Project 09 -multi-output - Energy saving dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7bf66cb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  get_ipython().run_line_magic('tensorflow_version', '2.x')\n",
    "except Exception:\n",
    "  pass\n",
    "'''\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, Input\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4e84a8",
   "metadata": {},
   "source": [
    " We define a few utilities for data conversion and visualization to make our code more neat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "158ae309",
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return (x - train_stats['mean']) / (train_stats['std'])\n",
    "\n",
    "def plot_diff(y_true, y_pred, title = ''):\n",
    "    plt.scatter(y_true, y_pred)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('True Values')\n",
    "    plt.ylabel('Predictions')\n",
    "    plt.axis('equal')\n",
    "    plt.axis('square')\n",
    "    plt.xlim(plt.xlim())\n",
    "    plt.ylim(plt.ylim())\n",
    "    plt.plot([-100, 100], [-100, 100])\n",
    "    plt.show()\n",
    "\n",
    "def plot_metrics(metric_name, title, ylim=5):\n",
    "    plt.title(title)\n",
    "    plt.ylim(0, ylim)\n",
    "    plt.plot(history.history[metric_name], color = 'blue', label = metric_name)\n",
    "    plt.plot(history.history['val_' + metric_name], color = 'green', label = 'val_' + metric_name)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a62c46a2",
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>0.64</td>\n",
       "      <td>784.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>220.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
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       "      <td>15.16</td>\n",
       "      <td>19.24</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.74</td>\n",
       "      <td>686.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>220.5</td>\n",
       "      <td>3.5</td>\n",
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       "      <td>0.25</td>\n",
       "      <td>4</td>\n",
       "      <td>12.43</td>\n",
       "      <td>15.35</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.66</td>\n",
       "      <td>759.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.10</td>\n",
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       "      <td>11.43</td>\n",
       "      <td>14.74</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.82</td>\n",
       "      <td>612.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>147.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "      <td>29.91</td>\n",
       "      <td>29.13</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.86</td>\n",
       "      <td>588.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2</td>\n",
       "      <td>31.64</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>0.82</td>\n",
       "      <td>612.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>147.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>23.67</td>\n",
       "      <td>24.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>0.76</td>\n",
       "      <td>661.5</td>\n",
       "      <td>416.5</td>\n",
       "      <td>122.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>23.93</td>\n",
       "      <td>29.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>0.79</td>\n",
       "      <td>637.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3</td>\n",
       "      <td>39.04</td>\n",
       "      <td>45.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>0.82</td>\n",
       "      <td>612.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>147.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>23.53</td>\n",
       "      <td>27.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>0.66</td>\n",
       "      <td>759.5</td>\n",
       "      <td>318.5</td>\n",
       "      <td>220.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.10</td>\n",
       "      <td>5</td>\n",
       "      <td>11.34</td>\n",
       "      <td>14.81</td>\n",
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       "<p>768 rows × 10 columns</p>\n",
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       "       X1     X2     X3     X4   X5  X6    X7  X8     Y1     Y2\n",
       "0    0.64  784.0  343.0  220.5  3.5   2  0.10   5  15.16  19.24\n",
       "1    0.74  686.0  245.0  220.5  3.5   5  0.25   4  12.43  15.35\n",
       "2    0.66  759.5  318.5  220.5  3.5   5  0.10   2  11.43  14.74\n",
       "3    0.82  612.5  318.5  147.0  7.0   3  0.40   1  29.91  29.13\n",
       "4    0.86  588.0  294.0  147.0  7.0   5  0.40   2  31.64  36.86\n",
       "..    ...    ...    ...    ...  ...  ..   ...  ..    ...    ...\n",
       "763  0.82  612.5  318.5  147.0  7.0   4  0.10   2  23.67  24.80\n",
       "764  0.76  661.5  416.5  122.5  7.0   5  0.00   0  23.93  29.40\n",
       "765  0.79  637.0  343.0  147.0  7.0   2  0.25   3  39.04  45.13\n",
       "766  0.82  612.5  318.5  147.0  7.0   2  0.10   1  23.53  27.31\n",
       "767  0.66  759.5  318.5  220.5  3.5   3  0.10   5  11.34  14.81\n",
       "\n",
       "[768 rows x 10 columns]"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify data URI\n",
    "\n",
    "# Use pandas excel reader\n",
    "df = pd.read_excel(\"ENB2012_data.xls\" , )\n",
    "df = df.sample(frac=1).reset_index(drop = True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61a7c825",
   "metadata": {},
   "outputs": [
    {
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       "      <td>35.99</td>\n",
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       "      <th>69</th>\n",
       "      <td>0.79</td>\n",
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       "      <th>356</th>\n",
       "      <td>0.79</td>\n",
       "      <td>637.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>147.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.10</td>\n",
       "      <td>3</td>\n",
       "      <td>36.97</td>\n",
       "      <td>35.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>0.86</td>\n",
       "      <td>588.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>147.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>3</td>\n",
       "      <td>31.84</td>\n",
       "      <td>31.60</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.86</td>\n",
       "      <td>588.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>147.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>5</td>\n",
       "      <td>31.12</td>\n",
       "      <td>36.26</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.76</td>\n",
       "      <td>661.5</td>\n",
       "      <td>416.5</td>\n",
       "      <td>122.50</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.10</td>\n",
       "      <td>2</td>\n",
       "      <td>33.16</td>\n",
       "      <td>33.91</td>\n",
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       "      <th>765</th>\n",
       "      <td>0.79</td>\n",
       "      <td>637.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>147.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.25</td>\n",
       "      <td>3</td>\n",
       "      <td>39.04</td>\n",
       "      <td>45.13</td>\n",
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       "       X1     X2     X3      X4   X5  X6    X7  X8     Y1     Y2\n",
       "12   0.64  784.0  343.0  220.50  3.5   3  0.10   4  15.19  19.25\n",
       "221  0.98  514.5  294.0  110.25  7.0   3  0.25   3  28.56  29.78\n",
       "181  0.66  759.5  318.5  220.50  3.5   4  0.10   4  11.33  15.00\n",
       "631  0.76  661.5  416.5  122.50  7.0   2  0.25   3  35.99  36.07\n",
       "69   0.79  637.0  343.0  147.00  7.0   4  0.40   2  42.74  38.84\n",
       "..    ...    ...    ...     ...  ...  ..   ...  ..    ...    ...\n",
       "356  0.79  637.0  343.0  147.00  7.0   4  0.10   3  36.97  35.10\n",
       "726  0.86  588.0  294.0  147.00  7.0   5  0.40   3  31.84  31.60\n",
       "149  0.86  588.0  294.0  147.00  7.0   4  0.40   5  31.12  36.26\n",
       "24   0.76  661.5  416.5  122.50  7.0   2  0.10   2  33.16  33.91\n",
       "765  0.79  637.0  343.0  147.00  7.0   2  0.25   3  39.04  45.13\n",
       "\n",
       "[614 rows x 10 columns]"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into train and test with 80 train / 20 test\n",
    "train, test = train_test_split(df, test_size = 0.2)\n",
    "\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49dc9d0f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>X1</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
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       "      <td>614.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.764821</td>\n",
       "      <td>671.236156</td>\n",
       "      <td>318.539902</td>\n",
       "      <td>176.348127</td>\n",
       "      <td>5.255700</td>\n",
       "      <td>3.508143</td>\n",
       "      <td>0.235098</td>\n",
