Notebooks/1-MLPs/1-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer Perceptrons for Regression\n",
    "\n",
    "**This notebook shows how to create a MLP regression model with TensorFlow 2 and Keras**\n",
    "\n",
    "## The Data\n",
    "\n",
    "We will be using the California Housing Prices dataset from Kaggle:\n",
    "\n",
    "https://www.kaggle.com/camnugent/california-housing-prices\n",
    "\n",
    "**Information**\n",
    "\n",
    "This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms because it requires rudimentary data cleaning, has an easily understandable list of variables and sits at an optimal size between being to toyish and too cumbersome.\n",
    "\n",
    "The data contains information from the 1990 California census. So although it may not help you with predicting current housing prices like the Zillow Zestimate dataset, it does provide an accessible introductory dataset for teaching people about the basics of machine learning.\n",
    "\n",
    "**Attributes**\n",
    "\n",
    "1. **longitude**: A measure of how far west a house is; a higher value is farther west\n",
    "2. **latitude**: A measure of how far north a house is; a higher value is farther north\n",
    "3. **housingMedianAge**: Median age of a house within a block; a lower number is a newer building\n",
    "4. **totalRooms**: Total number of rooms within a block\n",
    "5. **totalBedrooms**: Total number of bedrooms within a block\n",
    "6. **population**: Total number of people residing within a block\n",
    "7. **households**: Total number of households, a group of people residing within a home unit, for a block\n",
    "8. **medianIncome**: Median income for households within a block of houses (measured in tens of thousands of US Dollars)\n",
    "9. **medianHouseValue**: Median house value for households within a block (measured in US Dollars)\n",
    "10. **oceanProximity**: Location of the house w.r.t ocean/sea\n",
    "\n",
    "## Table of Contents\n",
    "- ### [Python libraries](#libraries)\n",
    "- ### [Data Exploration and Feature Engineering](#exploration)\n",
    "- ### [Split the Data](#split)\n",
    "- ### [Normalize the Data](#normalize)\n",
    "- ### [Create the Model](#model)\n",
    "- ### [Train the Model](#training)\n",
    "- ### [Evaluate the Model](#evaluation)\n",
    "- ### [Predictions](#predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='libraries'></a>\n",
    "## Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='exploration'></a>\n",
    "## Data Exploration and Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Read the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../Data/housing.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check for null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "median_house_value      0\n",
       "ocean_proximity         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop rows with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attribute statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>-119.570689</td>\n",
       "      <td>2.003578</td>\n",
       "      <td>-124.3500</td>\n",
       "      <td>-121.8000</td>\n",
       "      <td>-118.4900</td>\n",
       "      <td>-118.010</td>\n",
       "      <td>-114.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>35.633221</td>\n",
       "      <td>2.136348</td>\n",
       "      <td>32.5400</td>\n",
       "      <td>33.9300</td>\n",
       "      <td>34.2600</td>\n",
       "      <td>37.720</td>\n",
       "      <td>41.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>28.633094</td>\n",
       "      <td>12.591805</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>2636.504233</td>\n",
       "      <td>2185.269567</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1450.0000</td>\n",
       "      <td>2127.0000</td>\n",
       "      <td>3143.000</td>\n",
       "      <td>39320.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>296.0000</td>\n",
       "      <td>435.0000</td>\n",
       "      <td>647.000</td>\n",
       "      <td>6445.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>1424.946949</td>\n",
       "      <td>1133.208490</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>787.0000</td>\n",
       "      <td>1166.0000</td>\n",
       "      <td>1722.000</td>\n",
       "      <td>35682.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>499.433465</td>\n",
       "      <td>382.299226</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>280.0000</td>\n",
       "      <td>409.0000</td>\n",
       "      <td>604.000</td>\n",
       "      <td>6082.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>3.871162</td>\n",
       "      <td>1.899291</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>2.5637</td>\n",
       "      <td>3.5365</td>\n",
       "      <td>4.744</td>\n",
       "      <td>15.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>206864.413155</td>\n",
       "      <td>115435.667099</td>\n",
       "      <td>14999.0000</td>\n",
       "      <td>119500.0000</td>\n",
       "      <td>179700.0000</td>\n",
       "      <td>264700.000</td>\n",
       "      <td>500001.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count           mean            std         min  \\\n",
       "longitude           20433.0    -119.570689       2.003578   -124.3500   \n",
       "latitude            20433.0      35.633221       2.136348     32.5400   \n",
       "housing_median_age  20433.0      28.633094      12.591805      1.0000   \n",
       "total_rooms         20433.0    2636.504233    2185.269567      2.0000   \n",
       "total_bedrooms      20433.0     537.870553     421.385070      1.0000   \n",
       "population          20433.0    1424.946949    1133.208490      3.0000   \n",
       "households          20433.0     499.433465     382.299226      1.0000   \n",
       "median_income       20433.0       3.871162       1.899291      0.4999   \n",
       "median_house_value  20433.0  206864.413155  115435.667099  14999.0000   \n",
       "\n",
       "                            25%          50%         75%          max  \n",
       "longitude             -121.8000    -118.4900    -118.010    -114.3100  \n",
       "latitude                33.9300      34.2600      37.720      41.9500  \n",
       "housing_median_age      18.0000      29.0000      37.000      52.0000  \n",
       "total_rooms           1450.0000    2127.0000    3143.000   39320.0000  \n",
       "total_bedrooms         296.0000     435.0000     647.000    6445.0000  \n",
       "population             787.0000    1166.0000    1722.000   35682.0000  \n",
       "households             280.0000     409.0000     604.000    6082.0000  \n",
       "median_income            2.5637       3.5365       4.744      15.0001  \n",
       "median_house_value  119500.0000  179700.0000  264700.000  500001.0000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Spatial distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>POINT (-122.23000 37.88000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21.0</td>\n",
       "      <td>7099.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>POINT (-122.22000 37.86000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1467.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>POINT (-122.24000 37.85000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>POINT (-122.25000 37.85000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200.0</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>POINT (-122.25000 37.85000)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                41.0        880.0           129.0   \n",
       "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
       "2    -122.24     37.85                52.0       1467.0           190.0   \n",
       "3    -122.25     37.85                52.0       1274.0           235.0   \n",
       "4    -122.25     37.85                52.0       1627.0           280.0   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0       322.0       126.0         8.3252            452600.0        NEAR BAY   \n",
       "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY   \n",
       "2       496.0       177.0         7.2574            352100.0        NEAR BAY   \n",
       "3       558.0       219.0         5.6431            341300.0        NEAR BAY   \n",
       "4       565.0       259.0         3.8462            342200.0        NEAR BAY   \n",
       "\n",
       "                      geometry  \n",
       "0  POINT (-122.23000 37.88000)  \n",
       "1  POINT (-122.22000 37.86000)  \n",
       "2  POINT (-122.24000 37.85000)  \n",
       "3  POINT (-122.25000 37.85000)  \n",
       "4  POINT (-122.25000 37.85000)  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf = gpd.GeoDataFrame(df.copy(), geometry=gpd.points_from_xy(df.longitude, df.latitude))\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f921050>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(18,12))\n",
    "gdf.plot(ax=ax,column='median_house_value', markersize=gdf['median_house_value']*1e-4, alpha=0.5, legend=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Distribution of output variable (median house value)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121ac2c50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.distplot(df['median_house_value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121d88dd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.scatterplot(x='median_house_value',y='median_income',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove values >= 500000$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11e959610>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df[df['median_house_value'] < 500000]\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.distplot(df['median_house_value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Categorical data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND']\n"
