EntityEmbedding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cfIjMLrFkyNE"
   },
   "source": [
    "# Entity Embedding - Working Example \" House Prices\"\n",
    "\n",
    "**The purpose of this notebook is to demonstrate  the practical implementation of the so-called Entity Embedding  for Encoding Categorical Features for Training a Neural Network.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J5jgsouslC3s"
   },
   "source": [
    "## Loading Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OCi1WHyU4gUY"
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import matplotlib.pylab as plt\n",
    "import pandas as pd\n",
    "\n",
    "import datetime, warnings, scipy \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn import metrics, linear_model\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from keras.models import Sequential,Model\n",
    "from keras.models import Model as KerasModel\n",
    "from keras.layers import Input, Dense, Activation, Reshape\n",
    "from keras.layers import Concatenate, Dropout\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.utils import plot_model\n",
    "\n",
    "plt.rcParams[\"patch.force_edgecolor\"] = True\n",
    "plt.style.use('fivethirtyeight')\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"last_expr\"\n",
    "pd.options.display.max_columns = 50\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_mygCdZllF1W"
   },
   "source": [
    "##  The Ames Iowa Housing Data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "LYk3K-z4WXag",
    "outputId": "41936428-33ea-4fd1-bb90-e06ad7c9d535"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DataFrame size: (2930, 82)\n"
     ]
    }
   ],
   "source": [
    "url = 'http://www.amstat.org/publications/jse/v19n3/decock/AmesHousing.xls'\n",
    "# Load the file into a Pandas DataFrame\n",
    "data_df = pd.read_excel(url)\n",
    "print('DataFrame size:', data_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 271
    },
    "colab_type": "code",
    "id": "JukFGZWpi9CA",
    "outputId": "d1f768ad-d4ce-4ffa-95ca-a89ec5b14974"
   },
   "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>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Lot Config</th>\n",
       "      <th>Land Slope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition 1</th>\n",
       "      <th>Condition 2</th>\n",
       "      <th>Bldg Type</th>\n",
       "      <th>House Style</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Roof Style</th>\n",
       "      <th>Roof Matl</th>\n",
       "      <th>Exterior 1st</th>\n",
       "      <th>...</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Fireplace Qu</th>\n",
       "      <th>Garage Type</th>\n",
       "      <th>Garage Yr Blt</th>\n",
       "      <th>Garage Finish</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Garage Qual</th>\n",
       "      <th>Garage Cond</th>\n",
       "      <th>Paved Drive</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>528.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>P</td>\n",
       "      <td>210</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>393</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>522.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>527105010</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>212</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>189900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
       "0      1  526301100           20        RL         141.0     31770   Pave   \n",
       "1      2  526350040           20        RH          80.0     11622   Pave   \n",
       "2      3  526351010           20        RL          81.0     14267   Pave   \n",
       "3      4  526353030           20        RL          93.0     11160   Pave   \n",
       "4      5  527105010           60        RL          74.0     13830   Pave   \n",
       "\n",
       "  Alley Lot Shape Land Contour Utilities Lot Config Land Slope Neighborhood  \\\n",
       "0   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
       "1   NaN       Reg          Lvl    AllPub     Inside        Gtl        NAmes   \n",
       "2   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
       "3   NaN       Reg          Lvl    AllPub     Corner        Gtl        NAmes   \n",
       "4   NaN       IR1          Lvl    AllPub     Inside        Gtl      Gilbert   \n",
       "\n",
       "  Condition 1 Condition 2 Bldg Type House Style  Overall Qual  Overall Cond  \\\n",
       "0        Norm        Norm      1Fam      1Story             6             5   \n",
       "1       Feedr        Norm      1Fam      1Story             5             6   \n",
       "2        Norm        Norm      1Fam      1Story             6             6   \n",
       "3        Norm        Norm      1Fam      1Story             7             5   \n",
       "4        Norm        Norm      1Fam      2Story             5             5   \n",
       "\n",
       "   Year Built  Year Remod/Add Roof Style Roof Matl Exterior 1st    ...      \\\n",
       "0        1960            1960        Hip   CompShg      BrkFace    ...       \n",
       "1        1961            1961      Gable   CompShg      VinylSd    ...       \n",
       "2        1958            1958        Hip   CompShg      Wd Sdng    ...       \n",
       "3        1968            1968        Hip   CompShg      BrkFace    ...       \n",
       "4        1997            1998      Gable   CompShg      VinylSd    ...       \n",
       "\n",
       "  Fireplaces Fireplace Qu  Garage Type Garage Yr Blt Garage Finish  \\\n",
       "0          2           Gd       Attchd        1960.0           Fin   \n",
       "1          0          NaN       Attchd        1961.0           Unf   \n",
       "2          0          NaN       Attchd        1958.0           Unf   \n",
       "3          2           TA       Attchd        1968.0           Fin   \n",
       "4          1           TA       Attchd        1997.0           Fin   \n",
       "\n",
       "  Garage Cars Garage Area Garage Qual Garage Cond Paved Drive  Wood Deck SF  \\\n",
       "0         2.0       528.0          TA          TA           P           210   \n",
       "1         1.0       730.0          TA          TA           Y           140   \n",
       "2         1.0       312.0          TA          TA           Y           393   \n",
       "3         2.0       522.0          TA          TA           Y             0   \n",
       "4         2.0       482.0          TA          TA           Y           212   \n",
       "\n",
       "  Open Porch SF  Enclosed Porch  3Ssn Porch  Screen Porch Pool Area Pool QC  \\\n",
       "0            62               0           0             0         0     NaN   \n",
       "1             0               0           0           120         0     NaN   \n",
       "2            36               0           0             0         0     NaN   \n",
       "3             0               0           0             0         0     NaN   \n",
       "4            34               0           0             0         0     NaN   \n",
       "\n",
       "   Fence Misc Feature  Misc Val  Mo Sold  Yr Sold  Sale Type  Sale Condition  \\\n",
       "0    NaN          NaN         0        5     2010        WD           Normal   \n",
       "1  MnPrv          NaN         0        6     2010        WD           Normal   \n",
       "2    NaN         Gar2     12500        6     2010        WD           Normal   \n",
       "3    NaN          NaN         0        4     2010        WD           Normal   \n",
       "4  MnPrv          NaN         0        3     2010        WD           Normal   \n",
       "\n",
       "   SalePrice  \n",
       "0     215000  \n",
       "1     105000  \n",
       "2     172000  \n",
       "3     244000  \n",
       "4     189900  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b8kw-S5kl93c"
   },
   "source": [
    "## Sub-Feature Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kbvIG2u_l-pg"
   },
   "outputs": [],
   "source": [
    "# For same of demonstration, the following features are selected. \n",
    "features = ['Neighborhood','Bldg Type','Year Built','Roof Matl','Sale Condition','Sale Type','House Style','Gr Liv Area']\n",
    "\n",
    "# The aim is to predcit sale prices.\n",
    "target = ['SalePrice']\n",
    "data_df=data_df[features + target]\n",
    "data_df['Year Built']= data_df['Year Built'].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lK6T71rolwdF"
   },
   "source": [
    "## Data Preparation\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bh3TyaoTorwW"
   },
   "source": [
    "### *  ** Train/Val/Test Split**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kfO24esu87-C"
   },
   "outputs": [],
   "source": [
    "X_train, y_train = data_df.iloc[:2000][features], data_df.iloc[:2000][target]\n",
    "X_val, y_val = data_df.iloc[2000:2500][features], data_df.iloc[2000:2500][target]\n",
    "X_test = data_df.iloc[2500:][features]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DfwzHW8yoyhR"
   },
   "source": [
    "### *  ** Data Transformations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RSeV9dpcoy5B"
   },
   "outputs": [],
   "source": [
    "scalar=StandardScaler()\n",
    "scalar.fit(X_train['Gr Liv Area'].values.reshape(-1, 1))\n",
    "X_train['Gr Liv Area']=scalar.transform(X_train['Gr Liv Area'].values.reshape(-1, 1)) \n",
    "X_val['Gr Liv Area']=scalar.transform(X_val['Gr Liv Area'].values.reshape(-1, 1)) \n",
    "\n",
    "scalar.fit(y_train)\n",
    "y_train=scalar.transform(y_train)\n",
    "y_val=scalar.transform(y_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6Y2pVW81owsA"
   },
   "source": [
    "### *  ** Check for Mising Values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "ibJzQzgt4FjM",
    "outputId": "fdca09af-38a0-4a88-c711-7cf9b91603cc"
   },
   "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>variable</th>\n",
       "      <th>missing values</th>\n",
       "      <th>filling factor (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bldg Type</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Year Built</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Roof Matl</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sale Condition</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Sale Type</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>House Style</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Gr Liv Area</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SalePrice</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable  missing values  filling factor (%)\n",
       "0    Neighborhood               0               100.0\n",
       "1       Bldg Type               0               100.0\n",
       "2      Year Built               0               100.0\n",
       "3       Roof Matl               0               100.0\n",
       "4  Sale Condition               0               100.0\n",
       "5       Sale Type               0               100.0\n",
       "6     House Style               0               100.0\n",
       "7     Gr Liv Area               0               100.0\n",
       "8       SalePrice               0               100.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_df = data_df[features + target].isnull().sum(axis=0).reset_index()\n",
    "missing_df.columns = ['variable', 'missing values']\n",
    "missing_df['filling factor (%)']=(data_df[features + target].shape[0]-missing_df['missing values'])/data_df[features + target].shape[0]*100\n",
    "missing_df.sort_values('filling factor (%)').reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J7YXU2Ido9On"
   },
   "source": [
    "## *  **  Categorical Features Embedding Preparation**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uKoElRrZpquS"
   },
   "source": [
    "### *  ** Categorical Features and  Cardinality**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131
    },
    "colab_type": "code",
    "id": "JcJFmdh4-Tc3",
    "outputId": "23527775-98c9-4526-eb64-03fd1a10ef9b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neighborhood 28\n",
      "Bldg Type 5\n",
      "Year Built 118\n",
      "Roof Matl 8\n",
      "Sale Condition 6\n",
      "Sale Type 10\n",
      "House Style 8\n"
     ]
    }
   ],
   "source": [
    "embed_cols=[i for i in X_train.select_dtypes(include=['object'])]\n",
    "\n",
    "for i in embed_cols:\n",
    "    print(i,data_df[i].nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0yH75bdIp7p1"
   },
   "source": [
    "### *  ** Categorical Features To List Format**\n",
    "One need to convert data to list format to match the network structure. \n",
    "\n",
    "The following function takes the list of categorical features, and prepare such lists for the NN input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JFIcnq5UxYbZ"
   },
   "outputs": [],
   "source": [
    "embed_cols=[i for i in X_train.select_dtypes(include=['object'])]\n",
    "\n",
    "#converting data to list format to match the network structure\n",
    "def preproc(X_train, X_val, X_test):\n",
    "\n",
    "    input_list_train = []\n",
    "    input_list_val = []\n",
    "    input_list_test = []\n",
    "    \n",
    "    #the cols to be embedded: rescaling to range [0, # values)\n",
    "    for c in embed_cols:\n",
    "        raw_vals = np.unique(X_train[c])\n",
    "        val_map = {}\n",
    "        for i in range(len(raw_vals)):\n",
    "            val_map[raw_vals[i]] = i       \n",
    "        input_list_train.append(X_train[c].map(val_map).values)\n",
    "        input_list_val.append(X_val[c].map(val_map).fillna(0).values)\n",
    "        input_list_test.append(X_test[c].map(val_map).fillna(0).values)\n",
    "     \n",
    "    #the rest of the columns\n",
    "    other_cols = [c for c in X_train.columns if (not c in embed_cols)]\n",
    "    input_list_train.append(X_train[other_cols].values)\n",
    "    input_list_val.append(X_val[other_cols].values)\n",
    "    input_list_test.append(X_test[other_cols].values)\n",
    "    \n",
    "    return input_list_train, input_list_val, input_list_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "C67nVgly4Kkh",
    "outputId": "3c9f7c0e-913c-4221-c9d6-fe0bcf11a1ef"
   },
   "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>Neighborhood</th>\n",
       "      <th>Bldg Type</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Roof Matl</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>House Style</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NAmes</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1960</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>1Story</td>\n",
       "      <td>0.315074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NAmes</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1961</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>1Story</td>\n",
       "      <td>-1.224202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NAmes</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1958</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>1Story</td>\n",
       "      <td>-0.347220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NAmes</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1968</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1.234589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gilbert</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1997</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>2Story</td>\n",
       "      <td>0.260389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Neighborhood Bldg Type Year Built Roof Matl Sale Condition Sale Type  \\\n",
       "0        NAmes      1Fam       1960   CompShg         Normal       WD    \n",
       "1        NAmes      1Fam       1961   CompShg         Normal       WD    \n",
       "2        NAmes      1Fam       1958   CompShg         Normal       WD    \n",
       "3        NAmes      1Fam       1968   CompShg         Normal       WD    \n",
       "4      Gilbert      1Fam       1997   CompShg         Normal       WD    \n",
       "\n",
       "  House Style  Gr Liv Area  \n",
       "0      1Story     0.315074  \n",
       "1      1Story    -1.224202  \n",
       "2      1Story    -0.347220  \n",
       "3      1Story     1.234589  \n",
       "4      2Story     0.260389  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IkM3QOLLq1Wh"
   },
   "source": [
    "### *  ** Embedding Dimension - Hyperparamter**\n",
    "The choice of embedding dimension is optional, essentially it is a hyperparamter that one need to choose beforehand and investigate. One rule of thumb is to choose half of the cardinality of the categorical feature if that is is up to 50 in length. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131
    },
    "colab_type": "code",
    "id": "uF6AxZnW8pWq",
    "outputId": "80100157-122e-4e86-fe63-b1cffe64334c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorica Variable: Neighborhood Unique Categories: 26 Embedding Size: 13\n",
      "Categorica Variable: Bldg Type Unique Categories: 5 Embedding Size: 3\n",
      "Categorica Variable: Year Built Unique Categories: 116 Embedding Size: 50\n",
      "Categorica Variable: Roof Matl Unique Categories: 7 Embedding Size: 4\n",
      "Categorica Variable: Sale Condition Unique Categories: 6 Embedding Size: 3\n",
      "Categorica Variable: Sale Type Unique Categories: 9 Embedding Size: 5\n",
      "Categorica Variable: House Style Unique Categories: 8 Embedding Size: 4\n"
     ]
    }
   ],
   "source": [
    "for categorical_var in X_train.select_dtypes(include=['object']):\n",
    "    \n",
    "    cat_emb_name= categorical_var.replace(\" \", \"\")+'_Embedding'\n",
    "  \n",
    "    no_of_unique_cat  = X_train[categorical_var].nunique()\n",
    "    embedding_size = int(min(np.ceil((no_of_unique_cat)/2), 50 ))\n",
    "  \n",
    "    print('Categorica Variable:', categorical_var,\n",
    "        'Unique Categories:', no_of_unique_cat,\n",
    "        'Embedding Size:', embedding_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7yRLG8XgrmIP"
   },
   "source": [
    "### *  ** Proper Naming of Categorical Features for Labelling NN Layers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 131
    },
    "colab_type": "code",
    "id": "MIeQzUABmmdz",
    "outputId": "9b72e5a0-9966-4258-a6bf-3d9ecea3d22e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input_Neighborhood\n",
      "Input_BldgType\n",
      "Input_YearBuilt\n",
      "Input_RoofMatl\n",
      "Input_SaleCondition\n",
      "Input_SaleType\n",
      "Input_HouseStyle\n"
     ]
    }
   ],
   "source": [
    "for categorical_var in X_train.select_dtypes(include=['object']):\n",
    "    \n",
    "    input_name= 'Input_' + categorical_var.replace(\" \", \"\")\n",
    "    print(input_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5C8C-2IJr4QU"
   },
   "source": [
    "## *  ** Build Neural Network**\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E46u0SLgtFOe"
   },
   "source": [
    "### *  ** Putting it Altogether**\n",
    "Here we basically make the embeding layers one at a time and append, and at the end we concatenate it together with the numerical features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_xmTiqQ5XWXw"
   },
   "outputs": [],
   "source": [
    "input_models=[]\n",
    "output_embeddings=[]\n",
    "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "\n",
    "for categorical_var in X_train.select_dtypes(include=['object']):\n",
    "    \n",
    "    #Name of the categorical variable that will be used in the Keras Embedding layer\n",
    "    cat_emb_name= categorical_var.replace(\" \", \"\")+'_Embedding'\n",
    "  \n",
    "    # Define the embedding_size\n",
    "    no_of_unique_cat  = X_train[categorical_var].nunique()\n",
    "    embedding_size = int(min(np.ceil((no_of_unique_cat)/2), 50 ))\n",
    "  \n",
    "    #One Embedding Layer for each categorical variable\n",
    "    input_model = Input(shape=(1,))\n",
    "    output_model = Embedding(no_of_unique_cat, embedding_size, name=cat_emb_name)(input_model)\n",
    "    output_model = Reshape(target_shape=(embedding_size,))(output_model)    \n",
    "  \n",
    "    #Appending all the categorical inputs\n",
    "    input_models.append(input_model)\n",
    "  \n",
    "    #Appending all the embeddings\n",
    "    output_embeddings.append(output_model)\n",
    "  \n",
    "#Other non-categorical data columns (numerical). \n",
    "#I define single another network for the other columns and add them to our models list.\n",
    "input_numeric = Input(shape=(len(X_train.select_dtypes(include=numerics).columns.tolist()),))\n",
    "embedding_numeric = Dense(128)(input_numeric) \n",
    "input_models.append(input_numeric)\n",
    "output_embeddings.append(embedding_numeric)\n",
    "\n",
    "#At the end we concatenate altogther and add other Dense layers\n",
    "output = Concatenate()(output_embeddings)\n",
    "output = Dense(1000, kernel_initializer=\"uniform\")(output)\n",
    "output = Activation('relu')(output)\n",
    "output= Dropout(0.4)(output)\n",
    "output = Dense(512, kernel_initializer=\"uniform\")(output)\n",
    "output = Activation('relu')(output)\n",
    "output= Dropout(0.3)(output)\n",
    "output = Dense(1, activation='sigmoid')(output)\n",
    "\n",
    "model = Model(inputs=input_models, outputs=output)\n",
    "model.compile(loss='mean_squared_error', optimizer='Adam',metrics=['mse','mape'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FWO9TijAtMUw"
   },
   "source": [
    "### *  ** The Network Architecture**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydot_ng as pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 627
    },
    "colab_type": "code",
    "id": "r7QWxYqmcZA2",
    "outputId": "e9fc4cfd-2345-4718-d25d-bbc14fe11045",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "image/png": {
       "height": 575,
       "width": 1956
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, show_shapes=True, show_layer_names=True, to_file='model.png')\n",
    "from IPython.display import Image\n",
    "Image(retina=True, filename='model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1252
    },
    "colab_type": "code",
    "id": "TVvKBW9Es6ox",
    "outputId": "72000b39-8f5b-49fa-ee27-8a2f07c3c794"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_2 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_3 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_4 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_5 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_6 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "input_7 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Neighborhood_Embedding (Embeddi (None, 1, 13)        338         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "BldgType_Embedding (Embedding)  (None, 1, 3)         15          input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "YearBuilt_Embedding (Embedding) (None, 1, 50)        5800        input_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "RoofMatl_Embedding (Embedding)  (None, 1, 4)         28          input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "SaleCondition_Embedding (Embedd (None, 1, 3)         18          input_5[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "SaleType_Embedding (Embedding)  (None, 1, 5)         45          input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "HouseStyle_Embedding (Embedding (None, 1, 4)         32          input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_8 (InputLayer)            (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 13)           0           Neighborhood_Embedding[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 3)            0           BldgType_Embedding[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "reshape_3 (Reshape)             (None, 50)           0           YearBuilt_Embedding[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "reshape_4 (Reshape)             (None, 4)            0           RoofMatl_Embedding[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "reshape_5 (Reshape)             (None, 3)            0           SaleCondition_Embedding[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_6 (Reshape)             (None, 5)            0           SaleType_Embedding[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "reshape_7 (Reshape)             (None, 4)            0           HouseStyle_Embedding[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128)          256         input_8[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 210)          0           reshape_1[0][0]                  \n",
      "                                                                 reshape_2[0][0]                  \n",
      "                                                                 reshape_3[0][0]                  \n",
      "                                                                 reshape_4[0][0]                  \n",
