linear_regression_with_a_CarPriceDataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "wDlWLbfkJtvu"
   },
   "outputs": [],
   "source": [
    "#@title Copyright 2020 Google LLC. Double-click here for license information.\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TL5y5fY9Jy_x"
   },
   "source": [
    "# Linear Regression with a Real Dataset\n",
    "\n",
    "This Colab uses a real dataset to predict the prices of houses in California.   \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h8wtceyJj2uX"
   },
   "source": [
    "## Learning Objectives:\n",
    "\n",
    "After doing this Colab, you'll know how to do the following:\n",
    "\n",
    "  * Read a .csv file into a [pandas](https://developers.google.com/machine-learning/glossary/#pandas) DataFrame.\n",
    "  * Examine a [dataset](https://developers.google.com/machine-learning/glossary/#data_set). \n",
    "  * Experiment with different [features](https://developers.google.com/machine-learning/glossary/#feature) in building a model.\n",
    "  * Tune the model's [hyperparameters](https://developers.google.com/machine-learning/glossary/#hyperparameter)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JJZEgJQSjyK4"
   },
   "source": [
    "## The Dataset\n",
    "  \n",
    "The [dataset for this exercise](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California. The dataset is old but still provides a great opportunity to learn about machine learning programming."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "tX_umRMMsa3z"
   },
   "source": [
    "## Use the right version of TensorFlow\n",
    "\n",
    "The following hidden code cell ensures that the Colab will run on TensorFlow 2.X."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "lM75uNH-sTv2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%tensorflow_version` not found.\n"
     ]
    }
   ],
   "source": [
    "#@title Run on TensorFlow 2.x\n",
    "%tensorflow_version 2.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xchnxAsaKKqO"
   },
   "source": [
    "## Import relevant modules\n",
    "\n",
    "The following hidden code cell imports the necessary code to run the code in the rest of this Colaboratory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "9n9_cTveKmse"
   },
   "outputs": [],
   "source": [
    "#@title Import relevant modules\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# The following lines adjust the granularity of reporting. \n",
    "pd.options.display.max_rows = 10\n",
    "pd.options.display.float_format = \"{:.1f}\".format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X_TaJhU4KcuY"
   },
   "source": [
    "## The dataset\n",
    "\n",
    "Datasets are often stored on disk or at a URL in [.csv format](https://wikipedia.org/wiki/Comma-separated_values). \n",
    "\n",
    "A well-formed .csv file contains column names in the first row, followed by many rows of data.  A comma divides each value in each row. For example, here are the first five rows of the .csv file file holding the California Housing Dataset:\n",
    "\n",
    "```\n",
    "\"longitude\",\"latitude\",\"housing_median_age\",\"total_rooms\",\"total_bedrooms\",\"population\",\"households\",\"median_income\",\"median_house_value\"\n",
    "-114.310000,34.190000,15.000000,5612.000000,1283.000000,1015.000000,472.000000,1.493600,66900.000000\n",
    "-114.470000,34.400000,19.000000,7650.000000,1901.000000,1129.000000,463.000000,1.820000,80100.000000\n",
    "-114.560000,33.690000,17.000000,720.000000,174.000000,333.000000,117.000000,1.650900,85700.000000\n",
    "-114.570000,33.640000,14.000000,1501.000000,337.000000,515.000000,226.000000,3.191700,73400.000000\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sSFQkzNlj-l6"
   },
   "source": [
    "### Load the .csv file into a pandas DataFrame\n",
    "\n",
    "This Colab, like many machine learning programs, gathers the .csv file and stores the data in memory as a pandas Dataframe.  pandas is an open source Python library.  The primary datatype in pandas is a DataFrame.  You can imagine a pandas DataFrame as a spreadsheet in which each row is identified by a number and each column by a name. pandas is itself built on another open source Python library called NumPy. If you aren't familiar with these technologies, please view these two quick tutorials:\n",
    "\n",
    "*   [NumPy](https://colab.research.google.com/github/google/eng-edu/blob/master/ml/cc/exercises/numpy_ultraquick_tutorial.ipynb?utm_source=linearregressionreal-colab&utm_medium=colab&utm_campaign=colab-external&utm_content=numpy_tf2-colab&hl=en)\n",
    "*   [Pandas DataFrames](https://colab.research.google.com/github/google/eng-edu/blob/master/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb?utm_source=linearregressionreal-colab&utm_medium=colab&utm_campaign=colab-external&utm_content=pandas_tf2-colab&hl=en)\n",
    "\n",
    "The following code cell imports the .csv file into a pandas DataFrame and scales the values in the label (`median_house_value`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JZlvdpyYKx7V"
   },
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>CarName</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>doornumber</th>\n",
       "      <th>carbody</th>\n",
       "      <th>drivewheel</th>\n",
       "      <th>enginelocation</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>...</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>fuelsystem</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero giulia</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>alfa-romero stelvio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero Quadrifoglio</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5.5</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100ls</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5.5</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   car_ID  symboling                   CarName fueltype aspiration doornumber  \\\n",
       "0       1          3        alfa-romero giulia      gas        std        two   \n",
       "1       2          3       alfa-romero stelvio      gas        std        two   \n",
       "2       3          1  alfa-romero Quadrifoglio      gas        std        two   \n",
       "3       4          2               audi 100 ls      gas        std       four   \n",
       "4       5          2                audi 100ls      gas        std       four   \n",
       "\n",
       "       carbody drivewheel enginelocation  wheelbase  ...  enginesize  \\\n",
       "0  convertible        rwd          front       88.6  ...         130   \n",
       "1  convertible        rwd          front       88.6  ...         130   \n",
       "2    hatchback        rwd          front       94.5  ...         152   \n",
       "3        sedan        fwd          front       99.8  ...         109   \n",
       "4        sedan        4wd          front       99.4  ...         136   \n",
       "\n",
       "   fuelsystem  boreratio  stroke compressionratio horsepower  peakrpm citympg  \\\n",
       "0        mpfi        3.5     2.7              9.0        111      5.0      21   \n",
       "1        mpfi        3.5     2.7              9.0        111      5.0      21   \n",
       "2        mpfi        2.7     3.5              9.0        154      5.0      19   \n",
       "3        mpfi        3.2     3.4             10.0        102      5.5      24   \n",
       "4        mpfi        3.2     3.4              8.0        115      5.5      18   \n",
       "\n",
       "   highwaympg  price  \n",
       "0          27   13.5  \n",
       "1          27   16.5  \n",
       "2          26   16.5  \n",
       "3          30   13.9  \n",
       "4          22   17.4  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the dataset.\n",
    "training_df = pd.read_csv(\"CarPrice_Assignment.csv\")\n",
    "\n",
    "# Scale the label.\n",
    "training_df[\"price\"] /= 1000.0\n",
    "training_df[\"peakrpm\"] /= 1000.0\n",
    "\n",
    "training_df[\"curbweight\"] /= 1000.0\n",
    "\n",
    "\n",
    "\n",
    "# Print the first rows of the pandas DataFrame.\n",
    "training_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5inxx49n4U9u"
   },
   "source": [
    "Scaling `price` and `peakrpm` puts the value of each house in units of thousands. Scaling will keep loss values and learning rates in a friendlier range.  \n",
    "\n",
    "Although scaling a label is usually *not* essential, scaling features in a multi-feature model usually *is* essential."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yMysi6-3IAbu"
   },
   "source": [
    "## Examine the dataset\n",
    "\n",
    "A large part of most machine learning projects is getting to know your data. The pandas API provides a `describe` function that outputs the following statistics about every column in the DataFrame:\n",
    "\n",
    "* `count`, which is the number of rows in that column. Ideally, `count` contains the same value for every column. \n",
    "\n",
    "* `mean` and `std`, which contain the mean and standard deviation of the values in each column. \n",
    "\n",
    "* `min` and `max`, which contain the lowest and highest values in each column.\n",
    "\n",
    "* `25%`, `50%`, `75%`, which contain various [quantiles](https://developers.google.com/machine-learning/glossary/#quantile)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rnUSYKw4LUuh"
   },
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>205.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>103.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>98.8</td>\n",
       "      <td>174.0</td>\n",
       "      <td>65.9</td>\n",
       "      <td>53.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>126.9</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>10.1</td>\n",
       "      <td>104.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>25.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>59.3</td>\n",
       "      <td>1.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>41.6</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>86.6</td>\n",
       "      <td>141.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>47.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.5</td>\n",
       "      <td>166.3</td>\n",
       "      <td>64.1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2.1</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>173.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>54.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>154.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>102.4</td>\n",
       "      <td>183.1</td>\n",
       "      <td>66.9</td>\n",
       "      <td>55.5</td>\n",
       "      <td>2.9</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>9.4</td>\n",
       "      <td>116.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>205.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.9</td>\n",
       "      <td>208.1</td>\n",
       "      <td>72.3</td>\n",
       "      <td>59.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>326.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>23.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>49.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>45.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       car_ID  symboling  wheelbase  carlength  carwidth  carheight  \\\n",
       "count   205.0      205.0      205.0      205.0     205.0      205.0   \n",
       "mean    103.0        0.8       98.8      174.0      65.9       53.7   \n",
       "std      59.3        1.2        6.0       12.3       2.1        2.4   \n",
       "min       1.0       -2.0       86.6      141.1      60.3       47.8   \n",
       "25%      52.0        0.0       94.5      166.3      64.1       52.0   \n",
       "50%     103.0        1.0       97.0      173.2      65.5       54.1   \n",
       "75%     154.0        2.0      102.4      183.1      66.9       55.5   \n",
       "max     205.0        3.0      120.9      208.1      72.3       59.8   \n",
       "\n",
       "       curbweight  enginesize  boreratio  stroke  compressionratio  \\\n",
       "count       205.0       205.0      205.0   205.0             205.0   \n",
       "mean          2.6       126.9        3.3     3.3              10.1   \n",
       "std           0.5        41.6        0.3     0.3               4.0   \n",
       "min           1.5        61.0        2.5     2.1               7.0   \n",
       "25%           2.1        97.0        3.1     3.1               8.6   \n",
       "50%           2.4       120.0        3.3     3.3               9.0   \n",
       "75%           2.9       141.0        3.6     3.4               9.4   \n",
       "max           4.1       326.0        3.9     4.2              23.0   \n",
       "\n",
       "       horsepower  peakrpm  citympg  highwaympg  price  \n",
       "count       205.0    205.0    205.0       205.0  205.0  \n",
       "mean        104.1      5.1     25.2        30.8   13.3  \n",
       "std          39.5      0.5      6.5         6.9    8.0  \n",
       "min          48.0      4.2     13.0        16.0    5.1  \n",
       "25%          70.0      4.8     19.0        25.0    7.8  \n",
       "50%          95.0      5.2     24.0        30.0   10.3  \n",
       "75%         116.0      5.5     30.0        34.0   16.5  \n",
       "max         288.0      6.6     49.0        54.0   45.4  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get statistics on the dataset.\n",
    "training_df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f9pcW_Yjtoo8"
   },
   "source": [
    "### Task 1: Identify anomalies in the dataset\n",
    "\n",
    "Do you see any anomalies (strange values) in the data? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "UoS7NWRXEs1H"
   },
   "outputs": [],
   "source": [
    "#@title Double-click to view a possible answer.\n",
    "\n",
    "# The values seem sensible enough.\n",
    "\n",
    "# When you see anomalies in a column, become more careful\n",
    "# about using that column as a feature. That said,\n",
    "# anomalies in potential features sometimes mirror \n",
    "# anomalies in the label, which could make the column \n",
    "# be (or seem to be) a powerful feature.\n",
    "# Also, as you will see later in the course, you \n",
    "# might be able to represent (pre-process) raw data \n",
    "# in order to make columns into useful features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3014ezH3C7jT"
   },
   "source": [
    "## Define functions that build and train a model\n",
    "\n",
    "The following code defines two functions:\n",
    "\n",
    "  * `build_model(my_learning_rate)`, which builds a randomly-initialized model.\n",
    "  * `train_model(model, feature, label, epochs)`, which trains the model from the examples (feature and label) you pass. \n",
    "\n",
    "Since you don't need to understand model building code right now, we've hidden this code cell.  You may optionally double-click the following headline to see the code that builds and trains a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "pedD5GhlDC-y"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined the create_model and traing_model functions.\n"
     ]
    }
   ],
   "source": [
    "#@title Define the functions that build and train a model\n",
    "def build_model(my_learning_rate):\n",
    "  \"\"\"Create and compile a simple linear regression model.\"\"\"\n",
    "  # Most simple tf.keras models are sequential.\n",
    "  model = tf.keras.models.Sequential()\n",
    "\n",
    "  # Describe the topography of the model.\n",
    "  # The topography of a simple linear regression model\n",
    "  # is a single node in a single layer.\n",
    "  model.add(tf.keras.layers.Dense(units=1, \n",
    "                                  input_shape=(1,)))\n",
    "\n",
    "  # Compile the model topography into code that TensorFlow can efficiently\n",
    "  # execute. Configure training to minimize the model's mean squared error. \n",
    "  model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),\n",
    "                loss=\"mean_squared_error\",\n",
    "                metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "  return model        \n",
    "\n",
    "\n",
    "def train_model(model, df, feature, label, epochs, batch_size):\n",
    "  \"\"\"Train the model by feeding it data.\"\"\"\n",
    "\n",
    "  # Feed the model the feature and the label.\n",
    "  # The model will train for the specified number of epochs. \n",
    "  history = model.fit(x=df[feature],\n",
    "                      y=df[label],\n",
    "                      batch_size=None,\n",
    "                      epochs=epochs)\n",
    "\n",
    "  # Gather the trained model's weight and bias.\n",
    "  trained_weight = model.get_weights()[0]\n",
    "  trained_bias = model.get_weights()[1]\n",
    "\n",
    "  # The list of epochs is stored separately from the rest of history.\n",
    "  epochs = history.epoch\n",
    "  \n",
    "  # Isolate the error for each epoch.\n",
    "  hist = pd.DataFrame(history.history)\n",
    "\n",
    "  # To track the progression of training, we're going to take a snapshot\n",
    "  # of the model's root mean squared error at each epoch. \n",
    "  rmse = hist[\"root_mean_squared_error\"]\n",
    "\n",
    "  return trained_weight, trained_bias, epochs, rmse\n",
    "\n",
    "print(\"Defined the create_model and traing_model functions.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ak_TMAzGOIFq"
   },
   "source": [
    "## Define plotting functions\n",
    "\n",
    "The following [matplotlib](https://developers.google.com/machine-learning/glossary/#matplotlib) functions create the following plots:\n",
    "\n",
    "*  a scatter plot of the feature vs. the label, and a line showing the output of the trained model\n",
    "*  a loss curve\n",
    "\n",
    "You may optionally double-click the headline to see the matplotlib code, but note that writing matplotlib code is not an important part of learning ML programming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "QF0BFRXTOeR3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defined the plot_the_model and plot_the_loss_curve functions.\n"
     ]
    }
   ],
   "source": [
    "#@title Define the plotting functions\n",
    "def plot_the_model(trained_weight, trained_bias, feature, label):\n",
    "  \"\"\"Plot the trained model against 200 random training examples.\"\"\"\n",
    "\n",
    "  # Label the axes.\n",
    "  plt.xlabel(feature)\n",
    "  plt.ylabel(label)\n",
    "\n",
    "  # Create a scatter plot from 200 random points of the dataset.\n",
    "  random_examples = training_df.sample(n=200)\n",
    "  plt.scatter(random_examples[feature], random_examples[label])\n",
    "\n",
    "  # Create a red line representing the model. The red line starts\n",
    "  # at coordinates (x0, y0) and ends at coordinates (x1, y1).\n",
    "  x0 = 0\n",
    "  y0 = trained_bias\n",
    "  x1 = 10000\n",
    "  y1 = trained_bias + (trained_weight * x1)\n",
    "  plt.plot([x0, x1], [y0, y1], c='r')\n",
    "\n",
    "  # Render the scatter plot and the red line.\n",
    "  plt.show()\n",
    "\n",
    "\n",
    "def plot_the_loss_curve(epochs, rmse):\n",
    "  \"\"\"Plot a curve of loss vs. epoch.\"\"\"\n",
    "\n",
    "  plt.figure()\n",
    "  plt.xlabel(\"Epoch\")\n",
    "  plt.ylabel(\"Root Mean Squared Error\")\n",
    "\n",
    "  plt.plot(epochs, rmse, label=\"Loss\")\n",
    "  plt.legend()\n",
    "  plt.ylim([rmse.min()*0.97, rmse.max()])\n",
    "  plt.show()  \n",
    "\n",
    "print(\"Defined the plot_the_model and plot_the_loss_curve functions.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D-IXYVfvM4gD"
   },
   "source": [
    "## Call the model functions\n",
    "\n",
    "An important part of machine learning is determining which [features](https://developers.google.com/machine-learning/glossary/#feature) correlate with the [label](https://developers.google.com/machine-learning/glossary/#label). For example, real-life home-value prediction models typically rely on hundreds of features and synthetic features. However, this model relies on only one feature. For now, you'll arbitrarily use `total_rooms` as that feature. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "cellView": "both",
    "colab": {},
    "colab_type": "code",
    "id": "nj3v5EKQFY8s"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 39.4401 - root_mean_squared_error: 6.2801\n",
      "Epoch 2/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.9254 - root_mean_squared_error: 4.8914\n",
      "Epoch 3/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.4788 - root_mean_squared_error: 4.9476\n",
      "Epoch 4/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.7320 - root_mean_squared_error: 4.9731\n",
      "Epoch 5/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.0637 - root_mean_squared_error: 4.9055\n",
      "Epoch 6/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.3832 - root_mean_squared_error: 5.0382\n",
      "Epoch 7/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.9925 - root_mean_squared_error: 4.8982\n",
      "Epoch 8/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.3792 - root_mean_squared_error: 4.9375\n",
      "Epoch 9/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.4273 - root_mean_squared_error: 4.9424\n",
      "Epoch 10/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 24.6033 - root_mean_squared_error: 4.9602\n",
      "Epoch 11/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 24.6083 - root_mean_squared_error: 4.9607\n",
      "Epoch 12/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 25.0776 - root_mean_squared_error: 5.0078\n",
      "Epoch 13/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.6927 - root_mean_squared_error: 4.9692\n",
      "Epoch 14/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.6087 - root_mean_squared_error: 4.9607\n",
      "Epoch 15/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 24.4534 - root_mean_squared_error: 4.9450\n",
      "Epoch 16/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.3106 - root_mean_squared_error: 4.9306\n",
      "Epoch 17/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.0985 - root_mean_squared_error: 4.9090\n",
      "Epoch 18/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.7225 - root_mean_squared_error: 4.8706\n",
      "Epoch 19/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.2565 - root_mean_squared_error: 5.0256\n",
      "Epoch 20/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.9814 - root_mean_squared_error: 4.7939\n",
      "Epoch 21/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.2190 - root_mean_squared_error: 5.0218\n",
      "Epoch 22/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.8792 - root_mean_squared_error: 4.9879\n",
      "Epoch 23/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.9866 - root_mean_squared_error: 4.8976\n",
      "Epoch 24/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.7318 - root_mean_squared_error: 4.7678\n",
      "Epoch 25/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.8575 - root_mean_squared_error: 4.8844\n",
      "Epoch 26/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.7028 - root_mean_squared_error: 4.9702\n",
      "Epoch 27/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.6911 - root_mean_squared_error: 4.8673\n",
      "Epoch 28/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.1016 - root_mean_squared_error: 4.9093\n",
      "Epoch 29/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.7136 - root_mean_squared_error: 4.9713\n",