       "      <td>2.799674</td>\n",
       "      <td>22.301873</td>\n",
       "      <td>24.628371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.106496</td>\n",
       "      <td>88.513386</td>\n",
       "      <td>43.708339</td>\n",
       "      <td>45.294904</td>\n",
       "      <td>1.751418</td>\n",
       "      <td>1.123281</td>\n",
       "      <td>0.134147</td>\n",
       "      <td>1.537121</td>\n",
       "      <td>9.981266</td>\n",
       "      <td>9.434731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.620000</td>\n",
       "      <td>514.500000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>110.250000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.010000</td>\n",
       "      <td>10.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.660000</td>\n",
       "      <td>588.000000</td>\n",
       "      <td>294.000000</td>\n",
       "      <td>122.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>13.025000</td>\n",
       "      <td>15.802500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.760000</td>\n",
       "      <td>661.500000</td>\n",
       "      <td>318.500000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>19.125000</td>\n",
       "      <td>22.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.860000</td>\n",
       "      <td>759.500000</td>\n",
       "      <td>343.000000</td>\n",
       "      <td>220.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>31.470000</td>\n",
       "      <td>32.945000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.980000</td>\n",
       "      <td>808.500000</td>\n",
       "      <td>416.500000</td>\n",
       "      <td>220.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>43.100000</td>\n",
       "      <td>48.030000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               X1          X2          X3          X4          X5          X6  \\\n",
       "count  614.000000  614.000000  614.000000  614.000000  614.000000  614.000000   \n",
       "mean     0.764821  671.236156  318.539902  176.348127    5.255700    3.508143   \n",
       "std      0.106496   88.513386   43.708339   45.294904    1.751418    1.123281   \n",
       "min      0.620000  514.500000  245.000000  110.250000    3.500000    2.000000   \n",
       "25%      0.660000  588.000000  294.000000  122.500000    3.500000    3.000000   \n",
       "50%      0.760000  661.500000  318.500000  147.000000    7.000000    4.000000   \n",
       "75%      0.860000  759.500000  343.000000  220.500000    7.000000    5.000000   \n",
       "max      0.980000  808.500000  416.500000  220.500000    7.000000    5.000000   \n",
       "\n",
       "               X7          X8          Y1          Y2  \n",
       "count  614.000000  614.000000  614.000000  614.000000  \n",
       "mean     0.235098    2.799674   22.301873   24.628371  \n",
       "std      0.134147    1.537121    9.981266    9.434731  \n",
       "min      0.000000    0.000000    6.010000   10.900000  \n",
       "25%      0.100000    2.000000   13.025000   15.802500  \n",
       "50%      0.250000    3.000000   19.125000   22.625000  \n",
       "75%      0.400000    4.000000   31.470000   32.945000  \n",
       "max      0.400000    5.000000   43.100000   48.030000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_stats = train.describe()\n",
    "train_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "05ba9f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X1</th>\n",
       "      <td>614.0</td>\n",
       "      <td>0.764821</td>\n",
       "      <td>0.106496</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X2</th>\n",
       "      <td>614.0</td>\n",
       "      <td>671.236156</td>\n",
       "      <td>88.513386</td>\n",
       "      <td>514.50</td>\n",
       "      <td>588.00</td>\n",
       "      <td>661.50</td>\n",
       "      <td>759.50</td>\n",
       "      <td>808.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X3</th>\n",
       "      <td>614.0</td>\n",
       "      <td>318.539902</td>\n",
       "      <td>43.708339</td>\n",
       "      <td>245.00</td>\n",
       "      <td>294.00</td>\n",
       "      <td>318.50</td>\n",
       "      <td>343.00</td>\n",
       "      <td>416.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X4</th>\n",
       "      <td>614.0</td>\n",
       "      <td>176.348127</td>\n",
       "      <td>45.294904</td>\n",
       "      <td>110.25</td>\n",
       "      <td>122.50</td>\n",
       "      <td>147.00</td>\n",
       "      <td>220.50</td>\n",
       "      <td>220.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X5</th>\n",
       "      <td>614.0</td>\n",
       "      <td>5.255700</td>\n",
       "      <td>1.751418</td>\n",
       "      <td>3.50</td>\n",
       "      <td>3.50</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X6</th>\n",
       "      <td>614.0</td>\n",
       "      <td>3.508143</td>\n",
       "      <td>1.123281</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X7</th>\n",
       "      <td>614.0</td>\n",
       "      <td>0.235098</td>\n",
       "      <td>0.134147</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X8</th>\n",
       "      <td>614.0</td>\n",
       "      <td>2.799674</td>\n",
       "      <td>1.537121</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count        mean        std     min     25%     50%     75%     max\n",
       "X1  614.0    0.764821   0.106496    0.62    0.66    0.76    0.86    0.98\n",
       "X2  614.0  671.236156  88.513386  514.50  588.00  661.50  759.50  808.50\n",
       "X3  614.0  318.539902  43.708339  245.00  294.00  318.50  343.00  416.50\n",
       "X4  614.0  176.348127  45.294904  110.25  122.50  147.00  220.50  220.50\n",
       "X5  614.0    5.255700   1.751418    3.50    3.50    7.00    7.00    7.00\n",
       "X6  614.0    3.508143   1.123281    2.00    3.00    4.00    5.00    5.00\n",
       "X7  614.0    0.235098   0.134147    0.00    0.10    0.25    0.40    0.40\n",
       "X8  614.0    2.799674   1.537121    0.00    2.00    3.00    4.00    5.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Y1 and Y2 as the 2 outputs and format them as np arrays\n",
    "train_stats.pop('Y1')\n",
    "train_stats.pop('Y2')\n",
    "train_stats = train_stats.transpose()\n",
    "train_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "59bad4d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([15.19, 28.56, 11.33, 35.99, 42.74, 24.31, 39.01, 12.43, 14.53,\n",
       "        19.68, 12.3 , 38.82, 12.85, 14.51, 12.17, 14.32,  6.77, 15.23,\n",
       "        29.87,  6.85, 29.06, 16.76, 32.31, 15.09, 14.9 , 14.44, 10.72,\n",
       "        24.32, 30.05, 41.92, 14.6 , 28.64, 16.48, 38.84, 12.27, 25.7 ,\n",
       "        12.43, 32.4 , 15.4 , 10.07, 26.33, 35.45, 36.57, 29.67, 12.97,\n",
       "        29.62, 28.05, 14.33, 13.91, 12.34, 15.98, 29.14, 12.45, 10.35,\n",
       "        36.71, 16.66, 32.31, 15.55, 39.83, 11.6 , 39.84, 14.39, 33.12,\n",
       "        35.89, 28.67, 28.6 , 28.37, 35.56, 25.17, 15.09, 14.54, 31.28,\n",
       "        17.17, 18.9 , 40.19, 10.8 , 32.07, 38.57, 11.69, 32.82, 14.75,\n",
       "        25.48, 29.79, 36.13, 28.05, 12.72, 26.97, 11.53, 32.05, 15.37,\n",
       "        12.92, 15.34, 16.86, 15.55, 14.65, 29.08, 12.41, 28.52, 36.95,\n",
       "        32.4 , 16.64, 35.69, 24.63, 17.11, 13.04, 14.71, 14.6 , 39.04,\n",
       "        24.23, 15.23, 12.87, 10.68, 38.33, 18.71, 26.47, 32.31, 40.78,\n",
       "        26.45, 10.77, 10.78, 16.74, 32.29, 25.66, 36.06, 11.42, 14.62,\n",
       "        27.27, 12.86, 12.49, 32.84, 11.2 , 28.61,  6.37, 10.39, 11.32,\n",
       "        14.51, 12.18, 10.72,  6.4 , 26.84, 18.19, 11.61, 29.63, 28.64,\n",
       "        32.38, 28.55, 22.8 , 23.59, 17.05, 33.28, 14.96, 14.71, 10.15,\n",
       "        10.54, 29.22, 14.12,  6.01, 12.93, 15.3 , 32.72, 11.13,  8.6 ,\n",
       "        13.  , 12.97, 43.1 , 31.66, 25.98, 14.61, 15.41, 12.73, 36.64,\n",
       "        29.68, 10.7 , 17.14, 17.14, 24.38, 16.47, 42.77, 41.67, 32.82,\n",
       "        13.94,  6.07, 39.97, 13.18, 14.72, 24.77, 12.67, 15.09, 12.78,\n",
       "        23.87, 19.34, 12.43, 15.36, 10.46, 11.09, 16.93, 28.66, 11.59,\n",
       "        26.48, 14.37, 28.09, 12.73, 14.66, 11.42, 10.75,  8.45, 32.39,\n",
       "        23.86, 38.67, 11.34, 25.36, 14.7 , 32.69, 19.52, 35.94, 12.02,\n",
       "        13.01, 26.89, 32.31, 40.79, 32.68, 18.48, 19.13, 12.28, 25.36,\n",
       "        31.66, 11.98, 10.67, 12.84, 36.7 , 32.26, 14.19, 15.55, 11.16,\n",
       "        37.1 , 12.8 , 13.02, 20.71, 15.2 , 19.2 , 12.28, 10.43, 10.42,\n",
       "        13.02, 19.42, 29.88, 29.47, 31.63, 13.05, 28.01, 11.21, 42.11,\n",
       "        28.15, 40.42, 13.17, 12.2 , 13.  , 36.57, 32.24, 36.97, 12.03,\n",
       "        39.86, 36.95, 12.59, 12.41, 11.69, 37.26, 32.48, 29.9 , 14.32,\n",
       "        24.17, 40.78, 24.35, 14.71, 10.39, 39.31, 32.15, 40.  , 28.83,\n",
