13_광물 예측 - 과적합확인.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pygame 2.0.1 (SDL 2.0.14, Python 3.8.5)\n",
      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
     ]
    }
   ],
   "source": [
    "# 기본\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 경고 뜨지 않게 설정\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 그래프 설정\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic'\n",
    "# plt.rcParams['font.family'] = 'AppleGothic'\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.rcParams['figure.figsize'] = 20, 10\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 데이터 전처리 알고리즘\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 학습용과 검증용으로 나누는 함수\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 교차 검증\n",
    "# 지표를 하나만 설정할 경우\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# 지표를 하나 이상 설정할 경우\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "# 모델의 최적의 하이퍼파라미터를 찾기 위한 도구\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 평가함수\n",
    "# 분류용\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# 회귀용\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# 머신러닝 알고리즘 - 분류\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "\n",
    "# 머신러닝 알고리즘 - 회귀\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "\n",
    "# 차원축소\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "# 군집화\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import MeanShift\n",
    "from sklearn.cluster import estimate_bandwidth\n",
    "\n",
    "# ARIMA (시계열 예측)\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# 시간 측정을 위한 시간 모듈\n",
    "import datetime\n",
    "# 주식 정보를 읽어오기 위한 라이브러리\n",
    "from pandas_datareader import data\n",
    "\n",
    "# 형태소 백터를 생성하기 위한 라이브러리\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "# 형태소 백터를 학습 백터로 변환한다.\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "# 데이터 수집\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import re\n",
    "import time\n",
    "import os\n",
    "import json\n",
    "\n",
    "# 한국어 형태소 분석\n",
    "from konlpy.tag import Okt, Hannanum, Kkma, Mecab, Komoran\n",
    "\n",
    "# 워드 클라우드를 위한 라이브러리\n",
    "from collections import Counter\n",
    "import pytagcloud\n",
    "from IPython.display import Image\n",
    "\n",
    "# 출력 창 청소를 위한 함수\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# 저장\n",
    "import pickle\n",
    "\n",
    "# 딥러닝\n",
    "import tensorflow as tf\n",
    "\n",
    "# 딥러닝 모델 구조를 정의하는 것\n",
    "from tensorflow.keras.models import Sequential\n",
    "# 층구조를 정의하는 것\n",
    "from tensorflow.keras.layers import Dense\n",
    "# 활성화 함수를 정의하는 것\n",
    "from tensorflow.keras.layers import Activation\n",
    "\n",
    "# 다중 분류를 위한 원핫 인코딩\n",
    "# 결과데이터의 종류 수 만큼 결과데이터의 컬럼을 늘리는 작업\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "\n",
    "# 현재 프로젝트를 gpu에 할당한다.\n",
    "# 컴퓨터의 GPU는 메모리를 가지고 있다.\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "# gpu가 있다면..\n",
    "if len(gpus) > 0 :\n",
    "    try :\n",
    "        for gpu in gpus :\n",
    "            tf.config.experimental.set_memory_growth(gpu, True)\n",
    "    except RuntimeError as e :\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seed 설정\n",
    "np.random.seed(1)\n",
    "tf.random.set_seed(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
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       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
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       "\n",
       "       58      59  60  \n",
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       "[5 rows x 61 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 데이터를 읽어온다.\n",
    "df1 = pd.read_csv('data/sonar.csv', header=None)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.2028</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>0.2328</td>\n",
       "      <td>0.2684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0203</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.2529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.1354</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0042</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0181</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0       1       2       3       4       5       6       7       8   \\\n",
       "0    0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1    0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2    0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3    0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4    0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "203  0.0187  0.0346  0.0168  0.0177  0.0393  0.1630  0.2028  0.1694  0.2328   \n",
       "204  0.0323  0.0101  0.0298  0.0564  0.0760  0.0958  0.0990  0.1018  0.1030   \n",
       "205  0.0522  0.0437  0.0180  0.0292  0.0351  0.1171  0.1257  0.1178  0.1258   \n",
       "206  0.0303  0.0353  0.0490  0.0608  0.0167  0.1354  0.1465  0.1123  0.1945   \n",
       "207  0.0260  0.0363  0.0136  0.0272  0.0214  0.0338  0.0655  0.1400  0.1843   \n",
       "\n",
       "         9   ...      50      51      52      53      54      55      56  \\\n",
       "0    0.2111  ...  0.0232  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180   \n",
       "1    0.2872  ...  0.0125  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140   \n",
       "2    0.6194  ...  0.0033  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316   \n",
       "3    0.1264  ...  0.0241  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050   \n",
       "4    0.4459  ...  0.0156  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072   \n",
