an extremely competitive environment, where production companies constantly aim to release movies that do well in the box-office while also pleasing audiences. These companies will pour hundreds of millions of dollars into these movies in hopes of achieving success. In the dataset I have chosen, I have a list of 7668 movies from 1986 to 2016. By analyzing the data, I hope to find if there are any correlations between the commercial and popularity success of a movie alongside it's company. I also hope to look for any other correlations and create predictions based on these correlations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXPLORING THE DATA\n",
    "\n",
    "First, we load up the csv file through pandas and display the first 5 rows of the datatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>name</th>\n",
       "      <th>rating</th>\n",
       "      <th>genre</th>\n",
       "      <th>year</th>\n",
       "      <th>released</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "      <th>star</th>\n",
       "      <th>country</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>R</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1980</td>\n",
       "      <td>June 13, 1980 (United States)</td>\n",
       "      <td>8.4</td>\n",
       "      <td>927000.0</td>\n",
       "      <td>Stanley Kubrick</td>\n",
       "      <td>Stephen King</td>\n",
       "      <td>Jack Nicholson</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>19000000.0</td>\n",
       "      <td>46998772.0</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Blue Lagoon</td>\n",
       "      <td>R</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>1980</td>\n",
       "      <td>July 2, 1980 (United States)</td>\n",
       "      <td>5.8</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>Randal Kleiser</td>\n",
       "      <td>Henry De Vere Stacpoole</td>\n",
       "      <td>Brooke Shields</td>\n",
       "      <td>United States</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>58853106.0</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action</td>\n",
       "      <td>1980</td>\n",
       "      <td>June 20, 1980 (United States)</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>Irvin Kershner</td>\n",
       "      <td>Leigh Brackett</td>\n",
       "      <td>Mark Hamill</td>\n",
       "      <td>United States</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>538375067.0</td>\n",
       "      <td>Lucasfilm</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Airplane!</td>\n",
       "      <td>PG</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1980</td>\n",
       "      <td>July 2, 1980 (United States)</td>\n",
       "      <td>7.7</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>Jim Abrahams</td>\n",
       "      <td>Jim Abrahams</td>\n",
       "      <td>Robert Hays</td>\n",
       "      <td>United States</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>83453539.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caddyshack</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1980</td>\n",
       "      <td>July 25, 1980 (United States)</td>\n",
       "      <td>7.3</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>Harold Ramis</td>\n",
       "      <td>Brian Doyle-Murray</td>\n",
       "      <td>Chevy Chase</td>\n",
       "      <td>United States</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>39846344.0</td>\n",
       "      <td>Orion Pictures</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             name rating      genre  year  \\\n",
       "0                                     The Shining      R      Drama  1980   \n",
       "1                                 The Blue Lagoon      R  Adventure  1980   \n",
       "2  Star Wars: Episode V - The Empire Strikes Back     PG     Action  1980   \n",
       "3                                       Airplane!     PG     Comedy  1980   \n",
       "4                                      Caddyshack      R     Comedy  1980   \n",
       "\n",
       "                        released  score      votes         director  \\\n",
       "0  June 13, 1980 (United States)    8.4   927000.0  Stanley Kubrick   \n",
       "1   July 2, 1980 (United States)    5.8    65000.0   Randal Kleiser   \n",
       "2  June 20, 1980 (United States)    8.7  1200000.0   Irvin Kershner   \n",
       "3   July 2, 1980 (United States)    7.7   221000.0     Jim Abrahams   \n",
       "4  July 25, 1980 (United States)    7.3   108000.0     Harold Ramis   \n",
       "\n",
       "                    writer            star         country      budget  \\\n",
       "0             Stephen King  Jack Nicholson  United Kingdom  19000000.0   \n",
       "1  Henry De Vere Stacpoole  Brooke Shields   United States   4500000.0   \n",
       "2           Leigh Brackett     Mark Hamill   United States  18000000.0   \n",
       "3             Jim Abrahams     Robert Hays   United States   3500000.0   \n",
       "4       Brian Doyle-Murray     Chevy Chase   United States   6000000.0   \n",
       "\n",
       "         gross             company  runtime  \n",
       "0   46998772.0        Warner Bros.    146.0  \n",
       "1   58853106.0   Columbia Pictures    104.0  \n",
       "2  538375067.0           Lucasfilm    124.0  \n",
       "3   83453539.0  Paramount Pictures     88.0  \n",
       "4   39846344.0      Orion Pictures     98.0  "
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('movies.csv.zip')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to drop unnecessary columns, which include the release date, director, writer, star, country, rating, runtime, and genre of the movie, as we mainly want to focus on how well the movie performed based on it's budget, production company, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>1980</td>\n",
       "      <td>8.4</td>\n",
       "      <td>927000.0</td>\n",
       "      <td>19000000.0</td>\n",
       "      <td>46998772.0</td>\n",
       "      <td>Warner Bros.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Blue Lagoon</td>\n",
       "      <td>1980</td>\n",
       "      <td>5.8</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>58853106.0</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Star Wars: Episode V - The Empire Strikes Back</td>\n",
       "      <td>1980</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>18000000.0</td>\n",
       "      <td>538375067.0</td>\n",
       "      <td>Lucasfilm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Airplane!</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.7</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>83453539.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caddyshack</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.3</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>6000000.0</td>\n",
       "      <td>39846344.0</td>\n",
       "      <td>Orion Pictures</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             name  year  score      votes  \\\n",
       "0                                     The Shining  1980    8.4   927000.0   \n",
       "1                                 The Blue Lagoon  1980    5.8    65000.0   \n",
       "2  Star Wars: Episode V - The Empire Strikes Back  1980    8.7  1200000.0   \n",
       "3                                       Airplane!  1980    7.7   221000.0   \n",
       "4                                      Caddyshack  1980    7.3   108000.0   \n",
       "\n",
       "       budget        gross             company  \n",
       "0  19000000.0   46998772.0        Warner Bros.  \n",
       "1   4500000.0   58853106.0   Columbia Pictures  \n",
       "2  18000000.0  538375067.0           Lucasfilm  \n",
       "3   3500000.0   83453539.0  Paramount Pictures  \n",
       "4   6000000.0   39846344.0      Orion Pictures  "
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['released', 'director', 'writer', 'star', 'country', 'rating', 'runtime', 'genre'], axis=1)\n",
    "df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Missing Values in Budget Column: 28.31%\n",
      "Percentage of Missing Values in Gross Column: 2.46%\n",
      "Percentage of Missing Values in Score Column: 0.04%\n",
      "Percentage of Missing Values in Votes Column: 0.04%\n"
     ]
    }
   ],
   "source": [
    "print(f'Percentage of Missing Values in Budget Column: {(len(df[df['budget'].isnull()]) / 7668) * 100 :.2f}%')\n",
    "print(f'Percentage of Missing Values in Gross Column: {(len(df[df['gross'].isnull()]) / 7668) * 100 :.2f}%')\n",
    "print(f'Percentage of Missing Values in Score Column: {(len(df[df['score'].isnull()]) / 7668) * 100 :.2f}%')\n",
    "print(f'Percentage of Missing Values in Votes Column: {(len(df[df['votes'].isnull()]) / 7668) * 100 :.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are missing data values for most columns, but the columns that we will focus on that have missing values contain the score, votes, budget, and gross. Since the vote, score, and gross columns only have a small percentage of rows with missing values, we can easily drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df\n",
    "df_new.dropna(subset = ['score','votes','gross'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since 28.31% of the budget is missing, we need to use data imputation to replace the missing values. To figure out what measure of data we replace the missing data with, we have to display the budget data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='budget'>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df_new, x=\"budget\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there is a large number of outliers in the distribution of the budget for movies, so the best measure of spread would be the median. We can then impute the median of the budget into the missing values to fill them in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median : $21000000.0\n"
     ]
    }
   ],
   "source": [
    "# from sklearn.impute import SimpleImputer\n",
    "budget_median = df_new['budget'].median()\n",
    "print(f'Median : ${budget_median}')\n",
    "# imputer = SimpleImputer(missing_values = np.nan, strategy = 'median')\n",
    "# df['budget'] = imputer.fit(df['budget'])\n",
    "df_new['budget'] = df_new['budget'].fillna(budget_median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to analyze the major movie companies of this dataset, as we want to analyze major movies and compare the most well-known companies. I have decided to reduce the list to the top 5 companies, so that I can better analyze any correlation between the companies and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company\n",
       "Universal Pictures       376\n",
       "Warner Bros.             333\n",
       "Columbia Pictures        332\n",
       "Paramount Pictures       319\n",
       "Twentieth Century Fox    240\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['company'].value_counts().head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "company = df_new['company'].value_counts()\n",
    "top_5 = company.nlargest(5).index.tolist()\n",
    "subset_company = df_new[df_new['company'].isin(top_5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lf/1gn2p5yn6m96gw3zn57nrkq80000gn/T/ipykernel_2457/3519137803.py:1: 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",
      "  subset_company['net gain/loss'] = subset_company.apply(lambda row: row['gross'] - row['budget'], axis=1)\n"
     ]
    },
    {
     "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>net gain/loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>1980</td>\n",
       "      <td>8.4</td>\n",
       "      <td>927000.0</td>\n",
       "      <td>19000000.0</td>\n",
       "      <td>46998772.0</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>27998772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Blue Lagoon</td>\n",
       "      <td>1980</td>\n",
       "      <td>5.8</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>58853106.0</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "      <td>54353106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Airplane!</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.7</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>83453539.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>79953539.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Friday the 13th</td>\n",
       "      <td>1980</td>\n",
       "      <td>6.4</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>39754601.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>39204601.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Blues Brothers</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.9</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>115229890.0</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>88229890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>Tenet</td>\n",
       "      <td>2020</td>\n",
       "      <td>7.4</td>\n",
       "      <td>387000.0</td>\n",
       "      <td>205000000.0</td>\n",
       "      <td>363656624.0</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>158656624.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>The Invisible Man</td>\n",
       "      <td>2020</td>\n",
       "      <td>7.1</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>143151000.0</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>136151000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>Bad Boys for Life</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.6</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>90000000.0</td>\n",
       "      <td>426505244.0</td>\n",
       "      <td>Columbia Pictures</td>\n",
       "      <td>336505244.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Sonic the Hedgehog</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.5</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>85000000.0</td>\n",
       "      <td>319715683.0</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>234715683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>Dolittle</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.6</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>175000000.0</td>\n",
       "      <td>245487753.0</td>\n",
       "      <td>Universal Pictures</td>\n",
       "      <td>70487753.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1600 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  year  score     votes       budget        gross  \\\n",
       "0            The Shining  1980    8.4  927000.0   19000000.0   46998772.0   \n",
       "1        The Blue Lagoon  1980    5.8   65000.0    4500000.0   58853106.0   \n",
       "2              Airplane!  1980    7.7  221000.0    3500000.0   83453539.0   \n",
       "3        Friday the 13th  1980    6.4  123000.0     550000.0   39754601.0   \n",
       "4     The Blues Brothers  1980    7.9  188000.0   27000000.0  115229890.0   \n",
       "...                  ...   ...    ...       ...          ...          ...   \n",
       "1595               Tenet  2020    7.4  387000.0  205000000.0  363656624.0   \n",
       "1596   The Invisible Man  2020    7.1  186000.0    7000000.0  143151000.0   \n",
       "1597   Bad Boys for Life  2020    6.6  140000.0   90000000.0  426505244.0   \n",
       "1598  Sonic the Hedgehog  2020    6.5  102000.0   85000000.0  319715683.0   \n",
       "1599            Dolittle  2020    5.6   53000.0  175000000.0  245487753.0   \n",
       "\n",
       "                 company  net gain/loss  \n",
       "0           Warner Bros.     27998772.0  \n",
       "1      Columbia Pictures     54353106.0  \n",
       "2     Paramount Pictures     79953539.0  \n",
       "3     Paramount Pictures     39204601.0  \n",
       "4     Universal Pictures     88229890.0  \n",
       "...                  ...            ...  \n",
       "1595        Warner Bros.    158656624.0  \n",
       "1596  Universal Pictures    136151000.0  \n",
       "1597   Columbia Pictures    336505244.0  \n",
       "1598  Paramount Pictures    234715683.0  \n",
       "1599  Universal Pictures     70487753.0  \n",
       "\n",
       "[1600 rows x 8 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_company['net gain/loss'] = subset_company.apply(lambda row: row['gross'] - row['budget'], axis=1)\n",