       "        28.88,  6.05, 10.77, 22.58, 29.47, 23.54, 12.46, 12.63, 14.47,\n",
       "        10.39, 12.25, 35.48, 24.96, 15.55, 28.86, 28.15, 38.89, 36.26,\n",
       "        10.34, 16.86, 14.08, 11.46, 29.4 , 15.18, 12.27, 12.97, 24.59,\n",
       "        12.19, 19.36, 11.22, 28.65, 10.7 , 11.43, 28.62, 14.5 , 12.63,\n",
       "        29.52, 24.77, 14.42, 24.23, 10.36, 12.47, 16.44, 15.21, 22.89,\n",
       "        26.45, 36.9 , 29.27, 14.34, 14.6 , 24.03, 14.03, 14.92, 17.35,\n",
       "        29.91, 11.1 , 40.68, 36.59, 15.36, 36.43, 29.49, 15.3 , 14.62,\n",
       "        10.68, 11.43, 24.35, 14.52, 36.03, 18.31, 36.91,  6.79, 38.35,\n",
       "        28.67, 32.85, 24.63, 28.17, 14.18, 14.34, 13.68, 10.45, 28.63,\n",
       "        32.84, 12.93, 12.35, 28.91, 34.29, 26.91, 35.69, 11.16, 16.54,\n",
       "        10.56, 29.34, 29.6 , 12.42, 31.29,  7.18, 16.44, 36.86, 16.56,\n",
       "        13.95, 11.22, 32.72, 12.19, 15.12, 16.83, 40.71, 15.16, 14.37,\n",
       "        15.18, 14.21, 29.01, 14.33, 10.37, 11.45, 24.26, 25.74, 12.77,\n",
       "        32.46, 16.77, 15.34, 28.18, 29.54, 31.89, 10.53, 13.  , 28.7 ,\n",
       "        17.69, 36.45, 15.34, 40.12, 17.26, 14.48, 35.64, 28.57, 40.57,\n",
       "        14.61, 33.48, 19.48, 29.06, 23.84, 32.67, 12.82, 41.64, 28.93,\n",
       "        39.72, 36.45, 15.12, 35.84, 16.84, 12.18, 23.67, 39.81, 11.34,\n",
       "        36.28, 36.96, 13.86, 33.08,  7.1 , 10.34, 14.4 , 12.62, 28.4 ,\n",
       "        42.08, 39.68,  6.37, 24.37, 26.19, 29.03, 14.7 , 24.11, 19.  ,\n",
       "        38.65, 26.44, 10.85, 29.83, 32.21, 18.84, 15.32, 29.71, 26.  ,\n",
       "        41.09, 24.04, 23.89, 32.75, 16.62, 14.1 , 14.41, 18.48, 23.93,\n",
       "        32.53, 13.  , 13.  , 14.45, 32.  , 32.33, 27.03, 15.16, 15.19,\n",
       "        10.08, 27.03, 15.09, 10.36, 29.02, 36.86, 32.52, 32.46, 35.73,\n",
       "        42.62, 20.84, 36.77, 17.88, 18.46, 29.05, 29.53, 35.96, 14.5 ,\n",
       "        28.42, 31.53, 28.15, 11.44, 35.01, 11.22, 12.47, 13.99, 10.68,\n",
       "        40.03, 17.15, 32.96, 32.13, 24.28, 32.74, 29.92,  6.4 , 32.12,\n",
       "        16.55, 26.46, 16.9 , 15.16, 12.95, 39.32, 14.56, 25.49, 13.99,\n",
       "        28.4 , 16.92, 23.93, 23.53, 28.07, 33.13, 29.43, 11.69, 12.29,\n",
       "        24.94, 24.4 , 11.45, 15.29, 19.5 , 33.21, 14.16, 14.66, 35.05,\n",
       "        32.21, 33.09, 11.33, 28.41, 28.31, 25.37, 12.5 , 16.94, 12.63,\n",
       "        32.74, 26.28, 11.8 , 12.12, 32.38, 10.38, 35.78, 14.22,  8.49,\n",
       "        24.13, 40.11, 32.67, 38.98, 28.58, 15.42, 12.1 , 29.08, 31.64,\n",
       "         7.1 , 14.07, 34.72, 12.57, 36.47, 15.23, 16.73, 36.52, 16.69,\n",
       "        41.96, 14.42, 28.15, 10.71, 22.93, 35.24, 32.73, 24.03, 32.49,\n",
       "        10.32, 19.12, 32.33, 11.38, 32.46, 11.16, 15.12, 29.5 , 12.68,\n",
       "        13.97, 12.12, 32.68, 15.41, 35.65, 15.36, 32.12, 15.16, 42.5 ,\n",
       "        14.55, 12.88, 16.99, 15.08, 12.65, 25.17, 36.97, 31.84, 31.12,\n",
       "        33.16, 39.04]),\n",
       " array([19.25, 29.78, 15.  , 36.07, 38.84, 25.63, 45.28, 15.19, 16.9 ,\n",
       "        29.6 , 15.44, 39.37, 15.52, 17.1 , 15.2 , 16.9 , 11.73, 18.03,\n",
       "        27.89, 11.74, 29.34, 17.36, 34.25, 18.36, 18.22, 17.32, 14.2 ,\n",
       "        25.87, 28.61, 48.03, 15.14, 28.95, 16.61, 45.48, 15.27, 26.53,\n",
       "        15.59, 34.07, 19.24, 13.21, 31.06, 41.86, 35.39, 30.65, 14.27,\n",
       "        30.08, 33.91, 17.23, 14.89, 14.92, 24.93, 29.58, 15.1 , 13.65,\n",
       "        34.18, 20.46, 34.05, 21.33, 36.87, 13.7 , 38.48, 16.7 , 34.17,\n",
       "        43.3 , 29.43, 30.02, 29.28, 41.68, 26.08, 15.77, 16.43, 35.22,\n",
       "        17.21, 22.09, 37.41, 14.28, 36.16, 43.86, 14.76, 32.96, 16.44,\n",
       "        27.76, 29.92, 37.58, 32.35, 15.78, 27.25, 13.59, 30.11, 19.18,\n",
       "        16.14, 19.32, 20.01, 21.33, 15.38, 29.52, 15.28, 37.73, 36.87,\n",
       "        35.1 , 16.03, 36.93, 26.44, 20.43, 14.24, 17.39, 15.3 , 45.52,\n",
       "        25.02, 18.14, 15.85, 14.12, 44.16, 20.72, 31.01, 32.  , 39.48,\n",
       "        31.28, 16.75, 14.18, 16.  , 32.46, 26.3 , 34.94, 14.75, 17.22,\n",
       "        27.84, 16.17, 15.32, 32.88, 14.73, 30.2 , 11.29, 13.6 , 13.32,\n",
       "        16.5 , 15.22, 14.11, 11.72, 30.17, 20.21, 13.83, 30.93, 31.14,\n",
       "        31.53, 29.59, 28.61, 27.57, 23.77, 34.11, 17.64, 17.51, 13.36,\n",
       "        16.8 , 31.71, 16.63, 10.94, 15.85, 18.24, 33.78, 14.61, 12.07,\n",
       "        15.59, 14.5 , 39.41, 37.72, 26.96, 17.25, 19.37, 15.66, 37.01,\n",
       "        29.44, 13.87, 17.15, 17.2 , 25.91, 16.9 , 38.33, 46.94, 32.78,\n",
       "        16.7 , 10.9 , 40.85, 16.27, 18.1 , 29.79, 15.83, 18.36, 15.8 ,\n",
       "        24.93, 23.49, 15.35, 19.29, 13.72, 14.3 , 20.03, 34.73, 13.46,\n",
       "        30.91, 16.54, 34.33, 15.48, 17.37, 14.67, 14.27, 12.08, 30.66,\n",
       "        27.69, 43.8 , 14.87, 32.04, 17.  , 33.24, 22.72, 43.33, 14.03,\n",
       "        15.8 , 25.9 , 29.69, 44.87, 32.83, 21.54, 21.54, 15.64, 31.9 ,\n",
       "        36.2 , 14.72, 14.26, 16.26, 34.29, 33.34, 16.62, 21.33, 14.45,\n",
       "        35.29, 13.99, 16.06, 25.16, 19.43, 21.72, 14.74, 13.71, 13.39,\n",
       "        14.27, 22.53, 28.31, 29.45, 36.35, 15.89, 32.92, 14.55, 38.56,\n",
       "        30.19, 39.7 , 16.39, 14.94, 14.28, 36.1 , 33.94, 33.87, 13.79,\n",
       "        38.18, 37.2 , 14.24, 14.92, 13.72, 34.62, 35.48, 31.27, 16.35,\n",
       "        25.35, 39.55, 26.14, 17.01, 13.7 , 37.79, 36.21, 36.26, 29.36,\n",
       "        32.54, 11.19, 14.14, 28.51, 29.77, 24.61, 14.38, 15.81, 17.23,\n",
       "        13.5 , 15.23, 41.26, 29.43, 21.33, 32.83, 30.  , 39.66, 37.51,\n",
       "        13.72, 20.28, 17.02, 13.54, 32.93, 17.63, 14.97, 15.53, 26.29,\n",
       "        14.71, 22.73, 14.49, 33.54, 13.75, 14.83, 30.12, 17.03, 14.58,\n",
       "        28.43, 29.79, 17.2 , 25.72, 13.48, 14.6 , 17.11, 19.32, 28.88,\n",
       "        27.33, 34.43, 29.9 , 15.32, 16.85, 24.91, 15.47, 17.79, 21.19,\n",
       "        29.13, 14.67, 40.36, 36.44, 19.29, 36.62, 27.93, 18.15, 16.88,\n",
       "        14.21, 14.74, 25.64, 16.94, 42.86, 27.87, 37.45, 12.05, 43.66,\n",
       "        32.43, 32.95, 26.37, 30.1 , 16.99, 14.96, 15.19, 13.79, 30.19,\n",
       "        32.71, 15.63, 14.73, 29.64, 33.31, 25.6 , 40.66, 14.27, 16.88,\n",
       "        16.67, 33.37, 30.12, 15.31, 36.73, 12.4 , 17.1 , 34.25, 20.37,\n",
       "        14.89, 14.44, 33.23, 14.94, 19.37, 19.87, 40.63, 19.48, 16.61,\n",
       "        19.34, 16.74, 32.85, 16.99, 13.44, 14.86, 26.18, 28.27, 16.22,\n",
       "        33.64, 16.79, 17.86, 30.18, 33.98, 35.99, 13.79, 15.87, 29.61,\n",
       "        20.82, 36.81, 17.85, 37.26, 16.86, 16.74, 37.28, 29.69, 40.47,\n",
       "        15.07, 33.17, 22.72, 33.84, 27.54, 32.12, 16.03, 45.59, 28.2 ,\n",
       "        39.8 , 33.98, 19.26, 38.35, 20.56, 15.03, 24.8 , 37.76, 14.81,\n",
       "        37.35, 36.85, 16.1 , 34.11, 12.14, 13.36, 17.27, 14.15, 33.52,\n",
       "        38.23, 40.1 , 11.27, 25.11, 31.39, 32.92, 17.2 , 25.84, 22.25,\n",
       "        43.73, 27.17, 16.78, 29.82, 33.67, 22.49, 19.42, 28.02, 27.34,\n",
       "        47.01, 26.18, 24.77, 31.  , 19.9 , 16.57, 16.69, 20.78, 29.4 ,\n",
       "        33.04, 14.57, 14.47, 16.93, 37.58, 34.48, 25.81, 17.04, 19.3 ,\n",
       "        13.2 , 25.82, 17.2 , 13.43, 33.01, 37.28, 33.85, 35.56, 39.92,\n",
       "        39.07, 28.28, 43.14, 21.4 , 21.53, 29.67, 28.99, 40.99, 17.12,\n",
       "        34.19, 37.19, 29.79, 14.86, 33.14, 14.65, 15.14, 14.61, 14.3 ,\n",
       "        37.81, 16.99, 33.87, 32.28, 25.68, 30.53, 32.54, 11.67, 34.07,\n",
       "        16.56, 27.4 , 16.79, 19.24, 15.95, 38.17, 15.84, 28.14, 14.35,\n",
       "        34.52, 20.29, 29.68, 27.31, 34.14, 32.25, 28.38, 13.65, 15.4 ,\n",
       "        29.88, 26.02, 13.2 , 17.89, 27.3 , 34.35, 14.61, 17.74, 33.82,\n",
       "        33.95, 33.89, 15.  , 29.49, 34.15, 31.76, 15.5 , 16.58, 14.23,\n",
       "        33.88, 30.89, 14.49, 15.17, 33.62, 13.49, 43.12, 15.39, 12.22,\n",
       "        26.13, 40.77, 33.06, 45.97, 29.77, 19.34, 15.57, 33.93, 36.86,\n",
       "        12.4 , 16.11, 38.79, 15.42, 39.28, 18.14, 20.13, 37.29, 19.76,\n",
       "        37.7 , 16.87, 29.79, 13.8 , 28.68, 37.27, 34.01, 25.88, 32.83,\n",
       "        13.57, 21.93, 32.77, 14.54, 35.62, 14.39, 15.95, 29.56, 14.09,\n",
       "        16.08, 14.48, 36.12, 19.23, 41.07, 19.18, 34.7 , 18.06, 38.35,\n",
       "        17.25, 14.37, 19.65, 15.77, 15.85, 26.41, 35.1 , 31.6 , 36.26,\n",
       "        33.91, 45.13]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def format_output(data):\n",
    "    y1 = data.pop('Y1')\n",
    "    y1 = np.array(y1)\n",
    "    y2 = data.pop('Y2')\n",
    "    y2 = np.array(y2)\n",
    "    return y1, y2\n",
    "\n",