      "                                                                 reshape_5[0][0]                  \n",
      "                                                                 reshape_6[0][0]                  \n",
      "                                                                 reshape_7[0][0]                  \n",
      "                                                                 dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1000)         211000      concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 1000)         0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 1000)         0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 512)          512512      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 512)          0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 512)          0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1)            513         dropout_2[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 730,557\n",
      "Trainable params: 730,557\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nHa2vDF0ubNH"
   },
   "source": [
    "### *  ** Training**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u2Om7sLh6apd"
   },
   "outputs": [],
   "source": [
    "X_train_list,X_val_list,X_test_list = preproc(X_train,X_val, X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 32553
    },
    "colab_type": "code",
    "id": "DzzE64nlEbDX",
    "outputId": "f28f32c5-79eb-48d9-f9fc-04dc92a4818e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 2000 samples, validate on 500 samples\n",
      "Epoch 1/1000\n",
      " - 1s - loss: 1.1094 - mean_squared_error: 1.1094 - mean_absolute_percentage_error: 300.8905 - val_loss: 0.8694 - val_mean_squared_error: 0.8694 - val_mean_absolute_percentage_error: 273.3866\n",
      "Epoch 2/1000\n",
      " - 0s - loss: 0.8420 - mean_squared_error: 0.8420 - mean_absolute_percentage_error: 256.9126 - val_loss: 0.7448 - val_mean_squared_error: 0.7448 - val_mean_absolute_percentage_error: 211.2060\n",
      "Epoch 3/1000\n",
      " - 1s - loss: 0.7617 - mean_squared_error: 0.7617 - mean_absolute_percentage_error: 201.2317 - val_loss: 0.7306 - val_mean_squared_error: 0.7306 - val_mean_absolute_percentage_error: 163.0285\n",
      "Epoch 4/1000\n",
      " - 1s - loss: 0.7309 - mean_squared_error: 0.7309 - mean_absolute_percentage_error: 162.7063 - val_loss: 0.7126 - val_mean_squared_error: 0.7126 - val_mean_absolute_percentage_error: 141.2770\n",
      "Epoch 5/1000\n",
      " - 0s - loss: 0.6975 - mean_squared_error: 0.6975 - mean_absolute_percentage_error: 146.2286 - val_loss: 0.6919 - val_mean_squared_error: 0.6919 - val_mean_absolute_percentage_error: 125.2605\n",
      "Epoch 6/1000\n",
      " - 0s - loss: 0.6632 - mean_squared_error: 0.6632 - mean_absolute_percentage_error: 117.7723 - val_loss: 0.6696 - val_mean_squared_error: 0.6696 - val_mean_absolute_percentage_error: 118.5560\n",
      "Epoch 7/1000\n",
      " - 0s - loss: 0.6326 - mean_squared_error: 0.6326 - mean_absolute_percentage_error: 111.9353 - val_loss: 0.6525 - val_mean_squared_error: 0.6525 - val_mean_absolute_percentage_error: 116.8748\n",
      "Epoch 8/1000\n",
      " - 0s - loss: 0.6162 - mean_squared_error: 0.6162 - mean_absolute_percentage_error: 104.2752 - val_loss: 0.6499 - val_mean_squared_error: 0.6499 - val_mean_absolute_percentage_error: 126.3212\n",
      "Epoch 9/1000\n",
      " - 0s - loss: 0.6086 - mean_squared_error: 0.6086 - mean_absolute_percentage_error: 109.2474 - val_loss: 0.6505 - val_mean_squared_error: 0.6505 - val_mean_absolute_percentage_error: 134.3866\n",
      "Epoch 10/1000\n",
      " - 0s - loss: 0.6045 - mean_squared_error: 0.6045 - mean_absolute_percentage_error: 111.5787 - val_loss: 0.6502 - val_mean_squared_error: 0.6502 - val_mean_absolute_percentage_error: 137.9813\n",
      "Epoch 11/1000\n",
      " - 1s - loss: 0.6000 - mean_squared_error: 0.6000 - mean_absolute_percentage_error: 116.5356 - val_loss: 0.6529 - val_mean_squared_error: 0.6529 - val_mean_absolute_percentage_error: 139.5701\n",
      "Epoch 12/1000\n",
      " - 1s - loss: 0.5951 - mean_squared_error: 0.5951 - mean_absolute_percentage_error: 117.8058 - val_loss: 0.6494 - val_mean_squared_error: 0.6494 - val_mean_absolute_percentage_error: 134.1264\n",
      "Epoch 13/1000\n",
      " - 0s - loss: 0.5949 - mean_squared_error: 0.5949 - mean_absolute_percentage_error: 112.6739 - val_loss: 0.6497 - val_mean_squared_error: 0.6497 - val_mean_absolute_percentage_error: 131.6721\n",
      "Epoch 14/1000\n",
      " - 0s - loss: 0.5938 - mean_squared_error: 0.5938 - mean_absolute_percentage_error: 113.1802 - val_loss: 0.6461 - val_mean_squared_error: 0.6461 - val_mean_absolute_percentage_error: 128.8768\n",
      "Epoch 15/1000\n",
      " - 0s - loss: 0.5923 - mean_squared_error: 0.5923 - mean_absolute_percentage_error: 111.3771 - val_loss: 0.6458 - val_mean_squared_error: 0.6458 - val_mean_absolute_percentage_error: 129.3048\n",
      "Epoch 16/1000\n",
      " - 0s - loss: 0.5896 - mean_squared_error: 0.5896 - mean_absolute_percentage_error: 113.9494 - val_loss: 0.6477 - val_mean_squared_error: 0.6477 - val_mean_absolute_percentage_error: 130.1096\n",
      "Epoch 17/1000\n",
      " - 0s - loss: 0.5892 - mean_squared_error: 0.5892 - mean_absolute_percentage_error: 110.6980 - val_loss: 0.6414 - val_mean_squared_error: 0.6414 - val_mean_absolute_percentage_error: 124.9672\n",
      "Epoch 18/1000\n",
      " - 0s - loss: 0.5882 - mean_squared_error: 0.5882 - mean_absolute_percentage_error: 110.4897 - val_loss: 0.6462 - val_mean_squared_error: 0.6462 - val_mean_absolute_percentage_error: 127.9265\n",
      "Epoch 19/1000\n",
      " - 0s - loss: 0.5876 - mean_squared_error: 0.5876 - mean_absolute_percentage_error: 111.1219 - val_loss: 0.6401 - val_mean_squared_error: 0.6401 - val_mean_absolute_percentage_error: 122.4150\n",
      "Epoch 20/1000\n",
      " - 0s - loss: 0.5854 - mean_squared_error: 0.5854 - mean_absolute_percentage_error: 109.6461 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 122.6231\n",
      "Epoch 21/1000\n",
      " - 0s - loss: 0.5836 - mean_squared_error: 0.5836 - mean_absolute_percentage_error: 108.2462 - val_loss: 0.6399 - val_mean_squared_error: 0.6399 - val_mean_absolute_percentage_error: 120.0241\n",
      "Epoch 22/1000\n",
      " - 0s - loss: 0.5821 - mean_squared_error: 0.5821 - mean_absolute_percentage_error: 105.9068 - val_loss: 0.6398 - val_mean_squared_error: 0.6398 - val_mean_absolute_percentage_error: 118.3755\n",
      "Epoch 23/1000\n",
      " - 0s - loss: 0.5816 - mean_squared_error: 0.5816 - mean_absolute_percentage_error: 106.2874 - val_loss: 0.6395 - val_mean_squared_error: 0.6395 - val_mean_absolute_percentage_error: 118.0569\n",
      "Epoch 24/1000\n",
      " - 0s - loss: 0.5814 - mean_squared_error: 0.5814 - mean_absolute_percentage_error: 106.1483 - val_loss: 0.6393 - val_mean_squared_error: 0.6393 - val_mean_absolute_percentage_error: 117.8730\n",
      "Epoch 25/1000\n",
      " - 0s - loss: 0.5797 - mean_squared_error: 0.5797 - mean_absolute_percentage_error: 105.8677 - val_loss: 0.6407 - val_mean_squared_error: 0.6407 - val_mean_absolute_percentage_error: 120.2600\n",
      "Epoch 26/1000\n",
      " - 0s - loss: 0.5799 - mean_squared_error: 0.5799 - mean_absolute_percentage_error: 105.9412 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 119.9746\n",
      "Epoch 27/1000\n",
      " - 1s - loss: 0.5782 - mean_squared_error: 0.5782 - mean_absolute_percentage_error: 105.3566 - val_loss: 0.6414 - val_mean_squared_error: 0.6414 - val_mean_absolute_percentage_error: 120.5201\n",
      "Epoch 28/1000\n",
      " - 1s - loss: 0.5784 - mean_squared_error: 0.5784 - mean_absolute_percentage_error: 105.9219 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 121.2930\n",
      "Epoch 29/1000\n",
      " - 0s - loss: 0.5779 - mean_squared_error: 0.5779 - mean_absolute_percentage_error: 105.7695 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 118.2923\n",
      "Epoch 30/1000\n",
      " - 0s - loss: 0.5770 - mean_squared_error: 0.5770 - mean_absolute_percentage_error: 103.5891 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 119.3272\n",
      "Epoch 31/1000\n",
      " - 0s - loss: 0.5762 - mean_squared_error: 0.5762 - mean_absolute_percentage_error: 103.8702 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 118.3510\n",
      "Epoch 32/1000\n",
      " - 0s - loss: 0.5758 - mean_squared_error: 0.5758 - mean_absolute_percentage_error: 103.3601 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 117.9175\n",
      "Epoch 33/1000\n",
      " - 0s - loss: 0.5756 - mean_squared_error: 0.5756 - mean_absolute_percentage_error: 103.7015 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 116.9270\n",
      "Epoch 34/1000\n",
      " - 0s - loss: 0.5755 - mean_squared_error: 0.5755 - mean_absolute_percentage_error: 103.4590 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 117.5020\n",
      "Epoch 35/1000\n",
      " - 0s - loss: 0.5747 - mean_squared_error: 0.5747 - mean_absolute_percentage_error: 104.3903 - val_loss: 0.6408 - val_mean_squared_error: 0.6408 - val_mean_absolute_percentage_error: 116.3691\n",
      "Epoch 36/1000\n",
      " - 0s - loss: 0.5745 - mean_squared_error: 0.5745 - mean_absolute_percentage_error: 103.4048 - val_loss: 0.6405 - val_mean_squared_error: 0.6405 - val_mean_absolute_percentage_error: 116.0522\n",
      "Epoch 37/1000\n",
      " - 0s - loss: 0.5747 - mean_squared_error: 0.5747 - mean_absolute_percentage_error: 102.1486 - val_loss: 0.6401 - val_mean_squared_error: 0.6401 - val_mean_absolute_percentage_error: 115.4370\n",
      "Epoch 38/1000\n",
      " - 0s - loss: 0.5744 - mean_squared_error: 0.5744 - mean_absolute_percentage_error: 103.5211 - val_loss: 0.6394 - val_mean_squared_error: 0.6394 - val_mean_absolute_percentage_error: 115.1941\n",
      "Epoch 39/1000\n",
      " - 1s - loss: 0.5738 - mean_squared_error: 0.5738 - mean_absolute_percentage_error: 101.8770 - val_loss: 0.6385 - val_mean_squared_error: 0.6385 - val_mean_absolute_percentage_error: 112.8671\n",
      "Epoch 40/1000\n",
      " - 0s - loss: 0.5742 - mean_squared_error: 0.5742 - mean_absolute_percentage_error: 102.5374 - val_loss: 0.6396 - val_mean_squared_error: 0.6396 - val_mean_absolute_percentage_error: 117.1218\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 41/1000\n",
      " - 0s - loss: 0.5738 - mean_squared_error: 0.5738 - mean_absolute_percentage_error: 104.0026 - val_loss: 0.6383 - val_mean_squared_error: 0.6383 - val_mean_absolute_percentage_error: 115.2538\n",
      "Epoch 42/1000\n",
      " - 0s - loss: 0.5732 - mean_squared_error: 0.5732 - mean_absolute_percentage_error: 103.3374 - val_loss: 0.6378 - val_mean_squared_error: 0.6378 - val_mean_absolute_percentage_error: 114.7661\n",
      "Epoch 43/1000\n",
      " - 0s - loss: 0.5719 - mean_squared_error: 0.5719 - mean_absolute_percentage_error: 104.0965 - val_loss: 0.6377 - val_mean_squared_error: 0.6377 - val_mean_absolute_percentage_error: 115.3850\n",
      "Epoch 44/1000\n",
      " - 0s - loss: 0.5727 - mean_squared_error: 0.5727 - mean_absolute_percentage_error: 103.8663 - val_loss: 0.6373 - val_mean_squared_error: 0.6373 - val_mean_absolute_percentage_error: 114.9304\n",
      "Epoch 45/1000\n",
      " - 0s - loss: 0.5719 - mean_squared_error: 0.5719 - mean_absolute_percentage_error: 102.1785 - val_loss: 0.6373 - val_mean_squared_error: 0.6373 - val_mean_absolute_percentage_error: 115.2431\n",
      "Epoch 46/1000\n",
      " - 0s - loss: 0.5721 - mean_squared_error: 0.5721 - mean_absolute_percentage_error: 103.5363 - val_loss: 0.6373 - val_mean_squared_error: 0.6373 - val_mean_absolute_percentage_error: 115.8885\n",
      "Epoch 47/1000\n",
      " - 1s - loss: 0.5715 - mean_squared_error: 0.5715 - mean_absolute_percentage_error: 103.0390 - val_loss: 0.6375 - val_mean_squared_error: 0.6375 - val_mean_absolute_percentage_error: 116.0739\n",
      "Epoch 48/1000\n",
      " - 0s - loss: 0.5723 - mean_squared_error: 0.5723 - mean_absolute_percentage_error: 102.2850 - val_loss: 0.6370 - val_mean_squared_error: 0.6370 - val_mean_absolute_percentage_error: 114.9806\n",
      "Epoch 49/1000\n",
      " - 0s - loss: 0.5709 - mean_squared_error: 0.5709 - mean_absolute_percentage_error: 101.0941 - val_loss: 0.6374 - val_mean_squared_error: 0.6374 - val_mean_absolute_percentage_error: 117.1390\n",
      "Epoch 50/1000\n",
      " - 0s - loss: 0.5707 - mean_squared_error: 0.5707 - mean_absolute_percentage_error: 101.4306 - val_loss: 0.6372 - val_mean_squared_error: 0.6372 - val_mean_absolute_percentage_error: 117.3929\n",
      "Epoch 51/1000\n",
      " - 0s - loss: 0.5719 - mean_squared_error: 0.5719 - mean_absolute_percentage_error: 101.9217 - val_loss: 0.6392 - val_mean_squared_error: 0.6392 - val_mean_absolute_percentage_error: 120.3986\n",
      "Epoch 52/1000\n",
      " - 0s - loss: 0.5718 - mean_squared_error: 0.5718 - mean_absolute_percentage_error: 103.6407 - val_loss: 0.6368 - val_mean_squared_error: 0.6368 - val_mean_absolute_percentage_error: 116.2582\n",
      "Epoch 53/1000\n",
      " - 0s - loss: 0.5714 - mean_squared_error: 0.5714 - mean_absolute_percentage_error: 98.7100 - val_loss: 0.6374 - val_mean_squared_error: 0.6374 - val_mean_absolute_percentage_error: 117.6880\n",
      "Epoch 54/1000\n",
      " - 0s - loss: 0.5712 - mean_squared_error: 0.5712 - mean_absolute_percentage_error: 103.2689 - val_loss: 0.6382 - val_mean_squared_error: 0.6382 - val_mean_absolute_percentage_error: 120.7350\n",
      "Epoch 55/1000\n",
      " - 1s - loss: 0.5694 - mean_squared_error: 0.5694 - mean_absolute_percentage_error: 101.9659 - val_loss: 0.6362 - val_mean_squared_error: 0.6362 - val_mean_absolute_percentage_error: 116.1790\n",
      "Epoch 56/1000\n",
      " - 1s - loss: 0.5706 - mean_squared_error: 0.5706 - mean_absolute_percentage_error: 101.6868 - val_loss: 0.6365 - val_mean_squared_error: 0.6365 - val_mean_absolute_percentage_error: 118.8328\n",
      "Epoch 57/1000\n",
      " - 0s - loss: 0.5692 - mean_squared_error: 0.5692 - mean_absolute_percentage_error: 101.8620 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 116.4695\n",
      "Epoch 58/1000\n",
      " - 0s - loss: 0.5696 - mean_squared_error: 0.5696 - mean_absolute_percentage_error: 101.5455 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 118.5533\n",
      "Epoch 59/1000\n",
      " - 1s - loss: 0.5687 - mean_squared_error: 0.5687 - mean_absolute_percentage_error: 102.1899 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 117.8896\n",
      "Epoch 60/1000\n",
      " - 0s - loss: 0.5688 - mean_squared_error: 0.5688 - mean_absolute_percentage_error: 102.8249 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 116.7035\n",
      "Epoch 61/1000\n",
      " - 0s - loss: 0.5687 - mean_squared_error: 0.5687 - mean_absolute_percentage_error: 101.4338 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 118.1309\n",
      "Epoch 62/1000\n",
      " - 0s - loss: 0.5688 - mean_squared_error: 0.5688 - mean_absolute_percentage_error: 102.9855 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 119.5120\n",
      "Epoch 63/1000\n",
      " - 0s - loss: 0.5676 - mean_squared_error: 0.5676 - mean_absolute_percentage_error: 99.7299 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 117.3978\n",
      "Epoch 64/1000\n",
      " - 0s - loss: 0.5676 - mean_squared_error: 0.5676 - mean_absolute_percentage_error: 98.1336 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 117.9805\n",
      "Epoch 65/1000\n",
      " - 0s - loss: 0.5678 - mean_squared_error: 0.5678 - mean_absolute_percentage_error: 102.2036 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 119.3242\n",
      "Epoch 66/1000\n",
      " - 0s - loss: 0.5675 - mean_squared_error: 0.5675 - mean_absolute_percentage_error: 99.9395 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 118.5288\n",
      "Epoch 67/1000\n",
      " - 0s - loss: 0.5675 - mean_squared_error: 0.5675 - mean_absolute_percentage_error: 103.4309 - val_loss: 0.6352 - val_mean_squared_error: 0.6352 - val_mean_absolute_percentage_error: 117.7458\n",
      "Epoch 68/1000\n",
      " - 0s - loss: 0.5674 - mean_squared_error: 0.5674 - mean_absolute_percentage_error: 99.9028 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 115.8198\n",
      "Epoch 69/1000\n",
      " - 0s - loss: 0.5673 - mean_squared_error: 0.5673 - mean_absolute_percentage_error: 99.9313 - val_loss: 0.6342 - val_mean_squared_error: 0.6342 - val_mean_absolute_percentage_error: 117.8360\n",
      "Epoch 70/1000\n",
      " - 0s - loss: 0.5669 - mean_squared_error: 0.5669 - mean_absolute_percentage_error: 99.8281 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 118.3117\n",
      "Epoch 71/1000\n",
      " - 0s - loss: 0.5667 - mean_squared_error: 0.5667 - mean_absolute_percentage_error: 101.6792 - val_loss: 0.6342 - val_mean_squared_error: 0.6342 - val_mean_absolute_percentage_error: 117.3838\n",
      "Epoch 72/1000\n",
      " - 0s - loss: 0.5659 - mean_squared_error: 0.5659 - mean_absolute_percentage_error: 99.4210 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 115.8219\n",
      "Epoch 73/1000\n",
      " - 0s - loss: 0.5655 - mean_squared_error: 0.5655 - mean_absolute_percentage_error: 99.0091 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 118.4669\n",
      "Epoch 74/1000\n",
      " - 0s - loss: 0.5657 - mean_squared_error: 0.5657 - mean_absolute_percentage_error: 98.2269 - val_loss: 0.6353 - val_mean_squared_error: 0.6353 - val_mean_absolute_percentage_error: 119.7023\n",
      "Epoch 75/1000\n",
      " - 0s - loss: 0.5656 - mean_squared_error: 0.5656 - mean_absolute_percentage_error: 99.1220 - val_loss: 0.6351 - val_mean_squared_error: 0.6351 - val_mean_absolute_percentage_error: 118.6197\n",
      "Epoch 76/1000\n",
      " - 0s - loss: 0.5661 - mean_squared_error: 0.5661 - mean_absolute_percentage_error: 100.2601 - val_loss: 0.6349 - val_mean_squared_error: 0.6349 - val_mean_absolute_percentage_error: 118.4976\n",
      "Epoch 77/1000\n",
      " - 0s - loss: 0.5649 - mean_squared_error: 0.5649 - mean_absolute_percentage_error: 98.4516 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 116.7758\n",
      "Epoch 78/1000\n",
      " - 0s - loss: 0.5654 - mean_squared_error: 0.5654 - mean_absolute_percentage_error: 97.5460 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 118.4752\n",
      "Epoch 79/1000\n",
      " - 0s - loss: 0.5648 - mean_squared_error: 0.5648 - mean_absolute_percentage_error: 97.2495 - val_loss: 0.6356 - val_mean_squared_error: 0.6356 - val_mean_absolute_percentage_error: 119.3360\n",
      "Epoch 80/1000\n",
      " - 0s - loss: 0.5653 - mean_squared_error: 0.5653 - mean_absolute_percentage_error: 97.9161 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 118.0948\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 81/1000\n",
      " - 0s - loss: 0.5646 - mean_squared_error: 0.5646 - mean_absolute_percentage_error: 97.0029 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 118.5665\n",
      "Epoch 82/1000\n",
      " - 0s - loss: 0.5649 - mean_squared_error: 0.5649 - mean_absolute_percentage_error: 99.0771 - val_loss: 0.6349 - val_mean_squared_error: 0.6349 - val_mean_absolute_percentage_error: 116.7776\n",
      "Epoch 83/1000\n",
      " - 0s - loss: 0.5652 - mean_squared_error: 0.5652 - mean_absolute_percentage_error: 96.3167 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 116.8421\n",
      "Epoch 84/1000\n",
      " - 0s - loss: 0.5649 - mean_squared_error: 0.5649 - mean_absolute_percentage_error: 98.4222 - val_loss: 0.6362 - val_mean_squared_error: 0.6362 - val_mean_absolute_percentage_error: 120.4958\n",
      "Epoch 85/1000\n",
      " - 0s - loss: 0.5645 - mean_squared_error: 0.5645 - mean_absolute_percentage_error: 96.6089 - val_loss: 0.6359 - val_mean_squared_error: 0.6359 - val_mean_absolute_percentage_error: 116.3600\n",
      "Epoch 86/1000\n",
      " - 0s - loss: 0.5648 - mean_squared_error: 0.5648 - mean_absolute_percentage_error: 98.9236 - val_loss: 0.6367 - val_mean_squared_error: 0.6367 - val_mean_absolute_percentage_error: 119.7646\n",
      "Epoch 87/1000\n",
      " - 0s - loss: 0.5636 - mean_squared_error: 0.5636 - mean_absolute_percentage_error: 94.8090 - val_loss: 0.6357 - val_mean_squared_error: 0.6357 - val_mean_absolute_percentage_error: 117.7938\n",
      "Epoch 88/1000\n",
      " - 0s - loss: 0.5637 - mean_squared_error: 0.5637 - mean_absolute_percentage_error: 95.9818 - val_loss: 0.6366 - val_mean_squared_error: 0.6366 - val_mean_absolute_percentage_error: 120.2595\n",
      "Epoch 89/1000\n",
      " - 0s - loss: 0.5641 - mean_squared_error: 0.5641 - mean_absolute_percentage_error: 95.3848 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 114.7946\n",
      "Epoch 90/1000\n",
      " - 0s - loss: 0.5638 - mean_squared_error: 0.5638 - mean_absolute_percentage_error: 94.3396 - val_loss: 0.6357 - val_mean_squared_error: 0.6357 - val_mean_absolute_percentage_error: 117.9672\n",
      "Epoch 91/1000\n",
      " - 0s - loss: 0.5634 - mean_squared_error: 0.5634 - mean_absolute_percentage_error: 94.3456 - val_loss: 0.6358 - val_mean_squared_error: 0.6358 - val_mean_absolute_percentage_error: 119.0498\n",
      "Epoch 92/1000\n",
      " - 0s - loss: 0.5630 - mean_squared_error: 0.5630 - mean_absolute_percentage_error: 93.6274 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 117.8230\n",
      "Epoch 93/1000\n",
      " - 0s - loss: 0.5634 - mean_squared_error: 0.5634 - mean_absolute_percentage_error: 94.6026 - val_loss: 0.6349 - val_mean_squared_error: 0.6349 - val_mean_absolute_percentage_error: 118.0788\n",
      "Epoch 94/1000\n",
      " - 1s - loss: 0.5632 - mean_squared_error: 0.5632 - mean_absolute_percentage_error: 96.0974 - val_loss: 0.6353 - val_mean_squared_error: 0.6353 - val_mean_absolute_percentage_error: 118.7377\n",
      "Epoch 95/1000\n",
      " - 0s - loss: 0.5629 - mean_squared_error: 0.5629 - mean_absolute_percentage_error: 93.8607 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 116.9571\n",
      "Epoch 96/1000\n",
      " - 0s - loss: 0.5632 - mean_squared_error: 0.5632 - mean_absolute_percentage_error: 93.6143 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 118.3823\n",
      "Epoch 97/1000\n",
      " - 0s - loss: 0.5628 - mean_squared_error: 0.5628 - mean_absolute_percentage_error: 97.1261 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 117.0400\n",
      "Epoch 98/1000\n",
      " - 0s - loss: 0.5623 - mean_squared_error: 0.5623 - mean_absolute_percentage_error: 93.6935 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 118.1798\n",
      "Epoch 99/1000\n",
      " - 0s - loss: 0.5628 - mean_squared_error: 0.5628 - mean_absolute_percentage_error: 95.3588 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 117.3849\n",
      "Epoch 100/1000\n",
      " - 0s - loss: 0.5624 - mean_squared_error: 0.5624 - mean_absolute_percentage_error: 95.6079 - val_loss: 0.6349 - val_mean_squared_error: 0.6349 - val_mean_absolute_percentage_error: 119.5136\n",
      "Epoch 101/1000\n",
      " - 0s - loss: 0.5627 - mean_squared_error: 0.5627 - mean_absolute_percentage_error: 95.2548 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 116.1942\n",
      "Epoch 102/1000\n",
      " - 0s - loss: 0.5623 - mean_squared_error: 0.5623 - mean_absolute_percentage_error: 93.5344 - val_loss: 0.6356 - val_mean_squared_error: 0.6356 - val_mean_absolute_percentage_error: 119.1332\n",
      "Epoch 103/1000\n",
      " - 0s - loss: 0.5621 - mean_squared_error: 0.5621 - mean_absolute_percentage_error: 94.5716 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 116.1349\n",
      "Epoch 104/1000\n",
      " - 0s - loss: 0.5628 - mean_squared_error: 0.5628 - mean_absolute_percentage_error: 92.9041 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 117.2213\n",
      "Epoch 105/1000\n",
      " - 0s - loss: 0.5625 - mean_squared_error: 0.5625 - mean_absolute_percentage_error: 94.9723 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 117.7292\n",
      "Epoch 106/1000\n",
      " - 0s - loss: 0.5625 - mean_squared_error: 0.5625 - mean_absolute_percentage_error: 94.2051 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 117.0242\n",
      "Epoch 107/1000\n",
      " - 0s - loss: 0.5623 - mean_squared_error: 0.5623 - mean_absolute_percentage_error: 95.6726 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 116.9367\n",
      "Epoch 108/1000\n",
      " - 0s - loss: 0.5625 - mean_squared_error: 0.5625 - mean_absolute_percentage_error: 94.7234 - val_loss: 0.6328 - val_mean_squared_error: 0.6328 - val_mean_absolute_percentage_error: 115.4808\n",
      "Epoch 109/1000\n",
      " - 0s - loss: 0.5621 - mean_squared_error: 0.5621 - mean_absolute_percentage_error: 92.9271 - val_loss: 0.6361 - val_mean_squared_error: 0.6361 - val_mean_absolute_percentage_error: 119.6445\n",
      "Epoch 110/1000\n",
      " - 0s - loss: 0.5624 - mean_squared_error: 0.5624 - mean_absolute_percentage_error: 94.9096 - val_loss: 0.6328 - val_mean_squared_error: 0.6328 - val_mean_absolute_percentage_error: 114.0593\n",