     ]
    }
   ],
   "source": [
    "categories = list(df['ocean_proximity'].unique())\n",
    "print(categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11f510590>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['ocean_proximity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121ca6b50>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='ocean_proximity',y='median_house_value',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting categorical data into numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'NEAR BAY': 0, '<1H OCEAN': 1, 'INLAND': 2, 'NEAR OCEAN': 3, 'ISLAND': 4}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_cat = dict(zip(categories, np.arange(df['ocean_proximity'].nunique())))\n",
    "dict_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ocean_proximity'] = df['ocean_proximity'].apply(lambda x: dict_cat[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19448 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      "longitude             19448 non-null float64\n",
      "latitude              19448 non-null float64\n",
      "housing_median_age    19448 non-null float64\n",
      "total_rooms           19448 non-null float64\n",
      "total_bedrooms        19448 non-null float64\n",
      "population            19448 non-null float64\n",
      "households            19448 non-null float64\n",
      "median_income         19448 non-null float64\n",
      "median_house_value    19448 non-null float64\n",
      "ocean_proximity       19448 non-null int64\n",
      "dtypes: float64(9), int64(1)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='split'></a>\n",
    "## Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('median_house_value',axis=1)\n",
    "y = df['median_house_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13613, 9)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13613,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='normalize'></a>\n",
    "## Normalize the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='model'></a>\n",
    "## Create the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(20,activation='relu'))\n",
    "model.add(Dense(20,activation='relu'))\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dense(1,activation='linear'))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='training'></a>\n",
    "## Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 13613 samples, validate on 5835 samples\n",
      "Epoch 1/500\n",
      "13613/13613 [==============================] - 1s 69us/sample - loss: 45949140076.3952 - val_loss: 47105492453.9393\n",
      "Epoch 2/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 45918523145.5720 - val_loss: 47004558190.8675\n",
      "Epoch 3/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 45528864076.4070 - val_loss: 46067530021.7748\n",
      "Epoch 4/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 43290790764.3388 - val_loss: 41816166244.8644\n",
      "Epoch 5/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 36115305411.4461 - val_loss: 31069310742.2437\n",
      "Epoch 6/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 23151981617.4210 - val_loss: 16843344769.1188\n",
      "Epoch 7/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 12324310464.8510 - val_loss: 10280439803.2617\n",
      "Epoch 8/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 9590747669.2503 - val_loss: 9652251541.6514\n",
      "Epoch 9/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 9377873106.7726 - val_loss: 9546863711.1171\n",
      "Epoch 10/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 9292721369.6931 - val_loss: 9460756885.0372\n",
      "Epoch 11/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 9208828771.3121 - val_loss: 9371835404.8110\n",
      "Epoch 12/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 9123663996.9441 - val_loss: 9284700924.0953\n",
      "Epoch 13/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 9036756991.9248 - val_loss: 9191889157.4841\n",
      "Epoch 14/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 8947638958.2523 - val_loss: 9099961723.4153\n",
      "Epoch 15/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 8859001434.0034 - val_loss: 9002149236.9220\n",
      "Epoch 16/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 8766927979.6430 - val_loss: 8910256016.8254\n",
      "Epoch 17/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 8674490777.2088 - val_loss: 8809459348.1159\n",
      "Epoch 18/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 8578855599.2678 - val_loss: 8708252760.8871\n",
      "Epoch 19/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 8482129107.6753 - val_loss: 8616352172.7287\n",
      "Epoch 20/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 8384096405.1657 - val_loss: 8505190300.7589\n",
      "Epoch 21/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 8283588473.2770 - val_loss: 8400328928.1919\n",
      "Epoch 22/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 8178742265.1548 - val_loss: 8287769459.3426\n",
      "Epoch 23/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 8073722860.6679 - val_loss: 8175392640.5045\n",
      "Epoch 24/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 7963377960.0370 - val_loss: 8055898722.9779\n",
      "Epoch 25/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 7847663014.1847 - val_loss: 7933966964.5272\n",
      "Epoch 26/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 7728993513.7154 - val_loss: 7810339159.7025\n",
      "Epoch 27/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 7605740998.0037 - val_loss: 7674697981.6747\n",
      "Epoch 28/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 7476367354.9977 - val_loss: 7544494624.9049\n",
      "Epoch 29/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 7342529140.5568 - val_loss: 7405040744.6814\n",
      "Epoch 30/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 7201064733.9949 - val_loss: 7245785999.7724\n",
      "Epoch 31/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 7049751143.8067 - val_loss: 7096098587.0698\n",
      "Epoch 32/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 6899516481.3305 - val_loss: 6921862936.1741\n",
      "Epoch 33/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 6736362476.9688 - val_loss: 6751058567.5681\n",
      "Epoch 34/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 6568444664.8351 - val_loss: 6564786645.5308\n",
      "Epoch 35/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 6393894345.3511 - val_loss: 6385770067.3590\n",
      "Epoch 36/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 6218287990.7571 - val_loss: 6191991669.7995\n",
      "Epoch 37/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 6038346818.0827 - val_loss: 6004988389.5006\n",
      "Epoch 38/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 5864523584.2962 - val_loss: 5823926671.6847\n",
      "Epoch 39/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 5692439624.7399 - val_loss: 5660606976.9652\n",
      "Epoch 40/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 5537064838.2340 - val_loss: 5493158194.5858\n",
      "Epoch 41/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 5388660922.9648 - val_loss: 5344228333.6610\n",
      "Epoch 42/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 5257805874.3425 - val_loss: 5216255548.1940\n",
      "Epoch 43/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 5141890348.3247 - val_loss: 5103349008.3647\n",
      "Epoch 44/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 5041573753.2018 - val_loss: 5003223600.6115\n",
      "Epoch 45/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4954654774.0472 - val_loss: 4921585653.3827\n",
      "Epoch 46/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4880747270.4315 - val_loss: 4850185437.9983\n",
      "Epoch 47/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4816000079.3970 - val_loss: 4790140877.9846\n",
      "Epoch 48/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 4764463832.4519 - val_loss: 4740655862.1285\n",
      "Epoch 49/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4714332655.2254 - val_loss: 4695953228.5587\n",
      "Epoch 50/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4671785321.3299 - val_loss: 4657646934.2108\n",
      "Epoch 51/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4637411874.9407 - val_loss: 4625270182.6742\n",
      "Epoch 52/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4602558285.1968 - val_loss: 4604730760.5772\n",
      "Epoch 53/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 4570740215.3118 - val_loss: 4580559365.0015\n",
      "Epoch 54/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4549924403.1511 - val_loss: 4547204470.1505\n",
      "Epoch 55/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4527883476.9729 - val_loss: 4526262313.4163\n",
      "Epoch 56/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 4504157844.9400 - val_loss: 4506976407.6257\n",
