reto.ipynb

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    "name": "ipython",
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   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
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 },
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('weather-data.csv')\n",
    "df = df[['Date', 'TempHighF', 'TempAvgF', 'TempLowF', 'HumidityAvgPercent', 'SeaLevelPressureAvgInches', 'WindAvgMPH', 'PrecipitationSumInches']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         Date  TempHighF  TempAvgF  TempLowF HumidityAvgPercent  \\\n",
       "0  2013-12-21         74        60        45                 75   \n",
       "1  2013-12-22         56        48        39                 68   \n",
       "2  2013-12-23         58        45        32                 52   \n",
       "3  2013-12-24         61        46        31                 56   \n",
       "4  2013-12-25         58        50        41                 71   \n",
       "\n",
       "  SeaLevelPressureAvgInches WindAvgMPH PrecipitationSumInches  \n",
       "0                     29.68          4                   0.46  \n",
       "1                     30.13          6                      0  \n",
       "2                     30.49          3                      0  \n",
       "3                     30.45          4                      0  \n",
       "4                     30.33          2                      T  "
      ],
      "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>Date</th>\n      <th>TempHighF</th>\n      <th>TempAvgF</th>\n      <th>TempLowF</th>\n      <th>HumidityAvgPercent</th>\n      <th>SeaLevelPressureAvgInches</th>\n      <th>WindAvgMPH</th>\n      <th>PrecipitationSumInches</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2013-12-21</td>\n      <td>74</td>\n      <td>60</td>\n      <td>45</td>\n      <td>75</td>\n      <td>29.68</td>\n      <td>4</td>\n      <td>0.46</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2013-12-22</td>\n      <td>56</td>\n      <td>48</td>\n      <td>39</td>\n      <td>68</td>\n      <td>30.13</td>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2013-12-23</td>\n      <td>58</td>\n      <td>45</td>\n      <td>32</td>\n      <td>52</td>\n      <td>30.49</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2013-12-24</td>\n      <td>61</td>\n      <td>46</td>\n      <td>31</td>\n      <td>56</td>\n      <td>30.45</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2013-12-25</td>\n      <td>58</td>\n      <td>50</td>\n      <td>41</td>\n      <td>71</td>\n      <td>30.33</td>\n      <td>2</td>\n      <td>T</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   TempHighF  TempAvgF  TempLowF HumidityAvgPercent SeaLevelPressureAvgInches  \\\n",
       "0         74        60        45                 75                     29.68   \n",
       "1         56        48        39                 68                     30.13   \n",
       "2         58        45        32                 52                     30.49   \n",
       "3         61        46        31                 56                     30.45   \n",
       "4         58        50        41                 71                     30.33   \n",
       "\n",
       "  WindAvgMPH PrecipitationSumInches month day  \n",
       "0          4                   0.46    12  21  \n",
       "1          6                      0    12  22  \n",
       "2          3                      0    12  23  \n",
       "3          4                      0    12  24  \n",
       "4          2                      T    12  25  "
      ],
      "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>TempHighF</th>\n      <th>TempAvgF</th>\n      <th>TempLowF</th>\n      <th>HumidityAvgPercent</th>\n      <th>SeaLevelPressureAvgInches</th>\n      <th>WindAvgMPH</th>\n      <th>PrecipitationSumInches</th>\n      <th>month</th>\n      <th>day</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>74</td>\n      <td>60</td>\n      <td>45</td>\n      <td>75</td>\n      <td>29.68</td>\n      <td>4</td>\n      <td>0.46</td>\n      <td>12</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56</td>\n      <td>48</td>\n      <td>39</td>\n      <td>68</td>\n      <td>30.13</td>\n      <td>6</td>\n      <td>0</td>\n      <td>12</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>58</td>\n      <td>45</td>\n      <td>32</td>\n      <td>52</td>\n      <td>30.49</td>\n      <td>3</td>\n      <td>0</td>\n      <td>12</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>61</td>\n      <td>46</td>\n      <td>31</td>\n      <td>56</td>\n      <td>30.45</td>\n      <td>4</td>\n      <td>0</td>\n      <td>12</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>58</td>\n      <td>50</td>\n      <td>41</td>\n      <td>71</td>\n      <td>30.33</td>\n      <td>2</td>\n      <td>T</td>\n      <td>12</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 21
    }
   ],
   "source": [
    "df.Date.str.split('-', expand = True)\n",
    "# Split Date\n",
    "df1 = pd.concat([df, df.Date.str.split('-', expand = True)], axis=1)\n",
    "df1.drop(columns=['Date'], inplace=True)\n",
    "df1.rename(columns={0:'year', 1:'month', 2:'day'}, inplace=True)\n",
    "df1.drop(columns=['year'], inplace=True)\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   TempHighF  TempAvgF  TempLowF HumidityAvgPercent SeaLevelPressureAvgInches  \\\n",
       "0         74        60        45                 75                     29.68   \n",
       "1         56        48        39                 68                     30.13   \n",
       "2         58        45        32                 52                     30.49   \n",
       "3         61        46        31                 56                     30.45   \n",
       "4         58        50        41                 71                     30.33   \n",
       "\n",
       "  WindAvgMPH PrecipitationSumInches month day  TempHighC   TempAvgC  TempLowC  \n",
       "0          4                   0.46    12  21  23.333333  15.555556  7.222222  \n",
       "1          6                      0    12  22  13.333333   8.888889  3.888889  \n",
       "2          3                      0    12  23  14.444444   7.222222  0.000000  \n",
       "3          4                      0    12  24  16.111111   7.777778 -0.555556  \n",
       "4          2                      T    12  25  14.444444  10.000000  5.000000  "
      ],
      "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>TempHighF</th>\n      <th>TempAvgF</th>\n      <th>TempLowF</th>\n      <th>HumidityAvgPercent</th>\n      <th>SeaLevelPressureAvgInches</th>\n      <th>WindAvgMPH</th>\n      <th>PrecipitationSumInches</th>\n      <th>month</th>\n      <th>day</th>\n      <th>TempHighC</th>\n      <th>TempAvgC</th>\n      <th>TempLowC</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>74</td>\n      <td>60</td>\n      <td>45</td>\n      <td>75</td>\n      <td>29.68</td>\n      <td>4</td>\n      <td>0.46</td>\n      <td>12</td>\n      <td>21</td>\n      <td>23.333333</td>\n      <td>15.555556</td>\n      <td>7.222222</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56</td>\n      <td>48</td>\n      <td>39</td>\n      <td>68</td>\n      <td>30.13</td>\n      <td>6</td>\n      <td>0</td>\n      <td>12</td>\n      <td>22</td>\n      <td>13.333333</td>\n      <td>8.888889</td>\n      <td>3.888889</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>58</td>\n      <td>45</td>\n      <td>32</td>\n      <td>52</td>\n      <td>30.49</td>\n      <td>3</td>\n      <td>0</td>\n      <td>12</td>\n      <td>23</td>\n      <td>14.444444</td>\n      <td>7.222222</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>61</td>\n      <td>46</td>\n      <td>31</td>\n      <td>56</td>\n      <td>30.45</td>\n      <td>4</td>\n      <td>0</td>\n      <td>12</td>\n      <td>24</td>\n      <td>16.111111</td>\n      <td>7.777778</td>\n      <td>-0.555556</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>58</td>\n      <td>50</td>\n      <td>41</td>\n      <td>71</td>\n      <td>30.33</td>\n      <td>2</td>\n      <td>T</td>\n      <td>12</td>\n      <td>25</td>\n      <td>14.444444</td>\n      <td>10.000000</td>\n      <td>5.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "df1['TempHighC'], df1['TempAvgC'], df1['TempLowC'] = (df1['TempHighF']-32)*5/9, (df1['TempAvgF']-32)*5/9, (df1['TempLowF']-32)*5/9\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TempHighF                      int64\n",
       "TempAvgF                       int64\n",
       "TempLowF                       int64\n",
       "HumidityAvgPercent            object\n",
       "SeaLevelPressureAvgInches     object\n",
       "WindAvgMPH                    object\n",
       "PrecipitationSumInches        object\n",
       "month                         object\n",
       "day                           object\n",
       "TempHighC                    float64\n",
       "TempAvgC                     float64\n",
       "TempLowC                     float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "df1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "TempHighF                      int64\n",
       "TempAvgF                       int64\n",
       "TempLowF                       int64\n",
       "HumidityAvgPercent           float64\n",
       "SeaLevelPressureAvgInches    float32\n",
       "WindAvgMPH                   float64\n",
       "PrecipitationSumInches       float32\n",
       "month                          int32\n",
       "day                            int32\n",
       "TempHighC                    float64\n",
       "TempAvgC                     float64\n",
       "TempLowC                     float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "df2 = df1.copy()\n",
    "for c in ['HumidityAvgPercent', 'WindAvgMPH', 'month', 'day']:\n",
    "    df2[c] = df2[df2[c].apply(lambda x: x.isnumeric())][c].astype(str).astype(int)\n",
    "df2['PrecipitationSumInches'].replace({'T': None},inplace =True)\n",
