GITHUB_Deals_Prediction-Classification_TF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e4f27925-556d-4e44-b5c3-d1a1f78c34e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6fb173c7-b878-4ec8-8b58-6a21d3ab084d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   OrderID        100 non-null    object\n",
      " 1   OrderQuantity  100 non-null    int64 \n",
      " 2   OrderValue     100 non-null    int64 \n",
      " 3   Country        100 non-null    object\n",
      " 4   Industry       100 non-null    object\n",
      " 5   Deal Status    100 non-null    object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "deals= pd.read_csv('Sample_Data_Deals2.csv')\n",
    "deals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1bfac3a3-045d-4eb3-8df5-91a728b99dfd",
   "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>OrderID</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>OD38231</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Won</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        OrderID  Country       Industry Deal Status\n",
       "count       100      100            100         100\n",
       "unique        5        5              7           2\n",
       "top     OD38231  Germany  Manufacturing         Won\n",
       "freq         36       36             29          54"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deals.describe(include='O')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "18e3e42c-e958-422b-8828-a7d0dbea7461",
   "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>OrderQuantity</th>\n",
       "      <th>OrderValue</th>\n",
       "      <th>Country</th>\n",
       "      <th>Industry</th>\n",
       "      <th>Deal Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>371</td>\n",
       "      <td>383</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Won</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163</td>\n",
       "      <td>121</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Won</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>117</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Lost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>143</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Lost</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>165</td>\n",
       "      <td>148</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Lost</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderQuantity  OrderValue    Country       Industry Deal Status\n",
       "0            371         383     Canada     Technology         Won\n",
       "1            163         121     Canada        Finance         Won\n",
       "2            191         117  Australia  Manufacturing        Lost\n",
       "3            150         143  Australia  Manufacturing        Lost\n",
       "4            165         148  Australia  Manufacturing        Lost"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop Order ID\n",
    "deals1 = pd.DataFrame(deals.iloc[:,1:])\n",
    "deals1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "544eb58f-ead7-4a15-8e91-8e8d0bcf199e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Deal Status', ylabel='count'>"
      ]
     },
     "execution_count": 64,
     "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.countplot(x='Deal Status',data=deals1, hue='Deal Status', palette='flare') #Check if the dataset is well-balanced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4b8f44ff-9f37-4f4d-8db0-90090a1a42fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1eaac1a5dd0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='OrderQuantity', y='OrderValue', data=deals1, hue='Deal Status', palette='flare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e2624b25-f8dc-441f-8410-6cd1bf50ff8e",
   "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>OrderQuantity</th>\n",
       "      <th>OrderValue</th>\n",
       "      <th>Country_Canada</th>\n",
       "      <th>Country_China</th>\n",
       "      <th>Country_France</th>\n",
       "      <th>Country_Germany</th>\n",
       "      <th>Industry_Finance</th>\n",
       "      <th>Industry_Government</th>\n",
       "      <th>Industry_Healthcare</th>\n",
       "      <th>Industry_Manufacturing</th>\n",
       "      <th>Industry_Retail</th>\n",
       "      <th>Industry_Technology</th>\n",
       "      <th>Deal Status_Won</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>371</td>\n",
       "      <td>383</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>163</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>191</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>165</td>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderQuantity  OrderValue  Country_Canada  Country_China  Country_France  \\\n",
       "0            371         383               1              0               0   \n",
       "1            163         121               1              0               0   \n",
       "2            191         117               0              0               0   \n",
       "3            150         143               0              0               0   \n",
       "4            165         148               0              0               0   \n",
       "\n",
       "   Country_Germany  Industry_Finance  Industry_Government  \\\n",
       "0                0                 0                    0   \n",
       "1                0                 1                    0   \n",
       "2                0                 0                    0   \n",
       "3                0                 0                    0   \n",
       "4                0                 0                    0   \n",
       "\n",
       "   Industry_Healthcare  Industry_Manufacturing  Industry_Retail  \\\n",
       "0                    0                       0                0   \n",
       "1                    0                       0                0   \n",
       "2                    0                       1                0   \n",
       "3                    0                       1                0   \n",
       "4                    0                       1                0   \n",
       "\n",
       "   Industry_Technology  Deal Status_Won  \n",
       "0                    1                1  \n",
       "1                    0                1  \n",
       "2                    0                0  \n",
       "3                    0                0  \n",
       "4                    0                0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Encoding categorical data, drop orderID\n",
    "deals3 = pd.get_dummies(deals1, columns=['Country', 'Industry','Deal Status'], drop_first=True,dtype=int)\n",
    "deals3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "72c5d574-6c66-4777-8aa3-15b2b0e586ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create x and y datasets\n",
    "X = deals3.drop('Deal Status_Won',axis=1).values\n",
    "y = deals3['Deal Status_Won'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e47a5176-e5b2-4e78-ac64-344102009124",
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2c6ff97b-eeeb-4bb5-b456-e3922e641e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform your data\n",
    "scaler=MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0afcfaf4-73de-43f4-b35a-b02870e1f8ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75, 12)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "21faee33-c124-4c83-ab75-ebedaa4fb25d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 12)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "fa5f23a3-b393-46b9-b39c-4035479ea3d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 101ms/step - loss: 0.6936 - val_loss: 0.7130\n",