    "subset_company = subset_company.reset_index(drop=True)\n",
    "subset_company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANALYZING DATA\n",
    "\n",
    "We can begin by analyzing the data through displaying basic information about the data, searching for any patterns they may display."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name              object\n",
       "year               int64\n",
       "score            float64\n",
       "votes            float64\n",
       "budget           float64\n",
       "gross            float64\n",
       "company           object\n",
       "net gain/loss    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_company.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600, 8)"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_company.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first take a look at the score category. We can analyze the distribution of score and how it relates to the most popular film production companies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1600.000000\n",
       "mean        6.355375\n",
       "std         0.916984\n",
       "min         2.200000\n",
       "25%         5.800000\n",
       "50%         6.400000\n",
       "75%         7.000000\n",
       "max         9.000000\n",
       "Name: score, dtype: float64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_company['score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x29305d520>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(subset_company, x=\"score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there is a relatively normal distribution that's slightly skewed left between the scores of movies, averaging from a score of 6-7. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2918112b0>"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 705x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(subset_company, x=\"score\", hue=subset_company['company'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now see the score distribution between the top 5 companies. Notably, Warner Bros. tends to have higher scores, Universal Pictures tends to be have average to higher scores, and Columbia Pictures tends to have average to lower scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(data=subset_company, x='budget',y='gross')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the budget with the gross of movies from the top 5 production companies, we see that there are major outliers in gross that affect the visualization of the scatterplot. We can set a limit for the gross to get rid of some outliers, and get a better representation of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.0, 1000000000.0)]"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(data=subset_company, x='budget',y='gross')\n",
    "ax.set(ylim=(0,1000000000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now see a better visualization of the budget vs gross, in which we see a slight positive correlation. We will further explore this correlation later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'gross (in billions)')]"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAYAAABLkLalAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1tElEQVR4nO3deVxU1f8/8NfMwAwoMIAIgxuLiiaCS8pmIeaaUZiFWqgZJq655ZJpIorZ5paKZmn2UT/5kTRNo8wNMVk0hRD3FCUVUAMGF9aZ+f3Bb+6XYZMdHF7Px2Mecu8995z3PXOR95w5916RRqPRgIiIiIhIT4kbOgAiIiIiorrEhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSawYNHQBRQ1Or1bh79y5MTU0hEokaOhwiIiKqBI1Gg4cPH6JVq1YQiysew2XCS03e3bt30bZt24YOg4iIiKrhn3/+QZs2bSosw4SXmjxTU1MARb8wZmZmDRwNERERVUZ2djbatm0r/B2vCBNeavK00xjMzMyY8BIRET1jKjMdkRetEREREZFeY8JLRERERHqNCS8RERER6TUmvERERESk13jRGlE9S09Ph1KprLf25HI5bGxs6q09IiKixoYJL1E9Sk9Px+gxY1GQn1dvbRpKZdix/T9MeomIqMliwktUj5RKJQry85Dj2BdqI7nONnFOFoyTo5Dj4A21sXmttCfOVQI3TkCpVDLhJSKiJosJL1EDUBvJoW5uVfY2Y/NytxEREVHV8aI1IiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SWqQ7m5ubh69Spyc3MbOpRGjf1ERER1iQkvUR1KSUlBUFAQUlJSGjqURo39REREdYkJLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNYOGDoCISK1WAwBOnz4NpVKJ69evIykpCcbGxhg0aBC6du2KgwcP4u7du1AoFLC3t0dSUhIAwNXVFWKxGBkZGfj333/x999/Izc3Fy4uLnj99dchlUrLbFOlUiExMREPHjxAVlYWzM3NYWVlBVdXV0gkklLlMjIyYG5uDgDIysqCpaVlqbIly1taWsLZ2RkXLlwotx2VSoWEhAQkJCToHE/JNkqW6969O7p37y5sK95m586dhf5q1aoV/Pz8KuyH8uqtqN+0bZXVB3WhodolIv3AhLcBbdq0CXPnzkVmZiYMDIreikePHsHCwgIeHh44efKkUPbkyZPw9vbGlStX4OTk1FAhV1pkZCT69esnLBsZGcHR0REzZsxAUFBQA0ZGjU1UVBTWrl0LAPj2229LbT98+HC16v3jjz/w9ddfY8SIEZg0aVKpNsPCwpCWllZqP4VCgSlTpsDb27vCciXLllevNiEta18fHx/89ttvyMrKKvc4yiu3fft2mJubY8iQIYiMjCw3RqDo/xp/f/8y+2HVqlVl1jt79mzhuIqXL3l8JfugLjRUu0SkPziloQH169cPjx49wp9//imsO3nyJBQKBc6cOYMnT54I6yMjI9GqVatqJbsajQaFhYW1EnNJBQUFFW6/cuUKUlNTcfHiRUycOBGTJ0/G0aNHyy2fn59f2yFSIxYVFYXg4GC0bt1aZ32zZs0AAA4ODmXuV3xkz8jISPhZKpUK+9jY2ECtVmPXrl3YtGlTqTblcjkAwN3dHXPmzIG7uztEIhHkcjmCg4OxadMmBAcHw9HRERMmTAAAuLi4wMXFBSKRCBMmTICjoyOCg4MRFRUl1Ovo6IgNGzZg4cKFEIlEQnwdO3bUaUcsFmPXrl3IysqCi4sLxo0bBwAwNTUVYp0wYQLkcrlOuZUrV2LVqlVwcXFBVlYWdu3aBblcjg0bNuCNN94AABgaGgIA5s6dizlz5sDMzKzcfiiv3sWLFyMqKqpUee3xRUREYMOGDTp9UBcaql0i0i9MeBtQp06d0KpVK0RGRgrrIiMj4efnh/bt2yM6OlpnvXbEdMeOHejVqxdMTU2hUCjw9ttv4969ezplRSIRDh06hF69ekEmk+HkyZPw8fHB9OnTMW/ePFhaWkKhUGDJkiU6MSmVSgQFBcHa2hpmZmZ46aWX8NdffwnblyxZgu7du2Pr1q1wdHSETCaDRqMp9xitra2hUCjg4OCA6dOnw97eHufOnRO2+/j4YNq0aZg9ezasrKwwcOBAAMCJEyfg5uYGmUwGW1tbfPjhhzpJ+48//ggXFxcYGxujRYsWGDBgAB4/fly1N4AalEqlQlhYGDw9PYVRf5FIBA8PD/z888/w8PBAcnIyzM3NheQUADw8PHDw4EGIxUX/fZmZmQnbLC0tsXnzZnh5eQl1icVi7N69G/n5+UKbHh4eyMrKgpeXF1asWAFfX1+sWLECnp6eyM7Ohru7O8LDw+Hh4YGQkBAcOHAAXl5eWLt2LdauXQtPT08cPHgQISEh8PT0RFhYmHAsoaGh6Ny5M7Zs2QIPDw+YmJjAwsICDx8+xMsvv4wVK1bAw8MD6enpEIlEkEql+OKLL/Dbb7/By8sLP/30Ezw9PSGTyfDzzz8jKysLYrEYUqkUq1atwvPPP4+ePXti1apVkMlkwvQHR0dH7Nu3DxYWFvjll1/g5eWFHTt24OWXX0Z4eDgsLCwQHh6u0w9SqRSenp5Yu3atUK/2+GQyGcLCwqBSqXTeq9DQUDg7O6NZs2ZwdnZGaGgoPD09sXHjxjJHsmvrHKnPdolI/zDhbWA+Pj44fvy4sHz8+HH4+Pigb9++wvr8/HzExMQICW9+fj6WLVuGv/76C/v27UNycrIwOlTcvHnzsGLFCly6dAmurq4AgO+//x7NmzdHXFwcPv/8cyxdulT4ylij0eCVV15BWloaIiIicPbsWfTs2RP9+/dHRkaGUO/ff/+N3bt3Y8+ePcK8v6fRaDT47bff8M8//8Dd3V1n2/fffw8DAwOcOnUKX3/9Ne7cuYOhQ4eid+/e+Ouvv7Bx40Zs2bIFoaGhAIDU1FS89dZbCAwMxKVLlxAZGYnhw4dXmHgXl5eXh+zsbJ1XXbt16xauXr2KW7du1XlbFbXfmF4RERFIS0uDt7c39u3bB6DoPBkzZgwMDAyEUd9BgwZh8ODBwrG4u7vj8uXLwrzf4h/20tLSkJSUhICAAKSlpcHd3R1qtRpqtRr79+9HYmIi0tLS4ObmhvT0dAQEBAiJs1gsRkBAAFJTU9G6dWuoVCq4ubkhKSkJaWlpQtni5Yq3VbxMyXbGjx+PtLQ0JCYmQiwWw83NDSqVChqNBvn5+Thw4ICwv4GBAUaPHo28vDykp6cjPT0darUa+fn5wrxlAEhKSkJeXh7UajXS09Px9ddfQ6VSYfz48ZBKpUKMiYmJMDAwQGBgIFQqlU4/5OXlYfTo0UIfaPtB2742Zm354v1VvHzxtmpTQ7VLRPqHc3gbmI+PD2bNmoXCwkLk5OQgPj4e3t7eUKlU+OqrrwAAsbGxyMnJERLewMBAYX9HR0d89dVXcHNzw6NHj2BiYiJsW7p0qTBiquXq6org4GAARV+xrl+/HkePHsXAgQNx/PhxnD9/Hvfu3YNMJgMAfPnll9i3bx9+/PFHYRQuPz8f27dvR8uWLZ96fG3atAEA4Q/z0qVLS82569ChAz7//HNheeHChWjbti3Wr18PkUiEzp074+7du5g/fz4WL16M1NRUFBYWYvjw4bCzswNQ9FVzZa1YsQIhISGVLl8bli9fXq/tNbb2K/Lpp5/qLGunJOTl5QEAWrdurfNhxsjISOcDWEkZGRnw9PQEAOE8BoC7d+/C0tJSqKN4W+W1LZPJhLaKl9X+XLytkuuLt6Mto11fPC5tbMX3L28qR/HjLtkHt2/f1mmrZCza9cX7oby2iq8r3k55cZVsq7aU1ff10S4R6R8mvA2sX79+ePz4Mc6cOYPMzEw4OTnB2toaffv2xZgxY/D48WNERkaiXbt2cHR0BADEx8djyZIlSEhIQEZGhjDSlZKSgi5dugh19+rVq1R72pFeLVtbW2GE7OzZs3j06BFatGihUyYnJwfXr18Xlu3s7CqV7AJFc5JNTU2Rl5eH06dPY9q0abC0tMTkyZPLjfPSpUvw9PSESCQS1vXp0wePHj3C7du30a1bN/Tv3x8uLi4YPHgwBg0ahDfffBMWFhaVimnBggWYPXu2sJydnY22bdtWat/qWrhwIezs7HDr1q0GST617TcmV65cwcqVK/Hhhx/izz//xJEjRwAAycnJcHZ2FpLCO3fu6OyXm5sLhUJRbr2WlpZITk4G8H+JKwC0atVKSPRyc3N12tLS7qdtOy8vD7a2tqXKassVb6t4mZLtxMTECOVLxqWNrfj+xesseWxl/QwUfbj8888/ERMTA19fX50Yi8dQvB/K6oPix1eynbLKFi9fMqaa0tZX3+0Skf5hwtvAOnTogDZt2uD48ePIzMxE3759AUCY93rq1CkcP34cL730EgDg8ePHGDRoEAYNGoQdO3agZcuWSElJweDBg0td8NW8efNS7WkvZtESiURCwqxWq2Fra6szp1hLezum8uotj4ODg7Cvs7Mz4uLisHz5cp2Et2R9Go1GJ9nVrtPGK5FIcPjwYURHR+P333/HunXrsHDhQsTFxZU7ElScTCYrNcJW1+zs7Br07hoN3X5Z2rdvj507dyIqKgpvv/02jhw5ApFIhO3btyM0NFRIdH///XedEd64uDgMGTIEYrEYarUa1tbWwoc2hUKBrl27Ijg4GAqFAnFxccJX4X5+fpBIJFAoFDh9+jRsbGywc+dOhIaGCnXt3LkTtra2uHPnDiQSCU6fPg1fX18oFAqhLAChXPG2tOtDQ0Ph6uqq086WLVugUCjg6uoKtVqN06dPQyKRQK1Ww9DQEK+++ir27NmDnTt3YunSpdixYwdkMpnwu3P//n0YGBiga9euQj907doVMpkMBQUFaNmyJSZOnIgDBw5gy5YtGDRokBCjq6srCgsLsXXrVkgkEp1+yMzMxI4dO7B8+XKhn9RqtdC+hYWF8CG5eB8Un15QvN9KfqCuKW0/1ne7RKR/OIe3EejXrx8iIyMRGRkJHx8fYX3fvn1x6NAhxMbGCtMZLl++jAcPHuDTTz/Fiy++iM6dO+vMYayJnj17Ii0tDQYGBujQoYPOy8rKqlbakEgkyMnJqbBMly5dEB0drZPkREdHw9TUVJjXKRKJ0KdPH4SEhCA+Ph5SqRQ//fRTrcRI9UMikWDKlCmIiYnB5s2bARR9sImNjcVrr72G2NhYODg4ICsrC0qlUtgvNjYWvr6+wge14nOwMzIyMGHCBOH8iY2NhVqtxogRIyCVSoU2Y2NjYW5ujujoaCxYsAAHDhzAggULEBMTAzMzM8TFxcHf3x+xsbEIDg6Gr68voqOjMWPGDMyYMUMYRQ0ODkZMTAymTJkiHMuiRYtw+fJljB8/HrGxsXj06BEyMzNhamqKiIgILFiwALGxsbCxsRHm8M6dOxeDBw9GdHQ0Xn/9dcTExCAvLw+vvvoqzM3NhTm8s2fPxtmzZ3H27FnMnj1bmCpkbm6OGzduYNiwYcjMzMQrr7yC6OhoBAQEICIiAv7+/sjMzIS/v79OP2ivD5gxY4ZQr/b48vLyMGXKFEgkEp33atGiRbhw4QKePHmCCxcuYNGiRYiJicHkyZNr/b64DdUuEekfjvA2Av369cPUqVNRUFAgjPACRQnv5MmTkZubKyS87dq1g1Qqxbp16zBp0iQkJSVh2bJltRLHgAED4OnpiWHDhuGzzz5Dp06dcPfuXURERGDYsGFlTpF4mnv37iE3N1eY0rB9+3a8+eabFe4zZcoUrFmzBu+//z6mTZuGK1euIDg4GLNnz4ZYLEZcXByOHj2KQYMGwdraGnFxcbh//z6ee+45AMBPP/2EBQsW4PLly9XqB6o/3t7eCAkJEe7Dq6W9JV95X+0XvypfO20AKJpffvPmTQBAeno6xGJxqfvwatsMCwsDUDRiHBcXJ2zPzs5GSEgIvL290aVLF4SFhQl3TDl//rxQ7ptvvoGtra1QFoBQ79SpU0vFd+3aNaxcuVJYr9FoMGrUKPz22284f/68UPfDhw+FMt9++y1sbW11yn3wwQfC9uL34S3epvZ2gV9++SWAosRx1KhRZfbDqlWryqy35H14i/db8bZK9kFta6h2iUi/MOFtBPr164ecnBx07twZNjY2wvq+ffvi4cOHaN++vTDHtGXLlti2bRs++ugjfPXVV+jZsye+/PJLvPbaazWOQyQSISIiAgsXLkRgYCDu378PhUIBb29vnbiqolOnTgAAAwMDtG3bFhMnTix1K7SSWrdujYiICMydOxfdunWDpaUlxo8fj0WLFgEoug1VVFQU1qxZg+zsbNjZ2WHlypV4+eWXARTdWu3KlSvVipfqn7e3N6ytrTFp0iS899576NSpU50/ac3b2xt9+vR56pPWiperzJPWSpavzJPWJkyYUKknrZUsV/yJaBMmTKjWk9a08Vb2SWtlHV99PPGsodolIv0h0lT2Xk5Eeio7OxtyuRxKpVLnnq614erVqwgKCsLmzZvh5OQkLD/u8hrUzXWniYgfP0Dziz+Xua26tHVq22+sSvYTERHR01Tl7zfn8BIRERGRXmPCS0RERER6jQkvEREREek1JrxEREREpNeY8BIRERGRXmPCS0RERER6jQkvEREREek1JrxEREREpNeY8BIRERGRXmPCS1SH2rVrh82bN6Ndu3YNHUqjxn4iIqK6ZNDQARDpMyMjIz4qtxLYT0REVJc4wktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXDBo6AKKmSJyrLL0uJ0vn37pqh4iIqKlhwktUj+RyOQylMuDGiXLLGCdH1WqbhlIZ5HJ5rdZJRET0LGHCS1SPbGxssGP7f6BU1t/Iq1wuh42NTb21R0RE1Ngw4SWqZzY2NkxAiYiI6hEvWiMiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0Gh8tTFSP0tPToVQq66x+uVzOxxYTERGVwISXqJ6kp6dj9JixKMjPq7M2DKUy7Nj+Hya9RERExTDhJaonSqUSBfl5yHHsC7WRXGebOCcLxslRyHHwhtrYvFr1i3OVwI0TUCqVTHiJiIiKYcJLVM/URnKom1uVvc3YvNxtREREVD28aI2IiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElqkO5ubm4evUqcnNzGzqUOteUjpWIiJ4tTHiJ6lBKSgqCgoKQkpLS0KHUuaZ0rERE9GxhwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREes2goQN4Vi1ZsgT79u1DQkJCnbazbds2zJw5E1lZWQ0aS2XiICqPSqVCYmIiMjIyYG5uDgDIysqCpaUlXF1dAQCJiYl48OABsrKyYG5uDisrK7i6ukIikVSqXktLSzg7O+PChQullh88eICMjAxkZ2dDLBaje/fu6N69e4V1P01+fj7279+Pu3fvolWrVvDz8wMA7N+/H3fu3AEAPPfcc7Cysip1vCXbfVr/lBVnyWN/Wl/VRH22RURUF5pkwpuWlobly5fjl19+wZ07d2BtbY3u3btj5syZ6N+/f0OHV2Vz5szB+++/X6M6lixZgpCQEACAWCxGq1atMHjwYKxYsQItW7bEyJEjMXTo0ErXZ29vj5kzZ2LmzJk1iouefVFRUQgLC0NaWlqZ24sneCUpFApMmTIF3t7elapXIpFApVKVu6y1fft2mJubY/bs2WXW/TSbNm1CeHi4Tt1hYWEAAI1GI6zbt2/fU4/paf1TVh+UtU9FfVUT9dkWEVFdaXJTGm7evInnn38ex44dw+eff47z58/jt99+Q79+/TB16tSGDq9aTExM0KJFixrX4+zsjNTUVKSkpGDjxo04cOAAxo4dCwAwNjaGtbV1jduoqvz8/Hpvk2pPVFQUgoOD4ejoiAkTJgAAXFxc4OLiApFIhAEDBiArK0tIdt3d3TFnzhy4u7tDJBJBLpcjODgYUVFR5da7YcMGLFy4EABgZmYGkUiEN954AyKRCEZGRsI+7du3R5cuXQAA7dq1Q1ZWFhYvXlyq7qfZtGkTdu3aBTMzM8yZMwd79uxB7969odFohGS3V69eeOGFF4R9rKysIBKJMGHCBDg6OgrH9LT+KVm+rGOPiIjAhg0bSpWrDfXZFhFRXWpyCe+UKVMgEolw+vRpvPnmm3BycoKzszNmz56N2NhYoVxKSgr8/PxgYmICMzMzjBgxAunp6eXW6+PjU2o0c9iwYRg3bpywbG9vj9DQUIwdOxYmJiaws7PD/v37cf/+faEtFxcX/Pnnn6Xq37dvH5ycnGBkZISBAwfin3/+EbYtWbIE3bt3F5bPnDmDgQMHwsrKCnK5HH379sW5c+ee2jcGBgZQKBRo3bo1fH19MX36dPz+++/IycnBtm3bhJE4rZ9//hm9evWCkZERrKysMHz4cKEvbt26hVmzZkEkEkEkEpUZJwCsWbMG9vb2wvK4ceMwbNgwrFixAq1atYKTkxMA4M6dOxg5ciQsLCzQokUL+Pn54ebNm8J+kZGRcHNzQ/PmzWFubo4+ffrg1q1bTz1mqjsqlQphYWHw9PRESEgIDhw4AC8vL6xduxZr166Fh4cHjh8/Dnd3d0ilUshkMoSGhsLX1xcrVqyAp6cnsrOz4eHhgY0bNwqjqcXrDQ0NRefOnbFlyxZ4eXkhPDwcHh4e2LdvH9zd3WFiYgKxWAypVIpNmzZh/fr18PLyQn5+Pjw8PCCTyRAWFlbmKHBZ8vPzER4eDgsLC4SHh8PX1xempqY4d+4c5HI5xOKi/1JDQkLw999/w8PDAxYWFsjIyIC7uzsOHjyIkJAQeHp6IiwsrML+8fT01Cm/ceNG5Ofn6xy7s7MzmjVrBmdnZ4SGhgrlKns8lX3/6rotIqK61qQS3oyMDPz222+YOnUqmjdvXmq7NqHTaDQYNmwYMjIycOLECRw+fBjXr1/HyJEjaxzD6tWr0adPH8THx+OVV17BmDFjMHbsWIwePRrnzp1Dhw4dMHbsWJ2vRZ88eYLly5fj+++/x6lTp5CdnY1Ro0aV28bDhw/xzjvv4OTJk4iNjUXHjh0xdOhQPHz4sEqxGhsbQ61Wo7CwsNS2X375BcOHD8crr7yC+Ph4HD16FL169QIA7N27F23atMHSpUuRmpqK1NTUKrV79OhRXLp0CYcPH8bBgwfx5MkT9OvXDyYmJoiKisIff/wBExMTDBkyBPn5+SgsLMSwYcPQt29fJCYmIiYmBkFBQUKiXVJeXh6ys7N1XnXt1q1b9ZaA37p1C1evXq33V8njS0xMRFpaGgICApCUlCT8LBaLIRaL4ebmBpVKhTZt2iA/Px95eXlISkoCUDStJiAgAKmpqXBzc0NqaioSExNL1SsWi3WWDQwMhHpbt26N9PR0qNVq5OfnIykpSag3LS0N7u7uyMvLQ1pamlD30+zfvx8qlQrjx4+HgYGBzrrBgwdDrVYDAL755hukpaVhzJgxCAwMhFqtRuvWrZGamoqkpCQhhor6R3v82vKpqanYv3+/Trniiu9T2eOpSMl+rsu2iIjqWpOaw/v3339Do9Ggc+fOFZY7cuQIEhMTkZycjLZt2wIomvPn7OyMM2fOoHfv3tWOYejQoZg4cSIAYPHixdi4cSN69+4Nf39/AMD8+fPh6emJ9PR0KBQKAEBBQQHWr18Pd3d3AMD333+P5557DqdPn4abm1upNl566SWd5a+//hoWFhY4ceIEfH19KxXn5cuXsXHjRri5ucHU1LTU9uXLl2PUqFHCvF8A6NatGwDA0tISEokEpqamwjFURfPmzfHtt99CKpUCALZu3QqxWIxvv/1WSGK/++47mJubIzIyEr169YJSqYSvry/at28PoOhiofKsWLFCJ+76sHz5cr1sqyIZGRkAAAcHB8TExAg/a8lkMgC601a0+xQvqy2n3Va83rKWteXz8vLKjad4uZLtVuTu3bsAAE9Pz1LrWrVqJay7ffu20JaNjY1OPBkZGTr7l9c/xY9PW17bVvFyxZXsk5oo2V912RYRUV1rUgmvdtS0vJE/rUuXLqFt27ZCsgsAXbp0gbm5OS5dulSjhFd7RToA4Q+hi4tLqXX37t0TkkUDAwNh9BQAOnfuLMRSVsJ77949LF68GMeOHUN6ejpUKhWePHmClJSUCmM7f/48TExMoFKpkJeXBx8fH2zevLnMsgkJCcKcw9rm4uIiJLsAcPbsWfz999+lEu/c3Fxcv34dgwYNwrhx4zB48GAMHDgQAwYMwIgRI2Bra1tm/QsWLMDs2bOF5ezsbJ33ui5o55jWRzK6cOFC2NnZ1Xk7Jd26dUvn+CwtLQEAycnJOj87OzsD+L8EsPh7rS2nLVu8nHZbybpKLmvLF09oS+5XvN6S7VZEm9TGxMQIHx6167TJKAC0adMGf/75J5KTk3Hjxg2deCwtLYUYtPGU1T/aMsXLa9sqXq644vvUVFkx1VVbRER1rUlNaejYsSNEIhEuXbpUYTmNRlNmUlzeeqDoK77i0xCAopHZkgwNDYWftXWVtU771WjJ9U9bBxTNgz179izWrFmD6OhoJCQkoEWLFk+9AKxTp05ISEjAxYsXkZOTg2PHjqFDhw5lljU2Nq6wrrJUto9KTjdRq9V4/vnnkZCQoPO6evUq3n77bQBFI74xMTHw8vLC//73Pzg5OenMyS5OJpPBzMxM51XX7Ozs6i0JtbOzg5OTU72/Sh6fq6srFAoFdu7cia5duwo/q9VqqNVqnD59GhKJBLdv3xbm8Hbt2hVA0Xu+c+dO2Nra4vTp07C1tRU+LBavV61W6ywXFhYK9d65cwc2NjbCHN6uXbsK9SoUCsTFxUEmk0GhUOh8EK2In58fJBIJtmzZIkz10a47dOiQ8NX/hAkToFAosH37duEbijt37sDW1hZdu3YVYqiof7THry1va2sLPz8/nXLFFd+nssdTkZL9XJdtERHVtSaV8FpaWmLw4MHYsGEDHj9+XGq79krxLl26ICUlRefCsIsXL0KpVJb7VXnLli115qqqVCphPmJNFRYW6lzIduXKFWRlZZU7NePkyZOYPn06hg4dCmdnZ8hkMjx48OCp7UilUnTo0AEODg6lRsdKcnV1xdGjRyusq+TFLC1btkRaWppO0luZewf37NkT165dg7W1NTp06KDzksvlQrkePXpgwYIFiI6ORteuXfHf//73qXVT3ZFIJJgyZQpiYmIQHBwMX19fREdHY8aMGZgxYwZiY2PRr18/xMXFCXN4Fy1ahAMHDmDBggWIiYmBmZkZYmNjMXnyZOG+r8XrXbRoES5fvozx48cjOjoa/v7+iI2NxbBhwxAXF4dHjx4Jc3gnTpyIadOmITo6GlKpFLGxscjLy8OUKVMqfU9ZqVQKf39/ZGZmwt/fHwcOHEB2djZ69OgBpVIpJIbBwcFo3749YmNjkZmZCUtLS8TFxcHX1xfBwcGIiYnBlClTKuwf7SiytvzkyZMhlUp1jv3ChQt48uQJLly4gEWLFgnlauMeuSX7uS7bIiKqa01qSgNQdK9MLy8vuLm5YenSpXB1dUVhYSEOHz6MjRs34tKlSxgwYABcXV0REBCANWvWoLCwEFOmTEHfvn11phYU99JLL2H27Nn45Zdf0L59e6xevbrWHtJgaGiI999/H1999RUMDQ0xbdo0eHh4lDmdAQA6dOiA7du3o1evXsjOzsbcuXOrNSJbkeDgYPTv3x/t27fHqFGjUFhYiF9//RXz5s0DUHRHiqioKIwaNQoymQxWVlbw8fHB/fv38fnnn+PNN9/Eb7/9hl9//fWpI6wBAQH44osv4Ofnh6VLl6JNmzZISUnB3r17MXfuXBQUFGDz5s147bXX0KpVK1y5cgVXr14VbqlGDcfb2xshISEICwtDdHQ0gKKpM1pHjhzRuQ9vXFwc4uLihO3Z2dkICQkpdb/X4vUWv51gdnY2NBoN9uzZA6Bo2ouWdmoBUHQXlureh3fSpEkAgPDwcKxcuVJYr/3GRaPRlLrTivYD5zfffANbW1udY6qof8oqX96xlyxXG+qzLSKiutTkEl4HBwecO3cOy5cvxwcffIDU1FS0bNkSzz//PDZu3Aig6A/Xvn378P7778Pb2xtisRhDhgzBunXryq03MDAQf/31F8aOHQsDAwPMmjUL/fr1q5WYmzVrhvnz5+Ptt9/G7du38cILL2Dr1q3llt+6dSuCgoLQo0cPtGvXDp988gnmzJlTK7Fo+fj4IDw8HMuWLcOnn34KMzMznT9+S5cuxcSJE9G+fXvk5eVBo9HgueeeQ1hYGD755BMsW7YMb7zxBubMmVPuPGGtZs2aISoqCvPnz8fw4cPx8OFDtG7dGv3794eZmRlycnJw+fJlfP/99/j3339ha2uLadOmCRcHUsPy9vZGnz59av1JayXrrc8nrU2aNAmBgYG18qS1yvRPyTjLOva6evpZfbZFRFRXRJqSkyqJmpjs7GzI5XIolcpan8979epVBAUFCUl9UFAQHnd5DermVjrlxI8foPnFn8vcVlnaOjZv3izcv7g+FT/WhmifiIialqr8/W5Sc3iJiIiIqOlhwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktUh9q1a4fNmzejXbt2DR1KnWtKx0pERM8Wg4YOgEifGRkZNZnH7DalYyUiomcLR3iJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0mkFDB0DU1IhzlaXX5WTp/Ftb9RIRERETXqJ6I5fLYSiVATdOlFvGODmqRm0YSmWQy+U1qoOIiEjfMOElqic2NjbYsf0/UCrrbiRWLpfDxsamzuonIiJ6FjHhJapHNjY2TEiJiIjqGS9aIyIiIiK9xoSXiIiIiPQaE14iIiIi0mtMeImIiIhIrzHhJSIiIiK9xoSXiIiIiPQaE14iIiIi0mtMeImIiIhIrzHhJSIiIiK9xoSXiIiIiPQaHy1MVI/S09OhVCrrpG65XM7HFhMREZWBCS9RPUlPT8foMWNRkJ9XJ/UbSmXYsf0/THqJiIhKqFbCu2TJErz77ruws7Or7XiI9JZSqURBfh5yHPtCbSQX1otzsmCcHIUcB2+ojc2rVbc4VwncOAGlUsmEl4iIqIRqJbwHDhxAaGgo+vbti/Hjx2P48OEwMjKq7diI9JLaSA51c6vS643Ny1xPRERENVOti9bOnj2Lc+fOwdXVFbNmzYKtrS0mT56MM2fO1HZ8REREREQ1Uu27NLi6umL16tW4c+cOtm7dijt37qBPnz5wcXHB2rVr6+zCHCIiIiKiqqjxbcnUajXy8/ORl5cHjUYDS0tLbNy4EW3btsX//ve/2oiRiIiIiKjaqp3wnj17FtOmTYOtrS1mzZqFHj164NKlSzhx4gQuX76M4OBgTJ8+vTZjJSIiIiKqsmolvK6urvDw8EBycjK2bNmCf/75B59++ik6dOgglBk7dizu379fa4ESEREREVVHte7S4O/vj8DAQLRu3brcMi1btoRara52YEREREREtaFaCe/HH39c23EQEREREdWJaiW8KpUK27Ztw9GjR3Hv3r1SI7nHjh2rleCIiIiIiGqqWgnvjBkzsG3bNrzyyivo2rUrRCJRbcdFRERERFQrqpXw7tq1C7t378bQoUNrOx4iIiIiolpVrbs0SKVSnTsyEBERERE1VtVKeD/44AOsXbsWGo2mtuMh0iu5ubm4evUqcnNzGzqUOteUjpWIiJ4t1ZrS8Mcff+D48eP49ddf4ezsDENDQ53te/furZXgiJ51KSkpCAoKwubNmxs6lDpX/FidnJwaOhwiIiJBtRJec3NzvP7667UdCxERERFRratWwvvdd9/VdhxERERERHWiWnN4iYiIiIieFdUa4QWAH3/8Ebt370ZKSgry8/N1tp07d67GgRERERER1YZqjfB+9dVXePfdd2FtbY34+Hi4ubmhRYsWuHHjBl5++eXajpGIiIiIqNqqlfCGhYVh8+bNWL9+PaRSKebNm4fDhw9j+vTpUCqVtR0jEREREVG1VSvhTUlJgZeXFwDA2NgYDx8+BACMGTMGP/zwQ+1FR0RERERUQ9VKeBUKBf79918AgJ2dHWJjYwEAycnJfBgFERERETUq1Up4X3rpJRw4cAAAMH78eMyaNQsDBw7EyJEjeX9eIiIiImpUqnWXhs2bN0OtVgMAJk2aBEtLS/zxxx949dVXMWnSpFoNkBqHyMhI9OvXD5mZmTA3N2/ocKgRu3TpEk6cOIF79+7B2toaPXv2RPfu3SGRSAAAKpUKCQkJOHfuHO7duwcrKyvI5XJhapSZmRksLS1haWkJAMjKyoKlpSVcXV0BAAkJCUhISAAAdO/evVTdiYmJePDgATIyMvDw4UOIRCK4urpCLBbr1KXdp3hMCQkJUKvVMDU1xcOHDyEWi0u1UZH8/Hzs378fd+/eRatWreDn5wepVFprfVtftP2YkZEh/L6X13dERM+CaiW8YrEYYvH/DQ6PGDECI0aMqLWgqmPcuHH4/vvvAQAGBgZo27Ythg8fjpCQEDRv3rxBY2tI27Ztw8yZM5GVlfXUcu+++66wrFAo8OKLL+Kzzz6Dg4MDvLy8kJqaCrlcXql2fXx80L17d6xZs6YG0dOzRHs7wtWrV+us37lzJ8zNzTF79mwAwKpVq556PpbF3NwcBQUFePz4sbBu+/btOnWHhYUhLS3tqXUpFApMmTIF3t7eiIqKqjCm4m14e3uXW+emTZsQHh4OlUqls87f3/+ZGgiIioqqsB+L9x0R0bOi2g+eyMzMxJdffonx48fjvffew8qVK5GRkVGbsVXZkCFDkJqaihs3biA0NBRhYWGYM2dOterSaDQoLCys5QgbNzMzM6SmpuLu3bv473//i4SEBLz22mtQqVSQSqVQKBQQiUT1GlNBQUG9tkfVExUVhU2bNgnLXbp0wfTp09GlSxcARaODixcvxuLFi4XEsk2bNmXWVfxDVbt27SASiTBgwABkZWXh8ePHaNeuHVauXIlVq1bBxcVFqDs4OFhn3/bt2wttGBkZCesnTJgAR0dHBAcHY9OmTUJM7dq1E8o0a9ZMJ4asrCwEBwcjKiqqzJg3bdqEXbt2wczMDHPmzMGePXswZ84cmJmZYdeuXTp905hFRUUhODgYjo6OmDBhAgDAxcUFLi4uEIlEOn1XXl8QETVG1Up4T5w4AQcHB3z11VfIzMxERkYGvvrqKzg4OODEiRO1HWOlyWQyKBQKtG3bFm+//TYCAgKwb98+AMCOHTvQq1cvmJqaQqFQ4O2338a9e/eEfSMjIyESiXDo0CH06tULMpkMJ0+exPXr1+Hn5wcbGxuYmJigd+/eOHLkiE679vb2CA0NxdixY2FiYgI7Ozvs378f9+/fh5+fH0xMTODi4oI///xTZ789e/bA2dkZMpkM9vb2WLlypc52kUgkxK9lbm6Obdu2AQBu3rwJkUiEvXv3ol+/fmjWrBm6deuGmJgY4ZjeffddKJVKiEQiiEQiLFmypNz+E4lEUCgUsLW1Rb9+/RAcHIykpCT8/fffQv8UHwU7deoU+vbti2bNmsHCwgKDBw9GZmYmxo0bhxMnTmDt2rVCuzdv3sS2bdtKTYfYt2+fThK9ZMkSdO/eHVu3boWjoyNkMhk0Gg2USiWCgoJgbW0NMzMzvPTSS/jrr7+E/f766y/069cPpqamMDMzw/PPP1+qv6luqFQqhIWFwdDQEADg6uqK9evXY/jw4Vi/fj08PT11vtY3NDSEh4cHCgsLIZPJ4O7uDplMBrFYDENDQ2RnZ8PCwgIymQx5eXlwd3fH8ePHIZVKYW5ujvz8fHTv3h09e/bE2rVr4eHhIdSbmZkJmUwGT09PbNq0CYWFhbCwsIC5uTk8PDwgk8lw8OBBhISEwMPDA7t374ZMJoOHhwdyc3OFfX/++Wd4enpCJpMhPz8fHh4ekEql2Lhxo84ILlA0jSE8PBwWFhYIDw+Hr68vWrRoAV9fX531JR/Q09ho30dPT0+EhITgwIED8PLywtq1a7F27Vp4enoKfefp6VlmXxARNVbVSninTp2KESNGIDk5GXv37sXevXtx48YNjBo1ClOnTq3tGKvN2NhYGCHMz8/HsmXL8Ndff2Hfvn1ITk7GuHHjSu0zb948rFixApcuXYKrqysePXqEoUOH4siRI4iPj8fgwYPx6quvIiUlRWe/1atXo0+fPoiPj8crr7yCMWPGYOzYsRg9ejTOnTuHDh06YOzYscJdLM6ePYsRI0Zg1KhROH/+PJYsWYKPP/5YSGarYuHChZgzZw4SEhLg5OSEt956C4WFhfDy8sKaNWuEkdvU1NQqjXgbGxsDKHuUNSEhAf3794ezszNiYmKEOdwqlUr44zhhwgSh3bZt21a63b///hu7d+/Gnj17hLmar7zyCtLS0hAREYGzZ8+iZ8+e6N+/v/CtQkBAANq0aYMzZ87g7Nmz+PDDD4UErKS8vDxkZ2frvOrarVu3cOvWrXpp5+rVq/X6ioiIQFpamnCeDB06VJjyJBaLMXr0aJ1kr6CgAO7u7khLS0NeXh48PDyQl5cHtVqNgoICaDQaDBw4EHl5eUhPT0fr1q2hUqmQn5+P9957D2lpaUhMTBTqd3d3B1D0O37v3j3k5eVh9OjRSEpKQlpaGsaPH4+0tDS4u7sjLy8PqampSEpKgpubG9RqtZBUF9/XwMAAo0ePRl5eXql9tW1r7d+/HyqVCuPHj4eBge4sMQMDAwQGBkKlUmH//v119r7XhsTERKSlpSEgIEDou4CAAGEKW0BAgNB32p9L9gURUWNVrTm8169fx549e3QuXJBIJJg9ezb+85//1FpwNXH69Gn897//Rf/+/QEAgYGBwjZHR0d89dVXcHNzw6NHj2BiYiJsW7p0KQYOHCgst2jRAt26dROWQ0ND8dNPP+Hnn3/GtGnThPVDhw7FxIkTAQCLFy/Gxo0b0bt3b/j7+wMA5s+fD09PT6Snp0OhUGDVqlXo378/Pv74YwCAk5MTLl68iC+++KLMRLwic+bMwSuvvAIACAkJgbOzM/7++2907twZcrlcGLmtitu3b+OLL75AmzZt4OTkhAcPHuhs//zzz9GrVy+EhYUJ65ydnYWfpVIpmjVrVuV2gaLEZfv27WjZsiUA4NixYzh//jzu3bsHmUwGAPjyyy+xb98+/PjjjwgKCkJKSgrmzp2Lzp07AwA6duxYbv0rVqxASEhIleOqieXLl+tVOxVp1aqVzrKDg0OpMtr3seTPWra2tsLPeXl5ws+enp4AoDN9qqz9HRwchG86tPsUL5eRkaGzXHzKgzbe4nGX3Le4u3fv6rRTkna9tlxjpT2u4n1XvA+0P2dkZJT5PhARNWbVSnh79uyJS5cuoVOnTjrrL126hO7du9dGXNVy8OBBmJiYoLCwEAUFBfDz88O6desAAPHx8ViyZAkSEhKQkZEh3GUiJSVFmGcIAL169dKp8/HjxwgJCcHBgwdx9+5dFBYWIicnp9QIr/YKcgCwsbEBUDT3reS6e/fuQaFQ4NKlS/Dz89Opo0+fPlizZg1UKlWVroIu3rY2Ubh3756Q/FWWUqmEiYkJNBoNnjx5gp49e2Lv3r1lXmWekJAgJPO1zc7OTkh2gaLR8EePHqFFixY65XJycnD9+nUAwOzZs/Hee+9h+/btGDBgAPz9/dG+ffsy61+wYIFwkRMAZGdnV2kEujoWLlwIoO4T0oULF8LOzq5O2yjpypUrOtNx7t69q3NOJicnl9qneBJb/Get1NRU4efiyaY2EdPewaG8/ZOTk4Uy2n2Kl7O0tMTNmzeF5dzcXJ19nZ2ddeIuuW9x2gQ/JiYGvr6+pWLRtl/yg0Bjoz2u4n2n7Qvtz9pyxX8mInoWVCvhnT59OmbMmIG///5bmD8XGxuLDRs24NNPP9X5mqv4H7661q9fP2zcuBGGhoZo1aqV8JX248ePMWjQIAwaNAg7duxAy5YtkZKSgsGDB5eaV1fyjg5z587FoUOH8OWXX6JDhw4wNjbGm2++WWq/4l+fa+eklrVOm2hrNJpSF4CVfGiHSCQqta6s6QUVtVMVpqamOHfuHMRiMWxsbCq8u4V2ukNViMXiSh1PyXbVajVsbW0RGRlZqqx2TvCSJUvw9ttv45dffsGvv/6K4OBg7Nq1q8z7QstksjJHBetSfSWhdnZ2cHJyqpe2tNq3b4+dO3fi33//RUFBASIiIjBo0CCIxWKo1Wrs2LEDUqlU+J0xNDREXFwcFAoFMjMzERsbC5lMhoKCAkgkEhQWFuLw4cOQyWQwNzfHnTt3IJFIIJFI8O2330KhUAj/r6jVasTFxQGAMMdXqVRix44dWLp0KRQKBbZs2QKFQoG4uDjIZDJYWlqia9eu2L17tzBvOC4uDtbW1sK+y5Ytw44dOyCTyWBhYaGzb8n/0/z8/LBp0yZs2bIFQ4YM0ZnWUFhYiK1bt0IikZT6gNvYuLq6QqFQYOfOnQgJCRF+Dg0NBVB0tw1bW1t07doVwcHBsLW1rdf/34mIaqJaCe9bb70FoGi+a1nbtImaSCSq14samjdvjg4dOpRaf/nyZTx48ACffvqpMJJX2QuaTp48iXHjxgmJ06NHj3RGhqqrS5cu+OOPP3TWRUdHw8nJSRjdbdmypc5I17Vr1/DkyZMqtSOVSiv9HojF4jL7ryyurq44evRouVMDymq3ZcuWePjwIR4/fiwktdo5uhXp2bMn0tLSYGBgAHt7+3LLOTk5wcnJCbNmzcJbb72F7777jg9CqQcSiQRTpkzB4sWLARTNBZ02bRr69++Po0eP4uLFizrlCwoKEBsbizZt2iAvL09IWIGiBFYulyMzMxNA0YeTuLg49O/fH0eOHEF+fj7atWsnnDfbtm3D+fPnhXotLCxw7949xMTEYOLEiTAwMEBaWhqMjIyEJ0L6+voiODgYsbGxGDlyJHbt2oXY2Fi0a9dO2Pe1114TftekUiliY2MhEokwefLkUt++SKVS+Pv7Y9euXfD390dgYCA8PT0RExODrVu3IjMzE6NGjWr09+PVvo/BwcEIDg6Gr68vvv32W8yYMQMAkJSUhPfeew/BwcGIiYlBSEgI78dLRM+MaiW8ZX1F2Zi1a9cOUqkU69atw6RJk5CUlIRly5ZVat8OHTpg7969ePXVVyESifDxxx9Xa/S0pA8++AC9e/fGsmXLMHLkSMTExGD9+vU6c2JfeuklrF+/Hh4eHlCr1Zg/f365F2KVx97eHo8ePcLRo0fRrVs3NGvWTOeWS9W1YMECuLi4YMqUKZg0aRKkUimOHz8Of39/WFlZwd7eHnFxcbh58yZMTExgaWkJd3d3NGvWDB999BHef/99nD59ulIX6Q0YMACenp4YNmwYPvvsM3Tq1Al3795FREQEhg0bBmdnZ8ydOxdvvvkmHBwccPv2bZw5cwZvvPFGjY+TKsfb2xuTJk0Sbr918eJFnUTXwsICs2bNAvB/9+G9fft2mXUplUrhZ+3UoSNHjgj34U1JScEHH3wglCl5H16tGzduCD8Xn7LwzTffwNbWFiEhIfD29kaXLl2watUqnWlKxT9YpqSkCPGXd+9Z7X12w8PDdaZ3SCQSjBo16pm5D6+3tzdCQkIQFhaG6OhoABA+UACl+46I6FlRrYS3vucI1lTLli2xbds2fPTRR/jqq6/Qs2dPfPnll3jttdeeuu/q1asRGBgILy8vWFlZYf78+bVyVX/Pnj2xe/duLF68GMuWLYOtrS2WLl2qc8HaypUr8e6778Lb2xutWrXC2rVrcfbs2Sq14+XlhUmTJmHkyJH4999/ERwcXOGtySrLyckJv//+Oz766CO4ubnB2NgY7u7uwuj/nDlz8M4776BLly7IyclBcnIy7O3tsWPHDsydOxebN2/GgAEDsGTJEgQFBVXYlkgkQkREBBYuXIjAwEDcv38fCoUC3t7esLGxgUQiwb///ouxY8ciPT0dVlZWwkNHqP707NkTADBr1izcu3ev3Cet9enTp86etNanT58qP2nN29tbiKkmT1qbNGkSAgMDn/knrWn7g09aIyJ9ItKUnFRZSXfu3MGpU6dw7969UiOe06dPr5XgiOpDdnY25HI5lEolzMzMarXuq1evIigoCJs3bwYABAUF4XGX16BubiWUET9+gOYXfy61viq0dWzevLne5/BqFT/WhoqBiIiajqr8/a7WCO93330nfI3dokULnYuvRCIRE14iIiIiajSqlfBqHxG6YMEC4QbzRERERESNUbWy1SdPnmDUqFFMdomIiIio0atWxjp+/HiEh4fXdixERERERLWuWlMaVqxYAV9fX/z2229wcXEpdausVatW1UpwREREREQ1Va2E95NPPsGhQ4eERwuXvGiNiIiIiKixqFbCu2rVKmzdulXnnrFERERERI1RtebwymQy9OnTp7ZjISIiIiKqddVKeGfMmIF169bVdixERERERLWuWlMaTp8+jWPHjuHgwYNwdnYuddHa3r17ayU4omddu3btsHnzZrRr1w4pKSkNHU6dKn6sREREjUm1El5zc3MMHz68tmMh0jtGRkZN5jG7TelYiYjo2VLtRwsTERERET0LqpXwat2/fx9XrlyBSCSCk5MTWrZsWVtxERERERHVimpdtPb48WMEBgbC1tYW3t7eePHFF9GqVSuMHz8eT548qe0YiYiIiIiqrVoJ7+zZs3HixAkcOHAAWVlZyMrKwv79+3HixAl88MEHtR0jEREREVG1VWtKw549e/Djjz/Cx8dHWDd06FAYGxtjxIgR2LhxY23FR0RERERUI9Ua4X3y5AlsbGxKrbe2tuaUBiIiIiJqVKqV8Hp6eiI4OBi5ubnCupycHISEhMDT07PWgiMiIiIiqqlqTWlYs2YNXn75ZbRp0wbdunWDSCRCQkICZDIZfv/999qOkYiIiIio2qqV8Lq4uODatWvYsWMHLl++DI1Gg1GjRiEgIADGxsa1HSMRERERUbVVK+FdsWIFbGxsMGHCBJ31W7duxf379zF//vxaCY6IiIiIqKaqlfB+/fXX+O9//1tqvbOzM0aNGsWEl6gC4lyl7nJOls6/tVEnERER/Z9qJbxpaWmwtbUttb5ly5ZITU2tcVBE+kgul8NQKgNunChzu3FyVI3qN5TKIJfLa1QHERGRPqpWwtu2bVucOnUKDg4OOutPnTqFVq1a1UpgRPrGxsYGO7b/B0pl3YzGyuXyMm8XSERE1NRVK+F97733MHPmTBQUFOCll14CABw9ehTz5s3jk9aIKmBjY8OklIiIqJ5VK+GdN28eMjIyMGXKFOTn5wMAjIyMMH/+fCxYsKBWAyQiIiIiqgmRRqPRVHfnR48e4dKlSzA2NkbHjh0hk8lqMzaiepGdnQ25XA6lUgkzM7OGDoeIiIgqoSp/v6s1wqtlYmKC3r1716QKIiIiIqI6Va1HCxMRERERPSuY8BIRERGRXmPCS0RERER6jQkvEREREek1JrxEREREpNeY8BIRERGRXmPCS0RERER6jQkvEREREem1Gj14goiqLj09HUqlsk7qlsvlsLGxqZO6iYiInlVMeInqUXp6OkaPGYuC/Lw6qd9QKsOO7f9h0ktERFQME16ieqRUKlGQn4ccx75QG8l1tolzsmCcHIUcB2+ojc2rXLc4VwncOAGlUsmEl4iIqBgmvEQNQG0kh7q5VdnbjM3L3UZERERVx4vWiIiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXqI6lJubi6tXryI3N7ehQ6kXTe14iYjo2cCEl6gOpaSkICgoCCkpKQ0dSr1oasdLRETPBia8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXDBo6gGedj48PunfvjjVr1jR0KBUaN24csrKysG/fvmrtf/PmTTg4OCA+Ph7du3ev1dhI/1y5cgW3bt1CRkYGMjMz8ffff0MqlcLKygodO3ZEbGwscnNz0bZtW0ycOBHGxsZl1qNSqZCYmIgHDx4gKysL5ubmsLS0hFqtRmJiItRqNczMzGBpaQkrKyu4urpCIpHo7J+QkIBz587h3r17sLa2Rs+ePdG9e3edcrUlPz8f+/fvx927d9GqVSv4+flBKpVWuI/2GDMyMmBpaVnqGJ6msvvXtJ2aqkr7DR1rfcTQGI6RqClpkglveUnqvn378Prrr0Oj0VS6rr1798LQ0LCWI6x/kZGR6Nevn7BsZWWFXr164dNPP0W3bt3Qtm1bpKamwsrKqlL11TTBpmfTuXPnAAArV66sVPmzZ89i37596NOnD5YvX66zLSoqCmFhYUhLS6t0+wqFAlOmTIG3tzeioqKwatUqZGVl6ZTZuXMnzM3NMXv2bHh7e1e67qfZtGkTwsPDoVKpdNb5+/tj0qRJZe5T1jEWP4anqez+NW2npqrSfkPHWh8xNIZjJGpqOKWhhiwtLWFqalqnbRQUFNRp/cVduXIFqamp+OWXX5CZmYkhQ4ZAqVRCIpFAoVDAwKB+PyOpVCqo1ep6bZOqJyoqCl9//XWp9WKxWOdfLWtrawCARCLBqVOnsHDhQp26goODIZfLAQDu7u547bXXhO3aUdM2bdqgffv2wnq5XI7g4GBs2rQJwcHBQrLbpUsXTJ8+HV26dAEAZGVlITg4GFFRUTU9bABFie2uXbtgZmaGOXPmYM+ePZgzZw7MzMywa9cubNq0qdQ+2mN0dHTEhg0bEBERgQ0bNsDR0bFSsVV2/5q2U1NVab+hY62PGBrDMRI1RUx4K7BkyRJ0794d27dvh729PeRyOUaNGoWHDx8KZXx8fDBz5kwAwIIFC+Dh4VGqHldXVwQHBwvL3333HZ577jkYGRmhc+fOCAsLE7bdvHkTIpEIu3fvho+PD4yMjLBjxw7cunULr776KiwsLNC8eXM4OzsjIiICQFFSOH78eDg4OMDY2BidOnXC2rVrq3XM1tbWUCgUcHNzw8qVK5GWlobY2FghroSEBKHshQsX8Morr8DMzAympqZ48cUXcf36dSxZsgTff/899u/fD5FIBJFIhMjISERGRkIkEumMuCUkJEAkEuHmzZsAgG3btsHc3BwHDx5Ely5dIJPJcOvWLeTn52PevHlo3bo1mjdvDnd3d0RGRgr1VNQ/VPdUKhXCwsLg4uICAMK3HnK5HC1btoS5ubnwwUUqlUIsFuP+/fvw8PBAy5YtYWBggFOnTiEnJ0eoy8PDA1lZWfDy8kJoaCji4uIglUphYWEBlUoFc3NzFBYWYtOmTfD09IRMJoNSqYS7uzvCw8NhYGAAmUwGT09PrF+/HsOHD8f69evh6ekJqVQKqVSKjRs36ozIVkd+fj7Cw8NhYWGB8PBw+Pr6okWLFvD19dVZn5+fX6q/PD09ERoaCmdnZzRr1gzOzs4IDQ2Fp6dnhbFVdv/8/PwatVNTVTnOmvZJfcfbGOsnovIx4X2K69evY9++fTh48CAOHjyIEydO4NNPPy2zbEBAAOLi4nD9+nVh3YULF3D+/HkEBAQAAL755hssXLgQy5cvx6VLl/DJJ5/g448/xvfff69T1/z58zF9+nRcunQJgwcPxtSpU5GXl4eoqCicP38en332GUxMTAAAarUabdq0we7du3Hx4kUsXrwYH330EXbv3l2jY9fOqyxrhPnOnTvw9vaGkZERjh07hrNnzyIwMBCFhYWYM2cORowYgSFDhiA1NRWpqanw8vKqdLtPnjzBihUr8O233+LChQuwtrbGu+++i1OnTmHXrl1ITEyEv78/hgwZgmvXrgFAhf1TUl5eHrKzs3Vede3WrVu4evUqbt26VW9t1ecrIiICaWlpsLW1BfB/58zgwYORnp4Oe3t7AECvXr2Qn58PtVoNjUaD1q1bIy0tTfga9+uvv0ZiYiLS0tLg5uaG9PR0BAQEICkpCenp6cjPz8fAgQOhUqkwaNAgpKWlISkpCaNHj0ZeXh7S0tLQunVrqFQqFBQUIC8vD6NHj9YZZR49ejTy8/ORl5eH1NRUJCYm1qi/9+/fL3zoLPkNiIGBAQIDA6FSqbB//35hvfYYAwICSo18i8ViBAQEVBhbZfffv39/jdqpqaocZ037pL7jbYz1E1H5muQc3qpQq9XYtm2bMG1hzJgxOHr0aKn5hgDQtWtXuLq64r///S8+/vhjAEXzBXv37g0nJycAwLJly7By5UoMHz4cAODg4ICLFy/i66+/xjvvvCPUNXPmTKEMAKSkpOCNN94QRtAcHR2FbYaGhggJCRGWHRwcEB0djd27d2PEiBHVOu5///0XISEhMDU1hZubG548eaKzfcOGDZDL5di1a5cwmqc9RqAoWc7Ly4NCoahy2wUFBQgLC0O3bt0AFH3o+OGHH3D79m20atUKADBnzhz89ttv+O677/DJJ59U2D8lrVixQqe/6kNZ54s+tFXSoUOHdJa175d2dLdHjx74888/he15eXkAgM6dO+PYsWO4ffs2MjIyAABGRkYAis7nmJiYUnVq/83IyICnp2epOrUcHBwqXNa2V113794FAJ0YitOu15Yr3mbJWErGWF5sld1f22Z126mp6hxnQ8VavO66iqGu6yei8jHhfQp7e3udObq2tra4d+9eueUDAgKwdetWfPzxx9BoNPjhhx+EKQ/379/HP//8g/Hjx2PChAnCPoWFhcJcRa1evXrpLE+fPh2TJ0/G77//jgEDBuCNN96Aq6ursH3Tpk349ttvcevWLeTk5CA/P79ad1No06YNAODx48fo2LEjwsPDYW1tLUw50EpISMCLL75YJxfsSaVSnWM7d+4cNBqNTkINFCU2LVq0APD0/iluwYIFmD17trCcnZ2Ntm3b1vpxFLdw4ULY2dnh1q1bdZ6QatuqT1euXMHKlSsxePBgnaRXm3BpR7Pi4+N19pPJZACAy5cvAyg6/ywtLQEAubm5AIDk5GRhXfE6tf9aWloiOTm5VJ1aycnJcHZ21lkurnjd1aFNvGNiYuDr61tquzZZ15Yr3mbJ2ErGWF5sld1f22Z126mp6hxnQ8VavO66iqGu6yei8jXJKQ1mZmZQKpWl1mdlZcHMzExnXcmETiQSVXgR1dtvv42rV6/i3LlziI6Oxj///INRo0YB+L9Rrm+++QYJCQnCKykpCbGxsTr1NG/eXGf5vffew40bNzBmzBicP38evXr1wrp16wAAu3fvxqxZsxAYGIjff/8dCQkJePfdd3XmDFbWyZMn8ddff0GpVOLq1asYPHhwmeXKu41URbRJT/G7YJQ1XcLY2BgikUhYVqvVkEgkOHv2rE6/Xbp0SZirXFH/lCSTyWBmZqbzqmt2dnZwcnKql0RU21Z9voYOHQqFQoHU1FQA//d7c+jQIdjY2AgfmP78809hDq9IJMKdO3egUCiEC3UmTpwIV1dXKBQKnD59GjY2Nti5cye6du0KGxsbSKVSHD58GBKJBL///jsUCgW6du2KHTt2QCaTQaFQ4M6dO5BIJDA0NIRMJsOOHTuE3z21Wo0dO3ZAKpVCJpPB1ta23A9GleXn5weJRIItW7agsLBQZ1thYSG2bt0KiUQCPz8/Yb32GHfu3Fnq/xO1Wo2dO3dWGFtl9/fz86tROzVVleOsaZ/Ud7yNsX4iKl+TTHg7d+6s87Wq1pkzZ9CpU6ca1d2mTRt4e3tj586d2LlzJwYMGAAbGxsAgI2NDVq3bo0bN26gQ4cOOq/yvuIqrm3btpg0aRL27t2LDz74AN988w2AoiTVy8sLU6ZMQY8ePdChQwedecRV4eDggPbt2z81CXR1dcXJkyfLvYOEVCotdeFFy5YtAUBIigDoXARXnh49ekClUuHevXul+q34lIny+ofqnkQiwZQpU3D+/HkA//dBRqlU4v79+8jKyhI+8Gjn8LZs2RKxsbG4f/8+CgsL0adPHxgbGwt1xcbGwtzcHNHR0Vi0aBHc3d2Rn5+PzMxMSCQSZGVlwcDAABMnTkRMTAzy8vIgl8sRFxcHf39/FBYWIi8vDzExMZg2bRr27NmDadOmISYmBvn5+cjPz8fkyZNrfO9TqVQKf39/ZGZmwt/fHwcOHMCDBw9w4MABnfXF78erPcaYmBgsWrQIFy5cwJMnT3DhwgUsWrQIMTExFcZW2f2lUmmN2qmpqhxnTfukvuNtjPUTUfma5JSGKVOmYP369Zg6dSqCgoJgbGyMw4cPY8uWLdi+fXuN6w8ICMCSJUuQn5+P1atX62xbsmQJpk+fDjMzM7z88svIy8vDn3/+iczMTJ2v2UuaOXMmXn75ZTg5OSEzMxPHjh3Dc889BwDo0KED/vOf/+DQoUNwcHDA9u3bcebMmUol0dU1bdo0rFu3DqNGjcKCBQsgl8sRGxsLNzc3dOrUCfb29jh06BCuXLmCFi1aQC6Xo0OHDmjbti2WLFmC0NBQXLt2rVL3a3VyckJAQADGjh2LlStXokePHnjw4AGOHTsGFxcXDB06tML+ofrh7e2NiRMnlroFV/HR1eK0U4NUKlWp+/B6e3sjJCREuINJXFyczr7aby9u376tsz47OxshISHw9vZGly5dhPvwXrx4ERcvXhTKWVhYYNasWbV2z1PtfXbDw8N1zmmJRIJRo0aVeR/e4sc4depUYb2tra1wDBWp7P41baemqtJ+Q8daHzE0hmMkaoqaZMJrb2+PkydPYuHChRg0aBByc3Ph5OSEbdu2wd/fv8b1+/v74/3334dEIsGwYcN0tr333nto1qwZvvjiC8ybNw/NmzeHi4uLMM+3PCqVClOnTsXt27dhZmaGIUOGCMn0pEmTkJCQgJEjR0IkEuGtt97ClClT8Ouvv9b4WMrTokULHDt2DHPnzkXfvn0hkUjQvXt39OnTBwAwYcIEREZGolevXnj06BGOHz8OHx8f/PDDD5g8eTK6deuG3r17IzQ0tFJ9/t133yE0NBQffPAB7ty5gxYtWsDT0xNDhw59av9Q/enZsycA4IMPPoCRkVGNnrTm7e2NPn36VPtJa9r96+tJa5MmTUJgYGCVnrRW/Bir88Styu5f03ZqqirtN3Ss9RFDYzhGoqZGpKnKY8WI9FB2djbkcjmUSmWtz+e9evUqgoKCsHnzZjg5OQnLj7u8BnVz3afWiR8/QPOLP5e5rTK0+2vbagglj5eIiKiuVOXvd5Ocw0tERERETQcTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXqI61K5dO2zevBnt2rVr6FDqRVM7XiIiejYYNHQARPrMyMioST1it6kdLxERPRs4wktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNSa8RERERKTXmPASERERkV5jwktEREREeo0JLxERERHpNT5amKgBiHOVpdflZOn8Wxt1EhERERNeonoll8thKJUBN06UW8Y4Oara9RtKZZDL5dXen4iISB8x4SWqRzY2Ntix/T9QKutmNFYul8PGxqZO6iYiInpWMeElqmc2NjZMSomIiOoRL1ojIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSawYNHQBRU5Oeng6lUllv7cnlctjY2NRbe0RERI0NE16iepSeno7RY8aiID+v3to0lMqwY/t/mPQSEVGTxYSXqB4plUoU5Ochx7Ev1EbyMsuIc7JgnByFHAdvqI3Na9SeOFcJ3DgBpVLJhJeIiJosJrxEDUBtJIe6uVXFZYzNn1qGiIiIno4XrRERERGRXmPCS0RERER6jQkvEREREek1JrxEREREpNeY8BIRERGRXmPCS0RERER6jQkvEREREek1JrxEREREpNeY8BIRERGRXmPCS0RERER6jQkvEREREek1JrxEdSg3NxdXr15Fbm5uQ4fSaLGPiIiorjHhJapDKSkpCAoKQkpKSkOH0mixj4iIqK4x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivWbQ0AE0ZpGRkejXrx8yMzNhbm5e7Xq2bduGmTNnIisrq9ZiI9I3arUa8fHxePDgAbKysmBubg4rKyu4urpCIpEAAFQqFRISEhAfH4/09HTY2NigR48ecHFxwYULF5CRkQFLS8tS+yQmJlZYb1Vo6yvZVn5+Pvbv34+7d++iVatW8PPzg0QiKbNsTdohotL4+9J4NZb3psESXpFIVOH2d955B9u2baufYAD4+Pige/fuWLNmjbDOy8sLqampkMvlla7H3t4eM2fOxMyZM2slrj179mDdunWIj4+HSqWCo6Mj3nzzTUybNg2Wlpa10kZtJfY1sWTJEoSEhJRaf/jwYQwYMKABIqL6tnDhQvz777+l1isUCkyZMgUAsGrVqlIfHHfs2AGxWAy1Wl3mPmFhYUhLSyu3Xm9v70rHGBUVVao+hUKB9u3bIzY2FiqVSli/ceNGGBkZ4cmTJ1Vus7x2qhovUVPA35fGqzG9Nw02pSE1NVV4rVmzBmZmZjrr1q5d21ChCaRSKRQKxVOT87qycOFCjBw5Er1798avv/6KpKQkrFy5En/99Re2b9/eIDE9TUFBQbX3dXZ21jkHUlNT+Z9VE3Du3DkAgImJCUQiEdzd3TFnzhy4u7sDAORyOYKDg7F48WIh2e3SpQumT5+ONm3aAICQ7C5cuBAbNmyAo6MjFi9ejODgYOEDa/F6RSKRUG9UVFSl4oyKikJwcDAcHR2xYcMGREREYMOGDRCLxTh16hSMjIwwZ84c7NmzB6+99hrUajWePHmCAQMGCGUdHR2f2mZ57VRmX6Kmhr8vjVdje28aLOFVKBTCSy6XQyQSQaFQwMbGBi4uLjhy5IhQtnv37rC2thaWY2JiYGhoiEePHgEAlEolgoKCYG1tDTMzM7z00kv466+/hPJLlixB9+7dsX37dtjb20Mul2PUqFF4+PAhAGDcuHE4ceIE1q5dC5FIBJFIhJs3byIyMhIikUhnRCk6Ohre3t4wNjZG27ZtMX36dDx+/BhA0SjxrVu3MGvWLKGe4g4dOoTnnnsOJiYmGDJkCFJTU8vtn9OnT+OTTz7BypUr8cUXX8DLywv29vYYOHAg9uzZg3feeUcoe+DAATz//PMwMjKCo6MjQkJCUFhYKGwXiUT49ttv8frrr6NZs2bo2LEjfv75ZwDAzZs30a9fPwCAhYUFRCIRxo0bB6BotLr4iLf2vViyZIlO3Zs2bYKfnx+aN2+O0NBQdOjQAV9++aXOfklJSRCLxbh+/Xq5x2xgYKBzXigUCkilUgDA+fPn8dJLL8HY2BgtWrRAUFCQ8P5HRkZCKpXi5MmTQl0rV66ElZVVhX1MDU+lUiE8PBwA8OjRI3h6emLFihXw9fXFihUr4OXlBaVSCUNDQwCAoaEhPD09sX79evj5+aGgoADm5uaQyWSQyWTYsmULOnfujJCQEMhkMkilUmRlZcHLy0unXk9PT2RnZ8PDwwMbN27UGZktL86wsDB4enoiNDQUzs7Owu9Seno6DA0NYWJigpdffhnm5uY4ffo0PDw8YGFhgePHj8PAwADOzs4IDQ2Fp6dnuW2W105l9iVqavj70ng1xvem0V20JhKJ4O3tjcjISABAZmYmLl68iIKCAly8eBFAUYLz/PPPw8TEBBqNBq+88grS0tIQERGBs2fPomfPnujfvz8yMjKEeq9fv459+/bh4MGDOHjwIE6cOIFPP/0UALB27Vp4enpiwoQJwshi27ZtS8V2/vx5DB48GMOHD0diYiL+97//4Y8//sC0adMAAHv37kWbNm2wdOlSoR6tJ0+e4Msvv8T27dsRFRWFlJQUzJkzp9x+2LlzJ0xMTISvZUvSTj04dOgQRo8ejenTp+PixYv4+uuvsW3bNixfvlynfEhICEaMGIHExEQMHToUAQEByMjIQNu2bbFnzx4AwJUrV6o1uh4cHAw/Pz+cP38egYGBCAwMxHfffadTZuvWrXjxxRfRvn37KtUNFPXdkCFDYGFhgTNnziA8PBxHjhwR+t3HxwczZ87EmDFjoFQq8ddff2HhwoX45ptvYGtrW6q+vLw8ZGdn67zq2q1bt3D16lXcunWrztuqqP3G9oqIiBCmMfz7778ICAiAWFz035JYLEZAQADS0tKQn58PoOgbhNGjR0MsFiMxMRHp6ekYP3488vLykJeXh7S0NCQmJiIpKUlYl56eXma9qampcHNzQ2pqKhITEyvsv8TERKSlpenUAwD79++HSqWCv78/0tPTkZiYKJQdM2YMAgMDoVKpsH///lJtl9Vmee1UZl+ipoa/L41XY3xvGuVFaz4+Pti8eTOAoiHxbt26oV27doiMjESXLl0QGRkJHx8fAMDx48dx/vx53Lt3DzKZDADw5ZdfYt++ffjxxx8RFBQEoOgrz23btsHU1BQAMGbMGBw9ehTLly+HXC6HVCpFs2bNoFAoyo3riy++wNtvvy3Mz+3YsSO++uor9O3bFxs3boSlpSUkEglMTU1L1VNQUIBNmzYJCd+0adOwdOnSctu6du0aHB0dhZGt8ixfvhwffvihMOLr6OiIZcuWYd68eQgODhbKjRs3Dm+99RYA4JNPPsG6detw+vRpDBkyRJgLbG1tXa05vG+//TYCAwOF5XfffReLFy/G6dOn4ebmhoKCAuzYsQNffPFFhfWcP38eJiYmwnKXLl1w+vRp7Ny5Ezk5OfjPf/6D5s2bAwDWr1+PV199FZ999hlsbGwQGhqKI0eOICgoCBcuXMCYMWPw+uuvl9nOihUrypwvXJdKfgCpbw3dfmU5ODhUuFx8nfYDraenp8724h90n1av9v+MsvYpq86S9dy9excAMHToUPz3v//VqcfBwQE2NjY65cqKvzLtVGZfoqaGvy+NV2N8bxptwjtjxgw8ePAAJ06cgI+PD9q1a4cTJ04gKCgI0dHRQtJ59uxZPHr0CC1atNCpIycnR+frc3t7eyHZBQBbW1vcu3evSnGdPXsWf//9N3bu3Cms02g0UKvVSE5OxnPPPVfuvs2aNdMZ3Xxa+xqNplJzh8+ePYszZ87oJDQqlQq5ubl48uQJmjVrBgBwdXUVtjdv3hympqZVPv7y9OrVS2fZ1tYWr7zyCrZu3Qo3NzccPHgQubm58Pf3r7CeTp06CVMtgP9LRi5duoRu3boJyS4A9OnTB2q1GleuXIGNjQ2kUil27NgBV1dX2NnZlZqKUdyCBQswe/ZsYTk7O7vMEf3atHDhQtjZ2eHWrVsNknxq229srly5gpUrVwrLycnJcHZ21lkuSVtG+0EtJiZGZ3tZF3OWV29eXl65+5RVZ8l6WrVqBQCIiIgoVU9ycjJu3LihU65422W1WV47ldmXqKnh70vj1Rjfm0aZ8Hbt2hUtWrTAiRMncOLECSxduhRt27bF8uXLcebMGeTk5OCFF14AUDRya2trK0yBKK74aGXJkVKRSKRzVXdlqNVqTJw4EdOnTy+1rV27dhXuW1b7Go2m3PJOTk74448/UFBQUOEor1qtRkhICIYPH15qm5GRUYXtP+34xWJxqRjLuiiteCKq9d5772HMmDFYvXo1vvvuO4wcOVJIvssjlUrRoUOHUusrSv6Lr4+OjgZQ9IkxIyOjzLgACPM965OdnR2cnJzqtc3G1H552rdvj23btuHff/9FixYtsHPnToSGhgp3Xdi5cycUCgUyMjKQn58PQ0ND7NixA8uXL4erqytsbGywZcsW4f20sLCAq6sr1Gq1sM7c3LzMem1tbXH69GnY2trqfCAsi6urKxQKhU49AODn54dNmzYhPDwcNjY2Qj0KhQLbt2/HlStXIJFI4OfnBwA6bZfVZnntVGZfoqaGvy+NV2N8bxrdHF7g/+bx7t+/H0lJSXjxxRfh4uIiTAvo2bOnMFrbs2dPpKWlwcDAAB06dNB5WVlZVbpNqVT61MnTPXv2xIULF0q106FDB+HiqsrUUxlvv/02Hj16hLCwsDK3ay+k69mzJ65cuVJmTCXnzZRHG3vJuFu2bKkzDzk7O7vMEbeyDB06FM2bN8fGjRvx66+/6kx5qKouXbogISFBuDgQAE6dOgWxWCwkcdevX8esWbPwzTffwMPDA2PHjq3yBxqqfxKJRBj5NzExQUxMDBYsWIADBw5gwYIFiI6OhlwuFz5oFRQUICYmBtOmTcO+fftgaGiIrKwsYb7u+PHjcfnyZQQHByMvLw/5+fkwNzdHdHS0Tr0xMTEwMzNDbGwsJk+e/NR7QkokEkyZMgUxMTFYtGgRLly4gCdPnuDatWuwsbFBQUEBHj16hIiICGRmZqJ3796IjY1FZmYm+vXrh8LCQly4cAGLFi1CTExMuW2W105l9iVqavj70ng1xvemUY7wAkXTGmbNmoUePXrAzMwMAODt7Y2dO3fqfB09YMAAeHp6YtiwYfjss8/QqVMn3L17FxERERg2bFipr9vLY29vj7i4ONy8eRMmJiZlDrPPnz8fHh4emDp1KiZMmIDmzZvj0qVLOHz4MNatWyfUExUVhVGjRkEmk1Up6S7O3d0d8+bNwwcffIA7d+7g9ddfR6tWrfD3339j06ZNeOGFFzBjxgwsXrwYvr6+aNu2Lfz9/YWLec6fP4/Q0NBKtWVnZweRSISDBw9i6NChMDY2homJCV566SVs27YNr776KiwsLPDxxx9X+uSUSCQYN24cFixYgA4dOpSaZ1kVAQEBCA4OxjvvvIMlS5bg/v37eP/99zFmzBjY2NhApVJhzJgxGDRoEN599128/PLLcHFxwcqVKzF37txqt0v1o2fPngCK7tKg0WgQFxeHuLg4YXt2drYw51p7H96LFy8KF7ECEEZutdNFbG1thTny2g+N5dVb2VvfeXt7IyQkBGFhYZg6daqw3tbWFn369EFsbKzO9AyxWAwjIyMcOXJEuOuMra3tU9usqJ2qxEvUFPD3pfFqbO9No014+/XrB5VKJVycBgB9+/bFvn370LdvX2GdSCRCREQEFi5ciMDAQNy/fx8KhQLe3t7CBSOVMWfOHLzzzjvo0qULcnJyyhzJdHV1xYkTJ7Bw4UK8+OKL0Gg0aN++PUaOHCmUWbp0KSZOnIj27dsjLy+vwmkLT/PZZ5/h+eefx4YNG7Bp0yao1Wq0b98eb775pnCR2uDBg3Hw4EEsXboUn3/+OQwNDdG5c2e89957lW6ndevWCAkJwYcffoh3330XY8eOxbZt27BgwQLcuHEDvr6+kMvlWLZsWaVHeAFg/Pjx+OSTT2o0ugsUzX8+dOgQZsyYgd69e6NZs2Z44403sGrVKgBFF2TdvHkTBw4cAFD0dfK3336LESNGYODAgejevXuN2qf6sXz5cuTk5FT4RLQ+ffpU+Ulrffr0qbUnrXl7ewv11eWT1ipqh4h08fel8WpM741IU5OMjKgCp06dgo+PD27fvl2lDx/1LTs7G3K5HEqlUvg2obZcvXoVQUFB2Lx5M5ycnITlx11eg7p52aP/4scP0PzizxWWqSxtXdr2G6OSfURERFQZVfn73WhHeOnZlZeXh3/++Qcff/wxRowY0aiTXSIiItJ/jfKiNXq2/fDDD+jUqROUSiU+//zzhg6HiIiImjgmvFTrxo0bB5VKhbNnz6J169YNHQ4RERE1cUx4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeoDrVr1w6bN29Gu3btGjqURot9REREdY1PWiOqQ0ZGRnxc7lOwj4iIqK5xhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa0x4iYiIiEivMeElIiIiIr3GhJeIiIiI9BoTXiIiIiLSa3y0MFEDEOcqy9+Wk6Xzb121Q0RE1FQw4SWqR3K5HIZSGXDjxFPLGidH1UqbhlIZ5HJ5rdRFRET0LGLCS1SPbGxssGP7f6BU1t/Iq1wuh42NTb21R0RE1Ngw4SWqZzY2NkxAiYiI6hEvWiMiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK/xojVq8jQaDQAgOzu7gSMhIiKiytL+3db+Ha8IE15q8h4+fAgAaNu2bQNHQkRERFX18OHDp95vXqSpTFpMpMfUajXu3r0LU1NTiESiWq07Ozsbbdu2xT///AMzM7NarVvfse+qj31Xfey7mmH/VR/7ruo0Gg0ePnyIVq1aQSyueJYuR3ipyROLxWjTpk2dtmFmZsb/wKqJfVd97LvqY9/VDPuv+th3VVPZJ4nyojUiIiIi0mtMeImIiIhIrzHhJapDMpkMwcHBkMlkDR3KM4d9V33su+pj39UM+6/62Hd1ixetEREREZFe4wgvEREREek1JrxEREREpNeY8BIRERGRXmPCS0RERER6jQkvUQ2FhYXBwcEBRkZGeP7553Hy5MkKy584cQLPP/88jIyM4OjoiE2bNtVTpI1PVfouMjISIpGo1Ovy5cv1GHHjEBUVhVdffRWtWrWCSCTCvn37nroPz7siVe07nndFVqxYgd69e8PU1BTW1tYYNmwYrly58tT9eN4VqU7/8dyrXUx4iWrgf//7H2bOnImFCxciPj4eL774Il5++WWkpKSUWT45ORlDhw7Fiy++iPj4eHz00UeYPn069uzZU8+RN7yq9p3WlStXkJqaKrw6duxYTxE3Ho8fP0a3bt2wfv36SpXnefd/qtp3Wk39vDtx4gSmTp2K2NhYHD58GIWFhRg0aBAeP35c7j487/5PdfpPq6mfe7VGQ0TV5ubmppk0aZLOus6dO2s+/PDDMsvPmzdP07lzZ511EydO1Hh4eNRZjI1VVfvu+PHjGgCazMzMeoju2QFA89NPP1VYhudd2SrTdzzvynbv3j0NAM2JEyfKLcPzrnyV6T+ee7WLI7xE1ZSfn4+zZ89i0KBBOusHDRqE6OjoMveJiYkpVX7w4MH4888/UVBQUGexNjbV6TutHj16wNbWFv3798fx48frMky9wfOu5nje6VIqlQAAS0vLcsvwvCtfZfpPi+de7WDCS1RNDx48gEqlgo2Njc56GxsbpKWllblPWlpameULCwvx4MGDOou1salO39na2mLz5s3Ys2cP9u7di06dOqF///6Iioqqj5CfaTzvqo/nXWkajQazZ8/GCy+8gK5du5Zbjudd2Srbfzz3apdBQwdA9KwTiUQ6yxqNptS6p5Uva31TUJW+69SpEzp16iQse3p64p9//sGXX34Jb2/vOo1TH/C8qx6ed6VNmzYNiYmJ+OOPP55aluddaZXtP557tYsjvETVZGVlBYlEUmpE8t69e6VGNbQUCkWZ5Q0MDNCiRYs6i7WxqU7flcXDwwPXrl2r7fD0Ds+72tWUz7v3338fP//8M44fP442bdpUWJbnXWlV6b+yNOVzr6aY8BJVk1QqxfPPP4/Dhw/rrD98+DC8vLzK3MfT07NU+d9//x29evWCoaFhncXa2FSn78oSHx8PW1vb2g5P7/C8q11N8bzTaDSYNm0a9u7di2PHjsHBweGp+/C8+z/V6b+yNMVzr9Y02OVyRHpg165dGkNDQ82WLVs0Fy9e1MycOVPTvHlzzc2bNzUajUbz4YcfasaMGSOUv3HjhqZZs2aaWbNmaS5evKjZsmWLxtDQUPPjjz821CE0mKr23erVqzU//fST5urVq5qkpCTNhx9+qAGg2bNnT0MdQoN5+PChJj4+XhMfH68BoFm1apUmPj5ec+vWLY1Gw/OuIlXtO553RSZPnqyRy+WayMhITWpqqvB68uSJUIbnXfmq038892oXE16iGtqwYYPGzs5OI5VKNT179tS5zcw777yj6du3r075yMhITY8ePTRSqVRjb2+v2bhxYz1H3HhUpe8+++wzTfv27TVGRkYaCwsLzQsvvKD55ZdfGiDqhqe9XVHJ1zvvvKPRaHjeVaSqfcfzrkhZfQZA89133wlleN6Vrzr9x3Ovdok0mv8/g5yIiIiISA9xDi8RERER6TUmvERERESk15jwEhEREZFeY8JLRERERHqNCS8RERER6TUmvERERESk15jwEhEREZFeY8JLRERERHUiKioKr776Klq1agWRSIR9+/ZVuY7du3eje/fuaNasGezs7PDFF19UuQ4mvERE1Oh9/PHHCAoKEpZ9fHwwc+bMGtVZmTrs7e2xZs0aYbn4H+ybN29CJBIhISEBABAZGQmRSISsrKwaxVVZeXl5aNeuHc6ePVsv7RFVx+PHj9GtWzesX7++Wvv/+uuvCAgIwKRJk5CUlISwsDCsWrWqyvUZVKt1IiKiepKeno61a9ciMTFRWLd3714YGhrWedtnzpxB8+bNK1XWy8sLqampkMvldRxVEZlMhjlz5mD+/Pk4cuRIvbRJVFUvv/wyXn755XK35+fnY9GiRdi5cyeysrLQtWtXfPbZZ/Dx8QEAbN++HcOGDcOkSZMAAI6Ojpg/fz4+++wzTJ06FSKRqFJxcISXiIgqLT8/v97b3LJlCzw9PWFvby+ss7S0hKmpaZ233bJlSzRr1qxSZaVSKRQKRaX/ANeGgIAAnDx5EpcuXaq3Nolq07vvvotTp05h165dSExMhL+/P4YMGYJr164BKPomw8jISGcfY2Nj3L59G7du3ap0O0x4iYiaqIcPHyIgIADNmzeHra0tVq9eXeprfnt7e4SGhmLcuHGQy+WYMGECAGDPnj1wdnaGTCaDvb09Vq5cqVN3WFgYOnbsCCMjI9jY2ODNN98Utv34449wcXGBsbExWrRogQEDBuDx48flxrlr1y689tprOuvKivOTTz5BYGAgTE1N0a5dO2zevPmpfVBYWIhp06bB3NwcLVq0wKJFi6DRaHTqLT6loSJlTWl4Wj89Le78/HxMmzYNtra2MDIygr29PVasWCFsb9GiBby8vPDDDz9UKkaixuT69ev44YcfEB4ejhdffBHt27fHnDlz8MILL+C7774DAAwePBh79+7F0aNHoVarcfXqVeF3MjU1tdJtMeElImqiZs+ejVOnTuHnn3/G4cOHcfLkSZw7d65UuS+++AJdu3bF2bNn8fHHH+Ps2bMYMWIERo0ahfPnz2PJkiX4+OOPsW3bNgDAn3/+ienTp2Pp0qW4cuUKfvvtN3h7ewMo+gP11ltvITAwEJcuXUJkZCSGDx+uk2QWl5mZiaSkJPTq1eupx7Ny5Ur06tUL8fHxmDJlCiZPnozLly9XuM/3338PAwMDxMXF4auvvsLq1avx7bffPrWtynhaP1Um7q+++go///wzdu/ejStXrmDHjh06I90A4ObmhpMnT9ZKzET16dy5c9BoNHBycoKJiYnwOnHiBK5fvw4AmDBhAqZNmwZfX19IpVJ4eHhg1KhRAACJRFL5xjRERNTkZGdnawwNDTXh4eHCuqysLE2zZs00M2bMENbZ2dlphg0bprPv22+/rRk4cKDOurlz52q6dOmi0Wg0mj179mjMzMw02dnZpdo9e/asBoDm5s2blYozPj5eA0CTkpKis75v376l4hw9erSwrFarNdbW1pqNGzeWW3ffvn01zz33nEatVgvr5s+fr3nuued06l29erWwDEDz008/aTQajSY5OVkDQBMfH6/RaDSa48ePawBoMjMzNRrN0/upMnG///77mpdeekknxpLWrl2rsbe3L3c7UWNR/PdHo9Fodu3apZFIJJrLly9rrl27pvNKTU3V2bewsFBz+/ZtTV5eniYiIkIDQJOenl7ptjnCS0TUBN24cQMFBQVwc3MT1snlcnTq1KlU2ZKjq5cuXUKfPn101vXp0wfXrl2DSqXCwIEDYWdnB0dHR4wZMwY7d+7EkydPAADdunVD//794eLiAn9/f3zzzTfIzMwsN86cnBwAKDWHryyurq7CzyKRCAqFAvfu3atwHw8PD505t56ensJx1NTT+qkycY8bNw4JCQno1KkTpk+fjt9//71UO8bGxkL/Ej1LevToAZVKhXv37qFDhw46L4VCoVNWIpGgdevWkEql+OGHH+Dp6Qlra+tKt8WEl4ioCdL8/ykEJS+w0pQxtaDkXQo0Gk2F+5mamuLcuXP44YcfYGtri8WLF6Nbt27IysqCRCLB4cOH8euvv6JLly5Yt24dOnXqhOTk5DLjtLKyAoAKk2KtkndtEIlEUKvVT92vrjytn7Qqirtnz55ITk7GsmXLkJOTgxEjRujMhwaAjIwMtGzZspajJ6odjx49QkJCgnD7vuTkZCQkJCAlJQVOTk4ICAjA2LFjsXfvXiQnJ+PMmTP47LPPEBERAQB48OABNm3ahMuXLyMhIQEzZsxAeHh4pefWazHhJSJqgtq3bw9DQ0OcPn1aWJednS1cGV2RLl264I8//tBZFx0dDScnJ2FOnYGBAQYMGIDPP/8ciYmJuHnzJo4dOwagKKHr06cPQkJCEB8fD6lUip9++qncOM3MzHDx4sXqHmqFYmNjSy137NixanMDy1GZfqoMMzMzjBw5Et988w3+97//Yc+ePcjIyBC2JyUloUePHjWOl6gu/Pnnn+jRo4dwjs6ePRs9evTA4sWLAQDfffcdxo4diw8++ACdOnXCa6+9hri4OLRt21ao4/vvv0evXr3Qp08fXLhwAZGRkTrfTlUG78NLRNQEmZqa4p133sHcuXNhaWkJa2trBAcHQywWP/W2Wh988AF69+6NZcuWYeTIkYiJicH69esRFhYGADh48CBu3LgBb29vWFhYICIiAmq1Gp06dUJcXByOHj2KQYMGwdraGnFxcbh//z6ee+65MtsSi8UYMGAA/vjjDwwbNqy2uwH//PMPZs+ejYkTJ+LcuXNYt25dqTspVNfT+qkyVq9eDVtbW3Tv3h1isRjh4eFQKBQwNzcXypw8eRLLli2rlZiJapuPj0+5F6UCRd9whISEICQkpMztVlZWiImJqXEcTHiJiJqoVatWYdKkSfD19YWZmRnmzZuHf/7556nzZXv27Indu3dj8eLFWLZsGWxtbbF06VKMGzcOAGBubo69e/diyZIlyM3NRceOHfHDDz/A2dkZly5dQlRUFNasWYPs7GzY2dlh5cqVFd6YPigoCOPHj8fnn38Osbh2v5gcO3YscnJy4ObmBolEgvfff1/niW418bR+qgwTExN89tlnuHbtGiQSCXr37o2IiAihH2JiYqBUKktNcyAiXSJNRWk3ERE1GY8fP0br1q2xcuVKjB8/vqHDEWg0Gnh4eGDmzJl46623GjqcRsXf3x89evTARx991NChEDVqnMNLRNRExcfH44cffsD169dx7tw5BAQEAAD8/PwaODJdIpEImzdvRmFhYUOH0qjk5eWhW7dumDVrVkOHQtTocYSXiKiJio+Px3vvvYcrV65AKpXi+eefx6pVq+Di4tLQoRER1SomvERERESk1zilgYiIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivcaEl4iIiIj0GhNeIiIiItJrTHiJiIiISK8x4SUiIiIivfb/AB5KhgWyISnfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(subset_company, x='gross', y=subset_company['company'])\n",
    "ax.set(xlabel='gross (in billions)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'budget (by 100 million)')]"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(subset_company, x='budget', y=subset_company['company'])\n",
    "ax.set(xlabel='budget (by 100 million)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the boxplots for the top 5 companies alongside their budget and grossing, we can once again see that there are extreme outliers for both. This makes sense, as each movie production company has very few successful movies that reach such high grossing, and certain movies receive major budgets depending on the promise of the movie or projected grossing. However, we see that Warner Bros. has spent the most on movies in terms of budget, yet Twentieth Century Fox seems to have had the most commercially successful movies, revealing that even if the movie company spends a lot on budget, they may not receive the most success in the box-office."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = subset_company[[\"gross\", \"budget\", \"score\", \"votes\", 'net gain/loss']].corr()\n",
    "sns.heatmap(corr, annot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a heatmap, we now see the correlation between the different features we have in our dataset. Budget and gross have the strongest correlation, which helps prove our prediction that the more money you put into a movie, the better commercial success you receive. We also have a strong correlation between gross and votes, which also makes sense. As more people view a movie, the revenue of a movie increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACHINE LEARNING\n",
    "\n",
    "After looking through the data and searching for patterns, we can attempt to make predictions based on the data. We can start with making predictions on the specific movie company based on the features \"gross\", \"budget\", \"score\", and \"votes\", to see if there are any differing featuristics between the top 5 movie companies we have chosen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "budget R^2 Value: 0.00011393137092130523\n",
      "gross R^2 Value: 5.438703912674647e-06\n",
      "score R^2 Value: 0.003058345288662001\n",
      "votes R^2 Value: 0.0003162757310316566\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lf/1gn2p5yn6m96gw3zn57nrkq80000gn/T/ipykernel_2457/2064163978.py:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  subset_company['company'] = subset_company['company'].replace({'Universal Pictures': 0, 'Warner Bros.': 1, 'Columbia Pictures': 2, 'Paramount Pictures' : 3, 'Twentieth Century Fox' : 4})\n"
     ]
    }
   ],
   "source": [
    "features = ['budget','gross','score','votes']\n",
    "subset_company['company'] = subset_company['company'].replace({'Universal Pictures': 0, 'Warner Bros.': 1, 'Columbia Pictures': 2, 'Paramount Pictures' : 3, 'Twentieth Century Fox' : 4})\n",
    "\n",
    "for feature in features:\n",
    "    lm = LinearRegression()\n",
    "    lm.fit(subset_company[[feature]], subset_company['company'])\n",
    "    score = lm.score(subset_company[[feature]], subset_company['company'])\n",
    "    print(f'{feature} R^2 Value: {score}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I started with single variable linear regression, and fit each feature into a linear regression model to see which was the best predictor of the company, using the $R^2$ value to determine how well the model was. Unfortunately, none of the features can predict the company as shown by the scores. This shows us that none of the top 5 companies stand out in terms of commercial or populairty success, which we were starting to see through the analyzation of the data earlier. Although there are no correlations between any of the features and the company, we did learn that there was a strong correlation between budget and gross, which can try further exploring through a linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 = 0.46757209877600703\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(subset_company[['budget']], subset_company['gross'])\n",
    "score = lm.score(subset_company[['budget']], subset_company['gross'])\n",
    "print(f'R2 = {score}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting the model, we get an $R^2$ value of .46, which is pretty weak, revealing that there isn't a strong linear relationship between the budget and gross of a movie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also take a look at the relationship between votes and net gain/loss, which we saw a slightly strong correlation in earlier. Logically, a movie's votes and score will have an affect on the net gain/loss, as the more popular a movie is, the more people there will be to watch it, increasing box office. We can first try fitting it onto a linear regression, but this time validating the $R^2$ value using 5-fold validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average R-squared: 0.38664\n",
      "Standard Deviation of R-squared: 0.09345\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "features = ['votes']\n",
    "\n",
    "X = subset_company[features]\n",
    "y = subset_company['net gain/loss']\n",
    "# Setup 5-fold cross-validation\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=1)\n",
    "\n",
    "# Perform cross-validation\n",
    "r2_scores = cross_val_score(lm, X, y, cv=kf, scoring='r2')\n",
    "\n",
    "# Calculate average and standard deviation of R-squared scores\n",
    "average_r2 = np.mean(r2_scores)\n",
    "std_r2 = np.std(r2_scores)\n",
    "\n",
    "print(f'Average R-squared: {average_r2:.5f}')\n",
    "print(f'Standard Deviation of R-squared: {std_r2:.5f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we find a weak average $R^2$ value between votes and net gain/loss. However, we can instead try and fit the data onto a logistic regression model using classification. First, I wanted to see all the titles that had a negative revenue. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "432\n"