      "Epoch 30/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.4555 - root_mean_squared_error: 4.8431\n",
      "Epoch 31/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.0188 - root_mean_squared_error: 5.0019\n",
      "Epoch 32/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.5660 - root_mean_squared_error: 4.8545\n",
      "Epoch 33/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.6908 - root_mean_squared_error: 4.8673\n",
      "Epoch 34/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 25.3418 - root_mean_squared_error: 5.0341\n",
      "Epoch 35/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.9751 - root_mean_squared_error: 4.8964\n",
      "Epoch 36/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.1072 - root_mean_squared_error: 4.9099\n",
      "Epoch 37/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.7076 - root_mean_squared_error: 5.0703\n",
      "Epoch 38/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.8134 - root_mean_squared_error: 4.8799\n",
      "Epoch 39/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.8638 - root_mean_squared_error: 4.8851\n",
      "Epoch 40/50\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 23.8245 - root_mean_squared_error: 4.8810\n",
      "Epoch 41/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.4982 - root_mean_squared_error: 4.9496\n",
      "Epoch 42/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.3130 - root_mean_squared_error: 5.0312\n",
      "Epoch 43/50\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.8806 - root_mean_squared_error: 4.8868\n",
      "Epoch 44/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.8543 - root_mean_squared_error: 4.8841\n",
      "Epoch 45/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.2441 - root_mean_squared_error: 4.9238\n",
      "Epoch 46/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.3151 - root_mean_squared_error: 4.8286\n",
      "Epoch 47/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.2445 - root_mean_squared_error: 4.9239\n",
      "Epoch 48/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.8451 - root_mean_squared_error: 4.9845\n",
      "Epoch 49/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.8298 - root_mean_squared_error: 4.8816\n",
      "Epoch 50/50\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.5456 - root_mean_squared_error: 4.8524\n",
      "\n",
      "The learned weight for your model is 0.1284\n",
      "The learned bias for your model is -1.0491\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# The following variables are the hyperparameters.\n",
    "learning_rate = 0.01\n",
    "epochs = 50\n",
    "batch_size = 15\n",
    "\n",
    "# Specify the feature and the label.\n",
    "my_feature = \"horsepower\" \n",
    "my_label=\"price\" \n",
    "\n",
    "# Discard any pre-existing version of the model.\n",
    "my_model = None\n",
    "\n",
    "# Invoke the functions.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_df, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "print(\"\\nThe learned weight for your model is %.4f\" % weight)\n",
    "print(\"The learned bias for your model is %.4f\\n\" % bias )\n",
    "\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 9403.1299 - root_mean_squared_error: 96.9697\n",
      "Epoch 2/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 2503.2549 - root_mean_squared_error: 50.0325\n",
      "Epoch 3/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 635.8973 - root_mean_squared_error: 25.2170\n",
      "Epoch 4/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 110.7976 - root_mean_squared_error: 10.5260\n",
      "Epoch 5/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.6836 - root_mean_squared_error: 5.2615\n",
      "Epoch 6/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.9168 - root_mean_squared_error: 4.7871\n",
      "Epoch 7/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.4103 - root_mean_squared_error: 4.8384\n",
      "Epoch 8/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.1453 - root_mean_squared_error: 4.8110\n",
      "Epoch 9/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.7821 - root_mean_squared_error: 4.8767\n",
      "Epoch 10/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.4903 - root_mean_squared_error: 5.0488\n",
      "Epoch 11/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 40.3068 - root_mean_squared_error: 6.3488\n",
      "Epoch 12/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.6665 - root_mean_squared_error: 4.8648\n",
      "Epoch 13/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.4761 - root_mean_squared_error: 5.9562\n",
      "Epoch 14/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 27.1000 - root_mean_squared_error: 5.2058\n",
      "Epoch 15/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.0008 - root_mean_squared_error: 5.2916\n",
      "Epoch 16/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.6487 - root_mean_squared_error: 4.9647\n",
      "Epoch 17/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 48.4763 - root_mean_squared_error: 6.9625\n",
      "Epoch 18/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.6202 - root_mean_squared_error: 5.0616\n",
      "Epoch 19/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.8716 - root_mean_squared_error: 5.1838\n",
      "Epoch 20/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.6752 - root_mean_squared_error: 5.3549\n",
      "Epoch 21/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 43.7431 - root_mean_squared_error: 6.6139\n",
      "Epoch 22/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.0846 - root_mean_squared_error: 5.2995\n",
      "Epoch 23/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.4134 - root_mean_squared_error: 5.8663\n",
      "Epoch 24/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.6146 - root_mean_squared_error: 5.1589\n",
      "Epoch 25/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.7274 - root_mean_squared_error: 4.8711\n",
      "Epoch 26/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.6991 - root_mean_squared_error: 4.7644\n",
      "Epoch 27/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 51.7917 - root_mean_squared_error: 7.1966\n",
      "Epoch 28/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 28.7435 - root_mean_squared_error: 5.3613\n",
      "Epoch 29/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.6908 - root_mean_squared_error: 4.7635\n",
      "Epoch 30/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 41.2518 - root_mean_squared_error: 6.4228\n",
      "Epoch 31/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 22.9329 - root_mean_squared_error: 4.7888\n",
      "Epoch 32/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.3421 - root_mean_squared_error: 6.1108\n",
      "Epoch 33/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 25.8211 - root_mean_squared_error: 5.0815\n",
      "Epoch 34/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.0560 - root_mean_squared_error: 5.3904\n",
      "Epoch 35/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.4614 - root_mean_squared_error: 5.6090\n",
      "Epoch 36/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.6653 - root_mean_squared_error: 5.3540\n",
      "Epoch 37/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 41.7156 - root_mean_squared_error: 6.4588\n",
      "Epoch 38/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.4640 - root_mean_squared_error: 4.8440\n",
      "Epoch 39/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.1214 - root_mean_squared_error: 6.0927\n",
      "Epoch 40/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.9651 - root_mean_squared_error: 5.4740\n",
      "Epoch 41/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.7140 - root_mean_squared_error: 5.5420\n",
      "Epoch 42/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.3499 - root_mean_squared_error: 5.7749\n",
      "Epoch 43/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.8173 - root_mean_squared_error: 5.4605\n",
      "Epoch 44/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.6077 - root_mean_squared_error: 5.5324\n",
      "Epoch 45/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.4486 - root_mean_squared_error: 5.5180\n",
      "Epoch 46/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.9866 - root_mean_squared_error: 5.9989\n",
      "Epoch 47/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.9220 - root_mean_squared_error: 4.9922\n",
      "Epoch 48/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.5492 - root_mean_squared_error: 5.1526\n",
      "Epoch 49/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.0270 - root_mean_squared_error: 5.1017\n",
      "Epoch 50/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.8081 - root_mean_squared_error: 5.0802\n",
      "Epoch 51/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.3217 - root_mean_squared_error: 6.1091\n",
      "Epoch 52/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.2649 - root_mean_squared_error: 5.5014\n",
      "Epoch 53/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 25.7946 - root_mean_squared_error: 5.0788\n",
      "Epoch 54/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.9240 - root_mean_squared_error: 4.7879\n",
      "Epoch 55/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 44.1787 - root_mean_squared_error: 6.6467\n",
      "Epoch 56/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.6495 - root_mean_squared_error: 4.8631\n",
      "Epoch 57/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 52.5736 - root_mean_squared_error: 7.2508\n",
      "Epoch 58/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.4676 - root_mean_squared_error: 4.9465\n",
      "Epoch 59/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 33.5561 - root_mean_squared_error: 5.7928\n",
      "Epoch 60/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.0408 - root_mean_squared_error: 4.9031\n",
      "Epoch 61/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.6011 - root_mean_squared_error: 6.2929\n",
      "Epoch 62/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.1174 - root_mean_squared_error: 5.2074\n",
      "Epoch 63/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 27.2073 - root_mean_squared_error: 5.2161\n",
      "Epoch 64/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 22.6399 - root_mean_squared_error: 4.7581\n",
      "Epoch 65/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 48.9117 - root_mean_squared_error: 6.9937\n",
      "Epoch 66/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 26.0393 - root_mean_squared_error: 5.1029\n",
      "Epoch 67/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.8991 - root_mean_squared_error: 5.3758\n",
      "Epoch 68/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 38.5382 - root_mean_squared_error: 6.2079\n",
      "Epoch 69/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.0545 - root_mean_squared_error: 5.1044\n",
      "Epoch 70/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.7190 - root_mean_squared_error: 5.1690\n",
      "Epoch 71/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 27.9269 - root_mean_squared_error: 5.2846\n",
      "Epoch 72/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 26.8037 - root_mean_squared_error: 5.1772\n",
      "Epoch 73/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.1565 - root_mean_squared_error: 5.5818\n",
      "Epoch 74/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.3553 - root_mean_squared_error: 6.0295\n",
      "Epoch 75/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.5841 - root_mean_squared_error: 4.7523\n",
      "Epoch 76/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.1244 - root_mean_squared_error: 6.0930\n",
      "Epoch 77/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 37.2483 - root_mean_squared_error: 6.1031\n",
      "Epoch 78/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.5244 - root_mean_squared_error: 4.7460\n",
      "Epoch 79/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.9742 - root_mean_squared_error: 5.8287\n",
      "Epoch 80/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9839 - root_mean_squared_error: 5.9147\n",
      "Epoch 81/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.1702 - root_mean_squared_error: 4.9163\n",
      "Epoch 82/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.2008 - root_mean_squared_error: 5.9330\n",
      "Epoch 83/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.7213 - root_mean_squared_error: 4.9721\n",
      "Epoch 84/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.4736 - root_mean_squared_error: 6.2828\n",
      "Epoch 85/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 32.4966 - root_mean_squared_error: 5.7006\n",
      "Epoch 86/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.6099 - root_mean_squared_error: 4.9608\n",
      "Epoch 87/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 32.1085 - root_mean_squared_error: 5.6664\n",
      "Epoch 88/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 43.2959 - root_mean_squared_error: 6.5800\n",
      "Epoch 89/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.9549 - root_mean_squared_error: 4.7911\n",
      "Epoch 90/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.8640 - root_mean_squared_error: 6.3138\n",
      "Epoch 91/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.9419 - root_mean_squared_error: 4.9942\n",
      "Epoch 92/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.8090 - root_mean_squared_error: 6.1489\n",
      "Epoch 93/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.0741 - root_mean_squared_error: 6.2509\n",
      "Epoch 94/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.8180 - root_mean_squared_error: 5.3682\n",
      "Epoch 95/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.5099 - root_mean_squared_error: 5.4323\n",
      "Epoch 96/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 34.4795 - root_mean_squared_error: 5.8719\n",
      "Epoch 97/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 42.5101 - root_mean_squared_error: 6.5200\n",
      "Epoch 98/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 23.4457 - root_mean_squared_error: 4.8421\n",
      "Epoch 99/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 28.3380 - root_mean_squared_error: 5.3233\n",
      "Epoch 100/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 31.9372 - root_mean_squared_error: 5.6513\n",
      "Epoch 101/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 34.4121 - root_mean_squared_error: 5.8662\n",
      "Epoch 102/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.5961 - root_mean_squared_error: 5.1571\n",
      "Epoch 103/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.8816 - root_mean_squared_error: 5.8208\n",
      "Epoch 104/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.3946 - root_mean_squared_error: 5.0393\n",
      "Epoch 105/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 48.5593 - root_mean_squared_error: 6.9685\n",
      "Epoch 106/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.3632 - root_mean_squared_error: 4.8335\n",
      "Epoch 107/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.9795 - root_mean_squared_error: 4.8969\n",
      "Epoch 108/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.6108 - root_mean_squared_error: 5.9675\n",
      "Epoch 109/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.5642 - root_mean_squared_error: 5.0561\n",
      "Epoch 110/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.3376 - root_mean_squared_error: 5.5980\n",
      "Epoch 111/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.2857 - root_mean_squared_error: 5.7694\n",
      "Epoch 112/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.0324 - root_mean_squared_error: 5.1993\n",
      "Epoch 113/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.6881 - root_mean_squared_error: 5.0683\n",
      "Epoch 114/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 43.3093 - root_mean_squared_error: 6.5810\n",
      "Epoch 115/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.5783 - root_mean_squared_error: 4.8557\n",
      "Epoch 116/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.7707 - root_mean_squared_error: 6.0639\n",
      "Epoch 117/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 32.2773 - root_mean_squared_error: 5.6813\n",
      "Epoch 118/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.4998 - root_mean_squared_error: 4.8477\n",
      "Epoch 119/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 41.9545 - root_mean_squared_error: 6.4772\n",
      "Epoch 120/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 30.0999 - root_mean_squared_error: 5.4863\n",
      "Epoch 121/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.6421 - root_mean_squared_error: 5.3518\n",
      "Epoch 122/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.2950 - root_mean_squared_error: 5.4125\n",
      "Epoch 123/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 37.9519 - root_mean_squared_error: 6.1605\n",
      "Epoch 124/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.7495 - root_mean_squared_error: 4.7696\n",
      "Epoch 125/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 26.2582 - root_mean_squared_error: 5.1243\n",
      "Epoch 126/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.6098 - root_mean_squared_error: 5.2545\n",
      "Epoch 127/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.8190 - root_mean_squared_error: 6.3102\n",
      "Epoch 128/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.2478 - root_mean_squared_error: 5.4998\n",
      "Epoch 129/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.3819 - root_mean_squared_error: 5.0380\n",
      "Epoch 130/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.4454 - root_mean_squared_error: 6.0370\n",
      "Epoch 131/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 32.3970 - root_mean_squared_error: 5.6918\n",
      "Epoch 132/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.6791 - root_mean_squared_error: 4.8661\n",
      "Epoch 133/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.7254 - root_mean_squared_error: 5.4521\n",
      "Epoch 134/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.2352 - root_mean_squared_error: 5.3137\n",
      "Epoch 135/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.8614 - root_mean_squared_error: 5.1828\n",
      "Epoch 136/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.7592 - root_mean_squared_error: 5.8103\n",
      "Epoch 137/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.4038 - root_mean_squared_error: 5.2349\n",
      "Epoch 138/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 29.6633 - root_mean_squared_error: 5.4464\n",
      "Epoch 139/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 39.6440 - root_mean_squared_error: 6.2963\n",
      "Epoch 140/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.5935 - root_mean_squared_error: 5.6208\n",
      "Epoch 141/150\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 27.5791 - root_mean_squared_error: 5.2516\n",
      "Epoch 142/150\n",
      "7/7 [==============================] - 2s 224ms/step - loss: 39.3455 - root_mean_squared_error: 6.2726\n",
      "Epoch 143/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 1ms/step - loss: 31.8665 - root_mean_squared_error: 5.6450\n",
      "Epoch 144/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.1457 - root_mean_squared_error: 4.7059\n",
      "Epoch 145/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.1035 - root_mean_squared_error: 5.3948\n",
      "Epoch 146/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 28.1659 - root_mean_squared_error: 5.3072\n",
      "Epoch 147/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 46.2126 - root_mean_squared_error: 6.7980\n",
      "Epoch 148/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 22.8059 - root_mean_squared_error: 4.7756\n",
      "Epoch 149/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 31.1754 - root_mean_squared_error: 5.5835\n",
      "Epoch 150/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.4329 - root_mean_squared_error: 5.0431\n",
      "\n",
      "The learned weight for your model is 0.1496\n",
      "The learned bias for your model is -3.3757\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# The following variables are the hyperparameters.\n",
    "learning_rate = 0.05\n",
    "epochs = 150\n",
    "batch_size = 12\n",
    "\n",
    "# Specify the feature and the label.\n",
    "my_feature = \"horsepower\"  \n",
    "my_label=\"price\" \n",
    "\n",
    "# Discard any pre-existing version of the model.\n",
    "my_model = None\n",
    "\n",
    "# Invoke the functions.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_df, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "print(\"\\nThe learned weight for your model is %.4f\" % weight)\n",
    "print(\"The learned bias for your model is %.4f\\n\" % bias )\n",
    "\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Btp8zUNbYOcd"
   },
   "source": [
    "A certain amount of randomness plays into training a model. Consequently, you'll get different results each time you train the model. That said, given the dataset and the hyperparameters, the trained model will generally do a poor job describing the feature's relation to the label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1xNqWWos_zyk"
   },
   "source": [
    "## Use the model to make predictions\n",
    "\n",
    "You can use the trained model to make predictions. In practice, [you should make predictions on examples that are not used in training](https://developers.google.com/machine-learning/crash-course/training-and-test-sets/splitting-data). However, for this exercise, you'll just work with a subset of the same training dataset. A later Colab exercise will explore ways to make predictions on examples not used in training.\n",
    "\n",
    "First, run the following code to define the house prediction function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nH63BmncAcab"
   },
   "outputs": [],
   "source": [
    "def predict_house_values(n, feature, label):\n",
    "  \"\"\"Predict house values based on a feature.\"\"\"\n",
    "\n",
    "  batch = training_df[feature][1:1 + n]\n",
    "  predicted_values = my_model.predict_on_batch(x=batch)\n",
    "\n",
    "  print(\"feature   label          predicted\")\n",
    "  print(\"  value   value          value\")\n",
    "  print(\"          in $(K)        in $(K)\")\n",
    "  print(\"--------------------------------------\")\n",
    "  for i in range(n):\n",
    "    print (\"%5.0f %6.0f %15.0f\" % (training_df[feature][i],\n",
    "                                   training_df[label][i],\n",
    "                                   predicted_values[i][0] ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NbBNQujU5WjK"
   },
   "source": [
    "##### Now, invoke the house prediction function on 15 examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y_0DGBt0Kz_N",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in $(K)        in $(K)\n",
      "--------------------------------------\n",
      "  111     13              13\n",
      "  111     16              20\n",
      "  154     16              12\n",
      "  102     14              14\n",
      "  115     17              13\n",
      "  110     15              13\n",
      "  110     18              13\n",
      "  110     19              18\n",
      "  140     24              21\n",
      "  160     18              12\n",
