Ozone Level Detection(lab).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e2c932f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://archive.ics.uci.edu/ml/datasets/Ozone+Level+Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36a1b923",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.layers import *\n",
    "from keras.models import *\n",
    "from keras.utils import *\n",
    "from sklearn.preprocessing import *\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ae0c492",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>feat_0</th>\n",
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       "      <td>1/1/1998</td>\n",
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       "      <td>2.6</td>\n",
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       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.2</td>\n",
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       "      <td>0.6</td>\n",
       "      <td>6.94</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5790</td>\n",
       "      <td>17.9</td>\n",
       "      <td>41.3</td>\n",
       "      <td>10235</td>\n",
       "      <td>-40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/1998</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.9</td>\n",
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       "      <td>2.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.4</td>\n",
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       "      <td>0.49</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.54</td>\n",
       "      <td>5775</td>\n",
       "      <td>31.15</td>\n",
       "      <td>51.7</td>\n",
       "      <td>10195</td>\n",
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       "      <td>2.08</td>\n",
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       "      <th>4</th>\n",
       "      <td>1/5/1998</td>\n",
       "      <td>2.6</td>\n",
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       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.5</td>\n",
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       "      <td>-1.14</td>\n",
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       "      <td>12/30/2004</td>\n",
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       "      <td>-0.89</td>\n",
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       "      <th>2533</th>\n",
       "      <td>12/31/2004</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.7</td>\n",
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       "      <td>0.54</td>\n",
       "      <td>13.07</td>\n",
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       "      <td>5820</td>\n",
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       "      <td>39.35</td>\n",
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       "          feat_0 feat_1 feat_2 feat_3 feat_4 feat_5 feat_6 feat_7 feat_8  \\\n",
       "0       1/1/1998    0.8    1.8    2.4    2.1      2    2.1    1.5    1.7   \n",
       "1       1/2/1998    2.8    3.2    3.3    2.7    3.3    3.2    2.9    2.8   \n",
       "2       1/3/1998    2.9    2.8    2.6    2.1    2.2    2.5    2.5    2.7   \n",
       "3       1/4/1998    4.7    3.8    3.7    3.8    2.9    3.1    2.8    2.5   \n",
       "4       1/5/1998    2.6    2.1    1.6    1.4    0.9    1.5    1.2    1.4   \n",
       "...          ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "2529  12/27/2004    0.3    0.4    0.5    0.5    0.2    0.3    0.4    0.4   \n",
       "2530  12/28/2004      1    1.4    1.1    1.7    1.5    1.7    1.8    1.5   \n",
       "2531  12/29/2004    0.8    0.8    1.2    0.9    0.4    0.6    0.8    1.1   \n",
       "2532  12/30/2004    1.3    0.9    1.5    1.2    1.6    1.8    1.1      1   \n",
       "2533  12/31/2004    1.5    1.3    1.8    1.4    1.2    1.7    1.6    1.4   \n",
       "\n",
       "     feat_9  ... feat_64 feat_65 feat_66 feat_67 feat_68 feat_69 feat_70  \\\n",
       "0       1.9  ...    0.15   10.67   -1.56    5795   -12.1    17.9   10330   \n",
       "1       3.1  ...    0.48    8.39    3.84    5805   14.05      29   10275   \n",
       "2       2.2  ...     0.6    6.94     9.8    5790    17.9    41.3   10235   \n",
       "3       2.4  ...    0.49    8.73   10.54    5775   31.15    51.7   10195   \n",