    "df2['PrecipitationSumInches'] = pd.to_numeric(df2[\"PrecipitationSumInches\"], downcast=\"float\")\n",
    "df2['SeaLevelPressureAvgInches'].replace({'-': None},inplace =True)\n",
    "df2['SeaLevelPressureAvgInches'] = pd.to_numeric(df2[\"SeaLevelPressureAvgInches\"], downcast=\"float\")\n",
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Valores nulos en el Dataset: \n\nHumidityAvgPercent             2\nSeaLevelPressureAvgInches      3\nWindAvgMPH                     2\nPrecipitationSumInches       124\ndtype: int64\n\n"
     ]
    }
   ],
   "source": [
    "print(\"Valores nulos en el Dataset: \\n\\n{}\\n\".format(df2.isnull().sum(axis = 0)[df2.isnull().sum(axis = 0)>0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Valores nulos en el Dataset: \n\nSeries([], dtype: int64)\n\n"
     ]
    }
   ],
   "source": [
    "df2.dropna(inplace=True)\n",
    "print(\"Valores nulos en el Dataset: \\n\\n{}\\n\".format(df2.isnull().sum(axis = 0)[df2.isnull().sum(axis = 0)>0]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   TempHighF  TempAvgF  TempLowF  HumidityAvgPercent  \\\n",
       "0         74        60        45                75.0   \n",
       "1         56        48        39                68.0   \n",
       "2         58        45        32                52.0   \n",
       "3         61        46        31                56.0   \n",
       "5         57        48        39                63.0   \n",
       "\n",
       "   SeaLevelPressureAvgInches  WindAvgMPH  PrecipitationSumInches  month  day  \\\n",
       "0                  29.680000         4.0                    0.46     12   21   \n",
       "1                  30.129999         6.0                    0.00     12   22   \n",
       "2                  30.490000         3.0                    0.00     12   23   \n",
       "3                  30.450001         4.0                    0.00     12   24   \n",
       "5                  30.400000         3.0                    0.00     12   26   \n",
       "\n",
       "   TempHighC   TempAvgC  TempLowC  SeaLevelPressureAvgMmHg  \n",
       "0  23.333333  15.555556  7.222222               753.872009  \n",
       "1  13.333333   8.888889  3.888889               765.301941  \n",
       "2  14.444444   7.222222  0.000000               774.445984  \n",
       "3  16.111111   7.777778 -0.555556               773.429993  \n",
       "5  13.888889   8.888889  3.888889               772.159973  "
      ],
      "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>TempHighF</th>\n      <th>TempAvgF</th>\n      <th>TempLowF</th>\n      <th>HumidityAvgPercent</th>\n      <th>SeaLevelPressureAvgInches</th>\n      <th>WindAvgMPH</th>\n      <th>PrecipitationSumInches</th>\n      <th>month</th>\n      <th>day</th>\n      <th>TempHighC</th>\n      <th>TempAvgC</th>\n      <th>TempLowC</th>\n      <th>SeaLevelPressureAvgMmHg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>74</td>\n      <td>60</td>\n      <td>45</td>\n      <td>75.0</td>\n      <td>29.680000</td>\n      <td>4.0</td>\n      <td>0.46</td>\n      <td>12</td>\n      <td>21</td>\n      <td>23.333333</td>\n      <td>15.555556</td>\n      <td>7.222222</td>\n      <td>753.872009</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>56</td>\n      <td>48</td>\n      <td>39</td>\n      <td>68.0</td>\n      <td>30.129999</td>\n      <td>6.0</td>\n      <td>0.00</td>\n      <td>12</td>\n      <td>22</td>\n      <td>13.333333</td>\n      <td>8.888889</td>\n      <td>3.888889</td>\n      <td>765.301941</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>58</td>\n      <td>45</td>\n      <td>32</td>\n      <td>52.0</td>\n      <td>30.490000</td>\n      <td>3.0</td>\n      <td>0.00</td>\n      <td>12</td>\n      <td>23</td>\n      <td>14.444444</td>\n      <td>7.222222</td>\n      <td>0.000000</td>\n      <td>774.445984</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>61</td>\n      <td>46</td>\n      <td>31</td>\n      <td>56.0</td>\n      <td>30.450001</td>\n      <td>4.0</td>\n      <td>0.00</td>\n      <td>12</td>\n      <td>24</td>\n      <td>16.111111</td>\n      <td>7.777778</td>\n      <td>-0.555556</td>\n      <td>773.429993</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>57</td>\n      <td>48</td>\n      <td>39</td>\n      <td>63.0</td>\n      <td>30.400000</td>\n      <td>3.0</td>\n      <td>0.00</td>\n      <td>12</td>\n      <td>26</td>\n      <td>13.888889</td>\n      <td>8.888889</td>\n      <td>3.888889</td>\n      <td>772.159973</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 28
    }
   ],
   "source": [
    "df2['SeaLevelPressureAvgMmHg'] = df2['SeaLevelPressureAvgInches']*25.4\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example of making predictions for a regression problem\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = df2.drop(['PrecipitationSumInches'], axis=1)\n",
    "target = df2[['PrecipitationSumInches']]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state=133)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_5 (Dense)              (None, 16)                208       \n_________________________________________________________________\ndense_6 (Dense)              (None, 16)                272       \n_________________________________________________________________\ndense_7 (Dense)              (None, 1)                 17        \n=================================================================\nTotal params: 497\nTrainable params: 497\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# We'll use Keras to create a simple model architecture\n",
    "model = Sequential()\n",
    "# First layer takes a scalar input and feeds it through 16 \"neurons.\" The\n",
    "# neurons decide whether to activate based on the 'relu' activation function.\n",
    "model.add(Dense(16, activation='relu', input_shape=(12,)))\n",
    "# The new second layer may help the network learn more complex representations\n",
    "model.add(Dense(16, activation='relu'))\n",
    "# Final layer is a single neuron, since we want to output a single value\n",
    "model.add(Dense(1))\n",
    "# Compile the model using a standard optimizer and loss function for regression\n",
    "model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "# Show a summary of the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " mae: 0.1930 - val_loss: 0.1104 - val_mae: 0.1490\n",
      "Epoch 451/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1887 - mae: 0.1996 - val_loss: 0.1105 - val_mae: 0.1266\n",
      "Epoch 452/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1853 - mae: 0.1945 - val_loss: 0.1151 - val_mae: 0.1685\n",
      "Epoch 453/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1930 - mae: 0.1939 - val_loss: 0.1140 - val_mae: 0.1133\n",
      "Epoch 454/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1884 - mae: 0.1873 - val_loss: 0.1153 - val_mae: 0.1658\n",
      "Epoch 455/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1864 - mae: 0.1873 - val_loss: 0.1297 - val_mae: 0.2253\n",
      "Epoch 456/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1862 - mae: 0.1905 - val_loss: 0.1280 - val_mae: 0.1948\n",
      "Epoch 457/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1883 - mae: 0.1906 - val_loss: 0.1200 - val_mae: 0.1783\n",
      "Epoch 458/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1899 - mae: 0.1942 - val_loss: 0.1105 - val_mae: 0.1383\n",
      "Epoch 459/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1825 - mae: 0.1876 - val_loss: 0.1118 - val_mae: 0.1324\n",
      "Epoch 460/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1878 - mae: 0.1895 - val_loss: 0.1118 - val_mae: 0.1292\n",
      "Epoch 461/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1899 - mae: 0.1918 - val_loss: 0.1199 - val_mae: 0.1838\n",
      "Epoch 462/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1892 - mae: 0.1879 - val_loss: 0.1187 - val_mae: 0.1298\n",
      "Epoch 463/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1853 - mae: 0.1908 - val_loss: 0.1159 - val_mae: 0.1190\n",
      "Epoch 464/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1840 - mae: 0.1853 - val_loss: 0.1237 - val_mae: 0.1957\n",
      "Epoch 465/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1843 - mae: 0.1885 - val_loss: 0.1226 - val_mae: 0.1592\n",
      "Epoch 466/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1880 - mae: 0.1848 - val_loss: 0.1182 - val_mae: 0.1298\n",
      "Epoch 467/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1864 - mae: 0.1843 - val_loss: 0.1173 - val_mae: 0.1451\n",
      "Epoch 468/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1850 - mae: 0.1881 - val_loss: 0.1223 - val_mae: 0.1892\n",
      "Epoch 469/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1818 - mae: 0.1862 - val_loss: 0.1103 - val_mae: 0.1205\n",
      "Epoch 470/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1865 - mae: 0.1887 - val_loss: 0.1159 - val_mae: 0.1701\n",
      "Epoch 471/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1830 - mae: 0.1882 - val_loss: 0.1161 - val_mae: 0.1455\n",
      "Epoch 472/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1838 - mae: 0.1852 - val_loss: 0.1593 - val_mae: 0.2396\n",
      "Epoch 473/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1838 - mae: 0.1881 - val_loss: 0.1203 - val_mae: 0.1463\n",
      "Epoch 474/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1787 - mae: 0.1857 - val_loss: 0.1942 - val_mae: 0.3036\n",
      "Epoch 475/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1844 - mae: 0.1907 - val_loss: 0.1192 - val_mae: 0.1487\n",
      "Epoch 476/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1816 - mae: 0.1852 - val_loss: 0.1141 - val_mae: 0.1372\n",
      "Epoch 477/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1833 - mae: 0.1864 - val_loss: 0.1374 - val_mae: 0.2256\n",
      "Epoch 478/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1854 - mae: 0.1920 - val_loss: 0.1167 - val_mae: 0.1398\n",