      "  101     16              12\n",
      "  101     17              15\n",
      "  121     21              15\n",
      "  121     21              15\n",
      "  121     25              24\n"
     ]
    }
   ],
   "source": [
    "predict_house_values(15, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-gGaqArcpqY3"
   },
   "source": [
    "### Task 2: Judge the predictive power of the model\n",
    "\n",
    "Look at the preceding table. How close is the predicted value to the label value?  In other words, does your model accurately predict house values?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "yVpjhUFm9uID"
   },
   "outputs": [],
   "source": [
    "#@title Double-click to view the answer.\n",
    "\n",
    "# Most of the predicted values differ significantly\n",
    "# from the label value, so the trained model probably \n",
    "# doesn't have much predictive power. However, the\n",
    "# first 10 examples might not be representative of \n",
    "# the rest of the examples.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wLoqis3IUPSd"
   },
   "source": [
    "## Task 3: Try a different feature\n",
    "\n",
    "The `horsepower` feature had only a little predictive power. Would a different feature have greater predictive power?  Try using `citympg` as the feature instead of `total_rooms`. \n",
    "\n",
    "Note: When you change features, you might also need to change the hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "H0ab6HD4ZO75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 39.8603 - root_mean_squared_error: 6.3135\n",
      "Epoch 2/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 40.4350 - root_mean_squared_error: 6.3588\n",
      "Epoch 3/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.4901 - root_mean_squared_error: 6.2040\n",
      "Epoch 4/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.7297 - root_mean_squared_error: 6.3032\n",
      "Epoch 5/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.1872 - root_mean_squared_error: 6.2600\n",
      "Epoch 6/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.2933 - root_mean_squared_error: 6.1882\n",
      "Epoch 7/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.3650 - root_mean_squared_error: 6.1939\n",
      "Epoch 8/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.4498 - root_mean_squared_error: 6.2809\n",
      "Epoch 9/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.6431 - root_mean_squared_error: 6.2164\n",
      "Epoch 10/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.2524 - root_mean_squared_error: 6.1849\n",
      "Epoch 11/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.8382 - root_mean_squared_error: 6.3117\n",
      "Epoch 12/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.1223 - root_mean_squared_error: 6.1743\n",
      "Epoch 13/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.8516 - root_mean_squared_error: 6.2331\n",
      "Epoch 14/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.3866 - root_mean_squared_error: 6.1957\n",
      "Epoch 15/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.8452 - root_mean_squared_error: 6.2326\n",
      "Epoch 16/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.4756 - root_mean_squared_error: 6.1217\n",
      "Epoch 17/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 37.2790 - root_mean_squared_error: 6.1057\n",
      "Epoch 18/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.7860 - root_mean_squared_error: 6.0652\n",
      "Epoch 19/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.1407 - root_mean_squared_error: 6.0943\n",
      "Epoch 20/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.8352 - root_mean_squared_error: 6.3115\n",
      "Epoch 21/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.6979 - root_mean_squared_error: 6.1399\n",
      "Epoch 22/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.3864 - root_mean_squared_error: 6.0321\n",
      "Epoch 23/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.4785 - root_mean_squared_error: 6.1220\n",
      "Epoch 24/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 36.6950 - root_mean_squared_error: 6.0576\n",
      "Epoch 25/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 37.5479 - root_mean_squared_error: 6.1276\n",
      "Epoch 26/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.7055 - root_mean_squared_error: 6.0585\n",
      "Epoch 27/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.7239 - root_mean_squared_error: 6.2229\n",
      "Epoch 28/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2392 - root_mean_squared_error: 6.0199\n",
      "Epoch 29/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.8109 - root_mean_squared_error: 6.0672\n",
      "Epoch 30/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.8066 - root_mean_squared_error: 6.0668\n",
      "Epoch 31/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.1161 - root_mean_squared_error: 6.0097\n",
      "Epoch 32/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2072 - root_mean_squared_error: 6.0172\n",
      "Epoch 33/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 37.6017 - root_mean_squared_error: 6.1320\n",
      "Epoch 34/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 36.7007 - root_mean_squared_error: 6.0581\n",
      "Epoch 35/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 36.5616 - root_mean_squared_error: 6.0466\n",
      "Epoch 36/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2689 - root_mean_squared_error: 6.0224\n",
      "Epoch 37/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2533 - root_mean_squared_error: 6.0211\n",
      "Epoch 38/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.2534 - root_mean_squared_error: 6.1036\n",
      "Epoch 39/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 36.5384 - root_mean_squared_error: 6.0447\n",
      "Epoch 40/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 36.4408 - root_mean_squared_error: 6.0366\n",
      "Epoch 41/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.4962 - root_mean_squared_error: 6.0412\n",
      "Epoch 42/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.8982 - root_mean_squared_error: 5.9915\n",
      "Epoch 43/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.8314 - root_mean_squared_error: 6.0689\n",
      "Epoch 44/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.0952 - root_mean_squared_error: 6.0079\n",
      "Epoch 45/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.4768 - root_mean_squared_error: 5.9562\n",
      "Epoch 46/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.5481 - root_mean_squared_error: 5.9622\n",
      "Epoch 47/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.5210 - root_mean_squared_error: 6.1254\n",
      "Epoch 48/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.7125 - root_mean_squared_error: 5.9760\n",
      "Epoch 49/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.3141 - root_mean_squared_error: 5.9426\n",
      "Epoch 50/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 35.6835 - root_mean_squared_error: 5.9736\n",
      "Epoch 51/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.6790 - root_mean_squared_error: 5.9732\n",
      "Epoch 52/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.1292 - root_mean_squared_error: 6.0108\n",
      "Epoch 53/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.1162 - root_mean_squared_error: 5.9259\n",
      "Epoch 54/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.5761 - root_mean_squared_error: 6.0478\n",
      "Epoch 55/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.3531 - root_mean_squared_error: 6.0293\n",
      "Epoch 56/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8175 - root_mean_squared_error: 5.9006\n",
      "Epoch 57/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.3857 - root_mean_squared_error: 6.1144\n",
      "Epoch 58/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9644 - root_mean_squared_error: 5.9131\n",
      "Epoch 59/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.4511 - root_mean_squared_error: 5.9541\n",
      "Epoch 60/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.8122 - root_mean_squared_error: 6.0673\n",
      "Epoch 61/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.2454 - root_mean_squared_error: 5.9368\n",
      "Epoch 62/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.0620 - root_mean_squared_error: 5.9213\n",
      "Epoch 63/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8594 - root_mean_squared_error: 5.9042\n",
      "Epoch 64/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.1023 - root_mean_squared_error: 5.8397\n",
      "Epoch 65/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.8297 - root_mean_squared_error: 5.9858\n",
      "Epoch 66/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.4080 - root_mean_squared_error: 5.9505\n",
      "Epoch 67/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.8634 - root_mean_squared_error: 5.9886\n",
      "Epoch 68/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.2850 - root_mean_squared_error: 5.9401\n",
      "Epoch 69/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2037 - root_mean_squared_error: 6.0170\n",
      "Epoch 70/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9141 - root_mean_squared_error: 5.9088\n",
      "Epoch 71/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.3552 - root_mean_squared_error: 5.9460\n",
      "Epoch 72/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 34.7784 - root_mean_squared_error: 5.8973\n",
      "Epoch 73/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.5600 - root_mean_squared_error: 6.0465\n",
      "Epoch 74/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8035 - root_mean_squared_error: 5.8994\n",
      "Epoch 75/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.0955 - root_mean_squared_error: 5.9241\n",
      "Epoch 76/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.5860 - root_mean_squared_error: 5.8810\n",
      "Epoch 77/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.4224 - root_mean_squared_error: 5.9517\n",
      "Epoch 78/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.5542 - root_mean_squared_error: 5.8783\n",
      "Epoch 79/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.8440 - root_mean_squared_error: 5.9870\n",
      "Epoch 80/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.1140 - root_mean_squared_error: 5.9257\n",
      "Epoch 81/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.7481 - root_mean_squared_error: 5.9790\n",
      "Epoch 82/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.3367 - root_mean_squared_error: 5.9445\n",
      "Epoch 83/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.6782 - root_mean_squared_error: 5.8888\n",
      "Epoch 84/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.7232 - root_mean_squared_error: 5.8926\n",
      "Epoch 85/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.2367 - root_mean_squared_error: 5.9361\n",
      "Epoch 86/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.9772 - root_mean_squared_error: 5.9142\n",
      "Epoch 87/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.5302 - root_mean_squared_error: 5.9607\n",
      "Epoch 88/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.1028 - root_mean_squared_error: 5.8398\n",
      "Epoch 89/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.0497 - root_mean_squared_error: 6.0868\n",
      "Epoch 90/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.5738 - root_mean_squared_error: 5.9644\n",
      "Epoch 91/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.7396 - root_mean_squared_error: 5.8940\n",
      "Epoch 92/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.5073 - root_mean_squared_error: 5.8743\n",
      "Epoch 93/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.6800 - root_mean_squared_error: 5.8890\n",
      "Epoch 94/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.1881 - root_mean_squared_error: 5.9320\n",
      "Epoch 95/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 33.9432 - root_mean_squared_error: 5.8261\n",
      "Epoch 96/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.9342 - root_mean_squared_error: 5.9945\n",
      "Epoch 97/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.4834 - root_mean_squared_error: 5.9568\n",
      "Epoch 98/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.4328 - root_mean_squared_error: 5.8679\n",
      "Epoch 99/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.7202 - root_mean_squared_error: 5.8069\n",
      "Epoch 100/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.3857 - root_mean_squared_error: 5.9486\n",
      "Epoch 101/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.7805 - root_mean_squared_error: 5.8975\n",
      "Epoch 102/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.7782 - root_mean_squared_error: 5.9815\n",
      "Epoch 103/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.6337 - root_mean_squared_error: 5.8850\n",
      "Epoch 104/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.5948 - root_mean_squared_error: 5.9661\n",
      "Epoch 105/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 35.0099 - root_mean_squared_error: 5.9169\n",
      "Epoch 106/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2022 - root_mean_squared_error: 6.0168\n",
      "Epoch 107/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.7516 - root_mean_squared_error: 5.8950\n",
      "Epoch 108/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8517 - root_mean_squared_error: 5.9035\n",
      "Epoch 109/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.1718 - root_mean_squared_error: 5.9306\n",
      "Epoch 110/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9676 - root_mean_squared_error: 5.9133\n",
      "Epoch 111/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.5756 - root_mean_squared_error: 5.9645\n",
      "Epoch 112/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.0080 - root_mean_squared_error: 5.9168\n",
      "Epoch 113/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9686 - root_mean_squared_error: 5.9134\n",
      "Epoch 114/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8191 - root_mean_squared_error: 5.9008\n",
      "Epoch 115/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.8112 - root_mean_squared_error: 5.9842\n",
      "Epoch 116/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8065 - root_mean_squared_error: 5.8997\n",
      "Epoch 117/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9187 - root_mean_squared_error: 5.9092\n",
      "Epoch 118/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.1860 - root_mean_squared_error: 6.0155\n",
      "Epoch 119/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.1604 - root_mean_squared_error: 5.8447\n",
      "Epoch 120/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.8022 - root_mean_squared_error: 5.9835\n",
      "Epoch 121/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.4286 - root_mean_squared_error: 5.7817\n",
      "Epoch 122/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.0077 - root_mean_squared_error: 5.8316\n",
      "Epoch 123/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.4843 - root_mean_squared_error: 5.9569\n",
      "Epoch 124/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.0513 - root_mean_squared_error: 5.9204\n",
      "Epoch 125/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.1072 - root_mean_squared_error: 5.9251\n",
      "Epoch 126/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 33.9601 - root_mean_squared_error: 5.8275\n",
      "Epoch 127/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 33.8151 - root_mean_squared_error: 5.8151\n",
      "Epoch 128/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.2926 - root_mean_squared_error: 5.9408\n",
      "Epoch 129/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.5475 - root_mean_squared_error: 5.8777\n",
      "Epoch 130/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.0348 - root_mean_squared_error: 5.8339\n",
      "Epoch 131/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.4188 - root_mean_squared_error: 5.9514\n",
      "Epoch 132/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 35.1738 - root_mean_squared_error: 5.9308\n",
      "Epoch 133/150\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 34.4042 - root_mean_squared_error: 5.8655\n",
      "Epoch 134/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.6246 - root_mean_squared_error: 5.9686\n",
      "Epoch 135/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.1624 - root_mean_squared_error: 5.8449\n",
      "Epoch 136/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.8072 - root_mean_squared_error: 5.9839\n",
      "Epoch 137/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.7790 - root_mean_squared_error: 5.9816\n",
      "Epoch 138/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.6488 - root_mean_squared_error: 5.8863\n",
      "Epoch 139/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.7055 - root_mean_squared_error: 5.8911\n",
      "Epoch 140/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.0897 - root_mean_squared_error: 5.8386\n",
      "Epoch 141/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.3963 - root_mean_squared_error: 5.8648\n",
      "Epoch 142/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.6782 - root_mean_squared_error: 5.8888\n",
      "Epoch 143/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9494 - root_mean_squared_error: 5.9118\n",
      "Epoch 144/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.9481 - root_mean_squared_error: 5.9117\n",
      "Epoch 145/150\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.2501 - root_mean_squared_error: 5.9372\n",
      "Epoch 146/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.8445 - root_mean_squared_error: 5.9029\n",
      "Epoch 147/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.3785 - root_mean_squared_error: 5.8633\n",
      "Epoch 148/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.2942 - root_mean_squared_error: 5.9409\n",
      "Epoch 149/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.6458 - root_mean_squared_error: 5.8005\n",
      "Epoch 150/150\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.2678 - root_mean_squared_error: 6.0223\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi41LCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvSM8oowAAIABJREFUeJzsnXmcZGV5739P1Tl1Tu3Ve/d0z0zPxiwwMywjq6CARuMSkMQYMIioUW4UvNdowjVmEe8VueYmEqMgLrhxYwR3EU1kEdmZAWZgFpiFWXp6pvfu2vf3/vGe961Ta1d3V3U30+/386nPdG3nvFU9/T7n96zEGINCoVAoFADgWOgFKBQKhWLxoIyCQqFQKCTKKCgUCoVCooyCQqFQKCTKKCgUCoVCooyCQqFQKCTKKCgUCoVCooyCQqFQKCTKKCgUCoVCoi30AmZKe3s76+/vn/X7w8kMjozFsbbTB7fubNzCFAqFYhGzY8eOUcZYx3Sve80Zhf7+fmzfvn3W7//Kwwfwxd+8jLs+dB4uWtvewJUpFArF4oWIjtTzuiXnPjo4HAUAxFLZBV6JQqFQLD6WnlEY4UYhns4t8EoUCoVi8bGkjAJjDAdHYgCUUVAoFIpKvOZiCnNhOJJC1HIbxdPKfaRQLDUymQwGBgaQTCYXeilNwzRN9PX1Qdf1Wb1/SRkF4ToCgFhKKQWFYqkxMDAAv9+P/v5+ENFCL6fhMMYwNjaGgYEBrFq1albHWFLuI+E6AoB4RikFhWKpkUwm0dbWdkoaBAAgIrS1tc1JCS0tpTAchdflhK45EFdKQaFYkpyqBkEw18+3xJRCFKs7fPC6NMRUTEGhUCjKWFJG4dBIDGs6vPC4nEio7COFQrEA+Hy+hV5CTZaMUUikczg+mcCaDh88hoaYMgoKhUJRxpIxCodGeebRmk4fPLoTcVXRrFAoFgmHDx/GZZddhi1btuDyyy/H0aNHAQD33nsvzjjjDGzduhWXXHIJAGD37t0499xzceaZZ2LLli3Yv39/Q9eyZALNIvNoTYcPXsOJwcnMnI/5lYcPYEtfEBevm7bHVE32DIbx2IERfPiSNXNek0KhqI/P/mI39gyGG3rMTcsC+Id3nj7j991444247rrrcN111+Fb3/oWbrrpJvz0pz/FLbfcgt/85jfo7e3F5OQkAODOO+/Exz/+cbz3ve9FOp1GLtdYr0dTlQIRhYjoPiLaR0R7ieiCkuevIKJdRPQCEW0notc3ay2XrGvH9z94HvrbPfC4tIYUr33tdwfxsxcG53ycn+08js//ah/yeTbnYykUitceTz75JK655hoAwLXXXovHHnsMAHDRRRfh/e9/P77+9a/Lzf+CCy7A5z//edx22204cuQI3G53Q9fSbKVwO4BfM8b+hIhcADwlzz8I4OeMMUZEWwD8EMCGZiwk5HHh9et4V1SPy1kUUzg8GsPKNs+MUrkYY4imsg1prJfK5Pm/2TzcLtXOW6GYD2ZzRT/f3HnnnXj66adx//3345xzzsGOHTtwzTXX4LzzzsP999+Pt73tbfja176Gyy67rGHnbJpSIKIggEsAfBMAGGNpxtik/TWMsShjTFweewHMy6Wyx6XJ7KMDwxG88Z8eweMHxmZ0jHg6hzyDbJsxF1LZnHVMFedQKJYiF154IX7wgx8AAO655x5cfPHFAICDBw/ivPPOwy233IKOjg4cO3YMhw4dwurVq3HTTTfhiiuuwK5duxq6lmYqhVUARgDcTURbAewA8HHGWMz+IiJ6F4BbAXQCeHulAxHRhwF8GABWrFgx54V5DSdi6SwYYxiYSAAAdh2flEqiHoQxaKRSSGRURpRCcaoTj8fR19cn73/iE5/Al7/8ZVx//fX44he/iI6ODtx9990AgE996lPYv38/GGO4/PLLsXXrVtx222343ve+B13X0d3djU9/+tMNXV8zjYIG4GwANzLGniai2wHcDODv7C9ijP0EwE+I6BIAnwPwptIDMcbuAnAXAGzbtm3OasLtcoIx7q6ZSvCA84Hh6DTvKiaS5MagEUohaSkFVTuhUJz65PP5io8/9NBDZY/9+Mc/Lnvs5ptvxs0339zwdQmaGWgeADDAGHvaun8fuJGoCGPsUQCriajp49C8Lm4LY6ksJuOzMwoFpTD3jVwpBYVCsVhomlFgjJ0EcIyI1lsPXQ5gj/01RLSWrOguEZ0NwAAwM+f+LPBYwdx4OldkFGaS/RNJ8vc1JqaQl+tRKBSKhaTZ2Uc3ArjHyjw6BOB6IroBABhjdwL4YwDvI6IMgASA99gCz03DYymFeDqHiXha/jw4lUBfS2mCVGWiyUJMgTE2pyZUSUshKKWgUDSfuf69LnbmuoU21Sgwxl4AsK3k4Tttz98G4LZmrqESHoMrhVg6K2MKALB/OFpmFPJ5hk/dtwvXnLcC56xskY9HLIWQzTOksnmY+uxTSYVSUDEFhaK5mKaJsbGxU7Z9tpinYJrmrI+xZCqa7YiYQjyVw2Q8jb4WNwYmEjg4HMWl6zuLXjseT+NHzw2gM2AUGQWhFACuFuZmFFSgWaGYD/r6+jAwMICRkZGFXkrTEJPXZsuSNAqFmEIWk4kMVrV7kczksH+oPNg8EePupZNTxUMr7LGEWCqHtjk0PpQxBeU+Uiiaiq7rs55ItlRYMg3x7NgDzVPxDIJuHWs7fdg/HCl77ZhlFE5MJYoeF4FmYO7BZhFTSCqloFAoFpglaRS8hpWSms5iIp5GyKNjXacf+4ejZUGa8TqUwlyNgso+UigUi4UlaRREf6FYigeaQ24X1nb6EElmMRxJFb22oBSSRQYjUhJTmAuqTkGhUCwWlqRR8FhB4aFwCnkGSynwoEBpXEHEFFLZvKxpALg6EG6ouSgFxpitoln1PlIoFAvLkjQKmtMBQ3NgcJLHCUIeF1a08VTUgYl40WuF+wjgakEQSWbRHeBpX3NRCpkcgxAgSikoFIqFZkkaBYAHmwetTT7k1tHhNwCgqvsIAE6GC8HmaDKLLssozEUpiHRUQMUUFArFwrOEjYJmUwo6DM2JkEfHcKQ4oDweS2FZkG/+dqUQTWXRHRRKYfabeTKTt/2sjIJCoVhYlqxR8BpOjFiqIORxAQC6/CaGw8VKYTyWwfpuP5wOKspAiiR5KqupOxCbQyxAKQWFQrGYWLJGwe0q1O2FPDoAoDNglLmPxmMpdPgNdPoNqRTE1DW/qcFn6EWZSDNFpKMCKqagUCgWniVrFLy2sZdBNzcKHX4Dw+GCGmCMYTyWRqvXQHfQlEpBTF3zGRp8hnNOgWbhMiJSbS4UCsXCs2SNguiU6jM06E7+NXT6TYxEU7IeIZLKIpNjaPXq6AmasqpZBJZ9pgavoc3JKAilEHTrSikoFIoFZwkbBa4UhOsIADr9BjI5hgmrHkHUKLR6DXQH3LKATbiL/KYOr6FJI/E39+3Cl377ijzed544jG8+9mrNdYjCtZBbVzEFhUKx4CxZo+A1KhiFgEhL5W4ikY7a5nWhJ2gins4hkspKI+A3NPgMTQaaf7t3CL968YQ83t2Pv4ofPzdQcx0i0NzidaneRwqFYsFZskZBuI9Cbpd8TNQdiAyk8ahQCi6ZfnpyKimb4RXcRzkkMzmMxdI4OBJDMpNDJJnB4bE4wrbGeZUQKaktHhfimdycB2QoFArFXFjCRoErhWCJ+wgoFLCNxwpGocdWqyBmKYhAczSVxXGr5iGXZ3hlKIK9J3jH1XCidrxBKIWQW0cuz5DJ1W8UhsJJXPx/HsLh0Vjd71EoFIpaLMl5CkBBKbQUGQW+8Q9ZGUjj8YJREEOaTk4l5MQmnpKqIZrM4vhEodp592BYZhVFkhnk8wwOR+UpTyLQLGolEukcXFp9tvrgcBTHxhPYPxxFf7u3rvcoFApFLZasUZAxBZv7yO1ywm9osqhtPJaGoTngcTnh0kw4CBiYSKDF2sD9Bg80JzI5HLN6JjkI2DMYlplEeQZE01kETB2VSFmvE8YpkckhiMqvLUUEplUltEKhaBRL1n3k1ssDzQDQETAKgeZoGm1eF4gIutOB07r82DkwJQPNXsMJnzWbYf9QFE4HYevyEPacCGP3YFgeM5yoHleQSsHLDU18BtXRYlKbMgoKhaJRNNUoEFGIiO4jon1EtJeILih5/r1EtIuIXiSiJ4hoazPXY0cM2hGFa4JOv1EINMdSaPUVlMRZK1rw/NEJTCUycOtOaE6HPM4rQxF0B0xs7Qthz2AYB4YjWN3BXTq14grSKLgLSqFeRKtte1W0QqFQzIVmK4XbAfyaMbYBwFYAe0uefxXAGxhjmwF8DsBdTV6PpFCn4Cp6vCtgFgLN8Yx0FQHAOStbEElm8cKxSfhNbgzsRqE35MamngASmRwyOYYLVrcBAKZqKIVkJgciICCMwgzSUpX7SKFQNJqmGQUiCgK4BMA3AYAxlmaMTdpfwxh7gjE2Yd19CkBfs9ZTymldfqzt9GHTskDR451+7j7iLS5SaPMWjMLZK0IAgOePTsBniopoblxGo2ksC5lFx7tgDTcKtdJSU9k8TM0pjdRMlIIwCkopKBSKRtFMpbAKwAiAu4noeSL6BhHVSpH5IIAHKj1BRB8mou1EtH1kZKQhi1sWcuO3n3gDekPuosc7/SaSmTzCySzGo7zvkWBVuxctHh15xgvXAMBra6zX2+LGui4fNAfBZ2jY3BsEME1MIZODoTtkjGMmVc0JpRQUCkWDqWkUiMhJRPfM8tgagLMB3MEYOwtADMDNVc5zKbhR+JtKzzPG7mKMbWOMbevo6JjlcupDVDUfGI4ils6hzRZTICKcvaIFAApKwbQZhZAHhubEhh4/zugNyMymcI0uqslMHobmkHOjZ7LBK/eRQqFoNDVTUhljOSJaSUQuxli61msrMABggDH2tHX/PlQwCkS0BcA3APwhY2xshudoOGIC2we/8yycDsK2lS1Fz5+9sgUP7huG3+AxAJF9BADLQrzO4d+uPhtOB8FvaiCqHVNIZXMw9YL7aEZKIcONjX1Qj0KhUMyFeuoUDgF4nIh+Dn61DwBgjP1zrTcxxk4S0TEiWs8YexnA5QD22F9DRCsA/BjAtYyxVyodZ74Rc5cZA777gXNxnhUsFpxlxRV8JYFmAOhr4a4oeyGZz9CmTUk1tIL7SAWaFQrFQlKPUTho3RwA/DM8/o0A7iEiF7hxuZ6IbgAAxtidAP4eQBuAr1pVwlnG2LYZnqOhrGr34tarNuPCNW1Y2VYeAtnaF4LTQbIYrVgpuMteHzD1aQPNhuaU7qPZBJqTKtCsUCgaxLRGgTH2WQAgIp91P1rvwRljLwAo3eTvtD3/IQAfqvd48