       "4       1.3  ...       ?       ?       ?       ?       ?       ?       ?   \n",
       "...     ...  ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2529    1.3  ...    0.07    7.93   -4.41    5800   -25.6    21.8   10295   \n",
       "2530    2.1  ...    0.04    5.95   -1.14    5845   -19.4    19.1   10310   \n",
       "2531    1.5  ...    0.06     7.8   -0.64    5845    -9.6    35.2   10275   \n",
       "2532    1.9  ...    0.25    7.72   -0.89    5845   -19.6    34.2   10245   \n",
       "2533    1.6  ...    0.54   13.07    9.15    5820    1.95   39.35   10220   \n",
       "\n",
       "     feat_71 feat_72 feat_73  \n",
       "0        -55       0     0.0  \n",
       "1        -55       0     0.0  \n",
       "2        -40       0     0.0  \n",
       "3        -40    2.08     0.0  \n",
       "4          ?    0.58     0.0  \n",
       "...      ...     ...     ...  \n",
       "2529      65       0     0.0  \n",
       "2530      15       0     0.0  \n",
       "2531     -35       0     0.0  \n",
       "2532     -30    0.05     0.0  \n",
       "2533     -25       0     0.0  \n",
       "\n",
       "[2534 rows x 74 columns]"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "label = ['feat_{}'.format(i) for i in range(74)]\n",
    "df = pd.read_csv('eighthr.data', names = label)\n",
    "df"
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   "execution_count": 4,
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       "           feat_73\n",
       "count  2534.000000\n",
       "mean      0.063141\n",
       "std       0.243265\n",
       "min       0.000000\n",
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  {
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   "execution_count": 5,
   "id": "8f1a191c",
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2534 entries, 0 to 2533\n",
      "Data columns (total 74 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
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      " 18  feat_18  2534 non-null   object \n",
      " 19  feat_19  2534 non-null   object \n",
      " 20  feat_20  2534 non-null   object \n",
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      " 22  feat_22  2534 non-null   object \n",
      " 23  feat_23  2534 non-null   object \n",
      " 24  feat_24  2534 non-null   object \n",
      " 25  feat_25  2534 non-null   object \n",
      " 26  feat_26  2534 non-null   object \n",
      " 27  feat_27  2534 non-null   object \n",
      " 28  feat_28  2534 non-null   object \n",
      " 29  feat_29  2534 non-null   object \n",
      " 30  feat_30  2534 non-null   object \n",
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      " 33  feat_33  2534 non-null   object \n",
      " 34  feat_34  2534 non-null   object \n",
      " 35  feat_35  2534 non-null   object \n",
      " 36  feat_36  2534 non-null   object \n",
      " 37  feat_37  2534 non-null   object \n",
      " 38  feat_38  2534 non-null   object \n",
      " 39  feat_39  2534 non-null   object \n",
      " 40  feat_40  2534 non-null   object \n",
      " 41  feat_41  2534 non-null   object \n",
      " 42  feat_42  2534 non-null   object \n",
      " 43  feat_43  2534 non-null   object \n",
      " 44  feat_44  2534 non-null   object \n",
      " 45  feat_45  2534 non-null   object \n",
      " 46  feat_46  2534 non-null   object \n",
      " 47  feat_47  2534 non-null   object \n",
      " 48  feat_48  2534 non-null   object \n",
      " 49  feat_49  2534 non-null   object \n",
      " 50  feat_50  2534 non-null   object \n",
      " 51  feat_51  2534 non-null   object \n",
      " 52  feat_52  2534 non-null   object \n",
      " 53  feat_53  2534 non-null   object \n",
      " 54  feat_54  2534 non-null   object \n",
      " 55  feat_55  2534 non-null   object \n",
      " 56  feat_56  2534 non-null   object \n",
      " 57  feat_57  2534 non-null   object \n",
      " 58  feat_58  2534 non-null   object \n",
      " 59  feat_59  2534 non-null   object \n",
      " 60  feat_60  2534 non-null   object \n",
      " 61  feat_61  2534 non-null   object \n",
      " 62  feat_62  2534 non-null   object \n",
      " 63  feat_63  2534 non-null   object \n",
      " 64  feat_64  2534 non-null   object \n",
      " 65  feat_65  2534 non-null   object \n",