      "Epoch 2/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6840 - val_loss: 0.7122\n",
      "Epoch 3/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.7032 - val_loss: 0.7115\n",
      "Epoch 4/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.7038 - val_loss: 0.7111\n",
      "Epoch 5/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7037 - val_loss: 0.7104\n",
      "Epoch 6/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.7046 - val_loss: 0.7097\n",
      "Epoch 7/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.7403 - val_loss: 0.7097\n",
      "Epoch 8/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6940 - val_loss: 0.7091\n",
      "Epoch 9/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6923 - val_loss: 0.7077\n",
      "Epoch 10/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6851 - val_loss: 0.7059\n",
      "Epoch 11/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6998 - val_loss: 0.7040\n",
      "Epoch 12/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7004 - val_loss: 0.7019\n",
      "Epoch 13/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6932 - val_loss: 0.6997\n",
      "Epoch 14/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.7291 - val_loss: 0.6975\n",
      "Epoch 15/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6800 - val_loss: 0.6957\n",
      "Epoch 16/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6818 - val_loss: 0.6943\n",
      "Epoch 17/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.6861 - val_loss: 0.6932\n",
      "Epoch 18/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.6972 - val_loss: 0.6923\n",
      "Epoch 19/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.7046 - val_loss: 0.6911\n",
      "Epoch 20/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6960 - val_loss: 0.6898\n",
      "Epoch 21/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6766 - val_loss: 0.6890\n",
      "Epoch 22/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6714 - val_loss: 0.6883\n",
      "Epoch 23/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.7171 - val_loss: 0.6881\n",
      "Epoch 24/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.7015 - val_loss: 0.6876\n",
      "Epoch 25/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.7077 - val_loss: 0.6875\n",
      "Epoch 26/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6737 - val_loss: 0.6873\n",
      "Epoch 27/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.7171 - val_loss: 0.6875\n",
      "Epoch 28/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6840 - val_loss: 0.6877\n",
      "Epoch 29/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6807 - val_loss: 0.6876\n",
      "Epoch 30/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6817 - val_loss: 0.6872\n",
      "Epoch 31/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6628 - val_loss: 0.6866\n",
      "Epoch 32/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6828 - val_loss: 0.6858\n",
      "Epoch 33/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6803 - val_loss: 0.6852\n",
      "Epoch 34/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6875 - val_loss: 0.6845\n",
      "Epoch 35/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.7004 - val_loss: 0.6842\n",
      "Epoch 36/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6734 - val_loss: 0.6837\n",
      "Epoch 37/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6478 - val_loss: 0.6833\n",
      "Epoch 38/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6729 - val_loss: 0.6827\n",
      "Epoch 39/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6857 - val_loss: 0.6821\n",
      "Epoch 40/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6696 - val_loss: 0.6819\n",
      "Epoch 41/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6905 - val_loss: 0.6820\n",
      "Epoch 42/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6882 - val_loss: 0.6821\n",
      "Epoch 43/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6646 - val_loss: 0.6822\n",
      "Epoch 44/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6620 - val_loss: 0.6827\n",
      "Epoch 45/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.7070 - val_loss: 0.6833\n",
      "Epoch 46/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.7114 - val_loss: 0.6837\n",
      "Epoch 47/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6981 - val_loss: 0.6843\n",
      "Epoch 48/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6738 - val_loss: 0.6849\n",
      "Epoch 49/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6812 - val_loss: 0.6853\n",
      "Epoch 50/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6428 - val_loss: 0.6857\n",
      "Epoch 51/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6975 - val_loss: 0.6859\n",
      "Epoch 52/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6850 - val_loss: 0.6860\n",
      "Epoch 53/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6614 - val_loss: 0.6857\n",
      "Epoch 54/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6523 - val_loss: 0.6856\n",
      "Epoch 55/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6423 - val_loss: 0.6852\n",
      "Epoch 56/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6701 - val_loss: 0.6850\n",
      "Epoch 57/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6795 - val_loss: 0.6847\n",
      "Epoch 58/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.6546 - val_loss: 0.6845\n",
      "Epoch 59/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6711 - val_loss: 0.6845\n",
      "Epoch 60/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6635 - val_loss: 0.6849\n",
      "Epoch 61/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6812 - val_loss: 0.6849\n",
      "Epoch 62/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6816 - val_loss: 0.6848\n",
      "Epoch 63/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.7026 - val_loss: 0.6846\n",
      "Epoch 64/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6560 - val_loss: 0.6848\n",
      "Epoch 65/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6565 - val_loss: 0.6849\n",
      "Epoch 66/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6886 - val_loss: 0.6851\n",
      "Epoch 67/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6915 - val_loss: 0.6854\n",
      "Epoch 68/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6549 - val_loss: 0.6858\n",
      "Epoch 69/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6445 - val_loss: 0.6860\n",
      "Epoch 70/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6541 - val_loss: 0.6862\n",
      "Epoch 71/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6755 - val_loss: 0.6864\n",
      "Epoch 72/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6670 - val_loss: 0.6866\n",
      "Epoch 73/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6900 - val_loss: 0.6869\n",
      "Epoch 74/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.7008 - val_loss: 0.6872\n",
      "Epoch 75/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6750 - val_loss: 0.6873\n",
      "Epoch 76/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.7042 - val_loss: 0.6879\n",
      "Epoch 77/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6855 - val_loss: 0.6888\n",
      "Epoch 78/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6697 - val_loss: 0.6900\n",
      "Epoch 79/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.6431 - val_loss: 0.6909\n",
      "Epoch 80/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6687 - val_loss: 0.6914\n",
      "Epoch 81/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6741 - val_loss: 0.6918\n",
