class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import pandas as pd\r\n",
    "import numpy as np\r\n",
    "import json\r\n",
    "import seaborn as sns\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "path = \"./물금고등학교-익명의-숲_생활편/\"\r\n"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "source": [
    "with open(path + \"data/collect.json\", \"r\", encoding='UTF-8') as j:\r\n",
    "    json_file = json.load(j)\r\n",
    "df_original = pd.json_normalize(json_file['48:3F:DA:77:26:C0'])\r\n",
    "df_original['date'] = pd.to_datetime(df_original['date'], format='%Y-%m-%d %H:%M:%S')\r\n",
    "\r\n",
    "df_original.iloc[:, 1:] = df_original.iloc[:, 1:].astype(float)\r\n",
    "df_original.drop(columns=['date'], inplace=True)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "def ShowBoxPlot():\r\n",
    "    sns.set(font=\"Malgun Gothic\", \r\n",
    "        rc={\"axes.unicode_minus\":False},\r\n",
    "        style='darkgrid')\r\n",
    "\r\n",
    "    plt.figure(figsize=(25, 5))\r\n",
    "    plt.subplot(1, 5, 1)\r\n",
    "    sns.boxplot(y='values.tmp', data=df_original)\r\n",
    "    plt.subplot(1, 5, 2)\r\n",
    "    sns.boxplot(y='values.hum', data=df_original)\r\n",
    "    plt.subplot(1, 5, 3)\r\n",
    "    sns.boxplot(y='values.co2', data=df_original)\r\n",
    "    plt.subplot(1, 5, 4)\r\n",
    "    sns.boxplot(y='values.pm', data=df_original)\r\n",
    "    plt.subplot(1, 5, 5)\r\n",
    "    sns.boxplot(y='values.voc', data=df_original)\r\n",
    "    plt.show()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "source": [
    "df_pre = df_original\r\n",
    "df_pre['values.voc'] = df_pre['values.voc'].apply(lambda x: x > 200)\r\n",
    "df_pre['values.voc'].replace(True, 1, inplace=True)\r\n",
    "df_pre['values.voc'].replace(False, 0, inplace=True)\r\n",
    "\r\n",
    "sns.countplot(x=df_pre['values.voc'])\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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mYR8AAAC23UN05ZVX6mc/+1nAuvbt26tjx47W+kmTJikjI0MdOnSQ0+nUI488Io/Ho0GDBkmShg8frri4ON1///2aN2+evF6vZs2apbS0NIWGhkqSJk+erOeee04zZszQxIkTtWHDBr3++uvKyclp2RMGAACtlq03Vf+YhQsXKigoSCkpKaqpqVFSUpJeeOEFa3twcLDWrl2rKVOmyOPxqH379powYYLmzJljjenRo4dycnI0bdo0LV68WF27dtUrr7yipKQkO04JAAC0Qg6/3++3exKtnc/nk8vlUnV1dbPeT5Qw/bVm2zdwKSuaP97uKVy0sjnxdk8BaJW6Ze1utn1fyO9v259DBAAAYDeCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8RoVREOHDlVVVdVZ630+n4YOHXqxcwIAAGhRjQqiTZs2qba29qz1J0+e1Pvvv3/RkwIAAGhJbS5k8CeffGL9ed++ffJ6vdbruro65ebm6ic/+UnTzQ4AAKAFXFAQ9e3bVw6HQw6H45wfjYWFhenZZ59tsskBAAC0hAsKotLSUvn9fl1zzTXasWOHOnfubG0LCQlRZGSkgoODm3ySAAAAzemCgqh79+6SpPr6+maZDAAAgB0uKIjOdODAAW3cuFGVlZVnBVJWVtZFTwwAAKClNCqIXn75ZU2ZMkWdOnVSVFSUHA6Htc3hcBBEAADgktKoIHryySf129/+VjNnzmzq+QAAALS4Rj2H6MiRIxo7dmxTzwUAAMAWjQqisWPHav369U09FwAAAFs06iOznj176je/+Y22bdum+Ph4tW3bNmD7L3/5yyaZHAAAQEtoVBC99NJLCg8PV0FBgQoKCgK2ORwOgggAAFxSGhVEpaWlTT0PAAAA2zTqHiIAAIDLSaOuEE2cOPEHty9fvrxRkwEAALBDo4LoyJEjAa9PnTqlPXv2qKqq6pz/6SsAAEBr1qggWr169Vnr6uvrNWXKFP3TP/3TRU8KAACgJTXZPURBQUHKyMjQwoULm2qXAAAALaJJb6r+/PPPdfr06abcJQAAQLNr1EdmGRkZAa/9fr+++uor5eTkaMKECU0yMQAAgJbSqCDatWtXwOugoCB17txZCxYs+NFvoAEAALQ2jQqijRs3NvU8AAAAbNOoIGpw6NAhlZSUSJJ69eqlzp07N8mkAAAAWlKjbqo+fvy4Jk6cqC5dumjw4MEaPHiwoqOjNWnSJP39739v6jkCAAA0q0YFUUZGhgoKCrRmzRpVVVWpqqpK77zzjgoKCvSrX/2qqecIAADQrBr1kdmf//xnvfnmm7r11lutdSNHjlRYWJjuvvtuLV26tKnmBwAA0OwadYXo73//u9xu91nrIyMj+cgMAABcchoVRB6PR7Nnz9bJkyetdSdOnNDjjz8uj8fTZJMDAABoCY0KokWLFmnLli3q2rWrhg0bpmHDhikmJkZbtmzR4sWLz3s/S5cu1fXXXy+n0ymn0ymPx6P33nvP2n7y5EmlpaWpY8eOCg8PV0pKiioqKgL2UVZWpuTkZF1xxRWKjIzU9OnTz3pa9qZNm9SvXz+FhoaqZ8+eys7ObsxpAwCAy1Sjgig+Pl4HDhzQ3Llz1bdvX/Xt21dPP/20PvvsM1133XXnvZ+uXbvq6aefVlFRkT788EMNHTpUo0eP1t69eyVJ06ZN05o1a/TGG2+ooKBABw8e1JgxY6z319XVKTk5WbW1tdq6dateffVVZWdnKysryxpTWlqq5ORkDRkyRMXFxUpPT9eDDz6odevWNebUAQDAZcjh9/v9F/qmuXPnyu12n/VU6uXLl+vQoUOaOXNmoyfUoUMHzZ8/X3fddZc6d+6slStX6q677pIkffrpp4qNjVVhYaEGDRqk9957T7fffrsOHjxo3dO0bNkyzZw5U4cOHVJISIhmzpypnJwc7dmzxzrGuHHjVFVVpdzc3POak8/nk8vlUnV1tZxOZ6PP7cckTH+t2fYNXMqK5o+3ewoXrWxOvN1TAFqlblm7m23fF/L7u1FXiF588UX17t37rPXXXXedli1b1phdqq6uTn/60590/PhxeTweFRUV6dSpU0pMTLTG9O7dW926dVNhYaEkqbCwUPHx8QE3eCclJcnn81lXmQoLCwP20TCmYR/nUlNTI5/PF7AAAIDLV6OCyOv1qkuXLmet79y5s7766qsL2tfu3bsVHh6u0NBQTZ48WatXr1ZcXJy8Xq9CQkIUERERMN7tdsvr9Vrz+O633Rpe/9gYn8+nEydOnHNOc+fOlcvlspaYmJgLOicAAHBpaVQQNdxA/V1btmxRdHT0Be2rV69eKi4u1vbt2zVlyhRNmDBB+/bta8y0mkxmZqaqq6utpby83Nb5AACA5tWoBzM+9NBDSk9P16lTpzR06FBJUn5+vmbMmHHBT6oOCQlRz549JUkJCQnauXOnFi9erHvuuUe1tbWqqqoKuEpUUVGhqKgoSVJUVJR27NgRsL+Gb6GdOea730yrqKiQ0+lUWFjYOecUGhqq0NDQCzoPAABw6WpUEE2fPl3ffPON/v3f/121tbWSpHbt2mnmzJnKzMy8qAnV19erpqZGCQkJatu2rfLz85WSkiJJKikpUVlZmfWsI4/Ho9/+9reqrKxUZGSkJCkvL09Op1NxcXHWmP/5n/8JOEZeXh7PSwIAAJZGBZHD4dDvfvc7/eY3v9H+/fsVFhama6+99oKvqmRmZmrEiBHq1q2bjh49qpUrV2rTpk1at26dXC6XJk2apIyMDHXo0EFOp1OPPPKIPB6PBg0aJEkaPny44uLidP/992vevHnyer2aNWuW0tLSrLlMnjxZzz33nGbMmKGJEydqw4YNev3115WTk9OYUwcAAJehRgVRg/DwcA0YMKDR76+srNT48eP11VdfyeVy6frrr9e6dev0L//yL5KkhQsXKigoSCkpKaqpqVFSUpJeeOEF6/3BwcFau3atpkyZIo/Ho/bt22vChAmaM2eONaZHjx7KycnRtGnTtHjxYnXt2lWvvPKKkpKSGn/iAADgstKo5xCZhucQAfbiOUTA5euSfg4RAADA5YQgAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMazNYjmzp2rAQMG6Morr1RkZKTuuOMOlZSUBIw5efKk0tLS1LFjR4WHhyslJUUVFRUBY8rKypScnKwrrrhCkZGRmj59uk6fPh0wZtOmTerXr59CQ0PVs2dPZWdnN/fpAQCAS4StQVRQUKC0tDRt27ZNeXl5OnXqlIYPH67jx49bY6ZNm6Y1a9bojTfeUEFBgQ4ePKgxY8ZY2+vq6pScnKza2lpt3bpVr776qrKzs5WVlWWNKS0tVXJysoYMGaLi4mKlp6frwQcf1Lp161r0fAEAQOvk8Pv9frsn0eDQoUOKjIxUQUGBBg8erOrqanXu3FkrV67UXXfdJUn69NNPFRsbq8LCQg0aNEjvvfeebr/9dh08eFBut1uStGzZMs2cOVOHDh1SSEiIZs6cqZycHO3Zs8c61rhx41RVVaXc3Nyz5lFTU6Oamhrrtc/nU0xMjKqrq+V0Opvt/BOmv9Zs+wYuZUXzx9s9hYtWNife7ikArVK3rN3Ntm+fzyeXy3Vev79b1T1E1dXVkqQOHTpIkoqKinTq1CklJiZaY3r37q1u3bqpsLBQklRYWKj4+HgrhiQpKSlJPp9Pe/futcacuY+GMQ37+K65c+fK5XJZS0xMTNOdJAAAaHVaTRDV19crPT1dN910k372s59Jkrxer0JCQhQREREw1u12y+v1WmPOjKGG7Q3bfmiMz+fTiRMnzppLZmamqqurraW8vLxJzhEAALRObeyeQIO0tDTt2bNHH3zwgd1TUWhoqEJDQ+2eBgAAaCGt4grR1KlTtXbtWm3cuFFdu3a11kdFRam2tlZVVVUB4ysqKhQVFWWN+e63zhpe/9gYp9OpsLCwpj4dAABwibE1iPx+v6ZOnarVq1drw4YN6tGjR8D2hIQEtW3bVvn5+da6kpISlZWVyePxSJI8Ho92796tyspKa0xeXp6cTqfi4uKsMWfuo2FMwz4AAIDZbP3ILC0tTStXrtQ777yjK6+80rrnx+VyKSwsTC6XS5MmTVJGRoY6dOggp9OpRx55RB6PR4MGDZIkDR8+XHFxcbr//vs1b948eb1ezZo1S2lpadbHXpMnT9Zzzz2nGTNmaOLEidqwYYNef/115eTk2HbuAACg9bD1CtHSpUtVXV2tW2+9VV26dLGWVatWWWMWLlyo22+/XSkpKRo8eLCioqL01ltvWduDg4O1du1aBQcHy+Px6Be/+IXGjx+vOXPmWGN69OihnJwc5eXlqU+fPlqwYIFeeeUVJSUltej5AgCA1qlVPYeotbqQ5xhcDJ5DBJwbzyECLl88hwgAAKCVIIgAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABiPIAIAAMYjiAAAgPEIIgAAYDyCCAAAGI8gAgAAxiOIAACA8QgiAABgPIIIAAAYjyACAADGI4gAAIDxCCIAAGA8gggAABi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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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     },
     "metadata": {}