      "Epoch 479/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1801 - mae: 0.1805 - val_loss: 0.1190 - val_mae: 0.1126\n",
      "Epoch 480/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1856 - mae: 0.1837 - val_loss: 0.1326 - val_mae: 0.1833\n",
      "Epoch 481/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1805 - mae: 0.1867 - val_loss: 0.1172 - val_mae: 0.1168\n",
      "Epoch 482/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1837 - mae: 0.1821 - val_loss: 0.1196 - val_mae: 0.1424\n",
      "Epoch 483/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1833 - mae: 0.1809 - val_loss: 0.1234 - val_mae: 0.1591\n",
      "Epoch 484/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1840 - mae: 0.1822 - val_loss: 0.1742 - val_mae: 0.2324\n",
      "Epoch 485/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1818 - mae: 0.1846 - val_loss: 0.1165 - val_mae: 0.1274\n",
      "Epoch 486/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1843 - mae: 0.1904 - val_loss: 0.1198 - val_mae: 0.1655\n",
      "Epoch 487/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1865 - mae: 0.1786 - val_loss: 0.1163 - val_mae: 0.1409\n",
      "Epoch 488/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1812 - mae: 0.1791 - val_loss: 0.1149 - val_mae: 0.1400\n",
      "Epoch 489/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1806 - mae: 0.1804 - val_loss: 0.1259 - val_mae: 0.1498\n",
      "Epoch 490/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1784 - mae: 0.1749 - val_loss: 0.1161 - val_mae: 0.1629\n",
      "Epoch 491/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1807 - mae: 0.1813 - val_loss: 0.1205 - val_mae: 0.1684\n",
      "Epoch 492/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1829 - mae: 0.1859 - val_loss: 0.1203 - val_mae: 0.1395\n",
      "Epoch 493/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1833 - mae: 0.1806 - val_loss: 0.1328 - val_mae: 0.1587\n",
      "Epoch 494/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1812 - mae: 0.1819 - val_loss: 0.1239 - val_mae: 0.1488\n",
      "Epoch 495/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1839 - mae: 0.1788 - val_loss: 0.1208 - val_mae: 0.1665\n",
      "Epoch 496/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1798 - mae: 0.1805 - val_loss: 0.1215 - val_mae: 0.1524\n",
      "Epoch 497/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1793 - mae: 0.1807 - val_loss: 0.1432 - val_mae: 0.1848\n",
      "Epoch 498/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1798 - mae: 0.1841 - val_loss: 0.1287 - val_mae: 0.1531\n",
      "Epoch 499/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1825 - mae: 0.1808 - val_loss: 0.1432 - val_mae: 0.2325\n",
      "Epoch 500/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1795 - mae: 0.1828 - val_loss: 0.1325 - val_mae: 0.1489\n",
      "Epoch 501/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1755 - mae: 0.1755 - val_loss: 0.1187 - val_mae: 0.1413\n",
      "Epoch 502/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1773 - mae: 0.1843 - val_loss: 0.2258 - val_mae: 0.3272\n",
      "Epoch 503/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1734 - mae: 0.1893 - val_loss: 0.1560 - val_mae: 0.1669\n",
      "Epoch 504/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1844 - mae: 0.1841 - val_loss: 0.1109 - val_mae: 0.1195\n",
      "Epoch 505/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1776 - mae: 0.1743 - val_loss: 0.1139 - val_mae: 0.1519\n",
      "Epoch 506/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1713 - mae: 0.1869 - val_loss: 0.1224 - val_mae: 0.1543\n",
      "Epoch 507/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1798 - mae: 0.1795 - val_loss: 0.1211 - val_mae: 0.1381\n",
      "Epoch 508/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1754 - mae: 0.1747 - val_loss: 0.1259 - val_mae: 0.1472\n",
      "Epoch 509/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1757 - mae: 0.1765 - val_loss: 0.1183 - val_mae: 0.1326\n",
      "Epoch 510/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1737 - mae: 0.1781 - val_loss: 0.1336 - val_mae: 0.1533\n",
      "Epoch 511/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1784 - mae: 0.1796 - val_loss: 0.1868 - val_mae: 0.2628\n",
      "Epoch 512/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1764 - mae: 0.1780 - val_loss: 0.1414 - val_mae: 0.1855\n",
      "Epoch 513/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1780 - mae: 0.1806 - val_loss: 0.1219 - val_mae: 0.1516\n",
      "Epoch 514/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1799 - mae: 0.1798 - val_loss: 0.1174 - val_mae: 0.1461\n",
      "Epoch 515/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1736 - mae: 0.1761 - val_loss: 0.2893 - val_mae: 0.4042\n",
      "Epoch 516/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1774 - mae: 0.1983 - val_loss: 0.1122 - val_mae: 0.1182\n",
      "Epoch 517/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1782 - mae: 0.1813 - val_loss: 0.1151 - val_mae: 0.1471\n",
      "Epoch 518/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1730 - mae: 0.1784 - val_loss: 0.1276 - val_mae: 0.1699\n",
      "Epoch 519/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1732 - mae: 0.1804 - val_loss: 0.1169 - val_mae: 0.1198\n",
      "Epoch 520/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1774 - mae: 0.1743 - val_loss: 0.2398 - val_mae: 0.3469\n",
      "Epoch 521/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1773 - mae: 0.1966 - val_loss: 0.1190 - val_mae: 0.1548\n",
      "Epoch 522/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1769 - mae: 0.1800 - val_loss: 0.1298 - val_mae: 0.1713\n",
      "Epoch 523/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1743 - mae: 0.1779 - val_loss: 0.1152 - val_mae: 0.1576\n",
      "Epoch 524/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1795 - mae: 0.1792 - val_loss: 0.1407 - val_mae: 0.2005\n",
      "Epoch 525/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1758 - mae: 0.1837 - val_loss: 0.1369 - val_mae: 0.1899\n",
      "Epoch 526/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1772 - mae: 0.1829 - val_loss: 0.1141 - val_mae: 0.1510\n",
      "Epoch 527/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1731 - mae: 0.1839 - val_loss: 0.1287 - val_mae: 0.1670\n",
      "Epoch 528/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1753 - mae: 0.1792 - val_loss: 0.1072 - val_mae: 0.1158\n",
      "Epoch 529/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1742 - mae: 0.1804 - val_loss: 0.1387 - val_mae: 0.1890\n",
      "Epoch 530/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1774 - mae: 0.1832 - val_loss: 0.1198 - val_mae: 0.1792\n",
      "Epoch 531/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1734 - mae: 0.1811 - val_loss: 0.1141 - val_mae: 0.1151\n",
      "Epoch 532/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1707 - mae: 0.1792 - val_loss: 0.1080 - val_mae: 0.1193\n",
      "Epoch 533/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1786 - mae: 0.1783 - val_loss: 0.1238 - val_mae: 0.1637\n",
      "Epoch 534/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1736 - mae: 0.1725 - val_loss: 0.1133 - val_mae: 0.1469\n",
      "Epoch 535/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1725 - mae: 0.1753 - val_loss: 0.1374 - val_mae: 0.1724\n",
      "Epoch 536/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1716 - mae: 0.1856 - val_loss: 0.1104 - val_mae: 0.1354\n",
      "Epoch 537/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1747 - mae: 0.1791 - val_loss: 0.1373 - val_mae: 0.1796\n",
      "Epoch 538/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1720 - mae: 0.1807 - val_loss: 0.1057 - val_mae: 0.1216\n",
      "Epoch 539/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1716 - mae: 0.1796 - val_loss: 0.1268 - val_mae: 0.1653\n",
      "Epoch 540/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1758 - mae: 0.1809 - val_loss: 0.1307 - val_mae: 0.1529\n",
      "Epoch 541/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1713 - mae: 0.1830 - val_loss: 0.1375 - val_mae: 0.1850\n",
      "Epoch 542/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1734 - mae: 0.1812 - val_loss: 0.1082 - val_mae: 0.1256\n",
      "Epoch 543/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1719 - mae: 0.1817 - val_loss: 0.1217 - val_mae: 0.1247\n",
      "Epoch 544/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1757 - mae: 0.1732 - val_loss: 0.1432 - val_mae: 0.2099\n",
      "Epoch 545/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1751 - mae: 0.1780 - val_loss: 0.1233 - val_mae: 0.1513\n",
      "Epoch 546/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1711 - mae: 0.1776 - val_loss: 0.1233 - val_mae: 0.1606\n",
      "Epoch 547/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1686 - mae: 0.1841 - val_loss: 0.1244 - val_mae: 0.1362\n",
      "Epoch 548/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1721 - mae: 0.1745 - val_loss: 0.1131 - val_mae: 0.1534\n",
      "Epoch 549/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1741 - mae: 0.1777 - val_loss: 0.1368 - val_mae: 0.1864\n",
      "Epoch 550/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1748 - mae: 0.1799 - val_loss: 0.2600 - val_mae: 0.3616\n",
      "Epoch 551/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1803 - mae: 0.1889 - val_loss: 0.1127 - val_mae: 0.1291\n",
      "Epoch 552/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1766 - mae: 0.1784 - val_loss: 0.1259 - val_mae: 0.1682\n",
      "Epoch 553/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1715 - mae: 0.1777 - val_loss: 0.1093 - val_mae: 0.1240\n",
      "Epoch 554/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1799 - mae: 0.1804 - val_loss: 0.1125 - val_mae: 0.1308\n",
      "Epoch 555/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1747 - mae: 0.1831 - val_loss: 0.1553 - val_mae: 0.1979\n",
      "Epoch 556/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1766 - mae: 0.1830 - val_loss: 0.1317 - val_mae: 0.1366\n",