      "Epoch 82/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6571 - val_loss: 0.6920\n",
      "Epoch 83/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6466 - val_loss: 0.6919\n",
      "Epoch 84/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6678 - val_loss: 0.6915\n",
      "Epoch 85/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.6682 - val_loss: 0.6912\n",
      "Epoch 86/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6480 - val_loss: 0.6909\n",
      "Epoch 87/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6802 - val_loss: 0.6908\n",
      "Epoch 88/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6637 - val_loss: 0.6906\n",
      "Epoch 89/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6691 - val_loss: 0.6901\n",
      "Epoch 90/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6621 - val_loss: 0.6900\n",
      "Epoch 91/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6464 - val_loss: 0.6901\n",
      "Epoch 92/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6648 - val_loss: 0.6904\n",
      "Epoch 93/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6596 - val_loss: 0.6907\n",
      "Epoch 94/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6655 - val_loss: 0.6908\n",
      "Epoch 95/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6712 - val_loss: 0.6909\n",
      "Epoch 96/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6424 - val_loss: 0.6915\n",
      "Epoch 97/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6562 - val_loss: 0.6921\n",
      "Epoch 98/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6768 - val_loss: 0.6929\n",
      "Epoch 99/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6274 - val_loss: 0.6934\n",
      "Epoch 100/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6162 - val_loss: 0.6939\n",
      "Epoch 101/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6571 - val_loss: 0.6944\n",
      "Epoch 102/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6637 - val_loss: 0.6944\n",
      "Epoch 103/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.7007 - val_loss: 0.6946\n",
      "Epoch 104/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6239 - val_loss: 0.6947\n",
      "Epoch 105/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6586 - val_loss: 0.6946\n",
      "Epoch 106/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6559 - val_loss: 0.6942\n",
      "Epoch 107/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6361 - val_loss: 0.6942\n",
      "Epoch 108/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.6277 - val_loss: 0.6940\n",
      "Epoch 109/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6453 - val_loss: 0.6941\n",
      "Epoch 110/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6247 - val_loss: 0.6940\n",
      "Epoch 111/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6758 - val_loss: 0.6939\n",
      "Epoch 112/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.6586 - val_loss: 0.6938\n",
      "Epoch 113/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6672 - val_loss: 0.6935\n",
      "Epoch 114/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6677 - val_loss: 0.6934\n",
      "Epoch 115/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6402 - val_loss: 0.6934\n",
      "Epoch 116/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6840 - val_loss: 0.6934\n",
      "Epoch 117/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6654 - val_loss: 0.6935\n",
      "Epoch 118/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6694 - val_loss: 0.6939\n",
      "Epoch 119/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6604 - val_loss: 0.6941\n",
      "Epoch 120/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6407 - val_loss: 0.6941\n",
      "Epoch 121/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6688 - val_loss: 0.6942\n",
      "Epoch 122/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.6446 - val_loss: 0.6944\n",
      "Epoch 123/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6684 - val_loss: 0.6940\n",
      "Epoch 124/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6752 - val_loss: 0.6940\n",
      "Epoch 125/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6570 - val_loss: 0.6939\n",
      "Epoch 126/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6515 - val_loss: 0.6937\n",
      "Epoch 127/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6625 - val_loss: 0.6939\n",
      "Epoch 128/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6064 - val_loss: 0.6942\n",
      "Epoch 129/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6294 - val_loss: 0.6945\n",
      "Epoch 130/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.6538 - val_loss: 0.6949\n",
      "Epoch 131/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6286 - val_loss: 0.6953\n",
      "Epoch 132/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6564 - val_loss: 0.6956\n",
      "Epoch 133/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6342 - val_loss: 0.6954\n",
      "Epoch 134/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6415 - val_loss: 0.6953\n",
      "Epoch 135/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.6466 - val_loss: 0.6954\n",
      "Epoch 136/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6211 - val_loss: 0.6961\n",
      "Epoch 137/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6666 - val_loss: 0.6969\n",
      "Epoch 138/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5987 - val_loss: 0.6977\n",
      "Epoch 139/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5919 - val_loss: 0.6984\n",
      "Epoch 140/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6546 - val_loss: 0.6988\n",
      "Epoch 141/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6107 - val_loss: 0.6991\n",
      "Epoch 142/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6230 - val_loss: 0.6993\n",
      "Epoch 143/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6547 - val_loss: 0.6993\n",
      "Epoch 144/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6564 - val_loss: 0.6986\n",
      "Epoch 145/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6190 - val_loss: 0.6982\n",
      "Epoch 146/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6035 - val_loss: 0.6985\n",
      "Epoch 147/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6642 - val_loss: 0.6992\n",
      "Epoch 148/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6075 - val_loss: 0.6999\n",
      "Epoch 149/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.6150 - val_loss: 0.7011\n",
      "Epoch 150/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6375 - val_loss: 0.7022\n",
      "Epoch 151/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.6465 - val_loss: 0.7030\n",
      "Epoch 152/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6058 - val_loss: 0.7036\n",
      "Epoch 153/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6199 - val_loss: 0.7043\n",
      "Epoch 154/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6084 - val_loss: 0.7052\n",
      "Epoch 155/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6312 - val_loss: 0.7059\n",
      "Epoch 156/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6122 - val_loss: 0.7067\n",
      "Epoch 157/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6373 - val_loss: 0.7072\n",
      "Epoch 158/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6555 - val_loss: 0.7078\n",
      "Epoch 159/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6318 - val_loss: 0.7081\n",
      "Epoch 160/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6155 - val_loss: 0.7087\n",
      "Epoch 161/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5988 - val_loss: 0.7090\n",