      "Epoch 111/1000\n",
      " - 0s - loss: 0.5627 - mean_squared_error: 0.5627 - mean_absolute_percentage_error: 92.1629 - val_loss: 0.6361 - val_mean_squared_error: 0.6361 - val_mean_absolute_percentage_error: 118.9881\n",
      "Epoch 112/1000\n",
      " - 0s - loss: 0.5631 - mean_squared_error: 0.5631 - mean_absolute_percentage_error: 97.3680 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 116.4747\n",
      "Epoch 113/1000\n",
      " - 0s - loss: 0.5626 - mean_squared_error: 0.5626 - mean_absolute_percentage_error: 93.6803 - val_loss: 0.6331 - val_mean_squared_error: 0.6331 - val_mean_absolute_percentage_error: 115.1210\n",
      "Epoch 114/1000\n",
      " - 0s - loss: 0.5626 - mean_squared_error: 0.5626 - mean_absolute_percentage_error: 96.1334 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 117.9732\n",
      "Epoch 115/1000\n",
      " - 0s - loss: 0.5615 - mean_squared_error: 0.5615 - mean_absolute_percentage_error: 93.1475 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 115.2263\n",
      "Epoch 116/1000\n",
      " - 0s - loss: 0.5620 - mean_squared_error: 0.5620 - mean_absolute_percentage_error: 94.0162 - val_loss: 0.6350 - val_mean_squared_error: 0.6350 - val_mean_absolute_percentage_error: 117.6758\n",
      "Epoch 117/1000\n",
      " - 0s - loss: 0.5617 - mean_squared_error: 0.5617 - mean_absolute_percentage_error: 93.5917 - val_loss: 0.6331 - val_mean_squared_error: 0.6331 - val_mean_absolute_percentage_error: 114.8749\n",
      "Epoch 118/1000\n",
      " - 0s - loss: 0.5614 - mean_squared_error: 0.5614 - mean_absolute_percentage_error: 92.1845 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 117.4829\n",
      "Epoch 119/1000\n",
      " - 0s - loss: 0.5607 - mean_squared_error: 0.5607 - mean_absolute_percentage_error: 91.8475 - val_loss: 0.6349 - val_mean_squared_error: 0.6349 - val_mean_absolute_percentage_error: 117.9474\n",
      "Epoch 120/1000\n",
      " - 0s - loss: 0.5607 - mean_squared_error: 0.5607 - mean_absolute_percentage_error: 91.7879 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 116.6032\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 121/1000\n",
      " - 0s - loss: 0.5614 - mean_squared_error: 0.5614 - mean_absolute_percentage_error: 93.4241 - val_loss: 0.6330 - val_mean_squared_error: 0.6330 - val_mean_absolute_percentage_error: 114.4071\n",
      "Epoch 122/1000\n",
      " - 0s - loss: 0.5609 - mean_squared_error: 0.5609 - mean_absolute_percentage_error: 91.9092 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 117.2935\n",
      "Epoch 123/1000\n",
      " - 0s - loss: 0.5609 - mean_squared_error: 0.5609 - mean_absolute_percentage_error: 93.7234 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 116.2429\n",
      "Epoch 124/1000\n",
      " - 0s - loss: 0.5610 - mean_squared_error: 0.5610 - mean_absolute_percentage_error: 92.5909 - val_loss: 0.6333 - val_mean_squared_error: 0.6333 - val_mean_absolute_percentage_error: 116.2273\n",
      "Epoch 125/1000\n",
      " - 0s - loss: 0.5606 - mean_squared_error: 0.5606 - mean_absolute_percentage_error: 92.7318 - val_loss: 0.6328 - val_mean_squared_error: 0.6328 - val_mean_absolute_percentage_error: 114.5334\n",
      "Epoch 126/1000\n",
      " - 0s - loss: 0.5609 - mean_squared_error: 0.5609 - mean_absolute_percentage_error: 91.2576 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 116.7491\n",
      "Epoch 127/1000\n",
      " - 0s - loss: 0.5609 - mean_squared_error: 0.5609 - mean_absolute_percentage_error: 93.9567 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 116.5008\n",
      "Epoch 128/1000\n",
      " - 0s - loss: 0.5605 - mean_squared_error: 0.5605 - mean_absolute_percentage_error: 91.5039 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 116.9337\n",
      "Epoch 129/1000\n",
      " - 0s - loss: 0.5605 - mean_squared_error: 0.5605 - mean_absolute_percentage_error: 92.8210 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 116.1626\n",
      "Epoch 130/1000\n",
      " - 0s - loss: 0.5605 - mean_squared_error: 0.5605 - mean_absolute_percentage_error: 91.9831 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 117.2841\n",
      "Epoch 131/1000\n",
      " - 0s - loss: 0.5603 - mean_squared_error: 0.5603 - mean_absolute_percentage_error: 92.7110 - val_loss: 0.6328 - val_mean_squared_error: 0.6328 - val_mean_absolute_percentage_error: 116.7738\n",
      "Epoch 132/1000\n",
      " - 0s - loss: 0.5606 - mean_squared_error: 0.5606 - mean_absolute_percentage_error: 91.4419 - val_loss: 0.6323 - val_mean_squared_error: 0.6323 - val_mean_absolute_percentage_error: 116.6108\n",
      "Epoch 133/1000\n",
      " - 0s - loss: 0.5601 - mean_squared_error: 0.5601 - mean_absolute_percentage_error: 90.6449 - val_loss: 0.6334 - val_mean_squared_error: 0.6334 - val_mean_absolute_percentage_error: 117.6235\n",
      "Epoch 134/1000\n",
      " - 0s - loss: 0.5599 - mean_squared_error: 0.5599 - mean_absolute_percentage_error: 91.5728 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 116.6928\n",
      "Epoch 135/1000\n",
      " - 0s - loss: 0.5605 - mean_squared_error: 0.5605 - mean_absolute_percentage_error: 91.6260 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 118.8046\n",
      "Epoch 136/1000\n",
      " - 0s - loss: 0.5601 - mean_squared_error: 0.5601 - mean_absolute_percentage_error: 91.3303 - val_loss: 0.6329 - val_mean_squared_error: 0.6329 - val_mean_absolute_percentage_error: 114.4939\n",
      "Epoch 137/1000\n",
      " - 0s - loss: 0.5598 - mean_squared_error: 0.5598 - mean_absolute_percentage_error: 90.1253 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 117.9360\n",
      "Epoch 138/1000\n",
      " - 0s - loss: 0.5598 - mean_squared_error: 0.5598 - mean_absolute_percentage_error: 92.6363 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 117.8921\n",
      "Epoch 139/1000\n",
      " - 0s - loss: 0.5600 - mean_squared_error: 0.5600 - mean_absolute_percentage_error: 90.6612 - val_loss: 0.6329 - val_mean_squared_error: 0.6329 - val_mean_absolute_percentage_error: 115.9727\n",
      "Epoch 140/1000\n",
      " - 0s - loss: 0.5596 - mean_squared_error: 0.5596 - mean_absolute_percentage_error: 92.4072 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 118.5903\n",
      "Epoch 141/1000\n",
      " - 0s - loss: 0.5600 - mean_squared_error: 0.5600 - mean_absolute_percentage_error: 92.6129 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 117.8944\n",
      "Epoch 142/1000\n",
      " - 0s - loss: 0.5597 - mean_squared_error: 0.5597 - mean_absolute_percentage_error: 91.0720 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 117.4247\n",
      "Epoch 143/1000\n",
      " - 0s - loss: 0.5595 - mean_squared_error: 0.5595 - mean_absolute_percentage_error: 89.4452 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 117.3722\n",
      "Epoch 144/1000\n",
      " - 0s - loss: 0.5597 - mean_squared_error: 0.5597 - mean_absolute_percentage_error: 90.9966 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 118.5633\n",
      "Epoch 145/1000\n",
      " - 0s - loss: 0.5599 - mean_squared_error: 0.5599 - mean_absolute_percentage_error: 92.2874 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 116.0560\n",
      "Epoch 146/1000\n",
      " - 0s - loss: 0.5598 - mean_squared_error: 0.5598 - mean_absolute_percentage_error: 90.5360 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 116.7300\n",
      "Epoch 147/1000\n",
      " - 0s - loss: 0.5597 - mean_squared_error: 0.5597 - mean_absolute_percentage_error: 92.3698 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 118.6988\n",
      "Epoch 148/1000\n",
      " - 0s - loss: 0.5598 - mean_squared_error: 0.5598 - mean_absolute_percentage_error: 92.5805 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 116.8743\n",
      "Epoch 149/1000\n",
      " - 1s - loss: 0.5594 - mean_squared_error: 0.5594 - mean_absolute_percentage_error: 89.5149 - val_loss: 0.6334 - val_mean_squared_error: 0.6334 - val_mean_absolute_percentage_error: 116.0528\n",
      "Epoch 150/1000\n",
      " - 1s - loss: 0.5596 - mean_squared_error: 0.5596 - mean_absolute_percentage_error: 90.6661 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 118.7102\n",
      "Epoch 151/1000\n",
      " - 0s - loss: 0.5596 - mean_squared_error: 0.5596 - mean_absolute_percentage_error: 89.6802 - val_loss: 0.6333 - val_mean_squared_error: 0.6333 - val_mean_absolute_percentage_error: 115.5993\n",
      "Epoch 152/1000\n",
      " - 0s - loss: 0.5597 - mean_squared_error: 0.5597 - mean_absolute_percentage_error: 90.4730 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 118.7802\n",
      "Epoch 153/1000\n",
      " - 0s - loss: 0.5595 - mean_squared_error: 0.5595 - mean_absolute_percentage_error: 90.9128 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 117.7335\n",
      "Epoch 154/1000\n",
      " - 0s - loss: 0.5596 - mean_squared_error: 0.5596 - mean_absolute_percentage_error: 90.6443 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 117.3134\n",
      "Epoch 155/1000\n",
      " - 0s - loss: 0.5596 - mean_squared_error: 0.5596 - mean_absolute_percentage_error: 90.7277 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 116.5901\n",
      "Epoch 156/1000\n",
      " - 0s - loss: 0.5594 - mean_squared_error: 0.5594 - mean_absolute_percentage_error: 90.3754 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 118.4981\n",
      "Epoch 157/1000\n",
      " - 0s - loss: 0.5592 - mean_squared_error: 0.5592 - mean_absolute_percentage_error: 90.1177 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 116.0028\n",
      "Epoch 158/1000\n",
      " - 0s - loss: 0.5593 - mean_squared_error: 0.5593 - mean_absolute_percentage_error: 90.0828 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 117.6443\n",
      "Epoch 159/1000\n",
      " - 0s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 90.7422 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 117.1590\n",
      "Epoch 160/1000\n",
      " - 0s - loss: 0.5592 - mean_squared_error: 0.5592 - mean_absolute_percentage_error: 89.8165 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 117.2370\n"
     ]
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     "text": [
      "Epoch 161/1000\n",
      " - 0s - loss: 0.5594 - mean_squared_error: 0.5594 - mean_absolute_percentage_error: 91.5370 - val_loss: 0.6328 - val_mean_squared_error: 0.6328 - val_mean_absolute_percentage_error: 116.1568\n",
      "Epoch 162/1000\n",
      " - 0s - loss: 0.5592 - mean_squared_error: 0.5592 - mean_absolute_percentage_error: 89.4893 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 118.5758\n",
      "Epoch 163/1000\n",
      " - 0s - loss: 0.5593 - mean_squared_error: 0.5593 - mean_absolute_percentage_error: 90.6861 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 118.6764\n",
      "Epoch 164/1000\n",
      " - 0s - loss: 0.5590 - mean_squared_error: 0.5590 - mean_absolute_percentage_error: 89.4289 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 117.0789\n",
      "Epoch 165/1000\n",
      " - 1s - loss: 0.5594 - mean_squared_error: 0.5594 - mean_absolute_percentage_error: 90.5170 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 118.1091\n",
      "Epoch 166/1000\n",
      " - 0s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 88.7698 - val_loss: 0.6335 - val_mean_squared_error: 0.6335 - val_mean_absolute_percentage_error: 114.9000\n",
      "Epoch 167/1000\n",
      " - 0s - loss: 0.5592 - mean_squared_error: 0.5592 - mean_absolute_percentage_error: 90.1153 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 118.6302\n",
      "Epoch 168/1000\n",
      " - 1s - loss: 0.5596 - mean_squared_error: 0.5596 - mean_absolute_percentage_error: 90.3537 - val_loss: 0.6342 - val_mean_squared_error: 0.6342 - val_mean_absolute_percentage_error: 117.7347\n",
      "Epoch 169/1000\n",
      " - 0s - loss: 0.5595 - mean_squared_error: 0.5595 - mean_absolute_percentage_error: 90.3060 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 117.6759\n",
      "Epoch 170/1000\n",
      " - 0s - loss: 0.5598 - mean_squared_error: 0.5598 - mean_absolute_percentage_error: 89.3818 - val_loss: 0.6324 - val_mean_squared_error: 0.6324 - val_mean_absolute_percentage_error: 115.9651\n",
      "Epoch 171/1000\n",
      " - 1s - loss: 0.5593 - mean_squared_error: 0.5593 - mean_absolute_percentage_error: 88.7239 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 120.0085\n",
      "Epoch 172/1000\n",
      " - 1s - loss: 0.5595 - mean_squared_error: 0.5595 - mean_absolute_percentage_error: 91.5171 - val_loss: 0.6318 - val_mean_squared_error: 0.6318 - val_mean_absolute_percentage_error: 115.6025\n",
      "Epoch 173/1000\n",
      " - 0s - loss: 0.5591 - mean_squared_error: 0.5591 - mean_absolute_percentage_error: 89.3764 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 120.8108\n",
      "Epoch 174/1000\n",
      " - 1s - loss: 0.5593 - mean_squared_error: 0.5593 - mean_absolute_percentage_error: 90.3303 - val_loss: 0.6330 - val_mean_squared_error: 0.6330 - val_mean_absolute_percentage_error: 117.3772\n",
      "Epoch 175/1000\n",
      " - 1s - loss: 0.5591 - mean_squared_error: 0.5591 - mean_absolute_percentage_error: 89.6480 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 118.7721\n",
      "Epoch 176/1000\n",
      " - 0s - loss: 0.5592 - mean_squared_error: 0.5592 - mean_absolute_percentage_error: 91.2085 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 120.1942\n",
      "Epoch 177/1000\n",
      " - 0s - loss: 0.5587 - mean_squared_error: 0.5587 - mean_absolute_percentage_error: 89.2580 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 116.6654\n",
      "Epoch 178/1000\n",
      " - 1s - loss: 0.5592 - mean_squared_error: 0.5592 - mean_absolute_percentage_error: 88.6510 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 119.7925\n",
      "Epoch 179/1000\n",
      " - 1s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 89.3343 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 117.3197\n",
      "Epoch 180/1000\n",
      " - 1s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 88.6208 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 116.7271\n",
      "Epoch 181/1000\n",
      " - 1s - loss: 0.5588 - mean_squared_error: 0.5588 - mean_absolute_percentage_error: 89.2800 - val_loss: 0.6342 - val_mean_squared_error: 0.6342 - val_mean_absolute_percentage_error: 119.3899\n",
      "Epoch 182/1000\n",
      " - 0s - loss: 0.5588 - mean_squared_error: 0.5588 - mean_absolute_percentage_error: 88.3754 - val_loss: 0.6346 - val_mean_squared_error: 0.6346 - val_mean_absolute_percentage_error: 120.0137\n",
      "Epoch 183/1000\n",
      " - 0s - loss: 0.5587 - mean_squared_error: 0.5587 - mean_absolute_percentage_error: 89.1906 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 119.4008\n",
      "Epoch 184/1000\n",
      " - 0s - loss: 0.5586 - mean_squared_error: 0.5586 - mean_absolute_percentage_error: 88.0478 - val_loss: 0.6333 - val_mean_squared_error: 0.6333 - val_mean_absolute_percentage_error: 115.5800\n",
      "Epoch 185/1000\n",
      " - 1s - loss: 0.5588 - mean_squared_error: 0.5588 - mean_absolute_percentage_error: 88.1385 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 118.7195\n",
      "Epoch 186/1000\n",
      " - 0s - loss: 0.5588 - mean_squared_error: 0.5588 - mean_absolute_percentage_error: 90.3578 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 117.6287\n",
      "Epoch 187/1000\n",
      " - 0s - loss: 0.5588 - mean_squared_error: 0.5588 - mean_absolute_percentage_error: 88.8123 - val_loss: 0.6331 - val_mean_squared_error: 0.6331 - val_mean_absolute_percentage_error: 116.9935\n",
      "Epoch 188/1000\n",
      " - 0s - loss: 0.5586 - mean_squared_error: 0.5586 - mean_absolute_percentage_error: 89.3405 - val_loss: 0.6335 - val_mean_squared_error: 0.6335 - val_mean_absolute_percentage_error: 117.3701\n",
      "Epoch 189/1000\n",
      " - 0s - loss: 0.5587 - mean_squared_error: 0.5587 - mean_absolute_percentage_error: 87.6934 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 118.8346\n",
      "Epoch 190/1000\n",
      " - 0s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 90.8950 - val_loss: 0.6333 - val_mean_squared_error: 0.6333 - val_mean_absolute_percentage_error: 117.3857\n",
      "Epoch 191/1000\n",
      " - 0s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 87.7825 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 115.6193\n",
      "Epoch 192/1000\n",
      " - 0s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 89.0910 - val_loss: 0.6348 - val_mean_squared_error: 0.6348 - val_mean_absolute_percentage_error: 120.2836\n",
      "Epoch 193/1000\n",
      " - 0s - loss: 0.5585 - mean_squared_error: 0.5585 - mean_absolute_percentage_error: 89.9997 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 116.4799\n",
      "Epoch 194/1000\n",
      " - 0s - loss: 0.5586 - mean_squared_error: 0.5586 - mean_absolute_percentage_error: 87.5728 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 117.8552\n",
      "Epoch 195/1000\n",
      " - 0s - loss: 0.5585 - mean_squared_error: 0.5585 - mean_absolute_percentage_error: 88.8661 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 118.6286\n",
      "Epoch 196/1000\n",
      " - 0s - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_percentage_error: 89.1650 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 117.0253\n",
      "Epoch 197/1000\n",
      " - 1s - loss: 0.5587 - mean_squared_error: 0.5587 - mean_absolute_percentage_error: 88.2907 - val_loss: 0.6351 - val_mean_squared_error: 0.6351 - val_mean_absolute_percentage_error: 118.8532\n",
      "Epoch 198/1000\n",
      " - 0s - loss: 0.5587 - mean_squared_error: 0.5587 - mean_absolute_percentage_error: 87.8848 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 117.4794\n",
      "Epoch 199/1000\n",
      " - 0s - loss: 0.5587 - mean_squared_error: 0.5587 - mean_absolute_percentage_error: 87.3540 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 119.6724\n",
      "Epoch 200/1000\n",
      " - 0s - loss: 0.5584 - mean_squared_error: 0.5584 - mean_absolute_percentage_error: 87.8915 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 116.9278\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 201/1000\n",
      " - 0s - loss: 0.5585 - mean_squared_error: 0.5585 - mean_absolute_percentage_error: 87.7233 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 119.3896\n",
      "Epoch 202/1000\n",
      " - 0s - loss: 0.5586 - mean_squared_error: 0.5586 - mean_absolute_percentage_error: 87.9567 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 118.5845\n",
      "Epoch 203/1000\n",
      " - 0s - loss: 0.5584 - mean_squared_error: 0.5584 - mean_absolute_percentage_error: 87.2733 - val_loss: 0.6345 - val_mean_squared_error: 0.6345 - val_mean_absolute_percentage_error: 118.9622\n",
      "Epoch 204/1000\n",
      " - 0s - loss: 0.5585 - mean_squared_error: 0.5585 - mean_absolute_percentage_error: 89.0830 - val_loss: 0.6342 - val_mean_squared_error: 0.6342 - val_mean_absolute_percentage_error: 117.3667\n",
      "Epoch 205/1000\n",
      " - 0s - loss: 0.5582 - mean_squared_error: 0.5582 - mean_absolute_percentage_error: 88.1804 - val_loss: 0.6343 - val_mean_squared_error: 0.6343 - val_mean_absolute_percentage_error: 117.6041\n",
      "Epoch 206/1000\n",
      " - 0s - loss: 0.5585 - mean_squared_error: 0.5585 - mean_absolute_percentage_error: 87.3654 - val_loss: 0.6342 - val_mean_squared_error: 0.6342 - val_mean_absolute_percentage_error: 118.3750\n",
      "Epoch 207/1000\n",
      " - 0s - loss: 0.5585 - mean_squared_error: 0.5585 - mean_absolute_percentage_error: 88.8947 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 118.8454\n",
      "Epoch 208/1000\n",
      " - 0s - loss: 0.5584 - mean_squared_error: 0.5584 - mean_absolute_percentage_error: 87.8845 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 119.4476\n",
      "Epoch 209/1000\n",
      " - 0s - loss: 0.5584 - mean_squared_error: 0.5584 - mean_absolute_percentage_error: 87.6207 - val_loss: 0.6323 - val_mean_squared_error: 0.6323 - val_mean_absolute_percentage_error: 118.4508\n",
      "Epoch 210/1000\n",
      " - 0s - loss: 0.5583 - mean_squared_error: 0.5583 - mean_absolute_percentage_error: 88.9441 - val_loss: 0.6324 - val_mean_squared_error: 0.6324 - val_mean_absolute_percentage_error: 118.1568\n",
      "Epoch 211/1000\n",
      " - 0s - loss: 0.5583 - mean_squared_error: 0.5583 - mean_absolute_percentage_error: 88.1828 - val_loss: 0.6330 - val_mean_squared_error: 0.6330 - val_mean_absolute_percentage_error: 118.1416\n",
      "Epoch 212/1000\n",
      " - 0s - loss: 0.5584 - mean_squared_error: 0.5584 - mean_absolute_percentage_error: 88.0927 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 118.2288\n",
      "Epoch 213/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 88.4897 - val_loss: 0.6338 - val_mean_squared_error: 0.6338 - val_mean_absolute_percentage_error: 119.1451\n",
      "Epoch 214/1000\n",
      " - 0s - loss: 0.5582 - mean_squared_error: 0.5582 - mean_absolute_percentage_error: 87.6236 - val_loss: 0.6346 - val_mean_squared_error: 0.6346 - val_mean_absolute_percentage_error: 120.0110\n",
      "Epoch 215/1000\n",
      " - 0s - loss: 0.5582 - mean_squared_error: 0.5582 - mean_absolute_percentage_error: 90.3947 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 119.2526\n",
      "Epoch 216/1000\n",
      " - 0s - loss: 0.5583 - mean_squared_error: 0.5583 - mean_absolute_percentage_error: 88.0250 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 117.8693\n",
      "Epoch 217/1000\n",
      " - 0s - loss: 0.5582 - mean_squared_error: 0.5582 - mean_absolute_percentage_error: 88.4068 - val_loss: 0.6334 - val_mean_squared_error: 0.6334 - val_mean_absolute_percentage_error: 118.8633\n",
      "Epoch 218/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 86.6574 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 118.1475\n",
      "Epoch 219/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 87.5936 - val_loss: 0.6327 - val_mean_squared_error: 0.6327 - val_mean_absolute_percentage_error: 117.7711\n",
      "Epoch 220/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 86.2953 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 117.8267\n",
      "Epoch 221/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 88.0534 - val_loss: 0.6340 - val_mean_squared_error: 0.6340 - val_mean_absolute_percentage_error: 119.0670\n",
      "Epoch 222/1000\n",
      " - 0s - loss: 0.5581 - mean_squared_error: 0.5581 - mean_absolute_percentage_error: 87.0799 - val_loss: 0.6336 - val_mean_squared_error: 0.6336 - val_mean_absolute_percentage_error: 117.9357\n",
      "Epoch 223/1000\n",
      " - 1s - loss: 0.5581 - mean_squared_error: 0.5581 - mean_absolute_percentage_error: 87.3031 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 119.3410\n",
      "Epoch 224/1000\n",
      " - 1s - loss: 0.5581 - mean_squared_error: 0.5581 - mean_absolute_percentage_error: 87.4808 - val_loss: 0.6344 - val_mean_squared_error: 0.6344 - val_mean_absolute_percentage_error: 117.6772\n",
      "Epoch 225/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 87.4610 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 119.8926\n",
      "Epoch 226/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 86.9415 - val_loss: 0.6341 - val_mean_squared_error: 0.6341 - val_mean_absolute_percentage_error: 119.8898\n",
      "Epoch 227/1000\n",
      " - 0s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 87.5130 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 120.1323\n",
      "Epoch 228/1000\n",
      " - 0s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 88.3400 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 119.9809\n",
      "Epoch 229/1000\n",
      " - 1s - loss: 0.5582 - mean_squared_error: 0.5582 - mean_absolute_percentage_error: 87.4438 - val_loss: 0.6329 - val_mean_squared_error: 0.6329 - val_mean_absolute_percentage_error: 117.8746\n",
      "Epoch 230/1000\n",
      " - 0s - loss: 0.5579 - mean_squared_error: 0.5579 - mean_absolute_percentage_error: 88.3958 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 119.1277\n",
      "Epoch 231/1000\n",
      " - 1s - loss: 0.5579 - mean_squared_error: 0.5579 - mean_absolute_percentage_error: 87.3235 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 118.3085\n",
      "Epoch 232/1000\n",
      " - 1s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 87.7603 - val_loss: 0.6333 - val_mean_squared_error: 0.6333 - val_mean_absolute_percentage_error: 119.5082\n",
      "Epoch 233/1000\n",
      " - 1s - loss: 0.5579 - mean_squared_error: 0.5579 - mean_absolute_percentage_error: 88.5433 - val_loss: 0.6332 - val_mean_squared_error: 0.6332 - val_mean_absolute_percentage_error: 117.9202\n",
      "Epoch 234/1000\n",
      " - 1s - loss: 0.5579 - mean_squared_error: 0.5579 - mean_absolute_percentage_error: 86.0842 - val_loss: 0.6335 - val_mean_squared_error: 0.6335 - val_mean_absolute_percentage_error: 119.6768\n",
