{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhGuhbZ6M5tl"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "id": "AwOEIRJC6Une"
      },
      "outputs": [],
      "source": [
        "#@title 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": "code",
      "execution_count": 2,
      "metadata": {
        "cellView": "form",
        "id": "KyPEtTqk6VdG"
      },
      "outputs": [],
      "source": [
        "#@title MIT License\n",
        "#\n",
        "# Copyright (c) 2017 François Chollet\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIdT9iu_Z4Rb"
      },
      "source": [
        "# Basic regression: Predict fuel efficiency"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBIlTPscrIT9"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/regression\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/keras/regression.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHp3M9ZmrIxj"
      },
      "source": [
        "In a *regression* problem, the aim is to predict the output of a continuous value, like a price or a probability. Contrast this with a *classification* problem, where the aim is to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture).\n",
        "\n",
        "This tutorial uses the classic [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) dataset and demonstrates how to build models to predict the fuel efficiency of the late-1970s and early 1980s automobiles. To do this, you will provide the models with a description of many automobiles from that time period. This description includes attributes like cylinders, displacement, horsepower, and weight.\n",
        "\n",
        "This example uses the Keras API. (Visit the Keras [tutorials](https://www.tensorflow.org/tutorials/keras) and [guides](https://www.tensorflow.org/guide/keras) to learn more.)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "moB4tpEHxKB3"
      },
      "outputs": [],
      "source": [
        "# Use seaborn for pairplot.\n",
        "!pip install -q seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "1rRo8oNqZ-Rj"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "# Make NumPy printouts easier to read.\n",
        "np.set_printoptions(precision=3, suppress=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "9xQKvCJ85kCQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "721c9ab1-ae83-4f24-dc18-97b9ac46d660"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.13.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_72b0LCNbjx"
      },
      "source": [
        "## The Auto MPG dataset\n",
        "\n",
        "The dataset is available from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFh9ne3FZ-On"
      },
      "source": [
        "### Get the data\n",
        "First download and import the dataset using pandas:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "CiX2FI4gZtTt"
      },
      "outputs": [],
      "source": [
        "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n",
        "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
        "                'Acceleration', 'Model Year', 'Origin']\n",
        "\n",
        "raw_dataset = pd.read_csv(url, names=column_names,\n",
        "                          na_values='?', comment='\\t',\n",
        "                          sep=' ', skipinitialspace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "2oY3pMPagJrO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "7484a455-28d2-4e23-86f3-dd269d148222"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
              "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
              "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
              "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
              "\n",
              "     Model Year  Origin  \n",
              "393          82       1  \n",
              "394          82       2  \n",
              "395          82       1  \n",
              "396          82       1  \n",
              "397          82       1  "
            ],
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              "        const element = document.querySelector('#df-552d4ef1-6876-470a-a9d6-07c6e61bbb88');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset = raw_dataset.copy()\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MWuJTKEDM-f"
      },
      "source": [
        "### Clean the data\n",
        "\n",
        "The dataset contains a few unknown values:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "JEJHhN65a2VV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88dd4c42-f66f-48ac-ac0a-e3d361096a96"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MPG             0\n",
              "Cylinders       0\n",
              "Displacement    0\n",
              "Horsepower      6\n",
              "Weight          0\n",
              "Acceleration    0\n",
              "Model Year      0\n",
              "Origin          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UPN0KBHa_WI"
      },
      "source": [
        "Drop those rows to keep this initial tutorial simple:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "4ZUDosChC1UN"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XKitwaH4v8h"
      },
      "source": [
        "The `\"Origin\"` column is categorical, not numeric. So the next step is to one-hot encode the values in the column with [pd.get_dummies](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html).\n",
        "\n",