     ]
    }
   ],
   "source": [
    "titles = []\n",
    "for i in range(len(subset_company['net gain/loss'])):\n",
    "    if subset_company.loc[i,'net gain/loss'] < 0:\n",
    "        titles.append(subset_company.loc[i,'name'])\n",
    "\n",
    "print(len(titles))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to have two classes, whether a movie has a net gain or net loss in the box-office. I created a for-loop to create class 0, for when a movie has a net loss, and a class 1, if a movie has a positive net gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "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>name</th>\n",
       "      <th>year</th>\n",
       "      <th>score</th>\n",
       "      <th>votes</th>\n",
       "      <th>budget</th>\n",
       "      <th>gross</th>\n",
       "      <th>company</th>\n",
       "      <th>net gain/loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shining</td>\n",
       "      <td>1980</td>\n",
       "      <td>8.4</td>\n",
       "      <td>927000.0</td>\n",
       "      <td>19000000.0</td>\n",
       "      <td>46998772.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Blue Lagoon</td>\n",
       "      <td>1980</td>\n",
       "      <td>5.8</td>\n",
       "      <td>65000.0</td>\n",
       "      <td>4500000.0</td>\n",
       "      <td>58853106.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Airplane!</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.7</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>3500000.0</td>\n",
       "      <td>83453539.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Friday the 13th</td>\n",
       "      <td>1980</td>\n",
       "      <td>6.4</td>\n",
       "      <td>123000.0</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>39754601.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The Blues Brothers</td>\n",
       "      <td>1980</td>\n",
       "      <td>7.9</td>\n",
       "      <td>188000.0</td>\n",
       "      <td>27000000.0</td>\n",
       "      <td>115229890.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>Tenet</td>\n",
       "      <td>2020</td>\n",
       "      <td>7.4</td>\n",
       "      <td>387000.0</td>\n",
       "      <td>205000000.0</td>\n",
       "      <td>363656624.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1596</th>\n",
       "      <td>The Invisible Man</td>\n",
       "      <td>2020</td>\n",
       "      <td>7.1</td>\n",
       "      <td>186000.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>143151000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>Bad Boys for Life</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.6</td>\n",
       "      <td>140000.0</td>\n",
       "      <td>90000000.0</td>\n",
       "      <td>426505244.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>Sonic the Hedgehog</td>\n",
       "      <td>2020</td>\n",
       "      <td>6.5</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>85000000.0</td>\n",
       "      <td>319715683.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1599</th>\n",
       "      <td>Dolittle</td>\n",
       "      <td>2020</td>\n",
       "      <td>5.6</td>\n",
       "      <td>53000.0</td>\n",
       "      <td>175000000.0</td>\n",
       "      <td>245487753.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1600 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    name  year  score     votes       budget        gross  \\\n",
       "0            The Shining  1980    8.4  927000.0   19000000.0   46998772.0   \n",
       "1        The Blue Lagoon  1980    5.8   65000.0    4500000.0   58853106.0   \n",
       "2              Airplane!  1980    7.7  221000.0    3500000.0   83453539.0   \n",
       "3        Friday the 13th  1980    6.4  123000.0     550000.0   39754601.0   \n",
       "4     The Blues Brothers  1980    7.9  188000.0   27000000.0  115229890.0   \n",
       "...                  ...   ...    ...       ...          ...          ...   \n",
       "1595               Tenet  2020    7.4  387000.0  205000000.0  363656624.0   \n",
       "1596   The Invisible Man  2020    7.1  186000.0    7000000.0  143151000.0   \n",
       "1597   Bad Boys for Life  2020    6.6  140000.0   90000000.0  426505244.0   \n",
       "1598  Sonic the Hedgehog  2020    6.5  102000.0   85000000.0  319715683.0   \n",
       "1599            Dolittle  2020    5.6   53000.0  175000000.0  245487753.0   \n",
       "\n",
       "      company  net gain/loss  \n",
       "0           1            1.0  \n",
       "1           2            1.0  \n",
       "2           3            1.0  \n",
       "3           3            1.0  \n",
       "4           0            1.0  \n",
       "...       ...            ...  \n",
       "1595        1            1.0  \n",
       "1596        0            1.0  \n",
       "1597        2            1.0  \n",
       "1598        3            1.0  \n",
       "1599        0            1.0  \n",
       "\n",
       "[1600 rows x 8 columns]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(subset_company['net gain/loss'])):\n",
    "    if subset_company.loc[i,'net gain/loss'] < 0:\n",
    "        subset_company.loc[i,'net gain/loss'] = 0\n",
    "    elif subset_company.loc[i,'net gain/loss'] > 0:\n",
    "        subset_company.loc[i,'net gain/loss'] = 1\n",
    "\n",
    "subset_company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I then scaled the features using a standard scaler, with a mean of 0 and standard deviation of 1. I then fit the data onto a logistic regression model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "features = ['votes']\n",
    "subset_company[features] = scaler.fit_transform(subset_company[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 value: 0.811875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "X = subset_company[features]\n",
    "y = subset_company['net gain/loss']\n",
    "clf = LogisticRegression(multi_class='multinomial', penalty=None)\n",
    "clf.fit(X, y)\n",
    "print(f'R^2 value: {clf.score(X, y)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After fitting the model, we get a stronger $R^2$ value of 0.812, which reveals that with our model, we have a strong probability of predicting whether a movie will have a positive or negative revenue in the box-office based on how popular the movie is among viewers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXTRA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this portion, we will be using a different machine learning tool to create a model fitting votes to predict the net gain/loss. I downloaded the package TensorFlow, as it allows for efficient deep learning that's quick and reproducible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we're going to split our data into training and testing data, in order to have a data set dedicated towards training our model, and a set of data to test it on for accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X = subset_company[features]\n",
    "y = subset_company['net gain/loss']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras as keras\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From Tensorflow, we want to create a model using tf.keras.Sequential(), which will be our main model class that we fit our data onto. The Dense feature is a layer that allows us to build hidden layers within our model, which in this case is 2 hidden layers. The first hidden layer takes our raw data and passes it through a relu activation function, which takes our data and converts the output to a minimum of zero and an unlimited upward value. Our second hidden layer determines a true or false output, which in this case is whether a movie has a positive or negative net gain. We use the Sigmoid function to convert the value to 0 or 1, with 0 being that the movie had a negative gain and 1 being that the movie had a positive gain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alexnguyen/anaconda3/envs/math9/lib/python3.12/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"
     ]
    }
   ],
   "source": [
    "model = tf.keras.Sequential()\n",
    "Dense = tf.keras.layers.Dense\n",
    "model.add(Dense(units=32, activation='relu', input_dim=len(X_train.columns)))\n",
    "model.add(Dense(units=64, activation='relu'))\n",
    "model.add(Dense(units=1, activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to determine how well our fit is, so we want to know the binary cross entropy loss as well as the $R^2$ value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364us/step - accuracy: 0.6043 - loss: 0.6867\n",
      "Epoch 2/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364us/step - accuracy: 0.7258 - loss: 0.6465\n",
      "Epoch 3/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 325us/step - accuracy: 0.7390 - loss: 0.6126\n",
      "Epoch 4/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.7478 - loss: 0.5833\n",
      "Epoch 5/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280us/step - accuracy: 0.7269 - loss: 0.5764\n",
      "Epoch 6/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.7455 - loss: 0.5560\n",
      "Epoch 7/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.7368 - loss: 0.5490\n",
      "Epoch 8/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.7449 - loss: 0.5331\n",
      "Epoch 9/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.7216 - loss: 0.5420\n",
      "Epoch 10/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.7161 - loss: 0.5373\n",
      "Epoch 11/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.7310 - loss: 0.5223\n",
      "Epoch 12/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.7408 - loss: 0.5146\n",
      "Epoch 13/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.7254 - loss: 0.5131\n",
      "Epoch 14/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.7397 - loss: 0.4971\n",
      "Epoch 15/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.7287 - loss: 0.4989\n",
      "Epoch 16/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 278us/step - accuracy: 0.7081 - loss: 0.5126\n",
      "Epoch 17/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.7375 - loss: 0.4788\n",
      "Epoch 18/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.7322 - loss: 0.4817\n",
      "Epoch 19/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.7304 - loss: 0.4804\n",
      "Epoch 20/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320us/step - accuracy: 0.7118 - loss: 0.4940\n",
      "Epoch 21/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329us/step - accuracy: 0.7553 - loss: 0.4544\n",
      "Epoch 22/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.7546 - loss: 0.4566\n",
      "Epoch 23/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.7286 - loss: 0.4683\n",
      "Epoch 24/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.7331 - loss: 0.4602\n",
      "Epoch 25/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.7222 - loss: 0.4679\n",
      "Epoch 26/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.7236 - loss: 0.4626\n",
      "Epoch 27/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.7313 - loss: 0.4510\n",
      "Epoch 28/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.7534 - loss: 0.4320\n",
      "Epoch 29/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.7359 - loss: 0.4467\n",
      "Epoch 30/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.7000 - loss: 0.4681\n",
      "Epoch 31/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 339us/step - accuracy: 0.7290 - loss: 0.4496\n",
      "Epoch 32/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354us/step - accuracy: 0.7420 - loss: 0.4478\n",
      "Epoch 33/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329us/step - accuracy: 0.7282 - loss: 0.4290\n",
      "Epoch 34/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 370us/step - accuracy: 0.7392 - loss: 0.4380\n",
      "Epoch 35/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 399us/step - accuracy: 0.7621 - loss: 0.4323\n",
      "Epoch 36/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358us/step - accuracy: 0.7967 - loss: 0.4345\n",
      "Epoch 37/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 462us/step - accuracy: 0.7913 - loss: 0.4234\n",
      "Epoch 38/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 491us/step - accuracy: 0.7953 - loss: 0.4428\n",
      "Epoch 39/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 423us/step - accuracy: 0.8095 - loss: 0.4180\n",
      "Epoch 40/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311us/step - accuracy: 0.8085 - loss: 0.4105\n",
      "Epoch 41/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 896us/step - accuracy: 0.8119 - loss: 0.4181\n",
      "Epoch 42/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 375us/step - accuracy: 0.7946 - loss: 0.4228\n",
      "Epoch 43/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337us/step - accuracy: 0.8015 - loss: 0.4352\n",
      "Epoch 44/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 345us/step - accuracy: 0.8021 - loss: 0.4225\n",
      "Epoch 45/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395us/step - accuracy: 0.7972 - loss: 0.4337\n",
      "Epoch 46/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 410us/step - accuracy: 0.7972 - loss: 0.4285\n",
      "Epoch 47/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349us/step - accuracy: 0.8047 - loss: 0.4098\n",
      "Epoch 48/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 394us/step - accuracy: 0.7991 - loss: 0.4247\n",
      "Epoch 49/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 368us/step - accuracy: 0.8174 - loss: 0.4171\n",
      "Epoch 50/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.7978 - loss: 0.4312\n",
      "Epoch 51/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 377us/step - accuracy: 0.8260 - loss: 0.4054\n",
      "Epoch 52/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356us/step - accuracy: 0.8136 - loss: 0.4062\n",
      "Epoch 53/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 345us/step - accuracy: 0.8033 - loss: 0.4235\n",
      "Epoch 54/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354us/step - accuracy: 0.8181 - loss: 0.4177\n",
      "Epoch 55/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332us/step - accuracy: 0.8238 - loss: 0.4149\n",
      "Epoch 56/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374us/step - accuracy: 0.7962 - loss: 0.4248\n",
      "Epoch 57/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 338us/step - accuracy: 0.7827 - loss: 0.4319\n",
      "Epoch 58/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332us/step - accuracy: 0.8059 - loss: 0.4217\n",
      "Epoch 59/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 360us/step - accuracy: 0.8075 - loss: 0.4123\n",
      "Epoch 60/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331us/step - accuracy: 0.8099 - loss: 0.4135\n",
      "Epoch 61/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.8163 - loss: 0.4016\n",
      "Epoch 62/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8046 - loss: 0.4176\n",
      "Epoch 63/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.8236 - loss: 0.3956\n",
      "Epoch 64/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.7753 - loss: 0.4391\n",
      "Epoch 65/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.8127 - loss: 0.4040\n",
      "Epoch 66/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8155 - loss: 0.3969\n",
      "Epoch 67/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.7985 - loss: 0.4285\n",
      "Epoch 68/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8174 - loss: 0.4076\n",
      "Epoch 69/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.7966 - loss: 0.4171\n",
      "Epoch 70/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8145 - loss: 0.4152\n",
      "Epoch 71/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.7889 - loss: 0.4238\n",
      "Epoch 72/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8156 - loss: 0.4173\n",
      "Epoch 73/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8074 - loss: 0.4279\n",