      "Epoch 235/1000\n",
      " - 0s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 86.8347 - val_loss: 0.6330 - val_mean_squared_error: 0.6330 - val_mean_absolute_percentage_error: 118.3317\n",
      "Epoch 236/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 86.5636 - val_loss: 0.6339 - val_mean_squared_error: 0.6339 - val_mean_absolute_percentage_error: 118.5420\n",
      "Epoch 237/1000\n",
      " - 0s - loss: 0.5581 - mean_squared_error: 0.5581 - mean_absolute_percentage_error: 88.1333 - val_loss: 0.6337 - val_mean_squared_error: 0.6337 - val_mean_absolute_percentage_error: 120.9496\n",
      "Epoch 238/1000\n",
      " - 0s - loss: 0.5577 - mean_squared_error: 0.5577 - mean_absolute_percentage_error: 87.1540 - val_loss: 0.6326 - val_mean_squared_error: 0.6326 - val_mean_absolute_percentage_error: 117.4249\n",
      "Epoch 239/1000\n",
      " - 0s - loss: 0.5581 - mean_squared_error: 0.5581 - mean_absolute_percentage_error: 88.3546 - val_loss: 0.6320 - val_mean_squared_error: 0.6320 - val_mean_absolute_percentage_error: 120.5291\n",
      "Epoch 240/1000\n",
      " - 0s - loss: 0.5579 - mean_squared_error: 0.5579 - mean_absolute_percentage_error: 88.2714 - val_loss: 0.6331 - val_mean_squared_error: 0.6331 - val_mean_absolute_percentage_error: 119.6070\n"
     ]
    },
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     "text": [
      "Epoch 241/1000\n",
      " - 0s - loss: 0.5579 - mean_squared_error: 0.5579 - mean_absolute_percentage_error: 88.0675 - val_loss: 0.6329 - val_mean_squared_error: 0.6329 - val_mean_absolute_percentage_error: 119.9900\n",
      "Epoch 242/1000\n",
      " - 0s - loss: 0.5580 - mean_squared_error: 0.5580 - mean_absolute_percentage_error: 87.8265 - val_loss: 0.6326 - val_mean_squared_error: 0.6326 - val_mean_absolute_percentage_error: 118.4353\n",
      "Epoch 243/1000\n",
      " - 0s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 86.7383 - val_loss: 0.6324 - val_mean_squared_error: 0.6324 - val_mean_absolute_percentage_error: 118.8323\n",
      "Epoch 244/1000\n",
      " - 0s - loss: 0.5576 - mean_squared_error: 0.5576 - mean_absolute_percentage_error: 86.3140 - val_loss: 0.6325 - val_mean_squared_error: 0.6325 - val_mean_absolute_percentage_error: 117.5354\n",
      "Epoch 245/1000\n",
      " - 0s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 86.7380 - val_loss: 0.6326 - val_mean_squared_error: 0.6326 - val_mean_absolute_percentage_error: 118.3687\n",
      "Epoch 246/1000\n",
      " - 0s - loss: 0.5575 - mean_squared_error: 0.5575 - mean_absolute_percentage_error: 86.3931 - val_loss: 0.6325 - val_mean_squared_error: 0.6325 - val_mean_absolute_percentage_error: 120.5549\n",
      "Epoch 247/1000\n",
      " - 0s - loss: 0.5576 - mean_squared_error: 0.5576 - mean_absolute_percentage_error: 87.7671 - val_loss: 0.6314 - val_mean_squared_error: 0.6314 - val_mean_absolute_percentage_error: 118.8187\n",
      "Epoch 248/1000\n",
      " - 0s - loss: 0.5577 - mean_squared_error: 0.5577 - mean_absolute_percentage_error: 86.4139 - val_loss: 0.6305 - val_mean_squared_error: 0.6305 - val_mean_absolute_percentage_error: 119.1205\n",
      "Epoch 249/1000\n",
      " - 0s - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_percentage_error: 89.1300 - val_loss: 0.6304 - val_mean_squared_error: 0.6304 - val_mean_absolute_percentage_error: 118.8960\n",
      "Epoch 250/1000\n",
      " - 0s - loss: 0.5577 - mean_squared_error: 0.5577 - mean_absolute_percentage_error: 87.1801 - val_loss: 0.6294 - val_mean_squared_error: 0.6294 - val_mean_absolute_percentage_error: 116.5373\n",
      "Epoch 251/1000\n",
      " - 0s - loss: 0.5576 - mean_squared_error: 0.5576 - mean_absolute_percentage_error: 87.3186 - val_loss: 0.6293 - val_mean_squared_error: 0.6293 - val_mean_absolute_percentage_error: 116.9017\n",
      "Epoch 252/1000\n",
      " - 0s - loss: 0.5576 - mean_squared_error: 0.5576 - mean_absolute_percentage_error: 86.3497 - val_loss: 0.6313 - val_mean_squared_error: 0.6313 - val_mean_absolute_percentage_error: 119.0369\n",
      "Epoch 253/1000\n",
      " - 0s - loss: 0.5574 - mean_squared_error: 0.5574 - mean_absolute_percentage_error: 87.6024 - val_loss: 0.6310 - val_mean_squared_error: 0.6310 - val_mean_absolute_percentage_error: 118.5536\n",
      "Epoch 254/1000\n",
      " - 0s - loss: 0.5576 - mean_squared_error: 0.5576 - mean_absolute_percentage_error: 86.8442 - val_loss: 0.6301 - val_mean_squared_error: 0.6301 - val_mean_absolute_percentage_error: 118.0491\n",
      "Epoch 255/1000\n",
      " - 0s - loss: 0.5573 - mean_squared_error: 0.5573 - mean_absolute_percentage_error: 85.7988 - val_loss: 0.6305 - val_mean_squared_error: 0.6305 - val_mean_absolute_percentage_error: 118.2574\n",
      "Epoch 256/1000\n",
      " - 0s - loss: 0.5576 - mean_squared_error: 0.5576 - mean_absolute_percentage_error: 87.8217 - val_loss: 0.6312 - val_mean_squared_error: 0.6312 - val_mean_absolute_percentage_error: 121.1222\n",
      "Epoch 257/1000\n",
      " - 0s - loss: 0.5575 - mean_squared_error: 0.5575 - mean_absolute_percentage_error: 88.1220 - val_loss: 0.6299 - val_mean_squared_error: 0.6299 - val_mean_absolute_percentage_error: 119.6043\n",
      "Epoch 258/1000\n",
      " - 0s - loss: 0.5577 - mean_squared_error: 0.5577 - mean_absolute_percentage_error: 87.0747 - val_loss: 0.6286 - val_mean_squared_error: 0.6286 - val_mean_absolute_percentage_error: 117.0717\n",
      "Epoch 259/1000\n",
      " - 0s - loss: 0.5577 - mean_squared_error: 0.5577 - mean_absolute_percentage_error: 87.1083 - val_loss: 0.6299 - val_mean_squared_error: 0.6299 - val_mean_absolute_percentage_error: 119.5085\n",
      "Epoch 260/1000\n",
      " - 0s - loss: 0.5572 - mean_squared_error: 0.5572 - mean_absolute_percentage_error: 88.7258 - val_loss: 0.6305 - val_mean_squared_error: 0.6305 - val_mean_absolute_percentage_error: 118.9744\n",
      "Epoch 261/1000\n",
      " - 0s - loss: 0.5573 - mean_squared_error: 0.5573 - mean_absolute_percentage_error: 87.6558 - val_loss: 0.6301 - val_mean_squared_error: 0.6301 - val_mean_absolute_percentage_error: 116.7387\n",
      "Epoch 262/1000\n",
      " - 0s - loss: 0.5575 - mean_squared_error: 0.5575 - mean_absolute_percentage_error: 87.4525 - val_loss: 0.6303 - val_mean_squared_error: 0.6303 - val_mean_absolute_percentage_error: 118.7621\n",
      "Epoch 263/1000\n",
      " - 0s - loss: 0.5572 - mean_squared_error: 0.5572 - mean_absolute_percentage_error: 87.8062 - val_loss: 0.6309 - val_mean_squared_error: 0.6309 - val_mean_absolute_percentage_error: 119.2868\n",
      "Epoch 264/1000\n",
      " - 0s - loss: 0.5572 - mean_squared_error: 0.5572 - mean_absolute_percentage_error: 88.2301 - val_loss: 0.6306 - val_mean_squared_error: 0.6306 - val_mean_absolute_percentage_error: 118.9637\n",
      "Epoch 265/1000\n",
      " - 0s - loss: 0.5574 - mean_squared_error: 0.5574 - mean_absolute_percentage_error: 86.9277 - val_loss: 0.6304 - val_mean_squared_error: 0.6304 - val_mean_absolute_percentage_error: 117.7345\n",
      "Epoch 266/1000\n",
      " - 0s - loss: 0.5570 - mean_squared_error: 0.5570 - mean_absolute_percentage_error: 86.4131 - val_loss: 0.6313 - val_mean_squared_error: 0.6313 - val_mean_absolute_percentage_error: 118.7276\n",
      "Epoch 267/1000\n",
      " - 0s - loss: 0.5571 - mean_squared_error: 0.5571 - mean_absolute_percentage_error: 86.8586 - val_loss: 0.6316 - val_mean_squared_error: 0.6316 - val_mean_absolute_percentage_error: 118.9585\n",
      "Epoch 268/1000\n",
      " - 0s - loss: 0.5571 - mean_squared_error: 0.5571 - mean_absolute_percentage_error: 86.7763 - val_loss: 0.6335 - val_mean_squared_error: 0.6335 - val_mean_absolute_percentage_error: 116.5468\n",
      "Epoch 269/1000\n",
      " - 0s - loss: 0.5573 - mean_squared_error: 0.5573 - mean_absolute_percentage_error: 86.1422 - val_loss: 0.6314 - val_mean_squared_error: 0.6314 - val_mean_absolute_percentage_error: 119.8431\n",
      "Epoch 270/1000\n",
      " - 0s - loss: 0.5575 - mean_squared_error: 0.5575 - mean_absolute_percentage_error: 88.3518 - val_loss: 0.6323 - val_mean_squared_error: 0.6323 - val_mean_absolute_percentage_error: 118.1825\n",
      "Epoch 271/1000\n",
      " - 0s - loss: 0.5573 - mean_squared_error: 0.5573 - mean_absolute_percentage_error: 87.8718 - val_loss: 0.6326 - val_mean_squared_error: 0.6326 - val_mean_absolute_percentage_error: 118.9750\n",
      "Epoch 272/1000\n",
      " - 0s - loss: 0.5571 - mean_squared_error: 0.5571 - mean_absolute_percentage_error: 87.5078 - val_loss: 0.6328 - val_mean_squared_error: 0.6328 - val_mean_absolute_percentage_error: 120.4256\n",
      "Epoch 273/1000\n",
      " - 0s - loss: 0.5571 - mean_squared_error: 0.5571 - mean_absolute_percentage_error: 86.6881 - val_loss: 0.6335 - val_mean_squared_error: 0.6335 - val_mean_absolute_percentage_error: 117.8978\n",
      "Epoch 274/1000\n",
      " - 0s - loss: 0.5571 - mean_squared_error: 0.5571 - mean_absolute_percentage_error: 86.5581 - val_loss: 0.6324 - val_mean_squared_error: 0.6324 - val_mean_absolute_percentage_error: 117.9523\n",
      "Epoch 275/1000\n",
      " - 0s - loss: 0.5571 - mean_squared_error: 0.5571 - mean_absolute_percentage_error: 86.1607 - val_loss: 0.6329 - val_mean_squared_error: 0.6329 - val_mean_absolute_percentage_error: 118.4576\n",
      "Epoch 276/1000\n",
      " - 0s - loss: 0.5570 - mean_squared_error: 0.5570 - mean_absolute_percentage_error: 87.2244 - val_loss: 0.6347 - val_mean_squared_error: 0.6347 - val_mean_absolute_percentage_error: 117.7930\n",
      "Epoch 277/1000\n",
      " - 0s - loss: 0.5566 - mean_squared_error: 0.5566 - mean_absolute_percentage_error: 86.7871 - val_loss: 0.6362 - val_mean_squared_error: 0.6362 - val_mean_absolute_percentage_error: 119.1999\n",
      "Epoch 278/1000\n",
      " - 0s - loss: 0.5566 - mean_squared_error: 0.5566 - mean_absolute_percentage_error: 86.5554 - val_loss: 0.6399 - val_mean_squared_error: 0.6399 - val_mean_absolute_percentage_error: 120.3990\n",
      "Epoch 279/1000\n",
      " - 0s - loss: 0.5572 - mean_squared_error: 0.5572 - mean_absolute_percentage_error: 87.3763 - val_loss: 0.6375 - val_mean_squared_error: 0.6375 - val_mean_absolute_percentage_error: 119.1368\n",
      "Epoch 280/1000\n",
      " - 0s - loss: 0.5567 - mean_squared_error: 0.5567 - mean_absolute_percentage_error: 87.4005 - val_loss: 0.6376 - val_mean_squared_error: 0.6376 - val_mean_absolute_percentage_error: 117.1910\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 281/1000\n",
      " - 0s - loss: 0.5568 - mean_squared_error: 0.5568 - mean_absolute_percentage_error: 86.8869 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 120.2290\n",
      "Epoch 282/1000\n",
      " - 0s - loss: 0.5567 - mean_squared_error: 0.5567 - mean_absolute_percentage_error: 89.3595 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 121.3644\n",
      "Epoch 283/1000\n",
      " - 0s - loss: 0.5568 - mean_squared_error: 0.5568 - mean_absolute_percentage_error: 87.9535 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 118.0273\n",
      "Epoch 284/1000\n",
      " - 0s - loss: 0.5563 - mean_squared_error: 0.5563 - mean_absolute_percentage_error: 87.6296 - val_loss: 0.6404 - val_mean_squared_error: 0.6404 - val_mean_absolute_percentage_error: 118.4268\n",
      "Epoch 285/1000\n",
      " - 0s - loss: 0.5565 - mean_squared_error: 0.5565 - mean_absolute_percentage_error: 87.9981 - val_loss: 0.6402 - val_mean_squared_error: 0.6402 - val_mean_absolute_percentage_error: 119.2154\n",
      "Epoch 286/1000\n",
      " - 0s - loss: 0.5562 - mean_squared_error: 0.5562 - mean_absolute_percentage_error: 86.5465 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 120.5217\n",
      "Epoch 287/1000\n",
      " - 0s - loss: 0.5563 - mean_squared_error: 0.5563 - mean_absolute_percentage_error: 87.8287 - val_loss: 0.6385 - val_mean_squared_error: 0.6385 - val_mean_absolute_percentage_error: 121.6318\n",
      "Epoch 288/1000\n",
      " - 0s - loss: 0.5563 - mean_squared_error: 0.5563 - mean_absolute_percentage_error: 88.2816 - val_loss: 0.6383 - val_mean_squared_error: 0.6383 - val_mean_absolute_percentage_error: 120.4635\n",
      "Epoch 289/1000\n",
      " - 0s - loss: 0.5561 - mean_squared_error: 0.5561 - mean_absolute_percentage_error: 86.4942 - val_loss: 0.6389 - val_mean_squared_error: 0.6389 - val_mean_absolute_percentage_error: 118.6555\n",
      "Epoch 290/1000\n",
      " - 0s - loss: 0.5559 - mean_squared_error: 0.5559 - mean_absolute_percentage_error: 86.8057 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 119.4100\n",
      "Epoch 291/1000\n",
      " - 0s - loss: 0.5560 - mean_squared_error: 0.5560 - mean_absolute_percentage_error: 87.0722 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 120.9046\n",
      "Epoch 292/1000\n",
      " - 0s - loss: 0.5563 - mean_squared_error: 0.5563 - mean_absolute_percentage_error: 86.8068 - val_loss: 0.6413 - val_mean_squared_error: 0.6413 - val_mean_absolute_percentage_error: 119.2298\n",
      "Epoch 293/1000\n",
      " - 0s - loss: 0.5562 - mean_squared_error: 0.5562 - mean_absolute_percentage_error: 85.5723 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 119.5176\n",
      "Epoch 294/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 86.4271 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 122.1986\n",
      "Epoch 295/1000\n",
      " - 0s - loss: 0.5561 - mean_squared_error: 0.5561 - mean_absolute_percentage_error: 86.8185 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 120.7100\n",
      "Epoch 296/1000\n",
      " - 0s - loss: 0.5560 - mean_squared_error: 0.5560 - mean_absolute_percentage_error: 84.6961 - val_loss: 0.6404 - val_mean_squared_error: 0.6404 - val_mean_absolute_percentage_error: 119.8995\n",
      "Epoch 297/1000\n",
      " - 0s - loss: 0.5564 - mean_squared_error: 0.5564 - mean_absolute_percentage_error: 88.0330 - val_loss: 0.6394 - val_mean_squared_error: 0.6394 - val_mean_absolute_percentage_error: 121.2012\n",
      "Epoch 298/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 85.8709 - val_loss: 0.6395 - val_mean_squared_error: 0.6395 - val_mean_absolute_percentage_error: 119.5192\n",
      "Epoch 299/1000\n",
      " - 0s - loss: 0.5559 - mean_squared_error: 0.5559 - mean_absolute_percentage_error: 85.8436 - val_loss: 0.6401 - val_mean_squared_error: 0.6401 - val_mean_absolute_percentage_error: 120.4848\n",
      "Epoch 300/1000\n",
      " - 0s - loss: 0.5559 - mean_squared_error: 0.5559 - mean_absolute_percentage_error: 85.7432 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 119.2035\n",
      "Epoch 301/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 85.1535 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 120.5439\n",
      "Epoch 302/1000\n",
      " - 0s - loss: 0.5561 - mean_squared_error: 0.5561 - mean_absolute_percentage_error: 88.0499 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 121.4258\n",
      "Epoch 303/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 86.1077 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 119.5002\n",
      "Epoch 304/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 86.9337 - val_loss: 0.6408 - val_mean_squared_error: 0.6408 - val_mean_absolute_percentage_error: 120.7042\n",
      "Epoch 305/1000\n",
      " - 0s - loss: 0.5559 - mean_squared_error: 0.5559 - mean_absolute_percentage_error: 85.9535 - val_loss: 0.6406 - val_mean_squared_error: 0.6406 - val_mean_absolute_percentage_error: 119.2994\n",
      "Epoch 306/1000\n",
      " - 0s - loss: 0.5563 - mean_squared_error: 0.5563 - mean_absolute_percentage_error: 86.6654 - val_loss: 0.6404 - val_mean_squared_error: 0.6404 - val_mean_absolute_percentage_error: 119.6890\n",
      "Epoch 307/1000\n",
      " - 0s - loss: 0.5559 - mean_squared_error: 0.5559 - mean_absolute_percentage_error: 86.2030 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 118.3589\n",
      "Epoch 308/1000\n",
      " - 0s - loss: 0.5562 - mean_squared_error: 0.5562 - mean_absolute_percentage_error: 86.7586 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 123.5372\n",
      "Epoch 309/1000\n",
      " - 0s - loss: 0.5563 - mean_squared_error: 0.5563 - mean_absolute_percentage_error: 87.6409 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 117.8543\n",
      "Epoch 310/1000\n",
      " - 0s - loss: 0.5564 - mean_squared_error: 0.5564 - mean_absolute_percentage_error: 85.9574 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 120.3207\n",
      "Epoch 311/1000\n",
      " - 0s - loss: 0.5568 - mean_squared_error: 0.5568 - mean_absolute_percentage_error: 87.6938 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 120.1353\n",
      "Epoch 312/1000\n",
      " - 0s - loss: 0.5561 - mean_squared_error: 0.5561 - mean_absolute_percentage_error: 85.5440 - val_loss: 0.6472 - val_mean_squared_error: 0.6472 - val_mean_absolute_percentage_error: 118.9837\n",
      "Epoch 313/1000\n",
      " - 0s - loss: 0.5560 - mean_squared_error: 0.5560 - mean_absolute_percentage_error: 86.6079 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 122.7036\n",
      "Epoch 314/1000\n",
      " - 0s - loss: 0.5561 - mean_squared_error: 0.5561 - mean_absolute_percentage_error: 86.6671 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 118.2846\n",
      "Epoch 315/1000\n",
      " - 0s - loss: 0.5562 - mean_squared_error: 0.5562 - mean_absolute_percentage_error: 85.8048 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 120.2048\n",
      "Epoch 316/1000\n",
      " - 0s - loss: 0.5560 - mean_squared_error: 0.5560 - mean_absolute_percentage_error: 86.6966 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 123.5880\n",
      "Epoch 317/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 87.9140 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 119.0106\n",
      "Epoch 318/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 86.6506 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 118.5628\n",
      "Epoch 319/1000\n",
      " - 0s - loss: 0.5559 - mean_squared_error: 0.5559 - mean_absolute_percentage_error: 86.1919 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 117.8287\n",
      "Epoch 320/1000\n",
      " - 0s - loss: 0.5557 - mean_squared_error: 0.5557 - mean_absolute_percentage_error: 85.0607 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 118.7422\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 321/1000\n",
      " - 0s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 86.3307 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 121.0483\n",
      "Epoch 322/1000\n",
      " - 0s - loss: 0.5557 - mean_squared_error: 0.5557 - mean_absolute_percentage_error: 86.0507 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 119.4728\n",
      "Epoch 323/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 84.9062 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 117.9825\n",
      "Epoch 324/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 86.0277 - val_loss: 0.6407 - val_mean_squared_error: 0.6407 - val_mean_absolute_percentage_error: 120.2373\n",
      "Epoch 325/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 85.2611 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 118.4247\n",
      "Epoch 326/1000\n",
      " - 0s - loss: 0.5557 - mean_squared_error: 0.5557 - mean_absolute_percentage_error: 85.7352 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 119.8708\n",
      "Epoch 327/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 86.2760 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 118.2711\n",
      "Epoch 328/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 84.0522 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 119.2852\n",
      "Epoch 329/1000\n",
      " - 1s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 83.8624 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 120.1558\n",
      "Epoch 330/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 85.3777 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 119.2437\n",
      "Epoch 331/1000\n",
      " - 1s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.2328 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 118.8755\n",
      "Epoch 332/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 85.7582 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 119.8699\n",
      "Epoch 333/1000\n",
      " - 1s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 85.6382 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 119.4086\n",
      "Epoch 334/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.1848 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 119.1317\n",
      "Epoch 335/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 84.8979 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 120.9713\n",
      "Epoch 336/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 83.7405 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 118.4909\n",
      "Epoch 337/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 83.8075 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 117.6778\n",
      "Epoch 338/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 84.2691 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 120.0821\n",
      "Epoch 339/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 85.1290 - val_loss: 0.6406 - val_mean_squared_error: 0.6406 - val_mean_absolute_percentage_error: 120.3252\n",
      "Epoch 340/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 84.1353 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 117.8088\n",
      "Epoch 341/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.4915 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 119.2677\n",
      "Epoch 342/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 86.7200 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 121.6966\n",
      "Epoch 343/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.6129 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 119.1232\n",
      "Epoch 344/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 84.6213 - val_loss: 0.6404 - val_mean_squared_error: 0.6404 - val_mean_absolute_percentage_error: 118.5568\n",
      "Epoch 345/1000\n",
      " - 0s - loss: 0.5553 - mean_squared_error: 0.5553 - mean_absolute_percentage_error: 84.6573 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 120.1693\n",
      "Epoch 346/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 85.1009 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 121.4705\n",
      "Epoch 347/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 84.1161 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 120.7963\n",
      "Epoch 348/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 83.0595 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 122.2870\n",
      "Epoch 349/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 86.5595 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 120.0670\n",
      "Epoch 350/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.6345 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 118.2604\n",
      "Epoch 351/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 83.8577 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 120.2038\n",
      "Epoch 352/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.9314 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 120.4568\n",
      "Epoch 353/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 84.1510 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 119.5705\n",
      "Epoch 354/1000\n",
      " - 0s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 84.8203 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 121.4611\n",
      "Epoch 355/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 85.4481 - val_loss: 0.6413 - val_mean_squared_error: 0.6413 - val_mean_absolute_percentage_error: 119.4529\n",
      "Epoch 356/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 83.9658 - val_loss: 0.6406 - val_mean_squared_error: 0.6406 - val_mean_absolute_percentage_error: 120.3453\n",
      "Epoch 357/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 84.7532 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 119.4923\n",
      "Epoch 358/1000\n",
      " - 0s - loss: 0.5553 - mean_squared_error: 0.5553 - mean_absolute_percentage_error: 84.0195 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 121.1199\n",
      "Epoch 359/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 83.9977 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 119.3587\n",
      "Epoch 360/1000\n",
      " - 0s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 85.3408 - val_loss: 0.6407 - val_mean_squared_error: 0.6407 - val_mean_absolute_percentage_error: 118.6195\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 361/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 83.9421 - val_loss: 0.6404 - val_mean_squared_error: 0.6404 - val_mean_absolute_percentage_error: 119.3269\n",
      "Epoch 362/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 84.2701 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 119.5421\n",
      "Epoch 363/1000\n",