      "Epoch 162/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6786 - val_loss: 0.7090\n",
      "Epoch 163/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6040 - val_loss: 0.7089\n",
      "Epoch 164/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5921 - val_loss: 0.7091\n",
      "Epoch 165/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6616 - val_loss: 0.7101\n",
      "Epoch 166/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6562 - val_loss: 0.7109\n",
      "Epoch 167/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6011 - val_loss: 0.7115\n",
      "Epoch 168/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.6163 - val_loss: 0.7124\n",
      "Epoch 169/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6103 - val_loss: 0.7138\n",
      "Epoch 170/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6428 - val_loss: 0.7149\n",
      "Epoch 171/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6313 - val_loss: 0.7168\n",
      "Epoch 172/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6281 - val_loss: 0.7185\n",
      "Epoch 173/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6160 - val_loss: 0.7202\n",
      "Epoch 174/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5736 - val_loss: 0.7217\n",
      "Epoch 175/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6144 - val_loss: 0.7231\n",
      "Epoch 176/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6237 - val_loss: 0.7239\n",
      "Epoch 177/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6538 - val_loss: 0.7242\n",
      "Epoch 178/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6207 - val_loss: 0.7243\n",
      "Epoch 179/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5994 - val_loss: 0.7242\n",
      "Epoch 180/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6043 - val_loss: 0.7240\n",
      "Epoch 181/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5829 - val_loss: 0.7234\n",
      "Epoch 182/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6776 - val_loss: 0.7231\n",
      "Epoch 183/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6268 - val_loss: 0.7230\n",
      "Epoch 184/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6266 - val_loss: 0.7236\n",
      "Epoch 185/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6344 - val_loss: 0.7244\n",
      "Epoch 186/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6277 - val_loss: 0.7254\n",
      "Epoch 187/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6351 - val_loss: 0.7257\n",
      "Epoch 188/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.6416 - val_loss: 0.7258\n",
      "Epoch 189/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6591 - val_loss: 0.7259\n",
      "Epoch 190/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6020 - val_loss: 0.7258\n",
      "Epoch 191/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.6492 - val_loss: 0.7262\n",
      "Epoch 192/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6095 - val_loss: 0.7263\n",
      "Epoch 193/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6330 - val_loss: 0.7264\n",
      "Epoch 194/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5950 - val_loss: 0.7264\n",
      "Epoch 195/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5766 - val_loss: 0.7266\n",
      "Epoch 196/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6531 - val_loss: 0.7269\n",
      "Epoch 197/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5883 - val_loss: 0.7281\n",
      "Epoch 198/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6571 - val_loss: 0.7298\n",
      "Epoch 199/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6384 - val_loss: 0.7309\n",
      "Epoch 200/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5798 - val_loss: 0.7320\n",
      "Epoch 201/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6156 - val_loss: 0.7332\n",
      "Epoch 202/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6052 - val_loss: 0.7342\n",
      "Epoch 203/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.6063 - val_loss: 0.7350\n",
      "Epoch 204/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5937 - val_loss: 0.7362\n",
      "Epoch 205/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5890 - val_loss: 0.7378\n",
      "Epoch 206/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.6105 - val_loss: 0.7395\n",
      "Epoch 207/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.6444 - val_loss: 0.7408\n",
      "Epoch 208/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.6282 - val_loss: 0.7421\n",
      "Epoch 209/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6339 - val_loss: 0.7429\n",
      "Epoch 210/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.6050 - val_loss: 0.7437\n",
      "Epoch 211/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.6155 - val_loss: 0.7449\n",
      "Epoch 212/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6290 - val_loss: 0.7458\n",
      "Epoch 213/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5687 - val_loss: 0.7472\n",
      "Epoch 214/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5904 - val_loss: 0.7477\n",
      "Epoch 215/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6519 - val_loss: 0.7474\n",
      "Epoch 216/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.6136 - val_loss: 0.7479\n",
      "Epoch 217/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.6096 - val_loss: 0.7486\n",
      "Epoch 218/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5763 - val_loss: 0.7489\n",
      "Epoch 219/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6171 - val_loss: 0.7496\n",
      "Epoch 220/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6792 - val_loss: 0.7502\n",
      "Epoch 221/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.5807 - val_loss: 0.7509\n",
      "Epoch 222/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6323 - val_loss: 0.7514\n",
      "Epoch 223/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.5773 - val_loss: 0.7509\n",
      "Epoch 224/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6303 - val_loss: 0.7505\n",
      "Epoch 225/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6315 - val_loss: 0.7504\n",
      "Epoch 226/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6105 - val_loss: 0.7502\n",
      "Epoch 227/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6104 - val_loss: 0.7487\n",
      "Epoch 228/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5937 - val_loss: 0.7483\n",
      "Epoch 229/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6121 - val_loss: 0.7479\n",
      "Epoch 230/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6079 - val_loss: 0.7482\n",
      "Epoch 231/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5900 - val_loss: 0.7486\n",
      "Epoch 232/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 0.5634 - val_loss: 0.7488\n",
      "Epoch 233/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.6345 - val_loss: 0.7489\n",
      "Epoch 234/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.6454 - val_loss: 0.7493\n",
      "Epoch 235/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5724 - val_loss: 0.7504\n",
      "Epoch 236/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6244 - val_loss: 0.7520\n",
      "Epoch 237/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5890 - val_loss: 0.7536\n",
      "Epoch 238/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.6264 - val_loss: 0.7551\n",
      "Epoch 239/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5998 - val_loss: 0.7572\n",
      "Epoch 240/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.6040 - val_loss: 0.7601\n",
