diabetes_DL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning Based Diabetes Risk Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 461,
     "status": "ok",
     "timestamp": 1710913294554,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "xniTmBzwXz5o"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.4\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from sklearn.metrics import confusion_matrix , classification_report ,accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 33,
     "status": "ok",
     "timestamp": 1710913298660,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "n09uAAwxYyaT"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('dataset/diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
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    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1710913298660,
     "user": {
      "displayName": "Chinmoy Talukdar",
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    "id": "j7VLrY1SZokM",
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <td>88</td>\n",
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       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
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       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
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   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "758            1      106             76              0        0  37.5   \n",
       "759            6      190             92              0        0  35.5   \n",
       "760            2       88             58             26       16  28.4   \n",
       "761            9      170             74             31        0  44.0   \n",
       "762            9       89             62              0        0  22.5   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "758                     0.197   26        0  \n",
       "759                     0.278   66        1  \n",
       "760                     0.766   22        0  \n",
       "761                     0.403   43        1  \n",
       "762                     0.142   33        0  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  "
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
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   "source": [
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   "execution_count": 7,
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       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
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       "      <td>0.348958</td>\n",
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       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
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       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1710913298662,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "LJMyoqk-Z0xh",
    "outputId": "db0eac42-2002-445a-96fa-9f63acf7335c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7JTTiX3wZ-YG"
   },
   "source": [
    "**Visualization**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1710913298662,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "7xSuL6rQaL7R",
    "outputId": "72a5c974-40ab-4b3f-9201-71843c3c3cfe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_diabetes=df['Outcome'].value_counts()\n",
    "count_diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 497
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1710913298663,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "-hCZDGMnaQkr",
    "outputId": "81146201-46f6-4c89-93e6-f08dab114396"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2xvrOoc9N8nzgyCTnMHjs669V1eUjrUySJPW2vkA/FTgJ2As4Y8yyAl40qqIkSdLkTBjoVfUF4AtJ3lNV75vCmiTNYrufeeZ0lyBtsjVvfvOUf2ef29bel+RI4Hld0wVV9bXRliVJkiajz8tZ/h44Ebiu+zmxa5MkSTNEnyfFvRw4qKoegV+/nOUHwMmjLEySJPXX9z70nYemf28EdUiSpE3QZ4T+98APknyLwa1rz8PRuSRJM0qfi+LOTnIBg7esBXhXVf141IVJkqT++ozQqarVwLIR1yJJkjaSz3KXJKkBBrokSQ1Yb6An2SLJNVNVjCRJ2jjrDfTu3vMrk8ybonokSdJG6HNR3Fzg2iQXAz9b11hVR46sKkmSNCl9Av20kVchSZI2SZ/70C9Msg8wv6rOT7I9sOXoS5MkSX31eTnLfwe+AHy0a9oT+MoIa5IkSZPU57a1E4DnAPcDVNVNwO6jLEqSJE1On0B/qKp+uW4myVZAja4kSZI0WX0C/cIk7wa2S/IS4PPAV0dbliRJmow+gX4ScBdwNfBG4Dzgr0dZlCRJmpw+V7k/kmQJ8H0Gh9pvrCoPuUuSNINsMNCTvBz4CPB/Gbw+db8kb6yqfx11cZIkqZ8+D5b5J+CFVbUSIMkTgK8DBrokSTNEn3Poa9aFeedmYM2I6pEkSRthwhF6kld3k9cmOQ9YyuAc+tHAJVNQmyRJ6ml9h9xfMTR9J/D8bvouYJeRVSRJkiZtwkCvqjdMZSGSJGnj9bnKfT/grcC+w/19faokSTNHn6vcvwJ8jMHT4R4ZaTWSJGmj9An0/1dVHxx5JZIkaaP1CfQPJHkv8O/AQ+saq+rykVUlSZImpU+g/yHweuBF/OaQe3XzkiRpBugT6K8C9h9+haokSZpZ+jwp7kpg5xHXIUmSNkGfEfoewA1JLuHR59C9bU2SpBmiT6C/d+RVSJKkTdLnfegXTkUhkiRp4/V5UtwDDK5qB3gMsDXws6raaZSFSZKk/vqM0Hccnk/ySuCQURUkSZImr89V7o9SVV+hxz3oSfZO8q0k1ye5NsmJXfuuSZYnuan73GVonZOTrExyY5LDJlubJEmzVZ9D7q8emt0CWMBvDsGvz1rg7VV1eZIdgcuSLAf+DFhRVacnOQk4CXhXkgOAhcCBwOOB85M8saoentQeSZI0C/W5yn34vehrgVuBoza0UlWtBlZ30w8kuR7Ys1v3BV23JcAFwLu69nOq6iHgliQrGRzav6hHjZIkzWp9zqFv8nvRk+wLPB34PrBHF/ZU1eoku3fd9gS+N7Taqq5NkiRtwISBnuTU9axXVfW+Pl+Q5LHAF4H/WVX3J5mw63jfM872jgeOB5g3b16fEiRJat76Lor72Tg/AMcxOES+QUm2ZhDmn6mqL3XNdyaZ2y2fC6zp2lcBew+tvhdwx9htVtVZVbWgqhbMmTOnTxmSJDVvwkCvqn9a9wOcBWwHvAE4B9h/QxvOYCj+MeD6qjpjaNEyYFE3vQg4d6h9YZJtkuwHzAcunuT+SJI0K633HHqSXYG3Aa9jcAHbf66qn/Tc9nMYvHb16iRXdG3vBk4HliY5DrgNOBqgqq5NshS4jsHFdyd4hbskSf2s7xz6PwKvZjA6/8OqenAyG66q7zD+eXGAQydYZzGweDLfI0mS1n8O/e0M7gf/a+COJPd3Pw8kuX9qypMkSX1MOEKvqkk/RU6SJE0PQ1uSpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpASML9CQfT7ImyTVDbbsmWZ7kpu5zl6FlJydZmeTGJIeNqi5Jklo0yhH6J4HDx7SdBKyoqvnAim6eJAcAC4EDu3XOTLLlCGuTJKkpIwv0qvo2cO+Y5qOAJd30EuCVQ+3nVNVDVXULsBI4ZFS1SZLUmqk+h75HVa0G6D5379r3BG4f6reqa5MkST3MlIviMk5bjdsxOT7JpUkuveuuu0ZcliRJm4epDvQ7k8wF6D7XdO2rgL2H+u0F3DHeBqrqrKpaUFUL5syZM9JiJUnaXEx1oC8DFnXTi4Bzh9oXJtkmyX7AfODiKa5NkqTN1laj2nCSs4EXALslWQW8FzgdWJrkOOA24GiAqro2yVLgOmAtcEJVPTyq2iRJas3IAr2qXjvBokMn6L8YWDyqeiRJatlMuShOkiRtAgNdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDTDQJUlqgIEuSVIDDHRJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSAwx0SZIaYKBLktQAA12SpAYY6JIkNcBAlySpAQa6JEkNMNAlSWqAgS5JUgMMdEmSGmCgS5LUAANdkqQGGOiSJDXAQJckqQEGuiRJDZhxgZ7k8CQ3JlmZ5KTprkeSpM3BjAr0JFsCHwb+C3AA8NokB0xvVZIkzXwzKtCBQ4CVVXVzVf0SOAc4apprkiRpxptpgb4ncPvQ/KquTZIkrcdW013AGBmnrR7VITkeOL6bfTDJjSOvSqOyG3D3dBfRsnx2uivQDOXv3ojlhBNGufl9xmucaYG+Cth7aH4v4I7hDlV1FnDWVBal0UhyaVUtmO46pNnG3702zbRD7pcA85Psl+QxwEJg2TTXJEnSjDejRuhVtTbJW4B/A7YEPl5V105zWZIkzXgzKtABquo84LzprkNTwlMn0vTwd69BqaoN95IkSTPaTDuHLkmSNoKBrinn432l6ZHk40nWJLlmumvR756Brinl432lafVJ4PDpLkKjYaBrqvl4X2maVNW3gXunuw6NhoGuqebjfSVpBAx0TbUNPt5XkjR5Brqm2gYf7ytJmjwDXVPNx/tK0ggY6JpSVbUWWPd43+uBpT7eV5oaSc4GLgKelGRVkuOmuyb97vikOEmSGuAIXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJasBW012ApKmX5G+AB4GdgG9X1fnr6XsB8I6qurTntg8CHl9V5216pZL6MtClWayqTh3BZg8CFgAGujSFPOQuzRJJTuneQ38+8KSu7ZNJXtNNn5rkkiTXJDkryfBz949N8t1u2SFd/x2692tfkuQHSY7qnv73t8AxSa5Icsx4/br1D0xycdfvqiTzp/afiNQWA12aBZI8g8Fjdp8OvBo4eJxuH6qqg6vqKcB2wBFDy3aoqmcDbwY+3rWdAnyzqg4GXgj8I7A1cCrwuao6qKo+N16/JDsAbwI+UFUHMRjRr/pd7rM023jIXZod/hj4clX9HCDJeM/Pf2GSdwLbA7sC1wJf7ZadDYP3aSfZKcnOwEuBI5O8o+uzLTBvnO1O1O8i4JQkewFfqqqbNnEfpVnNQJdmjwmf85xkW+BMYEFV3d5dNLftetYtBq/C/ZOqunHMtp45dvPj9QOuT/J94OXAvyX5i6r6Zu+9kfQoHnKXZodvA69Ksl2SHYFXjFm+LrzvTvJY4DVjlh8DkOS5wE+r6qcMXrDz1nXn2pM8vev7ALDj0Lrj9kuyP3BzVX2QwRv3nrrpuynNXga6NAtU1eXA54ArgC8C/zFm+X3AvwBXA19h8JrbYT9J8l3gI8C6N3S9j8E586uSXNPNA3wLOGDdRXHr6XcMcE2SK4AnA//nd7Cr0qzl29YkSWqAI3RJkhpgoEuS1AADXZKkBhjokiQ1wECXJKkBBrokSQ0w0CVJaoCBLklSA/4/kCdeMET3Gd0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=count_diabetes.index,y=count_diabetes.values,palette='winter')\n",
    "plt.title('Diabetes Count')\n",
    "plt.xlabel('diabetes')\n",
    "plt.ylabel('Number of people')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 5385,
     "status": "ok",
     "timestamp": 1710913978669,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "u54HeW9jag0b",
    "outputId": "fe8be682-0fec-4fb3-e272-c1727ea98f9c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "for i, col in enumerate(['Pregnancies','Glucose','BloodPressure','SkinThickness','Insulin','DiabetesPedigreeFunction','BMI','Age']):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    sns.histplot(x=col,data=df,kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1710913307398,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "mLdVjs8qbo1o",
    "outputId": "8bc5e769-1b9a-4471-e1fe-fd0df8cc42ca"
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970   \n",
       "Outcome                      0.221898  0.466581       0.065068       0.074752   \n",
       "\n",
       "                           Insulin       BMI  DiabetesPedigreeFunction  \\\n",
       "Pregnancies              -0.073535  0.017683                 -0.033523   \n",
       "Glucose                   0.331357  0.221071                  0.137337   \n",
       "BloodPressure             0.088933  0.281805                  0.041265   \n",
       "SkinThickness             0.436783  0.392573                  0.183928   \n",
       "Insulin                   1.000000  0.197859                  0.185071   \n",
       "BMI                       0.197859  1.000000                  0.140647   \n",
       "DiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \n",
       "Age                      -0.042163  0.036242                  0.033561   \n",
       "Outcome                   0.130548  0.292695                  0.173844   \n",
       "\n",
       "                               Age   Outcome  \n",
       "Pregnancies               0.544341  0.221898  \n",
       "Glucose                   0.263514  0.466581  \n",
       "BloodPressure             0.239528  0.065068  \n",
       "SkinThickness            -0.113970  0.074752  \n",
       "Insulin                  -0.042163  0.130548  \n",
       "BMI                       0.036242  0.292695  \n",
       "DiabetesPedigreeFunction  0.033561  0.173844  \n",
       "Age                       1.000000  0.238356  \n",
       "Outcome                   0.238356  1.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 695
    },
    "executionInfo": {
     "elapsed": 1767,
     "status": "ok",
     "timestamp": 1710913309156,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "4zoQJ2l0cD0A",
    "outputId": "dbd2f5d2-1e45-43f2-dec4-5b97769ebae4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 10))\n",
    "sns.heatmap(df.corr(), annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C95RFkoMcWWR"
   },
   "source": [
    "Definining features and target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1710913309157,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "4Sk2RJMxcKrZ"
   },
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "Y=df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 31,
     "status": "ok",
     "timestamp": 1710913309159,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "LuETiVolcd64",
    "outputId": "b8fc2025-01cc-4d05-c174-d7705e42fd73"
   },
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "0                       0.627   50  \n",
       "1                       0.351   31  \n",
       "2                       0.672   32  \n",
       "3                       0.167   21  \n",
       "4                       2.288   33  \n",
       "..                        ...  ...  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  \n",
       "\n",
       "[768 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1710913309162,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "js3AGWD6chIx"
   },
   "outputs": [],
   "source": [
    "Y=Y.values.reshape(-1,1)\n",
    "#Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LQIIwBZ2cnvN"
   },
   "source": [
    "# Data Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1710913309163,
     "user": {
      "displayName": "Chinmoy Talukdar",
      "userId": "14881133516431433471"
     },
     "user_tz": -330
    },
    "id": "FEs3IlopclU3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 8)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=MinMaxScaler()\n",
    "X=scaler.fit_transform(X)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(691, 8) (77, 8) (691, 1) (77, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0BmC5yROcxTp"
   },
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6024 - loss: 0.6720\n",
      "Epoch 2/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6730 - loss: 0.6351\n",
      "Epoch 3/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6973 - loss: 0.5828\n",
      "Epoch 4/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6866 - loss: 0.5854\n",
      "Epoch 5/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7391 - loss: 0.5506\n",
      "Epoch 6/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7534 - loss: 0.5194\n",
      "Epoch 7/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7352 - loss: 0.4950\n",
      "Epoch 8/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7673 - loss: 0.4820\n",
      "Epoch 9/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7696 - loss: 0.4711\n",
      "Epoch 10/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7494 - loss: 0.4862\n",
      "Epoch 11/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7878 - loss: 0.4577\n",
      "Epoch 12/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7735 - loss: 0.4779\n",
      "Epoch 13/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8025 - loss: 0.4325\n",
      "Epoch 14/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7688 - loss: 0.4549\n",
      "Epoch 15/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8018 - loss: 0.4466\n",
      "Epoch 16/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7735 - loss: 0.4640\n",
      "Epoch 17/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7762 - loss: 0.4640\n",
      "Epoch 18/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7717 - loss: 0.4540\n",
      "Epoch 19/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7984 - loss: 0.4383\n",
      "Epoch 20/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7974 - loss: 0.4496\n",
      "Epoch 21/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7917 - loss: 0.4299\n",
      "Epoch 22/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8002 - loss: 0.4177\n",
      "Epoch 23/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8059 - loss: 0.4155\n",
      "Epoch 24/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8099 - loss: 0.4184\n",
      "Epoch 25/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7692 - loss: 0.4861\n",
      "Epoch 26/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7759 - loss: 0.4506\n",
      "Epoch 27/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7977 - loss: 0.4205\n",
      "Epoch 28/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7952 - loss: 0.4411\n",
      "Epoch 29/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8050 - loss: 0.4150\n",
      "Epoch 30/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7885 - loss: 0.4716\n",
      "Epoch 31/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8323 - loss: 0.3712\n",
      "Epoch 32/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7939 - loss: 0.4381\n",
      "Epoch 33/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8030 - loss: 0.4288\n",
      "Epoch 34/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7880 - loss: 0.4224\n",
      "Epoch 35/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7980 - loss: 0.4291\n",
      "Epoch 36/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8303 - loss: 0.4113\n",
      "Epoch 37/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8187 - loss: 0.4002\n",
      "Epoch 38/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8057 - loss: 0.4230\n",
      "Epoch 39/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8033 - loss: 0.4527\n",
      "Epoch 40/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8131 - loss: 0.4003\n",
      "Epoch 41/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8073 - loss: 0.4173\n",
      "Epoch 42/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8087 - loss: 0.4496\n",
      "Epoch 43/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8076 - loss: 0.4141\n",
      "Epoch 44/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8120 - loss: 0.3946\n",
      "Epoch 45/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8322 - loss: 0.4147\n",
      "Epoch 46/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7943 - loss: 0.4166\n",
      "Epoch 47/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7926 - loss: 0.4305\n",
      "Epoch 48/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7723 - loss: 0.4782\n",
      "Epoch 49/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8396 - loss: 0.3996\n",
      "Epoch 50/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8278 - loss: 0.3942\n",
      "Epoch 51/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7995 - loss: 0.4173\n",
      "Epoch 52/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8237 - loss: 0.3776\n",
      "Epoch 53/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8188 - loss: 0.3948\n",
      "Epoch 54/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8224 - loss: 0.4010\n",
      "Epoch 55/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8239 - loss: 0.3856\n",
      "Epoch 56/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8191 - loss: 0.4094\n",
      "Epoch 57/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8321 - loss: 0.3693\n",
      "Epoch 58/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8260 - loss: 0.3930\n",
      "Epoch 59/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8410 - loss: 0.3626\n",
      "Epoch 60/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8239 - loss: 0.3889\n",
      "Epoch 61/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8397 - loss: 0.3829\n",
      "Epoch 62/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8350 - loss: 0.3816\n",
      "Epoch 63/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7977 - loss: 0.4155\n",
      "Epoch 64/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8246 - loss: 0.3600\n",
      "Epoch 65/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8300 - loss: 0.3720\n",
      "Epoch 66/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8286 - loss: 0.3760\n",
      "Epoch 67/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8217 - loss: 0.4004\n",
      "Epoch 68/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8281 - loss: 0.3975\n",
      "Epoch 69/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8065 - loss: 0.4237\n",
      "Epoch 70/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8343 - loss: 0.3493\n",
      "Epoch 71/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8341 - loss: 0.3751\n",
      "Epoch 72/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8412 - loss: 0.3701\n",
      "Epoch 73/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8656 - loss: 0.3303\n",
      "Epoch 74/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8424 - loss: 0.3596\n",
      "Epoch 75/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8280 - loss: 0.3704\n",
      "Epoch 76/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8570 - loss: 0.3537\n",
      "Epoch 77/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8424 - loss: 0.3411\n",
      "Epoch 78/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8493 - loss: 0.3247\n",
      "Epoch 79/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8613 - loss: 0.3430\n",
      "Epoch 80/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8413 - loss: 0.3381\n",
      "Epoch 81/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8365 - loss: 0.3745\n",
      "Epoch 82/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8425 - loss: 0.3556\n",
      "Epoch 83/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8537 - loss: 0.3285\n",
      "Epoch 84/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8434 - loss: 0.3577\n",
      "Epoch 85/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8540 - loss: 0.3193\n",
      "Epoch 86/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8664 - loss: 0.3098\n",
      "Epoch 87/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8597 - loss: 0.3221\n",
      "Epoch 88/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8541 - loss: 0.3202\n",
      "Epoch 89/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8742 - loss: 0.3102\n",
      "Epoch 90/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8831 - loss: 0.3039\n",
      "Epoch 91/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8591 - loss: 0.3264\n",
      "Epoch 92/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8400 - loss: 0.3080\n",
      "Epoch 93/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8677 - loss: 0.3185\n",
      "Epoch 94/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8708 - loss: 0.2997\n",
      "Epoch 95/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8897 - loss: 0.2845\n",
      "Epoch 96/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8905 - loss: 0.2602\n",
      "Epoch 97/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8670 - loss: 0.3108\n",
      "Epoch 98/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8760 - loss: 0.3056\n",
      "Epoch 99/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8840 - loss: 0.2696\n",
      "Epoch 100/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8731 - loss: 0.2891\n",
      "Epoch 101/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8886 - loss: 0.2484\n",
      "Epoch 102/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8847 - loss: 0.2401\n",
      "Epoch 103/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8732 - loss: 0.3233\n",
      "Epoch 104/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8665 - loss: 0.2870\n",
      "Epoch 105/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8751 - loss: 0.2686\n",
      "Epoch 106/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8566 - loss: 0.2918\n",
      "Epoch 107/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8877 - loss: 0.2654\n",
      "Epoch 108/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8710 - loss: 0.2610\n",
      "Epoch 109/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8930 - loss: 0.2687\n",
      "Epoch 110/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8778 - loss: 0.2609\n",
      "Epoch 111/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8917 - loss: 0.2502\n",
      "Epoch 112/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8812 - loss: 0.2429\n",
      "Epoch 113/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8959 - loss: 0.2530\n",
      "Epoch 114/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9083 - loss: 0.2282\n",
      "Epoch 115/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8809 - loss: 0.2408\n",
      "Epoch 116/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9017 - loss: 0.2119\n",
      "Epoch 117/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8851 - loss: 0.2362\n",
      "Epoch 118/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8887 - loss: 0.2478\n",
      "Epoch 119/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9254 - loss: 0.2004\n",
      "Epoch 120/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9152 - loss: 0.2003\n",
      "Epoch 121/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9133 - loss: 0.1994\n",
      "Epoch 122/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9086 - loss: 0.1959\n",
      "Epoch 123/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8947 - loss: 0.2141\n",
      "Epoch 124/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8992 - loss: 0.2165\n",
      "Epoch 125/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9433 - loss: 0.1596\n",
      "Epoch 126/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9173 - loss: 0.1967\n",
      "Epoch 127/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9073 - loss: 0.1990\n",
      "Epoch 128/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9256 - loss: 0.1834\n",
      "Epoch 129/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8676 - loss: 0.2695\n",
      "Epoch 130/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9336 - loss: 0.1691\n",
      "Epoch 131/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9223 - loss: 0.1798\n",
      "Epoch 132/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9438 - loss: 0.1635\n",
      "Epoch 133/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9434 - loss: 0.1251\n",
      "Epoch 134/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9504 - loss: 0.1296\n",
      "Epoch 135/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9202 - loss: 0.1695\n",
      "Epoch 136/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9317 - loss: 0.1585\n",
      "Epoch 137/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9311 - loss: 0.1570\n",
      "Epoch 138/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9163 - loss: 0.1816\n",
      "Epoch 139/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9315 - loss: 0.1751\n",
      "Epoch 140/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9354 - loss: 0.1518\n",
      "Epoch 141/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9582 - loss: 0.1194\n",
      "Epoch 142/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9643 - loss: 0.1034\n",
      "Epoch 143/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9502 - loss: 0.1223\n",
      "Epoch 144/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9565 - loss: 0.1125\n",
      "Epoch 145/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9181 - loss: 0.1681\n",
      "Epoch 146/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9398 - loss: 0.1493\n",
      "Epoch 147/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9384 - loss: 0.1342\n",
      "Epoch 148/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8972 - loss: 0.2511\n",
      "Epoch 149/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9274 - loss: 0.1794\n",
      "Epoch 150/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9404 - loss: 0.1213\n",
      "Epoch 151/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9580 - loss: 0.1248\n",
      "Epoch 152/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.0975\n",
      "Epoch 153/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9656 - loss: 0.1001\n",
      "Epoch 154/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9570 - loss: 0.0938\n",
      "Epoch 155/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9631 - loss: 0.0969\n",
      "Epoch 156/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9616 - loss: 0.1016\n",
      "Epoch 157/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9521 - loss: 0.1457\n",
      "Epoch 158/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9489 - loss: 0.1107\n",
      "Epoch 159/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9751 - loss: 0.0894\n",
      "Epoch 160/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9764 - loss: 0.0823\n",
      "Epoch 161/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9845 - loss: 0.0625\n",
      "Epoch 162/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9566 - loss: 0.1168\n",
      "Epoch 163/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9627 - loss: 0.0870\n",
      "Epoch 164/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9693 - loss: 0.0856\n",
      "Epoch 165/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9701 - loss: 0.0777\n",
      "Epoch 166/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9599 - loss: 0.1004\n",
      "Epoch 167/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9524 - loss: 0.1092\n",
      "Epoch 168/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9497 - loss: 0.1399\n",
      "Epoch 169/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9283 - loss: 0.1777\n",
      "Epoch 170/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9603 - loss: 0.1074\n",
      "Epoch 171/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9762 - loss: 0.0768\n",
      "Epoch 172/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9657 - loss: 0.0839\n",
      "Epoch 173/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9836 - loss: 0.0660\n",
      "Epoch 174/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9836 - loss: 0.0505\n",
      "Epoch 175/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9791 - loss: 0.0653\n",
      "Epoch 176/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9885 - loss: 0.0478\n",
      "Epoch 177/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9722 - loss: 0.0928\n",
      "Epoch 178/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9671 - loss: 0.0841\n",
      "Epoch 179/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9471 - loss: 0.1060\n",
      "Epoch 180/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9406 - loss: 0.1148\n",
      "Epoch 181/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9716 - loss: 0.0677\n",
      "Epoch 182/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9819 - loss: 0.0578\n",
      "Epoch 183/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9516 - loss: 0.1161\n",
      "Epoch 184/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9764 - loss: 0.0764\n",
      "Epoch 185/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9781 - loss: 0.0589\n",
      "Epoch 186/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9858 - loss: 0.0400\n",
      "Epoch 187/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9936 - loss: 0.0302\n",
      "Epoch 188/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9743 - loss: 0.0724\n",
      "Epoch 189/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9724 - loss: 0.0761\n",
      "Epoch 190/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.0709\n",
      "Epoch 191/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9862 - loss: 0.0517\n",
      "Epoch 192/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9805 - loss: 0.0430\n",