      " 66  feat_66  2534 non-null   object \n",
      " 67  feat_67  2534 non-null   object \n",
      " 68  feat_68  2534 non-null   object \n",
      " 69  feat_69  2534 non-null   object \n",
      " 70  feat_70  2534 non-null   object \n",
      " 71  feat_71  2534 non-null   object \n",
      " 72  feat_72  2534 non-null   object \n",
      " 73  feat_73  2534 non-null   float64\n",
      "dtypes: float64(1), object(73)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36341fab",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3.4</td>\n",
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       "      <td>3.84</td>\n",
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       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.60</td>\n",
       "      <td>6.94</td>\n",
       "      <td>9.80</td>\n",
       "      <td>5790.0</td>\n",
       "      <td>17.90</td>\n",
       "      <td>41.30</td>\n",
       "      <td>10235.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.54</td>\n",
       "      <td>5775.0</td>\n",
       "      <td>31.15</td>\n",
       "      <td>51.70</td>\n",
       "      <td>10195.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2.6</td>\n",
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       "      <td>1.4</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
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       "      <td>1.1</td>\n",
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       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>5845.0</td>\n",
       "      <td>-19.60</td>\n",
       "      <td>34.20</td>\n",
       "      <td>10245.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2533</th>\n",
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       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.2</td>\n",
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       "      <td>13.07</td>\n",
       "      <td>9.15</td>\n",
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       "      <td>10220.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>2534 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "0        0.8     1.8     2.4     2.1     2.0     2.1     1.5     1.7     1.9   \n",
       "1        2.8     3.2     3.3     2.7     3.3     3.2     2.9     2.8     3.1   \n",
       "2        2.9     2.8     2.6     2.1     2.2     2.5     2.5     2.7     2.2   \n",
       "3        4.7     3.8     3.7     3.8     2.9     3.1     2.8     2.5     2.4   \n",
       "4        2.6     2.1     1.6     1.4     0.9     1.5     1.2     1.4     1.3   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2529     0.3     0.4     0.5     0.5     0.2     0.3     0.4     0.4     1.3   \n",
       "2530     1.0     1.4     1.1     1.7     1.5     1.7     1.8     1.5     2.1   \n",
       "2531     0.8     0.8     1.2     0.9     0.4     0.6     0.8     1.1     1.5   \n",
       "2532     1.3     0.9     1.5     1.2     1.6     1.8     1.1     1.0     1.9   \n",
       "2533     1.5     1.3     1.8     1.4     1.2     1.7     1.6     1.4     1.6   \n",
       "\n",
       "      feat_10  ...  feat_64  feat_65  feat_66  feat_67  feat_68  feat_69  \\\n",
       "0         2.3  ...     0.15    10.67    -1.56   5795.0   -12.10    17.90   \n",
       "1         3.4  ...     0.48     8.39     3.84   5805.0    14.05    29.00   \n",
       "2         2.5  ...     0.60     6.94     9.80   5790.0    17.90    41.30   \n",
       "3         3.1  ...     0.49     8.73    10.54   5775.0    31.15    51.70   \n",
       "4         1.4  ...      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "...       ...  ...      ...      ...      ...      ...      ...      ...   \n",
       "2529      2.2  ...     0.07     7.93    -4.41   5800.0   -25.60    21.80   \n",
       "2530      2.4  ...     0.04     5.95    -1.14   5845.0   -19.40    19.10   \n",
       "2531      1.5  ...     0.06     7.80    -0.64   5845.0    -9.60    35.20   \n",
       "2532      2.0  ...     0.25     7.72    -0.89   5845.0   -19.60    34.20   \n",
       "2533      3.0  ...     0.54    13.07     9.15   5820.0     1.95    39.35   \n",
       "\n",
       "      feat_70  feat_71  feat_72  feat_73  \n",
       "0     10330.0    -55.0     0.00      0.0  \n",