      "Epoch 241/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.5819 - val_loss: 0.7628\n",
      "Epoch 242/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5980 - val_loss: 0.7653\n",
      "Epoch 243/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6082 - val_loss: 0.7674\n",
      "Epoch 244/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5388 - val_loss: 0.7690\n",
      "Epoch 245/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.6049 - val_loss: 0.7703\n",
      "Epoch 246/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5778 - val_loss: 0.7720\n",
      "Epoch 247/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5892 - val_loss: 0.7728\n",
      "Epoch 248/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5804 - val_loss: 0.7736\n",
      "Epoch 249/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.5601 - val_loss: 0.7747\n",
      "Epoch 250/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.6022 - val_loss: 0.7759\n",
      "Epoch 251/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5693 - val_loss: 0.7782\n",
      "Epoch 252/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5858 - val_loss: 0.7800\n",
      "Epoch 253/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5563 - val_loss: 0.7809\n",
      "Epoch 254/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5708 - val_loss: 0.7819\n",
      "Epoch 255/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.6014 - val_loss: 0.7826\n",
      "Epoch 256/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5747 - val_loss: 0.7841\n",
      "Epoch 257/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5825 - val_loss: 0.7863\n",
      "Epoch 258/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5729 - val_loss: 0.7881\n",
      "Epoch 259/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.6337 - val_loss: 0.7895\n",
      "Epoch 260/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5870 - val_loss: 0.7896\n",
      "Epoch 261/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6190 - val_loss: 0.7894\n",
      "Epoch 262/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5690 - val_loss: 0.7890\n",
      "Epoch 263/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.6066 - val_loss: 0.7871\n",
      "Epoch 264/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5851 - val_loss: 0.7855\n",
      "Epoch 265/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5783 - val_loss: 0.7844\n",
      "Epoch 266/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.6019 - val_loss: 0.7834\n",
      "Epoch 267/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.6063 - val_loss: 0.7822\n",
      "Epoch 268/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5702 - val_loss: 0.7814\n",
      "Epoch 269/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5861 - val_loss: 0.7812\n",
      "Epoch 270/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6104 - val_loss: 0.7819\n",
      "Epoch 271/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5539 - val_loss: 0.7828\n",
      "Epoch 272/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5874 - val_loss: 0.7836\n",
      "Epoch 273/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.5372 - val_loss: 0.7848\n",
      "Epoch 274/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5918 - val_loss: 0.7863\n",
      "Epoch 275/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.5958 - val_loss: 0.7877\n",
      "Epoch 276/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6000 - val_loss: 0.7885\n",
      "Epoch 277/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5711 - val_loss: 0.7895\n",
      "Epoch 278/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5570 - val_loss: 0.7910\n",
      "Epoch 279/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5632 - val_loss: 0.7926\n",
      "Epoch 280/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5439 - val_loss: 0.7934\n",
      "Epoch 281/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5246 - val_loss: 0.7948\n",
      "Epoch 282/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5331 - val_loss: 0.7967\n",
      "Epoch 283/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5227 - val_loss: 0.7985\n",
      "Epoch 284/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5717 - val_loss: 0.8002\n",
      "Epoch 285/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5791 - val_loss: 0.8018\n",
      "Epoch 286/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5669 - val_loss: 0.8036\n",
      "Epoch 287/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5714 - val_loss: 0.8041\n",
      "Epoch 288/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5695 - val_loss: 0.8061\n",
      "Epoch 289/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6090 - val_loss: 0.8088\n",
      "Epoch 290/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5506 - val_loss: 0.8117\n",
      "Epoch 291/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5842 - val_loss: 0.8140\n",
      "Epoch 292/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5567 - val_loss: 0.8162\n",
      "Epoch 293/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5589 - val_loss: 0.8182\n",
      "Epoch 294/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5689 - val_loss: 0.8194\n",
      "Epoch 295/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5727 - val_loss: 0.8197\n",
      "Epoch 296/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5903 - val_loss: 0.8212\n",
      "Epoch 297/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5697 - val_loss: 0.8225\n",
      "Epoch 298/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5465 - val_loss: 0.8234\n",
      "Epoch 299/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.5711 - val_loss: 0.8246\n",
      "Epoch 300/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6177 - val_loss: 0.8252\n",
      "Epoch 301/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5603 - val_loss: 0.8252\n",
      "Epoch 302/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5683 - val_loss: 0.8244\n",
      "Epoch 303/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5348 - val_loss: 0.8239\n",
      "Epoch 304/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.6031 - val_loss: 0.8243\n",
      "Epoch 305/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5747 - val_loss: 0.8251\n",
      "Epoch 306/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5440 - val_loss: 0.8261\n",
      "Epoch 307/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5462 - val_loss: 0.8274\n",
      "Epoch 308/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.5701 - val_loss: 0.8290\n",
      "Epoch 309/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.6173 - val_loss: 0.8300\n",
      "Epoch 310/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5768 - val_loss: 0.8303\n",
      "Epoch 311/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5406 - val_loss: 0.8310\n",
      "Epoch 312/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5750 - val_loss: 0.8318\n",
      "Epoch 313/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5887 - val_loss: 0.8328\n",
      "Epoch 314/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5539 - val_loss: 0.8341\n",
      "Epoch 315/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.5702 - val_loss: 0.8353\n",
      "Epoch 316/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5666 - val_loss: 0.8369\n",
      "Epoch 317/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5836 - val_loss: 0.8376\n",
      "Epoch 318/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5444 - val_loss: 0.8394\n",
      "Epoch 319/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5934 - val_loss: 0.8402\n",