      "Epoch 74/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296us/step - accuracy: 0.8131 - loss: 0.4076\n",
      "Epoch 75/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307us/step - accuracy: 0.8144 - loss: 0.4122\n",
      "Epoch 76/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.8094 - loss: 0.4030\n",
      "Epoch 77/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8104 - loss: 0.4092\n",
      "Epoch 78/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.8354 - loss: 0.3959\n",
      "Epoch 79/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8166 - loss: 0.4151\n",
      "Epoch 80/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.7984 - loss: 0.4371\n",
      "Epoch 81/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312us/step - accuracy: 0.7928 - loss: 0.4274\n",
      "Epoch 82/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289us/step - accuracy: 0.7929 - loss: 0.4389\n",
      "Epoch 83/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8075 - loss: 0.4155\n",
      "Epoch 84/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.8246 - loss: 0.3789\n",
      "Epoch 85/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8113 - loss: 0.4031\n",
      "Epoch 86/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344us/step - accuracy: 0.8062 - loss: 0.4237\n",
      "Epoch 87/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 285us/step - accuracy: 0.8243 - loss: 0.4075\n",
      "Epoch 88/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.7996 - loss: 0.4437\n",
      "Epoch 89/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.8093 - loss: 0.4096\n",
      "Epoch 90/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.7928 - loss: 0.4303\n",
      "Epoch 91/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8109 - loss: 0.4054\n",
      "Epoch 92/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8239 - loss: 0.3882\n",
      "Epoch 93/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8173 - loss: 0.4055\n",
      "Epoch 94/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.8150 - loss: 0.3961\n",
      "Epoch 95/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288us/step - accuracy: 0.8102 - loss: 0.4116\n",
      "Epoch 96/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332us/step - accuracy: 0.7976 - loss: 0.4181\n",
      "Epoch 97/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.8049 - loss: 0.4013\n",
      "Epoch 98/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.8117 - loss: 0.4213\n",
      "Epoch 99/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8079 - loss: 0.4066\n",
      "Epoch 100/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.8076 - loss: 0.4260\n",
      "Epoch 101/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353us/step - accuracy: 0.8229 - loss: 0.3868\n",
      "Epoch 102/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8194 - loss: 0.3921\n",
      "Epoch 103/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.8122 - loss: 0.4052\n",
      "Epoch 104/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353us/step - accuracy: 0.8078 - loss: 0.3993\n",
      "Epoch 105/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 377us/step - accuracy: 0.7982 - loss: 0.4348\n",
      "Epoch 106/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 409us/step - accuracy: 0.8052 - loss: 0.3946\n",
      "Epoch 107/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359us/step - accuracy: 0.8027 - loss: 0.4198\n",
      "Epoch 108/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7809 - loss: 0.4348\n",
      "Epoch 109/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 531us/step - accuracy: 0.8022 - loss: 0.4245\n",
      "Epoch 110/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8031 - loss: 0.4022\n",
      "Epoch 111/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 327us/step - accuracy: 0.7925 - loss: 0.4209\n",
      "Epoch 112/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334us/step - accuracy: 0.7967 - loss: 0.4155\n",
      "Epoch 113/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 339us/step - accuracy: 0.8312 - loss: 0.3936\n",
      "Epoch 114/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356us/step - accuracy: 0.8061 - loss: 0.4132\n",
      "Epoch 115/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 350us/step - accuracy: 0.8039 - loss: 0.4139\n",
      "Epoch 116/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 377us/step - accuracy: 0.7990 - loss: 0.4273\n",
      "Epoch 117/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334us/step - accuracy: 0.7988 - loss: 0.4339\n",
      "Epoch 118/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 381us/step - accuracy: 0.8089 - loss: 0.4101\n",
      "Epoch 119/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 474us/step - accuracy: 0.7918 - loss: 0.4152\n",
      "Epoch 120/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359us/step - accuracy: 0.8005 - loss: 0.4271\n",
      "Epoch 121/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324us/step - accuracy: 0.8010 - loss: 0.4052\n",
      "Epoch 122/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8085 - loss: 0.4029\n",
      "Epoch 123/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329us/step - accuracy: 0.8120 - loss: 0.4026\n",
      "Epoch 124/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 349us/step - accuracy: 0.8126 - loss: 0.4145\n",
      "Epoch 125/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.8118 - loss: 0.4151\n",
      "Epoch 126/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.7974 - loss: 0.4114\n",
      "Epoch 127/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8090 - loss: 0.4060\n",
      "Epoch 128/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8160 - loss: 0.4193\n",
      "Epoch 129/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8038 - loss: 0.4209\n",
      "Epoch 130/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288us/step - accuracy: 0.8007 - loss: 0.4073\n",
      "Epoch 131/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.7978 - loss: 0.4190\n",
      "Epoch 132/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8098 - loss: 0.4179\n",
      "Epoch 133/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 325us/step - accuracy: 0.8057 - loss: 0.4174\n",
      "Epoch 134/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290us/step - accuracy: 0.8071 - loss: 0.4086\n",
      "Epoch 135/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8066 - loss: 0.4081\n",
      "Epoch 136/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8248 - loss: 0.3841\n",
      "Epoch 137/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8273 - loss: 0.4016\n",
      "Epoch 138/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8018 - loss: 0.4245 \n",
      "Epoch 139/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8251 - loss: 0.4060\n",
      "Epoch 140/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.8088 - loss: 0.4109\n",
      "Epoch 141/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8224 - loss: 0.3890\n",
      "Epoch 142/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330us/step - accuracy: 0.8186 - loss: 0.4066\n",
      "Epoch 143/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8103 - loss: 0.4178\n",
      "Epoch 144/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311us/step - accuracy: 0.7878 - loss: 0.4287\n",
      "Epoch 145/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.7902 - loss: 0.4304\n",
      "Epoch 146/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8050 - loss: 0.4092\n",
      "Epoch 147/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8228 - loss: 0.4028\n",
      "Epoch 148/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8095 - loss: 0.4007\n",
      "Epoch 149/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8045 - loss: 0.4073\n",
      "Epoch 150/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.8030 - loss: 0.4147\n",
      "Epoch 151/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8077 - loss: 0.4113\n",
      "Epoch 152/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 405us/step - accuracy: 0.7973 - loss: 0.4097\n",
      "Epoch 153/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8037 - loss: 0.4214\n",
      "Epoch 154/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8173 - loss: 0.4003\n",
      "Epoch 155/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 338us/step - accuracy: 0.8152 - loss: 0.3920\n",
      "Epoch 156/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8226 - loss: 0.3949\n",
      "Epoch 157/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344us/step - accuracy: 0.8216 - loss: 0.3991\n",
      "Epoch 158/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.8048 - loss: 0.4028\n",
      "Epoch 159/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8017 - loss: 0.4179\n",
      "Epoch 160/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8038 - loss: 0.4144\n",
      "Epoch 161/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8115 - loss: 0.3974\n",
      "Epoch 162/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8099 - loss: 0.4256 \n",
      "Epoch 163/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.7999 - loss: 0.4195\n",
      "Epoch 164/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8060 - loss: 0.4293\n",
      "Epoch 165/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8105 - loss: 0.4022\n",
      "Epoch 166/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.7990 - loss: 0.4192\n",
      "Epoch 167/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8067 - loss: 0.3969\n",
      "Epoch 168/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8263 - loss: 0.4049\n",
      "Epoch 169/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296us/step - accuracy: 0.8281 - loss: 0.3977\n",
      "Epoch 170/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8174 - loss: 0.4007\n",
      "Epoch 171/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.8066 - loss: 0.4311\n",
      "Epoch 172/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8301 - loss: 0.3868\n",
      "Epoch 173/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8217 - loss: 0.4091\n",
      "Epoch 174/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8223 - loss: 0.4101\n",
      "Epoch 175/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8084 - loss: 0.4249\n",
      "Epoch 176/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.8198 - loss: 0.3929\n",
      "Epoch 177/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307us/step - accuracy: 0.8035 - loss: 0.4157\n",
      "Epoch 178/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.8171 - loss: 0.3939\n",
      "Epoch 179/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290us/step - accuracy: 0.8181 - loss: 0.3889\n",
      "Epoch 180/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294us/step - accuracy: 0.7959 - loss: 0.4203\n",
      "Epoch 181/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8205 - loss: 0.4106\n",
      "Epoch 182/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291us/step - accuracy: 0.7965 - loss: 0.4188\n",
      "Epoch 183/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8019 - loss: 0.4091 \n",
      "Epoch 184/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 371us/step - accuracy: 0.8002 - loss: 0.4357\n",
      "Epoch 185/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8034 - loss: 0.4154\n",
      "Epoch 186/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8163 - loss: 0.4077\n",
      "Epoch 187/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8217 - loss: 0.4038\n",
      "Epoch 188/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.7873 - loss: 0.4229\n",
      "Epoch 189/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296us/step - accuracy: 0.8047 - loss: 0.4069\n",
      "Epoch 190/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8143 - loss: 0.4244\n",
      "Epoch 191/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.7850 - loss: 0.4389\n",
      "Epoch 192/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290us/step - accuracy: 0.8241 - loss: 0.3963\n",
      "Epoch 193/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290us/step - accuracy: 0.8041 - loss: 0.4201\n",
      "Epoch 194/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.7989 - loss: 0.4228\n",
      "Epoch 195/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294us/step - accuracy: 0.8379 - loss: 0.3889\n",
      "Epoch 196/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8214 - loss: 0.3854\n",
      "Epoch 197/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8176 - loss: 0.3940\n",
      "Epoch 198/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 421us/step - accuracy: 0.8037 - loss: 0.4284\n",
      "Epoch 199/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 508us/step - accuracy: 0.8048 - loss: 0.4135\n",
      "Epoch 200/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324us/step - accuracy: 0.8016 - loss: 0.4053\n",
      "Epoch 201/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356us/step - accuracy: 0.8118 - loss: 0.4169\n",
      "Epoch 202/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 345us/step - accuracy: 0.8203 - loss: 0.3969\n",
      "Epoch 203/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.7933 - loss: 0.4368\n",
      "Epoch 204/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8285 - loss: 0.3801\n",
      "Epoch 205/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311us/step - accuracy: 0.8185 - loss: 0.4096\n",
      "Epoch 206/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 293us/step - accuracy: 0.8077 - loss: 0.4186\n",
      "Epoch 207/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 335us/step - accuracy: 0.8180 - loss: 0.4012\n",
      "Epoch 208/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.8054 - loss: 0.4171\n",
      "Epoch 209/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330us/step - accuracy: 0.8260 - loss: 0.3872\n",
      "Epoch 210/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8016 - loss: 0.4293\n",
      "Epoch 211/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291us/step - accuracy: 0.8030 - loss: 0.4086\n",
      "Epoch 212/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8256 - loss: 0.3912\n",
      "Epoch 213/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8107 - loss: 0.4149\n",
      "Epoch 214/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8047 - loss: 0.4105\n",
      "Epoch 215/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.7889 - loss: 0.4238\n",
      "Epoch 216/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8131 - loss: 0.4053\n",
      "Epoch 217/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294us/step - accuracy: 0.8098 - loss: 0.4157\n",
      "Epoch 218/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8234 - loss: 0.3920\n",
      "Epoch 219/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8162 - loss: 0.4018\n",
      "Epoch 220/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286us/step - accuracy: 0.8003 - loss: 0.4252\n",
      "Epoch 221/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.8035 - loss: 0.4141\n",
      "Epoch 222/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290us/step - accuracy: 0.8197 - loss: 0.3888\n",
      "Epoch 223/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8325 - loss: 0.3919 \n",
      "Epoch 224/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8290 - loss: 0.3994\n",
      "Epoch 225/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291us/step - accuracy: 0.7981 - loss: 0.4125\n",
      "Epoch 226/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8341 - loss: 0.3889\n",
      "Epoch 227/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8127 - loss: 0.3957\n",
      "Epoch 228/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8163 - loss: 0.4028\n",
      "Epoch 229/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.7965 - loss: 0.4092\n",
      "Epoch 230/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8295 - loss: 0.3871\n",
      "Epoch 231/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8155 - loss: 0.3956\n",