      "Epoch 557/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1825 - mae: 0.1733 - val_loss: 0.1713 - val_mae: 0.2880\n",
      "Epoch 558/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1760 - mae: 0.1845 - val_loss: 0.1314 - val_mae: 0.1734\n",
      "Epoch 559/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1744 - mae: 0.1780 - val_loss: 0.1373 - val_mae: 0.1615\n",
      "Epoch 560/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1727 - mae: 0.1821 - val_loss: 0.1246 - val_mae: 0.1564\n",
      "Epoch 561/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1735 - mae: 0.1758 - val_loss: 0.1053 - val_mae: 0.1261\n",
      "Epoch 562/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1748 - mae: 0.1813 - val_loss: 0.1229 - val_mae: 0.1156\n",
      "Epoch 563/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1709 - mae: 0.1720 - val_loss: 0.1066 - val_mae: 0.1321\n",
      "Epoch 564/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1707 - mae: 0.1842 - val_loss: 0.1131 - val_mae: 0.1228\n",
      "Epoch 565/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1686 - mae: 0.1747 - val_loss: 0.1254 - val_mae: 0.1413\n",
      "Epoch 566/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1714 - mae: 0.1825 - val_loss: 0.1530 - val_mae: 0.1704\n",
      "Epoch 567/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1734 - mae: 0.1775 - val_loss: 0.1447 - val_mae: 0.1708\n",
      "Epoch 568/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1741 - mae: 0.1830 - val_loss: 0.1099 - val_mae: 0.1331\n",
      "Epoch 569/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1679 - mae: 0.1764 - val_loss: 0.1134 - val_mae: 0.1281\n",
      "Epoch 570/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1707 - mae: 0.1746 - val_loss: 0.1159 - val_mae: 0.1668\n",
      "Epoch 571/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1722 - mae: 0.1747 - val_loss: 0.3441 - val_mae: 0.4310\n",
      "Epoch 572/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1722 - mae: 0.1941 - val_loss: 0.1281 - val_mae: 0.1482\n",
      "Epoch 573/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1742 - mae: 0.1804 - val_loss: 0.1414 - val_mae: 0.1749\n",
      "Epoch 574/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1744 - mae: 0.1805 - val_loss: 0.1519 - val_mae: 0.2468\n",
      "Epoch 575/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1710 - mae: 0.1785 - val_loss: 0.1245 - val_mae: 0.1556\n",
      "Epoch 576/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1663 - mae: 0.1737 - val_loss: 0.1500 - val_mae: 0.1863\n",
      "Epoch 577/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1712 - mae: 0.1773 - val_loss: 0.1347 - val_mae: 0.1695\n",
      "Epoch 578/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1752 - mae: 0.1763 - val_loss: 0.1400 - val_mae: 0.1813\n",
      "Epoch 579/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1708 - mae: 0.1761 - val_loss: 0.1120 - val_mae: 0.1426\n",
      "Epoch 580/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1687 - mae: 0.1815 - val_loss: 0.1126 - val_mae: 0.1244\n",
      "Epoch 581/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1756 - mae: 0.1804 - val_loss: 0.1215 - val_mae: 0.1739\n",
      "Epoch 582/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1724 - mae: 0.1786 - val_loss: 0.1285 - val_mae: 0.2012\n",
      "Epoch 583/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1704 - mae: 0.1790 - val_loss: 0.1267 - val_mae: 0.1524\n",
      "Epoch 584/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1681 - mae: 0.1737 - val_loss: 0.1201 - val_mae: 0.1550\n",
      "Epoch 585/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1719 - mae: 0.1805 - val_loss: 0.1254 - val_mae: 0.1999\n",
      "Epoch 586/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1666 - mae: 0.1805 - val_loss: 0.1142 - val_mae: 0.1161\n",
      "Epoch 587/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1721 - mae: 0.1753 - val_loss: 0.1235 - val_mae: 0.1823\n",
      "Epoch 588/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1703 - mae: 0.1756 - val_loss: 0.1158 - val_mae: 0.1351\n",
      "Epoch 589/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1710 - mae: 0.1777 - val_loss: 0.1405 - val_mae: 0.1841\n",
      "Epoch 590/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1746 - mae: 0.1839 - val_loss: 0.1153 - val_mae: 0.1443\n",
      "Epoch 591/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1702 - mae: 0.1789 - val_loss: 0.1229 - val_mae: 0.1586\n",
      "Epoch 592/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1693 - mae: 0.1799 - val_loss: 0.1049 - val_mae: 0.1603\n",
      "Epoch 593/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1699 - mae: 0.1793 - val_loss: 0.1867 - val_mae: 0.2214\n",
      "Epoch 594/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1703 - mae: 0.1823 - val_loss: 0.1350 - val_mae: 0.1571\n",
      "Epoch 595/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1723 - mae: 0.1781 - val_loss: 0.1140 - val_mae: 0.1297\n",
      "Epoch 596/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1721 - mae: 0.1778 - val_loss: 0.1247 - val_mae: 0.1442\n",
      "Epoch 597/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1758 - mae: 0.1809 - val_loss: 0.1149 - val_mae: 0.1266\n",
      "Epoch 598/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1637 - mae: 0.1678 - val_loss: 0.1309 - val_mae: 0.1555\n",
      "Epoch 599/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1697 - mae: 0.1761 - val_loss: 0.1224 - val_mae: 0.1451\n",
      "Epoch 600/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1685 - mae: 0.1768 - val_loss: 0.1071 - val_mae: 0.1238\n"
     ]
    }
   ],
   "source": [
    "history_2 = model.fit(X_train, y_train, epochs=600, batch_size=16,\n",
    "validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Draw a graph of the loss, which is the distance between\n",
    "# the predicted and actual values during training and validation.\n",
    "loss = history_2.history['loss']\n",
    "val_loss = history_2.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_3\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_8 (Dense)              (None, 128)               1664      \n_________________________________________________________________\ndense_9 (Dense)              (None, 64)                8256      \n_________________________________________________________________\ndense_10 (Dense)             (None, 32)                2080      \n_________________________________________________________________\ndense_11 (Dense)             (None, 16)                528       \n_________________________________________________________________\ndense_12 (Dense)             (None, 1)                 17        \n=================================================================\nTotal params: 12,545\nTrainable params: 12,545\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# We'll use Keras to create a simple model architecture\n",
    "model2 = Sequential()\n",
    "# First layer takes a scalar input and feeds it through 16 \"neurons.\" The\n",
    "# neurons decide whether to activate based on the 'relu' activation function.\n",
    "model2.add(Dense(128, activation='relu', input_shape=(len(X_train.columns),)))\n",
    "# The new second layer may help the network learn more complex representations\n",
    "model2.add(Dense(64, activation='relu'))\n",
    "model2.add(Dense(32, activation='relu'))\n",
    "model2.add(Dense(16, activation='relu'))\n",
    "# Final layer is a single neuron, since we want to output a single value\n",
    "model2.add(Dense(1))\n",
    "# Compile the model using a standard optimizer and loss function for regression\n",
    "model2.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "# Show a summary of the model\n",
    "model2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " mae: 0.1876 - val_loss: 0.0947 - val_mae: 0.1532\n",
      "Epoch 451/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1648 - mae: 0.1753 - val_loss: 0.1099 - val_mae: 0.1308\n",
      "Epoch 452/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1699 - mae: 0.1786 - val_loss: 0.1105 - val_mae: 0.1530\n",
      "Epoch 453/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1660 - mae: 0.1779 - val_loss: 0.1205 - val_mae: 0.1920\n",
      "Epoch 454/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1795 - mae: 0.1808 - val_loss: 0.1040 - val_mae: 0.1658\n",
      "Epoch 455/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1674 - mae: 0.1758 - val_loss: 0.1214 - val_mae: 0.1943\n",
      "Epoch 456/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1714 - mae: 0.1858 - val_loss: 0.1140 - val_mae: 0.1566\n",
      "Epoch 457/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1751 - mae: 0.1824 - val_loss: 0.2298 - val_mae: 0.3705\n",
      "Epoch 458/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1803 - mae: 0.1849 - val_loss: 0.1122 - val_mae: 0.1406\n",
      "Epoch 459/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1703 - mae: 0.1863 - val_loss: 0.1091 - val_mae: 0.1741\n",
      "Epoch 460/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1569 - mae: 0.1733 - val_loss: 0.1903 - val_mae: 0.3672\n",
      "Epoch 461/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1575 - mae: 0.1844 - val_loss: 0.5274 - val_mae: 0.3717\n",
      "Epoch 462/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1738 - mae: 0.1801 - val_loss: 0.0868 - val_mae: 0.1465\n",
      "Epoch 463/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1813 - mae: 0.1837 - val_loss: 0.1294 - val_mae: 0.1103\n",
      "Epoch 464/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1436 - mae: 0.1802 - val_loss: 0.1020 - val_mae: 0.1277\n",
      "Epoch 465/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1703 - mae: 0.1800 - val_loss: 0.2410 - val_mae: 0.3154\n",
      "Epoch 466/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1636 - mae: 0.1775 - val_loss: 0.1107 - val_mae: 0.1170\n",