        "Note: You can set up the `tf.keras.Model` to do this kind of transformation for you but that's beyond the scope of this tutorial. Check out the [Classify structured data using Keras preprocessing layers](../structured_data/preprocessing_layers.ipynb) or [Load CSV data](../load_data/csv.ipynb) tutorials for examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "gWNTD2QjBWFJ"
      },
      "outputs": [],
      "source": [
        "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ulXz4J7PAUzk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "c1d00934-dd1a-4c95-d972-de11cadd5011"
      },
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
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              "\n",
              "     Model Year  Europe  Japan  USA  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cuym4yvk76vU"
      },
      "source": [
        "### Split the data into training and test sets\n",
        "\n",
        "Now, split the dataset into a training set and a test set. You will use the test set in the final evaluation of your models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "qn-IGhUE7_1H"
      },
      "outputs": [],
      "source": [
        "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
        "test_dataset = dataset.drop(train_dataset.index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4ubs136WLNp"
      },
      "source": [
        "### Inspect the data\n",
        "\n",
        "Review the joint distribution of a few pairs of columns from the training set.\n",
        "\n",
        "The top row suggests that the fuel efficiency (MPG) is a function of all the other parameters. The other rows indicate they are functions of each other."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "oRKO_x8gWKv-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "a61c8c58-f10f-419a-b876-a2306d32e93d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7c72111b0250>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gavKO_6DWRMP"
      },
      "source": [
        "Let's also check the overall statistics. Note how each feature covers a very different range:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "yi2FzC3T21jR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "outputId": "c20877d7-2710-450d-e332-8400cb1f1153"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              count         mean         std     min      25%     50%  \\\n",
              "MPG           314.0    23.310510    7.728652    10.0    17.00    22.0   \n",
              "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
              "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
              "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
              "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
              "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
              "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
              "Europe        314.0     0.178344    0.383413     0.0     0.00     0.0   \n",
              "Japan         314.0     0.197452    0.398712     0.0     0.00     0.0   \n",
              "USA           314.0     0.624204    0.485101     0.0     0.00     1.0   \n",
              "\n",
              "                  75%     max  \n",
              "MPG             28.95    46.6  \n",
              "Cylinders        8.00     8.0  \n",
              "Displacement   265.75   455.0  \n",
              "Horsepower     128.00   225.0  \n",
              "Weight        3608.00  5140.0  \n",
              "Acceleration    17.20    24.8  \n",
              "Model Year      79.00    82.0  \n",
              "Europe           0.00     1.0  \n",
              "Japan            0.00     1.0  \n",
              "USA              1.00     1.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-596ac6b6-a54a-437b-a3b4-3a4888a4508c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>314.0</td>\n",
              "      <td>23.310510</td>\n",
              "      <td>7.728652</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.00</td>\n",
              "      <td>22.0</td>\n",
              "      <td>28.95</td>\n",
              "      <td>46.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>314.0</td>\n",
              "      <td>5.477707</td>\n",
              "      <td>1.699788</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>314.0</td>\n",
              "      <td>195.318471</td>\n",
              "      <td>104.331589</td>\n",
              "      <td>68.0</td>\n",
              "      <td>105.50</td>\n",
              "      <td>151.0</td>\n",
              "      <td>265.75</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>314.0</td>\n",
              "      <td>104.869427</td>\n",
              "      <td>38.096214</td>\n",
              "      <td>46.0</td>\n",
              "      <td>76.25</td>\n",
              "      <td>94.5</td>\n",
              "      <td>128.00</td>\n",
              "      <td>225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>314.0</td>\n",
              "      <td>2990.251592</td>\n",
              "      <td>843.898596</td>\n",
              "      <td>1649.0</td>\n",
              "      <td>2256.50</td>\n",
              "      <td>2822.5</td>\n",
              "      <td>3608.00</td>\n",
              "      <td>5140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>314.0</td>\n",
              "      <td>15.559236</td>\n",
              "      <td>2.789230</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.80</td>\n",
              "      <td>15.5</td>\n",
              "      <td>17.20</td>\n",