wER4epzV1R93mtouPPPz8GGbm4fDc1hGQlNts6wE3DrNZVCMpODqTvgcjrgmOGgHRVoVigUjWZao0BEZwD4HoBW6/4ogPcxxnY3eW2Lljdv6pI/ExG8Lid6W8pVAgAE3dq0xWtu3QkigselzSimEFfFawqFosHUk5J6F4BPMMZWMsZWAvgrAF9v7rJeW/hNvSy1VTC9+ygHw2qAZ+rO2bmPlFJQKBQNop6Ygpcx9rC4wxh7ZJp6gyXHZ//odDlvoZTp3Ud5GDo3Ch6XU7auqAdhQFLKKCgUigZRV/YREf0duAsJAP4cPGissHiTzZ1USsDUp09J1XiQ2T1rpaDcRwqFojHU4z76AIAO8NTRHwFotx5T1EHQrSOWziGbq7xxp2xKwe1yzq6iOauUgkKhaAw1lQIROQH8LWPspnlazylHwM2/4kgyixavq+z5ZCYHw1IKHpez7vgAY0wGmlVMQaFQNIqaSoExlgPw+nlayymJqGeo5kJKZW1KQa9fKaSyeeStyZ3KfaRQKBpFPTGF561q5ntRXNH846at6hRCtMSulIHEGJPFawB3H9UbUxCuI5fmUEpBoVA0jHqMgglgDMBltscYeIxBMQ1iiE+lWoW0FWcQKalu3Vl38VrcNsZzKJwCY0zOjlYoFIrZUk9MYRdj7F/maT2nHCKmUMl9JNw+pl6IKdSvFLiRafG4MBRO8bkM1nEUCoVittQTU7h6ntZySiJiCpXcRykra0gWr80g+0i8rtUKXqdUXEGhUDSAetxHjxPRvwH4DxTHFJ5r2qpOIWRMoYJSEBu5MAoeXUM6m0cuz+B01HYFCaMgMpqS2RyC0Gu9RaFQKKalHqNwpvXvLbbHGIpjDIoqeF1OOB1UWylYbh/RjjuazCLoqb3Bi9hDqzVDWgWbFQpFI6inS+ql87GQUxUi3kG1ZkzBUgoiKD2VyExrFMqUQp3uo68/egjnr27D5r5gfR9AoVAsKarGFIjoS7afP17y3LebuKZTDt7/qDz7SHQ3FUrBbhQqkcszPPLycFHhWqtlPOpRCvk8w+cf2It7dxyb+YdQKBRLglqB5ktsP19X8tyWJqzllKVap1TRyE7EFAJm9UwlAHhw7xDef/ez2D0YlllKBaUwvVGIprNgDBiJpGb+IWbJ0bE4/uqHO5FW7b0VitcEtYwCVflZMUOCbh2T8UoxheKUVOEyEgbk0EgUn7p3JzJWPcPxyYT8tzT7qJ7pa5EkVxfD82gUHj84ih89N4Cj4/F5O6dCoZg9tYyCg4haiKjN9nMrEbUCUAnxM2B9tx+7B6cwFE4WPV6aklrqPnr45RHcu2MAh0d50tdQmG/mw+GkNAohd/1KQWRAzadSEOuMz6AluEKhWDhqGYUggB0AtgMIAHjOur8DM5/VvKS59vyVyOYZvv/UkaLHkyUpqaVGYSKWBgCcmOLGZNgyKkPhFBLpLEzdAbeLv7ee6WvCKAxHkmCMzekz1YsosoulGpcddf+uEzg4UvdUWIVCMQOqGgXGWD9jbDVjbFWF2+r5XORrnf52Ly7f0IV7nj5adEUvlIJwH7l1JzQHSaMwHudG4aRlFIYiwihwpeBxabJvUl1KISm6quYRTc3PlXszlMJf37ezzMAqFIrGUM88hVlDRCEiuo+I9hHRXiK6oOT5DUT0JBGliOiTzVzLQvPB16/CeCyNn71wXD4ms48spUBECNomtZUqBeE+GoqkkEjn4Nad0qDUM30tYgt2z1dcQRiFRhkhxhgSmVzdPaIUCsXMaKpRAHA7gF8zxjYA2Apgb8nz4wBuAvBPTV7HgnP+6lZs7AngnqePysdkRbOtZ1HQXZjUNi6NAg8wC/fRsFQKTphW2+166hTsVdXTxRX2D0VwYDgy7TGnIyGVQmM28WyeIc9UsZ5C0SyaZhSIKAie1vpNAGCMpRljk/bXMMaGGWPPAqg+r/IUgYhw6foO7BkMy/TMZElKKsBrGmRMIV5QCol0Trp/ToaTiGeEUZi5+wiY3ij87U9ewv/88Yv1fryqiG6usQYpBfE51QwJhaI51Cpea611q+PYqwCMALibiJ4nom8QkXc2iySiDxPRdiLaPjIyMptDLArWd/uRzTO8amUTpbJ5OB0E3VlsFMIlSuHkVBLDVjyhv82DyXgGk/E03C4ndKcDTgfVNZIzksxAdNeezn00FEk2JI1UBJobpRSEy02NIFUomkMtpSAyj3aAb+6vANhv/byjjmNrAM4GcAdj7CzwZno3z2aRjLG7GGPbGGPbOjo6ZnOIRcH6bp60te9kGIAYxVn8KxDuo3yeYcKqbTgxlZDxhM19IQDA4dEYPC5e7GZqjjrdR1l0+AzoTppWKYxF0xiOpOZcdCaMQaxBgeaCUlBGQaFoBrWyj0SW0W8BvJMx1s4YawPwDgD/WcexBwAMMMaetu7fB24kliyr233QHISXT3Jf/cGRKJaF3EWvCbo1hJNZRJJZ5PIMHX4D4WRW1ips7g0A4K4gt4u7jky9vtnO4WQGQbeODp8hlUclkpkcoile/Swyn2ZLTMQUGpSSKpWCch8pFE2hnpjC+YyxX4k7jLEHAFw43ZsYYycBHCOi9dZDlwPYM6tVniK4NAdWd3jxylAEuTzD9sMTOHdVsSdOKIWxGL+S39TDjcDOAR6O2dwbkq/16HajUIdSSGYQcOvoCJg1lYJwWwHAwOTcXEiyTkEphYby8skIfr5zcKGXoTgFqccoDBLRZ4io37r9LYB6/zfeCOAeItoF3oL780R0AxHdAABE1E1EAwA+AeAzRDRARIHZfJDXCqd1+bHvZAR7T4QRSWVxXolRCJg6cnmGgQmecbRpWcEouJwO6YIC+KQ2gAeqU3XFFLLwmxo6fEZNozAWLRiFwcm5KYV4k5RCPcV6pzLff+oIPt2ARACFopR65ilcDeAfAPwEfI7Co6hzGhtj7AUA20oevtP2/EkAfXWt9BRhQ7cfv9x1Ag/vGwYAvK6/XCkAwOEx7i4SSmHfiQi6gyZaPDpcmgPpbB5uK6Zg1KsUEhn0t3nhMzU8f3Si6utGYwWDcdwyTrMl0eCYgkjjXepKQbj46hnIpFDMhHrmKYwD+DgReRljseler6jN+m6+yf/7M0exvNVdIaZgGYVR7rbZaBmFbJ6hK2CCiNAVMHBsPCGVgqnXpxTCySwCbg1tXgPj8TQyuXxR5pNAKAUHAcfn6D6KN7hOQWQdLXWjkLaaJEZTWfl/RqFoBNO6j4joQiLaA6vwjIi2EtFXm76yU5T1Xdz9MziVxLn9bWXPlyqFnqApO6F2+g0AQJffBFBwH5na9IFmxhgiyQwCpo4OvwHGit1EdsaiXCms7fTJzqyzIZ9nssV3o+oUCkphabuPRFZYpEJLdoViLtQTU/gXAG8BMAYAjLGdKJ61oJgBfS1uuZmfu6ql7PmAVAoxuDQHPC4nugPcCHSV/Ou2KYXpNslkJo9MjsFv6tK4VIsrjMXSMDQH1nX5p40pTMTSeHFgqvI5beqlcXUKOXns+WrqtxhJSaOgus8qGktdFc2MsdJRXUtbu88Bh4OwzlIL566qrhSOjsfR6nGBiNAT5EagM2AU/espSUmNJDP4w9t/jzd+8WFc8W+P4aXjhc1azGgIuDV0WEahWlrqaDSFdp+BvpAbxycTyOerb75fe/QQ3nPXkxVfYzcEjWqIJ5QCYwUXiuCzv9iNr/3uYEPOs9gRSmG+Ghsqlg71GIVjRHQhAEZEutW4rrSHkWIGnLOiBStaPehv85Q9J5RCNs/kVLWekKUQ/CVKQbeK13Qnktkcdg+GsfdEGL0tbuwcmMIjLw/L4wo3Q8DU0Wm9v6pSiKbR5nOht8WNdDZfFHguZdAa+DMaLX9NwjYIqFGts+3qo1QdPbxvGI8dGG3IeRY7yn2kaBb1GIUbAHwUQC+A4+CppR9t5qJOdf76revxixtfD6LyrBG/oclWFK1ebiB6gjwYXXAflSoF7j4S7TO+cNUWtHldRfGAKWtGtN/U0O7jxqZaq4uxWAqtXheWWeet5UISxqBS7EEohXafC4lMDrkaiqNeUjZDUNoZNpzMLpkrZ+FGU+4jRaOpaRSIyAngWsbYexljXYyxTsbYnzPGxuZpfackpu6smjHicBD8BlcALR6+eS9v5Yqit4Vv0hu6A9CdJB83rEDzq1YcYlnIjb4Wt6x1AOzuIx2G5kSH38DTr45V9MuPR9No8xryfLXSUoXaOFGh8lm4jNp9RtH9uZCqohQYYwgnMogukU1SxRQUzaKmUWCM5QBcM09rUViIWc0i6+htZ3Tjhx+5AKvaeT/BjT0B7P7sW+V9Q3cglcnj0EgMq9q8cDoIfS2eos1cNNkLmPzY/+0Na/D4gTE88NLJonMzxjAaS6Pdch8BtdNShVIYrKAUElIpCKMwdxeS3RDYXUmJTA7ZPFsySkHEU5RRUDSaetxHjxHRvxHRxUR0trg1fWVLGKEihFHQnI6ydhguWyM9U3Mincvj0EhUGoreFjcGbEFisXkETK5C3nfBSmzqCeCWX+wp2kijqSzS2TzafC4ETB1+Q6vqPsrk8rJpX6XXxEuMwkzTUg8MR5EtCSYXK4XCz2HLPbZUlIKKKSiaRT1G4UwApwO4BcD/tW6n/FCchaTUKEyHmKnw6lgMqzq4UegrCRLb3UcANzT/+11nYCiSxJ2PFDJ2RO1Cm5dv5L0lbig79h5JlZSCmKXQ7uefYyZKgWdSPYo7HinOJipSCrafxeeLprM1s6UawcmpJK766uM1mwo2G+U+UjSLaY0CY+zSCrfL5mNxSxXh4hExhekQ09cYg1QKfZbrR2zo4UQWLqejqFX3WStacMm6DvzqxRPyMdGIr80KRp/W5cfjB0ZlLcLARFxOZBPxBAcBg1OV3EfFMYWZKIWJWAaZHMMPnj1WtMlXVwoZ+R3Em1ztvOfEFJ47Ool9J+Y+mW62qJRURbOoq06BiN5ORH9NRH8vbs1e2FJmtkoBAFYL91GIB6GFUYgkMwi4tbKMp8s2dOLQaEy25h61lILYyD/zjo1o9brwge88i3/+r1dw+f/9Ha771rMAgBErnrCus3KRm1AGHbOIKYgr/+OTCTx1qJDXYFcH9qZ4YZsbpVHV09UQ6bWNqr2YDcp9pGgW9bS5uBPAe8A7nhKAdwNY2eR1LWmEUZipUgBQFFMACplD4WQWfrM84+nS9Z0AgIetmgbpPvKJ1homvn3965DM5PCvD+5H0K3j+GQC8XQWoxEx+CeI0WiqrNVGWUxhBpuo/Qr4h9sLtZOpbE42gKsUUwCa71IRxiDaoNqL2SAUU1i5jxQNph6lcCFj7H0AJhhjnwVwAYDTmruspU1gpkpB40ohYGryPT5DQ8ijY2CCZw6FExkZZLazos2D1R1ePLRPGIVU2bnXdfnxw49cgG9f/zr8/Ts3AeAN+4RS2NoXBFA+kCeRzsFBQMjKpppJ+2yxsW9dHsIDL52Uc6tT2bw0mkVGwXbF3GyXykIrhWwuD+FRUzEFRaOpxygIZ3GciJYByADoad6SFJdv7MQ1562QPYqmQ7iPVnf4itxDfS1uWVQWsQbsVOKy9Z14+tA44uksxmJp+E0NhuYses3GngDeuL4T/W1ciRwZi2E0kobX5cSaDh+A8rhCPJ2Dx6XBZ9VdzEQpCLfIBy7qRyqbx69f4nGPZCaHkDAKdvdRwmYU5kkpNKpKe6bY23tEU8p9pGgs9RiFXxJRCMAXATwH4DCAf2/mopY6G7oD+Py7NsNRZ598w3IfiXiCoC/kKQS5wNh0AAAgAElEQVSarQE7lbh0QyfSuTwePzCGsVhaunsq0W+d4/BYnPdI8huy/XdpXCGe5iNDPYbTuj9zpXDhmnYQFWIjqWxeGrdUkVKwp9U2d6OUI0YXSCnY52YrpaBoNPVkH32OMTbJGPsReCxhA2Ps75q/NEW9iKv6VSVGobfFjeMTCVntG6gQUwD4oB+vy4l/+NlLeOLAaE23lc/Q0O4zcHg0hpEIb5zXbTXsOzFZrhS8LidcTgc0B80oAByxNfDzGZrc/JKZvHRHVco+Aprv64+nRExhYTZkEWAPeXREktkl3S12KfGGLz6Mrzx8oOnnmXbIDhG9r8JjYIx9tzlLUsyUNmsT39BTPMm0r8WNRCaH8VhazmeuhEtz4HNXnoGf7xzEvhMRnL+6teLrBP1tHhwei2E8lsaaDh9M3Yl2n6ui+8jt4hlPXkObsVJwaQ4YmhN+Q5MbcCqbg9fQ4KDyOoV2nwuj0TSic8zISWZyODAcxRm9wYrPxxo8YnSmCKXQ7jMwGc8gkeFuuoVicDIBt+6UDRwVjYcxhqPj8XkZLlWP++h1ttvFAP4RwB81cU2KGdLf7sWvbroYb9rYWfR4r+XW+V/370Uyk8eK1vKurIKrzu7Dt68/F099+nJ86i0bap5vZZsXh8dilvuIbwTLQm4cL3EfJTJZ2bTP63LO6Mo6ksrKwLjP1GScIJXJw9Scsl24IJzIysaBc72Cv3fHAK78yuMyuF2KUDyNGjE6U4RSEBcDC13F/aHvbMetDzSvcfKB4Qiu/MrjRckES41MjoGx4vTzZlGP++hG2+0vAJwNwNf0lSlmxKZlgbIahL4WbgR+8vxxvH1zD64+d0VDzrWq3YOhcAoT8YyMP/QEzYruI2EUPIZW0wefzzN878nD0m0USWZlgNpXohRM3SHbhQvCyQxavS64NAciczQKAxNxZPOsqGLbjlAKza6HqIZIRxXf/UKnpQ5OJTAUrt5efa48d2QSLxybxNGxuY2GfS0jfuf24tNmMZszxACsqueFRBQiovuIaB8R7SWiC0qeJyL6VyI6QES7VE+lxrK81Q3dSbh4XTv+5T1nNmzA+8q2QuxCDOxZFnJjcDJR5N9OpHNw6wWlUCtb5/ljk/i7n+3Gb3YPAeAxBVFX4TN1udGnMnkYmhOmVjxtLpzg7jG/oc35yllUaldTCnGpFBbWfSRqSRaygC2fZ5hKZJq6BvF7SCzhudxCHc6HUagnpvALAOIv3QFgE4Af1nn82wH8mjH2J0TkAlDqv/hDAOus23kA7rD+VTQAv6nj1//9Eixv8RQ10Jsr/TajIK5W+1o8iKVzmIhnZKC6SCm4aiuFPSfCAAp1EhFbtpTf0HDcqrdIZnMwhFKwbRKRJHc3+Uxtzu4jUdUdruY+WiTZR6I/1UJmIPFAd3PXII3CAhnhxYD4v16aKt4M6olO2ZvfZQEcYYwNTPcmIgqCz3J+PwAwxtIASvX4FQC+y/jl5VOWsuhhjJ2AoiGIGoJGsrK9YNuFURDxiiNjsSKj4LYCoF7DicHJ6leTewa5URAum0gyg3Yrm0q4j3J5hkyOwdScMHSnVAqMMRlI9xnanN060yoFyxgsWKA5V6wUFrL/0WRC/L6abxQaNef7tYhUCvoicB8xxn5nuz1ej0GwWAVgBMDdRPQ8EX2DiLwlr+kFYJ//PGA9VgQRfZiIthPR9pGRkTpPr2gWAVOXQU7R12ilNVr06HjB75tIZ+tXCoO84d5YrLDJFNxH3CUk/aq6A6bukPeTmTwyOYaAqRelr84WMSOieqCZn3fBUlIzIvto4d1Hk3ERA2q++2g+Mm8WK+J3vihiCkQUIaJwhVuEiMI13qqBB6XvYIydBR6LuHk2i2SM3cUY28YY29bR0TGbQygajDACIvuooBS4UWCMIZ4puI+8hrOqDz6by2PfSd5xVLiPojb3kc/QEEvn5JWiqTlgagX3UbikpmEum3Uuz+QaqmW7SKWwUDEFqRRm7j4aCidx7Teflp9xrkxaG3Ys3Zhxq5VQSsEeaF4E2UcAvgS+mfcC6APwNwC+xBjzM8YCNd43AGCAMfa0df8+cCNh5ziA5bb7fdZjikVOf7sXPkOT+fGm7kRXwJBKIZXNgzHAbVcKVTbrV0djUh6Px9LI5xmi6awcSyqMg3AtGbpTzqUGiqfKzSamkM8zaWAm4mnZV6iSUsjnmdycYumFKRwTMQXRMHEm2Ue7Bqbw+/2jePxgYybqTsYLHuFmKaeCUVi61duLyn0E4I8YY19ljEUYY2HG2B3gsYCaMMZOAjhGROuthy4HsKfkZT8H8D4rC+l8AFMqnvDa4GOXrsXtf3Zm0WMrW70ybVBsnB5b9lE8k6s4AEcEmTf2BDAWSyOa5sFL6T6yjINw65glgWb7ACFfHdlHU/FMUXrj3U8cxiX/52Hk8kzGE4DKgWaRAdPqdYGx4gK6Wnztdwfx8snGzF8QV41ul7Ouz2tHrF/EcOaK3XA2y4UUVu4jW/bR4lAKMSJ6LxE5ichBRO8FdwXVw40A7iGiXeAT3D5PRDcQ0Q3W878CcAjAAQBfB/CXM1y/YoFY3eHD5Ru7ih5b0ebBkXH+X0Nc1Qkl4TE0volmy/+w9wyG4XI6cG5/C8ZjaekO8duK14BCW29DFK+J9tGJwqhRn6lNW6fwpQdfwR/f+YS8yn/y4BiGIykMTiak4QEqKwVRsCZiKfVcHSczOdz6wL6iFuBzQSgFl9MBv6nNaDMWg4+EIZ4rIqYAzD3Y/PShMXzke9vL3FCl7qNEOofbfr1vQY3Eo6+M4KP3PIf7dgxUjT01kpTMPlocSuEaAH8KYAjAMPg8hWvqOThj7AUrFrCFMXYlY2yCMXYnY+xO63nGGPsoY2wNY2wzY2z7bD+IYuFZ2cqL2pKZnEwfdMuYgtUptULGzp4TYZzW7UNX0EQ8ncNwmFdG11YKNveRTSn4DQ3pbL6oaVwpo9E0RiIp2WRvr7VBHh6LSaXQ4tGLZjQIRMZRZ0AMDpp+IxRXuidsbUAODEeLXC8zQVw1ujTHjAPrYmOtVylkc3m8/raH8JPnK+eXNNIoPLp/BL/ZPVRUNMgYK6tTePbwOO545CC2H56Y0/nmwgMvncT9L57AJ+/dibf8y6NNP5/4nZuLwX3EGDvMGLuCMdZu3a5kjB1u+soUrzlW2DKQpPvI1uYCKN9EGWPYMxjGpp6AzGgScQlZp1BBKRj2QLMtplAwPtU3KPHci8enMJXIyPbih0dj0vCs6fBVvAIUyqBDjhid/mpVHEd0kWWM4c/uehK3/frlad9bCXshk9/UEJlBV1ixsY5GU3XNmB6PpzEwkcDeKqNHi2MKc7tiFr9fMRIW4EYsaykHcaEhfgcLqRRiqSxWtHpwwxvW4GQ42fQaivmsU6hqFIjoL4honfUzEdG3iGhKVR4rqrFSzlooGAWhFESzNDGIZzSawvu+9Qyu/eYzGIulsakngFarGEtkMBWyj3T5HoBvhqbulGl6YZu7SaiKWm4du1HYa3OjvDoax0gkBUNzoLfFXdEoyBGjgfqnyQklM2gZn6lEBqPRNJ4/Orsr3WL3kT6zmIJt86pHLYir9okqLT8mExlZtT73VGB+jvFo4Vz234EwaNIoVHBFzhexFG/DIrLwJmap+uplPiuaa53h4+CzEwDgagBbAawG8AnwSmWFooiVtgK2RKY4pnDGMt5xdNcAr0d4aN8wHn1lBJOJNLb0BfHG9Z2y6E3Miy5VCmLTMHUnDM2BdC6PXJ63BReGQry21gYlNvaXbEahw29YTf74PImgW6+YkloaU6inUE5sbCPRFNLZvFRCrwxFZpVRk87loTsJDgdZMYWZuY80q91JPXEFcfU+Ea+sAibjaSxv5Y0I59qDadxSCGOxykYhXtJzqt4gfyMYjaZwZKwQSo1aRqHFauM+f0ZhYQPNWcaY+I28A7zyeIwx9lsApUVoCgVCHu7Tr+Q+6vAb6A258cLAJADguSMTCLp1/Pyjr8fPP/Z69Ld7ZTHW4TFhFArFa0C5UgB4Jo69LbhQFfUohV0D3Ci0eV3YtrJFzojo8BsImDrCiUxZtlQhpsBnSNSTOy9iE4zxOoFj41wx5NnssoDS2bzcHPymNqPNOJ7OIeRxoa/FXdUlZEds0FOJwqb3w2ePYbdVbDiZyGC51XhxuoB3Jpev6fIR5xqrEuxPlBmF+VMKt/5qHz7yvR3yfiydhddwImSlBU9WMZqNwl642WxqnSFPRD1EZIKnk/7W9py7uctSvBYhIp6BNBaXm6fb1ur3zOUh7DzGjcKOIxM4Z2VL0XQ5oRRK3UdeS20IX7Np1SkA/GoxnChusw3U9m/H0lnoTsJUIoOH9o1g07IAVrV7cXQ8jpPhpFQKeQZES67k56IUAO5Csld9C+U0E1LZnOxlFTC5oqm3XiKZycHtcmBTT0BWkddCbNBCKTDG8JmfvYRvPvYqAJ7e2xU0oTloWsVy66/24U+/9mTV54XbaLyCUgh5dJv7SFSyz59RGAonizLTYik+10PUijRdKSySiua/B7Ad3IX0c8bYbgAgojeAp5EqFGWsbPPgpeNT+Kf/fBkhj1402nPr8iAGJhI4OBLF/uEozlnZUvRen6HB5XRgLJaG00HSoDgdBK/LidGICDQXlEIyU6oU+OO1pq/FUzls6QsB4OpjY08A/e1eZPMMB0ei6PBzowDwTa/4vZZR8NdvFOz1Diemkjg6Hkeb14WugIEXj9dnFL7wwD586DvPAuBKweXkf7rtPgPpbL7uduHxdBYeXcOmZQEcGo1N674SG7QIKEdSWaSzeRwZi4MxhslEBi0eva7U2F0Dk3jp+FTFzLBkJic/QyX3UXfALHMfpWpkmDWaqUSmZORrqfuo2UohD5fmKGuP3wyqGgXG2C/Bx29utOYoCLYDeE+zF6Z4bbKi1YuxWBouzYEffuQCGWgGgK3WRvztxw8DAM5eUWwUiEiqBb+pFf0B+ExNXikaRUohVzRqVLqPqly1MsYQS2exbWWL9K1v7PHLUaaMAR0+lzQypXEF0apDGoU63EdTiQx0Jz/X8ckEjo3HsbzVg829Iey03GnT8cTBUbx0nLua0tm8dCOI1FiRxjsdvEmhExt7AmAM0xbUiTjOZJyrERFjODwak00KQ25X0SCkahweiyPPivtjCezqYMwWaBYGtTtoSmUgjUKJUoins3jnlx+bdQC/FpOJNNLZvHTjxFJZeA2t4D6qEohvFKlsbl5UAjBNSipjLMsYmyh5LMYYizZ3WYrXKu86qxdXn7scP/nLi3Bal7/ouTN6g3AQcN+OATgdhK3Ly8ddis6fIotIYL9vWL2PAO4+mohn5JX9dO6jZCaPPOPZUGJ9G3sCRfOtO/wGAm5+nNIMpHg6C6eDEDA1aA6qeqX96CsjcvOaSmTQ5jUQ8ug4McXdR8tbPdjaF8Shkdi0V9iMsaLXpWxKodPPYxv1DrlJZviMCxEHGJrGmIjgbzbPEE1lpTtpLJaWsZGgR4ff0Gu6j2KprHS/iESC4vOkK/48lciACOj0G/K7LmQfFSuF4xMJvHh8Cv+1Z6jmZ6rFsfF4UT2JXIelBKLJrGx14jU0uDQHvC5n05VCMpOflyAzMLshOwpFVdZ3+3HrVVvklbQdr6HhtC4/EpkcTl8WqDhXuKAUiudJ+2z37e6jqUQGAxNxual7dCeIqisFERPwupzYujwIQ3NgTYcPbV6X7LUkYgpAeauLWIo3+SMieKoMDhoOJ/G+bz2DHz3HC77CSW60eoJuHBtP4PhkAita3djcx42iUADVGImkEE1lZdO5tOVKAIAuoRTqqDkACjMuxPc8VuEK9z+ePSo3QftV+2Q8I5UDAKlyQm592iwouzp4tYJREAajO2AW1SlMWSrQ49IKgeZ05UCzcO/snkMLj7+6dydusAWUAd4kURw7kszK8wtXZcjjmnUhYr0sGqWgUDQa4UIqdR0J2mzuIztiwzYsv6pwn+wenEKecWMEAA4Hweeq3upCXL17XBr+x5tPwz0fOg+6kx+z3zIs9phCOJHFkbEY/vKeHYins4inC2NCvVVmNwxatRiiWG0qwY1Cb8jEc0cnkMszrGj1yLjGrmlcSIdGi1Mh07m83CBEFlS9SiFhuY9avJYvvMQoDE4m8Dc/ehH37uAtOcZjaemqm4ini4KtLxy1jILHBb9ZOYVXYE/nPFTBKAjjs67LVxZTCLp1uF3OaQPNQkHsHpyadaPC0UgKOwemitSMXclFU1l5ISAKJVu8+rykpM5H5hFQp1Egol4iupCILhG3Zi9McWqydTnfCEuDzAJRwBYwK7uPhEIQ/+60snfsripvjSZx9j/oTr+Jbf2t8jmhNtp9howpTCUyeOClk/jViyfx4sAUYkXT5JwVU1KFf1+0zAgnsgi4NfQE3fJqenmrB61eF3pD7mnrBQ6NFG9QKZtS8BkavC4nhus0CkIpGBpvpjceK97IxfpEBthYLI3V7XxQ02Q8U6QcXjgmjIKOwDRKQRxvXacPr46We5+Fy2hdpx+T8QyyVntwYRQ8uhOZHEMml69apyB+56PRNIYjtb+Pk1PJigZdGLb7Xyz05bSnm4aTGWl8xP/JFo+rKe6jA8NRafhSmbx0mTabeuYp3AbgcQCfAfAp6/bJJq9LcYry1jO6cfW5K/DG9ZXnYoiYQrn7qKAUAMg/kF0Dk3A5Hehv8xS9tlqlsfBLe43yP7C1nT44HYQOvwGfS4OD+KYkagkOj8VkgBEQcx7KzzNkbUjCpTNlzY9eFipkcov5EytaPbIHUzUOjRQ20UgyaxmFwvq7AiaG6nQfJTKFudktXl3GDARiwzsyHkcml8dUIoO1ndwoTMTTGIulLFeYiVeGeZC64D6qoRTG42jx6NjSF6rsPoql+O+xXVQI82PZlYJYf7U6Bfv5X5omq+s9dz2Jf/mvV8oeF26iX+wclI9N2lyI0WRWnl+kSs/VfXRsPF6mbCLJDN52++9x3w7ugkxZY2jng3rOciWA9YyxtzHG3mnd/qjZC1OcmrR6Xbj1qs1lm76gmvtIXJWJPwzh0jgyFseaTh80Z+G/ctCt4/hk5U0yanMflXLdhf34/gfPg9fQ4HAQAm4dU4mMLNR6dZTXX9inyVW62hwpUwrcL74sxF09moPQE+QGoq/FjWMVsnH++T9fxhce2Ged164UslbxWuHzdviNurOPErYRqa0eF8ZLU24tI3d0LCZdS2Kkq1AK7T4XVrZ5IPaxgLswx6Ka2+bIWAwr27xY3eHFUDhV9r2NR9No87lkrGNcFs2VGIV0rmqg2V6wOF1c4cRUEgdHihVLMpNDOptHd8DEvpMR7B+KyDUIInajIJWCPmulsHtwCpd88WE8tG+46PGhcArpXF7+H0qV/M6bST1nOQSg8l+wQtFgxKZQmn0kjIRQCKatKG59V/Ec6rec3oWdxybxYoXCMOHuKT0+wI3JBWva5P2AqeNkOCl94IdHY7yS1TZ3ulKgWfj3RyIp5PIMkVQWQZtS6Gtxw2mlwy5v9WA4kiq76v3pC4P49hOvIp7O4tBoDL3WeyPJDNK24jWAK4Vq7pLByQT+7qcvIZPLI5vLI53LS6PW6nWVxRTEhjcwkZCfY3UHd6tNxjMYiabQ5jOkq82tO632IrzYz+5OGwonZbPBI2NxrGzzoN/qj3V4rFgtjMXSaPW60Ga5D+X0O0tleWRDxepKQVzl91u1MtVIZfnmf2Kq2JAK99e7t/XBQcAvdp2wPnfa9ppy91HI40I4mZnV5LmH9g6DMeCZw+NFj4vYjb0mYzFlH8UBvEBEXyOifxW3Zi9MsTSp6j4qUwqFP5DTuotTX68+dwX8hoavPXqw7PiFQPP0f2BBt47th8fBGFcmh8di3CdvraXa3GnhNhqLpeUVb8ByuQDcEAj6WvhmLzZPgG92xybiSGby+K89Qzg6HseZVixGuI8Mp90oGBgKJytepd+/6wS+99QRHBqJIZ4prjJv8bqK0j+BQhA3m2eysK4rYMJvaNx9FE2h3eeSm3vIKt6q1HPqpn9/Htd8/SmksjkMTiawstUjjUmpC2nMMjbi9z8WS8u22UG3Ltdsn4xXWqcQTfIkgM19oZpKQcQeBieL3XbC/bSmw4ezV7Tgsf18Hrw9A41ngRW7IENuHYxVn+ldi0etc+w6VmzEpFFIF2oyFpNS+DmAzwF4AsAO202haDiiAlpk/wh8ZUqh8F93fUk9hN/Ucc15K/CrF0+UuWZKpX8tgu6CW+CyDZ14dTSGSDIr24B7rdnRjDH8ctegvHK1ZwKJzS/o1tEVMOGgUqPAf7bHFQ6ORKVr5uu/P4RcnsmaDq4U8kVKodNv8nYfFQK9+4cLLpBkSefaVk+5UbC7dZ6zisBavS6EvDom42mMxdJo8xqyI674PQkjLjbWWCqLHUcmcGQsjh88cwx5Bqxo88qYwasj5UqhzeuS7sPxWBqJTA6ZHLPcR1b/K5siKgs0pzLwGRpOXxbA8ckEdg1M4uYf7SrqhMtfxz9j2OYKEvf5Z9HQ3+6VSkIEmkUrD2E4fbbsI2DmrS7CyQyeOzoJp4Pw0vGpoj5bwm0kzsWzjxaJUmCMfafSbT4Wp1h6rGj14AtXbcbbN/cUPV6mFGxSurRIDgDef1E/HESyR49AVCBXCjSXIja8oFvHhWvakcrmMRpNyXiE1+VELJXFkwfH8LH/9zzut9wNw5GkNG4HhqPyGLrTgdv+eAs+cFG/PIfoMDowUTBe4j1nLg/JGgaRvhpOFqekAoWq5pEKweb91rGmEpmyJoUtXhcStoFI/PspNwrtPhdaPC6MRtOYjGfQbnMflSoFsbE+c3gc2TyDg4DbH9wPgLdA8bg09ATNCkqBG4WQxwUirhzElbddKdjrJEpbZ0eSWfhMTXbkvfIrj+MHzx7Dr186WfY6gd2FFLENa+q2XHK5PFcrHpcTIY+OSKo8plBoijczo/DEgTHk8gxXntmLSCqLV20uNaEURFuVRaUUiGgdEd1HRHuI6JC4zcfiFEsPIsKfnbsCQU+xUiiNKTgcBJeTV5P2hsr7M/YE3Xjn1mW4b8dA0dVgPJ2FZr13OkRV86aSimdhUDyGhng6hwetIOErwxFkcnmMxdI4ozcAoHClLlJs371tOdZ2FoxYp9+E7iRZHQxwo+B0EP7bG9fIxzb2BKA7SQaaS2MKQHmtAmMMB4bKjYLYYOVVuW0zi6V4xbahOXBoJGZVb+sIunUZmG2zAs0AEHJb7j5DuI/4xvrEgVG4NAeuu7BfqhHxnlXtXhy0GYV4OotEJoc2nwGng9DicWEsli4yCsKQ2SfjVapT8JsaNvcG4Tc1XLS2HV6Xs8ytU2wUEmWP+00N3UETuTzDaDSFScuF5Td1GWgmshlX0RQvNjP30e/3j8DrcuJ66yLBXq8i+nxFbTGF+Zi6BtTnProbwB0AsgAuBfBdAN9v5qIUilJETyN7Wp6hO7Cuy1/UadXOtResRDSVxU9fOC4fs1ckT4eoVTh9WUAWtgGFK0ThRhJXogeGohiNpsBYYX6EVAqeyrkaTgehN+QuUgr7h6JY2erBG9d3wG9oaPe5bJtScZ0CwFtAAOUtK4bCKVnEN5XIyOKv0sFH9mBzLJWD1+WUKbMtHhcc1kYtrqrbfS6YOu+ftKaTfy/CfSQ2sccPjOGcFS34wEWr5AYqOstu6ePdcv/kjifwxMFRWfsgjFSbFesQLceDbl3GkEaifA1tPqPMfRS2YgpBj47tn3kTvvuBc9HmM8rcOvYsJbtSELEDv1mI/5yYSsq4hs/QELUCzV5XoTfXbGYqMMbw6P4RXLCmHRu6/XDrTuy0xRXKYgqLLNDsZow9CIAYY0cYY/8I4O3NXZZCUYwsXrP9YXT6DWztK++fJDhreQibegL43pNHZBBWTMyqB+E+Or03gJ6AKeW7PaYA8CCx00E4MBKVRWSnL+NK4aBlFAJVUnABHlc4ZospHBiJYm2nD4bmxHUX9uMPz+CuNL+pYTLBs1zsG4Soai7NQBIqBbCMgnQfWSmpJemfQKH7p7iqFzMuWmxGrc3a3H/60QvxiTevl2sD+NX2eCyNPSfCuHBNG5a3evDmjV3Y1BOQm+h/f9M6/OM7N2FwMoHrvvWMdFOJIHOr14WxaGWlIK6g27yucqWQLDRGNDRu+Fs8etmsA3tfrBOTdvcR34ADpibV18mpBKbiQiloUinY3Y8zmalweDSGN/3z73DJFx/GsfEELjmtHZrTgdOXBYo65o7I7CMRU1hE7iMAKSJyANhPRB8joncB8E33JgAgosNE9CIRvUBE2ys830JEP7FGfD5DRGfMcP2KJYLYdOxK4d8/fD7++q0bqr6HiPDn56/EvpMRufHE0lmZPTQdrdYf++nLgnA4SG6UHltKquBtm3twdDyOI1Zgu6/FgxaPLltelAbO7SxvdeO4pRTS2TwOj8awzkqz/eRb1uNzV54hvwORqmlXCqKquVQp7LdcR04HIZzIyEwpe0oqUGwU4mlenLei1Vv0GrHxAYUrekNzytTaglHI4KlDYwCAC9e2AwD+9eqz8N0Pnivfb+pOvP+iVfjpRy+C7nTgf92/t+hcbT4XxmIp6U5p9bmky0tslu0+A6lsvijjKlrB4AcrFJaJzV93UpH7KJzkzfe8VtwD4JXPU4kMQh6uFLhRyBUlKgRMDU4H1aUUfvbCIA6ORHH2iha8+5w+vHPLMgBcPe0enJKV3KMy0Mw70WZybFEphY8D8AC4CcA5AP4cwHUzOMeljLEzGWPbKjz3aQAvMMa2AHgf1JhPRRVkoLlIKZjTZhFdceYy+A0N33/qKICCe6Qe3r6lB1+++iwZyBZpmDKmYBmHvhY33nJ6FxiD3BA7A4ZsCuh0UM0U2L4WD0ajacTTvM9SNs9kFbEdv6FLV0tpTKQrYJa1utg/HEWLh7tC7O4j4YoRRnYtqT8AABvHSURBVK9YKfCUW5ElJFRByKYU2is1O3RpIOIb7uMHRuF1ObHFUnGm7qxYLNgZMPGRS9bIOIEIzrd5DRwbT+CrjxzEO7b0oDfkli4v4VYRqsI+UyGSzJYVPbZ49KKKZPE6AFjd7isJNHOj4nDwFu4upwMnwklMJtLSfRdNZcuMDxEh5K6vgO3BfUM4c3kIt//ZWfjiu7dKF97W5UEkM3m8MhQFY0wG1GPW/ApgfqauAfVlHz1rtcoeZ4xdzxj7Y8bYUw06/yYAD1nn2Qegn4i6GnRsxSmE1yhXCvW+7w3rO7D9CC8OElfC9eA3dbxz6zJ5X3ZildlH/N83ru+QhuPxA6NwEL+aFm2tg269ZgxD1ipMJGQMYl1neUaV39Rks7jS76EzYJR1Sj0wHMG6Tj9CHr3EfeSU63JQsS+cu9cKMQWhCkQw1eV0yKCyHdGI8KlDY7h3xwAu3dAJvY5g/l9cskp2erW7j9K5PPpa3Lj1qs3882oOOKgQaBZFbsKFlLPaWftKjELIrZcV6EVTWbicDqxs85QpBeF+IiJ0BQ2bUnBZo08z3H1UYuT4d1xbKQyFk9g1MIU3bSzf4jb3io65UzLDzGclMgiFt2jcR0R0ARHtAbDPur+ViL5a5/EZgP8koh1E9OEKz+8EcJV13HPBh/r0VVjDh4loOxFtHxkZqfPUilMJl+bAVWf34sI17TN+b2+LG0NTKeTzjF8JV7hqrQcRbBZKocdqW/HW03vQ3+aF00E4MhZHm8+A5nTIAHAt1xFQqFU4NhGXKaSiitiO3yx04yxVCp1+syj7iDGGV4aiWNvlQ9Bq11GakuqwZfoIxIYn6hDapPuIf4Y2n6uqgfObGp49PIHugIlbrqjPE+xxabj1qs1411m98veyttMHU3fgy1efJQPYRHwSn/gMrZYBEcFmUZBW6j7i1cbZomrjSDIDn6lhWchdFFMIJ4qVRk/AjSNjvIhQxBSEUii9sGjxuKbNPnrYylK7fGNn2XMr27xw607sOxmRhk+4K4UCmS/3UT1/HV8C8BbwIjYwxnbOoEvq6xljx4moE8B/EdE+xtijtue/AOB2InoBwIsAngdQ1jeAMXYXgLsAYNu2bbPriat4zfPPf3rmrN7XEzCRzuUxHk9bSmF2f1xvOK0Db9rYKVXBmg4fnvnby6UiWNnmwaGRmLzyFe6j0o6vpSxvEbUKCewfjqKvxV3RcPlNTRa1ubRS91GhqpmIMGoFatd1+jAZT2MoHC1zHwE8A6ko+8hqDb6i1YNrzluBy62rWqEUxNV8JYLWBvyN67bJ+EA9XLahC5dtKFw9v3PrMrxpY1fR1D4AcLt4saCpO6QLUCiFiBU8Lg3oC2M2lcjINUUtN1N30EQklUUkmZGZXQGbAe8KmnhwLx/Yw+tMCIxxF9aGkir6kMdVlEFWid/uHUZvyF1WbAlwF+NpXT7sOxmWLrL+di92D4ale2/RKAUAYIwdK3moronZjLHj1r/DAH4C4NyS58OWS+pM8JhCB9T8Z0WD6baaz/F2ybm63UelLAu58Y3rXlfUgkMYBIC3hbY/Jo3CNEqhw2/A0By445GD+OWuQdnSohT7VWzpVePGngBS2Tx2HOEBdZF5tK7Tj6CbZ+Ak0jk4qHhzKa1qjqVy8Bg8gPz5d23GJiuLSmyu9pnbpXzuitPx739xfsViwplSahD4Y4V24eLziwI22Y+oLKZQXlgmYgL2YDLAYwp2A94TLMyF5imp/DsYjaYrKAUdY7E0vvTbV3Ddt55BJlecLpvM5PDYgRFcvrGzqtLa0B3AvpORglGwlILoZGsulopmAMeI6EIAjIh0IvokgL3TvYmIvETkFz8D+AMAL5W8JkRE4pLiQwAeZYzNfmySQlEBe845d480549LxAGEUhCpotMZBSLCui4fxmNp3PCGNfi85UcvpdgoFP/p/sHp3TB1h6zJeOoQj6Gc1u1DwK1b2UfcdWbflFpL+h9Vco0Ahewj4cuvxLb+VjlNrhl49MJwo8KMbr752gvP7ARlDYF9JoIwCvxiQWSIhS3FIOgOFAx+yKMXHbvUTdXidWEkksKXfrsfv3tlpGwO9RMHR5HM5KXyqsT6bj/GY2nZlmOllQE2Ns9KoZ5LphvAs4J6ARwH8J8APlrH+7oA/MT6D6gB+H+MsV8T0Q0AwBi7E8BGAN8hIgZgN4APzvgTKBTTIIzC8Yk4EpnZxxSmQ6SRdgilUKWPUyW+dd3rQEQVx5gK7BtWqfvIZ2h486Zu/HLXCXzyD9bjO08cxps2dqHTbyLo1pHO5TERT5ddgbd4XZg4wjedTC6PdDYPX4XvJ2BqcOtO+V0uBKaoD3Fp8qpZNMWrFlMQSsEeBI4ms1gWctuUAg82lyqFbttnDbp1ZG1xiVLDefaKEFa2efC2zT2445GDODoWly3HAe468rqcOH91K6qxoYdfVDy2f5QXNVpuxfFo5eSCZjHtXwdjbBTAe2d6YMbYIQBbKzx+p+3nJwGcNtNjKxQzoc1nQHOQ7LdTb/HaTBFppAWlIGIK0xuFzsD0m639SrXUKADAlWcuwy92DuIv73kOU4kMPnbZWgAFo3RiKiHz/QWtXp5Kmc8zxK1CqUp1HESEe2+4AMtbPGXPzRcea+0+u1Kw0jVF5XZpd92QW4wdtc1ESGXgN/3oCpgg4mNTGWMytiCwG4WQ21XkEio1Cm89owdvPaMHw5EkNwo2pcAYw0N7h3Hxuo6aweIN3dxVt+v4FDp8hvx9F5TCAgeap2uPzRi7qfHLUSgaj9NB6AqYOGD17vHMMtA8HZt6AvjclWfgHVZBUlfAhMvpQHeg+tX/TLBvWJVcCZec1oEWj44nDo7h9WvbZWxCGIWTU8myeokWj8saTJ+RzQJ9Vb6fM3qb5xqqB7esJHcWYgoi0JwULSqqxBRKpqf5DA0uzYF2n4HByQRi6RzyrNDvCkCRKgq6dRmoB6p/Rx0+A27dWWQUdg+GcTKcrJh1ZKfV60Kn38BwJIV2nyENj8g4WwzuoxvAYwA/BDAIYPpmMQrFIqU7aOLgMFcKpTnmjYKIcO35K+V9n6Hh5zdeJIve5sp0SkF3OvCOLcvwvaeO4KOXrpWPS6MQTmJjT6DoPSKbaDyWlu6R2Qbim43b1l5EuI+S07iP/CYfqyoCzYwx2TgPAFa1ea2W6IW+R4IOnwEH8bx6v6khli4cu9p3RERY0eqRM6kB4Ld7h0AEXLqhtlEAgA09AQxHRtDuN+RnGa9Sm9Isav32ewC8G8B7wJvh/QeA+xhjkzXeo1AsSrqDpszMmc9NT7gEGoHd312ty+tNl6/Dtv6WIt+1MArJTL7MfSQ7fMbTMgC9aI1CBfdRyhZodlD58CSHg2T2FcAroDM5JrOU1nT68MBLJ2TzPbvh1ZwOdPh54z2Hg4qeq/UdrWjz4KjNKDy4dxhnLQ/VzNwSbOj249FXRtDuc8lziOrmBW9zwRgbY4zdyRi7FMD1AEIA9hDRtfOyMoWigfTYfPbNyj5qNkXuoyrpiR1+A1ec2VuUYWQPdJdumiKbaDSaLhtIv9jwVFIKtpRUn6FVTPcMeVzSBSOzlKwNl9dxZGS8qTT+0x0wZTquaOUB1I5LrWj14Oh4HIwxDIWTePH4VM2sIzui/qHDb8gYSiEldeGVAgCAiM4GcDWANwN4AGrqmuI1iD1oWG9DvMWGvw6lUAm7UXCXbPjdtlx90Rl0tsV9zaaW+4j3Paoc0BdtPoBCPYN4rUgOeN5qmFgakzh3VavsPitaeUQqtLmws6LVg0Qmh5FoCo+8XL2KuRLrhVHwGXBYPbPGF1Gg+RbwFtl7AfwAwP9kjJXP+1MoXgOInHSgepBwsePWeVFZLs8qxhSqYd8sPXqpUuCN3wYnE/Lqt1nZWXOl4D5ywtRK6xQyZRu6IOTWZXdVETsQn7FgFLhXvNSw/O3bNxXd95ncKNRUClbR2bHxOH6/fxRdAaNiFXMlNnQH8LFL1+KtZ3QD4AZQtL1YDBXNnwF3GW0FcCuA56wW1y8S0a55WZ1C0SCKlMIidY9MB1HBrz2TDcJp84eX1ik4HISekInBqaRtIP3i/H7s7iPN6YDmoEKgOVXeIVXQ4nHJmEK0pMitJ2jC63Ji13FuFOzZR5UQ76ulpkQzwVdH43ji4BguWtte11AngP+uPvmW9bIflt34LIbso1XzsgKFYh6wpxcuVp95PfhNDZPxzIzcRwB3IUWS2YrtI3qCJgYnE4im+NXsYv1+7IFmgLd9sFc0t1fpyxS0DdqJlLTDICKs7fRh5wAfcDNdTYk4dy3D2dfiBhGfyDceS+PidTNv4igQxsfpIGgz/J3PllqB5iO1bvOyOoWiQXT4eXoh0Lw6hfnAb/DGbNVGkFZDxBVK3UcArG6hCcRTvDfSfAU0Z4q7pGW5qTuKA81VNvQWjwvRVBaZXN4WaC68do3lQnI5HdNejftNHZo1w7oahuZET8DEQ/t4M72L1s7BKLhmrgznyuL87SsUDUa30gtdmqOuPv+LFb+pzSrgKIxCJaXQG3LjZDiJcDIDb5UMnsWAUApytobmLAk0V4kpWNlDk/EMohWK3ERcwW9O/9l9plbXd7S81YM849lE9qaJM6UwXEoZBYWi4XQH3a/ZdFSB39RnFGQW1DIKPUE38gw4NBJbtEFmAFjT6YXfNj/a1B22OoVMxeE/QKGZ31QiLbOP7O6ftR0FozAd/7+9ew+y8q7vOP7+sMsdwhIgQFgIxGAgmpAgprHFtCFObiZGa2ouTBs0ltHJVJqxMUaczjjtP220tqlRxKiJGmOnMVTGNggSW63GpIklgAlpCMEIEoHEBEQhXL794/mdh+Nhz+5Zds8+z7Kf18yZPc9lz373B2e/53c/b2obbzptbJf3VWKc34NaAhyNs69WSIXGNtlZ0sg5s7Kb0jas7rDF/qJtxOBjJqA1Im8+6iApnJo2C3p2595Otw0t2qxJJ7HhE5dyals2kqxSU3jt0BEOHDrS6egjyFZK3bv/EENbB/1OYp2ZRgZ1tZotwPvfejpfWvTmLu+rdDbP70F/AhztU+jLmkIjHwtu5Ni9kxd1cM6s1G552+vz2aH91c0XncG75x6zOWGX8ppCnT4FgF/uOcCk9uJWQe2uSp9CvpdCnZrC0T0VDrL3wLHzGaaOHZ5tM9pATaFRF8+eyMbte7jg9HE9ep2jfQp9l6w7m6dwPXADMEPSyqpLo4GXmx2YWW+bOXE0M/v5DuAzxo/M94rujsq+ArWT16BmZFaJm49qVUYfHR1mWn/yGmRLefy6g76H1pZBnNM+Jv903xtmTz6JZX/6ph6/zvHuTd4Tnf0P+BGwAxgPfKrq/F7A8xTM+pHOmo9GDxvMScNa2bO/4w12ymrY4BZe/e1Bdu7NNsk5uc6Q1HxLzt8crDvJ7d73nU9rS/k62EvV0ZyGnv5nRLwF2ERWQxgNbPPMZrP+ZVzan7heE0mlCak/dcQPGzyI/QcPszUtPldvNdpRQ1sZN3IID2/ayd79Hc9GHjn0+EZ1NVv1SKu+0khH858Aj5GtmPoe4FFJ1zQ7MDPrPQtmTeSzC+fWXW4hTwr9qabQmjUf/eylfbQMEu1jh3d4nyQ+dPFMHtnyEuu3vVrqEVa1ytrR/HHgzRGxE0DSBOC7wAPNDMzMes+Q1kFccfbkutcr/Qr96Q/m0MEtHDiU1RTaxw7vdP7JDb83ja88spXndu3rVyPQjk7UK1FNARhUSQjJSw1+n5n1E/2ypjB4UF5TOK2LjYwGtwzi41dmi9v15iijZhtZQJ9CI6WzStJ3gPvT8bXAfzQvJDPra1NSUijzPIVa2eijwzy/ex/vOq+ty/svOvMUll4xu8fDRPvSqJKNPgIgIm6V9MfA/HRqeUSsaOTFJW0lG610GDgUEfNqro8BvgZMS7F8MiK+3Hj4ZtYb+mPz0bDWFg4dCfbuP9RlTaHizy88vclR9a6jfQolmKdQ44fAQbLtSh/r5s+4KCJ217l2M/BURFyV+iqekXRfRPTvGUZm/cyZk0bTPnY4syb33vahzVa9cN/0cb03x6BMSjUktULSe8gSwTX0/uijAEYrW11qFNmkOA93NetjbSOG8N+3LeDcqV03w5RFdefr9OOY0NcflLVPYSnHP/oogNWSAvh8RCyvuf4ZYCXwC7I5ENdGxJHaF5G0GFgMMG3atAZ+rJmd6Co1hUGi7nDU/m7EkBbefs7kPu0HaSQp9GT00fyI2C7pFGCNpE0R8f2q65cC64AFwOvSPT+IiD3VL5KSyXKAefPmRYM/28xOYJWawqltw0s58aw3SOKuG+b26c9s5I/7KknfkbRI0iLg34GHGnnxiNievu4EVgDn19zyXuDByGwGngdmNRq8mQ1clURQbyazHZ8uk0JE3Ap8HjgnPZZHxEe6+j5JIyWNrjwHLgE21tz2AnBxumcicCawpTu/gJkNTJVhmqedoJ3MRWlo9FFEPAg8CCBpkKSFEXFfF982EViRdihqBb4eEaskfSC95jLgb4B7JG0ABNzWyUglM7PcsFRTOJ5VY62+zpbOPolsyOgUss7gNen4r4AngU6TQkRsAeZ0cH5Z1fNfkNUgzMy6ZXxaFfX1ddZzsuPTWU3hq8CvgEeA9wMfI/s0/86IWNcHsZmZ1TVz4mgeWvJWZk1yUuhNnSWF0yPibABJd5PtrTAtIvb3SWRmZl2Y3Y8m2/UXnXU0H6w8iYjDZPsoOCGYmZ3AOqspzJFUmS8gYHg6FhAR4RRtZnaCqZsUIuLEnA1iZmZ1eV8EMzPLOSmYmVnOScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws19kmOz0maSuwFzgMHIqIeTXXbwUWVsUyG5gQES83My4zM+tYU5NCclFE7O7oQkTcAdwBIOkq4BYnBDOz4pSp+eh64P6igzAzG8ianRQCWC3pCUmL690kaQRwGfDNOtcXS3pc0uO7du1qUqhmZtbspDA/IuYClwM3S7qwzn1XAT+s13QUEcsjYl5EzJswYUKzYjUzG/CamhQiYnv6uhNYAZxf59brcNORmVnhmpYUJI2UNLryHLgE2NjBfWOAPwS+1axYzMysMc0cfTQRWCGp8nO+HhGrJH0AICKWpfveBayOiH1NjMXMzBrQtKQQEVuAOR2cX1ZzfA9wT7PiMDOzxpVpSKqZmRXMScHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZjknBTMzyzkpmJlZzknBzMxyTgpmZpZzUjAzs5yTgpmZ5ZwUzMws56RgZmY5JwUzM8s5KZiZWc5JwczMck4KZmaWc1IwM7Ock4KZmeUUEUXH0C2SdgE/O85vHw/s7sVwmsEx9g7H2DscY8+VJb7TImJCVzf1u6TQE5Iej4h5RcfRGcfYOxxj73CMPVf2+Gq5+cjMzHJOCmZmlhtoSWF50QE0wDH2DsfYOxxjz5U9vt8xoPoUzMyscwOtpmBmZp0YMElB0mWSnpG0WdJHi44HQNJUSd+T9JSkn0paks6fLGmNpGfT17EFx9ki6X8lfTsdz5D0aCrLf5E0pOD42iQ9IGmTpKclvaWEZXhL+jfeKOl+ScOKLkdJX5K0U9LGqnMdlpsyd6ZY10uaW2CMd6R/6/WSVkhqq7p2e4rxGUmXFhVj1bUPSwpJ49NxIeXYHQMiKUhqAe4CLgfOAq6XdFaxUQFwCPhwRJwFXADcnOL6KLA2ImYCa9NxkZYAT1cd/x3w6Yg4A/gVcFMhUR31T8CqiJgFzCGLtTRlKGkK8CFgXkS8EWgBrqP4crwHuKzmXL1yuxyYmR6Lgc8VGOMa4I0RcQ7wf8DtAOm9cx3whvQ9n03v/SJiRNJU4BLgharTRZVjwwZEUgDOBzZHxJaIeA34BnB1wTERETsi4ifp+V6yP2ZTyGK7N912L/DOYiIESe3A24G707GABcAD6Zai4xsDXAh8ESAiXouIVyhRGSatwHBJrcAIYAcFl2NEfB94ueZ0vXK7GvhKZH4MtEmaXESMEbE6Ig6lwx8D7VUxfiMiDkTE88Bmsvd+n8eYfBr4CFDdcVtIOXbHQEkKU4CfVx1vS+dKQ9J04DzgUWBiROxIl14EJhYUFsA/kv3HPpKOxwGvVL0piy7LGcAu4MupietuSSMpURlGxHbgk2SfGHcArwJPUK5yrKhXbmV9D70PeCg9L02Mkq4GtkfEkzWXShNjPQMlKZSapFHAN4G/jIg91dciGx5WyBAxSVcCOyPiiSJ+foNagbnA5yLiPGAfNU1FRZYhQGqXv5osgZ0KjKSD5oayKbrcuiJpKVkT7H1Fx1JN0gjgY8BfFx3L8RgoSWE7MLXquD2dK5ykwWQJ4b6IeDCd/mWlSpm+7iwovD8A3iFpK1mT2wKy9vu21AwCxZflNmBbRDyajh8gSxJlKUOAtwHPR8SuiDgIPEhWtmUqx4p65Vaq95CkRcCVwMI4Oq6+LDG+juwDwJPpvdMO/ETSJMoTY10DJSn8DzAzjfYYQtYZtbLgmCrt818Eno6If6i6tBK4MT2/EfhWX8cGEBG3R0R7REwnK7OHI2Ih8D3gmqLjA4iIF4GfSzoznboYeIqSlGHyAnCBpBHp37wSY2nKsUq9clsJ/FkaPXMB8GpVM1OfknQZWZPmOyLiN1WXVgLXSRoqaQZZZ+5jfR1fRGyIiFMiYnp672wD5qb/q6Upx7oiYkA8gCvIRio8BywtOp4U03yy6vl6YF16XEHWbr8WeBb4LnByCWL9I+Db6fnpZG+2zcC/AkMLju1c4PFUjv8GjC1bGQKfADYBG4GvAkOLLkfgfrI+joNkf7huqldugMhG8D0HbCAbSVVUjJvJ2uUr75llVfcvTTE+A1xeVIw117cC44ssx+48PKPZzMxyA6X5yMzMGuCkYGZmOScFMzPLOSmYmVnOScHMzHJOCmY1JB2WtK7q0WuL6Uma3tFqmmZl0dr1LWYDzm8j4tyigzArgmsKZg2StFXS30vaIOkxSWek89MlPZzWx18raVo6PzGt9/9kevx+eqkWSV9Qtr/CaknDC/ulzGo4KZgda3hN89G1VddejYizgc+QrSAL8M/AvZGt738fcGc6fyfwXxExh2w9pp+m8zOBuyLiDcArwLub/PuYNcwzms1qSPp1RIzq4PxWYEFEbEkLGb4YEeMk7QYmR8TBdH5HRIyXtAtoj4gDVa8xHVgT2SY2SLoNGBwRf9v838ysa64pmHVP1HneHQeqnh/GfXtWIk4KZt1zbdXXR9LzH5GtIguwEPhBer4W+CDk+1yP6asgzY6XP6GYHWu4pHVVx6siojIsdayk9WSf9q9P5/6CbOe3W8l2gXtvOr8EWC7pJrIawQfJVtM0Ky33KZg1KPUpzIuI3UXHYtYsbj4yM7OcawpmZpZzTcHMzHJOCmZmlnNSMDOznJOCmZnlnBTMzCznpGBmZrn/B91pVHh672fwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in $(K)        in $(K)\n",