       "..      ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "203  0.2684  ...  0.0203  0.0116  0.0098  0.0199  0.0033  0.0101  0.0065   \n",
       "204  0.2154  ...  0.0051  0.0061  0.0093  0.0135  0.0063  0.0063  0.0034   \n",
       "205  0.2529  ...  0.0155  0.0160  0.0029  0.0051  0.0062  0.0089  0.0140   \n",
       "206  0.2354  ...  0.0042  0.0086  0.0046  0.0126  0.0036  0.0035  0.0034   \n",
       "207  0.2354  ...  0.0181  0.0146  0.0129  0.0047  0.0039  0.0061  0.0040   \n",
       "\n",
       "         57      58      59  \n",
       "0    0.0084  0.0090  0.0032  \n",
       "1    0.0049  0.0052  0.0044  \n",
       "2    0.0164  0.0095  0.0078  \n",
       "3    0.0044  0.0040  0.0117  \n",
       "4    0.0048  0.0107  0.0094  \n",
       "..      ...     ...     ...  \n",
       "203  0.0115  0.0193  0.0157  \n",
       "204  0.0032  0.0062  0.0067  \n",
       "205  0.0138  0.0077  0.0031  \n",
       "206  0.0079  0.0036  0.0048  \n",
       "207  0.0036  0.0061  0.0115  \n",
       "\n",
       "[208 rows x 60 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0      R\n",
       "1      R\n",
       "2      R\n",
       "3      R\n",
       "4      R\n",
       "      ..\n",
       "203    M\n",
       "204    M\n",
       "205    M\n",
       "206    M\n",
       "207    M\n",
       "Name: 60, Length: 208, dtype: object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 입력과 결과로 나눈다.\n",
    "X = df1.drop(60, axis=1)\n",
    "y = df1[60]\n",
    "\n",
    "display(X)\n",
    "display(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 문자열 변환\n",
    "encoder1 = LabelEncoder()\n",
    "encoder1.fit(y)\n",
    "y = encoder1.transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습 데이터와 검증 데이터로 나눈다.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y,\n",
    "                                                   test_size=0.3,\n",
    "                                                   random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 설정\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(24, input_dim=60))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(10))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 컴파일\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', \n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "29/29 [==============================] - 1s 3ms/step - loss: 0.6825 - accuracy: 0.5655\n",
      "Epoch 2/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.6674 - accuracy: 0.5655\n",
      "Epoch 3/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.6562 - accuracy: 0.6069\n",
      "Epoch 4/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.6442 - accuracy: 0.6207\n",
      "Epoch 5/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.6289 - accuracy: 0.6759\n",
      "Epoch 6/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.6161 - accuracy: 0.7103\n",
      "Epoch 7/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.6020 - accuracy: 0.6828\n",
      "Epoch 8/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.5889 - accuracy: 0.7517\n",
      "Epoch 9/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.5744 - accuracy: 0.7103\n",
      "Epoch 10/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.5563 - accuracy: 0.7379\n",
      "Epoch 11/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.5372 - accuracy: 0.7862\n",
      "Epoch 12/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.5148 - accuracy: 0.8138\n",
      "Epoch 13/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.4959 - accuracy: 0.7931\n",
      "Epoch 14/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.4840 - accuracy: 0.8138\n",
      "Epoch 15/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.4587 - accuracy: 0.7931\n",
      "Epoch 16/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.4511 - accuracy: 0.8069\n",
      "Epoch 17/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.4235 - accuracy: 0.8345\n",
      "Epoch 18/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.4130 - accuracy: 0.8552\n",
      "Epoch 19/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.4003 - accuracy: 0.8483\n",
      "Epoch 20/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3982 - accuracy: 0.8483\n",
      "Epoch 21/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3757 - accuracy: 0.8759\n",
      "Epoch 22/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3603 - accuracy: 0.8897\n",
      "Epoch 23/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3540 - accuracy: 0.8897\n",
      "Epoch 24/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.3394 - accuracy: 0.8897\n",
      "Epoch 25/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.3465 - accuracy: 0.8690\n",
      "Epoch 26/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3304 - accuracy: 0.8828\n",
      "Epoch 27/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3240 - accuracy: 0.8621\n",
      "Epoch 28/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3146 - accuracy: 0.8828\n",
      "Epoch 29/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.3005 - accuracy: 0.9241\n",
      "Epoch 30/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2902 - accuracy: 0.8690\n",
      "Epoch 31/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.3075 - accuracy: 0.8552\n",
      "Epoch 32/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2896 - accuracy: 0.9310\n",
      "Epoch 33/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.2771 - accuracy: 0.9241\n",