      "Epoch 57/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4483436815.7779 - val_loss: 4491947937.0584\n",
      "Epoch 58/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4464396214.5831 - val_loss: 4474486263.1376\n",
      "Epoch 59/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4448682699.0248 - val_loss: 4456128761.3751\n",
      "Epoch 60/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4432406135.1896 - val_loss: 4442359800.4538\n",
      "Epoch 61/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4414692518.1659 - val_loss: 4429718978.8408\n",
      "Epoch 62/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 4401200390.2999 - val_loss: 4413045312.6691\n",
      "Epoch 63/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4385161110.8770 - val_loss: 4400634760.8404\n",
      "Epoch 64/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 4368936476.6973 - val_loss: 4388668945.8125\n",
      "Epoch 65/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 4356169751.6386 - val_loss: 4373559862.3150\n",
      "Epoch 66/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 4342276644.8213 - val_loss: 4362438228.3243\n",
      "Epoch 67/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 4328834914.6727 - val_loss: 4351652279.6093\n",
      "Epoch 68/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4314207416.4825 - val_loss: 4338077437.8502\n",
      "Epoch 69/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 4304147499.2904 - val_loss: 4325551284.9330\n",
      "Epoch 70/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 4290199122.3307 - val_loss: 4312792167.1897\n",
      "Epoch 71/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4277408718.7671 - val_loss: 4299824093.0769\n",
      "Epoch 72/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4259469828.2877 - val_loss: 4301551642.1484\n",
      "Epoch 73/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4250699023.0444 - val_loss: 4277593760.4881\n",
      "Epoch 74/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 4235995731.2522 - val_loss: 4270364094.1025\n",
      "Epoch 75/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 4224530250.7897 - val_loss: 4252837218.4953\n",
      "Epoch 76/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 4212869714.6316 - val_loss: 4243398465.4149\n",
      "Epoch 77/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 4200170746.9789 - val_loss: 4229290462.7441\n",
      "Epoch 78/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4187509994.5429 - val_loss: 4220451829.8214\n",
      "Epoch 79/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4174285238.1130 - val_loss: 4210007278.3191\n",
      "Epoch 80/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4162666260.6673 - val_loss: 4195359996.3585\n",
      "Epoch 81/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4155784655.1620 - val_loss: 4184177639.4310\n",
      "Epoch 82/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4139283786.3760 - val_loss: 4178446744.1961\n",
      "Epoch 83/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4127651647.2619 - val_loss: 4162823367.5352\n",
      "Epoch 84/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4114551065.6696 - val_loss: 4150621571.1369\n",
      "Epoch 85/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4102760658.7350 - val_loss: 4139367566.5001\n",
      "Epoch 86/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4090955918.2076 - val_loss: 4128174204.7753\n",
      "Epoch 87/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4080318425.9376 - val_loss: 4117280289.0804\n",
      "Epoch 88/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4067949508.3864 - val_loss: 4105648537.7755\n",
      "Epoch 89/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4055841709.0675 - val_loss: 4095062857.8386\n",
      "Epoch 90/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4046403850.9072 - val_loss: 4090313878.5728\n",
      "Epoch 91/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4033491010.2332 - val_loss: 4075890271.5558\n",
      "Epoch 92/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 4020334614.9428 - val_loss: 4061561313.0255\n",
      "Epoch 93/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 4010778139.1928 - val_loss: 4050597274.1265\n",
      "Epoch 94/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3997181586.5705 - val_loss: 4039749426.8490\n",
      "Epoch 95/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3988051909.5147 - val_loss: 4028995897.2545\n",
      "Epoch 96/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3974818736.0764 - val_loss: 4018997635.1369\n",
      "Epoch 97/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3963106615.0627 - val_loss: 4007534843.6566\n",
      "Epoch 98/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3955414881.2435 - val_loss: 3997515002.4281\n",
      "Epoch 99/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3940635015.2307 - val_loss: 3985832118.6879\n",
      "Epoch 100/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3929896382.7635 - val_loss: 3976161535.1664\n",
      "Epoch 101/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3918713414.8217 - val_loss: 3965273390.0230\n",
      "Epoch 102/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3908493462.8393 - val_loss: 3954374744.7993\n",
      "Epoch 103/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3895722068.1172 - val_loss: 3943321045.4430\n",
      "Epoch 104/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3882823218.4365 - val_loss: 3932287500.3722\n",
      "Epoch 105/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3872548638.7095 - val_loss: 3921562271.2596\n",
      "Epoch 106/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3860383626.5217 - val_loss: 3911208390.3506\n",
      "Epoch 107/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3848926494.2581 - val_loss: 3902529809.4177\n",
      "Epoch 108/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3838144082.9325 - val_loss: 3890303595.3138\n",
      "Epoch 109/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3826164844.6585 - val_loss: 3884924461.5404\n",
      "Epoch 110/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3816408331.1329 - val_loss: 3868779815.8807\n",
      "Epoch 111/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3805836248.1322 - val_loss: 3859343232.0658\n",
      "Epoch 112/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3792008505.0372 - val_loss: 3849782011.9198\n",
      "Epoch 113/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3784060717.1897 - val_loss: 3838395475.5345\n",
      "Epoch 114/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3771250746.2596 - val_loss: 3828770834.0757\n",
      "Epoch 115/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3759900552.8104 - val_loss: 3828436958.1299\n",
      "Epoch 116/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3749961489.4704 - val_loss: 3822829182.7935\n",
      "Epoch 117/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3738177814.0965 - val_loss: 3795467066.9217\n",
      "Epoch 118/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3726678263.2742 - val_loss: 3787124104.7527\n",
      "Epoch 119/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3717017869.6716 - val_loss: 3775734615.1760\n",
      "Epoch 120/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3703217410.3319 - val_loss: 3768977778.6406\n",
      "Epoch 121/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3693986799.9777 - val_loss: 3756266340.8644\n",
      "Epoch 122/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3684337666.0310 - val_loss: 3747414887.1458\n",
      "Epoch 123/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3671511274.0163 - val_loss: 3741805018.1813\n",
      "Epoch 124/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3663213770.7051 - val_loss: 3728618318.0504\n",
      "Epoch 125/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3653385479.4658 - val_loss: 3720032323.3892\n",
      "Epoch 126/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3643920447.8260 - val_loss: 3712695447.9767\n",
      "Epoch 127/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3633268976.0153 - val_loss: 3708061867.8074\n",
      "Epoch 128/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3623761027.1499 - val_loss: 3700660034.8189\n",
      "Epoch 129/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3614142411.2692 - val_loss: 3687317302.0079\n",
      "Epoch 130/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3607964233.8494 - val_loss: 3679196056.4593\n",
      "Epoch 131/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3595992638.0771 - val_loss: 3669052995.6524\n",
      "Epoch 132/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3586426401.1542 - val_loss: 3660953812.5217\n",
      "Epoch 133/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3575287804.5022 - val_loss: 3665335183.5092\n",
      "Epoch 134/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3571173784.7763 - val_loss: 3643651330.3253\n",
      "Epoch 135/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3560962816.9215 - val_loss: 3638874895.7505\n",
      "Epoch 136/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3552210325.2221 - val_loss: 3628412624.7486\n",
      "Epoch 137/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3542468291.5401 - val_loss: 3619590802.9752\n",
      "Epoch 138/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3532790128.8521 - val_loss: 3613052687.3995\n",