              "      <td>24.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model Year</th>\n",
              "      <td>314.0</td>\n",
              "      <td>75.898089</td>\n",
              "      <td>3.675642</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.178344</td>\n",
              "      <td>0.383413</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.197452</td>\n",
              "      <td>0.398712</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>314.0</td>\n",
              "      <td>0.624204</td>\n",
              "      <td>0.485101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-596ac6b6-a54a-437b-a3b4-3a4888a4508c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-596ac6b6-a54a-437b-a3b4-3a4888a4508c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4fa9d717-121c-47e2-baba-294856821a3f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4fa9d717-121c-47e2-baba-294856821a3f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4fa9d717-121c-47e2-baba-294856821a3f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "train_dataset.describe().transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Db7Auq1yXUvh"
      },
      "source": [
        "### Split features from labels\n",
        "\n",
        "Separate the target value—the \"label\"—from the features. This label is the value that you will train the model to predict."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "t2sluJdCW7jN"
      },
      "outputs": [],
      "source": [
        "train_features = train_dataset.copy()\n",
        "test_features = test_dataset.copy()\n",
        "\n",
        "train_labels = train_features.pop('MPG')\n",
        "test_labels = test_features.pop('MPG')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRklxK5s388r"
      },
      "source": [
        "## Normalization\n",
        "\n",
        "In the table of statistics it's easy to see how different the ranges of each feature are:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IcmY6lKKbkw8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "bfcfda98-9f62-4f08-8212-b72a8bffd1b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     mean         std\n",
              "MPG             23.310510    7.728652\n",
              "Cylinders        5.477707    1.699788\n",
              "Displacement   195.318471  104.331589\n",
              "Horsepower     104.869427   38.096214\n",
              "Weight        2990.251592  843.898596\n",
              "Acceleration    15.559236    2.789230\n",
              "Model Year      75.898089    3.675642\n",
              "Europe           0.178344    0.383413\n",
              "Japan            0.197452    0.398712\n",
              "USA              0.624204    0.485101"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d952a819-ebc4-4da7-9005-3f5c2c410de6\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>23.310510</td>\n",
              "      <td>7.728652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>5.477707</td>\n",
              "      <td>1.699788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>195.318471</td>\n",
              "      <td>104.331589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>104.869427</td>\n",
              "      <td>38.096214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>2990.251592</td>\n",
              "      <td>843.898596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>15.559236</td>\n",
              "      <td>2.789230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model Year</th>\n",
              "      <td>75.898089</td>\n",
              "      <td>3.675642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Europe</th>\n",
              "      <td>0.178344</td>\n",
              "      <td>0.383413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Japan</th>\n",
              "      <td>0.197452</td>\n",
              "      <td>0.398712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>USA</th>\n",
              "      <td>0.624204</td>\n",
              "      <td>0.485101</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d952a819-ebc4-4da7-9005-3f5c2c410de6');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "train_dataset.describe().transpose()[['mean', 'std']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ywmerQ6dSox"
      },
      "source": [
        "It is good practice to normalize features that use different scales and ranges.\n",
        "\n",
        "One reason this is important is because the features are multiplied by the model weights. So, the scale of the outputs and the scale of the gradients are affected by the scale of the inputs.\n",
        "\n",
        "Although a model *might* converge without feature normalization, normalization makes training much more stable.\n",
        "\n",
        "Note: There is no advantage to normalizing the one-hot features—it is done here for simplicity. For more details on how to use the preprocessing layers, refer to the [Working with preprocessing layers](https://www.tensorflow.org/guide/keras/preprocessing_layers) guide and the [Classify structured data using Keras preprocessing layers](../structured_data/preprocessing_layers.ipynb) tutorial."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFJ6ISropeoo"
      },
      "source": [
        "### The Normalization layer\n",
        "\n",
        "The `tf.keras.layers.Normalization` is a clean and simple way to add feature normalization into your model.\n",
        "\n",
        "The first step is to create the layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "JlC5ooJrgjQF"
      },
      "outputs": [],