      " - 0s - loss: 0.5553 - mean_squared_error: 0.5553 - mean_absolute_percentage_error: 84.8396 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 119.6045\n",
      "Epoch 364/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.5639 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 120.0908\n",
      "Epoch 365/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 83.3669 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 119.8070\n",
      "Epoch 366/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 83.3100 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 118.8076\n",
      "Epoch 367/1000\n",
      " - 1s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 83.9353 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 120.7518\n",
      "Epoch 368/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 83.0538 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 120.4068\n",
      "Epoch 369/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 84.0682 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 120.7793\n",
      "Epoch 370/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 83.8321 - val_loss: 0.6413 - val_mean_squared_error: 0.6413 - val_mean_absolute_percentage_error: 120.1903\n",
      "Epoch 371/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 84.2696 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 119.6072\n",
      "Epoch 372/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 82.6796 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 121.1269\n",
      "Epoch 373/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 83.3941 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 121.2054\n",
      "Epoch 374/1000\n",
      " - 0s - loss: 0.5553 - mean_squared_error: 0.5553 - mean_absolute_percentage_error: 84.2584 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 120.0222\n",
      "Epoch 375/1000\n",
      " - 1s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 83.9311 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 118.7926\n",
      "Epoch 376/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 83.0261 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 122.3531\n",
      "Epoch 377/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 84.9022 - val_loss: 0.6413 - val_mean_squared_error: 0.6413 - val_mean_absolute_percentage_error: 121.9242\n",
      "Epoch 378/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 84.0912 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 117.5521\n",
      "Epoch 379/1000\n",
      " - 1s - loss: 0.5554 - mean_squared_error: 0.5554 - mean_absolute_percentage_error: 82.9790 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 120.8923\n",
      "Epoch 380/1000\n",
      " - 1s - loss: 0.5556 - mean_squared_error: 0.5556 - mean_absolute_percentage_error: 86.2556 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 121.5874\n",
      "Epoch 381/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 84.1609 - val_loss: 0.6403 - val_mean_squared_error: 0.6403 - val_mean_absolute_percentage_error: 121.6531\n",
      "Epoch 382/1000\n",
      " - 0s - loss: 0.5553 - mean_squared_error: 0.5553 - mean_absolute_percentage_error: 86.2613 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 122.2583\n",
      "Epoch 383/1000\n",
      " - 0s - loss: 0.5555 - mean_squared_error: 0.5555 - mean_absolute_percentage_error: 84.5568 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 120.6407\n",
      "Epoch 384/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 82.3156 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 121.5068\n",
      "Epoch 385/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.7096 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 120.7868\n",
      "Epoch 386/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 82.6094 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 121.0175\n",
      "Epoch 387/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 84.5271 - val_loss: 0.6405 - val_mean_squared_error: 0.6405 - val_mean_absolute_percentage_error: 122.8710\n",
      "Epoch 388/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 85.0525 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 122.2466\n",
      "Epoch 389/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.7906 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 119.8486\n",
      "Epoch 390/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 84.1025 - val_loss: 0.6408 - val_mean_squared_error: 0.6408 - val_mean_absolute_percentage_error: 122.5187\n",
      "Epoch 391/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 85.3336 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 124.4465\n",
      "Epoch 392/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 86.7437 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 122.4259\n",
      "Epoch 393/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 82.9265 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 121.1567\n",
      "Epoch 394/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 83.8526 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 120.4118\n",
      "Epoch 395/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.0544 - val_loss: 0.6407 - val_mean_squared_error: 0.6407 - val_mean_absolute_percentage_error: 122.0647\n",
      "Epoch 396/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.2643 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 123.1072\n",
      "Epoch 397/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 83.7745 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 124.6427\n",
      "Epoch 398/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 85.3483 - val_loss: 0.6413 - val_mean_squared_error: 0.6413 - val_mean_absolute_percentage_error: 123.1203\n",
      "Epoch 399/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 85.5647 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 122.7226\n",
      "Epoch 400/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 84.7460 - val_loss: 0.6403 - val_mean_squared_error: 0.6403 - val_mean_absolute_percentage_error: 126.6835\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 401/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 85.9637 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 123.2167\n",
      "Epoch 402/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 84.1759 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 123.8966\n",
      "Epoch 403/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 84.3824 - val_loss: 0.6403 - val_mean_squared_error: 0.6403 - val_mean_absolute_percentage_error: 121.4330\n",
      "Epoch 404/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 85.4504 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 122.3811\n",
      "Epoch 405/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 84.1498 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 125.0343\n",
      "Epoch 406/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 84.0763 - val_loss: 0.6402 - val_mean_squared_error: 0.6402 - val_mean_absolute_percentage_error: 123.8999\n",
      "Epoch 407/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.1123 - val_loss: 0.6403 - val_mean_squared_error: 0.6403 - val_mean_absolute_percentage_error: 121.7978\n",
      "Epoch 408/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.5097 - val_loss: 0.6406 - val_mean_squared_error: 0.6406 - val_mean_absolute_percentage_error: 123.6797\n",
      "Epoch 409/1000\n",
      " - 0s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 85.6914 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 124.1594\n",
      "Epoch 410/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 85.4292 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 124.3478\n",
      "Epoch 411/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.9838 - val_loss: 0.6440 - val_mean_squared_error: 0.6440 - val_mean_absolute_percentage_error: 124.1250\n",
      "Epoch 412/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.6396 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 122.1962\n",
      "Epoch 413/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.0810 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 123.9359\n",
      "Epoch 414/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 85.3357 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 123.9301\n",
      "Epoch 415/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 84.1796 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 122.0921\n",
      "Epoch 416/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 83.7262 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 124.4180\n",
      "Epoch 417/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 85.7572 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 123.7228\n",
      "Epoch 418/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 82.9520 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 122.1968\n",
      "Epoch 419/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.9015 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 123.2637\n",
      "Epoch 420/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 84.2340 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 125.2722\n",
      "Epoch 421/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.3692 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 124.1392\n",
      "Epoch 422/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.0310 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 122.2153\n",
      "Epoch 423/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 82.9824 - val_loss: 0.6403 - val_mean_squared_error: 0.6403 - val_mean_absolute_percentage_error: 122.8017\n",
      "Epoch 424/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 84.1214 - val_loss: 0.6404 - val_mean_squared_error: 0.6404 - val_mean_absolute_percentage_error: 124.3736\n",
      "Epoch 425/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 84.2396 - val_loss: 0.6403 - val_mean_squared_error: 0.6403 - val_mean_absolute_percentage_error: 122.8682\n",
      "Epoch 426/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.1244 - val_loss: 0.6395 - val_mean_squared_error: 0.6395 - val_mean_absolute_percentage_error: 120.8711\n",
      "Epoch 427/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 83.6881 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 124.2423\n",
      "Epoch 428/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 84.7660 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 124.2929\n",
      "Epoch 429/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 82.8075 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 121.5014\n",
      "Epoch 430/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.2289 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 123.7194\n",
      "Epoch 431/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 83.8237 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 125.3033\n",
      "Epoch 432/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.2600 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 122.6922\n",
      "Epoch 433/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.6681 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 121.6941\n",
      "Epoch 434/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.1045 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 121.9186\n",
      "Epoch 435/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 82.5990 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 122.9404\n",
      "Epoch 436/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.4580 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 126.7546\n",
      "Epoch 437/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 82.7731 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 123.2786\n",
      "Epoch 438/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 82.8064 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 126.0887\n",
      "Epoch 439/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 84.1142 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 124.6332\n",
      "Epoch 440/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.1990 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 123.1722\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 441/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 82.5290 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 123.1712\n",
      "Epoch 442/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 83.2134 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 123.4500\n",
      "Epoch 443/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.7800 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 123.4379\n",
      "Epoch 444/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.0803 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 122.8003\n",
      "Epoch 445/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.5272 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 121.0617\n",
      "Epoch 446/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 84.4777 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 126.7945\n",
      "Epoch 447/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.5289 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 125.4806\n",
      "Epoch 448/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.1005 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 121.6169\n",
      "Epoch 449/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 82.7791 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 124.3495\n",
      "Epoch 450/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 83.3561 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 123.4264\n",
      "Epoch 451/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 83.1254 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 125.3471\n",
      "Epoch 452/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 82.9763 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 125.0777\n",
      "Epoch 453/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.9066 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 121.1283\n",
      "Epoch 454/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.2751 - val_loss: 0.6413 - val_mean_squared_error: 0.6413 - val_mean_absolute_percentage_error: 122.7590\n",
      "Epoch 455/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 83.4230 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 123.0709\n",
      "Epoch 456/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.1066 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 123.2254\n",
      "Epoch 457/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.2659 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 125.3733\n",
      "Epoch 458/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 83.3265 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 123.6301\n",
      "Epoch 459/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.6147 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 122.5631\n",
      "Epoch 460/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 82.4585 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 122.2792\n",
      "Epoch 461/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.9519 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 126.3385\n",
      "Epoch 462/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 84.1671 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 124.3942\n",
      "Epoch 463/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.5485 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 121.7366\n",
      "Epoch 464/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.9333 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 123.6127\n",
      "Epoch 465/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 83.2018 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 122.3768\n",
      "Epoch 466/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 81.7498 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 123.1740\n",
      "Epoch 467/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.5255 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 124.1876\n",
      "Epoch 468/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 83.2605 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 121.1065\n",
      "Epoch 469/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 82.0935 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 122.9929\n",
      "Epoch 470/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 82.2799 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 122.8758\n",
      "Epoch 471/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 81.5698 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 121.5050\n",
      "Epoch 472/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.0470 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 125.1888\n",
      "Epoch 473/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 85.0129 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 123.8705\n",
      "Epoch 474/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.0849 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 119.9956\n",
      "Epoch 475/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 82.2150 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 124.5474\n",
      "Epoch 476/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 84.2412 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 124.3112\n",
      "Epoch 477/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.3592 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 124.3178\n",
      "Epoch 478/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 83.7931 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 124.8113\n",
      "Epoch 479/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 82.7548 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 123.4494\n",
      "Epoch 480/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 83.4710 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 122.3860\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 481/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.9555 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 122.0208\n",
      "Epoch 482/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.5408 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 122.1362\n",
      "Epoch 483/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.7054 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 124.1010\n",
      "Epoch 484/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.2449 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 122.4303\n",
      "Epoch 485/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.6338 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 123.3990\n",
      "Epoch 486/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 82.6806 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 122.1037\n",
      "Epoch 487/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 83.0488 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 123.7706\n",
      "Epoch 488/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.1935 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 124.9793\n",
      "Epoch 489/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 83.1454 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 122.0946\n",
      "Epoch 490/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.9100 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 121.8430\n",
      "Epoch 491/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.5798 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 123.8156\n",
      "Epoch 492/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.3611 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 126.0010\n",
      "Epoch 493/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 83.4010 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 122.9620\n",
      "Epoch 494/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.6357 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 121.8687\n",
      "Epoch 495/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.3123 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 123.2525\n",
      "Epoch 496/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.1693 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 123.4099\n",
      "Epoch 497/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.6835 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 125.1731\n",
      "Epoch 498/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.5033 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 122.9947\n",
      "Epoch 499/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.6240 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 120.0676\n",
      "Epoch 500/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.4697 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 123.6954\n",
      "Epoch 501/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.5572 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 124.2717\n",
      "Epoch 502/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.9462 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 122.1442\n",
      "Epoch 503/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.7062 - val_loss: 0.6397 - val_mean_squared_error: 0.6397 - val_mean_absolute_percentage_error: 121.8216\n",
      "Epoch 504/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 82.0908 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 121.0524\n",
      "Epoch 505/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.6406 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 124.1620\n",
      "Epoch 506/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 83.1637 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 123.5048\n",
      "Epoch 507/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 82.2442 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 120.3060\n",
      "Epoch 508/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.7395 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 122.7660\n",
      "Epoch 509/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.3930 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 124.8947\n",
      "Epoch 510/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 83.1472 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 123.8763\n",
      "Epoch 511/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.0731 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 122.1138\n",
      "Epoch 512/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.7313 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 124.5962\n",
      "Epoch 513/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 84.9060 - val_loss: 0.6464 - val_mean_squared_error: 0.6464 - val_mean_absolute_percentage_error: 124.3200\n",
      "Epoch 514/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.9874 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 125.8951\n",
      "Epoch 515/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.4165 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 122.0400\n",
      "Epoch 516/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.1063 - val_loss: 0.6458 - val_mean_squared_error: 0.6458 - val_mean_absolute_percentage_error: 122.1394\n",
      "Epoch 517/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.3665 - val_loss: 0.6466 - val_mean_squared_error: 0.6466 - val_mean_absolute_percentage_error: 127.6291\n",
      "Epoch 518/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.1425 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 121.6912\n",
      "Epoch 519/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 81.8388 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 121.9501\n",
      "Epoch 520/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.7204 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 123.0423\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 521/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.4262 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 122.7247\n",
      "Epoch 522/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.5114 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 123.7898\n",
      "Epoch 523/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 83.1093 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 121.6706\n",
      "Epoch 524/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.5666 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 120.6943\n",
      "Epoch 525/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.8941 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 122.6606\n",
      "Epoch 526/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 80.9286 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 123.0012\n",
      "Epoch 527/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.6628 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 121.6046\n",