      "--------------------------------------\n",
      "   21     13              16\n",
      "   21     16              18\n",
      "   19     16              14\n",
      "   24     14              19\n",
      "   18     17              18\n",
      "   19     15              18\n",
      "   19     18              18\n",
      "   19     19              19\n",
      "   17     24              20\n",
      "   16     18              14\n",
      "   23     16              14\n",
      "   23     17              16\n",
      "   21     21              16\n",
      "   21     21              17\n",
      "   20     25              20\n"
     ]
    }
   ],
   "source": [
    "my_feature = \"citympg\"   # Replace the ? with population or possibly\n",
    "                   # a different column name.\n",
    "\n",
    "# Experiment with the hyperparameters.\n",
    "learning_rate = 0.5\n",
    "epochs = 150\n",
    "batch_size = 120\n",
    "\n",
    "# Don't change anything below this line.\n",
    "model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_df, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)\n",
    "\n",
    "predict_house_values(15, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "107mDkW7U6mg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/18\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 2904.9360 - root_mean_squared_error: 53.8975\n",
      "Epoch 2/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 137.8519 - root_mean_squared_error: 11.7410\n",
      "Epoch 3/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.3759 - root_mean_squared_error: 4.7303\n",
      "Epoch 4/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.5549 - root_mean_squared_error: 4.5338\n",
      "Epoch 5/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.8105 - root_mean_squared_error: 4.5619\n",
      "Epoch 6/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.9947 - root_mean_squared_error: 4.6898\n",
      "Epoch 7/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.6237 - root_mean_squared_error: 4.5413\n",
      "Epoch 8/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.7583 - root_mean_squared_error: 6.0629\n",
      "Epoch 9/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.9612 - root_mean_squared_error: 5.2878\n",
      "Epoch 10/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.3883 - root_mean_squared_error: 6.0323\n",
      "Epoch 11/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.7346 - root_mean_squared_error: 5.8936\n",
      "Epoch 12/18\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.3677 - root_mean_squared_error: 4.8340\n",
      "Epoch 13/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 40.1610 - root_mean_squared_error: 6.3373\n",
      "Epoch 14/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.0479 - root_mean_squared_error: 6.2488\n",
      "Epoch 15/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.8337 - root_mean_squared_error: 4.8820\n",
      "Epoch 16/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.3600 - root_mean_squared_error: 6.0299\n",
      "Epoch 17/18\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.5340 - root_mean_squared_error: 5.1511\n",
      "Epoch 18/18\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 38.3675 - root_mean_squared_error: 6.1941\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in $(K)        in $(K)\n",
      "--------------------------------------\n",
      "  130     13              19\n",
      "  130     16              22\n",
      "  152     16              16\n",
      "  109     14              20\n",
      "  136     17              20\n",
      "  136     15              20\n",
      "  136     18              20\n",
      "  136     19              19\n",
      "  131     24              19\n",
      "  131     18              15\n"
     ]
    }
   ],
   "source": [
    "#@title Double-click to view a possible solution.\n",
    "\n",
    "my_feature = \"enginesize\" # Pick a feature other than \"total_rooms\"\n",
    "\n",
    "# Possibly, experiment with the hyperparameters.\n",
    "learning_rate = 0.05\n",
    "epochs = 18\n",
    "batch_size = 1\n",
    "\n",
    "# Don't change anything below.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_df, \n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "plot_the_model(weight, bias, my_feature, my_label)\n",
    "plot_the_loss_curve(epochs, rmse)\n",
    "\n",
    "predict_house_values(10, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nd_rHJ59AUtk"
   },
   "source": [
    "Did this variable produce better predictions than label?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "F0tPEtzcC-vK"
   },
   "outputs": [],
   "source": [
    "#@title Double-click to view the answer.\n",
    "\n",
    "# Training is not entirely deterministic, but population \n",
    "# typically converges at a slightly higher RMSE than \n",
    "# total_rooms.  So, enginesize appears to be about \n",
    "# the same or slightly worse at making predictions \n",
    "# than citympg."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C8uYpyGacsIg"
   },
   "source": [
    "## Task 4: Define a synthetic feature\n",
    "\n",
    "You have determined that `total_rooms` and `population` were not useful features.  That is, neither the total number of rooms in a neighborhood nor the neighborhood's population successfully predicted the median house price of that neighborhood. Perhaps though, the *ratio* of `total_rooms` to `population` might have some predictive power. That is, perhaps block density relates to median house value.\n",
    "\n",
    "To explore this hypothesis, do the following: \n",
    "\n",
    "1. Create a [synthetic feature](https://developers.google.com/machine-learning/glossary/#synthetic_feature) that's a ratio of `total_rooms` to `population`. (If you are new to pandas DataFrames, please study the [Pandas DataFrame Ultraquick Tutorial](https://colab.research.google.com/github/google/eng-edu/blob/master/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb?utm_source=linearregressionreal-colab&utm_medium=colab&utm_campaign=colab-external&utm_content=pandas_tf2-colab&hl=en).)\n",
    "2. Tune the three hyperparameters.\n",
    "3. Determine whether this synthetic feature produces \n",
    "   a lower loss value than any of the single features you \n",
    "   tried earlier in this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4Kx2xHSgdcpg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 199025344.0000 - root_mean_squared_error: 14107.6338\n",
      "Epoch 2/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 164376752.0000 - root_mean_squared_error: 12820.9502\n",
      "Epoch 3/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 141875264.0000 - root_mean_squared_error: 11911.1406\n",
      "Epoch 4/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 124497792.0000 - root_mean_squared_error: 11157.8574\n",
      "Epoch 5/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 109956304.0000 - root_mean_squared_error: 10486.0049\n",
      "Epoch 6/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 98432408.0000 - root_mean_squared_error: 9921.3105\n",
      "Epoch 7/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 88727712.0000 - root_mean_squared_error: 9419.5391\n",
      "Epoch 8/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 79894616.0000 - root_mean_squared_error: 8938.3789\n",
      "Epoch 9/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 72851968.0000 - root_mean_squared_error: 8535.3369\n",
      "Epoch 10/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 67834064.0000 - root_mean_squared_error: 8236.1436\n",
      "Epoch 11/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 63920688.0000 - root_mean_squared_error: 7995.0415\n",
      "Epoch 12/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 61211484.0000 - root_mean_squared_error: 7823.7769\n",
      "Epoch 13/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 59663656.0000 - root_mean_squared_error: 7724.2251\n",
      "Epoch 14/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58891620.0000 - root_mean_squared_error: 7674.0874\n",
      "Epoch 15/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58475584.0000 - root_mean_squared_error: 7646.9331\n",
      "Epoch 16/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58381856.0000 - root_mean_squared_error: 7640.8022\n",
      "Epoch 17/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58548944.0000 - root_mean_squared_error: 7651.7280\n",
      "Epoch 18/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58418960.0000 - root_mean_squared_error: 7643.2295\n",
      "Epoch 19/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58448048.0000 - root_mean_squared_error: 7645.1323\n",
      "Epoch 20/30\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58633656.0000 - root_mean_squared_error: 7657.2617\n",
      "Epoch 21/30\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58465836.0000 - root_mean_squared_error: 7646.2954\n",
      "Epoch 22/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58491992.0000 - root_mean_squared_error: 7648.0059\n",
      "Epoch 23/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58608888.0000 - root_mean_squared_error: 7655.6440\n",
      "Epoch 24/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58398212.0000 - root_mean_squared_error: 7641.8721\n",
      "Epoch 25/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58728992.0000 - root_mean_squared_error: 7663.4844\n",
      "Epoch 26/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58554992.0000 - root_mean_squared_error: 7652.1235\n",
      "Epoch 27/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58448920.0000 - root_mean_squared_error: 7645.1895\n",
      "Epoch 28/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58369828.0000 - root_mean_squared_error: 7640.0151\n",
      "Epoch 29/30\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58410832.0000 - root_mean_squared_error: 7642.6978\n",
      "Epoch 30/30\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58488532.0000 - root_mean_squared_error: 7647.7798\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:9 out of the last 14 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f81fad4d048> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in $           in $\n",
      "--------------------------------------\n",
      " 2730  13495           11878\n",
      " 2730  16500           12565\n",
      " 2888  16500           11382\n",
      " 2616  13950           10652\n",
      " 2448  17450           11243\n",
      " 2584  15250           11243\n",
      " 2584  17710           11243\n",
      " 2584  18920            9690\n",
      " 2227  23875            9121\n",
      " 2096  17859           10808\n",
      " 2484  16430           10808\n",
      " 2484  16925           14984\n",
      " 3444  20970           14984\n",
      " 3444  21105           14270\n",
      " 3280  24565           14549\n"
     ]
    }
   ],
   "source": [
    "# Define a synthetic feature named rooms_per_person\n",
    "training_df[\"para\"] = training_df[\"enginesize\"]*training_df[\"citympg\"] # write your code here.\n",
    "\n",
    "# Don't change the next two lines.\n",
    "my_feature = \"para\"\n",
    "my_label=\"price\" \n",
    "\n",
    "# Assign values to these three hyperparameters.\n",
    "learning_rate = 0.05\n",
    "epochs = 30\n",
    "batch_size =  15\n",
    "\n",
    "# Don't change anything below this line.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, rmse = train_model(my_model, training_df,\n",
    "                                         my_feature, my_label,\n",
    "                                         epochs, batch_size)\n",
    "\n",
    "plot_the_loss_curve(epochs, rmse)\n",
    "predict_house_values(15, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "xRfxp_3yofe3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 173320208.0000 - root_mean_squared_error: 13165.1133\n",
      "Epoch 2/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 142984992.0000 - root_mean_squared_error: 11957.6328\n",
      "Epoch 3/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 123573872.0000 - root_mean_squared_error: 11116.3789\n",
      "Epoch 4/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 108990936.0000 - root_mean_squared_error: 10439.8721\n",
      "Epoch 5/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 96653216.0000 - root_mean_squared_error: 9831.2363\n",
      "Epoch 6/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 85954464.0000 - root_mean_squared_error: 9271.1631\n",
      "Epoch 7/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 78281336.0000 - root_mean_squared_error: 8847.6738\n",
      "Epoch 8/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 71676160.0000 - root_mean_squared_error: 8466.1777\n",
      "Epoch 9/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 67157784.0000 - root_mean_squared_error: 8194.9854\n",
      "Epoch 10/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 63458728.0000 - root_mean_squared_error: 7966.0986\n",
      "Epoch 11/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 60858360.0000 - root_mean_squared_error: 7801.1768\n",
      "Epoch 12/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 59402968.0000 - root_mean_squared_error: 7707.3320\n",
      "Epoch 13/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58817236.0000 - root_mean_squared_error: 7669.2397\n",
      "Epoch 14/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58569712.0000 - root_mean_squared_error: 7653.0850\n",
      "Epoch 15/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58518188.0000 - root_mean_squared_error: 7649.7183\n",
      "Epoch 16/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58803796.0000 - root_mean_squared_error: 7668.3633\n",
      "Epoch 17/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58430100.0000 - root_mean_squared_error: 7643.9585\n",
      "Epoch 18/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58334884.0000 - root_mean_squared_error: 7637.7275\n",
      "Epoch 19/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58470828.0000 - root_mean_squared_error: 7646.6221\n",
      "Epoch 20/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58596092.0000 - root_mean_squared_error: 7654.8086\n",
      "Epoch 21/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58342360.0000 - root_mean_squared_error: 7638.2173\n",
      "Epoch 22/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58365512.0000 - root_mean_squared_error: 7639.7324\n",
      "Epoch 23/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58464732.0000 - root_mean_squared_error: 7646.2231\n",
      "Epoch 24/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58404596.0000 - root_mean_squared_error: 7642.2900\n",
      "Epoch 25/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58505132.0000 - root_mean_squared_error: 7648.8647\n",
      "Epoch 26/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58515916.0000 - root_mean_squared_error: 7649.5698\n",
      "Epoch 27/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58384048.0000 - root_mean_squared_error: 7640.9453\n",
      "Epoch 28/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58686068.0000 - root_mean_squared_error: 7660.6831\n",
      "Epoch 29/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58316836.0000 - root_mean_squared_error: 7636.5459\n",
      "Epoch 30/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58406732.0000 - root_mean_squared_error: 7642.4297\n",
      "Epoch 31/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58370316.0000 - root_mean_squared_error: 7640.0469\n",
      "Epoch 32/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58423092.0000 - root_mean_squared_error: 7643.5000\n",
      "Epoch 33/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58416428.0000 - root_mean_squared_error: 7643.0640\n",
      "Epoch 34/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58488456.0000 - root_mean_squared_error: 7647.7744\n",
      "Epoch 35/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58368496.0000 - root_mean_squared_error: 7639.9277\n",
      "Epoch 36/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58325688.0000 - root_mean_squared_error: 7637.1255\n",
      "Epoch 37/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58458380.0000 - root_mean_squared_error: 7645.8081\n",
      "Epoch 38/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58422252.0000 - root_mean_squared_error: 7643.4448\n",
      "Epoch 39/100\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 58423088.0000 - root_mean_squared_error: 7643.4995\n",
      "Epoch 40/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58466144.0000 - root_mean_squared_error: 7646.3159\n",
      "Epoch 41/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58452292.0000 - root_mean_squared_error: 7645.4097\n",
      "Epoch 42/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58502532.0000 - root_mean_squared_error: 7648.6948\n",
      "Epoch 43/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58355244.0000 - root_mean_squared_error: 7639.0605\n",
      "Epoch 44/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58339532.0000 - root_mean_squared_error: 7638.0317\n",
      "Epoch 45/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58379972.0000 - root_mean_squared_error: 7640.6787\n",
      "Epoch 46/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58527928.0000 - root_mean_squared_error: 7650.3550\n",
      "Epoch 47/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58474060.0000 - root_mean_squared_error: 7646.8335\n",
      "Epoch 48/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58354112.0000 - root_mean_squared_error: 7638.9863\n",
      "Epoch 49/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58318168.0000 - root_mean_squared_error: 7636.6333\n",
      "Epoch 50/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58351592.0000 - root_mean_squared_error: 7638.8213\n",
      "Epoch 51/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58335612.0000 - root_mean_squared_error: 7637.7754\n",
      "Epoch 52/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58501020.0000 - root_mean_squared_error: 7648.5962\n",
      "Epoch 53/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58404340.0000 - root_mean_squared_error: 7642.2734\n",
      "Epoch 54/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58583824.0000 - root_mean_squared_error: 7654.0068\n",
      "Epoch 55/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58571432.0000 - root_mean_squared_error: 7653.1973\n",
      "Epoch 56/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58707392.0000 - root_mean_squared_error: 7662.0747\n",
      "Epoch 57/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58404920.0000 - root_mean_squared_error: 7642.3110\n",
      "Epoch 58/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58511596.0000 - root_mean_squared_error: 7649.2871\n",
      "Epoch 59/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58522436.0000 - root_mean_squared_error: 7649.9956\n",
      "Epoch 60/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58400488.0000 - root_mean_squared_error: 7642.0210\n",
      "Epoch 61/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58741676.0000 - root_mean_squared_error: 7664.3120\n",
      "Epoch 62/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58557504.0000 - root_mean_squared_error: 7652.2876\n",
      "Epoch 63/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58386612.0000 - root_mean_squared_error: 7641.1133\n",
      "Epoch 64/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58522108.0000 - root_mean_squared_error: 7649.9746\n",
      "Epoch 65/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58417092.0000 - root_mean_squared_error: 7643.1074\n",
      "Epoch 66/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58342984.0000 - root_mean_squared_error: 7638.2578\n",
      "Epoch 67/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58403940.0000 - root_mean_squared_error: 7642.2471\n",
      "Epoch 68/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58490184.0000 - root_mean_squared_error: 7647.8877\n",
      "Epoch 69/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58370376.0000 - root_mean_squared_error: 7640.0508\n",
      "Epoch 70/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58435440.0000 - root_mean_squared_error: 7644.3076\n",
      "Epoch 71/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58652072.0000 - root_mean_squared_error: 7658.4639\n",
      "Epoch 72/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58470144.0000 - root_mean_squared_error: 7646.5771\n",
      "Epoch 73/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58434192.0000 - root_mean_squared_error: 7644.2261\n",
      "Epoch 74/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58456352.0000 - root_mean_squared_error: 7645.6753\n",
      "Epoch 75/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58326856.0000 - root_mean_squared_error: 7637.2021\n",