      "Epoch 320/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.5521 - val_loss: 0.8416\n",
      "Epoch 321/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5435 - val_loss: 0.8432\n",
      "Epoch 322/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5560 - val_loss: 0.8446\n",
      "Epoch 323/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5570 - val_loss: 0.8464\n",
      "Epoch 324/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.5638 - val_loss: 0.8474\n",
      "Epoch 325/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5650 - val_loss: 0.8492\n",
      "Epoch 326/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5741 - val_loss: 0.8512\n",
      "Epoch 327/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5459 - val_loss: 0.8535\n",
      "Epoch 328/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5985 - val_loss: 0.8560\n",
      "Epoch 329/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5342 - val_loss: 0.8591\n",
      "Epoch 330/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5983 - val_loss: 0.8618\n",
      "Epoch 331/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5675 - val_loss: 0.8637\n",
      "Epoch 332/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.5534 - val_loss: 0.8648\n",
      "Epoch 333/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5268 - val_loss: 0.8663\n",
      "Epoch 334/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5759 - val_loss: 0.8682\n",
      "Epoch 335/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5203 - val_loss: 0.8695\n",
      "Epoch 336/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5378 - val_loss: 0.8705\n",
      "Epoch 337/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5851 - val_loss: 0.8713\n",
      "Epoch 338/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5506 - val_loss: 0.8726\n",
      "Epoch 339/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.6133 - val_loss: 0.8727\n",
      "Epoch 340/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5881 - val_loss: 0.8729\n",
      "Epoch 341/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5538 - val_loss: 0.8721\n",
      "Epoch 342/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4951 - val_loss: 0.8704\n",
      "Epoch 343/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5144 - val_loss: 0.8686\n",
      "Epoch 344/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.5428 - val_loss: 0.8689\n",
      "Epoch 345/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.6023 - val_loss: 0.8704\n",
      "Epoch 346/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5811 - val_loss: 0.8715\n",
      "Epoch 347/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.5588 - val_loss: 0.8738\n",
      "Epoch 348/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5945 - val_loss: 0.8754\n",
      "Epoch 349/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.5626 - val_loss: 0.8771\n",
      "Epoch 350/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5534 - val_loss: 0.8787\n",
      "Epoch 351/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5614 - val_loss: 0.8803\n",
      "Epoch 352/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5314 - val_loss: 0.8816\n",
      "Epoch 353/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5601 - val_loss: 0.8831\n",
      "Epoch 354/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5517 - val_loss: 0.8854\n",
      "Epoch 355/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5496 - val_loss: 0.8883\n",
      "Epoch 356/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4747 - val_loss: 0.8913\n",
      "Epoch 357/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5385 - val_loss: 0.8942\n",
      "Epoch 358/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5082 - val_loss: 0.8969\n",
      "Epoch 359/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5112 - val_loss: 0.8996\n",
      "Epoch 360/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5074 - val_loss: 0.9013\n",
      "Epoch 361/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5335 - val_loss: 0.8995\n",
      "Epoch 362/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5315 - val_loss: 0.8971\n",
      "Epoch 363/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5786 - val_loss: 0.8953\n",
      "Epoch 364/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5244 - val_loss: 0.8947\n",
      "Epoch 365/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.5360 - val_loss: 0.8931\n",
      "Epoch 366/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5437 - val_loss: 0.8910\n",
      "Epoch 367/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5531 - val_loss: 0.8898\n",
      "Epoch 368/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.5695 - val_loss: 0.8895\n",
      "Epoch 369/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5345 - val_loss: 0.8898\n",
      "Epoch 370/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5086 - val_loss: 0.8904\n",
      "Epoch 371/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5532 - val_loss: 0.8909\n",
      "Epoch 372/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.5663 - val_loss: 0.8909\n",
      "Epoch 373/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.5312 - val_loss: 0.8923\n",
      "Epoch 374/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5902 - val_loss: 0.8924\n",
      "Epoch 375/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5536 - val_loss: 0.8914\n",
      "Epoch 376/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5538 - val_loss: 0.8904\n",
      "Epoch 377/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4994 - val_loss: 0.8906\n",
      "Epoch 378/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.5493 - val_loss: 0.8912\n",
      "Epoch 379/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5464 - val_loss: 0.8931\n",
      "Epoch 380/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5178 - val_loss: 0.8956\n",
      "Epoch 381/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5566 - val_loss: 0.8981\n",
      "Epoch 382/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5656 - val_loss: 0.9008\n",
      "Epoch 383/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5474 - val_loss: 0.9045\n",
      "Epoch 384/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5056 - val_loss: 0.9097\n",
      "Epoch 385/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5369 - val_loss: 0.9144\n",
      "Epoch 386/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5471 - val_loss: 0.9181\n",
      "Epoch 387/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5517 - val_loss: 0.9207\n",
      "Epoch 388/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.5050 - val_loss: 0.9232\n",
      "Epoch 389/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5221 - val_loss: 0.9268\n",
      "Epoch 390/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5823 - val_loss: 0.9300\n",
      "Epoch 391/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5136 - val_loss: 0.9340\n",
      "Epoch 392/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.5422 - val_loss: 0.9386\n",
      "Epoch 393/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5365 - val_loss: 0.9440\n",
      "Epoch 394/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5558 - val_loss: 0.9480\n",
      "Epoch 395/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5554 - val_loss: 0.9505\n",
      "Epoch 396/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.4832 - val_loss: 0.9517\n",
      "Epoch 397/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5383 - val_loss: 0.9524\n",
      "Epoch 398/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5539 - val_loss: 0.9520\n",
      "Epoch 399/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5924 - val_loss: 0.9533\n",