      "source": [
        "normalizer = tf.keras.layers.Normalization(axis=-1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "SQ5FTOsNbPrB"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYA2Ap6nVOha"
      },
      "source": [
        "Then, fit the state of the preprocessing layer to the data by calling `Normalization.adapt`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "CrBbbjbwV91f"
      },
      "outputs": [],
      "source": [
        "normalizer.adapt(np.array(train_features))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZccMR5yV9YV"
      },
      "source": [
        "Calculate the mean and variance, and store them in the layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "GGn-ukwxSPtx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20928659-3068-4bb3-c42e-338b65f1d2bb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[   5.478  195.318  104.869 2990.252   15.559   75.898    0.178    0.197\n",
            "     0.624]]\n"
          ]
        }
      ],
      "source": [
        "print(normalizer.mean.numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGWKaF9GSRuN"
      },
      "source": [
        "When the layer is called, it returns the input data, with each feature independently normalized:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "2l7zFL_XWIRu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c70f38b-49cc-458f-aec7-c26220d63d3e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First example: [[   4.    90.    75.  2125.    14.5   74.     0.     0.     1. ]]\n",
            "\n",
            "Normalized: [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5   0.78]]\n"
          ]
        }
      ],
      "source": [
        "first = np.array(train_features[:1])\n",
        "\n",
        "with np.printoptions(precision=2, suppress=True):\n",
        "  print('First example:', first)\n",
        "  print()\n",
        "  print('Normalized:', normalizer(first).numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6o3CrycBXA2s"
      },
      "source": [
        "## Linear regression\n",
        "\n",
        "Before building a deep neural network model, start with linear regression using one and several variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFby9n0tnHkw"
      },
      "source": [
        "### Linear regression with one variable\n",
        "\n",
        "Begin with a single-variable linear regression to predict `'MPG'` from `'Horsepower'`.\n",
        "\n",
        "Training a model with `tf.keras` typically starts by defining the model architecture. Use a `tf.keras.Sequential` model, which [represents a sequence of steps](https://www.tensorflow.org/guide/keras/sequential_model).\n",
        "\n",
        "There are two steps in your single-variable linear regression model:\n",
        "\n",
        "- Normalize the `'Horsepower'` input features using the `tf.keras.layers.Normalization` preprocessing layer.\n",
        "- Apply a linear transformation ($y = mx+b$) to produce 1 output using a linear layer (`tf.keras.layers.Dense`).\n",
        "\n",
        "The number of _inputs_ can either be set by the `input_shape` argument, or automatically when the model is run for the first time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp3gAFn3TPv8"
      },
      "source": [
        "First, create a NumPy array made of the `'Horsepower'` features. Then, instantiate the `tf.keras.layers.Normalization` and fit its state to the `horsepower` data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "1gJAy0fKs1TS"
      },
      "outputs": [],
      "source": [
        "horsepower = np.array(train_features['Horsepower'])\n",
        "\n",
        "horsepower_normalizer = layers.Normalization(input_shape=[1,], axis=None)\n",
        "horsepower_normalizer.adapt(horsepower)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NVlHJY2TWlC"
      },
      "source": [
        "Build the Keras Sequential model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "c0sXM7qLlKfZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d6563e5-99d7-4cb4-9fa5-9cb0564c2ec1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization_1 (Normaliza  (None, 1)                 3         \n",
            " tion)                                                           \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5 (24.00 Byte)\n",
            "Trainable params: 2 (8.00 Byte)\n",
            "Non-trainable params: 3 (16.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "horsepower_model = tf.keras.Sequential([\n",
        "    horsepower_normalizer,\n",
        "    layers.Dense(units=1)\n",
        "])\n",
        "\n",
        "horsepower_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eObQu9fDnXGL"
      },
      "source": [
        "This model will predict `'MPG'` from `'Horsepower'`.\n",
        "\n",
        "Run the untrained model on the first 10 'Horsepower' values. The output won't be good, but notice that it has the expected shape of `(10, 1)`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "UfV1HS6bns-s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31ceaf86-f206-43a4-d27f-12d9dedd1e42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 124ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.842],\n",
              "       [ 0.476],\n",
              "       [-1.555],\n",
              "       [ 1.181],\n",
              "       [ 1.068],\n",
              "       [ 0.419],\n",
              "       [ 1.265],\n",
              "       [ 1.068],\n",
              "       [ 0.278],\n",