      "Epoch 528/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.8838 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 124.2989\n",
      "Epoch 529/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 81.6123 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 124.0216\n",
      "Epoch 530/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.3574 - val_loss: 0.6475 - val_mean_squared_error: 0.6475 - val_mean_absolute_percentage_error: 122.8022\n",
      "Epoch 531/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.9159 - val_loss: 0.6480 - val_mean_squared_error: 0.6480 - val_mean_absolute_percentage_error: 123.9073\n",
      "Epoch 532/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 82.1909 - val_loss: 0.6462 - val_mean_squared_error: 0.6462 - val_mean_absolute_percentage_error: 121.0502\n",
      "Epoch 533/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.9676 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 122.6744\n",
      "Epoch 534/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.3771 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 122.5840\n",
      "Epoch 535/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.8958 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 121.7112\n",
      "Epoch 536/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.9413 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 120.9088\n",
      "Epoch 537/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.2668 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 121.8620\n",
      "Epoch 538/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 82.3921 - val_loss: 0.6472 - val_mean_squared_error: 0.6472 - val_mean_absolute_percentage_error: 121.3409\n",
      "Epoch 539/1000\n",
      " - 0s - loss: 0.5553 - mean_squared_error: 0.5553 - mean_absolute_percentage_error: 81.6376 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 121.4074\n",
      "Epoch 540/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 82.5510 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 119.9645\n",
      "Epoch 541/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.1265 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 121.8409\n",
      "Epoch 542/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.3496 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 123.0676\n",
      "Epoch 543/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 82.5141 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 120.6302\n",
      "Epoch 544/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.8249 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 120.9316\n",
      "Epoch 545/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 82.6641 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 122.7132\n",
      "Epoch 546/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.6262 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 122.2745\n",
      "Epoch 547/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.9038 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 122.3773\n",
      "Epoch 548/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.9914 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 123.7466\n",
      "Epoch 549/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.0412 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 124.4592\n",
      "Epoch 550/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.8013 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 121.7210\n",
      "Epoch 551/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.1732 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 122.6880\n",
      "Epoch 552/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.3189 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 121.8836\n",
      "Epoch 553/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.3557 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 123.2537\n",
      "Epoch 554/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.6591 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 124.2277\n",
      "Epoch 555/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.2342 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 121.1998\n",
      "Epoch 556/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.1012 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 120.7535\n",
      "Epoch 557/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.9938 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 124.1639\n",
      "Epoch 558/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.8705 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 122.4724\n",
      "Epoch 559/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.1965 - val_loss: 0.6408 - val_mean_squared_error: 0.6408 - val_mean_absolute_percentage_error: 120.6543\n",
      "Epoch 560/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.4496 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 122.0569\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 561/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.3124 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 121.7505\n",
      "Epoch 562/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.9207 - val_loss: 0.6407 - val_mean_squared_error: 0.6407 - val_mean_absolute_percentage_error: 123.3716\n",
      "Epoch 563/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.5562 - val_loss: 0.6414 - val_mean_squared_error: 0.6414 - val_mean_absolute_percentage_error: 121.5235\n",
      "Epoch 564/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.3006 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 121.6549\n",
      "Epoch 565/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.9404 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 123.0504\n",
      "Epoch 566/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.0920 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 120.7579\n",
      "Epoch 567/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.9595 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 121.7180\n",
      "Epoch 568/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 81.1880 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 122.8188\n",
      "Epoch 569/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.1928 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 122.9098\n",
      "Epoch 570/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.9515 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 122.0322\n",
      "Epoch 571/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.0360 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 119.9234\n",
      "Epoch 572/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.6453 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 123.1753\n",
      "Epoch 573/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.6799 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 122.4564\n",
      "Epoch 574/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.6395 - val_loss: 0.6414 - val_mean_squared_error: 0.6414 - val_mean_absolute_percentage_error: 120.7566\n",
      "Epoch 575/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.1063 - val_loss: 0.6414 - val_mean_squared_error: 0.6414 - val_mean_absolute_percentage_error: 121.6281\n",
      "Epoch 576/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.9612 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 122.4978\n",
      "Epoch 577/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.3843 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 121.1129\n",
      "Epoch 578/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.4549 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 123.3511\n",
      "Epoch 579/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.3688 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 123.0179\n",
      "Epoch 580/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.2244 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 121.4278\n",
      "Epoch 581/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.2949 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 123.2457\n",
      "Epoch 582/1000\n",
      " - 1s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 82.7899 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 120.6930\n",
      "Epoch 583/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.6692 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 119.9936\n",
      "Epoch 584/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.9182 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 123.0113\n",
      "Epoch 585/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.2491 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 120.4528\n",
      "Epoch 586/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.3624 - val_loss: 0.6408 - val_mean_squared_error: 0.6408 - val_mean_absolute_percentage_error: 121.6721\n",
      "Epoch 587/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.0027 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 122.0415\n",
      "Epoch 588/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.1976 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 120.7461\n",
      "Epoch 589/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.1533 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 121.3204\n",
      "Epoch 590/1000\n",
      " - 1s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.8971 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 121.8112\n",
      "Epoch 591/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.5321 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 120.8410\n",
      "Epoch 592/1000\n",
      " - 1s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.0337 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 120.2367\n",
      "Epoch 593/1000\n",
      " - 1s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.1561 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 121.2249\n",
      "Epoch 594/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.5628 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 120.6904\n",
      "Epoch 595/1000\n",
      " - 1s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.8738 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 121.3980\n",
      "Epoch 596/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.8710 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 120.4470\n",
      "Epoch 597/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.8901 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 120.3765\n",
      "Epoch 598/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.1287 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 121.4833\n",
      "Epoch 599/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.1683 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 121.9173\n",
      "Epoch 600/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.3617 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 120.8645\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 601/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.6240 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 122.6580\n",
      "Epoch 602/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.4846 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 122.9094\n",
      "Epoch 603/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.4239 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 121.0612\n",
      "Epoch 604/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.3672 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 121.2855\n",
      "Epoch 605/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.6699 - val_loss: 0.6414 - val_mean_squared_error: 0.6414 - val_mean_absolute_percentage_error: 121.1003\n",
      "Epoch 606/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.5991 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 120.5467\n",
      "Epoch 607/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.2450 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 120.6112\n",
      "Epoch 608/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.0932 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 121.5895\n",
      "Epoch 609/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.7768 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 121.3001\n",
      "Epoch 610/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 82.6097 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 121.7200\n",
      "Epoch 611/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.9835 - val_loss: 0.6410 - val_mean_squared_error: 0.6410 - val_mean_absolute_percentage_error: 120.8403\n",
      "Epoch 612/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.7124 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 120.4776\n",
      "Epoch 613/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.0877 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 121.3876\n",
      "Epoch 614/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.0172 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 121.8159\n",
      "Epoch 615/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.6024 - val_loss: 0.6419 - val_mean_squared_error: 0.6419 - val_mean_absolute_percentage_error: 122.4103\n",
      "Epoch 616/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.5863 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 122.8174\n",
      "Epoch 617/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.7416 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 121.9865\n",
      "Epoch 618/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 79.6263 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 120.0033\n",
      "Epoch 619/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6416 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 121.2296\n",
      "Epoch 620/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.9837 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 120.4151\n",
      "Epoch 621/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 79.8170 - val_loss: 0.6409 - val_mean_squared_error: 0.6409 - val_mean_absolute_percentage_error: 119.5499\n",
      "Epoch 622/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 82.2361 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 121.5756\n",
      "Epoch 623/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.8406 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 120.3384\n",
      "Epoch 624/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.9197 - val_loss: 0.6417 - val_mean_squared_error: 0.6417 - val_mean_absolute_percentage_error: 119.1288\n",
      "Epoch 625/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.7534 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 120.6926\n",
      "Epoch 626/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.0703 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 121.3308\n",
      "Epoch 627/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 79.9286 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 121.1020\n",
      "Epoch 628/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.0286 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 120.7268\n",
      "Epoch 629/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.9684 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 119.0621\n",
      "Epoch 630/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.6315 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 119.9876\n",
      "Epoch 631/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.0149 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 121.9296\n",
      "Epoch 632/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.0033 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 120.6038\n",
      "Epoch 633/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.0753 - val_loss: 0.6402 - val_mean_squared_error: 0.6402 - val_mean_absolute_percentage_error: 121.5388\n",
      "Epoch 634/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.6206 - val_loss: 0.6412 - val_mean_squared_error: 0.6412 - val_mean_absolute_percentage_error: 120.9642\n",
      "Epoch 635/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.0850 - val_loss: 0.6411 - val_mean_squared_error: 0.6411 - val_mean_absolute_percentage_error: 123.0284\n",
      "Epoch 636/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.6213 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 123.0078\n",
      "Epoch 637/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.5060 - val_loss: 0.6421 - val_mean_squared_error: 0.6421 - val_mean_absolute_percentage_error: 121.6842\n",
      "Epoch 638/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 80.0498 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 122.7052\n",
      "Epoch 639/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 82.0933 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 120.1678\n",
      "Epoch 640/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.4069 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 120.5535\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 641/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8039 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 122.7274\n",
      "Epoch 642/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.4541 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 121.7025\n",
      "Epoch 643/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.5279 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 121.9040\n",
      "Epoch 644/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.2449 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 122.4077\n",
      "Epoch 645/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.7426 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 121.0816\n",
      "Epoch 646/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.7858 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 123.4106\n",
      "Epoch 647/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.0288 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 121.9318\n",
      "Epoch 648/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8516 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 121.1218\n",
      "Epoch 649/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 79.5501 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 121.9445\n",
      "Epoch 650/1000\n",
      " - 1s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.4425 - val_loss: 0.6440 - val_mean_squared_error: 0.6440 - val_mean_absolute_percentage_error: 122.0453\n",
      "Epoch 651/1000\n",
      " - 1s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.5981 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 122.8910\n",
      "Epoch 652/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.1681 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 120.5657\n",
      "Epoch 653/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6858 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 120.4028\n",
      "Epoch 654/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.4966 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 121.2644\n",
      "Epoch 655/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.5151 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 123.0805\n",
      "Epoch 656/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.1469 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 121.9549\n",
      "Epoch 657/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.6263 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 121.3031\n",
      "Epoch 658/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.6529 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 121.6849\n",
      "Epoch 659/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 83.0023 - val_loss: 0.6440 - val_mean_squared_error: 0.6440 - val_mean_absolute_percentage_error: 120.3619\n",
      "Epoch 660/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.9373 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 121.4418\n",
      "Epoch 661/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 81.1891 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 122.2321\n",
      "Epoch 662/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 81.5348 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 121.6031\n",
      "Epoch 663/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.4245 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.6185\n",
      "Epoch 664/1000\n",
      " - 1s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.9278 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 122.1041\n",
      "Epoch 665/1000\n",
      " - 1s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.1454 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 121.3438\n",
      "Epoch 666/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.1597 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 123.4294\n",
      "Epoch 667/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 81.0336 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 122.0223\n",
      "Epoch 668/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.0686 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 120.8728\n",
      "Epoch 669/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.5550 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 121.8136\n",
      "Epoch 670/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.9275 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 122.6314\n",
      "Epoch 671/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 82.5884 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 119.9339\n",
      "Epoch 672/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8894 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 121.4606\n",
      "Epoch 673/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.6859 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 122.8587\n",
      "Epoch 674/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.1001 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 123.0524\n",
      "Epoch 675/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8341 - val_loss: 0.6440 - val_mean_squared_error: 0.6440 - val_mean_absolute_percentage_error: 123.6657\n",
      "Epoch 676/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.6230 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 121.5903\n",
      "Epoch 677/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.3467 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 120.9033\n",
      "Epoch 678/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.7136 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 123.1355\n",
      "Epoch 679/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.2824 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 123.3407\n",
      "Epoch 680/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 79.5186 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 123.6269\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 681/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.3822 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 121.7253\n",
      "Epoch 682/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6796 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 121.8686\n",
      "Epoch 683/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.5442 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 122.3180\n",
      "Epoch 684/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6125 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 121.2574\n",
      "Epoch 685/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4883 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 119.2565\n",
      "Epoch 686/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.0024 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 122.7059\n",
      "Epoch 687/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.9681 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 124.3775\n",
      "Epoch 688/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 82.4604 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 120.4427\n",
      "Epoch 689/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.9180 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 120.9993\n",
      "Epoch 690/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.0747 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 122.7160\n",
      "Epoch 691/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.8572 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 122.7805\n",
      "Epoch 692/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.8831 - val_loss: 0.6440 - val_mean_squared_error: 0.6440 - val_mean_absolute_percentage_error: 121.3262\n",
      "Epoch 693/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.3981 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 122.4339\n",
      "Epoch 694/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.6320 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 123.9400\n",
