02_0915_Salary(FCN).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f39687c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pandas import Series, DataFrame\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5bb603b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/Salary.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acb9d583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>39343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.3</td>\n",
       "      <td>46205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>37731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>43525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.2</td>\n",
       "      <td>39891.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.9</td>\n",
       "      <td>56642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>54445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>64445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.7</td>\n",
       "      <td>57189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.9</td>\n",
       "      <td>63218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.0</td>\n",
       "      <td>55794.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>56957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.1</td>\n",
       "      <td>57081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.5</td>\n",
       "      <td>61111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.9</td>\n",
       "      <td>67938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.1</td>\n",
       "      <td>66029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.3</td>\n",
       "      <td>83088.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.9</td>\n",
       "      <td>81363.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>93940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.8</td>\n",
       "      <td>91738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.1</td>\n",
       "      <td>98273.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.9</td>\n",
       "      <td>101302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.2</td>\n",
       "      <td>113812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.7</td>\n",
       "      <td>109431.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>105582.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.5</td>\n",
       "      <td>116969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>9.6</td>\n",
       "      <td>112635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.3</td>\n",
       "      <td>122391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10.5</td>\n",
       "      <td>121872.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    YearsExperience    Salary\n",
       "0               1.1   39343.0\n",
       "1               1.3   46205.0\n",
       "2               1.5   37731.0\n",
       "3               2.0   43525.0\n",
       "4               2.2   39891.0\n",
       "5               2.9   56642.0\n",
       "6               3.0   60150.0\n",
       "7               3.2   54445.0\n",
       "8               3.2   64445.0\n",
       "9               3.7   57189.0\n",
       "10              3.9   63218.0\n",
       "11              4.0   55794.0\n",
       "12              4.0   56957.0\n",
       "13              4.1   57081.0\n",
       "14              4.5   61111.0\n",
       "15              4.9   67938.0\n",
       "16              5.1   66029.0\n",
       "17              5.3   83088.0\n",
       "18              5.9   81363.0\n",
       "19              6.0   93940.0\n",
       "20              6.8   91738.0\n",
       "21              7.1   98273.0\n",
       "22              7.9  101302.0\n",
       "23              8.2  113812.0\n",
       "24              8.7  109431.0\n",
       "25              9.0  105582.0\n",
       "26              9.5  116969.0\n",
       "27              9.6  112635.0\n",
       "28             10.3  122391.0\n",
       "29             10.5  121872.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3b2f516",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19fd8b4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df['YearsExperience'].values\n",
    "y = df['Salary'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "896e49b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=423)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b32649b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.1,  1.3,  1.5,  2. ,  2.2,  2.9,  3. ,  3.2,  3.2,  3.7,  3.9,\n",
       "        4. ,  4. ,  4.1,  4.5,  4.9,  5.1,  5.3,  5.9,  6. ,  6.8,  7.1,\n",
       "        7.9,  8.2,  8.7,  9. ,  9.5,  9.6, 10.3, 10.5])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02898913",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 39343.,  46205.,  37731.,  43525.,  39891.,  56642.,  60150.,\n",