      "Epoch 34/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2698 - accuracy: 0.9310\n",
      "Epoch 35/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.2639 - accuracy: 0.9310\n",
      "Epoch 36/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2570 - accuracy: 0.9448\n",
      "Epoch 37/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2494 - accuracy: 0.9448\n",
      "Epoch 38/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2431 - accuracy: 0.9379\n",
      "Epoch 39/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2310 - accuracy: 0.9517\n",
      "Epoch 40/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2276 - accuracy: 0.9448\n",
      "Epoch 41/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2380 - accuracy: 0.9241\n",
      "Epoch 42/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2415 - accuracy: 0.9379\n",
      "Epoch 43/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2147 - accuracy: 0.9379\n",
      "Epoch 44/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.2263 - accuracy: 0.9241\n",
      "Epoch 45/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.2058 - accuracy: 0.9586\n",
      "Epoch 46/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1991 - accuracy: 0.9448\n",
      "Epoch 47/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1877 - accuracy: 0.9586\n",
      "Epoch 48/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1948 - accuracy: 0.9517\n",
      "Epoch 49/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1879 - accuracy: 0.9448\n",
      "Epoch 50/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1749 - accuracy: 0.9655\n",
      "Epoch 51/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1776 - accuracy: 0.9448\n",
      "Epoch 52/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1728 - accuracy: 0.9448\n",
      "Epoch 53/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1616 - accuracy: 0.9724\n",
      "Epoch 54/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1635 - accuracy: 0.9586\n",
      "Epoch 55/200\n",
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      "Epoch 56/200\n",
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      "Epoch 57/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1528 - accuracy: 0.9655\n",
      "Epoch 58/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1464 - accuracy: 0.9724\n",
      "Epoch 59/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1411 - accuracy: 0.9724\n",
      "Epoch 60/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1451 - accuracy: 0.9724\n",
      "Epoch 61/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1522 - accuracy: 0.9379\n",
      "Epoch 62/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1349 - accuracy: 0.9724\n",
      "Epoch 63/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1369 - accuracy: 0.9655\n",
      "Epoch 64/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1242 - accuracy: 0.9724\n",
      "Epoch 65/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1261 - accuracy: 0.9862\n",
      "Epoch 66/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1395 - accuracy: 0.9448\n",
      "Epoch 67/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1187 - accuracy: 0.9862\n",
      "Epoch 68/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.1144 - accuracy: 0.9862\n",
      "Epoch 69/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1184 - accuracy: 0.9793\n",
      "Epoch 70/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1051 - accuracy: 0.9931\n",
      "Epoch 71/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1080 - accuracy: 0.9862\n",
      "Epoch 72/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.1076 - accuracy: 0.9793\n",
      "Epoch 73/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0961 - accuracy: 0.9931\n",
      "Epoch 74/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0917 - accuracy: 0.9931\n",
      "Epoch 75/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0887 - accuracy: 0.9931\n",
      "Epoch 76/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0940 - accuracy: 0.9862\n",
      "Epoch 77/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0882 - accuracy: 0.9862\n",
      "Epoch 78/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0838 - accuracy: 0.9862\n",
      "Epoch 79/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0807 - accuracy: 0.9931\n",
      "Epoch 80/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0847 - accuracy: 0.9862\n",
      "Epoch 81/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0813 - accuracy: 0.9931\n",
      "Epoch 82/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0850 - accuracy: 0.9931\n",
      "Epoch 83/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0736 - accuracy: 0.9931\n",
      "Epoch 84/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0677 - accuracy: 0.9931\n",
      "Epoch 85/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0686 - accuracy: 0.9931\n",
      "Epoch 86/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0634 - accuracy: 0.9931\n",
      "Epoch 87/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0663 - accuracy: 1.0000\n",
      "Epoch 88/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0662 - accuracy: 0.9931\n",
      "Epoch 89/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0719 - accuracy: 0.9862\n",
      "Epoch 90/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0614 - accuracy: 0.9931\n",
      "Epoch 91/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0625 - accuracy: 0.9931\n",