      "Epoch 695/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.5471 - val_loss: 0.6458 - val_mean_squared_error: 0.6458 - val_mean_absolute_percentage_error: 123.1275\n",
      "Epoch 696/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.2685 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 123.4262\n",
      "Epoch 697/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.9087 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 121.5547\n",
      "Epoch 698/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.3076 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 120.3160\n",
      "Epoch 699/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 79.5125 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 123.1933\n",
      "Epoch 700/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 80.8351 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 122.9897\n",
      "Epoch 701/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 81.4756 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 119.4726\n",
      "Epoch 702/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 80.2446 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 120.2192\n",
      "Epoch 703/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 83.6917 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 122.5951\n",
      "Epoch 704/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 82.2551 - val_loss: 0.6416 - val_mean_squared_error: 0.6416 - val_mean_absolute_percentage_error: 121.4804\n",
      "Epoch 705/1000\n",
      " - 0s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 80.4394 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 125.2490\n",
      "Epoch 706/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 81.4174 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 121.0961\n",
      "Epoch 707/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 79.8597 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 119.8303\n",
      "Epoch 708/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 82.2308 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 121.7324\n",
      "Epoch 709/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.1479 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 122.7872\n",
      "Epoch 710/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 82.3324 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 122.6292\n",
      "Epoch 711/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.5028 - val_loss: 0.6422 - val_mean_squared_error: 0.6422 - val_mean_absolute_percentage_error: 121.0325\n",
      "Epoch 712/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.1290 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 120.1780\n",
      "Epoch 713/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.2400 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 122.1129\n",
      "Epoch 714/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.0982 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 124.2929\n",
      "Epoch 715/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 81.5435 - val_loss: 0.6415 - val_mean_squared_error: 0.6415 - val_mean_absolute_percentage_error: 121.7006\n",
      "Epoch 716/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 81.4529 - val_loss: 0.6418 - val_mean_squared_error: 0.6418 - val_mean_absolute_percentage_error: 119.3973\n",
      "Epoch 717/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.2809 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 121.1527\n",
      "Epoch 718/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.3175 - val_loss: 0.6426 - val_mean_squared_error: 0.6426 - val_mean_absolute_percentage_error: 124.0096\n",
      "Epoch 719/1000\n",
      " - 1s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.1445 - val_loss: 0.6423 - val_mean_squared_error: 0.6423 - val_mean_absolute_percentage_error: 122.4363\n",
      "Epoch 720/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.4154 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 121.6044\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 721/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.0079 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 122.5515\n",
      "Epoch 722/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.7305 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 123.4976\n",
      "Epoch 723/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.9899 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 122.2041\n",
      "Epoch 724/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.3194 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 120.3826\n",
      "Epoch 725/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.0973 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 120.8103\n",
      "Epoch 726/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.9572 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 122.4299\n",
      "Epoch 727/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.5253 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 120.2594\n",
      "Epoch 728/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4807 - val_loss: 0.6420 - val_mean_squared_error: 0.6420 - val_mean_absolute_percentage_error: 120.2277\n",
      "Epoch 729/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.8672 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 120.5406\n",
      "Epoch 730/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.3604 - val_loss: 0.6427 - val_mean_squared_error: 0.6427 - val_mean_absolute_percentage_error: 120.9591\n",
      "Epoch 731/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 81.5083 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 122.2197\n",
      "Epoch 732/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.8814 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 122.6086\n",
      "Epoch 733/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.7347 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 121.2353\n",
      "Epoch 734/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.5945 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 122.3394\n",
      "Epoch 735/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.4720 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 123.5697\n",
      "Epoch 736/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.8928 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 121.6781\n",
      "Epoch 737/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.4897 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 120.5215\n",
      "Epoch 738/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.2591 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 123.2467\n",
      "Epoch 739/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6612 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 122.9484\n",
      "Epoch 740/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.0470 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 120.7354\n",
      "Epoch 741/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8126 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 121.9921\n",
      "Epoch 742/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.5861 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 122.6729\n",
      "Epoch 743/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.7657 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 122.4512\n",
      "Epoch 744/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.9846 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 124.4172\n",
      "Epoch 745/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.5122 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 123.2571\n",
      "Epoch 746/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.7295 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 121.3668\n",
      "Epoch 747/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.4934 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 121.4051\n",
      "Epoch 748/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.5624 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 123.3940\n",
      "Epoch 749/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.2959 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 122.6528\n",
      "Epoch 750/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.7520 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 121.8709\n",
      "Epoch 751/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6076 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 120.0512\n",
      "Epoch 752/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.0450 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 120.9172\n",
      "Epoch 753/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 81.2400 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 123.4684\n",
      "Epoch 754/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.3785 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 121.8535\n",
      "Epoch 755/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.9773 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 120.9669\n",
      "Epoch 756/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.7648 - val_loss: 0.6435 - val_mean_squared_error: 0.6435 - val_mean_absolute_percentage_error: 122.5463\n",
      "Epoch 757/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.8965 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 121.7551\n",
      "Epoch 758/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.5352 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 121.8077\n",
      "Epoch 759/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.0073 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 123.3341\n",
      "Epoch 760/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.5291 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 119.3832\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 761/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.8750 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 119.8978\n",
      "Epoch 762/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.1418 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 124.1224\n",
      "Epoch 763/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.9115 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 124.4165\n",
      "Epoch 764/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.0311 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.7275\n",
      "Epoch 765/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8442 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 120.9370\n",
      "Epoch 766/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.0764 - val_loss: 0.6467 - val_mean_squared_error: 0.6467 - val_mean_absolute_percentage_error: 121.2523\n",
      "Epoch 767/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.7905 - val_loss: 0.6469 - val_mean_squared_error: 0.6469 - val_mean_absolute_percentage_error: 120.5898\n",
      "Epoch 768/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.4247 - val_loss: 0.6464 - val_mean_squared_error: 0.6464 - val_mean_absolute_percentage_error: 121.2018\n",
      "Epoch 769/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.1686 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 120.4900\n",
      "Epoch 770/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.5188 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.4300\n",
      "Epoch 771/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.2483 - val_loss: 0.6458 - val_mean_squared_error: 0.6458 - val_mean_absolute_percentage_error: 122.4649\n",
      "Epoch 772/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.6550 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.4906\n",
      "Epoch 773/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.8488 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 121.0753\n",
      "Epoch 774/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.8962 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 121.0444\n",
      "Epoch 775/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.4517 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 121.3470\n",
      "Epoch 776/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.1631 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 121.0682\n",
      "Epoch 777/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.4311 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.9274\n",
      "Epoch 778/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.1782 - val_loss: 0.6467 - val_mean_squared_error: 0.6467 - val_mean_absolute_percentage_error: 122.2669\n",
      "Epoch 779/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.8044 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 120.1260\n",
      "Epoch 780/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.2260 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 122.6471\n",
      "Epoch 781/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.8127 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 123.3164\n",
      "Epoch 782/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.9773 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 121.5364\n",
      "Epoch 783/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.0081 - val_loss: 0.6463 - val_mean_squared_error: 0.6463 - val_mean_absolute_percentage_error: 121.5660\n",
      "Epoch 784/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.9270 - val_loss: 0.6462 - val_mean_squared_error: 0.6462 - val_mean_absolute_percentage_error: 121.6631\n",
      "Epoch 785/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.4398 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 120.9145\n",
      "Epoch 786/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.4303 - val_loss: 0.6462 - val_mean_squared_error: 0.6462 - val_mean_absolute_percentage_error: 123.4689\n",
      "Epoch 787/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.2377 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 120.8089\n",
      "Epoch 788/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.7212 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 121.1376\n",
      "Epoch 789/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.2177 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 120.8979\n",
      "Epoch 790/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.1043 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 121.5902\n",
      "Epoch 791/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.2253 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 122.7605\n",
      "Epoch 792/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.2890 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 121.2267\n",
      "Epoch 793/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.2797 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 119.3774\n",
      "Epoch 794/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.3326 - val_loss: 0.6458 - val_mean_squared_error: 0.6458 - val_mean_absolute_percentage_error: 123.1173\n",
      "Epoch 795/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.2068 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 122.8001\n",
      "Epoch 796/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8730 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 119.7137\n",
      "Epoch 797/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.5711 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 122.9606\n",
      "Epoch 798/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.2226 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 123.1873\n",
      "Epoch 799/1000\n",
      " - 1s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.3373 - val_loss: 0.6464 - val_mean_squared_error: 0.6464 - val_mean_absolute_percentage_error: 122.5724\n",
      "Epoch 800/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.0350 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 122.8280\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 801/1000\n",
      " - 0s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 80.4417 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 119.0573\n",
      "Epoch 802/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 79.3560 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 119.7934\n",
      "Epoch 803/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 79.6003 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 123.9369\n",
      "Epoch 804/1000\n",
      " - 1s - loss: 0.5546 - mean_squared_error: 0.5546 - mean_absolute_percentage_error: 81.0768 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 121.4028\n",
      "Epoch 805/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 79.0933 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 120.3769\n",
      "Epoch 806/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 79.0997 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 120.4641\n",
      "Epoch 807/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.5863 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 121.2310\n",
      "Epoch 808/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4384 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 121.9993\n",
      "Epoch 809/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.3396 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 121.8755\n",
      "Epoch 810/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.0348 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 120.6615\n",
      "Epoch 811/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.6800 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 121.1683\n",
      "Epoch 812/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.7375 - val_loss: 0.6438 - val_mean_squared_error: 0.6438 - val_mean_absolute_percentage_error: 123.0671\n",
      "Epoch 813/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.8205 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 121.6229\n",
      "Epoch 814/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.3817 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 119.9389\n",
      "Epoch 815/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 78.9860 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 121.4336\n",
      "Epoch 816/1000\n",
      " - 1s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.0327 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 121.0178\n",
      "Epoch 817/1000\n",
      " - 1s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.2144 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 120.2170\n",
      "Epoch 818/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.0694 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 121.3115\n",
      "Epoch 819/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.2398 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 120.3987\n",
      "Epoch 820/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.2591 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 120.4765\n",
      "Epoch 821/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.5575 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 120.9069\n",
      "Epoch 822/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.1548 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 120.3002\n",
      "Epoch 823/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8388 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 122.2912\n",
      "Epoch 824/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8166 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 122.7957\n",
      "Epoch 825/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.1826 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 119.8578\n",
      "Epoch 826/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 78.6865 - val_loss: 0.6471 - val_mean_squared_error: 0.6471 - val_mean_absolute_percentage_error: 125.7280\n",
      "Epoch 827/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.8062 - val_loss: 0.6463 - val_mean_squared_error: 0.6463 - val_mean_absolute_percentage_error: 122.1902\n",
      "Epoch 828/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.4856 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 117.9024\n",
      "Epoch 829/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.0191 - val_loss: 0.6461 - val_mean_squared_error: 0.6461 - val_mean_absolute_percentage_error: 125.1382\n",
      "Epoch 830/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 79.9561 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 124.8784\n",
      "Epoch 831/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 80.5821 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 120.9306\n",
      "Epoch 832/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 79.8049 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 122.9418\n",
      "Epoch 833/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.7618 - val_loss: 0.6468 - val_mean_squared_error: 0.6468 - val_mean_absolute_percentage_error: 120.6497\n",
      "Epoch 834/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 78.6135 - val_loss: 0.6463 - val_mean_squared_error: 0.6463 - val_mean_absolute_percentage_error: 120.4585\n",
      "Epoch 835/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8174 - val_loss: 0.6473 - val_mean_squared_error: 0.6473 - val_mean_absolute_percentage_error: 124.3779\n",
      "Epoch 836/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.3934 - val_loss: 0.6465 - val_mean_squared_error: 0.6465 - val_mean_absolute_percentage_error: 123.0594\n",
      "Epoch 837/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4082 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 120.3602\n",
      "Epoch 838/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.3704 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.0397\n",
      "Epoch 839/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4390 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 123.8779\n",
      "Epoch 840/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.6859 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 122.7396\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 841/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.7640 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 121.9564\n",
      "Epoch 842/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8323 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 120.4106\n",
      "Epoch 843/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.6419 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 122.1586\n",
      "Epoch 844/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.5590 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 122.1728\n",
      "Epoch 845/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.8829 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 120.8034\n",
      "Epoch 846/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.5186 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 119.8655\n",
      "Epoch 847/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.5222 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 120.9890\n",
      "Epoch 848/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.4725 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 122.7352\n",
      "Epoch 849/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.1112 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 120.6652\n",
      "Epoch 850/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.6926 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 122.1443\n",
      "Epoch 851/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.0326 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 122.3955\n",
      "Epoch 852/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.3524 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 122.6632\n",
      "Epoch 853/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.4261 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 123.0614\n",
      "Epoch 854/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.1336 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 121.8264\n",
      "Epoch 855/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.1446 - val_loss: 0.6459 - val_mean_squared_error: 0.6459 - val_mean_absolute_percentage_error: 121.1447\n",
      "Epoch 856/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.2154 - val_loss: 0.6462 - val_mean_squared_error: 0.6462 - val_mean_absolute_percentage_error: 123.1507\n",
      "Epoch 857/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4806 - val_loss: 0.6465 - val_mean_squared_error: 0.6465 - val_mean_absolute_percentage_error: 123.3419\n",
      "Epoch 858/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.6357 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 120.6402\n",
      "Epoch 859/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.1664 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 121.2541\n",
      "Epoch 860/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.4482 - val_loss: 0.6455 - val_mean_squared_error: 0.6455 - val_mean_absolute_percentage_error: 122.7552\n",