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   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "source": [
    "df_pre.drop(columns=['values.voc'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>values.tmp</th>\n",
       "      <th>values.hum</th>\n",
       "      <th>values.co2</th>\n",
       "      <th>values.pm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>409.97</td>\n",
       "      <td>323.19</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>409.98</td>\n",
       "      <td>585.16</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>410.01</td>\n",
       "      <td>326.12</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>410.05</td>\n",
       "      <td>316.34</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>410.03</td>\n",
       "      <td>187.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10611</th>\n",
       "      <td>26.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1183.98</td>\n",
       "      <td>173.63</td>\n",
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       "    <tr>\n",
       "      <th>10612</th>\n",
       "      <td>26.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1183.99</td>\n",
       "      <td>163.85</td>\n",
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       "    <tr>\n",
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       "      <td>26.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1183.98</td>\n",
       "      <td>175.58</td>\n",
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       "      <th>10614</th>\n",
       "      <td>26.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1184.02</td>\n",
       "      <td>182.42</td>\n",
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       "    <tr>\n",
       "      <th>10615</th>\n",
       "      <td>26.6</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1184.01</td>\n",
       "      <td>151.14</td>\n",
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       "</table>\n",
       "<p>10616 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "       values.tmp  values.hum  values.co2  values.pm\n",
       "0            22.4        55.0      409.97     323.19\n",
       "1            22.4        54.0      409.98     585.16\n",
       "2            22.4        54.0      410.01     326.12\n",
       "3            22.4        54.0      410.05     316.34\n",
       "4            22.4        54.0      410.03     187.31\n",
       "...           ...         ...         ...        ...\n",
       "10611        26.6        64.0     1183.98     173.63\n",
       "10612        26.6        64.0     1183.99     163.85\n",
       "10613        26.6        64.0     1183.98     175.58\n",
       "10614        26.6        64.0     1184.02     182.42\n",
       "10615        26.6        65.0     1184.01     151.14\n",
       "\n",
       "[10616 rows x 4 columns]"
      ]
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     "metadata": {},
     "execution_count": 34
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   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "source": [
    "from sklearn.preprocessing import StandardScaler\r\n",
    "\r\n",
    "scaler = StandardScaler()\r\n",
    "\r\n",
    "scaled = scaler.fit_transform(df_pre.drop(columns=['values.voc']))\r\n",
    "\r\n",
    "df = pd.DataFrame(scaled)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "from tensorflow.keras.models import Sequential\r\n",