      "Epoch 139/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3524225727.5534 - val_loss: 3608195973.5061\n",
      "Epoch 140/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3516822723.1264 - val_loss: 3597548396.5861\n",
      "Epoch 141/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3506939138.2567 - val_loss: 3591828968.2207\n",
      "Epoch 142/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3499191968.4678 - val_loss: 3586537125.0509\n",
      "Epoch 143/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3490683878.0013 - val_loss: 3579476671.0238\n",
      "Epoch 144/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3485007707.9592 - val_loss: 3586659196.0295\n",
      "Epoch 145/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3476625123.7917 - val_loss: 3564549031.0252\n",
      "Epoch 146/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3467400208.3232 - val_loss: 3564246282.3979\n",
      "Epoch 147/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3463361269.4501 - val_loss: 3553883670.9018\n",
      "Epoch 148/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3456547227.2210 - val_loss: 3546298467.6799\n",
      "Epoch 149/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3447985882.2949 - val_loss: 3542444133.2593\n",
      "Epoch 150/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3441932705.4645 - val_loss: 3537098289.2257\n",
      "Epoch 151/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3434798888.4319 - val_loss: 3531085000.4127\n",
      "Epoch 152/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3427439240.7540 - val_loss: 3528274867.3097\n",
      "Epoch 153/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3424517749.1962 - val_loss: 3525972416.5594\n",
      "Epoch 154/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3418229302.4797 - val_loss: 3516018222.2423\n",
      "Epoch 155/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3411683894.7994 - val_loss: 3522757759.0567\n",
      "Epoch 156/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3412023910.8100 - val_loss: 3507671860.9549\n",
      "Epoch 157/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3401755156.8554 - val_loss: 3513639836.4956\n",
      "Epoch 158/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3399002902.1153 - val_loss: 3511122104.2674\n",
      "Epoch 159/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3396753688.9174 - val_loss: 3523758815.3145\n",
      "Epoch 160/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3391588348.5022 - val_loss: 3492411126.8305\n",
      "Epoch 161/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3384926964.2277 - val_loss: 3496066837.1907\n",
      "Epoch 162/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3382193819.8228 - val_loss: 3490394083.9212\n",
      "Epoch 163/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3376943491.1029 - val_loss: 3481421531.6291\n",
      "Epoch 164/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3371725598.5778 - val_loss: 3478313521.0502\n",
      "Epoch 165/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3368634024.6858 - val_loss: 3474393914.0442\n",
      "Epoch 166/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3364763606.1388 - val_loss: 3471539611.8814\n",
      "Epoch 167/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3360097528.2709 - val_loss: 3470864816.2386\n",
      "Epoch 168/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3359001285.6464 - val_loss: 3465800461.4691\n",
      "Epoch 169/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3355226745.0513 - val_loss: 3464240810.4912\n",
      "Epoch 170/500\n",
      "13613/13613 [==============================] - 0s 26us/sample - loss: 3350208779.4150 - val_loss: 3460759035.8759\n",
      "Epoch 171/500\n",
      "13613/13613 [==============================] - 0s 20us/sample - loss: 3346727662.9810 - val_loss: 3459832716.1748\n",
      "Epoch 172/500\n",
      "13613/13613 [==============================] - 0s 21us/sample - loss: 3344662101.5277 - val_loss: 3456571085.2387\n",
      "Epoch 173/500\n",
      "13613/13613 [==============================] - 0s 21us/sample - loss: 3343466173.6540 - val_loss: 3456945061.7967\n",
      "Epoch 174/500\n",
      "13613/13613 [==============================] - 0s 23us/sample - loss: 3338142908.1308 - val_loss: 3453214173.8667\n",
      "Epoch 175/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3335462870.5714 - val_loss: 3453058516.2146\n",
      "Epoch 176/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3334498731.3186 - val_loss: 3449897024.0548\n",
      "Epoch 177/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3330146780.9841 - val_loss: 3445397650.1854\n",
      "Epoch 178/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3326833322.3031 - val_loss: 3443833238.0901\n",
      "Epoch 179/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3324515254.6960 - val_loss: 3441262722.7421\n",
      "Epoch 180/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3322583839.4617 - val_loss: 3441030670.1272\n",
      "Epoch 181/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3322105687.5775 - val_loss: 3438648229.6213\n",
      "Epoch 182/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3319475352.4848 - val_loss: 3439376064.4278\n",
      "Epoch 183/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3317189656.6353 - val_loss: 3438065027.3124\n",
      "Epoch 184/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3314648727.4599 - val_loss: 3433985185.2778\n",
      "Epoch 185/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3313418063.4159 - val_loss: 3435878854.8771\n",
      "Epoch 186/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3311855697.2870 - val_loss: 3432121963.1383\n",
      "Epoch 187/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3309959283.2122 - val_loss: 3431661354.6009\n",
      "Epoch 188/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3308551273.7342 - val_loss: 3432188472.0699\n",
      "Epoch 189/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3306406696.7516 - val_loss: 3430267347.8636\n",
      "Epoch 190/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3305810809.1454 - val_loss: 3427556555.0451\n",
      "Epoch 191/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3305803112.4084 - val_loss: 3431069507.3453\n",
      "Epoch 192/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3303905682.1380 - val_loss: 3425369903.1637\n",
      "Epoch 193/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3301917404.0814 - val_loss: 3425588908.7726\n",
      "Epoch 194/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3300567209.6355 - val_loss: 3425984466.8984\n",
      "Epoch 195/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3299665591.4858 - val_loss: 3428601175.6147\n",
      "Epoch 196/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3298004792.2850 - val_loss: 3423086429.8447\n",
      "Epoch 197/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3296869789.5341 - val_loss: 3422311607.9164\n",
      "Epoch 198/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3296499490.8843 - val_loss: 3433292421.9887\n",
      "Epoch 199/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3297123558.4056 - val_loss: 3421337522.9587\n",
      "Epoch 200/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3293742090.4559 - val_loss: 3427434746.6036\n",
      "Epoch 201/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3294412973.5565 - val_loss: 3425930639.7724\n",
      "Epoch 202/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3292500287.5440 - val_loss: 3418286796.8877\n",
      "Epoch 203/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3289754595.6600 - val_loss: 3426659894.6660\n",
      "Epoch 204/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3293092463.7802 - val_loss: 3417098510.2588\n",
      "Epoch 205/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3288438382.1629 - val_loss: 3416642082.8353\n",
      "Epoch 206/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3288087288.1581 - val_loss: 3436820477.5431\n",
      "Epoch 207/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3291927546.6968 - val_loss: 3421527125.2895\n",
      "Epoch 208/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3287806296.1980 - val_loss: 3418804804.1789\n",
      "Epoch 209/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3287930122.8884 - val_loss: 3415026767.1472\n",
      "Epoch 210/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3284293289.8894 - val_loss: 3416509413.8516\n",
      "Epoch 211/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3287538908.5703 - val_loss: 3413902571.8622\n",
      "Epoch 212/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3283477825.9699 - val_loss: 3412913949.4389\n",
      "Epoch 213/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3284859228.5421 - val_loss: 3412224617.5589\n",
      "Epoch 214/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3283092088.5436 - val_loss: 3412053955.4550\n",
      "Epoch 215/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3283963893.5441 - val_loss: 3411984869.4129\n",
      "Epoch 216/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3280888334.2358 - val_loss: 3411010616.3332\n",
      "Epoch 217/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3280664929.3564 - val_loss: 3411467667.4578\n",