      "Epoch 232/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.8279 - loss: 0.3792\n",
      "Epoch 233/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8222 - loss: 0.3946\n",
      "Epoch 234/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8158 - loss: 0.3990\n",
      "Epoch 235/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 289us/step - accuracy: 0.8033 - loss: 0.3893\n",
      "Epoch 236/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8249 - loss: 0.3854\n",
      "Epoch 237/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8064 - loss: 0.4085\n",
      "Epoch 238/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8114 - loss: 0.4054\n",
      "Epoch 239/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 338us/step - accuracy: 0.7938 - loss: 0.4180\n",
      "Epoch 240/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320us/step - accuracy: 0.8170 - loss: 0.3935\n",
      "Epoch 241/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8098 - loss: 0.4099\n",
      "Epoch 242/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 457us/step - accuracy: 0.8014 - loss: 0.4194\n",
      "Epoch 243/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 338us/step - accuracy: 0.7884 - loss: 0.4236\n",
      "Epoch 244/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8053 - loss: 0.4174\n",
      "Epoch 245/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379us/step - accuracy: 0.8229 - loss: 0.3829\n",
      "Epoch 246/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344us/step - accuracy: 0.8035 - loss: 0.4169\n",
      "Epoch 247/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 325us/step - accuracy: 0.8043 - loss: 0.4277\n",
      "Epoch 248/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 344us/step - accuracy: 0.8034 - loss: 0.4042\n",
      "Epoch 249/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.7941 - loss: 0.4294\n",
      "Epoch 250/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288us/step - accuracy: 0.8052 - loss: 0.4083\n",
      "Epoch 251/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.8214 - loss: 0.4146\n",
      "Epoch 252/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316us/step - accuracy: 0.8102 - loss: 0.4097\n",
      "Epoch 253/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332us/step - accuracy: 0.8128 - loss: 0.3995\n",
      "Epoch 254/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.8023 - loss: 0.4086\n",
      "Epoch 255/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 334us/step - accuracy: 0.8043 - loss: 0.4168\n",
      "Epoch 256/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.7920 - loss: 0.4470\n",
      "Epoch 257/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 355us/step - accuracy: 0.8173 - loss: 0.3811\n",
      "Epoch 258/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8038 - loss: 0.4272\n",
      "Epoch 259/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.7909 - loss: 0.4291\n",
      "Epoch 260/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 930us/step - accuracy: 0.8069 - loss: 0.4140\n",
      "Epoch 261/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 602us/step - accuracy: 0.8246 - loss: 0.3982\n",
      "Epoch 262/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 296us/step - accuracy: 0.7822 - loss: 0.4277\n",
      "Epoch 263/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.7971 - loss: 0.4310\n",
      "Epoch 264/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.7943 - loss: 0.4301\n",
      "Epoch 265/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8012 - loss: 0.4207\n",
      "Epoch 266/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286us/step - accuracy: 0.8067 - loss: 0.4072\n",
      "Epoch 267/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.7934 - loss: 0.4099\n",
      "Epoch 268/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.7783 - loss: 0.4396\n",
      "Epoch 269/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 321us/step - accuracy: 0.8092 - loss: 0.4054\n",
      "Epoch 270/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 381us/step - accuracy: 0.8188 - loss: 0.4025\n",
      "Epoch 271/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.8067 - loss: 0.4237\n",
      "Epoch 272/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8223 - loss: 0.4074\n",
      "Epoch 273/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312us/step - accuracy: 0.7781 - loss: 0.4411\n",
      "Epoch 274/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369us/step - accuracy: 0.8101 - loss: 0.3983\n",
      "Epoch 275/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362us/step - accuracy: 0.8116 - loss: 0.3873\n",
      "Epoch 276/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7933 - loss: 0.4333\n",
      "Epoch 277/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 404us/step - accuracy: 0.7964 - loss: 0.4113\n",
      "Epoch 278/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379us/step - accuracy: 0.8052 - loss: 0.4239\n",
      "Epoch 279/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365us/step - accuracy: 0.8084 - loss: 0.4003\n",
      "Epoch 280/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.8236 - loss: 0.3937\n",
      "Epoch 281/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341us/step - accuracy: 0.8048 - loss: 0.4118\n",
      "Epoch 282/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.8083 - loss: 0.4083\n",
      "Epoch 283/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8031 - loss: 0.4090\n",
      "Epoch 284/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.8176 - loss: 0.3952\n",
      "Epoch 285/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8313 - loss: 0.3855\n",
      "Epoch 286/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8069 - loss: 0.4126\n",
      "Epoch 287/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 290us/step - accuracy: 0.8086 - loss: 0.4139\n",
      "Epoch 288/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8097 - loss: 0.4041\n",
      "Epoch 289/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 294us/step - accuracy: 0.8067 - loss: 0.4053\n",
      "Epoch 290/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365us/step - accuracy: 0.8057 - loss: 0.4066\n",
      "Epoch 291/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 269us/step - accuracy: 0.8273 - loss: 0.3879\n",
      "Epoch 292/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8084 - loss: 0.4024\n",
      "Epoch 293/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8044 - loss: 0.4213\n",
      "Epoch 294/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 988us/step - accuracy: 0.7974 - loss: 0.4056\n",
      "Epoch 295/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 384us/step - accuracy: 0.8192 - loss: 0.3893\n",
      "Epoch 296/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.8063 - loss: 0.4122\n",
      "Epoch 297/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8047 - loss: 0.4155\n",
      "Epoch 298/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8130 - loss: 0.4021\n",
      "Epoch 299/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.8162 - loss: 0.4029\n",
      "Epoch 300/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.8088 - loss: 0.4012\n",
      "Epoch 301/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.8030 - loss: 0.4064\n",
      "Epoch 302/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307us/step - accuracy: 0.8059 - loss: 0.4080\n",
      "Epoch 303/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342us/step - accuracy: 0.8266 - loss: 0.3878\n",
      "Epoch 304/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8099 - loss: 0.4060\n",
      "Epoch 305/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337us/step - accuracy: 0.8066 - loss: 0.4071\n",
      "Epoch 306/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 339us/step - accuracy: 0.8184 - loss: 0.3973\n",
      "Epoch 307/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.8148 - loss: 0.4042\n",
      "Epoch 308/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312us/step - accuracy: 0.8019 - loss: 0.4051\n",
      "Epoch 309/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 336us/step - accuracy: 0.8166 - loss: 0.3770\n",
      "Epoch 310/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8096 - loss: 0.4210 \n",
      "Epoch 311/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 474us/step - accuracy: 0.8081 - loss: 0.4034\n",
      "Epoch 312/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 399us/step - accuracy: 0.8284 - loss: 0.4067\n",
      "Epoch 313/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7880 - loss: 0.4165\n",
      "Epoch 314/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 673us/step - accuracy: 0.8084 - loss: 0.3951\n",
      "Epoch 315/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 702us/step - accuracy: 0.8076 - loss: 0.4101\n",
      "Epoch 316/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 882us/step - accuracy: 0.8015 - loss: 0.4272\n",
      "Epoch 317/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 711us/step - accuracy: 0.7905 - loss: 0.4219\n",
      "Epoch 318/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 451us/step - accuracy: 0.8085 - loss: 0.3973\n",
      "Epoch 319/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 425us/step - accuracy: 0.8075 - loss: 0.4020\n",
      "Epoch 320/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 600us/step - accuracy: 0.8255 - loss: 0.3903\n",
      "Epoch 321/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7965 - loss: 0.4179 \n",
      "Epoch 322/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 548us/step - accuracy: 0.8061 - loss: 0.4220\n",
      "Epoch 323/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8087 - loss: 0.4058\n",
      "Epoch 324/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316us/step - accuracy: 0.8262 - loss: 0.3933\n",
      "Epoch 325/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 418us/step - accuracy: 0.8180 - loss: 0.4009\n",
      "Epoch 326/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 360us/step - accuracy: 0.7865 - loss: 0.4278\n",
      "Epoch 327/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 373us/step - accuracy: 0.8104 - loss: 0.4082\n",
      "Epoch 328/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 355us/step - accuracy: 0.8294 - loss: 0.4028\n",
      "Epoch 329/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354us/step - accuracy: 0.8228 - loss: 0.3927\n",
      "Epoch 330/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365us/step - accuracy: 0.8035 - loss: 0.4129\n",
      "Epoch 331/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8221 - loss: 0.4002\n",
      "Epoch 332/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8211 - loss: 0.4028 \n",
      "Epoch 333/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7993 - loss: 0.4129\n",
      "Epoch 334/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 807us/step - accuracy: 0.8121 - loss: 0.4145\n",
      "Epoch 335/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8105 - loss: 0.4132 \n",
      "Epoch 336/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 445us/step - accuracy: 0.8036 - loss: 0.4154\n",
      "Epoch 337/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 320us/step - accuracy: 0.8193 - loss: 0.4019\n",
      "Epoch 338/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 592us/step - accuracy: 0.8158 - loss: 0.3840\n",
      "Epoch 339/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 551us/step - accuracy: 0.8172 - loss: 0.3963\n",
      "Epoch 340/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8133 - loss: 0.4012\n",
      "Epoch 341/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 747us/step - accuracy: 0.8004 - loss: 0.4281\n",
      "Epoch 342/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 404us/step - accuracy: 0.8065 - loss: 0.4119\n",
      "Epoch 343/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 343us/step - accuracy: 0.8167 - loss: 0.3890\n",
      "Epoch 344/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311us/step - accuracy: 0.8264 - loss: 0.3803\n",
      "Epoch 345/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8160 - loss: 0.3952\n",
      "Epoch 346/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8068 - loss: 0.4151\n",
      "Epoch 347/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 655us/step - accuracy: 0.7916 - loss: 0.4282\n",
      "Epoch 348/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8093 - loss: 0.4068\n",
      "Epoch 349/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358us/step - accuracy: 0.8147 - loss: 0.3965\n",
      "Epoch 350/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8144 - loss: 0.4116\n",
      "Epoch 351/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 373us/step - accuracy: 0.8098 - loss: 0.4248\n",
      "Epoch 352/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 422us/step - accuracy: 0.8074 - loss: 0.4011\n",
      "Epoch 353/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.8138 - loss: 0.3893\n",
      "Epoch 354/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8147 - loss: 0.3992\n",
      "Epoch 355/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8153 - loss: 0.4072\n",
      "Epoch 356/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337us/step - accuracy: 0.7988 - loss: 0.4298\n",
      "Epoch 357/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 332us/step - accuracy: 0.8278 - loss: 0.3830\n",
      "Epoch 358/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 346us/step - accuracy: 0.8003 - loss: 0.4114\n",
      "Epoch 359/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.7993 - loss: 0.3998\n",
      "Epoch 360/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8049 - loss: 0.4075\n",
      "Epoch 361/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 750us/step - accuracy: 0.8195 - loss: 0.3952\n",
      "Epoch 362/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 353us/step - accuracy: 0.8088 - loss: 0.4127\n",
      "Epoch 363/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 319us/step - accuracy: 0.8118 - loss: 0.4128\n",
      "Epoch 364/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8006 - loss: 0.4081\n",
      "Epoch 365/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8147 - loss: 0.3941\n",
      "Epoch 366/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 279us/step - accuracy: 0.8003 - loss: 0.4175\n",
      "Epoch 367/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8089 - loss: 0.3828\n",
      "Epoch 368/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 528us/step - accuracy: 0.8112 - loss: 0.3950\n",
      "Epoch 369/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 338us/step - accuracy: 0.8073 - loss: 0.4148\n",
      "Epoch 370/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 518us/step - accuracy: 0.7954 - loss: 0.4047\n",
      "Epoch 371/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 392us/step - accuracy: 0.8131 - loss: 0.3940\n",
      "Epoch 372/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 395us/step - accuracy: 0.8075 - loss: 0.4046\n",
      "Epoch 373/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 355us/step - accuracy: 0.8164 - loss: 0.3978\n",
      "Epoch 374/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 379us/step - accuracy: 0.7992 - loss: 0.4254\n",
      "Epoch 375/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 761us/step - accuracy: 0.8117 - loss: 0.4078\n",
      "Epoch 376/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 348us/step - accuracy: 0.7971 - loss: 0.4344\n",
      "Epoch 377/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.8277 - loss: 0.3985\n",
      "Epoch 378/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 321us/step - accuracy: 0.8148 - loss: 0.4064\n",
      "Epoch 379/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8129 - loss: 0.3993\n",
      "Epoch 380/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 297us/step - accuracy: 0.7994 - loss: 0.4286\n",
      "Epoch 381/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.8169 - loss: 0.4095\n",