      "Epoch 92/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0591 - accuracy: 0.9931\n",
      "Epoch 93/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0554 - accuracy: 0.9931\n",
      "Epoch 94/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0515 - accuracy: 0.9931\n",
      "Epoch 95/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0502 - accuracy: 1.0000\n",
      "Epoch 96/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0600 - accuracy: 0.9931\n",
      "Epoch 97/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0494 - accuracy: 1.0000\n",
      "Epoch 98/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0523 - accuracy: 0.9931\n",
      "Epoch 99/200\n",
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      "Epoch 100/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0439 - accuracy: 0.9931\n",
      "Epoch 101/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0421 - accuracy: 1.0000\n",
      "Epoch 102/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0417 - accuracy: 0.9931\n",
      "Epoch 103/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0407 - accuracy: 0.9931\n",
      "Epoch 104/200\n",
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      "Epoch 106/200\n",
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      "Epoch 107/200\n",
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      "Epoch 108/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0356 - accuracy: 1.0000\n",
      "Epoch 109/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0334 - accuracy: 0.9931\n",
      "Epoch 110/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0324 - accuracy: 1.0000\n",
      "Epoch 111/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0360 - accuracy: 0.9931\n",
      "Epoch 112/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0342 - accuracy: 1.0000\n",
      "Epoch 113/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0307 - accuracy: 1.0000\n",
      "Epoch 114/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0297 - accuracy: 1.0000\n",
      "Epoch 115/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0311 - accuracy: 1.0000\n",
      "Epoch 116/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0379 - accuracy: 1.0000\n",
      "Epoch 117/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0304 - accuracy: 0.9931\n",
      "Epoch 118/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0253 - accuracy: 1.0000\n",
      "Epoch 119/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0318 - accuracy: 0.9931\n",
      "Epoch 120/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0343 - accuracy: 0.9931\n",
      "Epoch 121/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0265 - accuracy: 1.0000\n",
      "Epoch 122/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0236 - accuracy: 1.0000\n",
      "Epoch 123/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0225 - accuracy: 1.0000\n",
      "Epoch 124/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0221 - accuracy: 1.0000\n",
      "Epoch 125/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0222 - accuracy: 1.0000\n",
      "Epoch 126/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0213 - accuracy: 1.0000\n",
      "Epoch 127/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0200 - accuracy: 1.0000\n",
      "Epoch 128/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0188 - accuracy: 1.0000\n",
      "Epoch 129/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0209 - accuracy: 1.0000\n",
      "Epoch 130/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0179 - accuracy: 1.0000\n",
      "Epoch 131/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0234 - accuracy: 1.0000\n",
      "Epoch 132/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0174 - accuracy: 1.0000\n",
      "Epoch 133/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0172 - accuracy: 1.0000\n",
      "Epoch 134/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0161 - accuracy: 1.0000\n",
      "Epoch 135/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 1.0000\n",
      "Epoch 136/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0154 - accuracy: 1.0000\n",
      "Epoch 137/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0147 - accuracy: 1.0000\n",
      "Epoch 138/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0197 - accuracy: 1.0000\n",
      "Epoch 139/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0166 - accuracy: 1.0000\n",
      "Epoch 140/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0140 - accuracy: 1.0000\n",
      "Epoch 141/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0142 - accuracy: 1.0000\n",
      "Epoch 142/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0148 - accuracy: 1.0000\n",
      "Epoch 143/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0123 - accuracy: 1.0000\n",
      "Epoch 144/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0119 - accuracy: 1.0000\n",
      "Epoch 145/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0117 - accuracy: 1.0000\n",
      "Epoch 146/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0113 - accuracy: 1.0000\n",
      "Epoch 147/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0106 - accuracy: 1.0000\n",
      "Epoch 148/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0104 - accuracy: 1.0000\n",
      "Epoch 149/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0138 - accuracy: 1.0000\n",
      "Epoch 150/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0105 - accuracy: 1.0000\n",