      "Epoch 218/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3282262480.7416 - val_loss: 3421776734.8977\n",
      "Epoch 219/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3279569449.3722 - val_loss: 3410512785.4396\n",
      "Epoch 220/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3277359951.9800 - val_loss: 3417060652.4435\n",
      "Epoch 221/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3279045496.6000 - val_loss: 3408413868.3338\n",
      "Epoch 222/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3278457160.1381 - val_loss: 3407928774.8771\n",
      "Epoch 223/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3277963021.4836 - val_loss: 3407528499.8581\n",
      "Epoch 224/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3275340363.0718 - val_loss: 3408188181.3662\n",
      "Epoch 225/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3276026798.9669 - val_loss: 3406650197.5966\n",
      "Epoch 226/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3273365471.4664 - val_loss: 3406625593.5177\n",
      "Epoch 227/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3274269397.5559 - val_loss: 3406245977.6768\n",
      "Epoch 228/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3275401087.1632 - val_loss: 3405634117.7584\n",
      "Epoch 229/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3272416929.5773 - val_loss: 3404656433.6206\n",
      "Epoch 230/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3276474978.3530 - val_loss: 3403833820.6382\n",
      "Epoch 231/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3270742954.8108 - val_loss: 3406117552.8089\n",
      "Epoch 232/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3272866471.0591 - val_loss: 3403372655.7889\n",
      "Epoch 233/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3269554672.1845 - val_loss: 3408037706.8915\n",
      "Epoch 234/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3271889140.0397 - val_loss: 3410996235.5825\n",
      "Epoch 235/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3275300360.4437 - val_loss: 3402948397.4087\n",
      "Epoch 236/500\n",
      "13613/13613 [==============================] - 0s 26us/sample - loss: 3268867344.2480 - val_loss: 3403472120.4977\n",
      "Epoch 237/500\n",
      "13613/13613 [==============================] - 1s 46us/sample - loss: 3270576376.3649 - val_loss: 3429744430.9882\n",
      "Epoch 238/500\n",
      "13613/13613 [==============================] - 0s 26us/sample - loss: 3270549554.2484 - val_loss: 3404364460.0706\n",
      "Epoch 239/500\n",
      "13613/13613 [==============================] - 0s 25us/sample - loss: 3268669308.4175 - val_loss: 3403732607.8464\n",
      "Epoch 240/500\n",
      "13613/13613 [==============================] - 0s 34us/sample - loss: 3265481905.2235 - val_loss: 3418983103.7258\n",
      "Epoch 241/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3266602363.0447 - val_loss: 3402444114.1745\n",
      "Epoch 242/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3269106180.4005 - val_loss: 3403022659.9596\n",
      "Epoch 243/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3266845965.3707 - val_loss: 3398621032.5498\n",
      "Epoch 244/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3264411986.8761 - val_loss: 3397965092.3709\n",
      "Epoch 245/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3267467982.0901 - val_loss: 3397653028.7657\n",
      "Epoch 246/500\n",
      "13613/13613 [==============================] - 1s 39us/sample - loss: 3265626868.2277 - val_loss: 3397814545.4177\n",
      "Epoch 247/500\n",
      "13613/13613 [==============================] - 0s 21us/sample - loss: 3265414662.1682 - val_loss: 3397065490.2074\n",
      "Epoch 248/500\n",
      "13613/13613 [==============================] - 0s 29us/sample - loss: 3263364036.5556 - val_loss: 3399212418.6105\n",
      "Epoch 249/500\n",
      "13613/13613 [==============================] - 0s 34us/sample - loss: 3264779150.4145 - val_loss: 3396097355.3302\n",
      "Epoch 250/500\n",
      "13613/13613 [==============================] - 0s 23us/sample - loss: 3262608365.7586 - val_loss: 3394360381.5979\n",
      "Epoch 251/500\n",
      "13613/13613 [==============================] - 0s 33us/sample - loss: 3261178841.8811 - val_loss: 3410610532.7767\n",
      "Epoch 252/500\n",
      "13613/13613 [==============================] - 0s 21us/sample - loss: 3262857699.1617 - val_loss: 3399254547.7429\n",
      "Epoch 253/500\n",
      "13613/13613 [==============================] - 0s 25us/sample - loss: 3261687751.6586 - val_loss: 3395414108.3092\n",
      "Epoch 254/500\n",
      "13613/13613 [==============================] - 0s 27us/sample - loss: 3261742931.0641 - val_loss: 3393562277.9284\n",
      "Epoch 255/500\n",
      "13613/13613 [==============================] - 0s 23us/sample - loss: 3258898829.1733 - val_loss: 3397038997.3004\n",
      "Epoch 256/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3260911135.0668 - val_loss: 3404098960.8254\n",
      "Epoch 257/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3258744518.2670 - val_loss: 3391505892.1844\n",
      "Epoch 258/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3258318823.8537 - val_loss: 3392207852.1693\n",
      "Epoch 259/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3256256298.4253 - val_loss: 3390422774.3040\n",
      "Epoch 260/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3258213987.2651 - val_loss: 3390248043.4015\n",
      "Epoch 261/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3256262876.8336 - val_loss: 3396587097.2380\n",
      "Epoch 262/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3257231633.4139 - val_loss: 3390077001.3560\n",
      "Epoch 263/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3255940445.8773 - val_loss: 3387279406.1546\n",
      "Epoch 264/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3255409061.5829 - val_loss: 3393787530.0250\n",
      "Epoch 265/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3253613585.6772 - val_loss: 3387537372.5505\n",
      "Epoch 266/500\n",
      "13613/13613 [==============================] - 0s 18us/sample - loss: 3252382011.1059 - val_loss: 3385567918.8785\n",
      "Epoch 267/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3253415668.5662 - val_loss: 3385129745.3299\n",
      "Epoch 268/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3254550823.5293 - val_loss: 3385917937.3464\n",
      "Epoch 269/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3252454316.1084 - val_loss: 3383177240.9200\n",
      "Epoch 270/500\n",
      "13613/13613 [==============================] - 0s 24us/sample - loss: 3252251316.7026 - val_loss: 3383955226.8065\n",
      "Epoch 271/500\n",
      "13613/13613 [==============================] - 0s 18us/sample - loss: 3249892673.7254 - val_loss: 3387543586.0456\n",
      "Epoch 272/500\n",
      "13613/13613 [==============================] - 0s 21us/sample - loss: 3250023102.8952 - val_loss: 3385424724.1926\n",
      "Epoch 273/500\n",
      "13613/13613 [==============================] - 0s 18us/sample - loss: 3250223348.8295 - val_loss: 3381050435.3892\n",
      "Epoch 274/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3248767180.6608 - val_loss: 3380715100.9234\n",
      "Epoch 275/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3246263919.2160 - val_loss: 3379721092.1021\n",
      "Epoch 276/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3245583330.2120 - val_loss: 3385049489.1763\n",
      "Epoch 277/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3245725277.2003 - val_loss: 3377615703.0005\n",
      "Epoch 278/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3243433591.2648 - val_loss: 3377035925.4320\n",
      "Epoch 279/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3243751842.2543 - val_loss: 3379871281.7522\n",
      "Epoch 280/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3244992617.8377 - val_loss: 3379071615.6710\n",
      "Epoch 281/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3243450190.1935 - val_loss: 3374548411.6456\n",
      "Epoch 282/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3240767610.8943 - val_loss: 3373962038.8853\n",
      "Epoch 283/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3240802811.2422 - val_loss: 3375669607.2336\n",
      "Epoch 284/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3243410627.3145 - val_loss: 3382142802.0867\n",
      "Epoch 285/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3239138165.7792 - val_loss: 3372890063.0375\n",
      "Epoch 286/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3239535289.4322 - val_loss: 3372950611.5345\n",
      "Epoch 287/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3238062342.8640 - val_loss: 3371888939.3906\n",
      "Epoch 288/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3239690324.3805 - val_loss: 3372891450.3075\n",
      "Epoch 289/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3237661402.3701 - val_loss: 3375760471.9219\n",
      "Epoch 290/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3240194239.1396 - val_loss: 3371927401.6027\n",
      "Epoch 291/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3237239422.2605 - val_loss: 3374551621.0564\n",
      "Epoch 292/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3236451256.3226 - val_loss: 3372718647.0170\n",
      "Epoch 293/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3236267540.5169 - val_loss: 3369990176.4662\n",