    "train_Y = format_output(train)\n",
    "test_Y = format_output(test)\n",
    "\n",
    "train_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30c6b99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the training and test data\n",
    "norm_train_X = norm(train)\n",
    "norm_test_X = norm(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36cdebc9",
   "metadata": {},
   "source": [
    "# Build the Model\n",
    "\n",
    "Here is how we'll build the model using the functional syntax. Notice that we can specify a list of outputs (i.e. `[y1_output, y2_output]`) when we instantiate the `Model()` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "582f74aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train .columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a8f6e121",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model layers.\n",
    "input_layer = Input(shape=(len(train .columns),))\n",
    "\n",
    "first_dense = Dense(units='128', activation='relu')(input_layer)\n",
    "\n",
    "second_dense = Dense(units='128', activation='relu')(first_dense)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3d6caf52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y1 output will be fed directly from the second dense\n",
    "y1_output = Dense (units='1', name = 'y1_output')(second_dense)\n",
    "third_dense = Dense(units = '64', activation = 'relu')(second_dense)\n",
    "\n",
    "# Y2 output will come via the third dense\n",
    "y2_output = Dense(units='1', name='y2_output')(third_dense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17ef463f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the model with the input layer and a list of output layers\n",
    "model = Model(inputs=input_layer, outputs=[y1_output, y2_output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b62185ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 8)]          0           []                               \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 128)          1152        ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 128)          16512       ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 64)           8256        ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " y1_output (Dense)              (None, 1)            129         ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " y2_output (Dense)              (None, 1)            65          ['dense_2[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 26,114\n",
      "Trainable params: 26,114\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef125ab4",
   "metadata": {},
   "source": [
    "# Configure parameters\n",
    "\n",
    "We specify the optimizer as well as the loss and metrics for each output.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "306300e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pooya\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\keras\\optimizers\\optimizer_v2\\gradient_descent.py:111: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Specify the optimizer, and compile the model with loss functions for both outputs\n",
    "optimizer = tf.keras.optimizers.SGD(lr=0.001)\n",
    "\n",
    "model.compile(optimizer = optimizer,\n",
    "              loss={'y1_output': 'mse', 'y2_output': 'mse'},\n",
    "              metrics = {'y1_output': tf.keras.metrics.RootMeanSquaredError(),\n",
    "                         'y2_output': tf.keras.metrics.RootMeanSquaredError()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "713c5709",
   "metadata": {},
   "source": [
    "# Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "70bea363",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 2.8052 - y1_output_loss: 0.6166 - y2_output_loss: 2.1885 - y1_output_root_mean_squared_error: 0.7853 - y2_output_root_mean_squared_error: 1.4794 - val_loss: 10.1932 - val_y1_output_loss: 3.3884 - val_y2_output_loss: 6.8048 - val_y1_output_root_mean_squared_error: 1.8408 - val_y2_output_root_mean_squared_error: 2.6086\n",
      "Epoch 2/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 4.2604 - y1_output_loss: 0.9991 - y2_output_loss: 3.2613 - y1_output_root_mean_squared_error: 0.9996 - y2_output_root_mean_squared_error: 1.8059 - val_loss: 2.8519 - val_y1_output_loss: 0.7189 - val_y2_output_loss: 2.1330 - val_y1_output_root_mean_squared_error: 0.8479 - val_y2_output_root_mean_squared_error: 1.4605\n",
      "Epoch 3/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 4.6957 - y1_output_loss: 1.0653 - y2_output_loss: 3.6304 - y1_output_root_mean_squared_error: 1.0321 - y2_output_root_mean_squared_error: 1.9054 - val_loss: 6.7254 - val_y1_output_loss: 2.6110 - val_y2_output_loss: 4.1144 - val_y1_output_root_mean_squared_error: 1.6159 - val_y2_output_root_mean_squared_error: 2.0284\n",
      "Epoch 4/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 3.5313 - y1_output_loss: 0.8061 - y2_output_loss: 2.7253 - y1_output_root_mean_squared_error: 0.8978 - y2_output_root_mean_squared_error: 1.6508 - val_loss: 8.3882 - val_y1_output_loss: 1.8301 - val_y2_output_loss: 6.5581 - val_y1_output_root_mean_squared_error: 1.3528 - val_y2_output_root_mean_squared_error: 2.5609\n",
      "Epoch 5/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 3.8149 - y1_output_loss: 0.8107 - y2_output_loss: 3.0042 - y1_output_root_mean_squared_error: 0.9004 - y2_output_root_mean_squared_error: 1.7332 - val_loss: 11.9092 - val_y1_output_loss: 1.8355 - val_y2_output_loss: 10.0737 - val_y1_output_root_mean_squared_error: 1.3548 - val_y2_output_root_mean_squared_error: 3.1739\n",
      "Epoch 6/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 7.1317 - y1_output_loss: 2.0872 - y2_output_loss: 5.0446 - y1_output_root_mean_squared_error: 1.4447 - y2_output_root_mean_squared_error: 2.2460 - val_loss: 3.1599 - val_y1_output_loss: 0.7484 - val_y2_output_loss: 2.4115 - val_y1_output_root_mean_squared_error: 0.8651 - val_y2_output_root_mean_squared_error: 1.5529\n",
      "Epoch 7/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 2.6436 - y1_output_loss: 0.5771 - y2_output_loss: 2.0664 - y1_output_root_mean_squared_error: 0.7597 - y2_output_root_mean_squared_error: 1.4375 - val_loss: 2.7887 - val_y1_output_loss: 0.7887 - val_y2_output_loss: 2.0000 - val_y1_output_root_mean_squared_error: 0.8881 - val_y2_output_root_mean_squared_error: 1.4142\n",
      "Epoch 8/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 3.4557 - y1_output_loss: 0.7176 - y2_output_loss: 2.7381 - y1_output_root_mean_squared_error: 0.8471 - y2_output_root_mean_squared_error: 1.6547 - val_loss: 3.2627 - val_y1_output_loss: 1.3689 - val_y2_output_loss: 1.8938 - val_y1_output_root_mean_squared_error: 1.1700 - val_y2_output_root_mean_squared_error: 1.3762\n",
      "Epoch 9/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 4.0569 - y1_output_loss: 0.8767 - y2_output_loss: 3.1801 - y1_output_root_mean_squared_error: 0.9363 - y2_output_root_mean_squared_error: 1.7833 - val_loss: 5.5891 - val_y1_output_loss: 1.5876 - val_y2_output_loss: 4.0015 - val_y1_output_root_mean_squared_error: 1.2600 - val_y2_output_root_mean_squared_error: 2.0004\n",
      "Epoch 10/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 10.8719 - y1_output_loss: 3.7866 - y2_output_loss: 7.0853 - y1_output_root_mean_squared_error: 1.9459 - y2_output_root_mean_squared_error: 2.6618 - val_loss: 8.0641 - val_y1_output_loss: 1.6320 - val_y2_output_loss: 6.4321 - val_y1_output_root_mean_squared_error: 1.2775 - val_y2_output_root_mean_squared_error: 2.5362\n",
      "Epoch 11/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 3.3138 - y1_output_loss: 0.8128 - y2_output_loss: 2.5010 - y1_output_root_mean_squared_error: 0.9016 - y2_output_root_mean_squared_error: 1.5815 - val_loss: 2.7892 - val_y1_output_loss: 0.6748 - val_y2_output_loss: 2.1144 - val_y1_output_root_mean_squared_error: 0.8215 - val_y2_output_root_mean_squared_error: 1.4541\n",
      "Epoch 12/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 3.1117 - y1_output_loss: 0.7016 - y2_output_loss: 2.4102 - y1_output_root_mean_squared_error: 0.8376 - y2_output_root_mean_squared_error: 1.5525 - val_loss: 4.5978 - val_y1_output_loss: 0.8620 - val_y2_output_loss: 3.7358 - val_y1_output_root_mean_squared_error: 0.9285 - val_y2_output_root_mean_squared_error: 1.9328\n",
      "Epoch 13/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 3.6828 - y1_output_loss: 0.7967 - y2_output_loss: 2.8860 - y1_output_root_mean_squared_error: 0.8926 - y2_output_root_mean_squared_error: 1.6988 - val_loss: 3.6466 - val_y1_output_loss: 0.8347 - val_y2_output_loss: 2.8119 - val_y1_output_root_mean_squared_error: 0.9136 - val_y2_output_root_mean_squared_error: 1.6769\n",
      "Epoch 14/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 2.5167 - y1_output_loss: 0.5480 - y2_output_loss: 1.9687 - y1_output_root_mean_squared_error: 0.7403 - y2_output_root_mean_squared_error: 1.4031 - val_loss: 2.4924 - val_y1_output_loss: 0.6357 - val_y2_output_loss: 1.8567 - val_y1_output_root_mean_squared_error: 0.7973 - val_y2_output_root_mean_squared_error: 1.3626\n",