    "from tensorflow.keras.layers import Dense\r\n",
    "\r\n",
    "# 필요한 라이브러리 불러옴\r\n",
    "import numpy as np\r\n",
    "import tensorflow as tf\r\n",
    "\r\n",
    "# 실행할 때마다 같은 결과를 출력하기 위해 설정하는 부분\r\n",
    "np.random.seed(3)\r\n",
    "tf.random.set_seed(3)\r\n",
    "\r\n",
    "# 환자의 기록과 수술 결과를 X와 Y로 구분하여 저장\r\n",
    "X = df_pre.iloc[:, :4]\r\n",
    "Y = df_pre.iloc[:, 4]\r\n",
    "\r\n",
    "# 딥러닝 구조를 결정(모델을 설정하고 실행하는 부분)\r\n",
    "model = Sequential()\r\n",
    "model.add(Dense(16, input_dim=4, activation='relu'))\r\n",
    "model.add(Dense(32, activation='relu'))\r\n",
    "model.add(Dense(1, activation='sigmoid'))\r\n",
    "\r\n",
    "# 딥러닝 실행\r\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['binary_accuracy'])\r\n",
    "model.fit(X, Y, epochs=100, batch_size=10, validation_batch_size=0.2)\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 1.6688 - binary_accuracy: 0.8072\n",
      "Epoch 2/100\n",
      "107/107 [==============================] - 0s 792us/step - loss: 0.1870 - binary_accuracy: 0.9610\n",
      "Epoch 3/100\n",
      "107/107 [==============================] - 0s 811us/step - loss: 0.1753 - binary_accuracy: 0.9634\n",
      "Epoch 4/100\n",
      "107/107 [==============================] - 0s 689us/step - loss: 0.1725 - binary_accuracy: 0.9630\n",
      "Epoch 5/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1720 - binary_accuracy: 0.9618\n",
      "Epoch 6/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1919 - binary_accuracy: 0.9561\n",
      "Epoch 7/100\n",
      "107/107 [==============================] - 0s 679us/step - loss: 0.1736 - binary_accuracy: 0.9603\n",
      "Epoch 8/100\n",
      "107/107 [==============================] - 0s 689us/step - loss: 0.1659 - binary_accuracy: 0.9607\n",
      "Epoch 9/100\n",
      "107/107 [==============================] - 0s 698us/step - loss: 0.1624 - binary_accuracy: 0.9615\n",
      "Epoch 10/100\n",
      "107/107 [==============================] - 0s 745us/step - loss: 0.1888 - binary_accuracy: 0.9531\n",
      "Epoch 11/100\n",
      "107/107 [==============================] - 0s 689us/step - loss: 0.1733 - binary_accuracy: 0.9583\n",
      "Epoch 12/100\n",
      "107/107 [==============================] - 0s 698us/step - loss: 0.1710 - binary_accuracy: 0.9575\n",
      "Epoch 13/100\n",
      "107/107 [==============================] - 0s 679us/step - loss: 0.1637 - binary_accuracy: 0.9584\n",
      "Epoch 14/100\n",
      "107/107 [==============================] - 0s 660us/step - loss: 0.1679 - binary_accuracy: 0.9581\n",
      "Epoch 15/100\n",
      "107/107 [==============================] - 0s 689us/step - loss: 0.1541 - binary_accuracy: 0.9603\n",
      "Epoch 16/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1588 - binary_accuracy: 0.9585\n",
      "Epoch 17/100\n",
      "107/107 [==============================] - 0s 717us/step - loss: 0.1613 - binary_accuracy: 0.9599\n",
      "Epoch 18/100\n",
      "107/107 [==============================] - 0s 651us/step - loss: 0.1530 - binary_accuracy: 0.9598\n",
      "Epoch 19/100\n",
      "107/107 [==============================] - 0s 755us/step - loss: 0.1608 - binary_accuracy: 0.9593\n",
      "Epoch 20/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1461 - binary_accuracy: 0.9626\n",
      "Epoch 21/100\n",