      "Epoch 76/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58300392.0000 - root_mean_squared_error: 7635.4692\n",
      "Epoch 77/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58536004.0000 - root_mean_squared_error: 7650.8823\n",
      "Epoch 78/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58392468.0000 - root_mean_squared_error: 7641.4966\n",
      "Epoch 79/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58557996.0000 - root_mean_squared_error: 7652.3198\n",
      "Epoch 80/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58557684.0000 - root_mean_squared_error: 7652.2993\n",
      "Epoch 81/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58452216.0000 - root_mean_squared_error: 7645.4048\n",
      "Epoch 82/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58526804.0000 - root_mean_squared_error: 7650.2812\n",
      "Epoch 83/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58557064.0000 - root_mean_squared_error: 7652.2588\n",
      "Epoch 84/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58538472.0000 - root_mean_squared_error: 7651.0439\n",
      "Epoch 85/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58326396.0000 - root_mean_squared_error: 7637.1719\n",
      "Epoch 86/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58378448.0000 - root_mean_squared_error: 7640.5791\n",
      "Epoch 87/100\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 58475864.0000 - root_mean_squared_error: 7646.9512\n",
      "Epoch 88/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58447524.0000 - root_mean_squared_error: 7645.0981\n",
      "Epoch 89/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58551276.0000 - root_mean_squared_error: 7651.8804\n",
      "Epoch 90/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58430592.0000 - root_mean_squared_error: 7643.9907\n",
      "Epoch 91/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58425100.0000 - root_mean_squared_error: 7643.6313\n",
      "Epoch 92/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58701972.0000 - root_mean_squared_error: 7661.7212\n",
      "Epoch 93/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58389752.0000 - root_mean_squared_error: 7641.3188\n",
      "Epoch 94/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58492996.0000 - root_mean_squared_error: 7648.0713\n",
      "Epoch 95/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58558904.0000 - root_mean_squared_error: 7652.3789\n",
      "Epoch 96/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58534304.0000 - root_mean_squared_error: 7650.7715\n",
      "Epoch 97/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58394348.0000 - root_mean_squared_error: 7641.6196\n",
      "Epoch 98/100\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 58427952.0000 - root_mean_squared_error: 7643.8179\n",
      "Epoch 99/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58319592.0000 - root_mean_squared_error: 7636.7266\n",
      "Epoch 100/100\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 58361920.0000 - root_mean_squared_error: 7639.4976\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:11 out of the last 11 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f81fad55598> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in $           in $\n",
      "--------------------------------------\n",
      " 2730  13495           11966\n",
      " 2730  16500           12658\n",
      " 2888  16500           11466\n",
      " 2616  13950           10730\n",
      " 2448  17450           11326\n",
      " 2584  15250           11326\n",
      " 2584  17710           11326\n",
      " 2584  18920            9762\n",
      " 2227  23875            9188\n",
      " 2096  17859           10888\n",
      " 2484  16430           10888\n",
      " 2484  16925           15094\n",
      " 3444  20970           15094\n",
      " 3444  21105           14376\n",
      " 3280  24565           14656\n"
     ]
    }
   ],
   "source": [
    "#@title Double-click to view a possible solution to Task 4.\n",
    "\n",
    "# Define a synthetic feature\n",
    "training_df[\"para\"] = training_df[\"enginesize\"] * training_df[\"citympg\"]\n",
    "my_feature = \"para\"\n",
    "my_label=\"price\" \n",
    "\n",
    "# Tune the hyperparameters.\n",
    "learning_rate = 0.05\n",
    "epochs = 100\n",
    "batch_size = 30\n",
    "\n",
    "# Don't change anything below this line.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, mae = train_model(my_model, training_df,\n",
    "                                        my_feature, my_label,\n",
    "                                        epochs, batch_size)\n",
    "\n",
    "plot_the_loss_curve(epochs, mae)\n",
    "predict_house_values(15, my_feature, my_label)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HBiDWursB1Wi"
   },
   "source": [
    "Based on the loss values, this synthetic feature produces a better model than the individual features you tried in Task 2 and Task 3. However, the model still isn't creating great predictions.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XEG_9oU9O54u"
   },
   "source": [
    "## Task 5. Find feature(s) whose raw values correlate with the label\n",
    "\n",
    "So far, we've relied on trial-and-error to identify possible features for the model.  Let's rely on statistics instead.\n",
    "\n",
    "A **correlation matrix** indicates how each attribute's raw values relate to the other attributes' raw values. Correlation values have the following meanings:\n",
    "\n",
    "  * `1.0`: perfect positive correlation; that is, when one attribute rises, the other attribute rises.\n",
    "  * `-1.0`: perfect negative correlation; that is, when one attribute rises, the other attribute falls. \n",
    "  * `0.0`: no correlation; the two column's [are not linearly related](https://en.wikipedia.org/wiki/Correlation_and_dependence#/media/File:Correlation_examples2.svg).\n",
    "\n",
    "In general, the higher the absolute value of a correlation value, the greater its predictive power. For example, a correlation value of -0.8 implies far more predictive power than a correlation of -0.2.\n",
    "\n",
    "The following code cell generates the correlation matrix for attributes of the California Housing Dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zFGKL45LO8Tt"
   },
   "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>car_ID</th>\n",
       "      <th>symboling</th>\n",
       "      <th>wheelbase</th>\n",
       "      <th>carlength</th>\n",
       "      <th>carwidth</th>\n",
       "      <th>carheight</th>\n",
       "      <th>curbweight</th>\n",
       "      <th>enginesize</th>\n",
       "      <th>boreratio</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compressionratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peakrpm</th>\n",
       "      <th>citympg</th>\n",
       "      <th>highwaympg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car_ID</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symboling</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wheelbase</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carlength</th>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carwidth</th>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peakrpm</th>\n",
       "      <td>-0.2</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citympg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highwaympg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            car_ID  symboling  wheelbase  carlength  carwidth  carheight  \\\n",
       "car_ID         1.0       -0.2        0.1        0.2       0.1        0.3   \n",
       "symboling     -0.2        1.0       -0.5       -0.4      -0.2       -0.5   \n",
       "wheelbase      0.1       -0.5        1.0        0.9       0.8        0.6   \n",
       "carlength      0.2       -0.4        0.9        1.0       0.8        0.5   \n",
       "carwidth       0.1       -0.2        0.8        0.8       1.0        0.3   \n",
       "...            ...        ...        ...        ...       ...        ...   \n",
       "horsepower    -0.0        0.1        0.4        0.6       0.6       -0.1   \n",
       "peakrpm       -0.2        0.3       -0.4       -0.3      -0.2       -0.3   \n",
       "citympg        0.0       -0.0       -0.5       -0.7      -0.6       -0.0   \n",
       "highwaympg     0.0        0.0       -0.5       -0.7      -0.7       -0.1   \n",
       "price         -0.1       -0.1        0.6        0.7       0.8        0.1   \n",
       "\n",
       "            curbweight  enginesize  boreratio  stroke  compressionratio  \\\n",
       "car_ID             0.1        -0.0        0.3    -0.2               0.2   \n",
       "symboling         -0.2        -0.1       -0.1    -0.0              -0.2   \n",
       "wheelbase          0.8         0.6        0.5     0.2               0.2   \n",
       "carlength          0.9         0.7        0.6     0.1               0.2   \n",
       "carwidth           0.9         0.7        0.6     0.2               0.2   \n",
       "...                ...         ...        ...     ...               ...   \n",
       "horsepower         0.8         0.8        0.6     0.1              -0.2   \n",
       "peakrpm           -0.3        -0.2       -0.3    -0.1              -0.4   \n",
       "citympg           -0.8        -0.7       -0.6    -0.0               0.3   \n",
       "highwaympg        -0.8        -0.7       -0.6    -0.0               0.3   \n",
       "price              0.8         0.9        0.6     0.1               0.1   \n",
       "\n",
       "            horsepower  peakrpm  citympg  highwaympg  price  \n",
       "car_ID            -0.0     -0.2      0.0         0.0   -0.1  \n",
       "symboling          0.1      0.3     -0.0         0.0   -0.1  \n",
       "wheelbase          0.4     -0.4     -0.5        -0.5    0.6  \n",
       "carlength          0.6     -0.3     -0.7        -0.7    0.7  \n",
       "carwidth           0.6     -0.2     -0.6        -0.7    0.8  \n",
       "...                ...      ...      ...         ...    ...  \n",
       "horsepower         1.0      0.1     -0.8        -0.8    0.8  \n",
       "peakrpm            0.1      1.0     -0.1        -0.1   -0.1  \n",
       "citympg           -0.8     -0.1      1.0         1.0   -0.7  \n",
       "highwaympg        -0.8     -0.1      1.0         1.0   -0.7  \n",
       "price              0.8     -0.1     -0.7        -0.7    1.0  \n",
       "\n",
       "[16 rows x 16 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate a correlation matrix.\n",
    "training_df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hp0r3NAVPEdt"
   },
   "source": [
    "The correlation matrix shows nine potential features (including a synthetic\n",
    "feature) and one label (`price`).  A strong negative correlation or strong positive correlation with the label suggests a potentially good feature.  \n",
    "\n",
    "**Your Task:** Determine which of these potential features appears to be the best candidate for a feature?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "cellView": "form",
    "colab": {},
    "colab_type": "code",
    "id": "RomQTd1OPVd0"
   },
   "outputs": [],
   "source": [
    "#@title Double-click here for the solution to Task 5\n",
    "\n",
    "# The `enginesize` correlates 0.9 with the label \n",
    "# (price), so `enginesize` might be a \n",
    "# good feature. The other seven potential features\n",
    "# all have a correlation relatively close to 0. \n",
    "\n",
    "# If time permits, try enginesize as the feature\n",
    "# and see whether the model improves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8RqvEbaVSlRt"
   },
   "source": [
    "Correlation matrices don't tell the entire story. In later exercises, you'll find additional ways to unlock predictive power from potential features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 230.4618 - root_mean_squared_error: 15.1810\n",
      "Epoch 2/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 192.7575 - root_mean_squared_error: 13.8837\n",
      "Epoch 3/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 167.8087 - root_mean_squared_error: 12.9541\n",
      "Epoch 4/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 146.4783 - root_mean_squared_error: 12.1028\n",
      "Epoch 5/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 128.3718 - root_mean_squared_error: 11.3301\n",
      "Epoch 6/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 112.0552 - root_mean_squared_error: 10.5856\n",
      "Epoch 7/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 98.6267 - root_mean_squared_error: 9.9311\n",
      "Epoch 8/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 86.2412 - root_mean_squared_error: 9.2866\n",
      "Epoch 9/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 75.9742 - root_mean_squared_error: 8.7163\n",
      "Epoch 10/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 66.9473 - root_mean_squared_error: 8.1821\n",
      "Epoch 11/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 59.3725 - root_mean_squared_error: 7.7054\n",
      "Epoch 12/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 53.4070 - root_mean_squared_error: 7.3080\n",
      "Epoch 13/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 49.3826 - root_mean_squared_error: 7.0273\n",
      "Epoch 14/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 46.1581 - root_mean_squared_error: 6.7940\n",
      "Epoch 15/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 43.9405 - root_mean_squared_error: 6.6288\n",
      "Epoch 16/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 42.5170 - root_mean_squared_error: 6.5205\n",
      "Epoch 17/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 41.4295 - root_mean_squared_error: 6.4366\n",
      "Epoch 18/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 41.1468 - root_mean_squared_error: 6.4146\n",
      "Epoch 19/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 40.9503 - root_mean_squared_error: 6.3992\n",
      "Epoch 20/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 40.5475 - root_mean_squared_error: 6.3677\n",
      "Epoch 21/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 40.3530 - root_mean_squared_error: 6.3524\n",
      "Epoch 22/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 40.2292 - root_mean_squared_error: 6.3426\n",
      "Epoch 23/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 39.9242 - root_mean_squared_error: 6.3186\n",
      "Epoch 24/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.6973 - root_mean_squared_error: 6.3006\n",
      "Epoch 25/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.6749 - root_mean_squared_error: 6.2988\n",
      "Epoch 26/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.2572 - root_mean_squared_error: 6.2656\n",
      "Epoch 27/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 39.1222 - root_mean_squared_error: 6.2548\n",
      "Epoch 28/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.7160 - root_mean_squared_error: 6.2222\n",
      "Epoch 29/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.6299 - root_mean_squared_error: 6.2153\n",
      "Epoch 30/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 38.2426 - root_mean_squared_error: 6.1841\n",
      "Epoch 31/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 38.0238 - root_mean_squared_error: 6.1663\n",
      "Epoch 32/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 37.8831 - root_mean_squared_error: 6.1549\n",
      "Epoch 33/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 37.6886 - root_mean_squared_error: 6.1391\n",
      "Epoch 34/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.4795 - root_mean_squared_error: 6.1220\n",
      "Epoch 35/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 37.3442 - root_mean_squared_error: 6.1110\n",
      "Epoch 36/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 37.1723 - root_mean_squared_error: 6.0969\n",
      "Epoch 37/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 36.9357 - root_mean_squared_error: 6.0775\n",
      "Epoch 38/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 36.6576 - root_mean_squared_error: 6.0546\n",
      "Epoch 39/500\n",
      "7/7 [==============================] - 0s 5ms/step - loss: 36.4983 - root_mean_squared_error: 6.0414\n",
      "Epoch 40/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 36.3194 - root_mean_squared_error: 6.0266\n",
      "Epoch 41/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.9608 - root_mean_squared_error: 5.9967\n",
      "Epoch 42/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 35.8311 - root_mean_squared_error: 5.9859\n",
      "Epoch 43/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.5892 - root_mean_squared_error: 5.9657\n",
      "Epoch 44/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 35.3852 - root_mean_squared_error: 5.9485\n",
      "Epoch 45/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 35.1423 - root_mean_squared_error: 5.9281\n",
      "Epoch 46/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 34.9796 - root_mean_squared_error: 5.9144\n",
      "Epoch 47/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.7276 - root_mean_squared_error: 5.8930\n",
      "Epoch 48/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.5435 - root_mean_squared_error: 5.8774\n",
      "Epoch 49/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 34.4821 - root_mean_squared_error: 5.8721\n",
      "Epoch 50/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.2577 - root_mean_squared_error: 5.8530\n",
      "Epoch 51/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 34.0298 - root_mean_squared_error: 5.8335\n",
      "Epoch 52/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.9566 - root_mean_squared_error: 5.8272\n",
      "Epoch 53/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.7781 - root_mean_squared_error: 5.8119\n",
      "Epoch 54/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.5470 - root_mean_squared_error: 5.7920\n",
      "Epoch 55/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.4154 - root_mean_squared_error: 5.7806\n",
      "Epoch 56/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.2165 - root_mean_squared_error: 5.7634\n",
      "Epoch 57/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 33.0704 - root_mean_squared_error: 5.7507\n",
      "Epoch 58/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 32.9276 - root_mean_squared_error: 5.7383\n",
      "Epoch 59/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 32.7423 - root_mean_squared_error: 5.7221\n",
      "Epoch 60/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 32.5606 - root_mean_squared_error: 5.7062\n",
      "Epoch 61/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 32.4465 - root_mean_squared_error: 5.6962\n",
      "Epoch 62/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 32.2422 - root_mean_squared_error: 5.6782\n",
      "Epoch 63/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 32.2755 - root_mean_squared_error: 5.6812\n",
      "Epoch 64/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.9029 - root_mean_squared_error: 5.6483\n",
      "Epoch 65/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 31.8419 - root_mean_squared_error: 5.6429\n",
      "Epoch 66/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.5510 - root_mean_squared_error: 5.6170\n",
      "Epoch 67/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 31.4776 - root_mean_squared_error: 5.6105\n",
      "Epoch 68/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.3547 - root_mean_squared_error: 5.5995\n",
      "Epoch 69/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.1044 - root_mean_squared_error: 5.5771\n",
      "Epoch 70/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 31.0557 - root_mean_squared_error: 5.5728\n",
      "Epoch 71/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 30.9377 - root_mean_squared_error: 5.5622\n",
      "Epoch 72/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 30.8061 - root_mean_squared_error: 5.5503\n",
      "Epoch 73/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.5770 - root_mean_squared_error: 5.5296\n",
      "Epoch 74/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.5889 - root_mean_squared_error: 5.5307\n",
      "Epoch 75/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 30.3284 - root_mean_squared_error: 5.5071\n",
      "Epoch 76/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.2055 - root_mean_squared_error: 5.4960\n",
      "Epoch 77/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 30.1605 - root_mean_squared_error: 5.4919\n",
      "Epoch 78/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 29.8956 - root_mean_squared_error: 5.4677\n",
      "Epoch 79/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.8634 - root_mean_squared_error: 5.4647\n",
      "Epoch 80/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.6008 - root_mean_squared_error: 5.4407\n",
      "Epoch 81/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 29.4585 - root_mean_squared_error: 5.4276\n",
      "Epoch 82/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.3449 - root_mean_squared_error: 5.4171\n",
      "Epoch 83/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 29.1747 - root_mean_squared_error: 5.4014\n",
      "Epoch 84/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 29.0462 - root_mean_squared_error: 5.3895\n",
      "Epoch 85/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.8446 - root_mean_squared_error: 5.3707\n",
      "Epoch 86/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.8704 - root_mean_squared_error: 5.3731\n",
      "Epoch 87/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.7025 - root_mean_squared_error: 5.3575\n",
      "Epoch 88/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.4439 - root_mean_squared_error: 5.3333\n",
      "Epoch 89/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.4036 - root_mean_squared_error: 5.3295\n",
      "Epoch 90/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.3027 - root_mean_squared_error: 5.3200\n",
      "Epoch 91/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.0403 - root_mean_squared_error: 5.2953\n",
      "Epoch 92/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 28.0091 - root_mean_squared_error: 5.2924\n",
      "Epoch 93/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.8422 - root_mean_squared_error: 5.2766\n",
      "Epoch 94/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 27.7777 - root_mean_squared_error: 5.2705\n",
      "Epoch 95/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.6573 - root_mean_squared_error: 5.2590\n",
      "Epoch 96/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.7497 - root_mean_squared_error: 5.2678\n",
      "Epoch 97/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 27.3462 - root_mean_squared_error: 5.2294\n",
      "Epoch 98/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.4551 - root_mean_squared_error: 5.2398\n",
      "Epoch 99/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.2504 - root_mean_squared_error: 5.2202\n",
      "Epoch 100/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 27.0800 - root_mean_squared_error: 5.2038\n",
      "Epoch 101/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 27.1685 - root_mean_squared_error: 5.2123\n",
      "Epoch 102/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.9489 - root_mean_squared_error: 5.1912\n",
      "Epoch 103/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.7965 - root_mean_squared_error: 5.1765\n",