      "Epoch 193/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9845 - loss: 0.0519\n",
      "Epoch 194/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9770 - loss: 0.0643\n",
      "Epoch 195/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9829 - loss: 0.0443\n",
      "Epoch 196/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9898 - loss: 0.0328\n",
      "Epoch 197/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9938 - loss: 0.0337\n",
      "Epoch 198/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9967 - loss: 0.0210\n",
      "Epoch 199/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9955 - loss: 0.0253\n",
      "Epoch 200/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9932 - loss: 0.0298\n",
      "Epoch 201/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9925 - loss: 0.0346\n",
      "Epoch 202/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9911 - loss: 0.0277\n",
      "Epoch 203/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9935 - loss: 0.0227\n",
      "Epoch 204/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9848 - loss: 0.0479\n",
      "Epoch 205/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9265 - loss: 0.3111\n",
      "Epoch 206/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9315 - loss: 0.1794\n",
      "Epoch 207/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9642 - loss: 0.0998\n",
      "Epoch 208/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9653 - loss: 0.0793\n",
      "Epoch 209/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9874 - loss: 0.0651\n",
      "Epoch 210/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9863 - loss: 0.0391\n",
      "Epoch 211/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9313 - loss: 0.2030\n",
      "Epoch 212/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9547 - loss: 0.1102\n",
      "Epoch 213/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9785 - loss: 0.0593\n",
      "Epoch 214/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9873 - loss: 0.0382\n",
      "Epoch 215/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9928 - loss: 0.0256\n",
      "Epoch 216/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9878 - loss: 0.0349\n",
      "Epoch 217/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9896 - loss: 0.0287\n",
      "Epoch 218/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9917 - loss: 0.0305\n",
      "Epoch 219/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9900 - loss: 0.0214\n",
      "Epoch 220/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9952 - loss: 0.0226\n",
      "Epoch 221/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9982 - loss: 0.0138\n",
      "Epoch 222/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9973 - loss: 0.0175\n",
      "Epoch 223/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9950 - loss: 0.0168   \n",
      "Epoch 224/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9875 - loss: 0.0443\n",
      "Epoch 225/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0186   \n",
      "Epoch 226/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9955 - loss: 0.0214\n",
      "Epoch 227/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0191   \n",
      "Epoch 228/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9924 - loss: 0.0185\n",
      "Epoch 229/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9899 - loss: 0.0192\n",
      "Epoch 230/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0112\n",
      "Epoch 231/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0101\n",
      "Epoch 232/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9785 - loss: 0.0591\n",
      "Epoch 233/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8356 - loss: 0.4694\n",
      "Epoch 234/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9392 - loss: 0.1514\n",
      "Epoch 235/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9663 - loss: 0.0835\n",
      "Epoch 236/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9966 - loss: 0.0355\n",
      "Epoch 237/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9961 - loss: 0.0283\n",
      "Epoch 238/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0228\n",
      "Epoch 239/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9964 - loss: 0.0224\n",
      "Epoch 240/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9963 - loss: 0.0205\n",
      "Epoch 241/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9945 - loss: 0.0202\n",
      "Epoch 242/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9973 - loss: 0.0142\n",
      "Epoch 243/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9960 - loss: 0.0153\n",
      "Epoch 244/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9898 - loss: 0.0297   \n",
      "Epoch 245/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9890 - loss: 0.0334\n",
      "Epoch 246/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9736 - loss: 0.0507\n",
      "Epoch 247/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9715 - loss: 0.0454\n",
      "Epoch 248/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9918 - loss: 0.0312\n",
      "Epoch 249/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9802 - loss: 0.0427\n",
      "Epoch 250/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9468 - loss: 0.1327\n",
      "Epoch 251/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9243 - loss: 0.2416\n",
      "Epoch 252/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9368 - loss: 0.1401\n",
      "Epoch 253/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9648 - loss: 0.0942\n",
      "Epoch 254/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9924 - loss: 0.0350\n",
      "Epoch 255/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0211\n",
      "Epoch 256/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9959 - loss: 0.0146\n",
      "Epoch 257/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9970 - loss: 0.0162\n",
      "Epoch 258/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9958 - loss: 0.0170\n",
      "Epoch 259/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9986 - loss: 0.0118\n",
      "Epoch 260/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0104   \n",
      "Epoch 261/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9946 - loss: 0.0116\n",
      "Epoch 262/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9986 - loss: 0.0079\n",
      "Epoch 263/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0069\n",
      "Epoch 264/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0088\n",
      "Epoch 265/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9979 - loss: 0.0137\n",
      "Epoch 266/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9899 - loss: 0.0262\n",
      "Epoch 267/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9899 - loss: 0.0294\n",
      "Epoch 268/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9499 - loss: 0.1556\n",
      "Epoch 269/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9505 - loss: 0.1496\n",
      "Epoch 270/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9285 - loss: 0.1778\n",
      "Epoch 271/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9621 - loss: 0.0724\n",
      "Epoch 272/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9939 - loss: 0.0237\n",
      "Epoch 273/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9941 - loss: 0.0147\n",
      "Epoch 274/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9937 - loss: 0.0188\n",
      "Epoch 275/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0137\n",
      "Epoch 276/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9977 - loss: 0.0127\n",
      "Epoch 277/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9944 - loss: 0.0129\n",
      "Epoch 278/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0091\n",
      "Epoch 279/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9969 - loss: 0.0098\n",
      "Epoch 280/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0119\n",
      "Epoch 281/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9989 - loss: 0.0082\n",
      "Epoch 282/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9962 - loss: 0.0134\n",
      "Epoch 283/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0070\n",
      "Epoch 284/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9960 - loss: 0.0105\n",
      "Epoch 285/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0072\n",
      "Epoch 286/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0068\n",
      "Epoch 287/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9979 - loss: 0.0100\n",
      "Epoch 288/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0061\n",
      "Epoch 289/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9980 - loss: 0.0089\n",
      "Epoch 290/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9970 - loss: 0.0146   \n",
      "Epoch 291/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0057\n",
      "Epoch 292/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9937 - loss: 0.0262\n",
      "Epoch 293/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9845 - loss: 0.0504\n",
      "Epoch 294/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9777 - loss: 0.0679\n",
      "Epoch 295/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9399 - loss: 0.1904\n",
      "Epoch 296/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9695 - loss: 0.0769\n",
      "Epoch 297/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9977 - loss: 0.0257\n",
      "Epoch 298/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9913 - loss: 0.0298\n",
      "Epoch 299/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9909 - loss: 0.0205\n",
      "Epoch 300/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0111\n",
      "Epoch 301/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9965 - loss: 0.0088\n",
      "Epoch 302/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0062\n",
      "Epoch 303/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0061\n",
      "Epoch 304/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0079\n",
      "Epoch 305/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0050\n",
      "Epoch 306/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0046\n",
      "Epoch 307/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0050\n",
      "Epoch 308/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0033\n",
      "Epoch 309/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9951 - loss: 0.0107\n",
      "Epoch 310/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9994 - loss: 0.0062\n",
      "Epoch 311/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9989 - loss: 0.0042\n",
      "Epoch 312/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0050\n",
      "Epoch 313/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0034   \n",
      "Epoch 314/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0061   \n",
      "Epoch 315/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0027   \n",
      "Epoch 316/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9966 - loss: 0.0149\n",
      "Epoch 317/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9985 - loss: 0.0091\n",
      "Epoch 318/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9898 - loss: 0.0451   \n",
      "Epoch 319/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.1178\n",
      "Epoch 320/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9364 - loss: 0.1861\n",
      "Epoch 321/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9713 - loss: 0.0908\n",
      "Epoch 322/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9572 - loss: 0.0964\n",
      "Epoch 323/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9738 - loss: 0.0576\n",
      "Epoch 324/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9623 - loss: 0.1197\n",
      "Epoch 325/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9933 - loss: 0.0246\n",
      "Epoch 326/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9973 - loss: 0.0133\n",
      "Epoch 327/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0121\n",
      "Epoch 328/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0070\n",
      "Epoch 329/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9990 - loss: 0.0060\n",
      "Epoch 330/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9947 - loss: 0.0103\n",
      "Epoch 331/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0043   \n",
      "Epoch 332/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0046\n",
      "Epoch 333/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9982 - loss: 0.0071\n",
      "Epoch 334/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0058   \n",
      "Epoch 335/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9934 - loss: 0.0121\n",
      "Epoch 336/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9970 - loss: 0.0080\n",
      "Epoch 337/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9941 - loss: 0.0104\n",
      "Epoch 338/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0034   \n",
      "Epoch 339/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0046\n",
      "Epoch 340/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0029\n",
      "Epoch 341/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0042   \n",
      "Epoch 342/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0023\n",
      "Epoch 343/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0045\n",
      "Epoch 344/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0027   \n",
      "Epoch 345/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0025\n",
      "Epoch 346/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0028\n",
      "Epoch 347/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0028   \n",
      "Epoch 348/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0027   \n",
      "Epoch 349/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0033\n",
      "Epoch 350/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0032\n",
      "Epoch 351/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9958 - loss: 0.0137\n",
      "Epoch 352/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9367 - loss: 0.1972\n",
      "Epoch 353/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8879 - loss: 0.3542\n",
      "Epoch 354/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9746 - loss: 0.0661\n",
      "Epoch 355/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0330\n",
      "Epoch 356/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0195\n",
      "Epoch 357/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0093\n",
      "Epoch 358/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0088\n",
      "Epoch 359/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0104\n",
      "Epoch 360/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0101\n",
      "Epoch 361/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9982 - loss: 0.0081\n",
      "Epoch 362/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0082\n",
      "Epoch 363/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0040\n",
      "Epoch 364/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0040\n",
      "Epoch 365/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9987 - loss: 0.0060\n",
      "Epoch 366/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9920 - loss: 0.0281\n",
      "Epoch 367/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9676 - loss: 0.0919\n",
      "Epoch 368/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9678 - loss: 0.1159\n",
      "Epoch 369/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9617 - loss: 0.1218\n",
      "Epoch 370/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9903 - loss: 0.0322\n",
      "Epoch 371/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9824 - loss: 0.0368\n",
      "Epoch 372/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9870 - loss: 0.0216\n",
      "Epoch 373/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0063\n",
      "Epoch 374/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9989 - loss: 0.0042\n",
      "Epoch 375/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0038\n",
      "Epoch 376/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0033   \n",
      "Epoch 377/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9985 - loss: 0.0043\n",
      "Epoch 378/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9986 - loss: 0.0088\n",
      "Epoch 379/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0057\n",
      "Epoch 380/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9996 - loss: 0.0052\n",
      "Epoch 381/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9951 - loss: 0.0081   \n",
      "Epoch 382/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0022\n",
      "Epoch 383/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9980 - loss: 0.0098\n",
      "Epoch 384/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9946 - loss: 0.0271\n",
      "Epoch 385/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9837 - loss: 0.0506\n",
      "Epoch 386/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7515 - loss: 1.0639\n",
      "Epoch 387/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8771 - loss: 0.2535\n",
      "Epoch 388/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9670 - loss: 0.1103\n",
      "Epoch 389/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9787 - loss: 0.0822\n",
      "Epoch 390/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9910 - loss: 0.0438\n",
      "Epoch 391/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9775 - loss: 0.0570\n",
      "Epoch 392/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9926 - loss: 0.0288\n",
      "Epoch 393/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9934 - loss: 0.0255\n",
      "Epoch 394/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9949 - loss: 0.0231\n",
      "Epoch 395/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0138\n",
      "Epoch 396/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9967 - loss: 0.0157\n",
      "Epoch 397/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0090\n",
      "Epoch 398/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9986 - loss: 0.0098\n",
      "Epoch 399/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0071\n",
      "Epoch 400/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0084\n",
      "Epoch 401/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0073\n",
      "Epoch 402/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9862 - loss: 0.0380\n",
      "Epoch 403/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9677 - loss: 0.1183\n",
      "Epoch 404/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9604 - loss: 0.1010\n",
      "Epoch 405/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9930 - loss: 0.0266\n",
      "Epoch 406/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0124\n",
      "Epoch 407/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0107\n",
      "Epoch 408/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0071\n",
      "Epoch 409/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0079\n",
      "Epoch 410/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0048   \n",
      "Epoch 411/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0042\n",
      "Epoch 412/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 413/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0031   \n",
      "Epoch 414/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9939 - loss: 0.0082\n",
      "Epoch 415/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 416/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 417/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0061\n",
      "Epoch 418/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 419/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0036\n",
      "Epoch 420/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0025   \n",
      "Epoch 421/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9985 - loss: 0.0061   \n",
      "Epoch 422/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 423/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0046\n",
      "Epoch 424/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9990 - loss: 0.0042\n",
      "Epoch 425/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9996 - loss: 0.0027\n",
      "Epoch 426/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9741 - loss: 0.0805\n",
      "Epoch 427/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9537 - loss: 0.1538\n",
      "Epoch 428/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9669 - loss: 0.0987\n",
      "Epoch 429/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9873 - loss: 0.0338\n",
      "Epoch 430/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9956 - loss: 0.0191\n",
      "Epoch 431/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9971 - loss: 0.0098\n",
      "Epoch 432/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0080\n",
      "Epoch 433/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9958 - loss: 0.0071   \n",
      "Epoch 434/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0058\n",
      "Epoch 435/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0060\n",
      "Epoch 436/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0058\n",
      "Epoch 437/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9994 - loss: 0.0033   \n",
      "Epoch 438/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 439/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0020   \n",
      "Epoch 440/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0025   \n",
      "Epoch 441/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9996 - loss: 0.0017   \n",
      "Epoch 442/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9980 - loss: 0.0035   \n",
      "Epoch 443/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 444/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0018   \n",
      "Epoch 445/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9980 - loss: 0.0039\n",
      "Epoch 446/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0011    \n",
      "Epoch 447/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9968 - loss: 0.0151\n",
      "Epoch 448/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9897 - loss: 0.0401\n",
      "Epoch 449/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9821 - loss: 0.0582   \n",
      "Epoch 450/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8987 - loss: 0.3277\n",
      "Epoch 451/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9530 - loss: 0.1287\n",
      "Epoch 452/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9971 - loss: 0.0375\n",
      "Epoch 453/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9872 - loss: 0.0344\n",
      "Epoch 454/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0150\n",
      "Epoch 455/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9988 - loss: 0.0109\n",
      "Epoch 456/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0083\n",
      "Epoch 457/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0060   \n",
      "Epoch 458/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0063   \n",
      "Epoch 459/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9974 - loss: 0.0060\n",
      "Epoch 460/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9975 - loss: 0.0095\n",
      "Epoch 461/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0062\n",
      "Epoch 462/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0041\n",
      "Epoch 463/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9994 - loss: 0.0034\n",
      "Epoch 464/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9953 - loss: 0.0170\n",
      "Epoch 465/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9956 - loss: 0.0096   \n",
      "Epoch 466/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9923 - loss: 0.0306\n",
      "Epoch 467/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9759 - loss: 0.0633\n",
      "Epoch 468/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9807 - loss: 0.0585\n",
      "Epoch 469/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9874 - loss: 0.0337\n",
      "Epoch 470/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9820 - loss: 0.0584\n",
      "Epoch 471/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9505 - loss: 0.1272\n",
      "Epoch 472/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9634 - loss: 0.1103\n",
      "Epoch 473/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0179\n",
      "Epoch 474/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0102\n",
      "Epoch 475/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0065\n",
      "Epoch 476/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0053\n",
      "Epoch 477/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0035   \n",
      "Epoch 478/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0034\n",
      "Epoch 479/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0043\n",
      "Epoch 480/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0051\n",
      "Epoch 481/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0023\n",
      "Epoch 482/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0039   \n",
      "Epoch 483/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0022\n",
      "Epoch 484/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0019   \n",
      "Epoch 485/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9990 - loss: 0.0033   \n",
      "Epoch 486/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9989 - loss: 0.0032   \n",
      "Epoch 487/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0025\n",
      "Epoch 488/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0019   \n",
      "Epoch 489/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 490/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 491/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0012\n",
      "Epoch 492/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0021   \n",
      "Epoch 493/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0012    \n",
      "Epoch 494/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0021   \n",
      "Epoch 495/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0031\n",
      "Epoch 496/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0026   \n",
      "Epoch 497/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9996 - loss: 0.0019   \n",
      "Epoch 498/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0012    \n",
      "Epoch 499/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0064\n",
      "Epoch 500/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0021    \n",
      "Epoch 501/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9914 - loss: 0.0588   \n",
      "Epoch 502/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9571 - loss: 0.1328\n",
      "Epoch 503/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9387 - loss: 0.2164\n",
      "Epoch 504/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9931 - loss: 0.0435\n",
      "Epoch 505/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9930 - loss: 0.0235\n",
      "Epoch 506/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9935 - loss: 0.0152\n",
      "Epoch 507/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0061\n",
      "Epoch 508/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9998 - loss: 0.0074\n",
      "Epoch 509/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0069\n",
      "Epoch 510/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0036\n",
      "Epoch 511/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0029\n",
      "Epoch 512/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0063   \n",
      "Epoch 513/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9984 - loss: 0.0034\n",
      "Epoch 514/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0029   \n",
      "Epoch 515/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 516/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9968 - loss: 0.0059\n",
      "Epoch 517/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0027   \n",
      "Epoch 518/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0038\n",
      "Epoch 519/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0016   \n",
      "Epoch 520/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9987 - loss: 0.0028   \n",
      "Epoch 521/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9979 - loss: 0.0035\n",
      "Epoch 522/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9977 - loss: 0.0041   \n",
      "Epoch 523/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0013   \n",
      "Epoch 524/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0014\n",
      "Epoch 525/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 526/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9999 - loss: 0.0011    \n",
      "Epoch 527/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0014   \n",
      "Epoch 528/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 529/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0017   \n",
      "Epoch 530/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0012    \n",
      "Epoch 531/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9964 - loss: 0.0035\n",
      "Epoch 532/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0014    \n",
      "Epoch 533/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9967 - loss: 0.0068   \n",
      "Epoch 534/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9859 - loss: 0.0575   \n",
      "Epoch 535/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8630 - loss: 0.5278\n",
      "Epoch 536/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8945 - loss: 0.2473\n",