      "Epoch 467/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1740 - mae: 0.1843 - val_loss: 0.1090 - val_mae: 0.1106\n",
      "Epoch 468/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1766 - mae: 0.1780 - val_loss: 0.1096 - val_mae: 0.1249\n",
      "Epoch 469/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1682 - mae: 0.1786 - val_loss: 0.0980 - val_mae: 0.1644\n",
      "Epoch 470/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1654 - mae: 0.1875 - val_loss: 0.1076 - val_mae: 0.1264\n",
      "Epoch 471/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1813 - mae: 0.1845 - val_loss: 0.1081 - val_mae: 0.1210\n",
      "Epoch 472/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1638 - mae: 0.1777 - val_loss: 0.1241 - val_mae: 0.1738\n",
      "Epoch 473/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1761 - mae: 0.1782 - val_loss: 0.1174 - val_mae: 0.1775\n",
      "Epoch 474/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1713 - mae: 0.1834 - val_loss: 0.1069 - val_mae: 0.1270\n",
      "Epoch 475/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1802 - mae: 0.1845 - val_loss: 0.1122 - val_mae: 0.1416\n",
      "Epoch 476/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1757 - mae: 0.1819 - val_loss: 0.1050 - val_mae: 0.1702\n",
      "Epoch 477/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1788 - mae: 0.1867 - val_loss: 0.0871 - val_mae: 0.1432\n",
      "Epoch 478/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1769 - mae: 0.1874 - val_loss: 0.1290 - val_mae: 0.2423\n",
      "Epoch 479/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.2025 - mae: 0.1842 - val_loss: 0.1260 - val_mae: 0.1867\n",
      "Epoch 480/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1775 - mae: 0.1897 - val_loss: 0.1336 - val_mae: 0.2181\n",
      "Epoch 481/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1760 - mae: 0.1825 - val_loss: 0.1101 - val_mae: 0.1232\n",
      "Epoch 482/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1684 - mae: 0.1788 - val_loss: 0.1871 - val_mae: 0.3706\n",
      "Epoch 483/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1628 - mae: 0.1880 - val_loss: 0.1107 - val_mae: 0.1399\n",
      "Epoch 484/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1724 - mae: 0.1808 - val_loss: 0.1168 - val_mae: 0.1194\n",
      "Epoch 485/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1717 - mae: 0.1814 - val_loss: 0.0943 - val_mae: 0.1399\n",
      "Epoch 486/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1606 - mae: 0.1818 - val_loss: 0.1325 - val_mae: 0.2447\n",
      "Epoch 487/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1652 - mae: 0.1789 - val_loss: 0.0952 - val_mae: 0.1374\n",
      "Epoch 488/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1573 - mae: 0.1729 - val_loss: 0.1248 - val_mae: 0.2118\n",
      "Epoch 489/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1721 - mae: 0.1806 - val_loss: 0.1791 - val_mae: 0.3600\n",
      "Epoch 490/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1755 - mae: 0.1879 - val_loss: 0.0904 - val_mae: 0.1302\n",
      "Epoch 491/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1800 - mae: 0.1847 - val_loss: 0.1566 - val_mae: 0.2980\n",
      "Epoch 492/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1801 - mae: 0.1896 - val_loss: 0.1062 - val_mae: 0.1276\n",
      "Epoch 493/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1686 - mae: 0.1744 - val_loss: 0.1613 - val_mae: 0.3041\n",
      "Epoch 494/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1779 - mae: 0.1839 - val_loss: 0.1119 - val_mae: 0.1572\n",
      "Epoch 495/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1618 - mae: 0.1771 - val_loss: 0.2906 - val_mae: 0.4102\n",
      "Epoch 496/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1711 - mae: 0.1832 - val_loss: 0.1212 - val_mae: 0.2103\n",
      "Epoch 497/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1682 - mae: 0.1867 - val_loss: 0.1168 - val_mae: 0.1529\n",
      "Epoch 498/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1561 - mae: 0.1787 - val_loss: 0.1091 - val_mae: 0.1359\n",
      "Epoch 499/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1648 - mae: 0.1789 - val_loss: 0.1096 - val_mae: 0.1754\n",
      "Epoch 500/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1718 - mae: 0.1838 - val_loss: 0.1143 - val_mae: 0.1700\n",
      "Epoch 501/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1773 - mae: 0.1792 - val_loss: 0.0844 - val_mae: 0.1524\n",
      "Epoch 502/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1748 - mae: 0.1836 - val_loss: 0.1020 - val_mae: 0.1551\n",
      "Epoch 503/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1788 - mae: 0.1811 - val_loss: 0.1151 - val_mae: 0.1605\n",
      "Epoch 504/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1631 - mae: 0.1854 - val_loss: 0.1041 - val_mae: 0.1563\n",
      "Epoch 505/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1560 - mae: 0.1777 - val_loss: 0.1275 - val_mae: 0.2305\n",
      "Epoch 506/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1700 - mae: 0.1844 - val_loss: 0.1172 - val_mae: 0.1694\n",
      "Epoch 507/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1639 - mae: 0.1807 - val_loss: 0.1086 - val_mae: 0.1378\n",
      "Epoch 508/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1639 - mae: 0.1813 - val_loss: 0.1091 - val_mae: 0.1475\n",
      "Epoch 509/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1576 - mae: 0.1884 - val_loss: 0.1081 - val_mae: 0.1069\n",
      "Epoch 510/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1764 - mae: 0.1871 - val_loss: 0.1156 - val_mae: 0.2033\n",
      "Epoch 511/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1671 - mae: 0.1822 - val_loss: 0.1049 - val_mae: 0.1206\n",
      "Epoch 512/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1632 - mae: 0.1835 - val_loss: 0.1066 - val_mae: 0.1249\n",
      "Epoch 513/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1628 - mae: 0.1803 - val_loss: 0.1082 - val_mae: 0.1378\n",
      "Epoch 514/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1580 - mae: 0.1791 - val_loss: 0.1002 - val_mae: 0.1389\n",
      "Epoch 515/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1568 - mae: 0.1789 - val_loss: 0.1093 - val_mae: 0.1297\n",
      "Epoch 516/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1791 - mae: 0.1796 - val_loss: 0.1105 - val_mae: 0.1545\n",
      "Epoch 517/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1775 - mae: 0.1871 - val_loss: 0.1361 - val_mae: 0.2461\n",
      "Epoch 518/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1755 - mae: 0.1803 - val_loss: 0.1440 - val_mae: 0.2256\n",
      "Epoch 519/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1585 - mae: 0.1799 - val_loss: 0.2172 - val_mae: 0.2510\n",
      "Epoch 520/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1710 - mae: 0.1797 - val_loss: 0.1146 - val_mae: 0.1478\n",
      "Epoch 521/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1739 - mae: 0.1832 - val_loss: 0.1036 - val_mae: 0.1751\n",
      "Epoch 522/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1593 - mae: 0.1811 - val_loss: 0.1293 - val_mae: 0.1960\n",
      "Epoch 523/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1616 - mae: 0.1797 - val_loss: 0.1082 - val_mae: 0.1377\n",
      "Epoch 524/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1552 - mae: 0.1812 - val_loss: 0.1327 - val_mae: 0.1547\n",
      "Epoch 525/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1665 - mae: 0.1829 - val_loss: 0.1146 - val_mae: 0.1242\n",
      "Epoch 526/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1729 - mae: 0.1775 - val_loss: 0.1060 - val_mae: 0.1796\n",
      "Epoch 527/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1642 - mae: 0.1813 - val_loss: 0.1068 - val_mae: 0.1298\n",
      "Epoch 528/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1760 - mae: 0.1866 - val_loss: 0.1119 - val_mae: 0.1835\n",
      "Epoch 529/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1647 - mae: 0.1831 - val_loss: 0.1061 - val_mae: 0.1641\n",
      "Epoch 530/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1696 - mae: 0.1817 - val_loss: 0.1395 - val_mae: 0.2102\n",
      "Epoch 531/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1605 - mae: 0.1780 - val_loss: 0.0973 - val_mae: 0.1559\n",
      "Epoch 532/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1468 - mae: 0.1740 - val_loss: 0.0944 - val_mae: 0.1552\n",
      "Epoch 533/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1517 - mae: 0.1763 - val_loss: 0.0970 - val_mae: 0.1461\n",
      "Epoch 534/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1673 - mae: 0.1789 - val_loss: 0.1281 - val_mae: 0.2021\n",
      "Epoch 535/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1620 - mae: 0.1749 - val_loss: 0.1128 - val_mae: 0.1688\n",
      "Epoch 536/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1720 - mae: 0.1798 - val_loss: 0.1048 - val_mae: 0.1252\n",
      "Epoch 537/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1519 - mae: 0.1717 - val_loss: 0.1123 - val_mae: 0.1646\n",
      "Epoch 538/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1690 - mae: 0.1849 - val_loss: 0.1177 - val_mae: 0.2047\n",
      "Epoch 539/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1800 - mae: 0.1804 - val_loss: 0.1214 - val_mae: 0.1762\n",
      "Epoch 540/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1712 - mae: 0.1852 - val_loss: 0.1217 - val_mae: 0.1818\n",
      "Epoch 541/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1689 - mae: 0.1808 - val_loss: 0.1149 - val_mae: 0.1447\n",
      "Epoch 542/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1609 - mae: 0.1801 - val_loss: 0.1257 - val_mae: 0.1944\n",
      "Epoch 543/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1655 - mae: 0.1800 - val_loss: 0.0860 - val_mae: 0.1269\n",
      "Epoch 544/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1529 - mae: 0.1704 - val_loss: 0.1090 - val_mae: 0.1333\n",