       "        54445.,  64445.,  57189.,  63218.,  55794.,  56957.,  57081.,\n",
       "        61111.,  67938.,  66029.,  83088.,  81363.,  93940.,  91738.,\n",
       "        98273., 101302., 113812., 109431., 105582., 116969., 112635.,\n",
       "       122391., 121872.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "979bb81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "496ded8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature가 1개로 normalization 의미X\n",
    "# import numpy as np\n",
    "\n",
    "# mean = np.mean(X_train, axis = 0)\n",
    "# std = np.std(X_train, axis = 0)\n",
    "\n",
    "\n",
    "# X_train = (x_train - mean) / std\n",
    "# X_test = (x_test - mean) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04c706da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_6 (Dense)             (None, 2)                 4         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 2)                 6         \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1)                 3         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13 (52.00 Byte)\n",
      "Trainable params: 13 (52.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(2,input_shape=(1,),activation='relu'))\n",
    "model.add(Dense(2,activation='relu'))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer = 'rmsprop', loss = 'mse', metrics = ['mse'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e3aa9fd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1/1 [==============================] - 1s 871ms/step - loss: 6074020352.0000 - mse: 6074020352.0000 - val_loss: 8219317760.0000 - val_mse: 8219317760.0000\n",
      "Epoch 2/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 6074017792.0000 - mse: 6074017792.0000 - val_loss: 8219314688.0000 - val_mse: 8219314688.0000\n",
      "Epoch 3/100\n",
      "1/1 [==============================] - 0s 57ms/step - loss: 6074014208.0000 - mse: 6074014208.0000 - val_loss: 8219311616.0000 - val_mse: 8219311616.0000\n",
      "Epoch 4/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 6074013184.0000 - mse: 6074013184.0000 - val_loss: 8219309568.0000 - val_mse: 8219309568.0000\n",
      "Epoch 5/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6074011136.0000 - mse: 6074011136.0000 - val_loss: 8219307520.0000 - val_mse: 8219307520.0000\n",
      "Epoch 6/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 6074009600.0000 - mse: 6074009600.0000 - val_loss: 8219305472.0000 - val_mse: 8219305472.0000\n",
      "Epoch 7/100\n",
      "1/1 [==============================] - 0s 53ms/step - loss: 6074008064.0000 - mse: 6074008064.0000 - val_loss: 8219302400.0000 - val_mse: 8219302400.0000\n",
      "Epoch 8/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6074007040.0000 - mse: 6074007040.0000 - val_loss: 8219300352.0000 - val_mse: 8219300352.0000\n",
      "Epoch 9/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6074004992.0000 - mse: 6074004992.0000 - val_loss: 8219298304.0000 - val_mse: 8219298304.0000\n",
      "Epoch 10/100\n",
      "1/1 [==============================] - 0s 55ms/step - loss: 6074003456.0000 - mse: 6074003456.0000 - val_loss: 8219297280.0000 - val_mse: 8219297280.0000\n",
      "Epoch 11/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6074003456.0000 - mse: 6074003456.0000 - val_loss: 8219295744.0000 - val_mse: 8219295744.0000\n",
      "Epoch 12/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6074001408.0000 - mse: 6074001408.0000 - val_loss: 8219293696.0000 - val_mse: 8219293696.0000\n",
      "Epoch 13/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 6074000896.0000 - mse: 6074000896.0000 - val_loss: 8219291648.0000 - val_mse: 8219291648.0000\n",
      "Epoch 14/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073999360.0000 - mse: 6073999360.0000 - val_loss: 8219290112.0000 - val_mse: 8219290112.0000\n",
      "Epoch 15/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073997824.0000 - mse: 6073997824.0000 - val_loss: 8219289088.0000 - val_mse: 8219289088.0000\n",
      "Epoch 16/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073997312.0000 - mse: 6073997312.0000 - val_loss: 8219287552.0000 - val_mse: 8219287552.0000\n",
      "Epoch 17/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 6073995776.0000 - mse: 6073995776.0000 - val_loss: 8219286016.0000 - val_mse: 8219286016.0000\n",
      "Epoch 18/100\n",
      "1/1 [==============================] - 0s 52ms/step - loss: 6073995264.0000 - mse: 6073995264.0000 - val_loss: 8219283456.0000 - val_mse: 8219283456.0000\n",
      "Epoch 19/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 6073993728.0000 - mse: 6073993728.0000 - val_loss: 8219282944.0000 - val_mse: 8219282944.0000\n",
      "Epoch 20/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 6073992704.0000 - mse: 6073992704.0000 - val_loss: 8219281408.0000 - val_mse: 8219281408.0000\n",