      "Epoch 15/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.3317 - y1_output_loss: 0.4165 - y2_output_loss: 1.9152 - y1_output_root_mean_squared_error: 0.6454 - y2_output_root_mean_squared_error: 1.3839 - val_loss: 3.1567 - val_y1_output_loss: 1.0261 - val_y2_output_loss: 2.1305 - val_y1_output_root_mean_squared_error: 1.0130 - val_y2_output_root_mean_squared_error: 1.4596\n",
      "Epoch 16/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.6646 - y1_output_loss: 0.4837 - y2_output_loss: 2.1809 - y1_output_root_mean_squared_error: 0.6955 - y2_output_root_mean_squared_error: 1.4768 - val_loss: 2.9029 - val_y1_output_loss: 1.0312 - val_y2_output_loss: 1.8717 - val_y1_output_root_mean_squared_error: 1.0155 - val_y2_output_root_mean_squared_error: 1.3681\n",
      "Epoch 17/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.5823 - y1_output_loss: 0.5125 - y2_output_loss: 2.0699 - y1_output_root_mean_squared_error: 0.7159 - y2_output_root_mean_squared_error: 1.4387 - val_loss: 2.7613 - val_y1_output_loss: 0.5289 - val_y2_output_loss: 2.2324 - val_y1_output_root_mean_squared_error: 0.7272 - val_y2_output_root_mean_squared_error: 1.4941\n",
      "Epoch 18/100\n",
      "62/62 [==============================] - 1s 11ms/step - loss: 2.5830 - y1_output_loss: 0.5703 - y2_output_loss: 2.0127 - y1_output_root_mean_squared_error: 0.7552 - y2_output_root_mean_squared_error: 1.4187 - val_loss: 2.4137 - val_y1_output_loss: 0.6004 - val_y2_output_loss: 1.8133 - val_y1_output_root_mean_squared_error: 0.7749 - val_y2_output_root_mean_squared_error: 1.3466\n",
      "Epoch 19/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 3.5837 - y1_output_loss: 0.8127 - y2_output_loss: 2.7711 - y1_output_root_mean_squared_error: 0.9015 - y2_output_root_mean_squared_error: 1.6647 - val_loss: 2.6779 - val_y1_output_loss: 0.6029 - val_y2_output_loss: 2.0750 - val_y1_output_root_mean_squared_error: 0.7765 - val_y2_output_root_mean_squared_error: 1.4405\n",
      "Epoch 20/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 3.1376 - y1_output_loss: 0.5871 - y2_output_loss: 2.5505 - y1_output_root_mean_squared_error: 0.7662 - y2_output_root_mean_squared_error: 1.5970 - val_loss: 10.3867 - val_y1_output_loss: 3.2277 - val_y2_output_loss: 7.1590 - val_y1_output_root_mean_squared_error: 1.7966 - val_y2_output_root_mean_squared_error: 2.6756\n",
      "Epoch 21/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 3.4121 - y1_output_loss: 0.7290 - y2_output_loss: 2.6831 - y1_output_root_mean_squared_error: 0.8538 - y2_output_root_mean_squared_error: 1.6380 - val_loss: 4.1447 - val_y1_output_loss: 0.8502 - val_y2_output_loss: 3.2945 - val_y1_output_root_mean_squared_error: 0.9221 - val_y2_output_root_mean_squared_error: 1.8151\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 22/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.5813 - y1_output_loss: 0.5002 - y2_output_loss: 2.0811 - y1_output_root_mean_squared_error: 0.7072 - y2_output_root_mean_squared_error: 1.4426 - val_loss: 2.6396 - val_y1_output_loss: 0.7509 - val_y2_output_loss: 1.8887 - val_y1_output_root_mean_squared_error: 0.8665 - val_y2_output_root_mean_squared_error: 1.3743\n",
      "Epoch 23/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.2616 - y1_output_loss: 0.4067 - y2_output_loss: 1.8549 - y1_output_root_mean_squared_error: 0.6377 - y2_output_root_mean_squared_error: 1.3619 - val_loss: 3.0330 - val_y1_output_loss: 0.6729 - val_y2_output_loss: 2.3601 - val_y1_output_root_mean_squared_error: 0.8203 - val_y2_output_root_mean_squared_error: 1.5363\n",
      "Epoch 24/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.0867 - y1_output_loss: 0.4246 - y2_output_loss: 1.6621 - y1_output_root_mean_squared_error: 0.6516 - y2_output_root_mean_squared_error: 1.2892 - val_loss: 2.9219 - val_y1_output_loss: 0.8073 - val_y2_output_loss: 2.1147 - val_y1_output_root_mean_squared_error: 0.8985 - val_y2_output_root_mean_squared_error: 1.4542\n",
      "Epoch 25/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.8044 - y1_output_loss: 0.5080 - y2_output_loss: 2.2964 - y1_output_root_mean_squared_error: 0.7127 - y2_output_root_mean_squared_error: 1.5154 - val_loss: 4.1953 - val_y1_output_loss: 1.1245 - val_y2_output_loss: 3.0708 - val_y1_output_root_mean_squared_error: 1.0604 - val_y2_output_root_mean_squared_error: 1.7524\n",
      "Epoch 26/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.2758 - y1_output_loss: 0.4496 - y2_output_loss: 1.8262 - y1_output_root_mean_squared_error: 0.6705 - y2_output_root_mean_squared_error: 1.3514 - val_loss: 2.2876 - val_y1_output_loss: 0.5423 - val_y2_output_loss: 1.7453 - val_y1_output_root_mean_squared_error: 0.7364 - val_y2_output_root_mean_squared_error: 1.3211\n",
      "Epoch 27/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.6865 - y1_output_loss: 0.4693 - y2_output_loss: 2.2172 - y1_output_root_mean_squared_error: 0.6851 - y2_output_root_mean_squared_error: 1.4890 - val_loss: 2.9600 - val_y1_output_loss: 0.7730 - val_y2_output_loss: 2.1870 - val_y1_output_root_mean_squared_error: 0.8792 - val_y2_output_root_mean_squared_error: 1.4788\n",
      "Epoch 28/100\n",
      "62/62 [==============================] - 1s 10ms/step - loss: 2.5151 - y1_output_loss: 0.4605 - y2_output_loss: 2.0546 - y1_output_root_mean_squared_error: 0.6786 - y2_output_root_mean_squared_error: 1.4334 - val_loss: 2.3020 - val_y1_output_loss: 0.5755 - val_y2_output_loss: 1.7266 - val_y1_output_root_mean_squared_error: 0.7586 - val_y2_output_root_mean_squared_error: 1.3140\n",
      "Epoch 29/100\n",
      "62/62 [==============================] - 1s 18ms/step - loss: 2.8053 - y1_output_loss: 0.5201 - y2_output_loss: 2.2852 - y1_output_root_mean_squared_error: 0.7212 - y2_output_root_mean_squared_error: 1.5117 - val_loss: 3.1720 - val_y1_output_loss: 0.9689 - val_y2_output_loss: 2.2031 - val_y1_output_root_mean_squared_error: 0.9843 - val_y2_output_root_mean_squared_error: 1.4843\n",
      "Epoch 30/100\n",
      "62/62 [==============================] - 1s 20ms/step - loss: 2.9879 - y1_output_loss: 0.5929 - y2_output_loss: 2.3949 - y1_output_root_mean_squared_error: 0.7700 - y2_output_root_mean_squared_error: 1.5476 - val_loss: 3.4671 - val_y1_output_loss: 0.7147 - val_y2_output_loss: 2.7524 - val_y1_output_root_mean_squared_error: 0.8454 - val_y2_output_root_mean_squared_error: 1.6590\n",
      "Epoch 31/100\n",
      "62/62 [==============================] - 2s 26ms/step - loss: 3.0029 - y1_output_loss: 0.6706 - y2_output_loss: 2.3323 - y1_output_root_mean_squared_error: 0.8189 - y2_output_root_mean_squared_error: 1.5272 - val_loss: 2.3619 - val_y1_output_loss: 0.6157 - val_y2_output_loss: 1.7462 - val_y1_output_root_mean_squared_error: 0.7847 - val_y2_output_root_mean_squared_error: 1.3214\n",
      "Epoch 32/100\n",
      "62/62 [==============================] - 2s 31ms/step - loss: 3.1315 - y1_output_loss: 0.7024 - y2_output_loss: 2.4292 - y1_output_root_mean_squared_error: 0.8381 - y2_output_root_mean_squared_error: 1.5586 - val_loss: 9.2406 - val_y1_output_loss: 1.6332 - val_y2_output_loss: 7.6074 - val_y1_output_root_mean_squared_error: 1.2780 - val_y2_output_root_mean_squared_error: 2.7582\n",
      "Epoch 33/100\n",
      "62/62 [==============================] - 1s 16ms/step - loss: 2.6250 - y1_output_loss: 0.5646 - y2_output_loss: 2.0604 - y1_output_root_mean_squared_error: 0.7514 - y2_output_root_mean_squared_error: 1.4354 - val_loss: 5.2602 - val_y1_output_loss: 1.4520 - val_y2_output_loss: 3.8082 - val_y1_output_root_mean_squared_error: 1.2050 - val_y2_output_root_mean_squared_error: 1.9515\n",
      "Epoch 34/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.8156 - y1_output_loss: 0.6061 - y2_output_loss: 2.2095 - y1_output_root_mean_squared_error: 0.7785 - y2_output_root_mean_squared_error: 1.4864 - val_loss: 5.2687 - val_y1_output_loss: 0.9246 - val_y2_output_loss: 4.3442 - val_y1_output_root_mean_squared_error: 0.9615 - val_y2_output_root_mean_squared_error: 2.0843\n",
      "Epoch 35/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 3.4607 - y1_output_loss: 0.7817 - y2_output_loss: 2.6790 - y1_output_root_mean_squared_error: 0.8841 - y2_output_root_mean_squared_error: 1.6368 - val_loss: 2.9343 - val_y1_output_loss: 0.8578 - val_y2_output_loss: 2.0765 - val_y1_output_root_mean_squared_error: 0.9262 - val_y2_output_root_mean_squared_error: 1.4410\n",
      "Epoch 36/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.0943 - y1_output_loss: 0.3873 - y2_output_loss: 1.7069 - y1_output_root_mean_squared_error: 0.6223 - y2_output_root_mean_squared_error: 1.3065 - val_loss: 7.7049 - val_y1_output_loss: 0.8916 - val_y2_output_loss: 6.8133 - val_y1_output_root_mean_squared_error: 0.9443 - val_y2_output_root_mean_squared_error: 2.6102\n",