      "Epoch 537/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9738 - loss: 0.0936\n",
      "Epoch 538/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9935 - loss: 0.0493\n",
      "Epoch 539/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9969 - loss: 0.0176\n",
      "Epoch 540/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0110\n",
      "Epoch 541/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9991 - loss: 0.0108\n",
      "Epoch 542/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9973 - loss: 0.0127\n",
      "Epoch 543/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0061\n",
      "Epoch 544/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9987 - loss: 0.0055\n",
      "Epoch 545/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0044\n",
      "Epoch 546/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0049\n",
      "Epoch 547/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0037\n",
      "Epoch 548/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9981 - loss: 0.0052\n",
      "Epoch 549/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0028   \n",
      "Epoch 550/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 551/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0036   \n",
      "Epoch 552/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0037   \n",
      "Epoch 553/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0041\n",
      "Epoch 554/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 555/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9990 - loss: 0.0035\n",
      "Epoch 556/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0025\n",
      "Epoch 557/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 558/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0020   \n",
      "Epoch 559/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 560/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016   \n",
      "Epoch 561/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0022   \n",
      "Epoch 562/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9978 - loss: 0.0024   \n",
      "Epoch 563/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0022   \n",
      "Epoch 564/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9990 - loss: 0.0025   \n",
      "Epoch 565/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016   \n",
      "Epoch 566/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0012    \n",
      "Epoch 567/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 568/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0026   \n",
      "Epoch 569/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9996 - loss: 0.0012    \n",
      "Epoch 570/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 571/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9992 - loss: 0.0039\n",
      "Epoch 572/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9997 - loss: 0.0026   \n",
      "Epoch 573/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9931 - loss: 0.0187\n",
      "Epoch 574/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9280 - loss: 0.3059\n",
      "Epoch 575/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9146 - loss: 0.1950\n",
      "Epoch 576/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9555 - loss: 0.0925\n",
      "Epoch 577/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9909 - loss: 0.0305\n",
      "Epoch 578/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9894 - loss: 0.0351\n",
      "Epoch 579/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9891 - loss: 0.0307\n",
      "Epoch 580/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9944 - loss: 0.0170\n",
      "Epoch 581/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0062\n",
      "Epoch 582/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0041\n",
      "Epoch 583/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0033\n",
      "Epoch 584/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0053   \n",
      "Epoch 585/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 586/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9993 - loss: 0.0024   \n",
      "Epoch 587/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9990 - loss: 0.0046\n",
      "Epoch 588/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0023   \n",
      "Epoch 589/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0022   \n",
      "Epoch 590/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0033\n",
      "Epoch 591/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9995 - loss: 0.0027\n",
      "Epoch 592/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0016   \n",
      "Epoch 593/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0014    \n",
      "Epoch 594/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0011    \n",
      "Epoch 595/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0019   \n",
      "Epoch 596/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0017   \n",
      "Epoch 597/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0015\n",
      "Epoch 598/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 9.7838e-04\n",
      "Epoch 599/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9983 - loss: 0.0025   \n",
      "Epoch 600/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0012    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x252e07bca90>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv1D, Flatten, MaxPooling1D, Dropout\n",
    "\n",
    "# Define the model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(filters=32, kernel_size=2, padding='same', activation='relu', input_shape=(X_train.shape[1], 1)))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Conv1D(filters=64, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Conv1D(filters=128, kernel_size=2, padding='same', activation='relu'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, Y_train, epochs=600, batch_size=16)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.97       500\n",
      "           1       0.95      0.95      0.95       268\n",
      "\n",
      "    accuracy                           0.97       768\n",
      "   macro avg       0.96      0.96      0.96       768\n",
      "weighted avg       0.97      0.97      0.97       768\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Y_pred = model.predict(X).round()\n",
    "cm=confusion_matrix(Y, Y_pred)\n",
    "\n",
    "print(classification_report(Y, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='YlGn')\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('True Values')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "# Binarize the output\n",
    "Y = label_binarize(Y, classes=[0, 1])\n",
    "\n",
    "# Get the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(Y.ravel(), Y_pred.ravel())\n",
    "\n",
    "# Calculate the AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "lw = 3\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.grid()\n",
    "plt.title('Receiver operating characteristic Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 996us/step\n",
      "[0.01707554]\n"
     ]
    }
   ],
   "source": [
    "sem = np.std(model.predict(X), axis=0) / np.sqrt(len(model.predict(X)))\n",
    "\n",
    "print(sem)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step - accuracy: 0.6214 - loss: 0.6718\n",
      "Epoch 2/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6613 - loss: 0.6497\n",
      "Epoch 3/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6700 - loss: 0.5994\n",
      "Epoch 4/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7039 - loss: 0.5820\n",
      "Epoch 5/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6954 - loss: 0.6280\n",
      "Epoch 6/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7135 - loss: 0.5679\n",
      "Epoch 7/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7606 - loss: 0.5309\n",
      "Epoch 8/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7225 - loss: 0.5599\n",
      "Epoch 9/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6873 - loss: 0.5940\n",
      "Epoch 10/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6999 - loss: 0.5779\n",
      "Epoch 11/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7175 - loss: 0.5526\n",
      "Epoch 12/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7203 - loss: 0.5728\n",
      "Epoch 13/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6740 - loss: 0.5986\n",
      "Epoch 14/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7325 - loss: 0.5425\n",
      "Epoch 15/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6980 - loss: 0.5847\n",
      "Epoch 16/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7206 - loss: 0.5539\n",
      "Epoch 17/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7274 - loss: 0.5475\n",
      "Epoch 18/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7458 - loss: 0.5484\n",
      "Epoch 19/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7099 - loss: 0.5809\n",
      "Epoch 20/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6988 - loss: 0.5712\n",
      "Epoch 21/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7456 - loss: 0.5499\n",
      "Epoch 22/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7438 - loss: 0.5514\n",
      "Epoch 23/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7318 - loss: 0.5637\n",
      "Epoch 24/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7432 - loss: 0.5214\n",
      "Epoch 25/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7415 - loss: 0.5318\n",
      "Epoch 26/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7090 - loss: 0.5687\n",
      "Epoch 27/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7267 - loss: 0.5617\n",
      "Epoch 28/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7379 - loss: 0.5404\n",
      "Epoch 29/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7008 - loss: 0.5689\n",
      "Epoch 30/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7658 - loss: 0.5279\n",
      "Epoch 31/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7270 - loss: 0.5531\n",
      "Epoch 32/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7070 - loss: 0.5568\n",
      "Epoch 33/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7427 - loss: 0.5371\n",
      "Epoch 34/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6966 - loss: 0.5749\n",
      "Epoch 35/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7285 - loss: 0.5483\n",
      "Epoch 36/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7524 - loss: 0.5226\n",
      "Epoch 37/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7632 - loss: 0.5092\n",
      "Epoch 38/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7580 - loss: 0.5188\n",
      "Epoch 39/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7598 - loss: 0.5192\n",
      "Epoch 40/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7415 - loss: 0.5189\n",
      "Epoch 41/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7375 - loss: 0.5272\n",
      "Epoch 42/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7618 - loss: 0.4898\n",
      "Epoch 43/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7616 - loss: 0.5076\n",
      "Epoch 44/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7494 - loss: 0.4987\n",
      "Epoch 45/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7396 - loss: 0.5014\n",
      "Epoch 46/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7728 - loss: 0.4858\n",
      "Epoch 47/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7735 - loss: 0.4805\n",
      "Epoch 48/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7748 - loss: 0.5133\n",
      "Epoch 49/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7679 - loss: 0.5005\n",
      "Epoch 50/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7792 - loss: 0.4878\n",
      "Epoch 51/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7431 - loss: 0.5042\n",
      "Epoch 52/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7856 - loss: 0.4549\n",
      "Epoch 53/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7713 - loss: 0.4811\n",
      "Epoch 54/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7078 - loss: 0.5349\n",
      "Epoch 55/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7710 - loss: 0.4602\n",
      "Epoch 56/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7522 - loss: 0.4753\n",
      "Epoch 57/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7887 - loss: 0.4759\n",
      "Epoch 58/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7527 - loss: 0.4864\n",
      "Epoch 59/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7506 - loss: 0.4756\n",
      "Epoch 60/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7333 - loss: 0.4998\n",
      "Epoch 61/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7438 - loss: 0.4930\n",
      "Epoch 62/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7673 - loss: 0.4735\n",
      "Epoch 63/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7649 - loss: 0.4595\n",
      "Epoch 64/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7567 - loss: 0.4976\n",
      "Epoch 65/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7593 - loss: 0.4971\n",
      "Epoch 66/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7790 - loss: 0.4734\n",
      "Epoch 67/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7677 - loss: 0.4749\n",
      "Epoch 68/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7261 - loss: 0.5189\n",
      "Epoch 69/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7577 - loss: 0.5012\n",
      "Epoch 70/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7208 - loss: 0.5113\n",
      "Epoch 71/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7705 - loss: 0.4824\n",
      "Epoch 72/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7644 - loss: 0.4647\n",
      "Epoch 73/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7402 - loss: 0.4784\n",
      "Epoch 74/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7688 - loss: 0.4764\n",
      "Epoch 75/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7667 - loss: 0.4612\n",
      "Epoch 76/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7423 - loss: 0.4930\n",
      "Epoch 77/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7636 - loss: 0.4781\n",
      "Epoch 78/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7573 - loss: 0.4669\n",
      "Epoch 79/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7761 - loss: 0.4399\n",
      "Epoch 80/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7218 - loss: 0.4871\n",
      "Epoch 81/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7747 - loss: 0.4675\n",
      "Epoch 82/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7776 - loss: 0.4927\n",
      "Epoch 83/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7920 - loss: 0.4624\n",
      "Epoch 84/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7699 - loss: 0.4889\n",
      "Epoch 85/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7756 - loss: 0.4576\n",
      "Epoch 86/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7613 - loss: 0.4609\n",
      "Epoch 87/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7807 - loss: 0.4801\n",
      "Epoch 88/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7466 - loss: 0.4940\n",
      "Epoch 89/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7520 - loss: 0.4882\n",
      "Epoch 90/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7391 - loss: 0.4910\n",
      "Epoch 91/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7559 - loss: 0.4927\n",
      "Epoch 92/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7875 - loss: 0.4503\n",
      "Epoch 93/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7543 - loss: 0.5163\n",
      "Epoch 94/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7454 - loss: 0.5061\n",
      "Epoch 95/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7938 - loss: 0.4437\n",
      "Epoch 96/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7395 - loss: 0.4687\n",
      "Epoch 97/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7633 - loss: 0.4698\n",
      "Epoch 98/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7692 - loss: 0.4870\n",
      "Epoch 99/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7785 - loss: 0.4469\n",
      "Epoch 100/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7687 - loss: 0.4511\n",
      "Epoch 101/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7684 - loss: 0.4684\n",
      "Epoch 102/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7830 - loss: 0.4430\n",
      "Epoch 103/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7653 - loss: 0.4554\n",
      "Epoch 104/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7721 - loss: 0.4494\n",
      "Epoch 105/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7853 - loss: 0.4593\n",
      "Epoch 106/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7735 - loss: 0.4792\n",
      "Epoch 107/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7740 - loss: 0.4491\n",
      "Epoch 108/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7988 - loss: 0.4614\n",
      "Epoch 109/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8062 - loss: 0.4311\n",
      "Epoch 110/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7709 - loss: 0.4491\n",
      "Epoch 111/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7543 - loss: 0.5033\n",
      "Epoch 112/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8055 - loss: 0.4417\n",
      "Epoch 113/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7554 - loss: 0.4707\n",
      "Epoch 114/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7470 - loss: 0.4972\n",
      "Epoch 115/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7949 - loss: 0.4232\n",
      "Epoch 116/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7771 - loss: 0.4466\n",
      "Epoch 117/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7636 - loss: 0.4728\n",
      "Epoch 118/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7585 - loss: 0.4731\n",
      "Epoch 119/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7851 - loss: 0.4471\n",
      "Epoch 120/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7583 - loss: 0.4445\n",
      "Epoch 121/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7519 - loss: 0.4841\n",
      "Epoch 122/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7234 - loss: 0.4921\n",
      "Epoch 123/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7557 - loss: 0.4618\n",
      "Epoch 124/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7685 - loss: 0.4526\n",
      "Epoch 125/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7849 - loss: 0.4268\n",
      "Epoch 126/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7916 - loss: 0.4431\n",
      "Epoch 127/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7874 - loss: 0.4603\n",
      "Epoch 128/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7837 - loss: 0.4439\n",
      "Epoch 129/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7803 - loss: 0.4618\n",
      "Epoch 130/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7599 - loss: 0.4509\n",
      "Epoch 131/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8048 - loss: 0.4125\n",
      "Epoch 132/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8002 - loss: 0.4212\n",
      "Epoch 133/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7784 - loss: 0.4357\n",
      "Epoch 134/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7860 - loss: 0.4360\n",
      "Epoch 135/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7763 - loss: 0.4478\n",
      "Epoch 136/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7782 - loss: 0.4395\n",
      "Epoch 137/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7960 - loss: 0.4193\n",
      "Epoch 138/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7832 - loss: 0.4264\n",
      "Epoch 139/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7963 - loss: 0.4116\n",
      "Epoch 140/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7635 - loss: 0.4418\n",
      "Epoch 141/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7630 - loss: 0.4617\n",
      "Epoch 142/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8011 - loss: 0.4177\n",
      "Epoch 143/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7967 - loss: 0.4265\n",
      "Epoch 144/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7764 - loss: 0.4473\n",
      "Epoch 145/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7941 - loss: 0.4554\n",
      "Epoch 146/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8046 - loss: 0.4416\n",
      "Epoch 147/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7722 - loss: 0.4447\n",
      "Epoch 148/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7744 - loss: 0.4307\n",
      "Epoch 149/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7534 - loss: 0.4603\n",
      "Epoch 150/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8108 - loss: 0.4266\n",
      "Epoch 151/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7890 - loss: 0.4039\n",
      "Epoch 152/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7891 - loss: 0.4355\n",
      "Epoch 153/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7779 - loss: 0.4467\n",
      "Epoch 154/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7510 - loss: 0.4293\n",
      "Epoch 155/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7974 - loss: 0.4245\n",
      "Epoch 156/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7718 - loss: 0.4381\n",
      "Epoch 157/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8051 - loss: 0.4011\n",
      "Epoch 158/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7930 - loss: 0.4268\n",
      "Epoch 159/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7859 - loss: 0.4194\n",
      "Epoch 160/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7844 - loss: 0.4445\n",
      "Epoch 161/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8136 - loss: 0.4156\n",
      "Epoch 162/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7754 - loss: 0.4530\n",
      "Epoch 163/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7484 - loss: 0.4335\n",
      "Epoch 164/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8076 - loss: 0.3931\n",
      "Epoch 165/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8059 - loss: 0.4186\n",
      "Epoch 166/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8015 - loss: 0.4036\n",
      "Epoch 167/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7953 - loss: 0.4134\n",
      "Epoch 168/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7823 - loss: 0.4159\n",
      "Epoch 169/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8048 - loss: 0.4073\n",
      "Epoch 170/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7990 - loss: 0.4118\n",
      "Epoch 171/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7726 - loss: 0.4109\n",
      "Epoch 172/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8102 - loss: 0.4025\n",
      "Epoch 173/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7908 - loss: 0.4312\n",
      "Epoch 174/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8193 - loss: 0.3764\n",
      "Epoch 175/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7841 - loss: 0.4062\n",
      "Epoch 176/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7974 - loss: 0.3945\n",
      "Epoch 177/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7965 - loss: 0.4340\n",
      "Epoch 178/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8078 - loss: 0.4016\n",
      "Epoch 179/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7462 - loss: 0.4883\n",
      "Epoch 180/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7987 - loss: 0.4130\n",
      "Epoch 181/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8225 - loss: 0.3941\n",
      "Epoch 182/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7723 - loss: 0.4679\n",
      "Epoch 183/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8051 - loss: 0.3989\n",
      "Epoch 184/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7913 - loss: 0.4078\n",
      "Epoch 185/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8274 - loss: 0.3653\n",
      "Epoch 186/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8108 - loss: 0.3901\n",
      "Epoch 187/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7739 - loss: 0.4465\n",
      "Epoch 188/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7953 - loss: 0.3996\n",
      "Epoch 189/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7936 - loss: 0.4163\n",
      "Epoch 190/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7814 - loss: 0.4161\n",
      "Epoch 191/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7372 - loss: 0.4643\n",
      "Epoch 192/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8044 - loss: 0.3924\n",
      "Epoch 193/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8318 - loss: 0.3629\n",
      "Epoch 194/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8201 - loss: 0.3757\n",
      "Epoch 195/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7967 - loss: 0.4017\n",
      "Epoch 196/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8046 - loss: 0.4067\n",
      "Epoch 197/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8231 - loss: 0.3635\n",
      "Epoch 198/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8215 - loss: 0.3857\n",
      "Epoch 199/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8123 - loss: 0.3894\n",
      "Epoch 200/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7958 - loss: 0.4243\n",
      "Epoch 201/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8156 - loss: 0.4329\n",
      "Epoch 202/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8191 - loss: 0.3974\n",
      "Epoch 203/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8193 - loss: 0.3697\n",
      "Epoch 204/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8148 - loss: 0.3794\n",
      "Epoch 205/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8103 - loss: 0.3686\n",
      "Epoch 206/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8154 - loss: 0.4065\n",
      "Epoch 207/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8145 - loss: 0.3666\n",
      "Epoch 208/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8591 - loss: 0.3394\n",
      "Epoch 209/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8097 - loss: 0.4005\n",
      "Epoch 210/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8217 - loss: 0.3869\n",
      "Epoch 211/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8479 - loss: 0.3661\n",
      "Epoch 212/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8286 - loss: 0.3968\n",
      "Epoch 213/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8320 - loss: 0.3665\n",
      "Epoch 214/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8316 - loss: 0.3681\n",
      "Epoch 215/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8274 - loss: 0.3571\n",
      "Epoch 216/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7976 - loss: 0.3942\n",
      "Epoch 217/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8038 - loss: 0.3827\n",
      "Epoch 218/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8396 - loss: 0.3293\n",
      "Epoch 219/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8330 - loss: 0.3670\n",
      "Epoch 220/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8080 - loss: 0.3944\n",
      "Epoch 221/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8519 - loss: 0.3380\n",
      "Epoch 222/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8199 - loss: 0.3866\n",
      "Epoch 223/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8102 - loss: 0.3752\n",
      "Epoch 224/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8053 - loss: 0.4048\n",
      "Epoch 225/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8282 - loss: 0.3746\n",
      "Epoch 226/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7989 - loss: 0.3999\n",
      "Epoch 227/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8131 - loss: 0.3795\n",
      "Epoch 228/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8323 - loss: 0.3396\n",
      "Epoch 229/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8133 - loss: 0.3658\n",
      "Epoch 230/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8146 - loss: 0.3836\n",
      "Epoch 231/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8482 - loss: 0.3477\n",
      "Epoch 232/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8278 - loss: 0.3775\n",
      "Epoch 233/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8208 - loss: 0.3939\n",
      "Epoch 234/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8496 - loss: 0.3450\n",
      "Epoch 235/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8163 - loss: 0.3746\n",
      "Epoch 236/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8365 - loss: 0.3552\n",
      "Epoch 237/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8385 - loss: 0.3687\n",
      "Epoch 238/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8049 - loss: 0.4178\n",
      "Epoch 239/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8318 - loss: 0.3569\n",
      "Epoch 240/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8199 - loss: 0.3706\n",
      "Epoch 241/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8368 - loss: 0.3531\n",
      "Epoch 242/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8363 - loss: 0.3596\n",
      "Epoch 243/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8430 - loss: 0.3401\n",
      "Epoch 244/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8462 - loss: 0.3420\n",