      "Epoch 545/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1598 - mae: 0.1762 - val_loss: 0.1799 - val_mae: 0.2182\n",
      "Epoch 546/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1705 - mae: 0.1861 - val_loss: 0.0883 - val_mae: 0.1709\n",
      "Epoch 547/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1932 - mae: 0.1867 - val_loss: 0.1004 - val_mae: 0.1790\n",
      "Epoch 548/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1678 - mae: 0.1782 - val_loss: 0.1814 - val_mae: 0.2594\n",
      "Epoch 549/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1660 - mae: 0.1736 - val_loss: 0.1474 - val_mae: 0.2392\n",
      "Epoch 550/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1610 - mae: 0.1811 - val_loss: 0.0890 - val_mae: 0.1345\n",
      "Epoch 551/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1623 - mae: 0.1722 - val_loss: 0.1310 - val_mae: 0.2371\n",
      "Epoch 552/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1740 - mae: 0.1804 - val_loss: 0.1232 - val_mae: 0.1632\n",
      "Epoch 553/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1488 - mae: 0.1764 - val_loss: 0.2106 - val_mae: 0.2696\n",
      "Epoch 554/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1727 - mae: 0.1817 - val_loss: 0.1038 - val_mae: 0.1380\n",
      "Epoch 555/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1741 - mae: 0.1769 - val_loss: 0.1058 - val_mae: 0.1604\n",
      "Epoch 556/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1642 - mae: 0.1804 - val_loss: 0.1230 - val_mae: 0.1787\n",
      "Epoch 557/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1567 - mae: 0.1685 - val_loss: 0.1095 - val_mae: 0.1421\n",
      "Epoch 558/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1626 - mae: 0.1844 - val_loss: 0.1196 - val_mae: 0.2017\n",
      "Epoch 559/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1513 - mae: 0.1746 - val_loss: 0.1161 - val_mae: 0.1414\n",
      "Epoch 560/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1737 - mae: 0.1819 - val_loss: 0.1088 - val_mae: 0.1627\n",
      "Epoch 561/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1584 - mae: 0.1730 - val_loss: 0.1189 - val_mae: 0.1868\n",
      "Epoch 562/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1683 - mae: 0.1802 - val_loss: 0.0996 - val_mae: 0.1319\n",
      "Epoch 563/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1563 - mae: 0.1780 - val_loss: 0.1099 - val_mae: 0.2063\n",
      "Epoch 564/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1555 - mae: 0.1779 - val_loss: 0.1067 - val_mae: 0.1610\n",
      "Epoch 565/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1617 - mae: 0.1813 - val_loss: 0.1055 - val_mae: 0.1447\n",
      "Epoch 566/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1632 - mae: 0.1802 - val_loss: 0.1228 - val_mae: 0.1950\n",
      "Epoch 567/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1520 - mae: 0.1764 - val_loss: 0.1077 - val_mae: 0.1700\n",
      "Epoch 568/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1720 - mae: 0.1842 - val_loss: 0.1221 - val_mae: 0.1438\n",
      "Epoch 569/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1618 - mae: 0.1764 - val_loss: 0.1149 - val_mae: 0.1724\n",
      "Epoch 570/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1643 - mae: 0.1783 - val_loss: 0.1139 - val_mae: 0.1657\n",
      "Epoch 571/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1612 - mae: 0.1763 - val_loss: 0.1605 - val_mae: 0.2348\n",
      "Epoch 572/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1640 - mae: 0.1744 - val_loss: 0.1271 - val_mae: 0.1363\n",
      "Epoch 573/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1732 - mae: 0.1837 - val_loss: 0.1419 - val_mae: 0.1839\n",
      "Epoch 574/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1777 - mae: 0.1831 - val_loss: 0.1255 - val_mae: 0.2705\n",
      "Epoch 575/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1619 - mae: 0.1811 - val_loss: 0.1120 - val_mae: 0.1282\n",
      "Epoch 576/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1659 - mae: 0.1769 - val_loss: 0.1243 - val_mae: 0.1698\n",
      "Epoch 577/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1656 - mae: 0.1802 - val_loss: 0.1499 - val_mae: 0.2254\n",
      "Epoch 578/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1596 - mae: 0.1738 - val_loss: 0.1129 - val_mae: 0.1107\n",
      "Epoch 579/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1655 - mae: 0.1776 - val_loss: 0.1154 - val_mae: 0.1376\n",
      "Epoch 580/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1638 - mae: 0.1770 - val_loss: 0.1210 - val_mae: 0.1928\n",
      "Epoch 581/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1610 - mae: 0.1750 - val_loss: 0.1087 - val_mae: 0.1589\n",
      "Epoch 582/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1605 - mae: 0.1783 - val_loss: 0.1205 - val_mae: 0.1398\n",
      "Epoch 583/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1517 - mae: 0.1752 - val_loss: 0.1416 - val_mae: 0.2143\n",
      "Epoch 584/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1747 - mae: 0.1740 - val_loss: 0.1090 - val_mae: 0.1477\n",
      "Epoch 585/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1568 - mae: 0.1795 - val_loss: 0.1281 - val_mae: 0.1309\n",
      "Epoch 586/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1454 - mae: 0.1684 - val_loss: 0.2390 - val_mae: 0.4257\n",
      "Epoch 587/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1795 - mae: 0.1904 - val_loss: 0.1148 - val_mae: 0.1665\n",
      "Epoch 588/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1521 - mae: 0.1759 - val_loss: 0.1076 - val_mae: 0.1393\n",
      "Epoch 589/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1661 - mae: 0.1749 - val_loss: 0.1203 - val_mae: 0.1900\n",
      "Epoch 590/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1727 - mae: 0.1777 - val_loss: 0.1166 - val_mae: 0.1975\n",
      "Epoch 591/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1598 - mae: 0.1784 - val_loss: 0.1120 - val_mae: 0.1358\n",
      "Epoch 592/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1636 - mae: 0.1700 - val_loss: 0.1426 - val_mae: 0.2252\n",
      "Epoch 593/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1744 - mae: 0.1768 - val_loss: 0.1357 - val_mae: 0.2239\n",
      "Epoch 594/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1817 - mae: 0.1809 - val_loss: 0.1259 - val_mae: 0.1332\n",
      "Epoch 595/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1788 - mae: 0.1792 - val_loss: 0.3180 - val_mae: 0.5015\n",
      "Epoch 596/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1855 - mae: 0.2040 - val_loss: 0.1106 - val_mae: 0.1505\n",
      "Epoch 597/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1775 - mae: 0.1891 - val_loss: 0.1689 - val_mae: 0.2200\n",
      "Epoch 598/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1698 - mae: 0.1839 - val_loss: 0.1170 - val_mae: 0.1546\n",
      "Epoch 599/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1640 - mae: 0.1799 - val_loss: 0.1159 - val_mae: 0.1555\n",
      "Epoch 600/600\n",
      "53/53 [==============================] - 0s 2ms/step - loss: 0.1622 - mae: 0.1803 - val_loss: 0.1154 - val_mae: 0.1727\n"
     ]
    }
   ],
   "source": [
    "history2 = model2.fit(X_train, y_train, epochs=600, batch_size=16,\n",
    "validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Draw a graph of the loss, which is the distance between\n",
    "# the predicted and actual values during training and validation.\n",
    "loss = history2.history['loss']\n",
    "val_loss = history2.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_4\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_13 (Dense)             (None, 512)               6656      \n_________________________________________________________________\ndense_14 (Dense)             (None, 256)               131328    \n_________________________________________________________________\ndense_15 (Dense)             (None, 128)               32896     \n_________________________________________________________________\ndense_16 (Dense)             (None, 64)                8256      \n_________________________________________________________________\ndense_17 (Dense)             (None, 32)                2080      \n_________________________________________________________________\ndense_18 (Dense)             (None, 16)                528       \n_________________________________________________________________\ndense_19 (Dense)             (None, 1)                 17        \n=================================================================\nTotal params: 181,761\nTrainable params: 181,761\nNon-trainable params: 0\n_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# We'll use Keras to create a simple model architecture\n",
    "model3 = Sequential()\n",
    "# First layer takes a scalar input and feeds it through 16 \"neurons.\" The\n",
    "# neurons decide whether to activate based on the 'relu' activation function.\n",
    "model3.add(Dense(512, activation='relu', input_shape=(len(X_train.columns),)))\n",
    "# The new second layer may help the network learn more complex representations\n",
    "model3.add(Dense(256, activation='relu'))\n",
    "model3.add(Dense(128, activation='relu'))\n",
    "model3.add(Dense(64, activation='relu'))\n",
    "model3.add(Dense(32, activation='relu'))\n",
    "model3.add(Dense(16, activation='relu'))\n",
    "# Final layer is a single neuron, since we want to output a single value\n",
    "model3.add(Dense(1))\n",
    "# Compile the model using a standard optimizer and loss function for regression\n",
    "model3.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "# Show a summary of the model\n",
    "model3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " mae: 0.1823 - val_loss: 0.1177 - val_mae: 0.1481\n",