      "Epoch 37/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.3278 - y1_output_loss: 0.4041 - y2_output_loss: 1.9237 - y1_output_root_mean_squared_error: 0.6357 - y2_output_root_mean_squared_error: 1.3870 - val_loss: 3.7281 - val_y1_output_loss: 1.1066 - val_y2_output_loss: 2.6215 - val_y1_output_root_mean_squared_error: 1.0519 - val_y2_output_root_mean_squared_error: 1.6191\n",
      "Epoch 38/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.0064 - y1_output_loss: 0.4533 - y2_output_loss: 1.5531 - y1_output_root_mean_squared_error: 0.6733 - y2_output_root_mean_squared_error: 1.2462 - val_loss: 2.6216 - val_y1_output_loss: 0.6558 - val_y2_output_loss: 1.9658 - val_y1_output_root_mean_squared_error: 0.8098 - val_y2_output_root_mean_squared_error: 1.4021\n",
      "Epoch 39/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 2.1984 - y1_output_loss: 0.4384 - y2_output_loss: 1.7600 - y1_output_root_mean_squared_error: 0.6621 - y2_output_root_mean_squared_error: 1.3266 - val_loss: 2.3222 - val_y1_output_loss: 0.6437 - val_y2_output_loss: 1.6785 - val_y1_output_root_mean_squared_error: 0.8023 - val_y2_output_root_mean_squared_error: 1.2956\n",
      "Epoch 40/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 2.9498 - y1_output_loss: 0.5652 - y2_output_loss: 2.3847 - y1_output_root_mean_squared_error: 0.7518 - y2_output_root_mean_squared_error: 1.5442 - val_loss: 3.3630 - val_y1_output_loss: 1.0680 - val_y2_output_loss: 2.2950 - val_y1_output_root_mean_squared_error: 1.0335 - val_y2_output_root_mean_squared_error: 1.5149\n",
      "Epoch 41/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 2.3223 - y1_output_loss: 0.4583 - y2_output_loss: 1.8639 - y1_output_root_mean_squared_error: 0.6770 - y2_output_root_mean_squared_error: 1.3653 - val_loss: 5.0431 - val_y1_output_loss: 1.2948 - val_y2_output_loss: 3.7483 - val_y1_output_root_mean_squared_error: 1.1379 - val_y2_output_root_mean_squared_error: 1.9360\n",
      "Epoch 42/100\n",
      "62/62 [==============================] - 1s 22ms/step - loss: 2.4865 - y1_output_loss: 0.5297 - y2_output_loss: 1.9569 - y1_output_root_mean_squared_error: 0.7278 - y2_output_root_mean_squared_error: 1.3989 - val_loss: 5.6951 - val_y1_output_loss: 1.4474 - val_y2_output_loss: 4.2477 - val_y1_output_root_mean_squared_error: 1.2031 - val_y2_output_root_mean_squared_error: 2.0610\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 43/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.9980 - y1_output_loss: 0.4231 - y2_output_loss: 1.5749 - y1_output_root_mean_squared_error: 0.6505 - y2_output_root_mean_squared_error: 1.2550 - val_loss: 7.8669 - val_y1_output_loss: 1.3160 - val_y2_output_loss: 6.5508 - val_y1_output_root_mean_squared_error: 1.1472 - val_y2_output_root_mean_squared_error: 2.5595\n",
      "Epoch 44/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 2.7689 - y1_output_loss: 0.5326 - y2_output_loss: 2.2363 - y1_output_root_mean_squared_error: 0.7298 - y2_output_root_mean_squared_error: 1.4954 - val_loss: 6.8162 - val_y1_output_loss: 1.1326 - val_y2_output_loss: 5.6835 - val_y1_output_root_mean_squared_error: 1.0643 - val_y2_output_root_mean_squared_error: 2.3840\n",
      "Epoch 45/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.4068 - y1_output_loss: 0.4855 - y2_output_loss: 1.9213 - y1_output_root_mean_squared_error: 0.6968 - y2_output_root_mean_squared_error: 1.3861 - val_loss: 2.4403 - val_y1_output_loss: 0.7619 - val_y2_output_loss: 1.6784 - val_y1_output_root_mean_squared_error: 0.8729 - val_y2_output_root_mean_squared_error: 1.2955\n",
      "Epoch 46/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.8608 - y1_output_loss: 0.5525 - y2_output_loss: 2.3083 - y1_output_root_mean_squared_error: 0.7433 - y2_output_root_mean_squared_error: 1.5193 - val_loss: 2.3238 - val_y1_output_loss: 0.7523 - val_y2_output_loss: 1.5714 - val_y1_output_root_mean_squared_error: 0.8674 - val_y2_output_root_mean_squared_error: 1.2536\n",
      "Epoch 47/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.2118 - y1_output_loss: 0.4196 - y2_output_loss: 1.7922 - y1_output_root_mean_squared_error: 0.6478 - y2_output_root_mean_squared_error: 1.3387 - val_loss: 3.7260 - val_y1_output_loss: 0.9070 - val_y2_output_loss: 2.8190 - val_y1_output_root_mean_squared_error: 0.9523 - val_y2_output_root_mean_squared_error: 1.6790\n",
      "Epoch 48/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.6748 - y1_output_loss: 0.5145 - y2_output_loss: 2.1602 - y1_output_root_mean_squared_error: 0.7173 - y2_output_root_mean_squared_error: 1.4698 - val_loss: 2.0230 - val_y1_output_loss: 0.5689 - val_y2_output_loss: 1.4541 - val_y1_output_root_mean_squared_error: 0.7542 - val_y2_output_root_mean_squared_error: 1.2059\n",
      "Epoch 49/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.0756 - y1_output_loss: 0.4138 - y2_output_loss: 1.6618 - y1_output_root_mean_squared_error: 0.6432 - y2_output_root_mean_squared_error: 1.2891 - val_loss: 4.0261 - val_y1_output_loss: 0.5943 - val_y2_output_loss: 3.4317 - val_y1_output_root_mean_squared_error: 0.7709 - val_y2_output_root_mean_squared_error: 1.8525\n",
      "Epoch 50/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 3.0210 - y1_output_loss: 0.5835 - y2_output_loss: 2.4375 - y1_output_root_mean_squared_error: 0.7639 - y2_output_root_mean_squared_error: 1.5612 - val_loss: 1.9947 - val_y1_output_loss: 0.6082 - val_y2_output_loss: 1.3865 - val_y1_output_root_mean_squared_error: 0.7799 - val_y2_output_root_mean_squared_error: 1.1775\n",
      "Epoch 51/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.2806 - y1_output_loss: 0.4737 - y2_output_loss: 1.8069 - y1_output_root_mean_squared_error: 0.6883 - y2_output_root_mean_squared_error: 1.3442 - val_loss: 2.3911 - val_y1_output_loss: 0.4930 - val_y2_output_loss: 1.8981 - val_y1_output_root_mean_squared_error: 0.7021 - val_y2_output_root_mean_squared_error: 1.3777\n",
      "Epoch 52/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.3225 - y1_output_loss: 0.5254 - y2_output_loss: 1.7971 - y1_output_root_mean_squared_error: 0.7248 - y2_output_root_mean_squared_error: 1.3406 - val_loss: 2.1687 - val_y1_output_loss: 0.6330 - val_y2_output_loss: 1.5357 - val_y1_output_root_mean_squared_error: 0.7956 - val_y2_output_root_mean_squared_error: 1.2392\n",
      "Epoch 53/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.8837 - y1_output_loss: 0.3605 - y2_output_loss: 1.5232 - y1_output_root_mean_squared_error: 0.6004 - y2_output_root_mean_squared_error: 1.2342 - val_loss: 10.2146 - val_y1_output_loss: 2.7358 - val_y2_output_loss: 7.4789 - val_y1_output_root_mean_squared_error: 1.6540 - val_y2_output_root_mean_squared_error: 2.7347\n",
      "Epoch 54/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.1060 - y1_output_loss: 0.4373 - y2_output_loss: 1.6686 - y1_output_root_mean_squared_error: 0.6613 - y2_output_root_mean_squared_error: 1.2918 - val_loss: 3.9284 - val_y1_output_loss: 0.6961 - val_y2_output_loss: 3.2323 - val_y1_output_root_mean_squared_error: 0.8343 - val_y2_output_root_mean_squared_error: 1.7979\n",
      "Epoch 55/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 2.3565 - y1_output_loss: 0.4881 - y2_output_loss: 1.8684 - y1_output_root_mean_squared_error: 0.6986 - y2_output_root_mean_squared_error: 1.3669 - val_loss: 2.4148 - val_y1_output_loss: 0.6540 - val_y2_output_loss: 1.7607 - val_y1_output_root_mean_squared_error: 0.8087 - val_y2_output_root_mean_squared_error: 1.3269\n",
      "Epoch 56/100\n",
      "62/62 [==============================] - 1s 16ms/step - loss: 1.8996 - y1_output_loss: 0.3978 - y2_output_loss: 1.5017 - y1_output_root_mean_squared_error: 0.6307 - y2_output_root_mean_squared_error: 1.2255 - val_loss: 2.2977 - val_y1_output_loss: 0.6366 - val_y2_output_loss: 1.6611 - val_y1_output_root_mean_squared_error: 0.7979 - val_y2_output_root_mean_squared_error: 1.2888\n",
      "Epoch 57/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 1.7718 - y1_output_loss: 0.3012 - y2_output_loss: 1.4706 - y1_output_root_mean_squared_error: 0.5488 - y2_output_root_mean_squared_error: 1.2127 - val_loss: 2.9527 - val_y1_output_loss: 0.8722 - val_y2_output_loss: 2.0805 - val_y1_output_root_mean_squared_error: 0.9339 - val_y2_output_root_mean_squared_error: 1.4424\n",
      "Epoch 58/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.9860 - y1_output_loss: 0.3959 - y2_output_loss: 1.5901 - y1_output_root_mean_squared_error: 0.6292 - y2_output_root_mean_squared_error: 1.2610 - val_loss: 1.9463 - val_y1_output_loss: 0.5510 - val_y2_output_loss: 1.3953 - val_y1_output_root_mean_squared_error: 0.7423 - val_y2_output_root_mean_squared_error: 1.1812\n",
      "Epoch 59/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.0456 - y1_output_loss: 0.3967 - y2_output_loss: 1.6489 - y1_output_root_mean_squared_error: 0.6298 - y2_output_root_mean_squared_error: 1.2841 - val_loss: 2.0879 - val_y1_output_loss: 0.4846 - val_y2_output_loss: 1.6033 - val_y1_output_root_mean_squared_error: 0.6961 - val_y2_output_root_mean_squared_error: 1.2662\n",