      "Epoch 245/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8336 - loss: 0.3502\n",
      "Epoch 246/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8409 - loss: 0.3484\n",
      "Epoch 247/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8309 - loss: 0.3515\n",
      "Epoch 248/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8469 - loss: 0.3052\n",
      "Epoch 249/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8383 - loss: 0.3606\n",
      "Epoch 250/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8514 - loss: 0.3090\n",
      "Epoch 251/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8602 - loss: 0.3285\n",
      "Epoch 252/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8464 - loss: 0.3260\n",
      "Epoch 253/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8366 - loss: 0.3239\n",
      "Epoch 254/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8428 - loss: 0.3400\n",
      "Epoch 255/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8192 - loss: 0.3839\n",
      "Epoch 256/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8265 - loss: 0.3408\n",
      "Epoch 257/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.8471 - loss: 0.3464\n",
      "Epoch 258/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8165 - loss: 0.3680\n",
      "Epoch 259/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8342 - loss: 0.3502\n",
      "Epoch 260/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8524 - loss: 0.3333\n",
      "Epoch 261/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8199 - loss: 0.3460\n",
      "Epoch 262/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8538 - loss: 0.3265\n",
      "Epoch 263/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8786 - loss: 0.2914\n",
      "Epoch 264/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8540 - loss: 0.3037\n",
      "Epoch 265/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8697 - loss: 0.2887\n",
      "Epoch 266/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8311 - loss: 0.3416\n",
      "Epoch 267/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8524 - loss: 0.3220\n",
      "Epoch 268/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8583 - loss: 0.3480\n",
      "Epoch 269/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8915 - loss: 0.2761\n",
      "Epoch 270/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8458 - loss: 0.3212\n",
      "Epoch 271/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8461 - loss: 0.3285\n",
      "Epoch 272/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8216 - loss: 0.3495\n",
      "Epoch 273/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8676 - loss: 0.2924\n",
      "Epoch 274/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8627 - loss: 0.3143\n",
      "Epoch 275/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8923 - loss: 0.2551\n",
      "Epoch 276/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8632 - loss: 0.3292\n",
      "Epoch 277/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8642 - loss: 0.3081\n",
      "Epoch 278/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8718 - loss: 0.3014\n",
      "Epoch 279/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8654 - loss: 0.2955\n",
      "Epoch 280/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8406 - loss: 0.3116\n",
      "Epoch 281/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8549 - loss: 0.3237\n",
      "Epoch 282/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8616 - loss: 0.2885\n",
      "Epoch 283/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8955 - loss: 0.2682\n",
      "Epoch 284/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8695 - loss: 0.3128\n",
      "Epoch 285/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8772 - loss: 0.3028\n",
      "Epoch 286/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8708 - loss: 0.2846\n",
      "Epoch 287/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8638 - loss: 0.2937\n",
      "Epoch 288/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8851 - loss: 0.2772\n",
      "Epoch 289/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8653 - loss: 0.3004\n",
      "Epoch 290/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8659 - loss: 0.2797\n",
      "Epoch 291/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8720 - loss: 0.2852\n",
      "Epoch 292/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8415 - loss: 0.3032\n",
      "Epoch 293/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8796 - loss: 0.2658\n",
      "Epoch 294/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8862 - loss: 0.2743\n",
      "Epoch 295/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8766 - loss: 0.2714\n",
      "Epoch 296/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8652 - loss: 0.2951\n",
      "Epoch 297/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8711 - loss: 0.2800\n",
      "Epoch 298/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8626 - loss: 0.2902\n",
      "Epoch 299/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8503 - loss: 0.3009\n",
      "Epoch 300/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8695 - loss: 0.3003\n",
      "Epoch 301/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8659 - loss: 0.2907\n",
      "Epoch 302/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8575 - loss: 0.3748\n",
      "Epoch 303/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8598 - loss: 0.2797\n",
      "Epoch 304/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8651 - loss: 0.2838\n",
      "Epoch 305/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8901 - loss: 0.2660\n",
      "Epoch 306/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8814 - loss: 0.2665\n",
      "Epoch 307/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9064 - loss: 0.2231\n",
      "Epoch 308/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9047 - loss: 0.2321\n",
      "Epoch 309/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8710 - loss: 0.2835\n",
      "Epoch 310/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8853 - loss: 0.2803\n",
      "Epoch 311/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8975 - loss: 0.2398\n",
      "Epoch 312/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9174 - loss: 0.2260\n",
      "Epoch 313/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8541 - loss: 0.3271\n",
      "Epoch 314/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8580 - loss: 0.3466\n",
      "Epoch 315/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8902 - loss: 0.2484\n",
      "Epoch 316/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8761 - loss: 0.2893\n",
      "Epoch 317/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9015 - loss: 0.2530\n",
      "Epoch 318/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9017 - loss: 0.2580\n",
      "Epoch 319/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8704 - loss: 0.2774\n",
      "Epoch 320/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8814 - loss: 0.2736\n",
      "Epoch 321/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8878 - loss: 0.2743\n",
      "Epoch 322/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9058 - loss: 0.2393\n",
      "Epoch 323/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9066 - loss: 0.2246\n",
      "Epoch 324/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8895 - loss: 0.2464\n",
      "Epoch 325/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9025 - loss: 0.2259\n",
      "Epoch 326/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9011 - loss: 0.2227\n",
      "Epoch 327/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8782 - loss: 0.2561\n",
      "Epoch 328/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9063 - loss: 0.2236\n",
      "Epoch 329/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8890 - loss: 0.2650\n",
      "Epoch 330/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9029 - loss: 0.2474\n",
      "Epoch 331/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8821 - loss: 0.2400\n",
      "Epoch 332/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8807 - loss: 0.2641\n",
      "Epoch 333/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9067 - loss: 0.2502\n",
      "Epoch 334/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8881 - loss: 0.2236\n",
      "Epoch 335/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8712 - loss: 0.2458\n",
      "Epoch 336/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9107 - loss: 0.2116\n",
      "Epoch 337/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9246 - loss: 0.1904\n",
      "Epoch 338/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9166 - loss: 0.2018\n",
      "Epoch 339/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9053 - loss: 0.2515\n",
      "Epoch 340/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8880 - loss: 0.2564\n",
      "Epoch 341/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9017 - loss: 0.2326\n",
      "Epoch 342/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8754 - loss: 0.2461\n",
      "Epoch 343/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9107 - loss: 0.2324\n",
      "Epoch 344/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9261 - loss: 0.2378\n",
      "Epoch 345/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9277 - loss: 0.2014\n",
      "Epoch 346/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8631 - loss: 0.2789\n",
      "Epoch 347/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9100 - loss: 0.2120\n",
      "Epoch 348/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9336 - loss: 0.1737\n",
      "Epoch 349/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9310 - loss: 0.1877\n",
      "Epoch 350/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9357 - loss: 0.1731\n",
      "Epoch 351/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9137 - loss: 0.2111\n",
      "Epoch 352/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9119 - loss: 0.1947\n",
      "Epoch 353/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9269 - loss: 0.1825\n",
      "Epoch 354/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9227 - loss: 0.1795\n",
      "Epoch 355/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9275 - loss: 0.1721\n",
      "Epoch 356/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9133 - loss: 0.1985\n",
      "Epoch 357/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9164 - loss: 0.2052\n",
      "Epoch 358/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9179 - loss: 0.2088\n",
      "Epoch 359/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9188 - loss: 0.2100\n",
      "Epoch 360/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9131 - loss: 0.2392\n",
      "Epoch 361/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9240 - loss: 0.1929\n",
      "Epoch 362/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9273 - loss: 0.1982\n",
      "Epoch 363/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9146 - loss: 0.2250\n",
      "Epoch 364/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9398 - loss: 0.1553\n",
      "Epoch 365/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9255 - loss: 0.1759\n",
      "Epoch 366/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8946 - loss: 0.2395\n",
      "Epoch 367/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9276 - loss: 0.1730\n",
      "Epoch 368/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9274 - loss: 0.1597\n",
      "Epoch 369/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9380 - loss: 0.1484\n",
      "Epoch 370/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9011 - loss: 0.1843\n",
      "Epoch 371/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9272 - loss: 0.1690\n",
      "Epoch 372/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9358 - loss: 0.1555\n",
      "Epoch 373/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9162 - loss: 0.1937\n",
      "Epoch 374/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9018 - loss: 0.2317\n",
      "Epoch 375/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9422 - loss: 0.1722\n",
      "Epoch 376/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9296 - loss: 0.1574\n",
      "Epoch 377/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9230 - loss: 0.1825\n",
      "Epoch 378/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9257 - loss: 0.1772\n",
      "Epoch 379/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9209 - loss: 0.1764\n",
      "Epoch 380/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9135 - loss: 0.1909\n",
      "Epoch 381/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9409 - loss: 0.1788\n",
      "Epoch 382/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9243 - loss: 0.1518\n",
      "Epoch 383/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9208 - loss: 0.2191\n",
      "Epoch 384/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9199 - loss: 0.1735\n",
      "Epoch 385/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9325 - loss: 0.1903\n",
      "Epoch 386/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9123 - loss: 0.1861\n",
      "Epoch 387/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9193 - loss: 0.1873\n",
      "Epoch 388/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9445 - loss: 0.1588\n",
      "Epoch 389/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9372 - loss: 0.1319\n",
      "Epoch 390/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9454 - loss: 0.1492\n",
      "Epoch 391/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9471 - loss: 0.1301\n",
      "Epoch 392/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9677 - loss: 0.0992\n",
      "Epoch 393/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9459 - loss: 0.1417\n",
      "Epoch 394/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9513 - loss: 0.1432\n",
      "Epoch 395/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9283 - loss: 0.1688\n",
      "Epoch 396/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9322 - loss: 0.1628\n",
      "Epoch 397/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9227 - loss: 0.2087\n",
      "Epoch 398/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9269 - loss: 0.1886\n",
      "Epoch 399/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9254 - loss: 0.1514\n",
      "Epoch 400/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9190 - loss: 0.1870\n",
      "Epoch 401/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9453 - loss: 0.1496\n",
      "Epoch 402/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9555 - loss: 0.1231\n",
      "Epoch 403/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9508 - loss: 0.1333\n",
      "Epoch 404/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9341 - loss: 0.1700\n",
      "Epoch 405/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9374 - loss: 0.1329\n",
      "Epoch 406/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9121 - loss: 0.1883\n",
      "Epoch 407/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9202 - loss: 0.1851\n",
      "Epoch 408/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9412 - loss: 0.1312\n",
      "Epoch 409/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9458 - loss: 0.1035\n",
      "Epoch 410/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9497 - loss: 0.1072\n",
      "Epoch 411/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9416 - loss: 0.1181\n",
      "Epoch 412/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9456 - loss: 0.1323\n",
      "Epoch 413/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9448 - loss: 0.1442\n",
      "Epoch 414/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9442 - loss: 0.1737\n",
      "Epoch 415/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9350 - loss: 0.1275\n",
      "Epoch 416/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9476 - loss: 0.1441\n",
      "Epoch 417/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9564 - loss: 0.1112\n",
      "Epoch 418/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9505 - loss: 0.1108\n",
      "Epoch 419/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9424 - loss: 0.1474\n",
      "Epoch 420/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9460 - loss: 0.1379\n",
      "Epoch 421/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9439 - loss: 0.1235\n",
      "Epoch 422/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9527 - loss: 0.1411\n",
      "Epoch 423/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9402 - loss: 0.1354\n",
      "Epoch 424/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9639 - loss: 0.1013\n",
      "Epoch 425/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9428 - loss: 0.1370\n",
      "Epoch 426/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9646 - loss: 0.0929\n",
      "Epoch 427/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9499 - loss: 0.1440\n",
      "Epoch 428/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9019 - loss: 0.2062\n",
      "Epoch 429/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9212 - loss: 0.2204\n",
      "Epoch 430/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9306 - loss: 0.1631\n",
      "Epoch 431/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9321 - loss: 0.1377\n",
      "Epoch 432/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9555 - loss: 0.1188\n",
      "Epoch 433/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9481 - loss: 0.1346\n",
      "Epoch 434/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9749 - loss: 0.0781\n",
      "Epoch 435/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9699 - loss: 0.0838\n",
      "Epoch 436/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9726 - loss: 0.0782\n",
      "Epoch 437/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9714 - loss: 0.0839\n",
      "Epoch 438/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9560 - loss: 0.1181\n",
      "Epoch 439/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9688 - loss: 0.0851\n",
      "Epoch 440/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9482 - loss: 0.1253\n",
      "Epoch 441/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9604 - loss: 0.0990\n",
      "Epoch 442/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9499 - loss: 0.0959\n",
      "Epoch 443/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9640 - loss: 0.0876\n",
      "Epoch 444/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9636 - loss: 0.1055\n",
      "Epoch 445/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9623 - loss: 0.0931\n",
      "Epoch 446/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9810 - loss: 0.0786\n",
      "Epoch 447/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9641 - loss: 0.0834\n",
      "Epoch 448/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9492 - loss: 0.1227\n",
      "Epoch 449/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9809 - loss: 0.0688\n",
      "Epoch 450/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9494 - loss: 0.0983\n",
      "Epoch 451/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9230 - loss: 0.1749\n",
      "Epoch 452/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9464 - loss: 0.1043\n",
      "Epoch 453/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9411 - loss: 0.1465\n",
      "Epoch 454/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9523 - loss: 0.1285\n",
      "Epoch 455/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9618 - loss: 0.1028\n",
      "Epoch 456/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9369 - loss: 0.1436\n",
      "Epoch 457/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9590 - loss: 0.1296\n",
      "Epoch 458/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9563 - loss: 0.1176\n",
      "Epoch 459/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9492 - loss: 0.1320\n",
      "Epoch 460/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9507 - loss: 0.1119\n",
      "Epoch 461/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9528 - loss: 0.1187\n",
      "Epoch 462/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9524 - loss: 0.1348\n",
      "Epoch 463/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9529 - loss: 0.1241\n",
      "Epoch 464/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9685 - loss: 0.0871\n",
      "Epoch 465/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9524 - loss: 0.1059\n",
      "Epoch 466/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9659 - loss: 0.0896\n",
      "Epoch 467/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9824 - loss: 0.0718\n",
      "Epoch 468/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9545 - loss: 0.1133\n",
      "Epoch 469/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9518 - loss: 0.1121\n",
      "Epoch 470/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9777 - loss: 0.0720\n",
      "Epoch 471/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9767 - loss: 0.0685\n",
      "Epoch 472/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9673 - loss: 0.0886\n",
      "Epoch 473/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9556 - loss: 0.1105\n",
      "Epoch 474/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9687 - loss: 0.0805\n",
      "Epoch 475/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9775 - loss: 0.0598\n",
      "Epoch 476/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9518 - loss: 0.0875\n",
      "Epoch 477/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9783 - loss: 0.0735\n",
      "Epoch 478/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9867 - loss: 0.0467\n",
      "Epoch 479/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9819 - loss: 0.0600\n",
      "Epoch 480/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9618 - loss: 0.1052\n",
      "Epoch 481/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8859 - loss: 0.2372\n",
      "Epoch 482/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9668 - loss: 0.1150\n",
      "Epoch 483/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9790 - loss: 0.0612\n",
      "Epoch 484/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9735 - loss: 0.0764\n",
      "Epoch 485/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9801 - loss: 0.0540\n",
      "Epoch 486/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9734 - loss: 0.0709\n",
      "Epoch 487/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9789 - loss: 0.0581\n",
      "Epoch 488/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9730 - loss: 0.0942\n",
      "Epoch 489/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9499 - loss: 0.1564\n",
      "Epoch 490/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9458 - loss: 0.1587\n",
      "Epoch 491/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9518 - loss: 0.1009\n",
      "Epoch 492/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9645 - loss: 0.0770\n",
      "Epoch 493/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9787 - loss: 0.0772\n",
      "Epoch 494/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9783 - loss: 0.0662\n",
      "Epoch 495/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9623 - loss: 0.0792\n",
      "Epoch 496/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9770 - loss: 0.0679\n",
      "Epoch 497/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.0500\n",
      "Epoch 498/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9820 - loss: 0.0527\n",
      "Epoch 499/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9674 - loss: 0.1030\n",
      "Epoch 500/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9765 - loss: 0.0559\n",
      "Epoch 501/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9693 - loss: 0.0679\n",
      "Epoch 502/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9624 - loss: 0.0899\n",
      "Epoch 503/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9855 - loss: 0.0639\n",
      "Epoch 504/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9787 - loss: 0.0483\n",
      "Epoch 505/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9685 - loss: 0.0790\n",
      "Epoch 506/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9391 - loss: 0.1659\n",
      "Epoch 507/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9670 - loss: 0.0948\n",
      "Epoch 508/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9650 - loss: 0.0864\n",
      "Epoch 509/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9337 - loss: 0.1645\n",
      "Epoch 510/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9826 - loss: 0.0588\n",
      "Epoch 511/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9811 - loss: 0.0585\n",
      "Epoch 512/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9803 - loss: 0.0755\n",
      "Epoch 513/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9779 - loss: 0.0723\n",
      "Epoch 514/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9907 - loss: 0.0324\n",
      "Epoch 515/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9675 - loss: 0.0730\n",
      "Epoch 516/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9780 - loss: 0.0560\n",
      "Epoch 517/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9939 - loss: 0.0373\n",
      "Epoch 518/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9813 - loss: 0.0597\n",
      "Epoch 519/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9903 - loss: 0.0429\n",
      "Epoch 520/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9810 - loss: 0.0597\n",
      "Epoch 521/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9818 - loss: 0.0370\n",
      "Epoch 522/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9747 - loss: 0.0971\n",
      "Epoch 523/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9746 - loss: 0.0730\n",
      "Epoch 524/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9703 - loss: 0.0704\n",
      "Epoch 525/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9741 - loss: 0.0565\n",
      "Epoch 526/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9781 - loss: 0.0670\n",
      "Epoch 527/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9727 - loss: 0.0776\n",
      "Epoch 528/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9547 - loss: 0.1355\n",
      "Epoch 529/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9769 - loss: 0.0723\n",
      "Epoch 530/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9777 - loss: 0.0622\n",
      "Epoch 531/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9519 - loss: 0.1086\n",
      "Epoch 532/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9951 - loss: 0.0395\n",
      "Epoch 533/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9896 - loss: 0.0393\n",
      "Epoch 534/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9775 - loss: 0.0477\n",
      "Epoch 535/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9791 - loss: 0.0575\n",
      "Epoch 536/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9914 - loss: 0.0455\n",
      "Epoch 537/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9855 - loss: 0.0609\n",
      "Epoch 538/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9901 - loss: 0.0395\n",
      "Epoch 539/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9933 - loss: 0.0341\n",
      "Epoch 540/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9920 - loss: 0.0400\n",
      "Epoch 541/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9809 - loss: 0.0518\n",
      "Epoch 542/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9886 - loss: 0.0403\n",
      "Epoch 543/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9768 - loss: 0.0704\n",
      "Epoch 544/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9660 - loss: 0.1027\n",
      "Epoch 545/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9661 - loss: 0.1092\n",
      "Epoch 546/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9797 - loss: 0.0577\n",
      "Epoch 547/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9679 - loss: 0.1058\n",
      "Epoch 548/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9830 - loss: 0.0523\n",
      "Epoch 549/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9805 - loss: 0.0651\n",
      "Epoch 550/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9856 - loss: 0.0622\n",
      "Epoch 551/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9777 - loss: 0.0589\n",
      "Epoch 552/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9896 - loss: 0.0416\n",
      "Epoch 553/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9704 - loss: 0.0885\n",
      "Epoch 554/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9808 - loss: 0.0404\n",
      "Epoch 555/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9781 - loss: 0.0573\n",
      "Epoch 556/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9550 - loss: 0.1205\n",
      "Epoch 557/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9632 - loss: 0.1018\n",
      "Epoch 558/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9924 - loss: 0.0392\n",
      "Epoch 559/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9754 - loss: 0.0638\n",
      "Epoch 560/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9480 - loss: 0.1179\n",
      "Epoch 561/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9760 - loss: 0.0719\n",
      "Epoch 562/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9826 - loss: 0.0566\n",
      "Epoch 563/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9791 - loss: 0.0615\n",
      "Epoch 564/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9926 - loss: 0.0316\n",