      "Epoch 294/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3234033139.3627 - val_loss: 3369278436.7986\n",
      "Epoch 295/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3234056554.2796 - val_loss: 3369546304.6691\n",
      "Epoch 296/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3236698002.4388 - val_loss: 3377459902.4973\n",
      "Epoch 297/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3235301493.4783 - val_loss: 3369021371.9088\n",
      "Epoch 298/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3232053315.8880 - val_loss: 3376621846.3314\n",
      "Epoch 299/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3231270475.7864 - val_loss: 3369742386.0154\n",
      "Epoch 300/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3231612366.1653 - val_loss: 3366997580.2516\n",
      "Epoch 301/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3232493934.0877 - val_loss: 3366366006.0079\n",
      "Epoch 302/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3231728415.5369 - val_loss: 3370106748.0295\n",
      "Epoch 303/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3235176195.7987 - val_loss: 3366689787.2617\n",
      "Epoch 304/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3231997609.0995 - val_loss: 3365446190.2423\n",
      "Epoch 305/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3230769236.8507 - val_loss: 3365168642.8956\n",
      "Epoch 306/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3229503244.8254 - val_loss: 3380676901.4238\n",
      "Epoch 307/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3229731806.9963 - val_loss: 3365139656.2372\n",
      "Epoch 308/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3228789308.3282 - val_loss: 3364813072.5402\n",
      "Epoch 309/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3228141095.4541 - val_loss: 3363546698.4967\n",
      "Epoch 310/500\n",
      "13613/13613 [==============================] - 0s 20us/sample - loss: 3226220867.2487 - val_loss: 3369001252.3709\n",
      "Epoch 311/500\n",
      "13613/13613 [==============================] - 0s 20us/sample - loss: 3231877699.6436 - val_loss: 3363403676.3201\n",
      "Epoch 312/500\n",
      "13613/13613 [==============================] - 0s 18us/sample - loss: 3228426759.7855 - val_loss: 3363032452.8919\n",
      "Epoch 313/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3228553810.0674 - val_loss: 3363808637.6967\n",
      "Epoch 314/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3225694998.1717 - val_loss: 3362298436.7054\n",
      "Epoch 315/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3226605586.2038 - val_loss: 3362388950.2327\n",
      "Epoch 316/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3225161184.7076 - val_loss: 3361856005.8790\n",
      "Epoch 317/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3224291986.0063 - val_loss: 3366349231.0101\n",
      "Epoch 318/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3225520634.4242 - val_loss: 3361275889.6096\n",
      "Epoch 319/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3225040492.5080 - val_loss: 3363331669.9037\n",
      "Epoch 320/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3224958237.8632 - val_loss: 3364123591.8423\n",
      "Epoch 321/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3224242143.2407 - val_loss: 3360674450.4487\n",
      "Epoch 322/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3223912065.0813 - val_loss: 3362077968.7157\n",
      "Epoch 323/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3223238447.1831 - val_loss: 3359741779.4907\n",
      "Epoch 324/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3221004589.0017 - val_loss: 3358522655.2816\n",
      "Epoch 325/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3221962512.5771 - val_loss: 3359719061.7830\n",
      "Epoch 326/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3221262350.2546 - val_loss: 3357902733.9297\n",
      "Epoch 327/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3220969238.9616 - val_loss: 3359937779.9349\n",
      "Epoch 328/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3223318100.8695 - val_loss: 3357664636.9947\n",
      "Epoch 329/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3222653265.3716 - val_loss: 3357722927.4269\n",
      "Epoch 330/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3221309676.7807 - val_loss: 3356046471.6559\n",
      "Epoch 331/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3218695180.9570 - val_loss: 3356126013.3786\n",
      "Epoch 332/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3219521890.9548 - val_loss: 3363334254.0339\n",
      "Epoch 333/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3219338651.1834 - val_loss: 3357455112.0288\n",
      "Epoch 334/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3218798147.7752 - val_loss: 3355651450.0113\n",
      "Epoch 335/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3218520799.1655 - val_loss: 3354872920.2728\n",
      "Epoch 336/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3218454096.6946 - val_loss: 3357094713.6932\n",
      "Epoch 337/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3216875456.7757 - val_loss: 3354299266.6982\n",
      "Epoch 338/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3217517808.7863 - val_loss: 3355477449.5095\n",
      "Epoch 339/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3220024184.1863 - val_loss: 3360372442.9272\n",
      "Epoch 340/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3214304485.1833 - val_loss: 3376436722.0483\n",
      "Epoch 341/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3216112619.0882 - val_loss: 3353288532.0171\n",
      "Epoch 342/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3215861518.0289 - val_loss: 3354231905.2230\n",
      "Epoch 343/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3216350537.1912 - val_loss: 3353194319.1033\n",
      "Epoch 344/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3214782081.8712 - val_loss: 3352126503.4859\n",
      "Epoch 345/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3215116647.3553 - val_loss: 3351404082.4542\n",
      "Epoch 346/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3214485825.8194 - val_loss: 3355380160.5594\n",
      "Epoch 347/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3214789879.5751 - val_loss: 3355765474.9998\n",
      "Epoch 348/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3218504358.3915 - val_loss: 3350838464.1645\n",
      "Epoch 349/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3212348751.2278 - val_loss: 3350273114.9052\n",
      "Epoch 350/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3211186801.9993 - val_loss: 3363418244.6725\n",
      "Epoch 351/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3212281669.2044 - val_loss: 3349186544.7321\n",
      "Epoch 352/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3213795466.1644 - val_loss: 3352738030.6701\n",
      "Epoch 353/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3210554937.1877 - val_loss: 3353649597.9270\n",
      "Epoch 354/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3211225615.6838 - val_loss: 3350224050.4761\n",
      "Epoch 355/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3209502705.1060 - val_loss: 3351876803.4989\n",
      "Epoch 356/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3208351151.6815 - val_loss: 3381041728.8446\n",
      "Epoch 357/500\n",
      "13613/13613 [==============================] - 0s 19us/sample - loss: 3213138128.7228 - val_loss: 3348876295.7217\n",
      "Epoch 358/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3208947839.2760 - val_loss: 3348705264.5566\n",
      "Epoch 359/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3209812696.0570 - val_loss: 3350877832.6211\n",
      "Epoch 360/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3209260344.3414 - val_loss: 3346841444.6889\n",
      "Epoch 361/500\n",
      "13613/13613 [==============================] - 0s 20us/sample - loss: 3207390399.5534 - val_loss: 3350270961.7851\n",
      "Epoch 362/500\n",
      "13613/13613 [==============================] - 0s 21us/sample - loss: 3207939206.5067 - val_loss: 3347779974.1203\n",
      "Epoch 363/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3207345551.8061 - val_loss: 3349593999.6847\n",
      "Epoch 364/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3206427615.9365 - val_loss: 3346028996.5957\n",
      "Epoch 365/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3206717514.9025 - val_loss: 3346948371.6991\n",
      "Epoch 366/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3207940982.6630 - val_loss: 3345525452.0103\n",
      "Epoch 367/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3207609528.7081 - val_loss: 3345712465.6480\n",
      "Epoch 368/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3207008879.5922 - val_loss: 3357998475.2096\n",
      "Epoch 369/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3208443374.9622 - val_loss: 3346662213.6267\n",
      "Epoch 370/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3204510584.4120 - val_loss: 3343149576.6869\n",
      "Epoch 371/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3206435326.0066 - val_loss: 3345357220.3051\n",
      "Epoch 372/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3205470366.7001 - val_loss: 3347160781.4142\n",