      "Epoch 104/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.6489 - root_mean_squared_error: 5.1623\n",
      "Epoch 105/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.6301 - root_mean_squared_error: 5.1604\n",
      "Epoch 106/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.5964 - root_mean_squared_error: 5.1572\n",
      "Epoch 107/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.3661 - root_mean_squared_error: 5.1348\n",
      "Epoch 108/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.3649 - root_mean_squared_error: 5.1347\n",
      "Epoch 109/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.2153 - root_mean_squared_error: 5.1201\n",
      "Epoch 110/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.1793 - root_mean_squared_error: 5.1166\n",
      "Epoch 111/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 26.0162 - root_mean_squared_error: 5.1006\n",
      "Epoch 112/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 26.0545 - root_mean_squared_error: 5.1044\n",
      "Epoch 113/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.7768 - root_mean_squared_error: 5.0771\n",
      "Epoch 114/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.6805 - root_mean_squared_error: 5.0676\n",
      "Epoch 115/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.7724 - root_mean_squared_error: 5.0767\n",
      "Epoch 116/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 25.5984 - root_mean_squared_error: 5.0595\n",
      "Epoch 117/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.4753 - root_mean_squared_error: 5.0473\n",
      "Epoch 118/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.4571 - root_mean_squared_error: 5.0455\n",
      "Epoch 119/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.2557 - root_mean_squared_error: 5.0255\n",
      "Epoch 120/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.1810 - root_mean_squared_error: 5.0181\n",
      "Epoch 121/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 25.1675 - root_mean_squared_error: 5.0167\n",
      "Epoch 122/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.9819 - root_mean_squared_error: 4.9982\n",
      "Epoch 123/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.9548 - root_mean_squared_error: 4.9955\n",
      "Epoch 124/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 24.8756 - root_mean_squared_error: 4.9875\n",
      "Epoch 125/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.7541 - root_mean_squared_error: 4.9754\n",
      "Epoch 126/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.6905 - root_mean_squared_error: 4.9689\n",
      "Epoch 127/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.6473 - root_mean_squared_error: 4.9646\n",
      "Epoch 128/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.5520 - root_mean_squared_error: 4.9550\n",
      "Epoch 129/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.4600 - root_mean_squared_error: 4.9457\n",
      "Epoch 130/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.4484 - root_mean_squared_error: 4.9445\n",
      "Epoch 131/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.3221 - root_mean_squared_error: 4.9317\n",
      "Epoch 132/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.2479 - root_mean_squared_error: 4.9242\n",
      "Epoch 133/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 24.2682 - root_mean_squared_error: 4.9263\n",
      "Epoch 134/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.0515 - root_mean_squared_error: 4.9042\n",
      "Epoch 135/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.0405 - root_mean_squared_error: 4.9031\n",
      "Epoch 136/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 24.0145 - root_mean_squared_error: 4.9005\n",
      "Epoch 137/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.9181 - root_mean_squared_error: 4.8906\n",
      "Epoch 138/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 23.8600 - root_mean_squared_error: 4.8847\n",
      "Epoch 139/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.7552 - root_mean_squared_error: 4.8739\n",
      "Epoch 140/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.7306 - root_mean_squared_error: 4.8714\n",
      "Epoch 141/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.5801 - root_mean_squared_error: 4.8559\n",
      "Epoch 142/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.5278 - root_mean_squared_error: 4.8505\n",
      "Epoch 143/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 23.5476 - root_mean_squared_error: 4.8526\n",
      "Epoch 144/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.4338 - root_mean_squared_error: 4.8408\n",
      "Epoch 145/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.3276 - root_mean_squared_error: 4.8299\n",
      "Epoch 146/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.3010 - root_mean_squared_error: 4.8271\n",
      "Epoch 147/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.2556 - root_mean_squared_error: 4.8224\n",
      "Epoch 148/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 23.1785 - root_mean_squared_error: 4.8144\n",
      "Epoch 149/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.1437 - root_mean_squared_error: 4.8108\n",
      "Epoch 150/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.0500 - root_mean_squared_error: 4.8010\n",
      "Epoch 151/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 23.0129 - root_mean_squared_error: 4.7972\n",
      "Epoch 152/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.9762 - root_mean_squared_error: 4.7934\n",
      "Epoch 153/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.9542 - root_mean_squared_error: 4.7910\n",
      "Epoch 154/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.7880 - root_mean_squared_error: 4.7737\n",
      "Epoch 155/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.8003 - root_mean_squared_error: 4.7750\n",
      "Epoch 156/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.7236 - root_mean_squared_error: 4.7669\n",
      "Epoch 157/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.7046 - root_mean_squared_error: 4.7649\n",
      "Epoch 158/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.6500 - root_mean_squared_error: 4.7592\n",
      "Epoch 159/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.5368 - root_mean_squared_error: 4.7473\n",
      "Epoch 160/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.5329 - root_mean_squared_error: 4.7469\n",
      "Epoch 161/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.5202 - root_mean_squared_error: 4.7455\n",
      "Epoch 162/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.3585 - root_mean_squared_error: 4.7285\n",
      "Epoch 163/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.3648 - root_mean_squared_error: 4.7291\n",
      "Epoch 164/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.2974 - root_mean_squared_error: 4.7220\n",
      "Epoch 165/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.2853 - root_mean_squared_error: 4.7207\n",
      "Epoch 166/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 22.1708 - root_mean_squared_error: 4.7086\n",
      "Epoch 167/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.1716 - root_mean_squared_error: 4.7087\n",
      "Epoch 168/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 22.2009 - root_mean_squared_error: 4.7118\n",
      "Epoch 169/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.0141 - root_mean_squared_error: 4.6919\n",
      "Epoch 170/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 22.0004 - root_mean_squared_error: 4.6905\n",
      "Epoch 171/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.9504 - root_mean_squared_error: 4.6851\n",
      "Epoch 172/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 22.0196 - root_mean_squared_error: 4.6925\n",
      "Epoch 173/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.9226 - root_mean_squared_error: 4.6822\n",
      "Epoch 174/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.8776 - root_mean_squared_error: 4.6774\n",
      "Epoch 175/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.8149 - root_mean_squared_error: 4.6706\n",
      "Epoch 176/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.7475 - root_mean_squared_error: 4.6634\n",
      "Epoch 177/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.7559 - root_mean_squared_error: 4.6643\n",
      "Epoch 178/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.6788 - root_mean_squared_error: 4.6561\n",
      "Epoch 179/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.7336 - root_mean_squared_error: 4.6619\n",
      "Epoch 180/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 21.7360 - root_mean_squared_error: 4.6622\n",
      "Epoch 181/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.5725 - root_mean_squared_error: 4.6446\n",
      "Epoch 182/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.5353 - root_mean_squared_error: 4.6406\n",
      "Epoch 183/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 21.5505 - root_mean_squared_error: 4.6423\n",
      "Epoch 184/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.5319 - root_mean_squared_error: 4.6402\n",
      "Epoch 185/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.4465 - root_mean_squared_error: 4.6310\n",
      "Epoch 186/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.5130 - root_mean_squared_error: 4.6382\n",
      "Epoch 187/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.4013 - root_mean_squared_error: 4.6262\n",
      "Epoch 188/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.4048 - root_mean_squared_error: 4.6265\n",
      "Epoch 189/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.3452 - root_mean_squared_error: 4.6201\n",
      "Epoch 190/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.2822 - root_mean_squared_error: 4.6133\n",
      "Epoch 191/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.2451 - root_mean_squared_error: 4.6092\n",
      "Epoch 192/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.2342 - root_mean_squared_error: 4.6081\n",
      "Epoch 193/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.2003 - root_mean_squared_error: 4.6044\n",
      "Epoch 194/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.2484 - root_mean_squared_error: 4.6096\n",
      "Epoch 195/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.0994 - root_mean_squared_error: 4.5934\n",
      "Epoch 196/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.0696 - root_mean_squared_error: 4.5902\n",
      "Epoch 197/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.0531 - root_mean_squared_error: 4.5884\n",
      "Epoch 198/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 21.1001 - root_mean_squared_error: 4.5935\n",
      "Epoch 199/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 21.1340 - root_mean_squared_error: 4.5972\n",
      "Epoch 200/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.9580 - root_mean_squared_error: 4.5780\n",
      "Epoch 201/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.9896 - root_mean_squared_error: 4.5814\n",
      "Epoch 202/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 20.9578 - root_mean_squared_error: 4.5780\n",
      "Epoch 203/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.8657 - root_mean_squared_error: 4.5679\n",
      "Epoch 204/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.9060 - root_mean_squared_error: 4.5723\n",
      "Epoch 205/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.9243 - root_mean_squared_error: 4.5743\n",
      "Epoch 206/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.8917 - root_mean_squared_error: 4.5707\n",
      "Epoch 207/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.8842 - root_mean_squared_error: 4.5699\n",
      "Epoch 208/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.8386 - root_mean_squared_error: 4.5649\n",
      "Epoch 209/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.8898 - root_mean_squared_error: 4.5705\n",
      "Epoch 210/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.7050 - root_mean_squared_error: 4.5503\n",
      "Epoch 211/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.7067 - root_mean_squared_error: 4.5505\n",
      "Epoch 212/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.6846 - root_mean_squared_error: 4.5480\n",
      "Epoch 213/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.7088 - root_mean_squared_error: 4.5507\n",
      "Epoch 214/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 20.6296 - root_mean_squared_error: 4.5420\n",
      "Epoch 215/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.6193 - root_mean_squared_error: 4.5409\n",
      "Epoch 216/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.6451 - root_mean_squared_error: 4.5437\n",
      "Epoch 217/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.5732 - root_mean_squared_error: 4.5358\n",
      "Epoch 218/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.5675 - root_mean_squared_error: 4.5351\n",
      "Epoch 219/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.5839 - root_mean_squared_error: 4.5369\n",
      "Epoch 220/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.5679 - root_mean_squared_error: 4.5352\n",
      "Epoch 221/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.5560 - root_mean_squared_error: 4.5339\n",
      "Epoch 222/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.4323 - root_mean_squared_error: 4.5202\n",
      "Epoch 223/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.5279 - root_mean_squared_error: 4.5308\n",
      "Epoch 224/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.4409 - root_mean_squared_error: 4.5212\n",
      "Epoch 225/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.5559 - root_mean_squared_error: 4.5339\n",
      "Epoch 226/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.4258 - root_mean_squared_error: 4.5195\n",
      "Epoch 227/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 20.3733 - root_mean_squared_error: 4.5137\n",
      "Epoch 228/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.3754 - root_mean_squared_error: 4.5139\n",
      "Epoch 229/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.3745 - root_mean_squared_error: 4.5138\n",
      "Epoch 230/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.3341 - root_mean_squared_error: 4.5093\n",
      "Epoch 231/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.2952 - root_mean_squared_error: 4.5050\n",
      "Epoch 232/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.3217 - root_mean_squared_error: 4.5080\n",
      "Epoch 233/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.2879 - root_mean_squared_error: 4.5042\n",
      "Epoch 234/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.2123 - root_mean_squared_error: 4.4958\n",
      "Epoch 235/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.2530 - root_mean_squared_error: 4.5003\n",
      "Epoch 236/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.3284 - root_mean_squared_error: 4.5087\n",
      "Epoch 237/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.2902 - root_mean_squared_error: 4.5045\n",
      "Epoch 238/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.2055 - root_mean_squared_error: 4.4951\n",
      "Epoch 239/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1283 - root_mean_squared_error: 4.4865\n",
      "Epoch 240/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1694 - root_mean_squared_error: 4.4910\n",
      "Epoch 241/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.2135 - root_mean_squared_error: 4.4959\n",
      "Epoch 242/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1294 - root_mean_squared_error: 4.4866\n",
      "Epoch 243/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1757 - root_mean_squared_error: 4.4917\n",
      "Epoch 244/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1591 - root_mean_squared_error: 4.4899\n",
      "Epoch 245/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.0432 - root_mean_squared_error: 4.4770\n",
      "Epoch 246/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1223 - root_mean_squared_error: 4.4858\n",
      "Epoch 247/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.1237 - root_mean_squared_error: 4.4859\n",
      "Epoch 248/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9822 - root_mean_squared_error: 4.4701\n",
      "Epoch 249/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 20.0385 - root_mean_squared_error: 4.4764\n",
      "Epoch 250/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 20.0774 - root_mean_squared_error: 4.4808\n",
      "Epoch 251/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.0765 - root_mean_squared_error: 4.4807\n",
      "Epoch 252/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9776 - root_mean_squared_error: 4.4696\n",
      "Epoch 253/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.9723 - root_mean_squared_error: 4.4690\n",
      "Epoch 254/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9337 - root_mean_squared_error: 4.4647\n",
      "Epoch 255/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 20.0214 - root_mean_squared_error: 4.4745\n",
      "Epoch 256/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.9314 - root_mean_squared_error: 4.4645\n",
      "Epoch 257/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 20.0100 - root_mean_squared_error: 4.4733\n",
      "Epoch 258/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9496 - root_mean_squared_error: 4.4665\n",
      "Epoch 259/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9183 - root_mean_squared_error: 4.4630\n",
      "Epoch 260/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8776 - root_mean_squared_error: 4.4584\n",
      "Epoch 261/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9039 - root_mean_squared_error: 4.4614\n",
      "Epoch 262/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9289 - root_mean_squared_error: 4.4642\n",
      "Epoch 263/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.9320 - root_mean_squared_error: 4.4645\n",
      "Epoch 264/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8272 - root_mean_squared_error: 4.4528\n",
      "Epoch 265/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.9587 - root_mean_squared_error: 4.4675\n",
      "Epoch 266/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8313 - root_mean_squared_error: 4.4532\n",
      "Epoch 267/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.8501 - root_mean_squared_error: 4.4553\n",
      "Epoch 268/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.8268 - root_mean_squared_error: 4.4527\n",
      "Epoch 269/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.8095 - root_mean_squared_error: 4.4508\n",
      "Epoch 270/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8018 - root_mean_squared_error: 4.4499\n",
      "Epoch 271/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.8282 - root_mean_squared_error: 4.4529\n",
      "Epoch 272/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8998 - root_mean_squared_error: 4.4609\n",
      "Epoch 273/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.7452 - root_mean_squared_error: 4.4436\n",
      "Epoch 274/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7515 - root_mean_squared_error: 4.4443\n",
      "Epoch 275/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.8842 - root_mean_squared_error: 4.4592\n",
      "Epoch 276/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8694 - root_mean_squared_error: 4.4575\n",
      "Epoch 277/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8329 - root_mean_squared_error: 4.4534\n",
      "Epoch 278/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7277 - root_mean_squared_error: 4.4416\n",
      "Epoch 279/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6755 - root_mean_squared_error: 4.4357\n",
      "Epoch 280/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7471 - root_mean_squared_error: 4.4438\n",
      "Epoch 281/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7110 - root_mean_squared_error: 4.4397\n",
      "Epoch 282/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6732 - root_mean_squared_error: 4.4354\n",
      "Epoch 283/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.8483 - root_mean_squared_error: 4.4551\n",
      "Epoch 284/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6471 - root_mean_squared_error: 4.4325\n",
      "Epoch 285/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 1ms/step - loss: 19.6397 - root_mean_squared_error: 4.4317\n",
      "Epoch 286/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7199 - root_mean_squared_error: 4.4407\n",
      "Epoch 287/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7824 - root_mean_squared_error: 4.4477\n",
      "Epoch 288/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7788 - root_mean_squared_error: 4.4473\n",
      "Epoch 289/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6410 - root_mean_squared_error: 4.4318\n",
      "Epoch 290/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.8223 - root_mean_squared_error: 4.4522\n",
      "Epoch 291/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6044 - root_mean_squared_error: 4.4277\n",
      "Epoch 292/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.6117 - root_mean_squared_error: 4.4285\n",
      "Epoch 293/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6542 - root_mean_squared_error: 4.4333\n",
      "Epoch 294/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6296 - root_mean_squared_error: 4.4305\n",
      "Epoch 295/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6816 - root_mean_squared_error: 4.4364\n",
      "Epoch 296/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6353 - root_mean_squared_error: 4.4312\n",
      "Epoch 297/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.7015 - root_mean_squared_error: 4.4386\n",
      "Epoch 298/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6388 - root_mean_squared_error: 4.4316\n",
      "Epoch 299/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5660 - root_mean_squared_error: 4.4233\n",
      "Epoch 300/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6061 - root_mean_squared_error: 4.4279\n",
      "Epoch 301/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5979 - root_mean_squared_error: 4.4270\n",
      "Epoch 302/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.6148 - root_mean_squared_error: 4.4289\n",
      "Epoch 303/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5595 - root_mean_squared_error: 4.4226\n",
      "Epoch 304/500\n",
      "7/7 [==============================] - ETA: 0s - loss: 11.0975 - root_mean_squared_error: 3.33 - 0s 2ms/step - loss: 19.6024 - root_mean_squared_error: 4.4275\n",
      "Epoch 305/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6388 - root_mean_squared_error: 4.4316\n",
      "Epoch 306/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5712 - root_mean_squared_error: 4.4239\n",
      "Epoch 307/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5948 - root_mean_squared_error: 4.4266\n",
      "Epoch 308/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6378 - root_mean_squared_error: 4.4315\n",
      "Epoch 309/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.5646 - root_mean_squared_error: 4.4232\n",
      "Epoch 310/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5070 - root_mean_squared_error: 4.4167\n",
      "Epoch 311/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4926 - root_mean_squared_error: 4.4150\n",