      "Epoch 565/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9911 - loss: 0.0413\n",
      "Epoch 566/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9798 - loss: 0.0499\n",
      "Epoch 567/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9738 - loss: 0.0961\n",
      "Epoch 568/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9523 - loss: 0.1292\n",
      "Epoch 569/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9612 - loss: 0.0968\n",
      "Epoch 570/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9762 - loss: 0.0528\n",
      "Epoch 571/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9842 - loss: 0.0439\n",
      "Epoch 572/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9794 - loss: 0.0562\n",
      "Epoch 573/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9853 - loss: 0.0453\n",
      "Epoch 574/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9750 - loss: 0.0745\n",
      "Epoch 575/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9891 - loss: 0.0432\n",
      "Epoch 576/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9877 - loss: 0.0354\n",
      "Epoch 577/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9796 - loss: 0.0436\n",
      "Epoch 578/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9803 - loss: 0.0507\n",
      "Epoch 579/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9865 - loss: 0.0403\n",
      "Epoch 580/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9752 - loss: 0.0730\n",
      "Epoch 581/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9887 - loss: 0.0406\n",
      "Epoch 582/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9847 - loss: 0.0576\n",
      "Epoch 583/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9569 - loss: 0.1395\n",
      "Epoch 584/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9809 - loss: 0.0530\n",
      "Epoch 585/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9755 - loss: 0.0682\n",
      "Epoch 586/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9717 - loss: 0.0825\n",
      "Epoch 587/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9848 - loss: 0.0612\n",
      "Epoch 588/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9866 - loss: 0.0369\n",
      "Epoch 589/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9831 - loss: 0.0460\n",
      "Epoch 590/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9873 - loss: 0.0416\n",
      "Epoch 591/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9930 - loss: 0.0322\n",
      "Epoch 592/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9862 - loss: 0.0373\n",
      "Epoch 593/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9748 - loss: 0.0638\n",
      "Epoch 594/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9971 - loss: 0.0208\n",
      "Epoch 595/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9745 - loss: 0.0598\n",
      "Epoch 596/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9838 - loss: 0.0505\n",
      "Epoch 597/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9935 - loss: 0.0373\n",
      "Epoch 598/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.0322\n",
      "Epoch 599/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9906 - loss: 0.0317\n",
      "Epoch 600/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9908 - loss: 0.0259\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x252e5eaadf0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
    "\n",
    "# Split the dataset into training and test sets\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=10)\n",
    "\n",
    "# Define the model\n",
    "model2 = Sequential()\n",
    "\n",
    "# Add the LSTM layers\n",
    "model2.add(LSTM(units=128, return_sequences=True, input_shape=(X_train.shape[1], 1)))\n",
    "model2.add(Dropout(0.2))\n",
    "\n",
    "model2.add(LSTM(units=64, return_sequences=True))\n",
    "model2.add(Dropout(0.2))\n",
    "\n",
    "model2.add(LSTM(units=32))\n",
    "model2.add(Dropout(0.2))\n",
    "\n",
    "# Add the output layer\n",
    "model2.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Compile the model\n",
    "model2.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model2.fit(X_train, Y_train, epochs=600, batch_size=16)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.98      0.97       500\n",
      "           1       0.95      0.93      0.94       268\n",
      "\n",
      "    accuracy                           0.96       768\n",
      "   macro avg       0.96      0.95      0.96       768\n",
      "weighted avg       0.96      0.96      0.96       768\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_pred = model2.predict(X).round()\n",
    "cm=confusion_matrix(Y, Y_pred)\n",
    "#print('Confusion Matrix:\\n', cm)\n",
    "\n",
    "print(classification_report(Y, Y_pred))\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='YlGn')\n",
    "plt.xlabel('Predicted Values')\n",
    "plt.ylabel('True Values')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
      "[0.0168207]\n"
     ]
    }
   ],
   "source": [
    "sem = np.std(model2.predict(X), axis=0) / np.sqrt(len(model2.predict(X)))\n",
    "\n",
    "print(sem)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Binarize the output\n",
    "Y = label_binarize(Y, classes=[0, 1])\n",
    "\n",
    "# Get the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(Y.ravel(), Y_pred.ravel())\n",
    "\n",
    "# Calculate the AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "lw = 3\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.grid()\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\anaconda3\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 974us/step - accuracy: 0.6599 - loss: 0.6653 \n",
      "Epoch 2/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6264 - loss: 0.6536 \n",
      "Epoch 3/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 958us/step - accuracy: 0.6408 - loss: 0.6308\n",
      "Epoch 4/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.6496 - loss: 0.6073\n",
      "Epoch 5/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7130 - loss: 0.5592\n",
      "Epoch 6/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7212 - loss: 0.5536\n",
      "Epoch 7/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7641 - loss: 0.5093\n",
      "Epoch 8/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 979us/step - accuracy: 0.7447 - loss: 0.5000\n",
      "Epoch 9/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7501 - loss: 0.4953\n",
      "Epoch 10/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7557 - loss: 0.4990\n",
      "Epoch 11/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7683 - loss: 0.4696 \n",
      "Epoch 12/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7826 - loss: 0.4626 \n",
      "Epoch 13/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7908 - loss: 0.4406 \n",
      "Epoch 14/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7551 - loss: 0.4938 \n",
      "Epoch 15/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7683 - loss: 0.4804\n",
      "Epoch 16/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.7539 - loss: 0.4805\n",
      "Epoch 17/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7867 - loss: 0.4780 \n",
      "Epoch 18/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7776 - loss: 0.4797 \n",
      "Epoch 19/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7739 - loss: 0.4500 \n",
      "Epoch 20/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7946 - loss: 0.4318\n",
      "Epoch 21/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7702 - loss: 0.4694\n",
      "Epoch 22/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7682 - loss: 0.4731\n",
      "Epoch 23/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7812 - loss: 0.4500 \n",
      "Epoch 24/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8082 - loss: 0.4178\n",
      "Epoch 25/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8069 - loss: 0.4189\n",
      "Epoch 26/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7597 - loss: 0.4876 \n",
      "Epoch 27/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8052 - loss: 0.4274 \n",
      "Epoch 28/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7835 - loss: 0.4333 \n",
      "Epoch 29/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8029 - loss: 0.4345\n",
      "Epoch 30/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7815 - loss: 0.4712\n",
      "Epoch 31/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.7658 - loss: 0.4614\n",
      "Epoch 32/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 973us/step - accuracy: 0.7747 - loss: 0.4553\n",
      "Epoch 33/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7762 - loss: 0.4415 \n",
      "Epoch 34/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7881 - loss: 0.4477 \n",
      "Epoch 35/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7829 - loss: 0.4719 \n",
      "Epoch 36/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7885 - loss: 0.4261 \n",
      "Epoch 37/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8022 - loss: 0.4507\n",
      "Epoch 38/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.7902 - loss: 0.4392\n",
      "Epoch 39/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7812 - loss: 0.4556\n",
      "Epoch 40/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7610 - loss: 0.4634 \n",
      "Epoch 41/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7706 - loss: 0.4589\n",
      "Epoch 42/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7700 - loss: 0.4763\n",
      "Epoch 43/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7727 - loss: 0.4636 \n",
      "Epoch 44/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7799 - loss: 0.4583\n",
      "Epoch 45/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8014 - loss: 0.4342\n",
      "Epoch 46/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - accuracy: 0.8130 - loss: 0.4197\n",
      "Epoch 47/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8251 - loss: 0.3853\n",
      "Epoch 48/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7625 - loss: 0.4657\n",
      "Epoch 49/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.7752 - loss: 0.4606\n",
      "Epoch 50/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7722 - loss: 0.4439 \n",
      "Epoch 51/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8055 - loss: 0.4272 \n",
      "Epoch 52/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.7874 - loss: 0.4450\n",
      "Epoch 53/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7934 - loss: 0.4339\n",
      "Epoch 54/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.7722 - loss: 0.4506\n",
      "Epoch 55/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7968 - loss: 0.4243\n",
      "Epoch 56/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7761 - loss: 0.4706\n",
      "Epoch 57/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7655 - loss: 0.4738\n",
      "Epoch 58/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7656 - loss: 0.4572\n",
      "Epoch 59/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 993us/step - accuracy: 0.8047 - loss: 0.4241\n",
      "Epoch 60/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.7975 - loss: 0.4220\n",
      "Epoch 61/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8164 - loss: 0.4111\n",
      "Epoch 62/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8068 - loss: 0.4200\n",
      "Epoch 63/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7707 - loss: 0.4404\n",
      "Epoch 64/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7857 - loss: 0.4457\n",
      "Epoch 65/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7854 - loss: 0.4267\n",
      "Epoch 66/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.7650 - loss: 0.4583\n",
      "Epoch 67/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8032 - loss: 0.4186\n",
      "Epoch 68/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.7978 - loss: 0.4110\n",
      "Epoch 69/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8030 - loss: 0.4174 \n",
      "Epoch 70/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8126 - loss: 0.4382\n",
      "Epoch 71/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7916 - loss: 0.4185\n",
      "Epoch 72/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.8039 - loss: 0.4174\n",
      "Epoch 73/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8002 - loss: 0.4322\n",
      "Epoch 74/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8028 - loss: 0.4137\n",
      "Epoch 75/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8240 - loss: 0.3864\n",
      "Epoch 76/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.7727 - loss: 0.4392\n",
      "Epoch 77/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.7571 - loss: 0.4563\n",
      "Epoch 78/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.7894 - loss: 0.4444\n",
      "Epoch 79/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.7963 - loss: 0.4167\n",
      "Epoch 80/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8186 - loss: 0.4126\n",
      "Epoch 81/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7914 - loss: 0.4242\n",
      "Epoch 82/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 917us/step - accuracy: 0.7967 - loss: 0.4001\n",
      "Epoch 83/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.7752 - loss: 0.4620\n",
      "Epoch 84/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8177 - loss: 0.4057\n",
      "Epoch 85/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8168 - loss: 0.3955\n",
      "Epoch 86/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8125 - loss: 0.4257\n",
      "Epoch 87/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8081 - loss: 0.4083\n",
      "Epoch 88/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8186 - loss: 0.4119\n",
      "Epoch 89/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8331 - loss: 0.3987\n",
      "Epoch 90/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8418 - loss: 0.4053\n",
      "Epoch 91/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7827 - loss: 0.4254\n",
      "Epoch 92/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8225 - loss: 0.4309\n",
      "Epoch 93/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8035 - loss: 0.4139\n",
      "Epoch 94/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8110 - loss: 0.4309 \n",
      "Epoch 95/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 976us/step - accuracy: 0.7978 - loss: 0.4044\n",
      "Epoch 96/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7984 - loss: 0.4302\n",
      "Epoch 97/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8049 - loss: 0.4341\n",
      "Epoch 98/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8154 - loss: 0.4093\n",
      "Epoch 99/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7805 - loss: 0.4254\n",
      "Epoch 100/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8217 - loss: 0.3985\n",
      "Epoch 101/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8329 - loss: 0.3882\n",
      "Epoch 102/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8067 - loss: 0.4174\n",
      "Epoch 103/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.7959 - loss: 0.4079\n",
      "Epoch 104/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8249 - loss: 0.3953\n",
      "Epoch 105/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8120 - loss: 0.4087\n",
      "Epoch 106/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8158 - loss: 0.3949\n",
      "Epoch 107/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7936 - loss: 0.4396\n",
      "Epoch 108/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.7896 - loss: 0.4210\n",
      "Epoch 109/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8211 - loss: 0.3710\n",
      "Epoch 110/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8314 - loss: 0.3897\n",
      "Epoch 111/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 941us/step - accuracy: 0.8011 - loss: 0.4099\n",
      "Epoch 112/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8276 - loss: 0.3935\n",
      "Epoch 113/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8182 - loss: 0.3925\n",
      "Epoch 114/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7982 - loss: 0.4159 \n",
      "Epoch 115/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8308 - loss: 0.3880\n",
      "Epoch 116/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8413 - loss: 0.3679\n",
      "Epoch 117/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 953us/step - accuracy: 0.8179 - loss: 0.3933\n",
      "Epoch 118/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8363 - loss: 0.3667 \n",
      "Epoch 119/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8221 - loss: 0.3944\n",
      "Epoch 120/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8220 - loss: 0.3793\n",
      "Epoch 121/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8308 - loss: 0.3809\n",
      "Epoch 122/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8068 - loss: 0.4056\n",
      "Epoch 123/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8221 - loss: 0.3882\n",
      "Epoch 124/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8069 - loss: 0.4241\n",
      "Epoch 125/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8478 - loss: 0.3819\n",
      "Epoch 126/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8224 - loss: 0.3805\n",
      "Epoch 127/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8199 - loss: 0.3869\n",
      "Epoch 128/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8221 - loss: 0.3845 \n",
      "Epoch 129/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8380 - loss: 0.3830\n",
      "Epoch 130/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8090 - loss: 0.3884\n",
      "Epoch 131/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8306 - loss: 0.3775\n",
      "Epoch 132/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8058 - loss: 0.4072\n",
      "Epoch 133/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8470 - loss: 0.3733 \n",
      "Epoch 134/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8230 - loss: 0.3890\n",
      "Epoch 135/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8198 - loss: 0.3718\n",
      "Epoch 136/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8294 - loss: 0.3837\n",
      "Epoch 137/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.8037 - loss: 0.4081\n",
      "Epoch 138/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8105 - loss: 0.3784\n",
      "Epoch 139/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8349 - loss: 0.3830\n",
      "Epoch 140/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8206 - loss: 0.4024\n",
      "Epoch 141/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 929us/step - accuracy: 0.8368 - loss: 0.3795\n",
      "Epoch 142/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - accuracy: 0.8132 - loss: 0.3936\n",
      "Epoch 143/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8317 - loss: 0.3737\n",
      "Epoch 144/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8471 - loss: 0.3440\n",
      "Epoch 145/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8136 - loss: 0.4001\n",
      "Epoch 146/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8212 - loss: 0.4106\n",
      "Epoch 147/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8372 - loss: 0.3758\n",
      "Epoch 148/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8019 - loss: 0.3923\n",
      "Epoch 149/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8309 - loss: 0.3636\n",
      "Epoch 150/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8350 - loss: 0.3753\n",
      "Epoch 151/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.7872 - loss: 0.4247\n",
      "Epoch 152/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8302 - loss: 0.3854\n",
      "Epoch 153/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8231 - loss: 0.4077\n",
      "Epoch 154/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8175 - loss: 0.3807\n",
      "Epoch 155/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8149 - loss: 0.4035\n",
      "Epoch 156/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8388 - loss: 0.3686\n",
      "Epoch 157/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8396 - loss: 0.3867 \n",
      "Epoch 158/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8321 - loss: 0.3700\n",
      "Epoch 159/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8491 - loss: 0.3592\n",
      "Epoch 160/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8397 - loss: 0.3729\n",
      "Epoch 161/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 978us/step - accuracy: 0.8254 - loss: 0.3728\n",
      "Epoch 162/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8437 - loss: 0.3633\n",
      "Epoch 163/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8313 - loss: 0.3697\n",
      "Epoch 164/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8261 - loss: 0.3741\n",
      "Epoch 165/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8649 - loss: 0.3467\n",
      "Epoch 166/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 958us/step - accuracy: 0.8093 - loss: 0.3896\n",
      "Epoch 167/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 972us/step - accuracy: 0.8306 - loss: 0.3847\n",
      "Epoch 168/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8294 - loss: 0.3859\n",
      "Epoch 169/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8333 - loss: 0.3771\n",
      "Epoch 170/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8121 - loss: 0.3878\n",
      "Epoch 171/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 958us/step - accuracy: 0.8247 - loss: 0.3958\n",
      "Epoch 172/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 977us/step - accuracy: 0.8322 - loss: 0.3877\n",
      "Epoch 173/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8255 - loss: 0.3835\n",
      "Epoch 174/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8301 - loss: 0.3685 \n",
      "Epoch 175/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8542 - loss: 0.3584\n",
      "Epoch 176/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8397 - loss: 0.3777\n",
      "Epoch 177/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8491 - loss: 0.3627\n",
      "Epoch 178/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8345 - loss: 0.3637\n",
      "Epoch 179/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8052 - loss: 0.3955 \n",
      "Epoch 180/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8440 - loss: 0.3642\n",
      "Epoch 181/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 970us/step - accuracy: 0.8421 - loss: 0.3621\n",
      "Epoch 182/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8161 - loss: 0.3898\n",
      "Epoch 183/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8352 - loss: 0.3788\n",
      "Epoch 184/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8361 - loss: 0.3683\n",
      "Epoch 185/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 947us/step - accuracy: 0.8443 - loss: 0.3600\n",
      "Epoch 186/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8580 - loss: 0.3344 \n",
      "Epoch 187/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8265 - loss: 0.3887\n",
      "Epoch 188/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 996us/step - accuracy: 0.8385 - loss: 0.3608\n",
      "Epoch 189/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8607 - loss: 0.3523\n",
      "Epoch 190/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8329 - loss: 0.3632 \n",
      "Epoch 191/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8367 - loss: 0.3694\n",
      "Epoch 192/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8113 - loss: 0.3926\n",
      "Epoch 193/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8344 - loss: 0.3727 \n",
      "Epoch 194/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8755 - loss: 0.3284\n",
      "Epoch 195/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 966us/step - accuracy: 0.8280 - loss: 0.3713\n",
      "Epoch 196/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8336 - loss: 0.3628\n",
      "Epoch 197/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8503 - loss: 0.3598\n",
      "Epoch 198/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8553 - loss: 0.3312\n",
      "Epoch 199/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8421 - loss: 0.3518\n",
      "Epoch 200/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 955us/step - accuracy: 0.8376 - loss: 0.3604\n",
      "Epoch 201/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8349 - loss: 0.3480\n",
      "Epoch 202/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8520 - loss: 0.3413\n",
      "Epoch 203/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8412 - loss: 0.3604\n",
      "Epoch 204/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8406 - loss: 0.3555\n",
      "Epoch 205/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8694 - loss: 0.3408\n",
      "Epoch 206/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 918us/step - accuracy: 0.8109 - loss: 0.3842\n",
      "Epoch 207/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8544 - loss: 0.3658\n",
      "Epoch 208/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 955us/step - accuracy: 0.8599 - loss: 0.3253\n",
      "Epoch 209/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8246 - loss: 0.3658 \n",
      "Epoch 210/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8517 - loss: 0.3355 \n",
      "Epoch 211/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8539 - loss: 0.3500 \n",
      "Epoch 212/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8523 - loss: 0.3425\n",
      "Epoch 213/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8053 - loss: 0.3927 \n",
      "Epoch 214/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8460 - loss: 0.3488\n",
      "Epoch 215/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8436 - loss: 0.3519 \n",
      "Epoch 216/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8384 - loss: 0.3548 \n",
      "Epoch 217/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8338 - loss: 0.3674 \n",
      "Epoch 218/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8596 - loss: 0.3322\n",
      "Epoch 219/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8597 - loss: 0.3424 \n",
      "Epoch 220/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8096 - loss: 0.4039 \n",
      "Epoch 221/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8165 - loss: 0.3735 \n",
      "Epoch 222/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8440 - loss: 0.3649 \n",
      "Epoch 223/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8621 - loss: 0.3458 \n",
      "Epoch 224/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8452 - loss: 0.3421\n",
      "Epoch 225/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - accuracy: 0.8394 - loss: 0.3632\n",
      "Epoch 226/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8613 - loss: 0.3434\n",
      "Epoch 227/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8305 - loss: 0.3435\n",
      "Epoch 228/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 982us/step - accuracy: 0.8407 - loss: 0.3452\n",
      "Epoch 229/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8313 - loss: 0.3563 \n",
      "Epoch 230/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8564 - loss: 0.3379\n",
      "Epoch 231/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8768 - loss: 0.2994\n",
      "Epoch 232/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8472 - loss: 0.3646\n",
      "Epoch 233/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8645 - loss: 0.3389 \n",
      "Epoch 234/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8275 - loss: 0.3553 \n",
      "Epoch 235/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8702 - loss: 0.3173 \n",
      "Epoch 236/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8759 - loss: 0.3082\n",
      "Epoch 237/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8612 - loss: 0.3280\n",
      "Epoch 238/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8664 - loss: 0.3447\n",
      "Epoch 239/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8580 - loss: 0.3347\n",
      "Epoch 240/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8598 - loss: 0.3386\n",
      "Epoch 241/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8636 - loss: 0.3363\n",
      "Epoch 242/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8591 - loss: 0.3344\n",
      "Epoch 243/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8578 - loss: 0.3516\n",
      "Epoch 244/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8328 - loss: 0.3643 \n",
      "Epoch 245/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8602 - loss: 0.3010\n",
      "Epoch 246/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8758 - loss: 0.3218\n",
      "Epoch 247/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8325 - loss: 0.3612\n",
      "Epoch 248/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8766 - loss: 0.3341\n",
      "Epoch 249/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8522 - loss: 0.3290\n",
      "Epoch 250/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8455 - loss: 0.3428\n",
      "Epoch 251/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 943us/step - accuracy: 0.8380 - loss: 0.3786\n",
      "Epoch 252/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8903 - loss: 0.3347 \n",
      "Epoch 253/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 962us/step - accuracy: 0.8534 - loss: 0.3374\n",
      "Epoch 254/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8366 - loss: 0.3582\n",