      "Epoch 60/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 2.2931 - y1_output_loss: 0.4721 - y2_output_loss: 1.8211 - y1_output_root_mean_squared_error: 0.6871 - y2_output_root_mean_squared_error: 1.3495 - val_loss: 5.2533 - val_y1_output_loss: 0.9805 - val_y2_output_loss: 4.2729 - val_y1_output_root_mean_squared_error: 0.9902 - val_y2_output_root_mean_squared_error: 2.0671\n",
      "Epoch 61/100\n",
      "62/62 [==============================] - 2s 24ms/step - loss: 2.3845 - y1_output_loss: 0.4441 - y2_output_loss: 1.9404 - y1_output_root_mean_squared_error: 0.6664 - y2_output_root_mean_squared_error: 1.3930 - val_loss: 3.4540 - val_y1_output_loss: 0.9353 - val_y2_output_loss: 2.5187 - val_y1_output_root_mean_squared_error: 0.9671 - val_y2_output_root_mean_squared_error: 1.5870\n",
      "Epoch 62/100\n",
      "62/62 [==============================] - 2s 26ms/step - loss: 2.9148 - y1_output_loss: 0.5332 - y2_output_loss: 2.3816 - y1_output_root_mean_squared_error: 0.7302 - y2_output_root_mean_squared_error: 1.5432 - val_loss: 2.0972 - val_y1_output_loss: 0.5521 - val_y2_output_loss: 1.5451 - val_y1_output_root_mean_squared_error: 0.7431 - val_y2_output_root_mean_squared_error: 1.2430\n",
      "Epoch 63/100\n",
      "62/62 [==============================] - 2s 27ms/step - loss: 1.9977 - y1_output_loss: 0.4167 - y2_output_loss: 1.5810 - y1_output_root_mean_squared_error: 0.6455 - y2_output_root_mean_squared_error: 1.2574 - val_loss: 2.8266 - val_y1_output_loss: 0.5877 - val_y2_output_loss: 2.2389 - val_y1_output_root_mean_squared_error: 0.7666 - val_y2_output_root_mean_squared_error: 1.4963\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 64/100\n",
      "62/62 [==============================] - 2s 31ms/step - loss: 2.3142 - y1_output_loss: 0.4593 - y2_output_loss: 1.8549 - y1_output_root_mean_squared_error: 0.6778 - y2_output_root_mean_squared_error: 1.3619 - val_loss: 2.0908 - val_y1_output_loss: 0.5327 - val_y2_output_loss: 1.5582 - val_y1_output_root_mean_squared_error: 0.7298 - val_y2_output_root_mean_squared_error: 1.2483\n",
      "Epoch 65/100\n",
      "62/62 [==============================] - 1s 22ms/step - loss: 1.7506 - y1_output_loss: 0.2953 - y2_output_loss: 1.4553 - y1_output_root_mean_squared_error: 0.5434 - y2_output_root_mean_squared_error: 1.2063 - val_loss: 1.8941 - val_y1_output_loss: 0.4440 - val_y2_output_loss: 1.4501 - val_y1_output_root_mean_squared_error: 0.6663 - val_y2_output_root_mean_squared_error: 1.2042\n",
      "Epoch 66/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.7825 - y1_output_loss: 0.3208 - y2_output_loss: 1.4617 - y1_output_root_mean_squared_error: 0.5664 - y2_output_root_mean_squared_error: 1.2090 - val_loss: 2.8513 - val_y1_output_loss: 0.7327 - val_y2_output_loss: 2.1186 - val_y1_output_root_mean_squared_error: 0.8560 - val_y2_output_root_mean_squared_error: 1.4555\n",
      "Epoch 67/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 2.2915 - y1_output_loss: 0.5099 - y2_output_loss: 1.7816 - y1_output_root_mean_squared_error: 0.7141 - y2_output_root_mean_squared_error: 1.3348 - val_loss: 2.0603 - val_y1_output_loss: 0.4877 - val_y2_output_loss: 1.5727 - val_y1_output_root_mean_squared_error: 0.6983 - val_y2_output_root_mean_squared_error: 1.2541\n",
      "Epoch 68/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 2.2410 - y1_output_loss: 0.3773 - y2_output_loss: 1.8637 - y1_output_root_mean_squared_error: 0.6143 - y2_output_root_mean_squared_error: 1.3652 - val_loss: 2.9049 - val_y1_output_loss: 0.6258 - val_y2_output_loss: 2.2791 - val_y1_output_root_mean_squared_error: 0.7911 - val_y2_output_root_mean_squared_error: 1.5097\n",
      "Epoch 69/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.8625 - y1_output_loss: 0.3297 - y2_output_loss: 1.5328 - y1_output_root_mean_squared_error: 0.5742 - y2_output_root_mean_squared_error: 1.2381 - val_loss: 2.4176 - val_y1_output_loss: 0.8155 - val_y2_output_loss: 1.6022 - val_y1_output_root_mean_squared_error: 0.9030 - val_y2_output_root_mean_squared_error: 1.2658\n",
      "Epoch 70/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 2.4518 - y1_output_loss: 0.5742 - y2_output_loss: 1.8777 - y1_output_root_mean_squared_error: 0.7577 - y2_output_root_mean_squared_error: 1.3703 - val_loss: 5.8222 - val_y1_output_loss: 1.1149 - val_y2_output_loss: 4.7074 - val_y1_output_root_mean_squared_error: 1.0559 - val_y2_output_root_mean_squared_error: 2.1696\n",
      "Epoch 71/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 3.5624 - y1_output_loss: 0.6855 - y2_output_loss: 2.8769 - y1_output_root_mean_squared_error: 0.8279 - y2_output_root_mean_squared_error: 1.6961 - val_loss: 1.8179 - val_y1_output_loss: 0.4975 - val_y2_output_loss: 1.3204 - val_y1_output_root_mean_squared_error: 0.7053 - val_y2_output_root_mean_squared_error: 1.1491\n",
      "Epoch 72/100\n",
      "62/62 [==============================] - 1s 16ms/step - loss: 2.0386 - y1_output_loss: 0.3799 - y2_output_loss: 1.6588 - y1_output_root_mean_squared_error: 0.6163 - y2_output_root_mean_squared_error: 1.2879 - val_loss: 1.7357 - val_y1_output_loss: 0.4216 - val_y2_output_loss: 1.3141 - val_y1_output_root_mean_squared_error: 0.6493 - val_y2_output_root_mean_squared_error: 1.1463\n",
      "Epoch 73/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 2.1078 - y1_output_loss: 0.3932 - y2_output_loss: 1.7146 - y1_output_root_mean_squared_error: 0.6271 - y2_output_root_mean_squared_error: 1.3094 - val_loss: 1.7470 - val_y1_output_loss: 0.4716 - val_y2_output_loss: 1.2754 - val_y1_output_root_mean_squared_error: 0.6867 - val_y2_output_root_mean_squared_error: 1.1293\n",
      "Epoch 74/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.7255 - y1_output_loss: 0.2982 - y2_output_loss: 1.4273 - y1_output_root_mean_squared_error: 0.5461 - y2_output_root_mean_squared_error: 1.1947 - val_loss: 3.2724 - val_y1_output_loss: 0.8610 - val_y2_output_loss: 2.4115 - val_y1_output_root_mean_squared_error: 0.9279 - val_y2_output_root_mean_squared_error: 1.5529\n",
      "Epoch 75/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 1.7711 - y1_output_loss: 0.3544 - y2_output_loss: 1.4167 - y1_output_root_mean_squared_error: 0.5953 - y2_output_root_mean_squared_error: 1.1903 - val_loss: 2.6131 - val_y1_output_loss: 0.5855 - val_y2_output_loss: 2.0276 - val_y1_output_root_mean_squared_error: 0.7652 - val_y2_output_root_mean_squared_error: 1.4239\n",
      "Epoch 76/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.5468 - y1_output_loss: 0.2677 - y2_output_loss: 1.2790 - y1_output_root_mean_squared_error: 0.5174 - y2_output_root_mean_squared_error: 1.1309 - val_loss: 1.9095 - val_y1_output_loss: 0.4550 - val_y2_output_loss: 1.4545 - val_y1_output_root_mean_squared_error: 0.6745 - val_y2_output_root_mean_squared_error: 1.2060\n",
      "Epoch 77/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 2.0843 - y1_output_loss: 0.4430 - y2_output_loss: 1.6414 - y1_output_root_mean_squared_error: 0.6655 - y2_output_root_mean_squared_error: 1.2812 - val_loss: 2.6432 - val_y1_output_loss: 0.6547 - val_y2_output_loss: 1.9885 - val_y1_output_root_mean_squared_error: 0.8092 - val_y2_output_root_mean_squared_error: 1.4101\n",
      "Epoch 78/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.7330 - y1_output_loss: 0.3406 - y2_output_loss: 1.3924 - y1_output_root_mean_squared_error: 0.5836 - y2_output_root_mean_squared_error: 1.1800 - val_loss: 3.7560 - val_y1_output_loss: 0.4747 - val_y2_output_loss: 3.2813 - val_y1_output_root_mean_squared_error: 0.6890 - val_y2_output_root_mean_squared_error: 1.8114\n",
      "Epoch 79/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.5833 - y1_output_loss: 0.2773 - y2_output_loss: 1.3060 - y1_output_root_mean_squared_error: 0.5266 - y2_output_root_mean_squared_error: 1.1428 - val_loss: 2.2334 - val_y1_output_loss: 0.4809 - val_y2_output_loss: 1.7525 - val_y1_output_root_mean_squared_error: 0.6935 - val_y2_output_root_mean_squared_error: 1.3238\n",
      "Epoch 80/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.6299 - y1_output_loss: 0.2957 - y2_output_loss: 1.3342 - y1_output_root_mean_squared_error: 0.5438 - y2_output_root_mean_squared_error: 1.1551 - val_loss: 1.5966 - val_y1_output_loss: 0.4227 - val_y2_output_loss: 1.1739 - val_y1_output_root_mean_squared_error: 0.6502 - val_y2_output_root_mean_squared_error: 1.0835\n",
      "Epoch 81/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.7284 - y1_output_loss: 0.3587 - y2_output_loss: 1.3697 - y1_output_root_mean_squared_error: 0.5989 - y2_output_root_mean_squared_error: 1.1703 - val_loss: 4.4821 - val_y1_output_loss: 0.5218 - val_y2_output_loss: 3.9603 - val_y1_output_root_mean_squared_error: 0.7223 - val_y2_output_root_mean_squared_error: 1.9901\n",
      "Epoch 82/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.9661 - y1_output_loss: 0.3849 - y2_output_loss: 1.5812 - y1_output_root_mean_squared_error: 0.6204 - y2_output_root_mean_squared_error: 1.2575 - val_loss: 2.0290 - val_y1_output_loss: 0.4809 - val_y2_output_loss: 1.5481 - val_y1_output_root_mean_squared_error: 0.6935 - val_y2_output_root_mean_squared_error: 1.2442\n",