      "Epoch 451/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1883 - mae: 0.2083 - val_loss: 0.1006 - val_mae: 0.1304\n",
      "Epoch 452/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1715 - mae: 0.1818 - val_loss: 0.1079 - val_mae: 0.2098\n",
      "Epoch 453/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1687 - mae: 0.1884 - val_loss: 0.1631 - val_mae: 0.2473\n",
      "Epoch 454/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1704 - mae: 0.1797 - val_loss: 0.1232 - val_mae: 0.1325\n",
      "Epoch 455/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1876 - mae: 0.1817 - val_loss: 0.1236 - val_mae: 0.1556\n",
      "Epoch 456/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.2144 - mae: 0.2022 - val_loss: 0.1302 - val_mae: 0.1326\n",
      "Epoch 457/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1864 - mae: 0.1713 - val_loss: 0.1309 - val_mae: 0.1985\n",
      "Epoch 458/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.2350 - mae: 0.2161 - val_loss: 0.1078 - val_mae: 0.1438\n",
      "Epoch 459/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1821 - mae: 0.1876 - val_loss: 0.1333 - val_mae: 0.1361\n",
      "Epoch 460/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1760 - mae: 0.1751 - val_loss: 0.1355 - val_mae: 0.1505\n",
      "Epoch 461/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.2706 - mae: 0.2056 - val_loss: 0.1285 - val_mae: 0.1518\n",
      "Epoch 462/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1959 - mae: 0.1929 - val_loss: 0.1247 - val_mae: 0.1515\n",
      "Epoch 463/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1825 - mae: 0.1856 - val_loss: 0.1309 - val_mae: 0.1870\n",
      "Epoch 464/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1757 - mae: 0.1844 - val_loss: 0.1131 - val_mae: 0.1707\n",
      "Epoch 465/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1845 - mae: 0.2020 - val_loss: 0.1113 - val_mae: 0.1815\n",
      "Epoch 466/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1730 - mae: 0.1866 - val_loss: 0.1579 - val_mae: 0.1745\n",
      "Epoch 467/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1730 - mae: 0.1853 - val_loss: 0.1349 - val_mae: 0.2808\n",
      "Epoch 468/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1671 - mae: 0.1889 - val_loss: 0.0954 - val_mae: 0.1543\n",
      "Epoch 469/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1765 - mae: 0.1858 - val_loss: 0.1104 - val_mae: 0.1344\n",
      "Epoch 470/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1749 - mae: 0.1865 - val_loss: 0.1505 - val_mae: 0.2303\n",
      "Epoch 471/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1768 - mae: 0.1918 - val_loss: 0.0957 - val_mae: 0.1584\n",
      "Epoch 472/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1733 - mae: 0.1875 - val_loss: 0.1950 - val_mae: 0.3412\n",
      "Epoch 473/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1733 - mae: 0.1932 - val_loss: 0.1243 - val_mae: 0.1688\n",
      "Epoch 474/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1727 - mae: 0.1817 - val_loss: 0.1303 - val_mae: 0.1957\n",
      "Epoch 475/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1727 - mae: 0.1838 - val_loss: 0.1139 - val_mae: 0.1322\n",
      "Epoch 476/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1710 - mae: 0.1880 - val_loss: 0.1090 - val_mae: 0.1277\n",
      "Epoch 477/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1808 - mae: 0.1797 - val_loss: 0.1079 - val_mae: 0.1749\n",
      "Epoch 478/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1570 - mae: 0.1808 - val_loss: 0.1137 - val_mae: 0.1235\n",
      "Epoch 479/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1793 - mae: 0.1819 - val_loss: 0.1104 - val_mae: 0.1598\n",
      "Epoch 480/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1670 - mae: 0.1812 - val_loss: 0.1149 - val_mae: 0.1633\n",
      "Epoch 481/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1702 - mae: 0.1859 - val_loss: 0.1138 - val_mae: 0.2054\n",
      "Epoch 482/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1606 - mae: 0.1806 - val_loss: 0.1968 - val_mae: 0.3573\n",
      "Epoch 483/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1763 - mae: 0.1920 - val_loss: 0.1180 - val_mae: 0.1744\n",
      "Epoch 484/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1599 - mae: 0.1781 - val_loss: 0.1934 - val_mae: 0.2222\n",
      "Epoch 485/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1670 - mae: 0.1814 - val_loss: 0.1093 - val_mae: 0.1373\n",
      "Epoch 486/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1761 - mae: 0.1876 - val_loss: 0.2417 - val_mae: 0.4031\n",
      "Epoch 487/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1711 - mae: 0.1931 - val_loss: 0.1066 - val_mae: 0.1298\n",
      "Epoch 488/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1789 - mae: 0.1882 - val_loss: 0.1039 - val_mae: 0.1844\n",
      "Epoch 489/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1629 - mae: 0.1820 - val_loss: 0.0868 - val_mae: 0.1344\n",
      "Epoch 490/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1719 - mae: 0.1841 - val_loss: 0.1067 - val_mae: 0.1378\n",
      "Epoch 491/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1744 - mae: 0.1855 - val_loss: 0.1248 - val_mae: 0.1728\n",
      "Epoch 492/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1755 - mae: 0.1927 - val_loss: 0.1140 - val_mae: 0.2208\n",
      "Epoch 493/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1732 - mae: 0.1867 - val_loss: 0.1008 - val_mae: 0.1478\n",
      "Epoch 494/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1682 - mae: 0.1855 - val_loss: 0.1028 - val_mae: 0.1717\n",
      "Epoch 495/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1746 - mae: 0.1864 - val_loss: 0.1060 - val_mae: 0.1185\n",
      "Epoch 496/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1793 - mae: 0.1905 - val_loss: 0.1211 - val_mae: 0.1922\n",
      "Epoch 497/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1738 - mae: 0.1819 - val_loss: 0.1227 - val_mae: 0.1828\n",
      "Epoch 498/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1661 - mae: 0.1838 - val_loss: 0.1036 - val_mae: 0.1531\n",
      "Epoch 499/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1705 - mae: 0.1828 - val_loss: 0.2102 - val_mae: 0.2531\n",
      "Epoch 500/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1879 - mae: 0.1933 - val_loss: 0.1138 - val_mae: 0.1265\n",
      "Epoch 501/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1653 - mae: 0.1833 - val_loss: 0.0967 - val_mae: 0.1487\n",
      "Epoch 502/600\n",
      "53/53 [==============================] - 0s 5ms/step - loss: 0.1724 - mae: 0.1818 - val_loss: 0.1377 - val_mae: 0.1406\n",
      "Epoch 503/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1752 - mae: 0.1810 - val_loss: 0.1066 - val_mae: 0.1395\n",
      "Epoch 504/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1698 - mae: 0.1852 - val_loss: 0.0983 - val_mae: 0.1340\n",
      "Epoch 505/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1672 - mae: 0.1809 - val_loss: 0.1143 - val_mae: 0.1662\n",
      "Epoch 506/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1587 - mae: 0.1833 - val_loss: 0.1818 - val_mae: 0.2524\n",
      "Epoch 507/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1846 - mae: 0.1861 - val_loss: 0.1068 - val_mae: 0.1211\n",
      "Epoch 508/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1790 - mae: 0.1912 - val_loss: 0.1007 - val_mae: 0.1673\n",
      "Epoch 509/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1668 - mae: 0.1805 - val_loss: 0.2183 - val_mae: 0.4144\n",
      "Epoch 510/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1711 - mae: 0.1929 - val_loss: 0.2021 - val_mae: 0.3962\n",
      "Epoch 511/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1713 - mae: 0.1963 - val_loss: 0.2425 - val_mae: 0.4090\n",
      "Epoch 512/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1736 - mae: 0.1880 - val_loss: 0.0928 - val_mae: 0.1302\n",
      "Epoch 513/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1667 - mae: 0.1873 - val_loss: 0.1242 - val_mae: 0.1946\n",
      "Epoch 514/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1692 - mae: 0.1869 - val_loss: 0.0955 - val_mae: 0.1318\n",
      "Epoch 515/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1712 - mae: 0.1829 - val_loss: 0.0979 - val_mae: 0.1839\n",
      "Epoch 516/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1762 - mae: 0.1824 - val_loss: 0.1073 - val_mae: 0.1639\n",
      "Epoch 517/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1694 - mae: 0.1849 - val_loss: 0.0965 - val_mae: 0.1592\n",
      "Epoch 518/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1699 - mae: 0.1847 - val_loss: 0.1074 - val_mae: 0.1576\n",
      "Epoch 519/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1607 - mae: 0.1828 - val_loss: 0.1174 - val_mae: 0.1918\n",
      "Epoch 520/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1653 - mae: 0.1876 - val_loss: 0.0952 - val_mae: 0.1615\n",
      "Epoch 521/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1671 - mae: 0.1739 - val_loss: 0.1027 - val_mae: 0.1852\n",
      "Epoch 522/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1720 - mae: 0.1892 - val_loss: 0.1090 - val_mae: 0.1532\n",
      "Epoch 523/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1653 - mae: 0.1811 - val_loss: 0.1019 - val_mae: 0.1309\n",
      "Epoch 524/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1612 - mae: 0.1777 - val_loss: 0.0988 - val_mae: 0.1408\n",
      "Epoch 525/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1693 - mae: 0.1793 - val_loss: 0.1281 - val_mae: 0.1755\n",
      "Epoch 526/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1591 - mae: 0.1754 - val_loss: 0.1353 - val_mae: 0.1177\n",
      "Epoch 527/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1738 - mae: 0.1801 - val_loss: 0.1006 - val_mae: 0.1294\n",
      "Epoch 528/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1585 - mae: 0.1765 - val_loss: 0.1080 - val_mae: 0.1362\n",