      "Epoch 255/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 961us/step - accuracy: 0.8673 - loss: 0.3274\n",
      "Epoch 256/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8435 - loss: 0.3454\n",
      "Epoch 257/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8569 - loss: 0.3211\n",
      "Epoch 258/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8530 - loss: 0.3297 \n",
      "Epoch 259/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8575 - loss: 0.3404 \n",
      "Epoch 260/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8780 - loss: 0.3144\n",
      "Epoch 261/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8635 - loss: 0.3194\n",
      "Epoch 262/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8489 - loss: 0.3189\n",
      "Epoch 263/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8609 - loss: 0.3395\n",
      "Epoch 264/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8402 - loss: 0.3343\n",
      "Epoch 265/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8555 - loss: 0.3337 \n",
      "Epoch 266/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8640 - loss: 0.3195\n",
      "Epoch 267/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8412 - loss: 0.3375\n",
      "Epoch 268/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8629 - loss: 0.3099\n",
      "Epoch 269/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8299 - loss: 0.3499\n",
      "Epoch 270/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8577 - loss: 0.3210\n",
      "Epoch 271/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8512 - loss: 0.3308\n",
      "Epoch 272/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8635 - loss: 0.3143\n",
      "Epoch 273/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.8699 - loss: 0.3077\n",
      "Epoch 274/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8461 - loss: 0.3595\n",
      "Epoch 275/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 917us/step - accuracy: 0.8355 - loss: 0.3424\n",
      "Epoch 276/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8691 - loss: 0.3193\n",
      "Epoch 277/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8709 - loss: 0.3133\n",
      "Epoch 278/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8599 - loss: 0.3300\n",
      "Epoch 279/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8595 - loss: 0.3297\n",
      "Epoch 280/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8794 - loss: 0.3108\n",
      "Epoch 281/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8514 - loss: 0.3043\n",
      "Epoch 282/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8367 - loss: 0.3531\n",
      "Epoch 283/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8787 - loss: 0.2910\n",
      "Epoch 284/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8688 - loss: 0.3049\n",
      "Epoch 285/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8620 - loss: 0.3035\n",
      "Epoch 286/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8698 - loss: 0.3058\n",
      "Epoch 287/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8338 - loss: 0.3423\n",
      "Epoch 288/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8568 - loss: 0.3152\n",
      "Epoch 289/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8737 - loss: 0.3111\n",
      "Epoch 290/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8681 - loss: 0.3429\n",
      "Epoch 291/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8631 - loss: 0.3097\n",
      "Epoch 292/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8593 - loss: 0.3424\n",
      "Epoch 293/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8659 - loss: 0.3172 \n",
      "Epoch 294/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8680 - loss: 0.2953\n",
      "Epoch 295/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8638 - loss: 0.2974\n",
      "Epoch 296/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8517 - loss: 0.3327\n",
      "Epoch 297/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8823 - loss: 0.3031\n",
      "Epoch 298/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8716 - loss: 0.3101\n",
      "Epoch 299/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8517 - loss: 0.3232\n",
      "Epoch 300/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8748 - loss: 0.3010\n",
      "Epoch 301/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 938us/step - accuracy: 0.8642 - loss: 0.3041\n",
      "Epoch 302/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8700 - loss: 0.2936\n",
      "Epoch 303/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8788 - loss: 0.2931\n",
      "Epoch 304/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8801 - loss: 0.3074\n",
      "Epoch 305/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8768 - loss: 0.2898\n",
      "Epoch 306/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8557 - loss: 0.3280\n",
      "Epoch 307/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8552 - loss: 0.3198\n",
      "Epoch 308/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8662 - loss: 0.3216\n",
      "Epoch 309/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8734 - loss: 0.3164\n",
      "Epoch 310/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8922 - loss: 0.2908\n",
      "Epoch 311/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8770 - loss: 0.3038\n",
      "Epoch 312/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - accuracy: 0.8590 - loss: 0.3051\n",
      "Epoch 313/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8497 - loss: 0.3436\n",
      "Epoch 314/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8521 - loss: 0.3113\n",
      "Epoch 315/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 961us/step - accuracy: 0.8531 - loss: 0.3424\n",
      "Epoch 316/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 944us/step - accuracy: 0.8722 - loss: 0.3052\n",
      "Epoch 317/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8714 - loss: 0.3089\n",
      "Epoch 318/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8645 - loss: 0.3123\n",
      "Epoch 319/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8457 - loss: 0.3155\n",
      "Epoch 320/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8694 - loss: 0.2889\n",
      "Epoch 321/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8849 - loss: 0.3061\n",
      "Epoch 322/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8569 - loss: 0.3202\n",
      "Epoch 323/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8596 - loss: 0.3385\n",
      "Epoch 324/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8547 - loss: 0.3093\n",
      "Epoch 325/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8872 - loss: 0.2837 \n",
      "Epoch 326/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 964us/step - accuracy: 0.8789 - loss: 0.2692\n",
      "Epoch 327/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8958 - loss: 0.2724\n",
      "Epoch 328/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8710 - loss: 0.3116\n",
      "Epoch 329/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8686 - loss: 0.2955\n",
      "Epoch 330/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 941us/step - accuracy: 0.8772 - loss: 0.2896\n",
      "Epoch 331/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8889 - loss: 0.2689\n",
      "Epoch 332/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8733 - loss: 0.2942\n",
      "Epoch 333/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8591 - loss: 0.2952\n",
      "Epoch 334/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8887 - loss: 0.2862\n",
      "Epoch 335/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8669 - loss: 0.3069\n",
      "Epoch 336/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8554 - loss: 0.3148\n",
      "Epoch 337/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8532 - loss: 0.3107\n",
      "Epoch 338/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8600 - loss: 0.3043\n",
      "Epoch 339/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 956us/step - accuracy: 0.8836 - loss: 0.2890\n",
      "Epoch 340/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8946 - loss: 0.2638\n",
      "Epoch 341/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8842 - loss: 0.2874\n",
      "Epoch 342/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8755 - loss: 0.2752\n",
      "Epoch 343/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8733 - loss: 0.2877\n",
      "Epoch 344/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8754 - loss: 0.2849\n",
      "Epoch 345/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8617 - loss: 0.3047\n",
      "Epoch 346/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8711 - loss: 0.2815\n",
      "Epoch 347/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8892 - loss: 0.2891\n",
      "Epoch 348/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8732 - loss: 0.2799\n",
      "Epoch 349/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8670 - loss: 0.3044\n",
      "Epoch 350/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8802 - loss: 0.3048\n",
      "Epoch 351/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8616 - loss: 0.2916\n",
      "Epoch 352/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8509 - loss: 0.3176 \n",
      "Epoch 353/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8553 - loss: 0.3182\n",
      "Epoch 354/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8787 - loss: 0.3028\n",
      "Epoch 355/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8839 - loss: 0.2982\n",
      "Epoch 356/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8450 - loss: 0.3245\n",
      "Epoch 357/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8730 - loss: 0.2993\n",
      "Epoch 358/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8846 - loss: 0.3123\n",
      "Epoch 359/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8762 - loss: 0.2957\n",
      "Epoch 360/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945us/step - accuracy: 0.8398 - loss: 0.3417\n",
      "Epoch 361/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8776 - loss: 0.2956\n",
      "Epoch 362/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - accuracy: 0.8667 - loss: 0.2749\n",
      "Epoch 363/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8933 - loss: 0.2748\n",
      "Epoch 364/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8906 - loss: 0.2710\n",
      "Epoch 365/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8508 - loss: 0.3119\n",
      "Epoch 366/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8842 - loss: 0.2723 \n",
      "Epoch 367/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8894 - loss: 0.2759\n",
      "Epoch 368/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8909 - loss: 0.2600\n",
      "Epoch 369/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8497 - loss: 0.3030\n",
      "Epoch 370/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8638 - loss: 0.2915\n",
      "Epoch 371/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8904 - loss: 0.2844\n",
      "Epoch 372/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8868 - loss: 0.2547\n",
      "Epoch 373/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8608 - loss: 0.3175\n",
      "Epoch 374/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8874 - loss: 0.2642\n",
      "Epoch 375/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8677 - loss: 0.2959\n",
      "Epoch 376/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8559 - loss: 0.2917 \n",
      "Epoch 377/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8917 - loss: 0.2747\n",
      "Epoch 378/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8388 - loss: 0.3057\n",
      "Epoch 379/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8643 - loss: 0.2920\n",
      "Epoch 380/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 946us/step - accuracy: 0.8648 - loss: 0.2913\n",
      "Epoch 381/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8795 - loss: 0.2928\n",
      "Epoch 382/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9013 - loss: 0.2626\n",
      "Epoch 383/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8896 - loss: 0.2719\n",
      "Epoch 384/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8420 - loss: 0.3161 \n",
      "Epoch 385/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8686 - loss: 0.3086\n",
      "Epoch 386/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8777 - loss: 0.3017\n",
      "Epoch 387/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8738 - loss: 0.3067\n",
      "Epoch 388/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8769 - loss: 0.2689\n",
      "Epoch 389/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - accuracy: 0.8693 - loss: 0.3174\n",
      "Epoch 390/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8962 - loss: 0.2564\n",
      "Epoch 391/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8841 - loss: 0.2832 \n",
      "Epoch 392/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8865 - loss: 0.2732\n",
      "Epoch 393/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8732 - loss: 0.2761\n",
      "Epoch 394/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8761 - loss: 0.2873\n",
      "Epoch 395/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8860 - loss: 0.2861\n",
      "Epoch 396/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 966us/step - accuracy: 0.8862 - loss: 0.2867\n",
      "Epoch 397/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8926 - loss: 0.2734 \n",
      "Epoch 398/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - accuracy: 0.8852 - loss: 0.2740\n",
      "Epoch 399/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8668 - loss: 0.3014\n",
      "Epoch 400/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8944 - loss: 0.2682\n",
      "Epoch 401/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8809 - loss: 0.2845\n",
      "Epoch 402/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8707 - loss: 0.2678\n",
      "Epoch 403/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 973us/step - accuracy: 0.8667 - loss: 0.2983\n",
      "Epoch 404/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8779 - loss: 0.3031\n",
      "Epoch 405/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 929us/step - accuracy: 0.8828 - loss: 0.3055\n",
      "Epoch 406/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8797 - loss: 0.2783\n",
      "Epoch 407/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8875 - loss: 0.2798\n",
      "Epoch 408/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8446 - loss: 0.3238\n",
      "Epoch 409/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8835 - loss: 0.2611\n",
      "Epoch 410/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8707 - loss: 0.2875\n",
      "Epoch 411/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945us/step - accuracy: 0.8785 - loss: 0.2820\n",
      "Epoch 412/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8739 - loss: 0.2624\n",
      "Epoch 413/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 932us/step - accuracy: 0.8817 - loss: 0.2976\n",
      "Epoch 414/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8813 - loss: 0.2739\n",
      "Epoch 415/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.8510 - loss: 0.2841\n",
      "Epoch 416/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8762 - loss: 0.2690\n",
      "Epoch 417/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8714 - loss: 0.2817\n",
      "Epoch 418/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8933 - loss: 0.2585\n",
      "Epoch 419/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8773 - loss: 0.2935\n",
      "Epoch 420/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8854 - loss: 0.2723\n",
      "Epoch 421/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - accuracy: 0.8691 - loss: 0.2891\n",
      "Epoch 422/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8620 - loss: 0.2809\n",
      "Epoch 423/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8863 - loss: 0.2674\n",
      "Epoch 424/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 955us/step - accuracy: 0.8894 - loss: 0.2640\n",
      "Epoch 425/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8901 - loss: 0.2498\n",
      "Epoch 426/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - accuracy: 0.8748 - loss: 0.2784\n",
      "Epoch 427/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8686 - loss: 0.2840\n",
      "Epoch 428/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8881 - loss: 0.2721\n",
      "Epoch 429/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8819 - loss: 0.2702\n",
      "Epoch 430/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8829 - loss: 0.2551\n",
      "Epoch 431/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 943us/step - accuracy: 0.8985 - loss: 0.2345\n",
      "Epoch 432/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8918 - loss: 0.2536 \n",
      "Epoch 433/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8853 - loss: 0.2861\n",
      "Epoch 434/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8858 - loss: 0.2739\n",
      "Epoch 435/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8806 - loss: 0.2697\n",
      "Epoch 436/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8802 - loss: 0.2827\n",
      "Epoch 437/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8731 - loss: 0.3039\n",
      "Epoch 438/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 953us/step - accuracy: 0.8786 - loss: 0.2839\n",
      "Epoch 439/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9015 - loss: 0.2548\n",
      "Epoch 440/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 972us/step - accuracy: 0.8998 - loss: 0.2603\n",
      "Epoch 441/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8933 - loss: 0.2528\n",
      "Epoch 442/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8978 - loss: 0.2604 \n",
      "Epoch 443/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8726 - loss: 0.2765\n",
      "Epoch 444/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8822 - loss: 0.2645\n",
      "Epoch 445/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8716 - loss: 0.2796\n",
      "Epoch 446/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8927 - loss: 0.2693\n",
      "Epoch 447/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 927us/step - accuracy: 0.8887 - loss: 0.2590\n",
      "Epoch 448/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8789 - loss: 0.2878\n",
      "Epoch 449/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8762 - loss: 0.2700\n",
      "Epoch 450/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8655 - loss: 0.2985 \n",
      "Epoch 451/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8924 - loss: 0.2769 \n",
      "Epoch 452/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 950us/step - accuracy: 0.8956 - loss: 0.2585\n",
      "Epoch 453/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8582 - loss: 0.3072\n",
      "Epoch 454/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8776 - loss: 0.2518\n",
      "Epoch 455/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8752 - loss: 0.2660 \n",
      "Epoch 456/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8967 - loss: 0.2561\n",
      "Epoch 457/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9025 - loss: 0.2375\n",
      "Epoch 458/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8775 - loss: 0.2784 \n",
      "Epoch 459/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 992us/step - accuracy: 0.9004 - loss: 0.2544\n",
      "Epoch 460/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8765 - loss: 0.2767 \n",
      "Epoch 461/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8842 - loss: 0.2905 \n",
      "Epoch 462/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8742 - loss: 0.2834 \n",
      "Epoch 463/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9055 - loss: 0.2585\n",
      "Epoch 464/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8938 - loss: 0.2511 \n",
      "Epoch 465/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8807 - loss: 0.2874\n",
      "Epoch 466/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 978us/step - accuracy: 0.8960 - loss: 0.2615\n",
      "Epoch 467/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8937 - loss: 0.2499\n",
      "Epoch 468/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8873 - loss: 0.2472\n",
      "Epoch 469/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.8941 - loss: 0.2604\n",
      "Epoch 470/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8945 - loss: 0.2489\n",
      "Epoch 471/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9118 - loss: 0.2347\n",
      "Epoch 472/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9144 - loss: 0.2305\n",
      "Epoch 473/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9075 - loss: 0.2356 \n",
      "Epoch 474/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8821 - loss: 0.2941\n",
      "Epoch 475/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8914 - loss: 0.2594\n",
      "Epoch 476/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8772 - loss: 0.2582\n",
      "Epoch 477/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8867 - loss: 0.2531\n",
      "Epoch 478/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9103 - loss: 0.2312\n",
      "Epoch 479/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8717 - loss: 0.3085\n",
      "Epoch 480/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8926 - loss: 0.2356\n",
      "Epoch 481/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8652 - loss: 0.2826\n",
      "Epoch 482/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8786 - loss: 0.2746\n",
      "Epoch 483/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8997 - loss: 0.2512\n",
      "Epoch 484/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8781 - loss: 0.2889\n",
      "Epoch 485/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8766 - loss: 0.2615\n",
      "Epoch 486/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8845 - loss: 0.2457\n",
      "Epoch 487/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 962us/step - accuracy: 0.9052 - loss: 0.2558\n",
      "Epoch 488/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9059 - loss: 0.2451\n",
      "Epoch 489/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8798 - loss: 0.2488\n",
      "Epoch 490/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9001 - loss: 0.2487 \n",
      "Epoch 491/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8991 - loss: 0.2439\n",
      "Epoch 492/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8904 - loss: 0.2500\n",
      "Epoch 493/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 959us/step - accuracy: 0.8989 - loss: 0.2487\n",
      "Epoch 494/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8928 - loss: 0.2476\n",
      "Epoch 495/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - accuracy: 0.9088 - loss: 0.2394\n",
      "Epoch 496/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8896 - loss: 0.2751\n",
      "Epoch 497/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9042 - loss: 0.2509\n",
      "Epoch 498/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8849 - loss: 0.2539\n",
      "Epoch 499/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8733 - loss: 0.2609\n",
      "Epoch 500/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9009 - loss: 0.2265\n",
      "Epoch 501/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9128 - loss: 0.2281\n",
      "Epoch 502/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 970us/step - accuracy: 0.8913 - loss: 0.2647\n",
      "Epoch 503/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9009 - loss: 0.2351\n",
      "Epoch 504/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8914 - loss: 0.2570\n",
      "Epoch 505/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8978 - loss: 0.2582\n",
      "Epoch 506/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8949 - loss: 0.2329 \n",
      "Epoch 507/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9129 - loss: 0.2586\n",
      "Epoch 508/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8782 - loss: 0.2693\n",
      "Epoch 509/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - accuracy: 0.9052 - loss: 0.2476\n",
      "Epoch 510/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9085 - loss: 0.2355\n",
      "Epoch 511/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 970us/step - accuracy: 0.8857 - loss: 0.2613\n",
      "Epoch 512/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8940 - loss: 0.2486\n",
      "Epoch 513/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9205 - loss: 0.2361\n",
      "Epoch 514/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9092 - loss: 0.2334\n",
      "Epoch 515/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9052 - loss: 0.2258\n",
      "Epoch 516/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8978 - loss: 0.2352 \n",
      "Epoch 517/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8947 - loss: 0.2572\n",
      "Epoch 518/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 961us/step - accuracy: 0.8913 - loss: 0.2532\n",
      "Epoch 519/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.8872 - loss: 0.2544\n",
      "Epoch 520/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.8921 - loss: 0.2363\n",
      "Epoch 521/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9217 - loss: 0.2287\n",
      "Epoch 522/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.8769 - loss: 0.2706\n",
      "Epoch 523/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8935 - loss: 0.2604\n",
      "Epoch 524/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9024 - loss: 0.2417 \n",
      "Epoch 525/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9012 - loss: 0.2490\n",
      "Epoch 526/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8940 - loss: 0.2575\n",
      "Epoch 527/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8923 - loss: 0.2386\n",
      "Epoch 528/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9247 - loss: 0.2209\n",
      "Epoch 529/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8922 - loss: 0.2451\n",
      "Epoch 530/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9138 - loss: 0.2300\n",
      "Epoch 531/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9135 - loss: 0.2335\n",
      "Epoch 532/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - accuracy: 0.8880 - loss: 0.2625\n",
      "Epoch 533/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8917 - loss: 0.2342\n",
      "Epoch 534/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8954 - loss: 0.2419\n",
      "Epoch 535/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9083 - loss: 0.2416\n",
      "Epoch 536/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8851 - loss: 0.2452\n",
      "Epoch 537/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8937 - loss: 0.2492\n",
      "Epoch 538/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9227 - loss: 0.2208\n",
      "Epoch 539/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8878 - loss: 0.2483\n",
      "Epoch 540/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9138 - loss: 0.2283\n",
      "Epoch 541/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.9103 - loss: 0.2292\n",
      "Epoch 542/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9125 - loss: 0.2156\n",
      "Epoch 543/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8961 - loss: 0.2294\n",
      "Epoch 544/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8853 - loss: 0.2652\n",
      "Epoch 545/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.9167 - loss: 0.2077\n",
      "Epoch 546/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8944 - loss: 0.2432\n",
      "Epoch 547/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9031 - loss: 0.2294\n",
      "Epoch 548/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9186 - loss: 0.2244\n",
      "Epoch 549/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8949 - loss: 0.2476\n",
      "Epoch 550/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9011 - loss: 0.2396\n",
      "Epoch 551/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8936 - loss: 0.2446\n",
      "Epoch 552/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - accuracy: 0.8995 - loss: 0.2343\n",
      "Epoch 553/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9067 - loss: 0.2193\n",
      "Epoch 554/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9129 - loss: 0.2261 \n",
      "Epoch 555/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - accuracy: 0.9249 - loss: 0.2220\n",
      "Epoch 556/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8751 - loss: 0.2593\n",
      "Epoch 557/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8943 - loss: 0.2436\n",
      "Epoch 558/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 929us/step - accuracy: 0.8990 - loss: 0.2581\n",
      "Epoch 559/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.9143 - loss: 0.2218\n",
      "Epoch 560/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.8986 - loss: 0.2293\n",
      "Epoch 561/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.8881 - loss: 0.2458\n",
      "Epoch 562/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9138 - loss: 0.2300\n",
      "Epoch 563/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 959us/step - accuracy: 0.8938 - loss: 0.2269\n",
      "Epoch 564/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9186 - loss: 0.2243\n",