      "107/107 [==============================] - 0s 738us/step - loss: 0.1471 - binary_accuracy: 0.9620\n",
      "Epoch 22/100\n",
      "107/107 [==============================] - 0s 679us/step - loss: 0.1541 - binary_accuracy: 0.9612\n",
      "Epoch 23/100\n",
      "107/107 [==============================] - 0s 783us/step - loss: 0.1551 - binary_accuracy: 0.9590\n",
      "Epoch 24/100\n",
      "107/107 [==============================] - 0s 782us/step - loss: 0.1411 - binary_accuracy: 0.9633\n",
      "Epoch 25/100\n",
      "107/107 [==============================] - 0s 717us/step - loss: 0.1402 - binary_accuracy: 0.9624\n",
      "Epoch 26/100\n",
      "107/107 [==============================] - 0s 717us/step - loss: 0.2152 - binary_accuracy: 0.9470\n",
      "Epoch 27/100\n",
      "107/107 [==============================] - 0s 726us/step - loss: 0.1537 - binary_accuracy: 0.9603\n",
      "Epoch 28/100\n",
      "107/107 [==============================] - 0s 764us/step - loss: 0.1502 - binary_accuracy: 0.9625\n",
      "Epoch 29/100\n",
      "107/107 [==============================] - 0s 679us/step - loss: 0.1344 - binary_accuracy: 0.9657\n",
      "Epoch 30/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1507 - binary_accuracy: 0.9615\n",
      "Epoch 31/100\n",
      "107/107 [==============================] - 0s 764us/step - loss: 0.1367 - binary_accuracy: 0.9644\n",
      "Epoch 32/100\n",
      "107/107 [==============================] - 0s 698us/step - loss: 0.1478 - binary_accuracy: 0.9630\n",
      "Epoch 33/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1451 - binary_accuracy: 0.9615\n",
      "Epoch 34/100\n",
      "107/107 [==============================] - 0s 660us/step - loss: 0.1338 - binary_accuracy: 0.9650\n",
      "Epoch 35/100\n",
      "107/107 [==============================] - 0s 670us/step - loss: 0.1579 - binary_accuracy: 0.9611\n",
      "Epoch 36/100\n",
      "107/107 [==============================] - 0s 764us/step - loss: 0.1402 - binary_accuracy: 0.9629\n",
      "Epoch 37/100\n",
      "107/107 [==============================] - 0s 755us/step - loss: 0.1320 - binary_accuracy: 0.9661\n",
      "Epoch 38/100\n",
      "107/107 [==============================] - 0s 679us/step - loss: 0.1949 - binary_accuracy: 0.9521\n",
      "Epoch 39/100\n",
      "107/107 [==============================] - 0s 698us/step - loss: 0.1428 - binary_accuracy: 0.9648\n",
      "Epoch 40/100\n",
      "107/107 [==============================] - 0s 726us/step - loss: 0.1435 - binary_accuracy: 0.9652\n",
      "Epoch 41/100\n",
      "107/107 [==============================] - 0s 698us/step - loss: 0.1516 - binary_accuracy: 0.9613\n",
      "Epoch 42/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1241 - binary_accuracy: 0.9678\n",
      "Epoch 43/100\n",
      "107/107 [==============================] - 0s 689us/step - loss: 0.1258 - binary_accuracy: 0.9644\n",
      "Epoch 44/100\n",
      "107/107 [==============================] - 0s 783us/step - loss: 0.1569 - binary_accuracy: 0.9608\n",
      "Epoch 45/100\n",
      "107/107 [==============================] - 0s 802us/step - loss: 0.1683 - binary_accuracy: 0.9580\n",
      "Epoch 46/100\n",
      "107/107 [==============================] - 0s 670us/step - loss: 0.1609 - binary_accuracy: 0.9602\n",
      "Epoch 47/100\n",
      "107/107 [==============================] - 0s 679us/step - loss: 0.1630 - binary_accuracy: 0.9608\n",