      "Epoch 529/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1753 - mae: 0.1879 - val_loss: 0.1425 - val_mae: 0.1857\n",
      "Epoch 530/600\n",
      "53/53 [==============================] - 0s 5ms/step - loss: 0.1631 - mae: 0.1827 - val_loss: 0.1047 - val_mae: 0.1193\n",
      "Epoch 531/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.2275 - mae: 0.2051 - val_loss: 0.1121 - val_mae: 0.1406\n",
      "Epoch 532/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1523 - mae: 0.1616 - val_loss: 0.1176 - val_mae: 0.1275\n",
      "Epoch 533/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1542 - mae: 0.1762 - val_loss: 0.1072 - val_mae: 0.1448\n",
      "Epoch 534/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1520 - mae: 0.1827 - val_loss: 0.1142 - val_mae: 0.1703\n",
      "Epoch 535/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1788 - mae: 0.1850 - val_loss: 0.1305 - val_mae: 0.2059\n",
      "Epoch 536/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1537 - mae: 0.1862 - val_loss: 0.1104 - val_mae: 0.1243\n",
      "Epoch 537/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1807 - mae: 0.1876 - val_loss: 0.1500 - val_mae: 0.2601\n",
      "Epoch 538/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1643 - mae: 0.1860 - val_loss: 0.1096 - val_mae: 0.1835\n",
      "Epoch 539/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1905 - mae: 0.1797 - val_loss: 0.0815 - val_mae: 0.1341\n",
      "Epoch 540/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1580 - mae: 0.1741 - val_loss: 0.1051 - val_mae: 0.1253\n",
      "Epoch 541/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1740 - mae: 0.1868 - val_loss: 0.1038 - val_mae: 0.1487\n",
      "Epoch 542/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1607 - mae: 0.1829 - val_loss: 0.1685 - val_mae: 0.3238\n",
      "Epoch 543/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1765 - mae: 0.1953 - val_loss: 0.1027 - val_mae: 0.1153\n",
      "Epoch 544/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1609 - mae: 0.1762 - val_loss: 0.1016 - val_mae: 0.1379\n",
      "Epoch 545/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1652 - mae: 0.1854 - val_loss: 0.1043 - val_mae: 0.1321\n",
      "Epoch 546/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1661 - mae: 0.1829 - val_loss: 0.1464 - val_mae: 0.2458\n",
      "Epoch 547/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1838 - mae: 0.1827 - val_loss: 0.1246 - val_mae: 0.1291\n",
      "Epoch 548/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1833 - mae: 0.1830 - val_loss: 0.2833 - val_mae: 0.4405\n",
      "Epoch 549/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.2489 - mae: 0.2076 - val_loss: 0.0898 - val_mae: 0.1628\n",
      "Epoch 550/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.2013 - mae: 0.1945 - val_loss: 0.1435 - val_mae: 0.2676\n",
      "Epoch 551/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1729 - mae: 0.1955 - val_loss: 0.1588 - val_mae: 0.1518\n",
      "Epoch 552/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1712 - mae: 0.1777 - val_loss: 0.1097 - val_mae: 0.1484\n",
      "Epoch 553/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1628 - mae: 0.1795 - val_loss: 0.1105 - val_mae: 0.1411\n",
      "Epoch 554/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1646 - mae: 0.1868 - val_loss: 0.1052 - val_mae: 0.1454\n",
      "Epoch 555/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1745 - mae: 0.1810 - val_loss: 0.2283 - val_mae: 0.3716\n",
      "Epoch 556/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1721 - mae: 0.1921 - val_loss: 0.1033 - val_mae: 0.1110\n",
      "Epoch 557/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1629 - mae: 0.1806 - val_loss: 0.0922 - val_mae: 0.1551\n",
      "Epoch 558/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1652 - mae: 0.1828 - val_loss: 0.1095 - val_mae: 0.1545\n",
      "Epoch 559/600\n",
      "53/53 [==============================] - 0s 5ms/step - loss: 0.1635 - mae: 0.1820 - val_loss: 0.1040 - val_mae: 0.1305\n",
      "Epoch 560/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1753 - mae: 0.1827 - val_loss: 0.1184 - val_mae: 0.1677\n",
      "Epoch 561/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1731 - mae: 0.1848 - val_loss: 0.1053 - val_mae: 0.1378\n",
      "Epoch 562/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1563 - mae: 0.1882 - val_loss: 0.3355 - val_mae: 0.2410\n",
      "Epoch 563/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1762 - mae: 0.1814 - val_loss: 0.0831 - val_mae: 0.1343\n",
      "Epoch 564/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1652 - mae: 0.1867 - val_loss: 0.1027 - val_mae: 0.1368\n",
      "Epoch 565/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1742 - mae: 0.1813 - val_loss: 0.1041 - val_mae: 0.1208\n",
      "Epoch 566/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1594 - mae: 0.1808 - val_loss: 0.1233 - val_mae: 0.1790\n",
      "Epoch 567/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1879 - mae: 0.1847 - val_loss: 0.1238 - val_mae: 0.1369\n",
      "Epoch 568/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1683 - mae: 0.1829 - val_loss: 0.1463 - val_mae: 0.2128\n",
      "Epoch 569/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1933 - mae: 0.1871 - val_loss: 0.1279 - val_mae: 0.2002\n",
      "Epoch 570/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1790 - mae: 0.1837 - val_loss: 0.1077 - val_mae: 0.1488\n",
      "Epoch 571/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.2005 - mae: 0.1969 - val_loss: 0.1185 - val_mae: 0.1719\n",
      "Epoch 572/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1782 - mae: 0.1845 - val_loss: 0.1379 - val_mae: 0.1784\n",
      "Epoch 573/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1735 - mae: 0.1916 - val_loss: 0.1055 - val_mae: 0.1415\n",
      "Epoch 574/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1645 - mae: 0.1806 - val_loss: 0.1508 - val_mae: 0.1876\n",
      "Epoch 575/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1652 - mae: 0.1729 - val_loss: 0.1094 - val_mae: 0.1635\n",
      "Epoch 576/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1775 - mae: 0.1874 - val_loss: 0.1040 - val_mae: 0.1373\n",
      "Epoch 577/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1771 - mae: 0.1904 - val_loss: 0.1025 - val_mae: 0.1247\n",
      "Epoch 578/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1606 - mae: 0.1779 - val_loss: 0.1073 - val_mae: 0.1553\n",
      "Epoch 579/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1677 - mae: 0.1820 - val_loss: 0.1598 - val_mae: 0.3069\n",
      "Epoch 580/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1746 - mae: 0.1833 - val_loss: 0.1502 - val_mae: 0.2240\n",
      "Epoch 581/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1771 - mae: 0.1828 - val_loss: 0.1274 - val_mae: 0.2184\n",
      "Epoch 582/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1761 - mae: 0.1848 - val_loss: 0.1090 - val_mae: 0.1260\n",
      "Epoch 583/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1686 - mae: 0.1949 - val_loss: 0.1042 - val_mae: 0.1317\n",
      "Epoch 584/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1808 - mae: 0.1818 - val_loss: 0.1188 - val_mae: 0.1408\n",
      "Epoch 585/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1740 - mae: 0.1784 - val_loss: 0.1134 - val_mae: 0.1307\n",
      "Epoch 586/600\n",
      "53/53 [==============================] - 0s 3ms/step - loss: 0.1651 - mae: 0.1761 - val_loss: 0.1679 - val_mae: 0.2400\n",
      "Epoch 587/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1813 - mae: 0.1908 - val_loss: 0.1117 - val_mae: 0.1418\n",
      "Epoch 588/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1737 - mae: 0.1788 - val_loss: 0.1037 - val_mae: 0.1351\n",
      "Epoch 589/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1808 - mae: 0.1843 - val_loss: 0.1865 - val_mae: 0.3408\n",
      "Epoch 590/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1904 - mae: 0.1899 - val_loss: 0.1410 - val_mae: 0.2216\n",
      "Epoch 591/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1663 - mae: 0.1822 - val_loss: 0.1719 - val_mae: 0.3554\n",
      "Epoch 592/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1514 - mae: 0.1844 - val_loss: 0.1160 - val_mae: 0.1597\n",
      "Epoch 593/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1692 - mae: 0.1829 - val_loss: 0.1447 - val_mae: 0.1647\n",
      "Epoch 594/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1792 - mae: 0.1866 - val_loss: 0.1444 - val_mae: 0.2237\n",
      "Epoch 595/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1736 - mae: 0.1874 - val_loss: 0.1029 - val_mae: 0.1271\n",
      "Epoch 596/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1667 - mae: 0.1867 - val_loss: 0.1106 - val_mae: 0.1380\n",
      "Epoch 597/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1564 - mae: 0.1769 - val_loss: 0.1181 - val_mae: 0.1789\n",
      "Epoch 598/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1612 - mae: 0.1811 - val_loss: 0.1100 - val_mae: 0.1298\n",
      "Epoch 599/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1554 - mae: 0.1731 - val_loss: 0.1040 - val_mae: 0.1282\n",
      "Epoch 600/600\n",
      "53/53 [==============================] - 0s 4ms/step - loss: 0.1734 - mae: 0.1806 - val_loss: 0.1083 - val_mae: 0.1408\n"
     ]
    }
   ],
   "source": [
    "history3 = model3.fit(X_train, y_train, epochs=600, batch_size=16,\n",
    "validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "#Draw a graph of the loss, which is the distance between\n",
    "# the predicted and actual values during training and validation.\n",
    "loss = history3.history['loss']\n",
    "val_loss = history3.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model3.save('keras_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflowjs as tfjs\n",
    "\n",
    "tfjs.converters.save_keras_model(model3,'./')"
   ]
  }
 ]
}