      "Epoch 400/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.5595 - val_loss: 0.9555\n",
      "Epoch 401/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5553 - val_loss: 0.9578\n",
      "Epoch 402/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5471 - val_loss: 0.9603\n",
      "Epoch 403/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5433 - val_loss: 0.9619\n",
      "Epoch 404/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5032 - val_loss: 0.9618\n",
      "Epoch 405/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.5251 - val_loss: 0.9636\n",
      "Epoch 406/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5520 - val_loss: 0.9657\n",
      "Epoch 407/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5153 - val_loss: 0.9688\n",
      "Epoch 408/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5235 - val_loss: 0.9711\n",
      "Epoch 409/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5226 - val_loss: 0.9724\n",
      "Epoch 410/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5587 - val_loss: 0.9726\n",
      "Epoch 411/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5330 - val_loss: 0.9723\n",
      "Epoch 412/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.4812 - val_loss: 0.9728\n",
      "Epoch 413/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5076 - val_loss: 0.9742\n",
      "Epoch 414/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5490 - val_loss: 0.9762\n",
      "Epoch 415/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5184 - val_loss: 0.9785\n",
      "Epoch 416/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5497 - val_loss: 0.9812\n",
      "Epoch 417/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4723 - val_loss: 0.9835\n",
      "Epoch 418/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.5271 - val_loss: 0.9852\n",
      "Epoch 419/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5230 - val_loss: 0.9851\n",
      "Epoch 420/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.5226 - val_loss: 0.9855\n",
      "Epoch 421/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5292 - val_loss: 0.9864\n",
      "Epoch 422/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5873 - val_loss: 0.9880\n",
      "Epoch 423/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4991 - val_loss: 0.9902\n",
      "Epoch 424/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5340 - val_loss: 0.9931\n",
      "Epoch 425/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5206 - val_loss: 0.9959\n",
      "Epoch 426/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.4959 - val_loss: 0.9968\n",
      "Epoch 427/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.5372 - val_loss: 0.9955\n",
      "Epoch 428/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4912 - val_loss: 0.9935\n",
      "Epoch 429/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5810 - val_loss: 0.9906\n",
      "Epoch 430/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5006 - val_loss: 0.9884\n",
      "Epoch 431/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.4607 - val_loss: 0.9891\n",
      "Epoch 432/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5359 - val_loss: 0.9925\n",
      "Epoch 433/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5215 - val_loss: 0.9955\n",
      "Epoch 434/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5234 - val_loss: 0.9970\n",
      "Epoch 435/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5299 - val_loss: 0.9987\n",
      "Epoch 436/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4678 - val_loss: 1.0018\n",
      "Epoch 437/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4941 - val_loss: 1.0044\n",
      "Epoch 438/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5348 - val_loss: 1.0054\n",
      "Epoch 439/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4842 - val_loss: 1.0061\n",
      "Epoch 440/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4861 - val_loss: 1.0071\n",
      "Epoch 441/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5370 - val_loss: 1.0077\n",
      "Epoch 442/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 0.5422 - val_loss: 1.0095\n",
      "Epoch 443/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.4616 - val_loss: 1.0118\n",
      "Epoch 444/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5670 - val_loss: 1.0137\n",
      "Epoch 445/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5471 - val_loss: 1.0155\n",
      "Epoch 446/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.4937 - val_loss: 1.0172\n",
      "Epoch 447/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.4902 - val_loss: 1.0196\n",
      "Epoch 448/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5483 - val_loss: 1.0213\n",
      "Epoch 449/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.5081 - val_loss: 1.0223\n",
      "Epoch 450/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5626 - val_loss: 1.0237\n",
      "Epoch 451/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.4884 - val_loss: 1.0257\n",
      "Epoch 452/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5464 - val_loss: 1.0253\n",
      "Epoch 453/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.4504 - val_loss: 1.0260\n",
      "Epoch 454/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5163 - val_loss: 1.0272\n",
      "Epoch 455/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4797 - val_loss: 1.0290\n",
      "Epoch 456/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4973 - val_loss: 1.0317\n",
      "Epoch 457/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5505 - val_loss: 1.0355\n",
      "Epoch 458/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5787 - val_loss: 1.0366\n",
      "Epoch 459/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5288 - val_loss: 1.0374\n",
      "Epoch 460/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4451 - val_loss: 1.0392\n",
      "Epoch 461/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.4802 - val_loss: 1.0408\n",
      "Epoch 462/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5111 - val_loss: 1.0413\n",
      "Epoch 463/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5093 - val_loss: 1.0422\n",
      "Epoch 464/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 0.5845 - val_loss: 1.0421\n",
      "Epoch 465/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5444 - val_loss: 1.0414\n",
      "Epoch 466/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4944 - val_loss: 1.0416\n",
      "Epoch 467/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.5027 - val_loss: 1.0421\n",
      "Epoch 468/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5195 - val_loss: 1.0428\n",
      "Epoch 469/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.4827 - val_loss: 1.0449\n",
      "Epoch 470/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4850 - val_loss: 1.0475\n",
      "Epoch 471/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5028 - val_loss: 1.0502\n",
      "Epoch 472/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.5370 - val_loss: 1.0523\n",
      "Epoch 473/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5038 - val_loss: 1.0553\n",
      "Epoch 474/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5261 - val_loss: 1.0588\n",
      "Epoch 475/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4654 - val_loss: 1.0634\n",
      "Epoch 476/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5370 - val_loss: 1.0690\n",
      "Epoch 477/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5461 - val_loss: 1.0749\n",
      "Epoch 478/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5044 - val_loss: 1.0811\n",