       "1     10275.0    -55.0     0.00      0.0  \n",
       "2     10235.0    -40.0     0.00      0.0  \n",
       "3     10195.0    -40.0     2.08      0.0  \n",
       "4         NaN      NaN     0.58      0.0  \n",
       "...       ...      ...      ...      ...  \n",
       "2529  10295.0     65.0     0.00      0.0  \n",
       "2530  10310.0     15.0     0.00      0.0  \n",
       "2531  10275.0    -35.0     0.00      0.0  \n",
       "2532  10245.0    -30.0     0.05      0.0  \n",
       "2533  10220.0    -25.0     0.00      0.0  \n",
       "\n",
       "[2534 rows x 73 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data type을 숫자로 바꿈 / 날짜를 지움\n",
    "df = df.apply(pd.to_numeric, errors = 'coerce')\n",
    "df = df.drop(['feat_0'], axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "34706c22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 제거\n",
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c35c2ca8",
   "metadata": {},
   "outputs": [
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       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.39</td>\n",
       "      <td>3.84</td>\n",
       "      <td>5805.0</td>\n",
       "      <td>14.05</td>\n",
       "      <td>29.00</td>\n",
       "      <td>10275.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.60</td>\n",
       "      <td>6.94</td>\n",
       "      <td>9.80</td>\n",
       "      <td>5790.0</td>\n",
       "      <td>17.90</td>\n",
       "      <td>41.30</td>\n",
       "      <td>10235.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.49</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.54</td>\n",
       "      <td>5775.0</td>\n",
       "      <td>31.15</td>\n",
       "      <td>51.70</td>\n",
       "      <td>10195.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.84</td>\n",
       "      <td>6.86</td>\n",
       "      <td>25.60</td>\n",
       "      <td>5695.0</td>\n",
       "      <td>26.75</td>\n",
       "      <td>48.45</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</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>2529</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>7.93</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>-25.60</td>\n",
       "      <td>21.80</td>\n",
       "      <td>10295.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2530</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5.95</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>5845.0</td>\n",
       "      <td>-19.40</td>\n",
       "      <td>19.10</td>\n",
       "      <td>10310.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2531</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>7.80</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>5845.0</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>35.20</td>\n",
       "      <td>10275.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25</td>\n",
       "      <td>7.72</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>5845.0</td>\n",
       "      <td>-19.60</td>\n",
       "      <td>34.20</td>\n",
       "      <td>10245.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.54</td>\n",
       "      <td>13.07</td>\n",
       "      <td>9.15</td>\n",
       "      <td>5820.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>39.35</td>\n",
       "      <td>10220.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1847 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "0        0.8     1.8     2.4     2.1     2.0     2.1     1.5     1.7     1.9   \n",
       "1        2.8     3.2     3.3     2.7     3.3     3.2     2.9     2.8     3.1   \n",
       "2        2.9     2.8     2.6     2.1     2.2     2.5     2.5     2.7     2.2   \n",
       "3        4.7     3.8     3.7     3.8     2.9     3.1     2.8     2.5     2.4   \n",
       "6        3.7     3.2     3.8     5.1     6.0     7.0     6.3     6.4     6.3   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2529     0.3     0.4     0.5     0.5     0.2     0.3     0.4     0.4     1.3   \n",
       "2530     1.0     1.4     1.1     1.7     1.5     1.7     1.8     1.5     2.1   \n",
       "2531     0.8     0.8     1.2     0.9     0.4     0.6     0.8     1.1     1.5   \n",
       "2532     1.3     0.9     1.5     1.2     1.6     1.8     1.1     1.0     1.9   \n",