      "Epoch 21/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 6073991680.0000 - mse: 6073991680.0000 - val_loss: 8219279360.0000 - val_mse: 8219279360.0000\n",
      "Epoch 22/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073991168.0000 - mse: 6073991168.0000 - val_loss: 8219277824.0000 - val_mse: 8219277824.0000\n",
      "Epoch 23/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 6073989632.0000 - mse: 6073989632.0000 - val_loss: 8219276800.0000 - val_mse: 8219276800.0000\n",
      "Epoch 24/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073989120.0000 - mse: 6073989120.0000 - val_loss: 8219275776.0000 - val_mse: 8219275776.0000\n",
      "Epoch 25/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073988608.0000 - mse: 6073988608.0000 - val_loss: 8219273728.0000 - val_mse: 8219273728.0000\n",
      "Epoch 26/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 6073987072.0000 - mse: 6073987072.0000 - val_loss: 8219272704.0000 - val_mse: 8219272704.0000\n",
      "Epoch 27/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073986560.0000 - mse: 6073986560.0000 - val_loss: 8219271168.0000 - val_mse: 8219271168.0000\n",
      "Epoch 28/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073985536.0000 - mse: 6073985536.0000 - val_loss: 8219269632.0000 - val_mse: 8219269632.0000\n",
      "Epoch 29/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073984512.0000 - mse: 6073984512.0000 - val_loss: 8219268608.0000 - val_mse: 8219268608.0000\n",
      "Epoch 30/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 6073983488.0000 - mse: 6073983488.0000 - val_loss: 8219267072.0000 - val_mse: 8219267072.0000\n",
      "Epoch 31/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073982976.0000 - mse: 6073982976.0000 - val_loss: 8219265536.0000 - val_mse: 8219265536.0000\n",
      "Epoch 32/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073981440.0000 - mse: 6073981440.0000 - val_loss: 8219264512.0000 - val_mse: 8219264512.0000\n",
      "Epoch 33/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 6073980928.0000 - mse: 6073980928.0000 - val_loss: 8219262976.0000 - val_mse: 8219262976.0000\n",
      "Epoch 34/100\n",
      "1/1 [==============================] - 0s 57ms/step - loss: 6073980416.0000 - mse: 6073980416.0000 - val_loss: 8219261440.0000 - val_mse: 8219261440.0000\n",
      "Epoch 35/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073978880.0000 - mse: 6073978880.0000 - val_loss: 8219260416.0000 - val_mse: 8219260416.0000\n",
      "Epoch 36/100\n",
      "1/1 [==============================] - 0s 52ms/step - loss: 6073977344.0000 - mse: 6073977344.0000 - val_loss: 8219258880.0000 - val_mse: 8219258880.0000\n",
      "Epoch 37/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 6073976832.0000 - mse: 6073976832.0000 - val_loss: 8219257344.0000 - val_mse: 8219257344.0000\n",
      "Epoch 38/100\n",
      "1/1 [==============================] - 0s 39ms/step - loss: 6073976320.0000 - mse: 6073976320.0000 - val_loss: 8219256832.0000 - val_mse: 8219256832.0000\n",
      "Epoch 39/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 6073974784.0000 - mse: 6073974784.0000 - val_loss: 8219255296.0000 - val_mse: 8219255296.0000\n",
      "Epoch 40/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 6073974272.0000 - mse: 6073974272.0000 - val_loss: 8219253248.0000 - val_mse: 8219253248.0000\n",
      "Epoch 41/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073973248.0000 - mse: 6073973248.0000 - val_loss: 8219252736.0000 - val_mse: 8219252736.0000\n",
      "Epoch 42/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 6073972224.0000 - mse: 6073972224.0000 - val_loss: 8219251200.0000 - val_mse: 8219251200.0000\n",
      "Epoch 43/100\n",
      "1/1 [==============================] - 0s 62ms/step - loss: 6073972224.0000 - mse: 6073972224.0000 - val_loss: 8219250176.0000 - val_mse: 8219250176.0000\n",
      "Epoch 44/100\n",
      "1/1 [==============================] - 0s 56ms/step - loss: 6073970688.0000 - mse: 6073970688.0000 - val_loss: 8219248640.0000 - val_mse: 8219248640.0000\n",
      "Epoch 45/100\n",
      "1/1 [==============================] - 0s 57ms/step - loss: 6073970176.0000 - mse: 6073970176.0000 - val_loss: 8219247104.0000 - val_mse: 8219247104.0000\n",
      "Epoch 46/100\n",
      "1/1 [==============================] - 0s 57ms/step - loss: 6073969152.0000 - mse: 6073969152.0000 - val_loss: 8219246080.0000 - val_mse: 8219246080.0000\n",
      "Epoch 47/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073968128.0000 - mse: 6073968128.0000 - val_loss: 8219245056.0000 - val_mse: 8219245056.0000\n",
      "Epoch 48/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 6073968128.0000 - mse: 6073968128.0000 - val_loss: 8219244032.0000 - val_mse: 8219244032.0000\n",