      "Epoch 48/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1556 - binary_accuracy: 0.9619\n",
      "Epoch 49/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1589 - binary_accuracy: 0.9602\n",
      "Epoch 50/100\n",
      "107/107 [==============================] - 0s 670us/step - loss: 0.1194 - binary_accuracy: 0.9673\n",
      "Epoch 51/100\n",
      "107/107 [==============================] - 0s 755us/step - loss: 0.1207 - binary_accuracy: 0.9682\n",
      "Epoch 52/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1303 - binary_accuracy: 0.9622\n",
      "Epoch 53/100\n",
      "107/107 [==============================] - 0s 689us/step - loss: 0.1485 - binary_accuracy: 0.9636\n",
      "Epoch 54/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1513 - binary_accuracy: 0.9598\n",
      "Epoch 55/100\n",
      "107/107 [==============================] - 0s 768us/step - loss: 0.1394 - binary_accuracy: 0.9644\n",
      "Epoch 56/100\n",
      "107/107 [==============================] - 0s 660us/step - loss: 0.1245 - binary_accuracy: 0.9659\n",
      "Epoch 57/100\n",
      "107/107 [==============================] - 0s 830us/step - loss: 0.1364 - binary_accuracy: 0.9649\n",
      "Epoch 58/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.2043 - binary_accuracy: 0.9514\n",
      "Epoch 59/100\n",
      "107/107 [==============================] - 0s 670us/step - loss: 0.1746 - binary_accuracy: 0.9586\n",
      "Epoch 60/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1632 - binary_accuracy: 0.9610\n",
      "Epoch 61/100\n",
      "107/107 [==============================] - 0s 750us/step - loss: 0.1591 - binary_accuracy: 0.9629\n",
      "Epoch 62/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1697 - binary_accuracy: 0.9590\n",
      "Epoch 63/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1196 - binary_accuracy: 0.9675\n",
      "Epoch 64/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1596 - binary_accuracy: 0.9596\n",
      "Epoch 65/100\n",
      "107/107 [==============================] - 0s 792us/step - loss: 0.1371 - binary_accuracy: 0.9651\n",
      "Epoch 66/100\n",
      "107/107 [==============================] - 0s 755us/step - loss: 0.1389 - binary_accuracy: 0.9622\n",
      "Epoch 67/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1425 - binary_accuracy: 0.9642\n",
      "Epoch 68/100\n",
      "107/107 [==============================] - 0s 887us/step - loss: 0.1506 - binary_accuracy: 0.9623\n",
      "Epoch 69/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1497 - binary_accuracy: 0.9631\n",
      "Epoch 70/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1500 - binary_accuracy: 0.9625\n",
      "Epoch 71/100\n",
      "107/107 [==============================] - 0s 783us/step - loss: 0.1228 - binary_accuracy: 0.9654\n",
      "Epoch 72/100\n",
      "107/107 [==============================] - 0s 755us/step - loss: 0.1255 - binary_accuracy: 0.9649\n",
      "Epoch 73/100\n",
      "107/107 [==============================] - 0s 821us/step - loss: 0.1285 - binary_accuracy: 0.9647\n",
      "Epoch 74/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1294 - binary_accuracy: 0.9655\n",
      "Epoch 75/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1198 - binary_accuracy: 0.9677\n",
      "Epoch 76/100\n",