       "2533     1.5     1.3     1.8     1.4     1.2     1.7     1.6     1.4     1.6   \n",
       "\n",
       "      feat_10  ...  feat_64  feat_65  feat_66  feat_67  feat_68  feat_69  \\\n",
       "0         2.3  ...     0.15    10.67    -1.56   5795.0   -12.10    17.90   \n",
       "1         3.4  ...     0.48     8.39     3.84   5805.0    14.05    29.00   \n",
       "2         2.5  ...     0.60     6.94     9.80   5790.0    17.90    41.30   \n",
       "3         3.1  ...     0.49     8.73    10.54   5775.0    31.15    51.70   \n",
       "6         5.4  ...     0.84     6.86    25.60   5695.0    26.75    48.45   \n",
       "...       ...  ...      ...      ...      ...      ...      ...      ...   \n",
       "2529      2.2  ...     0.07     7.93    -4.41   5800.0   -25.60    21.80   \n",
       "2530      2.4  ...     0.04     5.95    -1.14   5845.0   -19.40    19.10   \n",
       "2531      1.5  ...     0.06     7.80    -0.64   5845.0    -9.60    35.20   \n",
       "2532      2.0  ...     0.25     7.72    -0.89   5845.0   -19.60    34.20   \n",
       "2533      3.0  ...     0.54    13.07     9.15   5820.0     1.95    39.35   \n",
       "\n",
       "      feat_70  feat_71  feat_72  feat_73  \n",
       "0     10330.0    -55.0     0.00      0.0  \n",
       "1     10275.0    -55.0     0.00      0.0  \n",
       "2     10235.0    -40.0     0.00      0.0  \n",
       "3     10195.0    -40.0     2.08      0.0  \n",
       "6     10040.0    -80.0     0.18      0.0  \n",
       "...       ...      ...      ...      ...  \n",
       "2529  10295.0     65.0     0.00      0.0  \n",
       "2530  10310.0     15.0     0.00      0.0  \n",
       "2531  10275.0    -35.0     0.00      0.0  \n",
       "2532  10245.0    -30.0     0.05      0.0  \n",
       "2533  10220.0    -25.0     0.00      0.0  \n",
       "\n",
       "[1847 rows x 73 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "242e07eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.0\n",
       "1       0.0\n",
       "2       0.0\n",
       "3       0.0\n",
       "6       0.0\n",
       "       ... \n",
       "2529    0.0\n",
       "2530    0.0\n",
       "2531    0.0\n",
       "2532    0.0\n",
       "2533    0.0\n",
       "Name: feat_73, Length: 1847, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 마지막 데이터 - 정답 데이터\n",
    "Y = df['feat_73']\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc324222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1719\n",
       "1.0     128\n",
       "Name: feat_73, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a77e8d53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       ...,\n",
       "       [1., 0.],\n",
       "       [1., 0.],\n",
       "       [1., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorical - 인코딩\n",
    "Y = to_categorical(Y)\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5fd61ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['feat_73'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d62a3152",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feat_1</th>\n",
       "      <th>feat_2</th>\n",
       "      <th>feat_3</th>\n",
       "      <th>feat_4</th>\n",
       "      <th>feat_5</th>\n",
       "      <th>feat_6</th>\n",
       "      <th>feat_7</th>\n",
       "      <th>feat_8</th>\n",
       "      <th>feat_9</th>\n",
       "      <th>feat_10</th>\n",
       "      <th>...</th>\n",
       "      <th>feat_63</th>\n",
       "      <th>feat_64</th>\n",
       "      <th>feat_65</th>\n",
       "      <th>feat_66</th>\n",
       "      <th>feat_67</th>\n",
       "      <th>feat_68</th>\n",
       "      <th>feat_69</th>\n",
       "      <th>feat_70</th>\n",
       "      <th>feat_71</th>\n",
       "      <th>feat_72</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.5</td>\n",
       "      <td>0.15</td>\n",
       "      <td>10.67</td>\n",
       "      <td>-1.56</td>\n",
       "      <td>5795.0</td>\n",
       "      <td>-12.10</td>\n",
       "      <td>17.90</td>\n",
       "      <td>10330.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>0.48</td>\n",
       "      <td>8.39</td>\n",
       "      <td>3.84</td>\n",
       "      <td>5805.0</td>\n",
       "      <td>14.05</td>\n",
       "      <td>29.00</td>\n",