      "Epoch 49/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 6073966592.0000 - mse: 6073966592.0000 - val_loss: 8219241984.0000 - val_mse: 8219241984.0000\n",
      "Epoch 50/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 46ms/step - loss: 6073965056.0000 - mse: 6073965056.0000 - val_loss: 8219240960.0000 - val_mse: 8219240960.0000\n",
      "Epoch 51/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 6073965056.0000 - mse: 6073965056.0000 - val_loss: 8219239936.0000 - val_mse: 8219239936.0000\n",
      "Epoch 52/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073964032.0000 - mse: 6073964032.0000 - val_loss: 8219238912.0000 - val_mse: 8219238912.0000\n",
      "Epoch 53/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073963008.0000 - mse: 6073963008.0000 - val_loss: 8219237888.0000 - val_mse: 8219237888.0000\n",
      "Epoch 54/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073962496.0000 - mse: 6073962496.0000 - val_loss: 8219236352.0000 - val_mse: 8219236352.0000\n",
      "Epoch 55/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073960960.0000 - mse: 6073960960.0000 - val_loss: 8219234816.0000 - val_mse: 8219234816.0000\n",
      "Epoch 56/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 6073960960.0000 - mse: 6073960960.0000 - val_loss: 8219233792.0000 - val_mse: 8219233792.0000\n",
      "Epoch 57/100\n",
      "1/1 [==============================] - 0s 41ms/step - loss: 6073959936.0000 - mse: 6073959936.0000 - val_loss: 8219232768.0000 - val_mse: 8219232768.0000\n",
      "Epoch 58/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 6073958400.0000 - mse: 6073958400.0000 - val_loss: 8219231744.0000 - val_mse: 8219231744.0000\n",
      "Epoch 59/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073958400.0000 - mse: 6073958400.0000 - val_loss: 8219230208.0000 - val_mse: 8219230208.0000\n",
      "Epoch 60/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073956864.0000 - mse: 6073956864.0000 - val_loss: 8219228672.0000 - val_mse: 8219228672.0000\n",
      "Epoch 61/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073956864.0000 - mse: 6073956864.0000 - val_loss: 8219227648.0000 - val_mse: 8219227648.0000\n",
      "Epoch 62/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073955840.0000 - mse: 6073955840.0000 - val_loss: 8219226112.0000 - val_mse: 8219226112.0000\n",
      "Epoch 63/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073954304.0000 - mse: 6073954304.0000 - val_loss: 8219225600.0000 - val_mse: 8219225600.0000\n",
      "Epoch 64/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073954304.0000 - mse: 6073954304.0000 - val_loss: 8219224064.0000 - val_mse: 8219224064.0000\n",
      "Epoch 65/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073953792.0000 - mse: 6073953792.0000 - val_loss: 8219223552.0000 - val_mse: 8219223552.0000\n",
      "Epoch 66/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 6073952768.0000 - mse: 6073952768.0000 - val_loss: 8219222016.0000 - val_mse: 8219222016.0000\n",
      "Epoch 67/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073951744.0000 - mse: 6073951744.0000 - val_loss: 8219220480.0000 - val_mse: 8219220480.0000\n",
      "Epoch 68/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 6073951744.0000 - mse: 6073951744.0000 - val_loss: 8219219456.0000 - val_mse: 8219219456.0000\n",
      "Epoch 69/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073949696.0000 - mse: 6073949696.0000 - val_loss: 8219219456.0000 - val_mse: 8219219456.0000\n",
      "Epoch 70/100\n",
      "1/1 [==============================] - 0s 50ms/step - loss: 6073948672.0000 - mse: 6073948672.0000 - val_loss: 8219217920.0000 - val_mse: 8219217920.0000\n",
      "Epoch 71/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073948672.0000 - mse: 6073948672.0000 - val_loss: 8219216384.0000 - val_mse: 8219216384.0000\n",
      "Epoch 72/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073947648.0000 - mse: 6073947648.0000 - val_loss: 8219215360.0000 - val_mse: 8219215360.0000\n",
      "Epoch 73/100\n",
      "1/1 [==============================] - 0s 53ms/step - loss: 6073947648.0000 - mse: 6073947648.0000 - val_loss: 8219213824.0000 - val_mse: 8219213824.0000\n",
      "Epoch 74/100\n",
      "1/1 [==============================] - 0s 49ms/step - loss: 6073946112.0000 - mse: 6073946112.0000 - val_loss: 8219212288.0000 - val_mse: 8219212288.0000\n",
      "Epoch 75/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073945600.0000 - mse: 6073945600.0000 - val_loss: 8219211776.0000 - val_mse: 8219211776.0000\n",
      "Epoch 76/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073944576.0000 - mse: 6073944576.0000 - val_loss: 8219211264.0000 - val_mse: 8219211264.0000\n",