      "Epoch 479/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5082 - val_loss: 1.0864\n",
      "Epoch 480/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.5097 - val_loss: 1.0902\n",
      "Epoch 481/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.4665 - val_loss: 1.0929\n",
      "Epoch 482/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4822 - val_loss: 1.0947\n",
      "Epoch 483/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5269 - val_loss: 1.0968\n",
      "Epoch 484/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.5638 - val_loss: 1.0970\n",
      "Epoch 485/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5142 - val_loss: 1.0978\n",
      "Epoch 486/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.4943 - val_loss: 1.0990\n",
      "Epoch 487/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.4550 - val_loss: 1.1013\n",
      "Epoch 488/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5389 - val_loss: 1.1026\n",
      "Epoch 489/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4875 - val_loss: 1.1036\n",
      "Epoch 490/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5026 - val_loss: 1.1040\n",
      "Epoch 491/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4723 - val_loss: 1.1055\n",
      "Epoch 492/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 0.4616 - val_loss: 1.1084\n",
      "Epoch 493/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.5326 - val_loss: 1.1114\n",
      "Epoch 494/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.4789 - val_loss: 1.1145\n",
      "Epoch 495/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5342 - val_loss: 1.1166\n",
      "Epoch 496/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5232 - val_loss: 1.1185\n",
      "Epoch 497/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 0.4841 - val_loss: 1.1197\n",
      "Epoch 498/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 0.5530 - val_loss: 1.1190\n",
      "Epoch 499/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5412 - val_loss: 1.1192\n",
      "Epoch 500/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5082 - val_loss: 1.1214\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1eab7c54390>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define model\n",
    "model=Sequential()\n",
    "model.add(Dense(30,activation='relu'))\n",
    "model.add(Dropout(0.5)) #prevents overfitting\n",
    "model.add(Dense(15,activation='relu'))\n",
    "model.add(Dropout(0.5))  #prevents overfitting\n",
    "#BINARY CLASSIFICATION USES SIGMOID ACTIVATION FUNCTION FOR OUTPUT LAYER\n",
    "model.add(Dense(1,activation='sigmoid'))  #output layer\n",
    "#Compile\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam')\n",
    "\n",
    "#Model Fitting\n",
    "model.fit(X_train, y_train, epochs=500, validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "0915c338-37b6-4c1a-936f-38c7106df6bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 103,
     "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": [
    "losses= pd.DataFrame(model.history.history)\n",
    "losses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "f49a18cd-7ef0-4cf5-a191-3a14b51d171d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Early stopping to prevent overfitting\n",
    "early_stop = EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d5d4e73f-15ff-4567-bf82-0bbacae9dc42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.5082 - val_loss: 1.1252\n",
      "Epoch 2/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5229 - val_loss: 1.1291\n",
      "Epoch 3/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5226 - val_loss: 1.1325\n",
      "Epoch 4/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5404 - val_loss: 1.1343\n",
      "Epoch 5/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5426 - val_loss: 1.1365\n",
      "Epoch 6/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5217 - val_loss: 1.1388\n",
      "Epoch 7/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.4785 - val_loss: 1.1408\n",
      "Epoch 8/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 0.5067 - val_loss: 1.1409\n",
      "Epoch 9/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5035 - val_loss: 1.1405\n",
      "Epoch 10/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.5422 - val_loss: 1.1389\n",
      "Epoch 11/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.4645 - val_loss: 1.1391\n",
      "Epoch 12/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5191 - val_loss: 1.1402\n",
      "Epoch 13/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4928 - val_loss: 1.1417\n",
      "Epoch 14/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4637 - val_loss: 1.1442\n",
      "Epoch 15/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4847 - val_loss: 1.1471\n",
      "Epoch 16/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.4921 - val_loss: 1.1498\n",
      "Epoch 17/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 0.5273 - val_loss: 1.1508\n",
      "Epoch 18/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.4736 - val_loss: 1.1510\n",
      "Epoch 19/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 0.5577 - val_loss: 1.1504\n",
      "Epoch 20/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.4605 - val_loss: 1.1491\n",
      "Epoch 21/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 0.4902 - val_loss: 1.1475\n",
      "Epoch 22/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5107 - val_loss: 1.1482\n",
      "Epoch 23/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4831 - val_loss: 1.1500\n",
      "Epoch 24/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5182 - val_loss: 1.1520\n",
      "Epoch 25/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5136 - val_loss: 1.1547\n",
      "Epoch 26/500\n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.5138 - val_loss: 1.1584\n",
      "Epoch 26: early stopping\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1eab7ed9f50>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model fit WITH callbacks\n",
    "model.fit(X_train, y_train, epochs=500, validation_data=(X_test,y_test), callbacks=[early_stop])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8186078d-9fdf-400f-8321-ce16339938b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 106,
     "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": [
    "#Check val loss\n",
    "losses2= pd.DataFrame(model.history.history)\n",
    "losses2.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "10d56f7c-fe3b-4a9c-a2c2-b8184f3b755d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "classes_x=np.argmax(predictions,axis=1)\n",
    "print(classes_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "108c542c-cc9c-449f-bcdb-ee74faed5f66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      1.00      0.61        11\n",
      "           1       0.00      0.00      0.00        14\n",
      "\n",
      "    accuracy                           0.44        25\n",
      "   macro avg       0.22      0.50      0.31        25\n",
      "weighted avg       0.19      0.44      0.27        25\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\neogi\\Documents\\Python_venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "C:\\Users\\neogi\\Documents\\Python_venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "C:\\Users\\neogi\\Documents\\Python_venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,classes_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "ceae6613-1f43-4360-a69c-45224a04018b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  0]\n",
      " [14  0]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,classes_x))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}