      "Epoch 312/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4911 - root_mean_squared_error: 4.4149\n",
      "Epoch 313/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5673 - root_mean_squared_error: 4.4235\n",
      "Epoch 314/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.5155 - root_mean_squared_error: 4.4176\n",
      "Epoch 315/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5715 - root_mean_squared_error: 4.4240\n",
      "Epoch 316/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6107 - root_mean_squared_error: 4.4284\n",
      "Epoch 317/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5378 - root_mean_squared_error: 4.4202\n",
      "Epoch 318/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5081 - root_mean_squared_error: 4.4168\n",
      "Epoch 319/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6074 - root_mean_squared_error: 4.4280\n",
      "Epoch 320/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5394 - root_mean_squared_error: 4.4203\n",
      "Epoch 321/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5154 - root_mean_squared_error: 4.4176\n",
      "Epoch 322/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5776 - root_mean_squared_error: 4.4247\n",
      "Epoch 323/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4604 - root_mean_squared_error: 4.4114\n",
      "Epoch 324/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4341 - root_mean_squared_error: 4.4084\n",
      "Epoch 325/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4592 - root_mean_squared_error: 4.4113\n",
      "Epoch 326/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4951 - root_mean_squared_error: 4.4153\n",
      "Epoch 327/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5239 - root_mean_squared_error: 4.4186\n",
      "Epoch 328/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5824 - root_mean_squared_error: 4.4252\n",
      "Epoch 329/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4642 - root_mean_squared_error: 4.4118\n",
      "Epoch 330/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4267 - root_mean_squared_error: 4.4076\n",
      "Epoch 331/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.5041 - root_mean_squared_error: 4.4163\n",
      "Epoch 332/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.4767 - root_mean_squared_error: 4.4132\n",
      "Epoch 333/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5029 - root_mean_squared_error: 4.4162\n",
      "Epoch 334/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4778 - root_mean_squared_error: 4.4134\n",
      "Epoch 335/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4560 - root_mean_squared_error: 4.4109\n",
      "Epoch 336/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4443 - root_mean_squared_error: 4.4096\n",
      "Epoch 337/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.5127 - root_mean_squared_error: 4.4173\n",
      "Epoch 338/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4298 - root_mean_squared_error: 4.4079\n",
      "Epoch 339/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4422 - root_mean_squared_error: 4.4093\n",
      "Epoch 340/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4209 - root_mean_squared_error: 4.4069\n",
      "Epoch 341/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3893 - root_mean_squared_error: 4.4033\n",
      "Epoch 342/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.5128 - root_mean_squared_error: 4.4173\n",
      "Epoch 343/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4892 - root_mean_squared_error: 4.4147\n",
      "Epoch 344/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4235 - root_mean_squared_error: 4.4072\n",
      "Epoch 345/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4133 - root_mean_squared_error: 4.4060\n",
      "Epoch 346/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.4872 - root_mean_squared_error: 4.4144\n",
      "Epoch 347/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.4004 - root_mean_squared_error: 4.4046\n",
      "Epoch 348/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4813 - root_mean_squared_error: 4.4138\n",
      "Epoch 349/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3972 - root_mean_squared_error: 4.4042\n",
      "Epoch 350/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4452 - root_mean_squared_error: 4.4097\n",
      "Epoch 351/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3580 - root_mean_squared_error: 4.3998\n",
      "Epoch 352/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3390 - root_mean_squared_error: 4.3976\n",
      "Epoch 353/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5576 - root_mean_squared_error: 4.4224\n",
      "Epoch 354/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.6107 - root_mean_squared_error: 4.4284\n",
      "Epoch 355/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3606 - root_mean_squared_error: 4.4001\n",
      "Epoch 356/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4264 - root_mean_squared_error: 4.4075\n",
      "Epoch 357/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3446 - root_mean_squared_error: 4.3982\n",
      "Epoch 358/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4741 - root_mean_squared_error: 4.4130\n",
      "Epoch 359/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3597 - root_mean_squared_error: 4.4000\n",
      "Epoch 360/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4738 - root_mean_squared_error: 4.4129\n",
      "Epoch 361/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3864 - root_mean_squared_error: 4.4030\n",
      "Epoch 362/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3882 - root_mean_squared_error: 4.4032\n",
      "Epoch 363/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3955 - root_mean_squared_error: 4.4040\n",
      "Epoch 364/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5156 - root_mean_squared_error: 4.4176\n",
      "Epoch 365/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4358 - root_mean_squared_error: 4.4086\n",
      "Epoch 366/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3528 - root_mean_squared_error: 4.3992\n",
      "Epoch 367/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.4390 - root_mean_squared_error: 4.4090\n",
      "Epoch 368/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3172 - root_mean_squared_error: 4.3951\n",
      "Epoch 369/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4341 - root_mean_squared_error: 4.4084\n",
      "Epoch 370/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3535 - root_mean_squared_error: 4.3993\n",
      "Epoch 371/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3547 - root_mean_squared_error: 4.3994\n",
      "Epoch 372/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4084 - root_mean_squared_error: 4.4055\n",
      "Epoch 373/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3686 - root_mean_squared_error: 4.4010\n",
      "Epoch 374/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3563 - root_mean_squared_error: 4.3996\n",
      "Epoch 375/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4521 - root_mean_squared_error: 4.4105\n",
      "Epoch 376/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3990 - root_mean_squared_error: 4.4044\n",
      "Epoch 377/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3253 - root_mean_squared_error: 4.3961\n",
      "Epoch 378/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3452 - root_mean_squared_error: 4.3983\n",
      "Epoch 379/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.5180 - root_mean_squared_error: 4.4179\n",
      "Epoch 380/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3026 - root_mean_squared_error: 4.3935\n",
      "Epoch 381/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3054 - root_mean_squared_error: 4.3938\n",
      "Epoch 382/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3406 - root_mean_squared_error: 4.3978\n",
      "Epoch 383/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.4051 - root_mean_squared_error: 4.4051\n",
      "Epoch 384/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3976 - root_mean_squared_error: 4.4043\n",
      "Epoch 385/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3904 - root_mean_squared_error: 4.4035\n",
      "Epoch 386/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4760 - root_mean_squared_error: 4.4132\n",
      "Epoch 387/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3728 - root_mean_squared_error: 4.4015\n",
      "Epoch 388/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3366 - root_mean_squared_error: 4.3973\n",
      "Epoch 389/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4276 - root_mean_squared_error: 4.4077\n",
      "Epoch 390/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2757 - root_mean_squared_error: 4.3904\n",
      "Epoch 391/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3142 - root_mean_squared_error: 4.3948\n",
      "Epoch 392/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2974 - root_mean_squared_error: 4.3929\n",
      "Epoch 393/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3396 - root_mean_squared_error: 4.3977\n",
      "Epoch 394/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4658 - root_mean_squared_error: 4.4120\n",
      "Epoch 395/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3583 - root_mean_squared_error: 4.3998\n",
      "Epoch 396/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3730 - root_mean_squared_error: 4.4015\n",
      "Epoch 397/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3618 - root_mean_squared_error: 4.4002\n",
      "Epoch 398/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3156 - root_mean_squared_error: 4.3950\n",
      "Epoch 399/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3860 - root_mean_squared_error: 4.4030\n",
      "Epoch 400/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2992 - root_mean_squared_error: 4.3931\n",
      "Epoch 401/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4632 - root_mean_squared_error: 4.4117\n",
      "Epoch 402/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3365 - root_mean_squared_error: 4.3973\n",
      "Epoch 403/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4274 - root_mean_squared_error: 4.4076\n",
      "Epoch 404/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3018 - root_mean_squared_error: 4.3934\n",
      "Epoch 405/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 19.3242 - root_mean_squared_error: 4.3959\n",
      "Epoch 406/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 19.3773 - root_mean_squared_error: 4.4020\n",
      "Epoch 407/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3341 - root_mean_squared_error: 4.3971\n",
      "Epoch 408/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3698 - root_mean_squared_error: 4.4011\n",
      "Epoch 409/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2967 - root_mean_squared_error: 4.3928\n",
      "Epoch 410/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3205 - root_mean_squared_error: 4.3955\n",
      "Epoch 411/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3388 - root_mean_squared_error: 4.3976\n",
      "Epoch 412/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3386 - root_mean_squared_error: 4.3976\n",
      "Epoch 413/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3458 - root_mean_squared_error: 4.3984\n",
      "Epoch 414/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3141 - root_mean_squared_error: 4.3948\n",
      "Epoch 415/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3154 - root_mean_squared_error: 4.3949\n",
      "Epoch 416/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2810 - root_mean_squared_error: 4.3910\n",
      "Epoch 417/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2743 - root_mean_squared_error: 4.3903\n",
      "Epoch 418/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3493 - root_mean_squared_error: 4.3988\n",
      "Epoch 419/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3442 - root_mean_squared_error: 4.3982\n",
      "Epoch 420/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3731 - root_mean_squared_error: 4.4015\n",
      "Epoch 421/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3013 - root_mean_squared_error: 4.3933\n",
      "Epoch 422/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3688 - root_mean_squared_error: 4.4010\n",
      "Epoch 423/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3066 - root_mean_squared_error: 4.3939\n",
      "Epoch 424/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3153 - root_mean_squared_error: 4.3949\n",
      "Epoch 425/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4084 - root_mean_squared_error: 4.4055\n",
      "Epoch 426/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 3ms/step - loss: 19.2930 - root_mean_squared_error: 4.3924\n",
      "Epoch 427/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3092 - root_mean_squared_error: 4.3942\n",
      "Epoch 428/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3294 - root_mean_squared_error: 4.3965\n",
      "Epoch 429/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3244 - root_mean_squared_error: 4.3960\n",
      "Epoch 430/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3297 - root_mean_squared_error: 4.3966\n",
      "Epoch 431/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2621 - root_mean_squared_error: 4.3889\n",
      "Epoch 432/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3813 - root_mean_squared_error: 4.4024\n",
      "Epoch 433/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3189 - root_mean_squared_error: 4.3953\n",
      "Epoch 434/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3174 - root_mean_squared_error: 4.3952\n",
      "Epoch 435/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3147 - root_mean_squared_error: 4.3948\n",
      "Epoch 436/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3846 - root_mean_squared_error: 4.4028\n",
      "Epoch 437/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2666 - root_mean_squared_error: 4.3894\n",
      "Epoch 438/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3902 - root_mean_squared_error: 4.4034\n",
      "Epoch 439/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3161 - root_mean_squared_error: 4.3950\n",
      "Epoch 440/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2809 - root_mean_squared_error: 4.3910\n",
      "Epoch 441/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3100 - root_mean_squared_error: 4.3943\n",
      "Epoch 442/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2755 - root_mean_squared_error: 4.3904\n",
      "Epoch 443/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3179 - root_mean_squared_error: 4.3952\n",
      "Epoch 444/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3243 - root_mean_squared_error: 4.3959\n",
      "Epoch 445/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3103 - root_mean_squared_error: 4.3943\n",
      "Epoch 446/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2700 - root_mean_squared_error: 4.3898\n",
      "Epoch 447/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3527 - root_mean_squared_error: 4.3992\n",
      "Epoch 448/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3458 - root_mean_squared_error: 4.3984\n",
      "Epoch 449/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3027 - root_mean_squared_error: 4.3935\n",
      "Epoch 450/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3718 - root_mean_squared_error: 4.4013\n",
      "Epoch 451/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2728 - root_mean_squared_error: 4.3901\n",
      "Epoch 452/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2471 - root_mean_squared_error: 4.3871\n",
      "Epoch 453/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4749 - root_mean_squared_error: 4.4130\n",
      "Epoch 454/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2538 - root_mean_squared_error: 4.3879\n",
      "Epoch 455/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3052 - root_mean_squared_error: 4.3938\n",
      "Epoch 456/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2858 - root_mean_squared_error: 4.3916\n",
      "Epoch 457/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3150 - root_mean_squared_error: 4.3949\n",
      "Epoch 458/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3991 - root_mean_squared_error: 4.4044\n",
      "Epoch 459/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4004 - root_mean_squared_error: 4.4046\n",
      "Epoch 460/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2926 - root_mean_squared_error: 4.3923\n",
      "Epoch 461/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2917 - root_mean_squared_error: 4.3922\n",
      "Epoch 462/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2792 - root_mean_squared_error: 4.3908\n",
      "Epoch 463/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3788 - root_mean_squared_error: 4.4021\n",
      "Epoch 464/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3310 - root_mean_squared_error: 4.3967\n",
      "Epoch 465/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2751 - root_mean_squared_error: 4.3903\n",
      "Epoch 466/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2522 - root_mean_squared_error: 4.3877\n",
      "Epoch 467/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2828 - root_mean_squared_error: 4.3912\n",
      "Epoch 468/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 19.2693 - root_mean_squared_error: 4.3897\n",
      "Epoch 469/500\n",
      "7/7 [==============================] - 0s 4ms/step - loss: 19.4743 - root_mean_squared_error: 4.4130\n",
      "Epoch 470/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2595 - root_mean_squared_error: 4.3886\n",
      "Epoch 471/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3213 - root_mean_squared_error: 4.3956\n",
      "Epoch 472/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3044 - root_mean_squared_error: 4.3937\n",
      "Epoch 473/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.3221 - root_mean_squared_error: 4.3957\n",
      "Epoch 474/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3432 - root_mean_squared_error: 4.3981\n",
      "Epoch 475/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3141 - root_mean_squared_error: 4.3948\n",
      "Epoch 476/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3346 - root_mean_squared_error: 4.3971\n",
      "Epoch 477/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3593 - root_mean_squared_error: 4.3999\n",
      "Epoch 478/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2652 - root_mean_squared_error: 4.3892\n",
      "Epoch 479/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.4346 - root_mean_squared_error: 4.4085\n",
      "Epoch 480/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3589 - root_mean_squared_error: 4.3999\n",
      "Epoch 481/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2968 - root_mean_squared_error: 4.3928\n",
      "Epoch 482/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3540 - root_mean_squared_error: 4.3993\n",
      "Epoch 483/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2769 - root_mean_squared_error: 4.3905\n",
      "Epoch 484/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2873 - root_mean_squared_error: 4.3917\n",
      "Epoch 485/500\n",
      "7/7 [==============================] - 0s 3ms/step - loss: 19.2490 - root_mean_squared_error: 4.3874\n",
      "Epoch 486/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2733 - root_mean_squared_error: 4.3901\n",
      "Epoch 487/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3451 - root_mean_squared_error: 4.3983\n",
      "Epoch 488/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3414 - root_mean_squared_error: 4.3979\n",
      "Epoch 489/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2796 - root_mean_squared_error: 4.3909\n",
      "Epoch 490/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3008 - root_mean_squared_error: 4.3933\n",
      "Epoch 491/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2816 - root_mean_squared_error: 4.3911\n",
      "Epoch 492/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3657 - root_mean_squared_error: 4.4006\n",
      "Epoch 493/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2849 - root_mean_squared_error: 4.3915\n",
      "Epoch 494/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2550 - root_mean_squared_error: 4.3880\n",
      "Epoch 495/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2887 - root_mean_squared_error: 4.3919\n",
      "Epoch 496/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3362 - root_mean_squared_error: 4.3973\n",
      "Epoch 497/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 2ms/step - loss: 19.2556 - root_mean_squared_error: 4.3881\n",
      "Epoch 498/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.2532 - root_mean_squared_error: 4.3878\n",
      "Epoch 499/500\n",
      "7/7 [==============================] - 0s 2ms/step - loss: 19.3074 - root_mean_squared_error: 4.3940\n",
      "Epoch 500/500\n",
      "7/7 [==============================] - 0s 1ms/step - loss: 19.3307 - root_mean_squared_error: 4.3967\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature   label          predicted\n",
      "  value   value          value\n",
      "          in $(K)        in $(K)\n",
      "--------------------------------------\n",
      "    3     13              13\n",
      "    3     16              17\n",
      "    3     16              10\n",
      "    2     14              17\n",
      "    3     17              13\n",
      "    3     15              17\n",
      "    3     18              18\n",
      "    3     19              20\n",
      "    3     24              19\n",
      "    3     18              11\n",
      "    2     16              11\n",
      "    2     17              15\n",
      "    3     21              16\n",
      "    3     21              19\n",
      "    3     25              22\n",
      "    3     31              24\n",
      "    3     41              25\n",
      "    4     37              -0\n",
      "    1      5               5\n",
      "    2      6               5\n",
      "    2      7               5\n",
      "    2      6               5\n",
      "    2      6               8\n",
      "    2      8               6\n",
      "    2      6               6\n"
     ]
    }
   ],
   "source": [
    "#@title Double-click to view a possible solution to Task 4.\n",
    "\n",
    "# Using median_income\n",
    "#tried `enginesize`  and `horsepower` initially as corr are 0.9 and 0.8 resp\n",
    "#my_feature = \"enginesize\"\n",
    "\n",
    "\n",
    "my_feature = \"curbweight\" \n",
    "my_label=\"price\" \n",
    "\n",
    "# Tune the hyperparameters.\n",
    "learning_rate = 0.04\n",
    "epochs = 500\n",
    "batch_size = 25\n",
    "\n",
    "# Don't change anything below this line.\n",
    "my_model = build_model(learning_rate)\n",
    "weight, bias, epochs, mae = train_model(my_model, training_df,\n",
    "                                        my_feature, my_label,\n",
    "                                        epochs, batch_size)\n",
    "\n",
    "plot_the_loss_curve(epochs, mae)\n",
    "predict_house_values(25, my_feature, my_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Linear Regression with a Real Dataset.ipynb",
   "private_outputs": true,
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}