      "Epoch 382/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.7954 - loss: 0.4350\n",
      "Epoch 383/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 324us/step - accuracy: 0.7998 - loss: 0.4024\n",
      "Epoch 384/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312us/step - accuracy: 0.8093 - loss: 0.3983\n",
      "Epoch 385/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 347us/step - accuracy: 0.8012 - loss: 0.4239\n",
      "Epoch 386/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.8105 - loss: 0.4091\n",
      "Epoch 387/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8046 - loss: 0.4225\n",
      "Epoch 388/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 336us/step - accuracy: 0.7902 - loss: 0.4137\n",
      "Epoch 389/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 878us/step - accuracy: 0.8202 - loss: 0.3974\n",
      "Epoch 390/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 340us/step - accuracy: 0.8158 - loss: 0.4011\n",
      "Epoch 391/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.8213 - loss: 0.3818\n",
      "Epoch 392/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331us/step - accuracy: 0.8137 - loss: 0.4040\n",
      "Epoch 393/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8002 - loss: 0.4159\n",
      "Epoch 394/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 327us/step - accuracy: 0.8014 - loss: 0.4084\n",
      "Epoch 395/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8188 - loss: 0.3975\n",
      "Epoch 396/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 336us/step - accuracy: 0.8197 - loss: 0.3974\n",
      "Epoch 397/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8051 - loss: 0.4011\n",
      "Epoch 398/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 361us/step - accuracy: 0.8324 - loss: 0.4015\n",
      "Epoch 399/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 410us/step - accuracy: 0.8050 - loss: 0.4125\n",
      "Epoch 400/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 362us/step - accuracy: 0.8001 - loss: 0.4164\n",
      "Epoch 401/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.8251 - loss: 0.3884\n",
      "Epoch 402/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8164 - loss: 0.4053\n",
      "Epoch 403/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 383us/step - accuracy: 0.8080 - loss: 0.3963\n",
      "Epoch 404/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.7979 - loss: 0.4200\n",
      "Epoch 405/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 403us/step - accuracy: 0.8141 - loss: 0.4059\n",
      "Epoch 406/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8294 - loss: 0.3942\n",
      "Epoch 407/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 460us/step - accuracy: 0.8146 - loss: 0.4027\n",
      "Epoch 408/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 354us/step - accuracy: 0.7897 - loss: 0.4178\n",
      "Epoch 409/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.8110 - loss: 0.4185\n",
      "Epoch 410/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 321us/step - accuracy: 0.7970 - loss: 0.4188\n",
      "Epoch 411/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 384us/step - accuracy: 0.8149 - loss: 0.3989\n",
      "Epoch 412/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8121 - loss: 0.4069\n",
      "Epoch 413/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 322us/step - accuracy: 0.8149 - loss: 0.4072\n",
      "Epoch 414/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 276us/step - accuracy: 0.8036 - loss: 0.4122\n",
      "Epoch 415/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 283us/step - accuracy: 0.8136 - loss: 0.3821\n",
      "Epoch 416/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8134 - loss: 0.3947\n",
      "Epoch 417/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330us/step - accuracy: 0.8103 - loss: 0.3916\n",
      "Epoch 418/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7996 - loss: 0.4300\n",
      "Epoch 419/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 752us/step - accuracy: 0.8076 - loss: 0.4177\n",
      "Epoch 420/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 348us/step - accuracy: 0.7799 - loss: 0.4525\n",
      "Epoch 421/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 347us/step - accuracy: 0.8073 - loss: 0.4171\n",
      "Epoch 422/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311us/step - accuracy: 0.8097 - loss: 0.3879\n",
      "Epoch 423/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 330us/step - accuracy: 0.8074 - loss: 0.4190\n",
      "Epoch 424/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 304us/step - accuracy: 0.8204 - loss: 0.3781\n",
      "Epoch 425/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 314us/step - accuracy: 0.8136 - loss: 0.4079\n",
      "Epoch 426/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 431us/step - accuracy: 0.7959 - loss: 0.4224\n",
      "Epoch 427/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 382us/step - accuracy: 0.8060 - loss: 0.4036\n",
      "Epoch 428/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 411us/step - accuracy: 0.8074 - loss: 0.3994\n",
      "Epoch 429/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8106 - loss: 0.4004 \n",
      "Epoch 430/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 600us/step - accuracy: 0.7967 - loss: 0.4236\n",
      "Epoch 431/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 337us/step - accuracy: 0.8129 - loss: 0.3896\n",
      "Epoch 432/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.8051 - loss: 0.4168\n",
      "Epoch 433/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8159 - loss: 0.4010\n",
      "Epoch 434/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307us/step - accuracy: 0.8008 - loss: 0.4127\n",
      "Epoch 435/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8052 - loss: 0.4089\n",
      "Epoch 436/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8103 - loss: 0.4110\n",
      "Epoch 437/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312us/step - accuracy: 0.8062 - loss: 0.3970\n",
      "Epoch 438/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8182 - loss: 0.3976\n",
      "Epoch 439/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8043 - loss: 0.4063\n",
      "Epoch 440/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.7952 - loss: 0.4214\n",
      "Epoch 441/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 316us/step - accuracy: 0.8152 - loss: 0.4029\n",
      "Epoch 442/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8114 - loss: 0.3978\n",
      "Epoch 443/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.8022 - loss: 0.4034\n",
      "Epoch 444/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8204 - loss: 0.4054\n",
      "Epoch 445/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 793us/step - accuracy: 0.8180 - loss: 0.3892\n",
      "Epoch 446/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8097 - loss: 0.4112\n",
      "Epoch 447/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 311us/step - accuracy: 0.8215 - loss: 0.3847\n",
      "Epoch 448/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.8242 - loss: 0.3936\n",
      "Epoch 449/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 323us/step - accuracy: 0.8039 - loss: 0.4128\n",
      "Epoch 450/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8022 - loss: 0.4142\n",
      "Epoch 451/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292us/step - accuracy: 0.8030 - loss: 0.3975\n",
      "Epoch 452/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8084 - loss: 0.4043\n",
      "Epoch 453/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.8100 - loss: 0.3987\n",
      "Epoch 454/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.8030 - loss: 0.4026\n",
      "Epoch 455/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342us/step - accuracy: 0.8089 - loss: 0.4082\n",
      "Epoch 456/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 331us/step - accuracy: 0.8112 - loss: 0.4082\n",
      "Epoch 457/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 478us/step - accuracy: 0.8091 - loss: 0.4087\n",
      "Epoch 458/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8041 - loss: 0.4062\n",
      "Epoch 459/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 341us/step - accuracy: 0.8182 - loss: 0.3967\n",
      "Epoch 460/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306us/step - accuracy: 0.7991 - loss: 0.4122\n",
      "Epoch 461/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 315us/step - accuracy: 0.7962 - loss: 0.4213\n",
      "Epoch 462/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 333us/step - accuracy: 0.8281 - loss: 0.3757\n",
      "Epoch 463/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 342us/step - accuracy: 0.8114 - loss: 0.4117\n",
      "Epoch 464/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 313us/step - accuracy: 0.8051 - loss: 0.4091\n",
      "Epoch 465/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 329us/step - accuracy: 0.8098 - loss: 0.3956\n",
      "Epoch 466/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 317us/step - accuracy: 0.8069 - loss: 0.4126\n",
      "Epoch 467/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364us/step - accuracy: 0.7999 - loss: 0.4204\n",
      "Epoch 468/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8007 - loss: 0.4206\n",
      "Epoch 469/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 295us/step - accuracy: 0.8143 - loss: 0.3951\n",
      "Epoch 470/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356us/step - accuracy: 0.7925 - loss: 0.4247\n",
      "Epoch 471/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 374us/step - accuracy: 0.8102 - loss: 0.4016\n",
      "Epoch 472/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7988 - loss: 0.4289 \n",
      "Epoch 473/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 308us/step - accuracy: 0.8030 - loss: 0.4158\n",
      "Epoch 474/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.8099 - loss: 0.4082\n",
      "Epoch 475/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8066 - loss: 0.3986\n",
      "Epoch 476/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.7975 - loss: 0.4179\n",
      "Epoch 477/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301us/step - accuracy: 0.8086 - loss: 0.4088\n",
      "Epoch 478/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307us/step - accuracy: 0.7984 - loss: 0.4177\n",
      "Epoch 479/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 299us/step - accuracy: 0.8323 - loss: 0.3819\n",
      "Epoch 480/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8127 - loss: 0.3947\n",
      "Epoch 481/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8187 - loss: 0.3974\n",
      "Epoch 482/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.8066 - loss: 0.4038\n",
      "Epoch 483/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310us/step - accuracy: 0.8097 - loss: 0.4053\n",
      "Epoch 484/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 305us/step - accuracy: 0.8122 - loss: 0.4081\n",
      "Epoch 485/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 302us/step - accuracy: 0.7986 - loss: 0.4075\n",
      "Epoch 486/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 298us/step - accuracy: 0.7991 - loss: 0.4202\n",
      "Epoch 487/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 303us/step - accuracy: 0.8116 - loss: 0.4045\n",
      "Epoch 488/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8124 - loss: 0.3998 \n",
      "Epoch 489/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300us/step - accuracy: 0.8022 - loss: 0.4135\n",
      "Epoch 490/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 318us/step - accuracy: 0.8169 - loss: 0.3919\n",
      "Epoch 491/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 336us/step - accuracy: 0.8039 - loss: 0.4028\n",
      "Epoch 492/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 351us/step - accuracy: 0.8102 - loss: 0.3905\n",
      "Epoch 493/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 350us/step - accuracy: 0.8042 - loss: 0.4159\n",
      "Epoch 494/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 540us/step - accuracy: 0.8213 - loss: 0.3943\n",
      "Epoch 495/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 387us/step - accuracy: 0.7956 - loss: 0.4092\n",
      "Epoch 496/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 367us/step - accuracy: 0.8132 - loss: 0.4040\n",
      "Epoch 497/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 326us/step - accuracy: 0.8099 - loss: 0.3987\n",
      "Epoch 498/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 328us/step - accuracy: 0.8220 - loss: 0.3948\n",
      "Epoch 499/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 309us/step - accuracy: 0.8048 - loss: 0.4176\n",
      "Epoch 500/500\n",
      "\u001b[1m40/40\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 977us/step - accuracy: 0.8064 - loss: 0.4034\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x2943114f0>"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, epochs=500, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 452us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.815625"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat = model.predict(X_test)\n",
    "y_hat = [0 if x <0.5 else 1 for x in y_hat]\n",
    "\n",
    "\n",
    "accuracy_score(y_test,y_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training our model for different periods of time, I've found that the $R^2$ value of the model is around the same compared to our logistic regression model. However, this model is much nicer to use as it allows us to determine how long we want to train our model for, while also allowing us to save the model and reproduce it for later use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SUMMARY\n",
    "\n",
    "After exploring the dataset of movie info and using analysis methods such as linear regression and classification, I was able to come up with results both expected and unexpected. \n",
    "\n",
    "My initial attempt to find a correlation between votes, score, gross, and budget in relation to a company was unsuccessful. Using a linear regression to fit the features produced extremely low accuracy . With this, I found out that between the 5 most frequent companies in my dataset, the relation between the individual features and individual companies were not significant, such that no company performed noticably better or worse within one of the features. We can conclude that the companies in question are all similar competitors against each other, with no company being extremely differentiable from the rest.\n",
    "\n",
    "I then explored another correlation between the budget and gross of a movie, fitting the budget onto a linear regression model to predict the gross. Once again, it produced a weak accuracy score, revealing that the amount of budget does not always determine how much a movie will make.\n",
    "\n",
    "However, when exploring the relation between votes and the net gain/loss of revenue for a movie, I found a good correlation. Although a linear regression model including validation with KNN produced a low average $R^2$ value, fitting the data onto a logistic regression gave a strong accuracy score, using binary classification. My two classes consisted of either a positive or negative net revenue for the movie, and we were able to get around 0.8 for the $R^2$ value, which is strong. We were also able to get a similar score using Tensorflow. This correlation makes sense, as the more people watch a movie, the more revenue a movie will gross.\n",
    "\n",
    "# REFERENCES\n",
    "\n",
    "Source of Dataset: https://www.kaggle.com/datasets/danielgrijalvas/movies?resource=download\n",
    "\n",
    "Tutorial for Tensorflow: https://www.youtube.com/watch?v=6_2hzRopPbQ&ab_channel=NicholasRenotte\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "math9",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}