      "Epoch 77/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073944064.0000 - mse: 6073944064.0000 - val_loss: 8219209728.0000 - val_mse: 8219209728.0000\n",
      "Epoch 78/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073943552.0000 - mse: 6073943552.0000 - val_loss: 8219208192.0000 - val_mse: 8219208192.0000\n",
      "Epoch 79/100\n",
      "1/1 [==============================] - 0s 43ms/step - loss: 6073942528.0000 - mse: 6073942528.0000 - val_loss: 8219207168.0000 - val_mse: 8219207168.0000\n",
      "Epoch 80/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073942016.0000 - mse: 6073942016.0000 - val_loss: 8219206144.0000 - val_mse: 8219206144.0000\n",
      "Epoch 81/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 6073941504.0000 - mse: 6073941504.0000 - val_loss: 8219205632.0000 - val_mse: 8219205632.0000\n",
      "Epoch 82/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 6073940480.0000 - mse: 6073940480.0000 - val_loss: 8219204096.0000 - val_mse: 8219204096.0000\n",
      "Epoch 83/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 6073939456.0000 - mse: 6073939456.0000 - val_loss: 8219203072.0000 - val_mse: 8219203072.0000\n",
      "Epoch 84/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 6073938432.0000 - mse: 6073938432.0000 - val_loss: 8219201536.0000 - val_mse: 8219201536.0000\n",
      "Epoch 85/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073937920.0000 - mse: 6073937920.0000 - val_loss: 8219200000.0000 - val_mse: 8219200000.0000\n",
      "Epoch 86/100\n",
      "1/1 [==============================] - 0s 54ms/step - loss: 6073937408.0000 - mse: 6073937408.0000 - val_loss: 8219200000.0000 - val_mse: 8219200000.0000\n",
      "Epoch 87/100\n",
      "1/1 [==============================] - 0s 46ms/step - loss: 6073936384.0000 - mse: 6073936384.0000 - val_loss: 8219198976.0000 - val_mse: 8219198976.0000\n",
      "Epoch 88/100\n",
      "1/1 [==============================] - 0s 40ms/step - loss: 6073935872.0000 - mse: 6073935872.0000 - val_loss: 8219197440.0000 - val_mse: 8219197440.0000\n",
      "Epoch 89/100\n",
      "1/1 [==============================] - 0s 51ms/step - loss: 6073935360.0000 - mse: 6073935360.0000 - val_loss: 8219195904.0000 - val_mse: 8219195904.0000\n",
      "Epoch 90/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 6073935360.0000 - mse: 6073935360.0000 - val_loss: 8219195392.0000 - val_mse: 8219195392.0000\n",
      "Epoch 91/100\n",
      "1/1 [==============================] - 0s 45ms/step - loss: 6073933824.0000 - mse: 6073933824.0000 - val_loss: 8219194880.0000 - val_mse: 8219194880.0000\n",
      "Epoch 92/100\n",
      "1/1 [==============================] - 0s 39ms/step - loss: 6073933312.0000 - mse: 6073933312.0000 - val_loss: 8219193344.0000 - val_mse: 8219193344.0000\n",
      "Epoch 93/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 6073932288.0000 - mse: 6073932288.0000 - val_loss: 8219191808.0000 - val_mse: 8219191808.0000\n",
      "Epoch 94/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073931776.0000 - mse: 6073931776.0000 - val_loss: 8219191808.0000 - val_mse: 8219191808.0000\n",
      "Epoch 95/100\n",
      "1/1 [==============================] - 0s 44ms/step - loss: 6073931264.0000 - mse: 6073931264.0000 - val_loss: 8219190784.0000 - val_mse: 8219190784.0000\n",
      "Epoch 96/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073930240.0000 - mse: 6073930240.0000 - val_loss: 8219189248.0000 - val_mse: 8219189248.0000\n",
      "Epoch 97/100\n",
      "1/1 [==============================] - 0s 47ms/step - loss: 6073929728.0000 - mse: 6073929728.0000 - val_loss: 8219188736.0000 - val_mse: 8219188736.0000\n",
      "Epoch 98/100\n",
      "1/1 [==============================] - 0s 42ms/step - loss: 6073929216.0000 - mse: 6073929216.0000 - val_loss: 8219187200.0000 - val_mse: 8219187200.0000\n",
      "Epoch 99/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 41ms/step - loss: 6073928192.0000 - mse: 6073928192.0000 - val_loss: 8219186688.0000 - val_mse: 8219186688.0000\n",
      "Epoch 100/100\n",
      "1/1 [==============================] - 0s 48ms/step - loss: 6073928192.0000 - mse: 6073928192.0000 - val_loss: 8219185152.0000 - val_mse: 8219185152.0000\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs = 100, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3cd43727",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 35ms/step - loss: 8219160576.0000 - mse: 8219160576.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[8219160576.0, 8219160576.0]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8742eea3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loss 체크\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(range(100), history.history['loss'], label='train loss')\n",
    "plt.plot(range(100), history.history['val_loss'], label='test loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}