      "Epoch 861/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.2798 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 120.7513\n",
      "Epoch 862/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.2192 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 119.9192\n",
      "Epoch 863/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.2280 - val_loss: 0.6461 - val_mean_squared_error: 0.6461 - val_mean_absolute_percentage_error: 121.8144\n",
      "Epoch 864/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.1954 - val_loss: 0.6465 - val_mean_squared_error: 0.6465 - val_mean_absolute_percentage_error: 122.9599\n",
      "Epoch 865/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.9956 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 120.8490\n",
      "Epoch 866/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.1158 - val_loss: 0.6461 - val_mean_squared_error: 0.6461 - val_mean_absolute_percentage_error: 120.9349\n",
      "Epoch 867/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.5636 - val_loss: 0.6464 - val_mean_squared_error: 0.6464 - val_mean_absolute_percentage_error: 121.2090\n",
      "Epoch 868/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4345 - val_loss: 0.6462 - val_mean_squared_error: 0.6462 - val_mean_absolute_percentage_error: 121.4860\n",
      "Epoch 869/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.3056 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 121.7962\n",
      "Epoch 870/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.5976 - val_loss: 0.6461 - val_mean_squared_error: 0.6461 - val_mean_absolute_percentage_error: 121.2959\n",
      "Epoch 871/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8668 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 119.9574\n",
      "Epoch 872/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8044 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 122.5712\n",
      "Epoch 873/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.6686 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 123.8438\n",
      "Epoch 874/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.9508 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 120.3143\n",
      "Epoch 875/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 78.2470 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 120.8889\n",
      "Epoch 876/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.9543 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 123.4200\n",
      "Epoch 877/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.5422 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 121.4975\n",
      "Epoch 878/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.7936 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 122.5434\n",
      "Epoch 879/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.1956 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 123.4255\n",
      "Epoch 880/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 81.5963 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 121.3201\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 881/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 82.1819 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 121.0017\n",
      "Epoch 882/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.6619 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 123.0986\n",
      "Epoch 883/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.0418 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 120.9976\n",
      "Epoch 884/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.2370 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 120.7483\n",
      "Epoch 885/1000\n",
      " - 1s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 77.6488 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 122.5959\n",
      "Epoch 886/1000\n",
      " - 1s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 77.9062 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 122.5789\n",
      "Epoch 887/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.5786 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 121.2181\n",
      "Epoch 888/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.8501 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 122.2478\n",
      "Epoch 889/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 77.5969 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 120.9256\n",
      "Epoch 890/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.1054 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 121.5946\n",
      "Epoch 891/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.7352 - val_loss: 0.6450 - val_mean_squared_error: 0.6450 - val_mean_absolute_percentage_error: 123.4898\n",
      "Epoch 892/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.7282 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 121.5911\n",
      "Epoch 893/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.2108 - val_loss: 0.6452 - val_mean_squared_error: 0.6452 - val_mean_absolute_percentage_error: 119.8990\n",
      "Epoch 894/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.1955 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 121.6077\n",
      "Epoch 895/1000\n",
      " - 1s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 77.9608 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 122.2087\n",
      "Epoch 896/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.1762 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 122.3536\n",
      "Epoch 897/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.7496 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 121.9809\n",
      "Epoch 898/1000\n",
      " - 1s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.3040 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 120.3160\n",
      "Epoch 899/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.0172 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 121.2766\n",
      "Epoch 900/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.0612 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 122.7548\n",
      "Epoch 901/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.1053 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 122.4798\n",
      "Epoch 902/1000\n",
      " - 0s - loss: 0.5535 - mean_squared_error: 0.5535 - mean_absolute_percentage_error: 79.2488 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 121.5191\n",
      "Epoch 903/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.6484 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 121.1770\n",
      "Epoch 904/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.0859 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 120.7114\n",
      "Epoch 905/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.2661 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 121.4004\n",
      "Epoch 906/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 77.9321 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 122.9207\n",
      "Epoch 907/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.5421 - val_loss: 0.6441 - val_mean_squared_error: 0.6441 - val_mean_absolute_percentage_error: 121.3346\n",
      "Epoch 908/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 77.8988 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 120.7650\n",
      "Epoch 909/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.4922 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 121.5193\n",
      "Epoch 910/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.3252 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 122.0753\n",
      "Epoch 911/1000\n",
      " - 1s - loss: 0.5535 - mean_squared_error: 0.5535 - mean_absolute_percentage_error: 79.4670 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 121.4042\n",
      "Epoch 912/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.8380 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 120.7896\n",
      "Epoch 913/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.5487 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 123.3207\n",
      "Epoch 914/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.4672 - val_loss: 0.6461 - val_mean_squared_error: 0.6461 - val_mean_absolute_percentage_error: 122.0550\n",
      "Epoch 915/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.6986 - val_loss: 0.6454 - val_mean_squared_error: 0.6454 - val_mean_absolute_percentage_error: 119.9277\n",
      "Epoch 916/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.6375 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 122.6392\n",
      "Epoch 917/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.3483 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 123.4848\n",
      "Epoch 918/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 77.9803 - val_loss: 0.6443 - val_mean_squared_error: 0.6443 - val_mean_absolute_percentage_error: 122.0385\n",
      "Epoch 919/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.6425 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 122.7378\n",
      "Epoch 920/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.7119 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 122.8396\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 921/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.4466 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 121.2893\n",
      "Epoch 922/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.5352 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 121.8551\n",
      "Epoch 923/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.5198 - val_loss: 0.6466 - val_mean_squared_error: 0.6466 - val_mean_absolute_percentage_error: 122.6791\n",
      "Epoch 924/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.0395 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 120.5504\n",
      "Epoch 925/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.7587 - val_loss: 0.6447 - val_mean_squared_error: 0.6447 - val_mean_absolute_percentage_error: 122.0268\n",
      "Epoch 926/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.5720 - val_loss: 0.6448 - val_mean_squared_error: 0.6448 - val_mean_absolute_percentage_error: 123.1076\n",
      "Epoch 927/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.0844 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 121.8629\n",
      "Epoch 928/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.2719 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 121.3698\n",
      "Epoch 929/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.2747 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 120.8134\n",
      "Epoch 930/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.4999 - val_loss: 0.6428 - val_mean_squared_error: 0.6428 - val_mean_absolute_percentage_error: 120.6707\n",
      "Epoch 931/1000\n",
      " - 0s - loss: 0.5535 - mean_squared_error: 0.5535 - mean_absolute_percentage_error: 78.3730 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 120.8245\n",
      "Epoch 932/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 78.3512 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 121.8604\n",
      "Epoch 933/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.7951 - val_loss: 0.6429 - val_mean_squared_error: 0.6429 - val_mean_absolute_percentage_error: 120.2953\n",
      "Epoch 934/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.9371 - val_loss: 0.6425 - val_mean_squared_error: 0.6425 - val_mean_absolute_percentage_error: 120.9339\n",
      "Epoch 935/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.2797 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 122.9164\n",
      "Epoch 936/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.0283 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 121.3135\n",
      "Epoch 937/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 80.0262 - val_loss: 0.6431 - val_mean_squared_error: 0.6431 - val_mean_absolute_percentage_error: 120.8833\n",
      "Epoch 938/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.3371 - val_loss: 0.6440 - val_mean_squared_error: 0.6440 - val_mean_absolute_percentage_error: 121.4972\n",
      "Epoch 939/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.3766 - val_loss: 0.6433 - val_mean_squared_error: 0.6433 - val_mean_absolute_percentage_error: 120.7709\n",
      "Epoch 940/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 79.2198 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 121.0545\n",
      "Epoch 941/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.3833 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 122.0273\n",
      "Epoch 942/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.3881 - val_loss: 0.6432 - val_mean_squared_error: 0.6432 - val_mean_absolute_percentage_error: 120.8363\n",
      "Epoch 943/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.6045 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 120.0172\n",
      "Epoch 944/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.9160 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 122.6144\n",
      "Epoch 945/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.5593 - val_loss: 0.6430 - val_mean_squared_error: 0.6430 - val_mean_absolute_percentage_error: 121.7456\n",
      "Epoch 946/1000\n",
      " - 0s - loss: 0.5535 - mean_squared_error: 0.5535 - mean_absolute_percentage_error: 78.0045 - val_loss: 0.6424 - val_mean_squared_error: 0.6424 - val_mean_absolute_percentage_error: 119.3952\n",
      "Epoch 947/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 82.1086 - val_loss: 0.6434 - val_mean_squared_error: 0.6434 - val_mean_absolute_percentage_error: 121.3031\n",
      "Epoch 948/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.1209 - val_loss: 0.6436 - val_mean_squared_error: 0.6436 - val_mean_absolute_percentage_error: 121.7982\n",
      "Epoch 949/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.5759 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 121.1858\n",
      "Epoch 950/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.3546 - val_loss: 0.6457 - val_mean_squared_error: 0.6457 - val_mean_absolute_percentage_error: 121.8157\n",
      "Epoch 951/1000\n",
      " - 0s - loss: 0.5536 - mean_squared_error: 0.5536 - mean_absolute_percentage_error: 78.5375 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 120.7399\n",
      "Epoch 952/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 77.3864 - val_loss: 0.6449 - val_mean_squared_error: 0.6449 - val_mean_absolute_percentage_error: 119.8913\n",
      "Epoch 953/1000\n",
      " - 0s - loss: 0.5535 - mean_squared_error: 0.5535 - mean_absolute_percentage_error: 79.0517 - val_loss: 0.6456 - val_mean_squared_error: 0.6456 - val_mean_absolute_percentage_error: 121.8022\n",
      "Epoch 954/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.7326 - val_loss: 0.6445 - val_mean_squared_error: 0.6445 - val_mean_absolute_percentage_error: 122.6980\n",
      "Epoch 955/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.0376 - val_loss: 0.6466 - val_mean_squared_error: 0.6466 - val_mean_absolute_percentage_error: 123.2380\n",
      "Epoch 956/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 80.5745 - val_loss: 0.6477 - val_mean_squared_error: 0.6477 - val_mean_absolute_percentage_error: 123.1578\n",
      "Epoch 957/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.8668 - val_loss: 0.6488 - val_mean_squared_error: 0.6488 - val_mean_absolute_percentage_error: 120.4075\n",
      "Epoch 958/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.2626 - val_loss: 0.6490 - val_mean_squared_error: 0.6490 - val_mean_absolute_percentage_error: 121.7566\n",
      "Epoch 959/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 79.4870 - val_loss: 0.6499 - val_mean_squared_error: 0.6499 - val_mean_absolute_percentage_error: 126.0607\n",
      "Epoch 960/1000\n",
      " - 1s - loss: 0.5558 - mean_squared_error: 0.5558 - mean_absolute_percentage_error: 81.6161 - val_loss: 0.6472 - val_mean_squared_error: 0.6472 - val_mean_absolute_percentage_error: 118.9464\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 961/1000\n",
      " - 1s - loss: 0.5551 - mean_squared_error: 0.5551 - mean_absolute_percentage_error: 80.4089 - val_loss: 0.6468 - val_mean_squared_error: 0.6468 - val_mean_absolute_percentage_error: 124.1005\n",
      "Epoch 962/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 82.1500 - val_loss: 0.6442 - val_mean_squared_error: 0.6442 - val_mean_absolute_percentage_error: 123.4751\n",
      "Epoch 963/1000\n",
      " - 0s - loss: 0.5550 - mean_squared_error: 0.5550 - mean_absolute_percentage_error: 81.4845 - val_loss: 0.6437 - val_mean_squared_error: 0.6437 - val_mean_absolute_percentage_error: 119.3414\n",
      "Epoch 964/1000\n",
      " - 0s - loss: 0.5549 - mean_squared_error: 0.5549 - mean_absolute_percentage_error: 79.9776 - val_loss: 0.6439 - val_mean_squared_error: 0.6439 - val_mean_absolute_percentage_error: 120.3623\n",
      "Epoch 965/1000\n",
      " - 0s - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_percentage_error: 81.8392 - val_loss: 0.6446 - val_mean_squared_error: 0.6446 - val_mean_absolute_percentage_error: 120.7372\n",
      "Epoch 966/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 79.6262 - val_loss: 0.6444 - val_mean_squared_error: 0.6444 - val_mean_absolute_percentage_error: 120.4243\n",
      "Epoch 967/1000\n",
      " - 1s - loss: 0.5547 - mean_squared_error: 0.5547 - mean_absolute_percentage_error: 81.0284 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 124.2835\n",
      "Epoch 968/1000\n",
      " - 0s - loss: 0.5548 - mean_squared_error: 0.5548 - mean_absolute_percentage_error: 79.6812 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 119.7896\n",
      "Epoch 969/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 78.6391 - val_loss: 0.6463 - val_mean_squared_error: 0.6463 - val_mean_absolute_percentage_error: 121.0869\n",
      "Epoch 970/1000\n",
      " - 0s - loss: 0.5545 - mean_squared_error: 0.5545 - mean_absolute_percentage_error: 80.8186 - val_loss: 0.6480 - val_mean_squared_error: 0.6480 - val_mean_absolute_percentage_error: 124.8296\n",
      "Epoch 971/1000\n",
      " - 0s - loss: 0.5543 - mean_squared_error: 0.5543 - mean_absolute_percentage_error: 80.1636 - val_loss: 0.6486 - val_mean_squared_error: 0.6486 - val_mean_absolute_percentage_error: 124.0082\n",
      "Epoch 972/1000\n",
      " - 0s - loss: 0.5544 - mean_squared_error: 0.5544 - mean_absolute_percentage_error: 81.9891 - val_loss: 0.6488 - val_mean_squared_error: 0.6488 - val_mean_absolute_percentage_error: 121.5224\n",
      "Epoch 973/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 79.6897 - val_loss: 0.6483 - val_mean_squared_error: 0.6483 - val_mean_absolute_percentage_error: 121.0591\n",
      "Epoch 974/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 80.7818 - val_loss: 0.6481 - val_mean_squared_error: 0.6481 - val_mean_absolute_percentage_error: 122.2530\n",
      "Epoch 975/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.7407 - val_loss: 0.6474 - val_mean_squared_error: 0.6474 - val_mean_absolute_percentage_error: 122.0451\n",
      "Epoch 976/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.6689 - val_loss: 0.6470 - val_mean_squared_error: 0.6470 - val_mean_absolute_percentage_error: 122.9021\n",
      "Epoch 977/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.6685 - val_loss: 0.6474 - val_mean_squared_error: 0.6474 - val_mean_absolute_percentage_error: 121.6459\n",
      "Epoch 978/1000\n",
      " - 0s - loss: 0.5542 - mean_squared_error: 0.5542 - mean_absolute_percentage_error: 81.0504 - val_loss: 0.6481 - val_mean_squared_error: 0.6481 - val_mean_absolute_percentage_error: 121.5246\n",
      "Epoch 979/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 79.4042 - val_loss: 0.6475 - val_mean_squared_error: 0.6475 - val_mean_absolute_percentage_error: 123.7362\n",
      "Epoch 980/1000\n",
      " - 1s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 80.5390 - val_loss: 0.6473 - val_mean_squared_error: 0.6473 - val_mean_absolute_percentage_error: 121.9297\n",
      "Epoch 981/1000\n",
      " - 0s - loss: 0.5541 - mean_squared_error: 0.5541 - mean_absolute_percentage_error: 78.8060 - val_loss: 0.6473 - val_mean_squared_error: 0.6473 - val_mean_absolute_percentage_error: 121.1047\n",
      "Epoch 982/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.1766 - val_loss: 0.6465 - val_mean_squared_error: 0.6465 - val_mean_absolute_percentage_error: 122.9368\n",
      "Epoch 983/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 80.0743 - val_loss: 0.6468 - val_mean_squared_error: 0.6468 - val_mean_absolute_percentage_error: 121.3806\n",
      "Epoch 984/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 83.3282 - val_loss: 0.6475 - val_mean_squared_error: 0.6475 - val_mean_absolute_percentage_error: 121.9111\n",
      "Epoch 985/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 82.9522 - val_loss: 0.6475 - val_mean_squared_error: 0.6475 - val_mean_absolute_percentage_error: 122.6195\n",
      "Epoch 986/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.8111 - val_loss: 0.6476 - val_mean_squared_error: 0.6476 - val_mean_absolute_percentage_error: 121.9254\n",
      "Epoch 987/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.3657 - val_loss: 0.6477 - val_mean_squared_error: 0.6477 - val_mean_absolute_percentage_error: 121.8558\n",
      "Epoch 988/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.7539 - val_loss: 0.6476 - val_mean_squared_error: 0.6476 - val_mean_absolute_percentage_error: 122.9285\n",
      "Epoch 989/1000\n",
      " - 0s - loss: 0.5540 - mean_squared_error: 0.5540 - mean_absolute_percentage_error: 79.6692 - val_loss: 0.6472 - val_mean_squared_error: 0.6472 - val_mean_absolute_percentage_error: 120.6621\n",
      "Epoch 990/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.1993 - val_loss: 0.6467 - val_mean_squared_error: 0.6467 - val_mean_absolute_percentage_error: 120.5338\n",
      "Epoch 991/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 77.9956 - val_loss: 0.6471 - val_mean_squared_error: 0.6471 - val_mean_absolute_percentage_error: 123.0678\n",
      "Epoch 992/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 79.9277 - val_loss: 0.6476 - val_mean_squared_error: 0.6476 - val_mean_absolute_percentage_error: 123.8219\n",
      "Epoch 993/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 80.5336 - val_loss: 0.6474 - val_mean_squared_error: 0.6474 - val_mean_absolute_percentage_error: 121.1618\n",
      "Epoch 994/1000\n",
      " - 0s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 78.4104 - val_loss: 0.6471 - val_mean_squared_error: 0.6471 - val_mean_absolute_percentage_error: 121.3049\n",
      "Epoch 995/1000\n",
      " - 1s - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_percentage_error: 79.0203 - val_loss: 0.6468 - val_mean_squared_error: 0.6468 - val_mean_absolute_percentage_error: 123.5525\n",
      "Epoch 996/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.4488 - val_loss: 0.6460 - val_mean_squared_error: 0.6460 - val_mean_absolute_percentage_error: 122.8157\n",
      "Epoch 997/1000\n",
      " - 1s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.9632 - val_loss: 0.6453 - val_mean_squared_error: 0.6453 - val_mean_absolute_percentage_error: 121.9745\n",
      "Epoch 998/1000\n",
      " - 1s - loss: 0.5535 - mean_squared_error: 0.5535 - mean_absolute_percentage_error: 78.7118 - val_loss: 0.6451 - val_mean_squared_error: 0.6451 - val_mean_absolute_percentage_error: 122.5868\n",
      "Epoch 999/1000\n",
      " - 0s - loss: 0.5538 - mean_squared_error: 0.5538 - mean_absolute_percentage_error: 78.7288 - val_loss: 0.6458 - val_mean_squared_error: 0.6458 - val_mean_absolute_percentage_error: 122.4844\n",
      "Epoch 1000/1000\n",
      " - 0s - loss: 0.5539 - mean_squared_error: 0.5539 - mean_absolute_percentage_error: 78.6491 - val_loss: 0.6467 - val_mean_squared_error: 0.6467 - val_mean_absolute_percentage_error: 122.0675\n"
     ]
    }
   ],
   "source": [
    "history  =  model.fit(X_train_list,y_train,validation_data=(X_val_list,y_val) , epochs =  1000 , batch_size = 512, verbose= 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "id": "rNaQi-3ImF9d",
    "outputId": "9a8109bf-720a-40eb-fe4a-c6c74eb2f814"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "id": "PbNSwswi8UdO",
    "outputId": "92c68a6f-919f-4591-9eba-e1d781d2fc3a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['mean_absolute_percentage_error'])\n",
    "plt.plot(history.history['val_mean_absolute_percentage_error'])\n",
    "plt.title('mean_squared_error')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "EntityEmbedding.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "nbTranslate": {
   "displayLangs": [
    "*"
   ],
   "hotkey": "alt-t",
   "langInMainMenu": true,
   "sourceLang": "en",
   "targetLang": "fr",
   "useGoogleTranslate": true
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}