      "Epoch 373/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3203047885.3190 - val_loss: 3350487126.3424\n",
      "Epoch 374/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3202121664.8133 - val_loss: 3354122488.8487\n",
      "Epoch 375/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3205092936.3450 - val_loss: 3342848712.8514\n",
      "Epoch 376/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3205685487.8272 - val_loss: 3342993861.6487\n",
      "Epoch 377/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3203166030.4004 - val_loss: 3345672288.1700\n",
      "Epoch 378/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3202016451.7376 - val_loss: 3340874418.0374\n",
      "Epoch 379/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3201997300.3594 - val_loss: 3341033258.4254\n",
      "Epoch 380/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3200730190.2687 - val_loss: 3348968281.6329\n",
      "Epoch 381/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3202761982.7776 - val_loss: 3339635285.7282\n",
      "Epoch 382/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3203764411.3597 - val_loss: 3341430886.6632\n",
      "Epoch 383/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3202928183.3635 - val_loss: 3340548825.1722\n",
      "Epoch 384/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3200671203.9609 - val_loss: 3340258135.7025\n",
      "Epoch 385/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3199382470.4362 - val_loss: 3341175004.7698\n",
      "Epoch 386/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3203368620.0144 - val_loss: 3339912832.3729\n",
      "Epoch 387/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3201469178.1139 - val_loss: 3340104498.1470\n",
      "Epoch 388/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3200892136.9256 - val_loss: 3337647414.0956\n",
      "Epoch 389/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3198258520.5554 - val_loss: 3338482800.4908\n",
      "Epoch 390/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3198888287.5510 - val_loss: 3347848903.9740\n",
      "Epoch 391/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3198390343.9312 - val_loss: 3336674320.8473\n",
      "Epoch 392/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3199689612.4963 - val_loss: 3337173089.0475\n",
      "Epoch 393/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3197976656.2433 - val_loss: 3338853613.9681\n",
      "Epoch 394/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3197815107.3803 - val_loss: 3336500195.2192\n",
      "Epoch 395/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3198547068.3799 - val_loss: 3339703825.4615\n",
      "Epoch 396/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3196657342.9328 - val_loss: 3343981852.6492\n",
      "Epoch 397/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3198117249.2882 - val_loss: 3337997928.8569\n",
      "Epoch 398/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3197052762.2855 - val_loss: 3336305532.0295\n",
      "Epoch 399/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3196898860.9171 - val_loss: 3337566185.1859\n",
      "Epoch 400/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3195116708.6614 - val_loss: 3336504135.3817\n",
      "Epoch 401/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3195200023.8454 - val_loss: 3344686345.5205\n",
      "Epoch 402/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3197003806.2581 - val_loss: 3336389509.1551\n",
      "Epoch 403/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3195430100.9917 - val_loss: 3348792218.3897\n",
      "Epoch 404/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3197885515.8428 - val_loss: 3334615746.5337\n",
      "Epoch 405/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3196046661.8062 - val_loss: 3335590921.1256\n",
      "Epoch 406/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3193114608.5983 - val_loss: 3335066160.6992\n",
      "Epoch 407/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3193561027.5778 - val_loss: 3335707947.9171\n",
      "Epoch 408/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3196601145.0372 - val_loss: 3332958001.4451\n",
      "Epoch 409/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3191536170.8014 - val_loss: 3331841466.4171\n",
      "Epoch 410/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3193266588.7819 - val_loss: 3331884711.3323\n",
      "Epoch 411/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3194064805.6205 - val_loss: 3343186331.1794\n",
      "Epoch 412/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3195051657.8823 - val_loss: 3335461369.2435\n",
      "Epoch 413/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3191180905.6120 - val_loss: 3336968435.2329\n",
      "Epoch 414/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3193206859.4291 - val_loss: 3331228012.4984\n",
      "Epoch 415/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3191094759.1955 - val_loss: 3330753347.5208\n",
      "Epoch 416/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3192980648.8363 - val_loss: 3330952944.6883\n",
      "Epoch 417/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3191205390.8188 - val_loss: 3329675815.3981\n",
      "Epoch 418/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3191586655.0433 - val_loss: 3332611496.8679\n",
      "Epoch 419/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3187617381.8885 - val_loss: 3341953384.1111\n",
      "Epoch 420/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3194508410.5182 - val_loss: 3335262462.6399\n",
      "Epoch 421/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3189088664.9080 - val_loss: 3339072294.7400\n",
      "Epoch 422/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3191039162.8520 - val_loss: 3328450762.6941\n",
      "Epoch 423/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3191762891.7394 - val_loss: 3329839238.2519\n",
      "Epoch 424/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3187810271.9929 - val_loss: 3329419768.0151\n",
      "Epoch 425/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3188799590.1142 - val_loss: 3328524405.9311\n",
      "Epoch 426/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3188534968.8210 - val_loss: 3335564595.8142\n",
      "Epoch 427/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3190312559.1220 - val_loss: 3326922452.4339\n",
      "Epoch 428/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3187980417.7583 - val_loss: 3327234749.0934\n",
      "Epoch 429/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3186706475.9862 - val_loss: 3349199581.3841\n",
      "Epoch 430/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3189567152.1516 - val_loss: 3326615615.3529\n",
      "Epoch 431/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3186984321.8712 - val_loss: 3325964320.8171\n",
      "Epoch 432/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3186384996.8730 - val_loss: 3325722917.5993\n",
      "Epoch 433/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3186695139.2651 - val_loss: 3325944839.0197\n",
      "Epoch 434/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3186159170.6845 - val_loss: 3326437634.8518\n",
      "Epoch 435/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3187460404.6367 - val_loss: 3325346822.6687\n",
      "Epoch 436/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3185361385.2265 - val_loss: 3325149278.5906\n",
      "Epoch 437/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3187498060.0873 - val_loss: 3329896229.1606\n",
      "Epoch 438/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3184020089.6907 - val_loss: 3324187962.4829\n",
      "Epoch 439/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3187604588.3576 - val_loss: 3325608364.9042\n",
      "Epoch 440/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3186203751.4682 - val_loss: 3324331308.7945\n",
      "Epoch 441/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3184827752.3802 - val_loss: 3324214685.4608\n",
      "Epoch 442/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3185208264.4108 - val_loss: 3323735919.0430\n",
      "Epoch 443/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3182399407.2113 - val_loss: 3326989650.1745\n",
      "Epoch 444/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3183805823.9906 - val_loss: 3322853419.8732\n",
      "Epoch 445/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3182220290.0310 - val_loss: 3324439297.1846\n",
      "Epoch 446/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3182839250.2085 - val_loss: 3321311663.5366\n",
      "Epoch 447/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3181969630.4509 - val_loss: 3327385123.5373\n",
      "Epoch 448/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3181260819.6142 - val_loss: 3326732888.7116\n",
      "Epoch 449/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3181937560.9080 - val_loss: 3321915084.7123\n",
      "Epoch 450/500\n",
      "13613/13613 [==============================] - 0s 17us/sample - loss: 3182349893.0352 - val_loss: 3322986608.6663\n",
      "Epoch 451/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3181970186.4559 - val_loss: 3319924148.8014\n",
      "Epoch 452/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3178370791.4776 - val_loss: 3327952089.2600\n",
      "Epoch 453/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3179678231.6574 - val_loss: 3325364255.9397\n",