      "Epoch 83/100\n",
      "62/62 [==============================] - 1s 15ms/step - loss: 2.2548 - y1_output_loss: 0.3844 - y2_output_loss: 1.8704 - y1_output_root_mean_squared_error: 0.6200 - y2_output_root_mean_squared_error: 1.3676 - val_loss: 1.9809 - val_y1_output_loss: 0.5972 - val_y2_output_loss: 1.3837 - val_y1_output_root_mean_squared_error: 0.7728 - val_y2_output_root_mean_squared_error: 1.1763\n",
      "Epoch 84/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.5885 - y1_output_loss: 0.2893 - y2_output_loss: 1.2991 - y1_output_root_mean_squared_error: 0.5379 - y2_output_root_mean_squared_error: 1.1398 - val_loss: 2.7328 - val_y1_output_loss: 0.4197 - val_y2_output_loss: 2.3132 - val_y1_output_root_mean_squared_error: 0.6478 - val_y2_output_root_mean_squared_error: 1.5209\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 85/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.7661 - y1_output_loss: 0.3209 - y2_output_loss: 1.4452 - y1_output_root_mean_squared_error: 0.5664 - y2_output_root_mean_squared_error: 1.2022 - val_loss: 4.2427 - val_y1_output_loss: 1.3443 - val_y2_output_loss: 2.8985 - val_y1_output_root_mean_squared_error: 1.1594 - val_y2_output_root_mean_squared_error: 1.7025\n",
      "Epoch 86/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.9858 - y1_output_loss: 0.4085 - y2_output_loss: 1.5773 - y1_output_root_mean_squared_error: 0.6391 - y2_output_root_mean_squared_error: 1.2559 - val_loss: 1.6646 - val_y1_output_loss: 0.4797 - val_y2_output_loss: 1.1849 - val_y1_output_root_mean_squared_error: 0.6926 - val_y2_output_root_mean_squared_error: 1.0885\n",
      "Epoch 87/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.8963 - y1_output_loss: 0.3776 - y2_output_loss: 1.5187 - y1_output_root_mean_squared_error: 0.6145 - y2_output_root_mean_squared_error: 1.2324 - val_loss: 2.0428 - val_y1_output_loss: 0.4782 - val_y2_output_loss: 1.5646 - val_y1_output_root_mean_squared_error: 0.6915 - val_y2_output_root_mean_squared_error: 1.2509\n",
      "Epoch 88/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.6410 - y1_output_loss: 0.3065 - y2_output_loss: 1.3345 - y1_output_root_mean_squared_error: 0.5537 - y2_output_root_mean_squared_error: 1.1552 - val_loss: 1.5822 - val_y1_output_loss: 0.4816 - val_y2_output_loss: 1.1006 - val_y1_output_root_mean_squared_error: 0.6940 - val_y2_output_root_mean_squared_error: 1.0491\n",
      "Epoch 89/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.7609 - y1_output_loss: 0.3223 - y2_output_loss: 1.4386 - y1_output_root_mean_squared_error: 0.5678 - y2_output_root_mean_squared_error: 1.1994 - val_loss: 1.6624 - val_y1_output_loss: 0.4323 - val_y2_output_loss: 1.2301 - val_y1_output_root_mean_squared_error: 0.6575 - val_y2_output_root_mean_squared_error: 1.1091\n",
      "Epoch 90/100\n",
      "62/62 [==============================] - 1s 22ms/step - loss: 1.7108 - y1_output_loss: 0.2755 - y2_output_loss: 1.4353 - y1_output_root_mean_squared_error: 0.5248 - y2_output_root_mean_squared_error: 1.1980 - val_loss: 2.5450 - val_y1_output_loss: 0.9018 - val_y2_output_loss: 1.6431 - val_y1_output_root_mean_squared_error: 0.9497 - val_y2_output_root_mean_squared_error: 1.2818\n",
      "Epoch 91/100\n",
      "62/62 [==============================] - 1s 18ms/step - loss: 1.3908 - y1_output_loss: 0.2814 - y2_output_loss: 1.1093 - y1_output_root_mean_squared_error: 0.5305 - y2_output_root_mean_squared_error: 1.0532 - val_loss: 1.5671 - val_y1_output_loss: 0.3960 - val_y2_output_loss: 1.1711 - val_y1_output_root_mean_squared_error: 0.6293 - val_y2_output_root_mean_squared_error: 1.0822\n",
      "Epoch 92/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.4289 - y1_output_loss: 0.3026 - y2_output_loss: 1.1263 - y1_output_root_mean_squared_error: 0.5501 - y2_output_root_mean_squared_error: 1.0613 - val_loss: 1.5257 - val_y1_output_loss: 0.4060 - val_y2_output_loss: 1.1197 - val_y1_output_root_mean_squared_error: 0.6372 - val_y2_output_root_mean_squared_error: 1.0581\n",
      "Epoch 93/100\n",
      "62/62 [==============================] - 1s 12ms/step - loss: 1.5155 - y1_output_loss: 0.2951 - y2_output_loss: 1.2204 - y1_output_root_mean_squared_error: 0.5432 - y2_output_root_mean_squared_error: 1.1047 - val_loss: 2.8917 - val_y1_output_loss: 0.5391 - val_y2_output_loss: 2.3526 - val_y1_output_root_mean_squared_error: 0.7342 - val_y2_output_root_mean_squared_error: 1.5338\n",
      "Epoch 94/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.6753 - y1_output_loss: 0.3107 - y2_output_loss: 1.3645 - y1_output_root_mean_squared_error: 0.5574 - y2_output_root_mean_squared_error: 1.1681 - val_loss: 5.7475 - val_y1_output_loss: 1.9972 - val_y2_output_loss: 3.7504 - val_y1_output_root_mean_squared_error: 1.4132 - val_y2_output_root_mean_squared_error: 1.9366\n",
      "Epoch 95/100\n",
      "62/62 [==============================] - 1s 13ms/step - loss: 1.6798 - y1_output_loss: 0.3584 - y2_output_loss: 1.3214 - y1_output_root_mean_squared_error: 0.5987 - y2_output_root_mean_squared_error: 1.1495 - val_loss: 5.0891 - val_y1_output_loss: 0.6085 - val_y2_output_loss: 4.4806 - val_y1_output_root_mean_squared_error: 0.7800 - val_y2_output_root_mean_squared_error: 2.1167\n",
      "Epoch 96/100\n",
      "62/62 [==============================] - 1s 14ms/step - loss: 1.9186 - y1_output_loss: 0.3433 - y2_output_loss: 1.5753 - y1_output_root_mean_squared_error: 0.5859 - y2_output_root_mean_squared_error: 1.2551 - val_loss: 1.8471 - val_y1_output_loss: 0.4280 - val_y2_output_loss: 1.4191 - val_y1_output_root_mean_squared_error: 0.6542 - val_y2_output_root_mean_squared_error: 1.1913\n",
      "Epoch 97/100\n",
      "62/62 [==============================] - 1s 23ms/step - loss: 1.6676 - y1_output_loss: 0.3247 - y2_output_loss: 1.3429 - y1_output_root_mean_squared_error: 0.5698 - y2_output_root_mean_squared_error: 1.1588 - val_loss: 8.8017 - val_y1_output_loss: 1.0574 - val_y2_output_loss: 7.7443 - val_y1_output_root_mean_squared_error: 1.0283 - val_y2_output_root_mean_squared_error: 2.7829\n",
      "Epoch 98/100\n",
      "62/62 [==============================] - 1s 20ms/step - loss: 1.8635 - y1_output_loss: 0.2883 - y2_output_loss: 1.5752 - y1_output_root_mean_squared_error: 0.5369 - y2_output_root_mean_squared_error: 1.2551 - val_loss: 2.4721 - val_y1_output_loss: 0.4739 - val_y2_output_loss: 1.9982 - val_y1_output_root_mean_squared_error: 0.6884 - val_y2_output_root_mean_squared_error: 1.4136\n",
      "Epoch 99/100\n",
      "62/62 [==============================] - 1s 19ms/step - loss: 1.7841 - y1_output_loss: 0.4033 - y2_output_loss: 1.3807 - y1_output_root_mean_squared_error: 0.6351 - y2_output_root_mean_squared_error: 1.1750 - val_loss: 1.6135 - val_y1_output_loss: 0.4871 - val_y2_output_loss: 1.1264 - val_y1_output_root_mean_squared_error: 0.6980 - val_y2_output_root_mean_squared_error: 1.0613\n",
      "Epoch 100/100\n",
      "62/62 [==============================] - 1s 18ms/step - loss: 2.3793 - y1_output_loss: 0.4124 - y2_output_loss: 1.9670 - y1_output_root_mean_squared_error: 0.6421 - y2_output_root_mean_squared_error: 1.4025 - val_loss: 1.5968 - val_y1_output_loss: 0.5181 - val_y2_output_loss: 1.0787 - val_y1_output_root_mean_squared_error: 0.7198 - val_y2_output_root_mean_squared_error: 1.0386\n"
     ]
    }
   ],
   "source": [
    "# Train the model for 20 epochs\n",
    "history = model.fit(norm_train_X, train_Y,\n",
    "                    epochs = 100, batch_size = 10, validation_data = (norm_test_X, test_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6947b33d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 9ms/step - loss: 1.5968 - y1_output_loss: 0.5181 - y2_output_loss: 1.0787 - y1_output_root_mean_squared_error: 0.7198 - y2_output_root_mean_squared_error: 1.0386\n",
      "Loss = 1.5968260765075684, Y1_loss = 0.5181348919868469, Y1_mse = 0.7198158502578735, Y2_loss = 1.0786911249160767, Y2_mse = 1.0386005640029907\n"
     ]
    }
   ],
   "source": [
    "# Test the model and print loss and mse for both outputs\n",
    "loss, Y1_loss, Y2_loss, Y1_rmse, Y2_rmse = model.evaluate(x=norm_test_X, y=test_Y)\n",
    "print(\"Loss = {}, Y1_loss = {}, Y1_mse = {}, Y2_loss = {}, Y2_mse = {}\".format(loss, Y1_loss, Y1_rmse, Y2_loss, Y2_rmse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b086d2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 5ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and mse\n",
    "Y_pred = model.predict(norm_test_X)\n",
    "plot_diff(test_Y[0], Y_pred[0], title='Y1')\n",
    "plot_diff(test_Y[1], Y_pred[1], title='Y2')\n",
    "plot_metrics(metric_name='y1_output_root_mean_squared_error', title='Y1 RMSE', ylim=6)\n",
    "plot_metrics(metric_name='y2_output_root_mean_squared_error', title='Y2 RMSE', ylim=7)"
   ]
  },
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   "source": []
  }
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