       "      <td>10275.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.9</td>\n",
       "      <td>0.60</td>\n",
       "      <td>6.94</td>\n",
       "      <td>9.80</td>\n",
       "      <td>5790.0</td>\n",
       "      <td>17.90</td>\n",
       "      <td>41.30</td>\n",
       "      <td>10235.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.8</td>\n",
       "      <td>0.49</td>\n",
       "      <td>8.73</td>\n",
       "      <td>10.54</td>\n",
       "      <td>5775.0</td>\n",
       "      <td>31.15</td>\n",
       "      <td>51.70</td>\n",
       "      <td>10195.0</td>\n",
       "      <td>-40.0</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>0.84</td>\n",
       "      <td>6.86</td>\n",
       "      <td>25.60</td>\n",
       "      <td>5695.0</td>\n",
       "      <td>26.75</td>\n",
       "      <td>48.45</td>\n",
       "      <td>10040.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>0.18</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>2529</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.4</td>\n",
       "      <td>0.07</td>\n",
       "      <td>7.93</td>\n",
       "      <td>-4.41</td>\n",
       "      <td>5800.0</td>\n",
       "      <td>-25.60</td>\n",
       "      <td>21.80</td>\n",
       "      <td>10295.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2530</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>...</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>5.95</td>\n",
       "      <td>-1.14</td>\n",
       "      <td>5845.0</td>\n",
       "      <td>-19.40</td>\n",
       "      <td>19.10</td>\n",
       "      <td>10310.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2531</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.8</td>\n",
       "      <td>0.06</td>\n",
       "      <td>7.80</td>\n",
       "      <td>-0.64</td>\n",
       "      <td>5845.0</td>\n",
       "      <td>-9.60</td>\n",
       "      <td>35.20</td>\n",
       "      <td>10275.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>0.25</td>\n",
       "      <td>7.72</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>5845.0</td>\n",
       "      <td>-19.60</td>\n",
       "      <td>34.20</td>\n",
       "      <td>10245.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.9</td>\n",
       "      <td>0.54</td>\n",
       "      <td>13.07</td>\n",
       "      <td>9.15</td>\n",
       "      <td>5820.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>39.35</td>\n",
       "      <td>10220.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1847 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      feat_1  feat_2  feat_3  feat_4  feat_5  feat_6  feat_7  feat_8  feat_9  \\\n",
       "0        0.8     1.8     2.4     2.1     2.0     2.1     1.5     1.7     1.9   \n",
       "1        2.8     3.2     3.3     2.7     3.3     3.2     2.9     2.8     3.1   \n",
       "2        2.9     2.8     2.6     2.1     2.2     2.5     2.5     2.7     2.2   \n",
       "3        4.7     3.8     3.7     3.8     2.9     3.1     2.8     2.5     2.4   \n",
       "6        3.7     3.2     3.8     5.1     6.0     7.0     6.3     6.4     6.3   \n",
       "...      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
       "2529     0.3     0.4     0.5     0.5     0.2     0.3     0.4     0.4     1.3   \n",
       "2530     1.0     1.4     1.1     1.7     1.5     1.7     1.8     1.5     2.1   \n",
       "2531     0.8     0.8     1.2     0.9     0.4     0.6     0.8     1.1     1.5   \n",
       "2532     1.3     0.9     1.5     1.2     1.6     1.8     1.1     1.0     1.9   \n",
       "2533     1.5     1.3     1.8     1.4     1.2     1.7     1.6     1.4     1.6   \n",
       "\n",
       "      feat_10  ...  feat_63  feat_64  feat_65  feat_66  feat_67  feat_68  \\\n",
       "0         2.3  ...    -15.5     0.15    10.67    -1.56   5795.0   -12.10   \n",
       "1         3.4  ...    -14.5     0.48     8.39     3.84   5805.0    14.05   \n",
       "2         2.5  ...    -15.9     0.60     6.94     9.80   5790.0    17.90   \n",
       "3         3.1  ...    -16.8     0.49     8.73    10.54   5775.0    31.15   \n",
       "6         5.4  ...    -11.8     0.84     6.86    25.60   5695.0    26.75   \n",
       "...       ...  ...      ...      ...      ...      ...      ...      ...   \n",