      "Epoch 565/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8868 - loss: 0.2586\n",
      "Epoch 566/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9090 - loss: 0.2399\n",
      "Epoch 567/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8815 - loss: 0.2439 \n",
      "Epoch 568/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9215 - loss: 0.2202\n",
      "Epoch 569/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 960us/step - accuracy: 0.8950 - loss: 0.2413\n",
      "Epoch 570/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9072 - loss: 0.2298\n",
      "Epoch 571/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9212 - loss: 0.2120\n",
      "Epoch 572/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8865 - loss: 0.2657\n",
      "Epoch 573/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9097 - loss: 0.2254\n",
      "Epoch 574/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9102 - loss: 0.2024\n",
      "Epoch 575/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8986 - loss: 0.2349\n",
      "Epoch 576/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9222 - loss: 0.2222\n",
      "Epoch 577/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8672 - loss: 0.2907\n",
      "Epoch 578/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9323 - loss: 0.1991\n",
      "Epoch 579/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9142 - loss: 0.1977\n",
      "Epoch 580/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8918 - loss: 0.2329\n",
      "Epoch 581/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8976 - loss: 0.2422\n",
      "Epoch 582/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.8906 - loss: 0.2482\n",
      "Epoch 583/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8904 - loss: 0.2504\n",
      "Epoch 584/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9158 - loss: 0.2080 \n",
      "Epoch 585/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9027 - loss: 0.2625\n",
      "Epoch 586/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9161 - loss: 0.2254\n",
      "Epoch 587/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8921 - loss: 0.2302\n",
      "Epoch 588/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9101 - loss: 0.2177\n",
      "Epoch 589/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9104 - loss: 0.2201\n",
      "Epoch 590/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.8945 - loss: 0.2230\n",
      "Epoch 591/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9221 - loss: 0.1995 \n",
      "Epoch 592/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9187 - loss: 0.2049\n",
      "Epoch 593/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9157 - loss: 0.2158\n",
      "Epoch 594/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - accuracy: 0.9084 - loss: 0.2280\n",
      "Epoch 595/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9023 - loss: 0.2339\n",
      "Epoch 596/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 953us/step - accuracy: 0.9216 - loss: 0.2034\n",
      "Epoch 597/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9021 - loss: 0.2284\n",
      "Epoch 598/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.9176 - loss: 0.2133\n",
      "Epoch 599/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - accuracy: 0.9167 - loss: 0.2408\n",
      "Epoch 600/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - accuracy: 0.9173 - loss: 0.2128\n",
      "Accuracy: 74.03%\n",
      "Epoch 1/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7076 - loss: 0.5600   \n",
      "Epoch 2/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7699 - loss: 0.4650 \n",
      "Epoch 3/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 981us/step - accuracy: 0.8177 - loss: 0.4041\n",
      "Epoch 4/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8218 - loss: 0.4012\n",
      "Epoch 5/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8140 - loss: 0.3743 \n",
      "Epoch 6/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 983us/step - accuracy: 0.8615 - loss: 0.3436\n",
      "Epoch 7/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8661 - loss: 0.3294\n",
      "Epoch 8/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8381 - loss: 0.3337 \n",
      "Epoch 9/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8877 - loss: 0.2903\n",
      "Epoch 10/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8984 - loss: 0.2950\n",
      "Epoch 11/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.8686 - loss: 0.3094\n",
      "Epoch 12/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8725 - loss: 0.2918\n",
      "Epoch 13/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8669 - loss: 0.2967\n",
      "Epoch 14/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8449 - loss: 0.3188 \n",
      "Epoch 15/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8637 - loss: 0.2773\n",
      "Epoch 16/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8586 - loss: 0.2856\n",
      "Epoch 17/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8902 - loss: 0.2563\n",
      "Epoch 18/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.8606 - loss: 0.3123\n",
      "Epoch 19/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8697 - loss: 0.2588\n",
      "Epoch 20/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8965 - loss: 0.2444\n",
      "Epoch 21/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8911 - loss: 0.2540 \n",
      "Epoch 22/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8709 - loss: 0.2735\n",
      "Epoch 23/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8725 - loss: 0.2795\n",
      "Epoch 24/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9148 - loss: 0.2264\n",
      "Epoch 25/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 990us/step - accuracy: 0.9043 - loss: 0.2415\n",
      "Epoch 26/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9081 - loss: 0.2301\n",
      "Epoch 27/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.8876 - loss: 0.2389\n",
      "Epoch 28/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8936 - loss: 0.2454 \n",
      "Epoch 29/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9017 - loss: 0.2334\n",
      "Epoch 30/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8872 - loss: 0.2468 \n",
      "Epoch 31/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8974 - loss: 0.2549\n",
      "Epoch 32/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9201 - loss: 0.2054 \n",
      "Epoch 33/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9136 - loss: 0.2166 \n",
      "Epoch 34/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9284 - loss: 0.2014\n",
      "Epoch 35/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9227 - loss: 0.2162\n",
      "Epoch 36/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9361 - loss: 0.1939\n",
      "Epoch 37/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9229 - loss: 0.2065 \n",
      "Epoch 38/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9256 - loss: 0.1988 \n",
      "Epoch 39/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9277 - loss: 0.1960\n",
      "Epoch 40/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8982 - loss: 0.2010\n",
      "Epoch 41/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8946 - loss: 0.2257 \n",
      "Epoch 42/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9176 - loss: 0.1965 \n",
      "Epoch 43/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9257 - loss: 0.1875\n",
      "Epoch 44/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9133 - loss: 0.2078\n",
      "Epoch 45/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9057 - loss: 0.2210\n",
      "Epoch 46/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 984us/step - accuracy: 0.9066 - loss: 0.2468\n",
      "Epoch 47/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9402 - loss: 0.1595 \n",
      "Epoch 48/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9117 - loss: 0.2011\n",
      "Epoch 49/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9336 - loss: 0.1786 \n",
      "Epoch 50/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9263 - loss: 0.1694 \n",
      "Epoch 51/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9095 - loss: 0.1973\n",
      "Epoch 52/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.9336 - loss: 0.1766\n",
      "Epoch 53/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9052 - loss: 0.2186\n",
      "Epoch 54/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9336 - loss: 0.1883\n",
      "Epoch 55/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9332 - loss: 0.1581\n",
      "Epoch 56/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9337 - loss: 0.1822 \n",
      "Epoch 57/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9350 - loss: 0.1644 \n",
      "Epoch 58/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9193 - loss: 0.1903\n",
      "Epoch 59/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9301 - loss: 0.1485\n",
      "Epoch 60/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9485 - loss: 0.1449 \n",
      "Epoch 61/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9520 - loss: 0.1499\n",
      "Epoch 62/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9183 - loss: 0.1904 \n",
      "Epoch 63/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9248 - loss: 0.1585\n",
      "Epoch 64/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9443 - loss: 0.1545\n",
      "Epoch 65/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9255 - loss: 0.2020\n",
      "Epoch 66/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9236 - loss: 0.1635\n",
      "Epoch 67/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9367 - loss: 0.1470\n",
      "Epoch 68/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9408 - loss: 0.1752\n",
      "Epoch 69/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9475 - loss: 0.1505 \n",
      "Epoch 70/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9638 - loss: 0.1231\n",
      "Epoch 71/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9374 - loss: 0.1507\n",
      "Epoch 72/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.8704 - loss: 0.2871\n",
      "Epoch 73/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9356 - loss: 0.1771\n",
      "Epoch 74/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9557 - loss: 0.1308\n",
      "Epoch 75/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9489 - loss: 0.1253 \n",
      "Epoch 76/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9536 - loss: 0.1334\n",
      "Epoch 77/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9408 - loss: 0.1677\n",
      "Epoch 78/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - accuracy: 0.9510 - loss: 0.1560\n",
      "Epoch 79/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9664 - loss: 0.1235\n",
      "Epoch 80/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9486 - loss: 0.1298\n",
      "Epoch 81/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9550 - loss: 0.1392\n",
      "Epoch 82/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9650 - loss: 0.1136\n",
      "Epoch 83/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9566 - loss: 0.1294\n",
      "Epoch 84/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9640 - loss: 0.1074\n",
      "Epoch 85/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9693 - loss: 0.1100\n",
      "Epoch 86/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9679 - loss: 0.1169 \n",
      "Epoch 87/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9620 - loss: 0.1050 \n",
      "Epoch 88/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9567 - loss: 0.1195\n",
      "Epoch 89/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9561 - loss: 0.1235 \n",
      "Epoch 90/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9271 - loss: 0.1577 \n",
      "Epoch 91/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9515 - loss: 0.1139 \n",
      "Epoch 92/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9593 - loss: 0.1008 \n",
      "Epoch 93/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9647 - loss: 0.1129\n",
      "Epoch 94/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9313 - loss: 0.1578 \n",
      "Epoch 95/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9603 - loss: 0.1199 \n",
      "Epoch 96/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9704 - loss: 0.1044\n",
      "Epoch 97/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9066 - loss: 0.2039 \n",
      "Epoch 98/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9458 - loss: 0.1301 \n",
      "Epoch 99/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9823 - loss: 0.0833 \n",
      "Epoch 100/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9789 - loss: 0.0898 \n",
      "Epoch 101/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9617 - loss: 0.1083\n",
      "Epoch 102/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9446 - loss: 0.1324\n",
      "Epoch 103/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9745 - loss: 0.1010 \n",
      "Epoch 104/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9823 - loss: 0.0744\n",
      "Epoch 105/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9709 - loss: 0.0943\n",
      "Epoch 106/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9705 - loss: 0.0985\n",
      "Epoch 107/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9590 - loss: 0.1065\n",
      "Epoch 108/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9865 - loss: 0.0671 \n",
      "Epoch 109/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9679 - loss: 0.0869 \n",
      "Epoch 110/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9691 - loss: 0.0883\n",
      "Epoch 111/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9788 - loss: 0.0820\n",
      "Epoch 112/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9282 - loss: 0.1643\n",
      "Epoch 113/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9565 - loss: 0.1093\n",
      "Epoch 114/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9502 - loss: 0.1128 \n",
      "Epoch 115/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9631 - loss: 0.0899 \n",
      "Epoch 116/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9680 - loss: 0.0946\n",
      "Epoch 117/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9718 - loss: 0.0809\n",
      "Epoch 118/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9823 - loss: 0.0743 \n",
      "Epoch 119/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9565 - loss: 0.0959 \n",
      "Epoch 120/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9601 - loss: 0.1049\n",
      "Epoch 121/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9476 - loss: 0.1184 \n",
      "Epoch 122/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9750 - loss: 0.0765 \n",
      "Epoch 123/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9733 - loss: 0.0797 \n",
      "Epoch 124/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9735 - loss: 0.0832 \n",
      "Epoch 125/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9675 - loss: 0.1142 \n",
      "Epoch 126/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9822 - loss: 0.0665\n",
      "Epoch 127/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1000us/step - accuracy: 0.9888 - loss: 0.0630\n",
      "Epoch 128/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9800 - loss: 0.0664\n",
      "Epoch 129/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9868 - loss: 0.0542 \n",
      "Epoch 130/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9867 - loss: 0.0584 \n",
      "Epoch 131/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9791 - loss: 0.0747\n",
      "Epoch 132/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9685 - loss: 0.0823\n",
      "Epoch 133/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9851 - loss: 0.0633 \n",
      "Epoch 134/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9870 - loss: 0.0535\n",
      "Epoch 135/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9829 - loss: 0.0592\n",
      "Epoch 136/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.9896 - loss: 0.0542\n",
      "Epoch 137/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9893 - loss: 0.0526\n",
      "Epoch 138/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9785 - loss: 0.0652 \n",
      "Epoch 139/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9762 - loss: 0.0641\n",
      "Epoch 140/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9568 - loss: 0.0992\n",
      "Epoch 141/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9802 - loss: 0.0564\n",
      "Epoch 142/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9803 - loss: 0.0688 \n",
      "Epoch 143/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9850 - loss: 0.0521\n",
      "Epoch 144/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9663 - loss: 0.0980\n",
      "Epoch 145/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9677 - loss: 0.0872 \n",
      "Epoch 146/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9749 - loss: 0.0637\n",
      "Epoch 147/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9748 - loss: 0.0916 \n",
      "Epoch 148/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9680 - loss: 0.0734 \n",
      "Epoch 149/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9832 - loss: 0.0598 \n",
      "Epoch 150/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 0.9779 - loss: 0.0601\n",
      "Epoch 151/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9545 - loss: 0.1284\n",
      "Epoch 152/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 0.9352 - loss: 0.1332\n",
      "Epoch 153/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9791 - loss: 0.0565 \n",
      "Epoch 154/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 995us/step - accuracy: 0.9955 - loss: 0.0433\n",
      "Epoch 155/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - accuracy: 0.9883 - loss: 0.0493\n",
      "Epoch 156/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9851 - loss: 0.0511\n",
      "Epoch 157/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9956 - loss: 0.0347 \n",
      "Epoch 158/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 987us/step - accuracy: 0.9693 - loss: 0.0694\n",
      "Epoch 159/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9842 - loss: 0.0503\n",
      "Epoch 160/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9846 - loss: 0.0539\n",
      "Epoch 161/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9906 - loss: 0.0401 \n",
      "Epoch 162/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9914 - loss: 0.0376\n",
      "Epoch 163/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9919 - loss: 0.0465\n",
      "Epoch 164/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9882 - loss: 0.0339 \n",
      "Epoch 165/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9874 - loss: 0.0444\n",
      "Epoch 166/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9942 - loss: 0.0362\n",
      "Epoch 167/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9945 - loss: 0.0318 \n",
      "Epoch 168/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9985 - loss: 0.0367 \n",
      "Epoch 169/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9972 - loss: 0.0288 \n",
      "Epoch 170/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 996us/step - accuracy: 0.9973 - loss: 0.0317\n",
      "Epoch 171/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9985 - loss: 0.0265\n",
      "Epoch 172/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.9896 - loss: 0.0346\n",
      "Epoch 173/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9928 - loss: 0.0329\n",
      "Epoch 174/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9994 - loss: 0.0285\n",
      "Epoch 175/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9925 - loss: 0.0343 \n",
      "Epoch 176/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9859 - loss: 0.0423 \n",
      "Epoch 177/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9888 - loss: 0.0332\n",
      "Epoch 178/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9291 - loss: 0.1676\n",
      "Epoch 179/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9622 - loss: 0.0874 \n",
      "Epoch 180/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9604 - loss: 0.0915\n",
      "Epoch 181/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9926 - loss: 0.0360 \n",
      "Epoch 182/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9980 - loss: 0.0254\n",
      "Epoch 183/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9840 - loss: 0.0475 \n",
      "Epoch 184/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9890 - loss: 0.0419\n",
      "Epoch 185/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9900 - loss: 0.0431 \n",
      "Epoch 186/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9566 - loss: 0.1196 \n",
      "Epoch 187/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9748 - loss: 0.0825 \n",
      "Epoch 188/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9848 - loss: 0.0353\n",
      "Epoch 189/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9995 - loss: 0.0251\n",
      "Epoch 190/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9841 - loss: 0.0645 \n",
      "Epoch 191/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9655 - loss: 0.0758 \n",
      "Epoch 192/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9903 - loss: 0.0324 \n",
      "Epoch 193/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9938 - loss: 0.0353\n",
      "Epoch 194/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9962 - loss: 0.0194 \n",
      "Epoch 195/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9993 - loss: 0.0200\n",
      "Epoch 196/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9941 - loss: 0.0238 \n",
      "Epoch 197/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9944 - loss: 0.0264 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 198/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9858 - loss: 0.0447\n",
      "Epoch 199/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9900 - loss: 0.0379 \n",
      "Epoch 200/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9997 - loss: 0.0178\n",
      "Epoch 201/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9964 - loss: 0.0230\n",
      "Epoch 202/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9850 - loss: 0.0427 \n",
      "Epoch 203/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9914 - loss: 0.0351\n",
      "Epoch 204/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9887 - loss: 0.0337\n",
      "Epoch 205/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9959 - loss: 0.0224\n",
      "Epoch 206/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9912 - loss: 0.0265\n",
      "Epoch 207/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9974 - loss: 0.0234 \n",
      "Epoch 208/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9938 - loss: 0.0239 \n",
      "Epoch 209/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9858 - loss: 0.0322     \n",
      "Epoch 210/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9922 - loss: 0.0269\n",
      "Epoch 211/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9992 - loss: 0.0169\n",
      "Epoch 212/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9942 - loss: 0.0256 \n",
      "Epoch 213/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9993 - loss: 0.0179\n",
      "Epoch 214/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9984 - loss: 0.0128 \n",
      "Epoch 215/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 973us/step - accuracy: 0.9963 - loss: 0.0155\n",
      "Epoch 216/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9880 - loss: 0.0307 \n",
      "Epoch 217/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9802 - loss: 0.0506\n",
      "Epoch 218/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9281 - loss: 0.1536\n",
      "Epoch 219/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9331 - loss: 0.2136\n",
      "Epoch 220/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9603 - loss: 0.0980\n",
      "Epoch 221/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9948 - loss: 0.0384\n",
      "Epoch 222/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9905 - loss: 0.0334\n",
      "Epoch 223/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9860 - loss: 0.0428\n",
      "Epoch 224/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9851 - loss: 0.0423 \n",
      "Epoch 225/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9824 - loss: 0.0522\n",
      "Epoch 226/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9875 - loss: 0.0323 \n",
      "Epoch 227/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9970 - loss: 0.0173\n",
      "Epoch 228/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9993 - loss: 0.0150 \n",
      "Epoch 229/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9977 - loss: 0.0166\n",
      "Epoch 230/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9992 - loss: 0.0135 \n",
      "Epoch 231/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9966 - loss: 0.0168\n",
      "Epoch 232/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9990 - loss: 0.0134 \n",
      "Epoch 233/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9971 - loss: 0.0158\n",
      "Epoch 234/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9986 - loss: 0.0123\n",
      "Epoch 235/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9967 - loss: 0.0201\n",
      "Epoch 236/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9920 - loss: 0.0234\n",
      "Epoch 237/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9990 - loss: 0.0129\n",
      "Epoch 238/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9998 - loss: 0.0125\n",
      "Epoch 239/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9991 - loss: 0.0107   \n",
      "Epoch 240/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9974 - loss: 0.0160\n",
      "Epoch 241/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9932 - loss: 0.0253 \n",
      "Epoch 242/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9995 - loss: 0.0162 \n",
      "Epoch 243/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 0.9974 - loss: 0.0156\n",
      "Epoch 244/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9998 - loss: 0.0121\n",
      "Epoch 245/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9904 - loss: 0.0270 \n",
      "Epoch 246/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9949 - loss: 0.0203\n",
      "Epoch 247/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9998 - loss: 0.0162 \n",
      "Epoch 248/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9942 - loss: 0.0178   \n",
      "Epoch 249/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9914 - loss: 0.0262 \n",
      "Epoch 250/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9867 - loss: 0.0462 \n",
      "Epoch 251/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9896 - loss: 0.0304 \n",
      "Epoch 252/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9907 - loss: 0.0302 \n",
      "Epoch 253/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9705 - loss: 0.0909 \n",
      "Epoch 254/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9789 - loss: 0.0781 \n",
      "Epoch 255/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9846 - loss: 0.0440\n",
      "Epoch 256/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9892 - loss: 0.0349 \n",
      "Epoch 257/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9982 - loss: 0.0141 \n",
      "Epoch 258/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9982 - loss: 0.0134 \n",
      "Epoch 259/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9991 - loss: 0.0152 \n",
      "Epoch 260/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9982 - loss: 0.0125 \n",
      "Epoch 261/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9992 - loss: 0.0115 \n",
      "Epoch 262/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9971 - loss: 0.0118\n",
      "Epoch 263/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9966 - loss: 0.0110 \n",
      "Epoch 264/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0119\n",
      "Epoch 265/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 978us/step - accuracy: 0.9998 - loss: 0.0062\n",
      "Epoch 266/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9990 - loss: 0.0145 \n",
      "Epoch 267/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9962 - loss: 0.0193\n",
      "Epoch 268/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 985us/step - accuracy: 0.9987 - loss: 0.0111\n",
      "Epoch 269/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9978 - loss: 0.0124 \n",
      "Epoch 270/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9885 - loss: 0.0320\n",
      "Epoch 271/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9805 - loss: 0.0752 \n",
      "Epoch 272/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9905 - loss: 0.0337 \n",
      "Epoch 273/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9987 - loss: 0.0174 \n",
      "Epoch 274/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.9987 - loss: 0.0107\n",
      "Epoch 275/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9993 - loss: 0.0105\n",
      "Epoch 276/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9982 - loss: 0.0128 \n",
      "Epoch 277/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9984 - loss: 0.0128\n",
      "Epoch 278/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9897 - loss: 0.0228\n",
      "Epoch 279/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9984 - loss: 0.0140\n",