      "Epoch 454/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3181924617.6002 - val_loss: 3326406835.8800\n",
      "Epoch 455/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3181808446.8952 - val_loss: 3319454553.9839\n",
      "Epoch 456/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3179403553.3046 - val_loss: 3318497534.4644\n",
      "Epoch 457/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3179625282.5529 - val_loss: 3318828644.8206\n",
      "Epoch 458/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3180107333.5994 - val_loss: 3318181735.8478\n",
      "Epoch 459/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3179189996.6867 - val_loss: 3318912812.8823\n",
      "Epoch 460/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3179506906.4829 - val_loss: 3318045926.5974\n",
      "Epoch 461/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3177895973.8744 - val_loss: 3325781435.9966\n",
      "Epoch 462/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3176740263.4635 - val_loss: 3318212755.9404\n",
      "Epoch 463/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3178264742.9181 - val_loss: 3315963385.5945\n",
      "Epoch 464/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3178280073.6190 - val_loss: 3317611069.1592\n",
      "Epoch 465/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3176936034.4753 - val_loss: 3317613556.9440\n",
      "Epoch 466/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3176890525.0076 - val_loss: 3316667114.8970\n",
      "Epoch 467/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3176608411.4279 - val_loss: 3316060208.4360\n",
      "Epoch 468/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3177919552.6911 - val_loss: 3319064374.0079\n",
      "Epoch 469/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3177637019.8604 - val_loss: 3314666172.3914\n",
      "Epoch 470/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3175284461.4765 - val_loss: 3328385427.8087\n",
      "Epoch 471/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3179133150.9022 - val_loss: 3314623726.3191\n",
      "Epoch 472/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3174500853.4689 - val_loss: 3315452754.7887\n",
      "Epoch 473/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3173305164.3882 - val_loss: 3321331468.0651\n",
      "Epoch 474/500\n",
      "13613/13613 [==============================] - 0s 12us/sample - loss: 3179725240.8774 - val_loss: 3313802446.7304\n",
      "Epoch 475/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3173990621.3038 - val_loss: 3314063505.6590\n",
      "Epoch 476/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3176473510.6548 - val_loss: 3313161309.5376\n",
      "Epoch 477/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3175516975.2960 - val_loss: 3313474202.6968\n",
      "Epoch 478/500\n",
      "13613/13613 [==============================] - 0s 27us/sample - loss: 3175010308.3441 - val_loss: 3313069735.5078\n",
      "Epoch 479/500\n",
      "13613/13613 [==============================] - 0s 24us/sample - loss: 3172314358.5596 - val_loss: 3312134315.9829\n",
      "Epoch 480/500\n",
      "13613/13613 [==============================] - 0s 26us/sample - loss: 3177201431.1120 - val_loss: 3311823419.4043\n",
      "Epoch 481/500\n",
      "13613/13613 [==============================] - 0s 24us/sample - loss: 3171152236.1319 - val_loss: 3311480814.8017\n",
      "Epoch 482/500\n",
      "13613/13613 [==============================] - 0s 23us/sample - loss: 3172060860.9206 - val_loss: 3313270238.7441\n",
      "Epoch 483/500\n",
      "13613/13613 [==============================] - 0s 24us/sample - loss: 3171792770.5670 - val_loss: 3310875140.8260\n",
      "Epoch 484/500\n",
      "13613/13613 [==============================] - 0s 16us/sample - loss: 3174193177.0302 - val_loss: 3313936588.0980\n",
      "Epoch 485/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3173269819.7452 - val_loss: 3310677920.0055\n",
      "Epoch 486/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3173427111.0309 - val_loss: 3311780218.8888\n",
      "Epoch 487/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3168783005.7786 - val_loss: 3312476430.0833\n",
      "Epoch 488/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3172710256.2598 - val_loss: 3312117552.3921\n",
      "Epoch 489/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3171230435.7353 - val_loss: 3309848889.2545\n",
      "Epoch 490/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3169142996.6908 - val_loss: 3308897805.9517\n",
      "Epoch 491/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3170309993.6684 - val_loss: 3313298952.5114\n",
      "Epoch 492/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3167406864.2292 - val_loss: 3310764521.2737\n",
      "Epoch 493/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3167740952.1463 - val_loss: 3315822258.9148\n",
      "Epoch 494/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3167497865.0173 - val_loss: 3310434117.4512\n",
      "Epoch 495/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3167430905.3993 - val_loss: 3307442971.6840\n",
      "Epoch 496/500\n",
      "13613/13613 [==============================] - 0s 13us/sample - loss: 3168941776.1963 - val_loss: 3312334169.8961\n",
      "Epoch 497/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3169577872.5677 - val_loss: 3307674076.4627\n",
      "Epoch 498/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3167287596.8136 - val_loss: 3306617621.5417\n",
      "Epoch 499/500\n",
      "13613/13613 [==============================] - 0s 15us/sample - loss: 3165803805.0828 - val_loss: 3314477007.3885\n",
      "Epoch 500/500\n",
      "13613/13613 [==============================] - 0s 14us/sample - loss: 3166986446.5978 - val_loss: 3309353556.4997\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1412c5810>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x=X_train, y=y_train.values, \n",
    "          validation_data=(X_test,y_test.values),\n",
    "          epochs=500,\n",
    "          batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Save the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.save('regression_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = load_model('regression_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='evaluation'></a>\n",
    "## Evaluate the Model\n",
    "Check common regression metrics [here](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, explained_variance_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Training and validation losses\n",
    "\n",
    "We can compare these two variables to check for overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = pd.DataFrame(model.history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1419a3790>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare predictions with the real test labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Root mean square error**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root mean square error: 57526.98116103994\n"
     ]
    }
   ],
   "source": [
    "rmse = np.sqrt(mean_squared_error(y_test,test_predictions))\n",
    "print('Root mean square error:', rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192017.66742081448"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['median_house_value'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173700.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['median_house_value'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.959212573375986"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse/df['median_house_value'].mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explained variance score**\n",
    "\n",
    "The best possible score is 1.0, lower values are worse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6547776464761177"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance_score(y_test,test_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scatter plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x141b33690>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Our predictions\n",
    "plt.scatter(y_test,test_predictions, edgecolor='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Error distribution**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 73800., 359100.,  94700., ..., 140600.,  96200., 187500.])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([123779.625, 284532.2  , 101685.97 , ..., 141234.27 , 119725.62 ,\n",
       "       148026.06 ], dtype=float32)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions.reshape((test_predictions.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15200ce10>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors = np.array(y_test) - test_predictions.reshape((test_predictions.shape[0]))\n",
    "sns.distplot(errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='predictions'></a>\n",
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True house priece: 452600.0\n"
     ]
    }
   ],
   "source": [
    "single_house = df.drop('median_house_value',axis=1).iloc[0]\n",
    "print(\"True house priece:\", df['median_house_value'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_house = scaler.transform(single_house.values.reshape(-1, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted house priece: 418815.0\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted house priece:\", model.predict(single_house)[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}