       "2529      2.2  ...    -12.4     0.07     7.93    -4.41   5800.0   -25.60   \n",
       "2530      2.4  ...    -12.0     0.04     5.95    -1.14   5845.0   -19.40   \n",
       "2531      1.5  ...    -11.8     0.06     7.80    -0.64   5845.0    -9.60   \n",
       "2532      2.0  ...    -10.8     0.25     7.72    -0.89   5845.0   -19.60   \n",
       "2533      3.0  ...    -11.9     0.54    13.07     9.15   5820.0     1.95   \n",
       "\n",
       "      feat_69  feat_70  feat_71  feat_72  \n",
       "0       17.90  10330.0    -55.0     0.00  \n",
       "1       29.00  10275.0    -55.0     0.00  \n",
       "2       41.30  10235.0    -40.0     0.00  \n",
       "3       51.70  10195.0    -40.0     2.08  \n",
       "6       48.45  10040.0    -80.0     0.18  \n",
       "...       ...      ...      ...      ...  \n",
       "2529    21.80  10295.0     65.0     0.00  \n",
       "2530    19.10  10310.0     15.0     0.00  \n",
       "2531    35.20  10275.0    -35.0     0.00  \n",
       "2532    34.20  10245.0    -30.0     0.05  \n",
       "2533    39.35  10220.0    -25.0     0.00  \n",
       "\n",
       "[1847 rows x 72 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7b25404",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape:(1747, 72), Y_train.shape:(1747, 2), X_test.shape:(100, 72), Y_test.shape:(100, 2)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.asarray(df[:-100].values.tolist(), dtype = np.float64)\n",
    "X_test = np.asarray(df[-100:].values.tolist(), dtype = np.float64)\n",
    "\n",
    "Y_train = Y[:-100]\n",
    "Y_test = Y[-100:]\n",
    "\n",
    "print(f'X_train.shape:{X_train.shape}, Y_train.shape:{Y_train.shape}, X_test.shape:{X_test.shape}, Y_test.shape:{Y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9fef04b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (sample, timestep, features) = N x T x D(features 개수, 차원 개수)\n",
    "# X_train, Y_train -> (N x T x D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ab2046f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[:1700]\n",
    "Y_train = Y_train[:1700]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "023dc2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1, 10, 72)\n",
    "Y_train = Y_train.reshape(-1, 10, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8e6c1af4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape:(170, 10, 72), Y_train.shape:(170, 10, 2)\n"
     ]
    }
   ],
   "source": [
    "print(f'X_train.shape:{X_train.shape}, Y_train.shape:{Y_train.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1bd4260",
   "metadata": {},
   "source": [
    "### Build a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cb7f976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 10, 72)]          0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 10, 128)           102912    \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10, 2)             258       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 103,170\n",
      "Trainable params: 103,170\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 정확도 얼마인지?(accuracy)\n",
    "# LSTM + GlobalMaxPool1D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "i = Input(shape = (10, 72))\n",
    "x = LSTM(128, input_shape = (10, 72), return_sequences = True)(i) # many to many\n",
    "x = Dense(2, activation = 'softmax')(x)\n",
    "model = Model(i, x)\n",
    "model.compile(loss = 'categorical_crossentropy',\n",
    "              optimizer = 'adam',\n",
    "              metrics = ['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "43a2d39c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "4/4 [==============================] - 2s 171ms/step - loss: 0.6374 - accuracy: 0.7008 - val_loss: 0.5299 - val_accuracy: 0.9615\n",
      "Epoch 2/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.5289 - accuracy: 0.9127 - val_loss: 0.4618 - val_accuracy: 0.9615\n",
      "Epoch 3/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.4749 - accuracy: 0.9127 - val_loss: 0.4047 - val_accuracy: 0.9615\n",
      "Epoch 4/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.4205 - accuracy: 0.9127 - val_loss: 0.3152 - val_accuracy: 0.9615\n",
      "Epoch 5/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.3642 - accuracy: 0.9127 - val_loss: 0.2834 - val_accuracy: 0.9615\n",