      "107/107 [==============================] - 0s 745us/step - loss: 0.1304 - binary_accuracy: 0.9660\n",
      "Epoch 77/100\n",
      "107/107 [==============================] - 0s 802us/step - loss: 0.1161 - binary_accuracy: 0.9673\n",
      "Epoch 78/100\n",
      "107/107 [==============================] - 0s 726us/step - loss: 0.1295 - binary_accuracy: 0.9648\n",
      "Epoch 79/100\n",
      "107/107 [==============================] - 0s 802us/step - loss: 0.1288 - binary_accuracy: 0.9662\n",
      "Epoch 80/100\n",
      "107/107 [==============================] - 0s 962us/step - loss: 0.1347 - binary_accuracy: 0.9639\n",
      "Epoch 81/100\n",
      "107/107 [==============================] - 0s 793us/step - loss: 0.1220 - binary_accuracy: 0.9671\n",
      "Epoch 82/100\n",
      "107/107 [==============================] - 0s 849us/step - loss: 0.1423 - binary_accuracy: 0.9622\n",
      "Epoch 83/100\n",
      "107/107 [==============================] - 0s 934us/step - loss: 0.1486 - binary_accuracy: 0.9630\n",
      "Epoch 84/100\n",
      "107/107 [==============================] - 0s 840us/step - loss: 0.1542 - binary_accuracy: 0.9602\n",
      "Epoch 85/100\n",
      "107/107 [==============================] - 0s 972us/step - loss: 0.1354 - binary_accuracy: 0.9635\n",
      "Epoch 86/100\n",
      "107/107 [==============================] - 0s 774us/step - loss: 0.1214 - binary_accuracy: 0.9675\n",
      "Epoch 87/100\n",
      "107/107 [==============================] - 0s 887us/step - loss: 0.1267 - binary_accuracy: 0.9647\n",
      "Epoch 88/100\n",
      "107/107 [==============================] - 0s 896us/step - loss: 0.1390 - binary_accuracy: 0.9635\n",
      "Epoch 89/100\n",
      "107/107 [==============================] - 0s 906us/step - loss: 0.1434 - binary_accuracy: 0.9651\n",
      "Epoch 90/100\n",
      "107/107 [==============================] - 0s 698us/step - loss: 0.1352 - binary_accuracy: 0.9647\n",
      "Epoch 91/100\n",
      "107/107 [==============================] - 0s 726us/step - loss: 0.1216 - binary_accuracy: 0.9668\n",
      "Epoch 92/100\n",
      "107/107 [==============================] - 0s 755us/step - loss: 0.1180 - binary_accuracy: 0.9672\n",
      "Epoch 93/100\n",
      "107/107 [==============================] - 0s 775us/step - loss: 0.1400 - binary_accuracy: 0.9623\n",
      "Epoch 94/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1230 - binary_accuracy: 0.9670\n",
      "Epoch 95/100\n",
      "107/107 [==============================] - 0s 848us/step - loss: 0.1316 - binary_accuracy: 0.9653\n",
      "Epoch 96/100\n",
      "107/107 [==============================] - 0s 736us/step - loss: 0.1200 - binary_accuracy: 0.9655\n",
      "Epoch 97/100\n",
      "107/107 [==============================] - 0s 708us/step - loss: 0.1101 - binary_accuracy: 0.9699\n",
      "Epoch 98/100\n",
      "107/107 [==============================] - 0s 991us/step - loss: 0.1435 - binary_accuracy: 0.9652\n",
      "Epoch 99/100\n",
      "107/107 [==============================] - 0s 868us/step - loss: 0.1598 - binary_accuracy: 0.9607\n",
      "Epoch 100/100\n",
      "107/107 [==============================] - 0s 1ms/step - loss: 0.1168 - binary_accuracy: 0.9663\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x20a571e7d48>"
      ]
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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