      "Epoch 280/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9834 - loss: 0.0473\n",
      "Epoch 281/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9897 - loss: 0.0239 \n",
      "Epoch 282/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9899 - loss: 0.0308\n",
      "Epoch 283/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9940 - loss: 0.0184\n",
      "Epoch 284/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9932 - loss: 0.0377\n",
      "Epoch 285/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9841 - loss: 0.0461 \n",
      "Epoch 286/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9957 - loss: 0.0132 \n",
      "Epoch 287/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9991 - loss: 0.0065\n",
      "Epoch 288/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0069 \n",
      "Epoch 289/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9993 - loss: 0.0076\n",
      "Epoch 290/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0061 \n",
      "Epoch 291/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0061\n",
      "Epoch 292/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9994 - loss: 0.0057   \n",
      "Epoch 293/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9971 - loss: 0.0122\n",
      "Epoch 294/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9861 - loss: 0.0342 \n",
      "Epoch 295/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9937 - loss: 0.0279 \n",
      "Epoch 296/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9986 - loss: 0.0081 \n",
      "Epoch 297/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 0.9961 - loss: 0.0202   \n",
      "Epoch 298/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9923 - loss: 0.0206 \n",
      "Epoch 299/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9998 - loss: 0.0128\n",
      "Epoch 300/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9940 - loss: 0.0144 \n",
      "Epoch 301/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0099 \n",
      "Epoch 302/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0061 \n",
      "Epoch 303/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0057   \n",
      "Epoch 304/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9991 - loss: 0.0061 \n",
      "Epoch 305/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9989 - loss: 0.0082 \n",
      "Epoch 306/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9998 - loss: 0.0069 \n",
      "Epoch 307/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9982 - loss: 0.0084\n",
      "Epoch 308/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9905 - loss: 0.0281\n",
      "Epoch 309/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9706 - loss: 0.0578\n",
      "Epoch 310/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9458 - loss: 0.1529\n",
      "Epoch 311/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9384 - loss: 0.1987 \n",
      "Epoch 312/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 0.9616 - loss: 0.1146\n",
      "Epoch 313/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9723 - loss: 0.0624\n",
      "Epoch 314/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9975 - loss: 0.0197\n",
      "Epoch 315/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0107\n",
      "Epoch 316/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0086 \n",
      "Epoch 317/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0075\n",
      "Epoch 318/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0070\n",
      "Epoch 319/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0061 \n",
      "Epoch 320/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0066 \n",
      "Epoch 321/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0054\n",
      "Epoch 322/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0063 \n",
      "Epoch 323/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0048\n",
      "Epoch 324/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0059 \n",
      "Epoch 325/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0040 \n",
      "Epoch 326/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0048\n",
      "Epoch 327/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0050\n",
      "Epoch 328/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0045 \n",
      "Epoch 329/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0038\n",
      "Epoch 330/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0043\n",
      "Epoch 331/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0046 \n",
      "Epoch 332/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0043 \n",
      "Epoch 333/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0054\n",
      "Epoch 334/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0038\n",
      "Epoch 335/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0037 \n",
      "Epoch 336/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 337/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0026   \n",
      "Epoch 338/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0042\n",
      "Epoch 339/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9993 - loss: 0.0063\n",
      "Epoch 340/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9997 - loss: 0.0060\n",
      "Epoch 341/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9995 - loss: 0.0049\n",
      "Epoch 342/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9968 - loss: 0.0107     \n",
      "Epoch 343/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9998 - loss: 0.0101 \n",
      "Epoch 344/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9971 - loss: 0.0173 \n",
      "Epoch 345/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0040 \n",
      "Epoch 346/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0032 \n",
      "Epoch 347/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0033 \n",
      "Epoch 348/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0027   \n",
      "Epoch 349/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9967 - loss: 0.0153     \n",
      "Epoch 350/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9546 - loss: 0.1091 \n",
      "Epoch 351/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9422 - loss: 0.1611 \n",
      "Epoch 352/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9843 - loss: 0.0512 \n",
      "Epoch 353/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9777 - loss: 0.0596 \n",
      "Epoch 354/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9801 - loss: 0.0524 \n",
      "Epoch 355/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9978 - loss: 0.0150 \n",
      "Epoch 356/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0104\n",
      "Epoch 357/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0046\n",
      "Epoch 358/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0036\n",
      "Epoch 359/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - accuracy: 1.0000 - loss: 0.0039\n",
      "Epoch 360/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 361/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 1.0000 - loss: 0.0035\n",
      "Epoch 362/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 363/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 364/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0031\n",
      "Epoch 365/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0030 \n",
      "Epoch 366/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0031\n",
      "Epoch 367/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0031 \n",
      "Epoch 368/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 1.0000 - loss: 0.0027   \n",
      "Epoch 369/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0030\n",
      "Epoch 370/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 1.0000 - loss: 0.0030\n",
      "Epoch 371/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 372/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 1.0000 - loss: 0.0029   \n",
      "Epoch 373/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9996 - loss: 0.0050\n",
      "Epoch 374/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9980 - loss: 0.0084\n",
      "Epoch 375/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9867 - loss: 0.0292\n",
      "Epoch 376/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9981 - loss: 0.0146\n",
      "Epoch 377/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9901 - loss: 0.0273\n",
      "Epoch 378/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9989 - loss: 0.0137 \n",
      "Epoch 379/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9802 - loss: 0.0640\n",
      "Epoch 380/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9224 - loss: 0.2592\n",
      "Epoch 381/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9596 - loss: 0.1151\n",
      "Epoch 382/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 0.9792 - loss: 0.0540\n",
      "Epoch 383/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 0.9981 - loss: 0.0127\n",
      "Epoch 384/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0050 \n",
      "Epoch 385/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0043\n",
      "Epoch 386/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0040 \n",
      "Epoch 387/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 994us/step - accuracy: 1.0000 - loss: 0.0036\n",
      "Epoch 388/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0034\n",
      "Epoch 389/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 390/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0035 \n",
      "Epoch 391/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0036 \n",
      "Epoch 392/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0025 \n",
      "Epoch 393/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 394/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 395/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 396/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0025 \n",
      "Epoch 397/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0024     \n",
      "Epoch 398/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0023\n",
      "Epoch 399/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9988 - loss: 0.0053     \n",
      "Epoch 400/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0031\n",
      "Epoch 401/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0023\n",
      "Epoch 402/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 1.0000 - loss: 0.0025\n",
      "Epoch 403/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0026\n",
      "Epoch 404/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0024   \n",
      "Epoch 405/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 406/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0022\n",
      "Epoch 407/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0021 \n",
      "Epoch 408/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0019 \n",
      "Epoch 409/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0017   \n",
      "Epoch 410/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 411/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0018 \n",
      "Epoch 412/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 413/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 991us/step - accuracy: 1.0000 - loss: 0.0022\n",
      "Epoch 414/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 415/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0060\n",
      "Epoch 416/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9919 - loss: 0.0252\n",
      "Epoch 417/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9417 - loss: 0.1762\n",
      "Epoch 418/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9351 - loss: 0.1631 \n",
      "Epoch 419/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 976us/step - accuracy: 0.9497 - loss: 0.1661\n",
      "Epoch 420/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9445 - loss: 0.1600\n",
      "Epoch 421/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 0.9870 - loss: 0.0605\n",
      "Epoch 422/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 0.9971 - loss: 0.0178\n",
      "Epoch 423/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0062 \n",
      "Epoch 424/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0064\n",
      "Epoch 425/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0053 \n",
      "Epoch 426/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0042\n",
      "Epoch 427/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 1.0000 - loss: 0.0048\n",
      "Epoch 428/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0043 \n",
      "Epoch 429/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0035\n",
      "Epoch 430/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0044\n",
      "Epoch 431/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0035 \n",
      "Epoch 432/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 433/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0030\n",
      "Epoch 434/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 435/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0031 \n",
      "Epoch 436/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0027 \n",
      "Epoch 437/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 438/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0024 \n",
      "Epoch 439/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0024     \n",
      "Epoch 440/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0022 \n",
      "Epoch 441/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0023 \n",
      "Epoch 442/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0020 \n",
      "Epoch 443/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0023     \n",
      "Epoch 444/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0024\n",
      "Epoch 445/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0021 \n",
      "Epoch 446/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0019\n",
      "Epoch 447/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0021 \n",
      "Epoch 448/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 1.0000 - loss: 0.0020   \n",
      "Epoch 449/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0020 \n",
      "Epoch 450/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0020 \n",
      "Epoch 451/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0022 \n",
      "Epoch 452/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0017   \n",
      "Epoch 453/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 454/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0015\n",
      "Epoch 455/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0019 \n",
      "Epoch 456/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0018 \n",
      "Epoch 457/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0014 \n",
      "Epoch 458/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0017     \n",
      "Epoch 459/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0014   \n",
      "Epoch 460/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0015\n",
      "Epoch 461/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0014   \n",
      "Epoch 462/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0014     \n",
      "Epoch 463/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0013   \n",
      "Epoch 464/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0015     \n",
      "Epoch 465/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0014\n",
      "Epoch 466/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0014   \n",
      "Epoch 467/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0012   \n",
      "Epoch 468/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9813 - loss: 0.0466\n",
      "Epoch 469/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 0.9241 - loss: 0.2346\n",
      "Epoch 470/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9405 - loss: 0.1361\n",
      "Epoch 471/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 0.9773 - loss: 0.0443\n",
      "Epoch 472/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 984us/step - accuracy: 0.9938 - loss: 0.0226\n",
      "Epoch 473/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0068\n",
      "Epoch 474/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0040\n",
      "Epoch 475/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0037\n",
      "Epoch 476/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0035\n",
      "Epoch 477/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0036\n",
      "Epoch 478/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0031 \n",
      "Epoch 479/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0029\n",
      "Epoch 480/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 975us/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 481/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 482/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0023 \n",
      "Epoch 483/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0023     \n",
      "Epoch 484/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0022 \n",
      "Epoch 485/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0019   \n",
      "Epoch 486/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0020 \n",
      "Epoch 487/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0017   \n",
      "Epoch 488/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0019   \n",
      "Epoch 489/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0019 \n",
      "Epoch 490/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 491/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0015     \n",
      "Epoch 492/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0018\n",
      "Epoch 493/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 494/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0016\n",
      "Epoch 495/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0018 \n",
      "Epoch 496/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0019 \n",
      "Epoch 497/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 498/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0015 \n",
      "Epoch 499/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 500/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0015     \n",
      "Epoch 501/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011 \n",
      "Epoch 502/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0013 \n",
      "Epoch 503/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0013 \n",
      "Epoch 504/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0014 \n",
      "Epoch 505/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011   \n",
      "Epoch 506/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0015\n",
      "Epoch 507/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0012   \n",
      "Epoch 508/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 974us/step - accuracy: 1.0000 - loss: 9.2304e-04\n",
      "Epoch 509/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0013 \n",
      "Epoch 510/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0011   \n",
      "Epoch 511/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 9.9511e-04 \n",
      "Epoch 512/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011 \n",
      "Epoch 513/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0015 \n",
      "Epoch 514/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0014     \n",
      "Epoch 515/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011 \n",
      "Epoch 516/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0013   \n",
      "Epoch 517/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0032\n",
      "Epoch 518/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0016     \n",
      "Epoch 519/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0019 \n",
      "Epoch 520/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0011\n",
      "Epoch 521/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 985us/step - accuracy: 1.0000 - loss: 0.0010\n",
      "Epoch 522/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0010     \n",
      "Epoch 523/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9501 - loss: 0.1593     \n",
      "Epoch 524/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9345 - loss: 0.2251 \n",
      "Epoch 525/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9429 - loss: 0.1639 \n",
      "Epoch 526/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9588 - loss: 0.0978 \n",
      "Epoch 527/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9894 - loss: 0.0323 \n",
      "Epoch 528/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9947 - loss: 0.0168 \n",
      "Epoch 529/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0078 \n",
      "Epoch 530/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0043     \n",
      "Epoch 531/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0042 \n",
      "Epoch 532/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0046\n",
      "Epoch 533/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0038 \n",
      "Epoch 534/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0037 \n",
      "Epoch 535/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0039\n",
      "Epoch 536/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0032 \n",
      "Epoch 537/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0033 \n",
      "Epoch 538/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0028 \n",
      "Epoch 539/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0026 \n",
      "Epoch 540/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0026\n",
      "Epoch 541/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0026\n",
      "Epoch 542/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0025     \n",
      "Epoch 543/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0019   \n",
      "Epoch 544/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0021   \n",
      "Epoch 545/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 546/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0021 \n",
      "Epoch 547/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0017 \n",
      "Epoch 548/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0020     \n",
      "Epoch 549/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0021\n",
      "Epoch 550/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0016 \n",
      "Epoch 551/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0019\n",
      "Epoch 552/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0020\n",
      "Epoch 553/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0018 \n",
      "Epoch 554/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0019 \n",
      "Epoch 555/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 556/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - accuracy: 1.0000 - loss: 0.0015   \n",
      "Epoch 557/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0016   \n",
      "Epoch 558/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0016 \n",
      "Epoch 559/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0016 \n",
      "Epoch 560/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0017\n",
      "Epoch 561/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0014\n",
      "Epoch 562/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0013     \n",
      "Epoch 563/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0013   \n",
      "Epoch 564/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0012     \n",
      "Epoch 565/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0016   \n",
      "Epoch 566/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0014\n",
      "Epoch 567/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0013   \n",
      "Epoch 568/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011     \n",
      "Epoch 569/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 984us/step - accuracy: 1.0000 - loss: 0.0013   \n",
      "Epoch 570/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 0.0011\n",
      "Epoch 571/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0012\n",
      "Epoch 572/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0013\n",
      "Epoch 573/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0010 \n",
      "Epoch 574/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011     \n",
      "Epoch 575/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0011   \n",
      "Epoch 576/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0011 \n",
      "Epoch 577/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0012     \n",
      "Epoch 578/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 9.8332e-04\n",
      "Epoch 579/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 9.8838e-04 \n",
      "Epoch 580/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0010     \n",
      "Epoch 581/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0010    \n",
      "Epoch 582/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0011\n",
      "Epoch 583/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 999us/step - accuracy: 1.0000 - loss: 9.2792e-04\n",
      "Epoch 584/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 7.8808e-04\n",
      "Epoch 585/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 9.4396e-04\n",
      "Epoch 586/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 8.4213e-04 \n",
      "Epoch 587/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 7.5535e-04 \n",
      "Epoch 588/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0010   \n",
      "Epoch 589/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0012     \n",
      "Epoch 590/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 997us/step - accuracy: 1.0000 - loss: 0.0011\n",
      "Epoch 591/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.0013     \n",
      "Epoch 592/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 7.4959e-04 \n",
      "Epoch 593/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 7.0176e-04 \n",
      "Epoch 594/600\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 7.8412e-04 \n",
      "Epoch 595/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 9.7695e-04\n",
      "Epoch 596/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 9.3221e-04 \n",
      "Epoch 597/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 8.9709e-04 \n",
      "Epoch 598/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - accuracy: 1.0000 - loss: 9.2047e-04\n",
      "Epoch 599/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 6.5176e-04 \n",
      "Epoch 600/600\n",
      "\u001b[1m44/44\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 7.4777e-04 \n",
      "Accuracy: 68.83%\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# Split the dataset \n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=10)\n",
    "\n",
    "# Define the model\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=32, kernel_size=3, activation='relu', input_shape=(X.shape[1], 1)))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, to_categorical(Y_train), epochs=600, batch_size=16)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(X_test, to_categorical(Y_test), verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1] * 100))\n",
    "\n",
    "# Transfer Learning\n",
    "new_model = Sequential()\n",
    "\n",
    "# Add layers from the old model to the new model, except the last Dense layer\n",
    "for layer in model.layers[:-1]:\n",
    "    new_model.add(layer)\n",
    "\n",
    "# Add new layers for transfer learning\n",
    "new_model.add(Dense(100, activation='relu'))\n",
    "new_model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "# Compile the new model\n",
    "new_model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the new model\n",
    "new_model.fit(X_train, to_categorical(Y_train), epochs=600, batch_size=16)\n",
    "\n",
    "# Evaluate the new model\n",
    "scores = new_model.evaluate(X_test, to_categorical(Y_test), verbose=0)\n",
    "print(\"Accuracy: %.2f%%\" % (scores[1] * 100))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 841us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.98      0.98       500\n",
      "           1       0.96      0.95      0.96       268\n",
      "\n",
      "    accuracy                           0.97       768\n",
      "   macro avg       0.97      0.96      0.97       768\n",
      "weighted avg       0.97      0.97      0.97       768\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "y_pred = new_model.predict(X)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "y_true = np.argmax(to_categorical(Y), axis=1)\n",
    "\n",
    "print(classification_report(y_true, y_pred))\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "plt.figure(figsize=(14, 10))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"YlGn\", cbar=True)\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 737us/step\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 825us/step\n",
      "[0.01707086 0.01707087]\n"
     ]
    }
   ],
   "source": [
    "sem = np.std(new_model.predict(X), axis=0) / np.sqrt(len(new_model.predict(X)))\n",
    "\n",
    "print(sem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Binarize the output\n",
    "Y = label_binarize(Y, classes=[0, 1])\n",
    "\n",
    "# Get the ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(Y.ravel(), y_pred.ravel())\n",
    "\n",
    "# Calculate the AUC\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "lw = 3\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.3f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.grid()\n",
    "plt.title('Receiver operating characteristic curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}