      "Epoch 6/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3428 - accuracy: 0.9127 - val_loss: 0.2596 - val_accuracy: 0.9615\n",
      "Epoch 7/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3290 - accuracy: 0.9127 - val_loss: 0.2411 - val_accuracy: 0.9615\n",
      "Epoch 8/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3180 - accuracy: 0.9127 - val_loss: 0.2272 - val_accuracy: 0.9615\n",
      "Epoch 9/30\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.3103 - accuracy: 0.9127 - val_loss: 0.2167 - val_accuracy: 0.9615\n",
      "Epoch 10/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.3063 - accuracy: 0.9127 - val_loss: 0.2084 - val_accuracy: 0.9615\n",
      "Epoch 11/30\n",
      "4/4 [==============================] - 0s 12ms/step - loss: 0.3026 - accuracy: 0.9127 - val_loss: 0.2022 - val_accuracy: 0.9615\n",
      "Epoch 12/30\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.3005 - accuracy: 0.9127 - val_loss: 0.1974 - val_accuracy: 0.9615\n",
      "Epoch 13/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2993 - accuracy: 0.9127 - val_loss: 0.1936 - val_accuracy: 0.9615\n",
      "Epoch 14/30\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.2980 - accuracy: 0.9127 - val_loss: 0.1908 - val_accuracy: 0.9615\n",
      "Epoch 15/30\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.2977 - accuracy: 0.9127 - val_loss: 0.1885 - val_accuracy: 0.9615\n",
      "Epoch 16/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2973 - accuracy: 0.9127 - val_loss: 0.1869 - val_accuracy: 0.9615\n",
      "Epoch 17/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2971 - accuracy: 0.9127 - val_loss: 0.1857 - val_accuracy: 0.9615\n",
      "Epoch 18/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1847 - val_accuracy: 0.9615\n",
      "Epoch 19/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2968 - accuracy: 0.9127 - val_loss: 0.1841 - val_accuracy: 0.9615\n",
      "Epoch 20/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2968 - accuracy: 0.9127 - val_loss: 0.1835 - val_accuracy: 0.9615\n",
      "Epoch 21/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1829 - val_accuracy: 0.9615\n",
      "Epoch 22/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1824 - val_accuracy: 0.9615\n",
      "Epoch 23/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2968 - accuracy: 0.9127 - val_loss: 0.1821 - val_accuracy: 0.9615\n",
      "Epoch 24/30\n",
      "4/4 [==============================] - 0s 15ms/step - loss: 0.2968 - accuracy: 0.9127 - val_loss: 0.1820 - val_accuracy: 0.9615\n",
      "Epoch 25/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2968 - accuracy: 0.9127 - val_loss: 0.1817 - val_accuracy: 0.9615\n",
      "Epoch 26/30\n",
      "4/4 [==============================] - 0s 16ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1815 - val_accuracy: 0.9615\n",
      "Epoch 27/30\n",
      "4/4 [==============================] - 0s 14ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1816 - val_accuracy: 0.9615\n",
      "Epoch 28/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1815 - val_accuracy: 0.9615\n",
      "Epoch 29/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2969 - accuracy: 0.9127 - val_loss: 0.1816 - val_accuracy: 0.9615\n",
      "Epoch 30/30\n",
      "4/4 [==============================] - 0s 13ms/step - loss: 0.2968 - accuracy: 0.9127 - val_loss: 0.1817 - val_accuracy: 0.9615\n"
     ]
    }
   ],
   "source": [
    "r = model.fit(X_train, Y_train, epochs = 30, validation_split = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "abd890e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = X_test.reshape(-1, 10, 72)\n",
    "Y_test = Y_test.reshape(-1, 10, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ef80d044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 18ms/step - loss: 0.1382 - accuracy: 0.9800\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.13815702497959137, 0.9800000190734863]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = model.evaluate(X_test, Y_test)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f5b7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ([])"
   ]
  }
 ],
 "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",
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