      "Epoch 151/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0103 - accuracy: 1.0000\n",
      "Epoch 152/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0096 - accuracy: 1.0000\n",
      "Epoch 153/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0096 - accuracy: 1.0000\n",
      "Epoch 154/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0094 - accuracy: 1.0000\n",
      "Epoch 155/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0095 - accuracy: 1.0000\n",
      "Epoch 156/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0094 - accuracy: 1.0000\n",
      "Epoch 157/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0090 - accuracy: 1.0000\n",
      "Epoch 158/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0083 - accuracy: 1.0000\n",
      "Epoch 159/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0078 - accuracy: 1.0000\n",
      "Epoch 160/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0084 - accuracy: 1.0000\n",
      "Epoch 161/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0078 - accuracy: 1.0000\n",
      "Epoch 162/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0077 - accuracy: 1.0000\n",
      "Epoch 163/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0076 - accuracy: 1.0000\n",
      "Epoch 164/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0075 - accuracy: 1.0000\n",
      "Epoch 165/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0071 - accuracy: 1.0000\n",
      "Epoch 166/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0076 - accuracy: 1.0000\n",
      "Epoch 167/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0073 - accuracy: 1.0000\n",
      "Epoch 168/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0075 - accuracy: 1.0000\n",
      "Epoch 169/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0069 - accuracy: 1.0000\n",
      "Epoch 170/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0069 - accuracy: 1.0000\n",
      "Epoch 171/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0062 - accuracy: 1.0000\n",
      "Epoch 172/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 1.0000\n",
      "Epoch 173/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0061 - accuracy: 1.0000\n",
      "Epoch 174/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 1.0000\n",
      "Epoch 175/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0069 - accuracy: 1.0000\n",
      "Epoch 176/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0065 - accuracy: 1.0000\n",
      "Epoch 177/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0056 - accuracy: 1.0000\n",
      "Epoch 178/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0052 - accuracy: 1.0000\n",
      "Epoch 179/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 1.0000\n",
      "Epoch 180/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0048 - accuracy: 1.0000\n",
      "Epoch 181/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000\n",
      "Epoch 182/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000\n",
      "Epoch 183/200\n",
      "29/29 [==============================] - 0s 3ms/step - loss: 0.0046 - accuracy: 1.0000\n",
      "Epoch 184/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 1.0000\n",
      "Epoch 185/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0044 - accuracy: 1.0000\n",
      "Epoch 186/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0046 - accuracy: 1.0000\n",
      "Epoch 187/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 1.0000\n",
      "Epoch 188/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000\n",
      "Epoch 189/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000\n",
      "Epoch 190/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0040 - accuracy: 1.0000\n",
      "Epoch 191/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 1.0000\n",
      "Epoch 192/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0052 - accuracy: 1.0000\n",
      "Epoch 193/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0045 - accuracy: 1.0000\n",
      "Epoch 194/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 1.0000\n",
      "Epoch 195/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 1.0000\n",
      "Epoch 196/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0039 - accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0038 - accuracy: 1.0000\n",
      "Epoch 198/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000\n",
      "Epoch 199/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Epoch 200/200\n",
      "29/29 [==============================] - 0s 2ms/step - loss: 0.0031 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1c716484eb0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 학습한다.\n",
    "model.fit(X_train, y_train, epochs=200, batch_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 2ms/step - loss: 0.0036 - accuracy: 1.0000\n",
      "손실률 : 0.003597313305363059\n",
      "정확도 : 1.0\n"
     ]
    }
   ],
   "source": [
    "# 학습 데이터에 대한 평가\n",
    "a1 = model.evaluate(X_train, y_train)\n",
    "print(f'손실률 : {a1[0]}')\n",
    "print(f'정확도 : {a1[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 3ms/step - loss: 1.1075 - accuracy: 0.7619\n",
      "손실률 : 1.107460379600525\n",
      "정확도 : 0.761904776096344\n"
     ]
    }
   ],
   "source": [
    "# 검증 데이터에 대한 평가\n",
    "a1 = model.evaluate(X_test, y_test)\n",
    "print(f'손실률 : {a1[0]}')\n",
    "print(f'정확도 : {a1[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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