              "       [ 0.476]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "horsepower_model.predict(horsepower[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSkanJlmmFBX"
      },
      "source": [
        "Once the model is built, configure the training procedure using the Keras `Model.compile` method. The most important arguments to compile are the `loss` and the `optimizer`, since these define what will be optimized (`mean_absolute_error`) and how (using the `tf.keras.optimizers.Adam`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "JxA_3lpOm-SK"
      },
      "outputs": [],
      "source": [
        "horsepower_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "    loss='mean_absolute_error')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3q1I9TwnRSC"
      },
      "source": [
        "Use Keras `Model.fit` to execute the training for 100 epochs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-iSrNy59nRAp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eb7fc6b-8857-48d9-9096-e49e5a6aed1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6.36 s, sys: 211 ms, total: 6.57 s\n",
            "Wall time: 10.1 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = horsepower_model.fit(\n",
        "    train_features['Horsepower'],\n",
        "    train_labels,\n",
        "    epochs=100,\n",
        "    # Suppress logging.\n",
        "    verbose=0,\n",
        "    # Calculate validation results on 20% of the training data.\n",
        "    validation_split = 0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQm3pc0FYPQB"
      },
      "source": [
        "Visualize the model's training progress using the stats stored in the `history` object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "YCAwD_y4AdC3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c1916561-bb4f-4279-a121-6f25aa33c7ba"
      },
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        {
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              "        loss  val_loss  epoch\n",
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              "97  3.803072  4.190271     97\n",
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              "\n",
              "\n",
              "<div id=\"df-ceb1e2d8-1b6a-4b65-a702-80f02669ff74\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ceb1e2d8-1b6a-4b65-a702-80f02669ff74')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ceb1e2d8-1b6a-4b65-a702-80f02669ff74 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "9E54UoZunqhc"
      },
      "outputs": [],
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.ylim([0, 10])\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error [MPG]')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "yYsQYrIZyqjz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "264b1833-de4b-4b4d-9e65-3cdbe4d079d0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMNrt8X2ebXd"
      },
      "source": [
        "Collect the results on the test set for later:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "kDZ8EvNYrDtx"
      },
      "outputs": [],
      "source": [
        "test_results = {}\n",
        "\n",
        "test_results['horsepower_model'] = horsepower_model.evaluate(\n",
        "    test_features['Horsepower'],\n",
        "    test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0qutYAKwoda"
      },
      "source": [
        "Since this is a single variable regression, it's easy to view the model's predictions as a function of the input:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "xDS2JEtOn9Jn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "946c43a8-af70-4b82-9211-8d10873320c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 3ms/step\n"
          ]
        }
      ],
      "source": [
        "x = tf.linspace(0.0, 250, 251)\n",
        "y = horsepower_model.predict(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "rttFCTU8czsI"
      },
      "outputs": [],
      "source": [
        "def plot_horsepower(x, y):\n",
        "  plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n",
        "  plt.plot(x, y, color='k', label='Predictions')\n",
        "  plt.xlabel('Horsepower')\n",
        "  plt.ylabel('MPG')\n",
        "  plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "7l9ZiAOEUNBL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "cdce738b-1e5e-453a-89ec-505cbf10b23f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5a3scUUUhEong5eWF2NhYPHr0yNTDISNxdXWFp6dnmc7BQIYqPPmeluKLPfI9Lbq0SNC2k7e2xxFVJBKJBPXq1ePyUgVhY2NTppkYOQYyVKFp2tMiwss9Ld39PbVaZtK2k3drX/eyDZyonBKLxazsSzrhIiRVaLrsadGGvJM38L/O3XLs5E1EpH8MZKhCM8SeFnbyJiIyHi4tUYVmqD0t7ORNRGQcZjMjs3jxYohEIkydOlXxWHZ2NkJDQ1GpUiU4Ojpi4MCBSEhIMN0gqdyR72lRFV6I8DJ7qTR7WqzEIgTVqYT+zaohqE4lBjFERAZgFoHMxYsX8e2336JJkyZFHp82bRr27t2LHTt24NSpU3j69CkGDBhgolFSecQ9LUREls3kgUxGRgbefvttrF+/Hm5uborHU1NTsXHjRnz11Vfo2rUrWrRogc2bN+PcuXM4f/68CUdM5Q33tBARWS6T75EJDQ1F3759ERwcjM8//1zxeFRUFPLy8hAcHKx4rGHDhqhZsyYiIiLQtm1bpefLyclBTk6O4u9paWkGGff9+/exdOlShIWFFQnAyDJpu6eFHa2JiMyLSQOZn3/+GZcvX8bFixdLPBcfHw+JRAJXV9cij3t4eCA+Pl7lOcPCwrBgwQJ9D7WECRMm4MiRI/j999/xzTffYNCgQRCJ+IVmyeR7WlRhR2siIvNjsqWlJ0+eYMqUKdi2bZteix/NmjULqampij9PnjzR27kLmzdvHvz8/JCQkIDBgwfjjTfewD///GOQ9yLTY0drIiLzZLJAJioqComJiQgMDIS1tTWsra1x6tQprFy5EtbW1vDw8EBubi5SUlKKvC4hIUFtXwapVApnZ+cifwyhXbt2uHLlCubOnQsbGxvs3r0b/v7+WLduHWQymUHekwyjQCYg4n4Sdl/9BxH3k0p0pmZHayIi8yUSBMEkv33T09NLNAYbPXo0GjZsiI8//hg1atRAlSpVEB4ejoEDBwIA7ty5g4YNG6rdI1NcWloaXFxckJqaarCg5ubNmxg7diwiIyMBAB06dMD69evRoEEDg7wf6Y82y0UR95Pw1nrNG8zDQ9qyozURkZ5o+/1tshkZJycnBAQEFPnj4OCASpUqISAgAC4uLhgzZgymT5+OEydOICoqCqNHj0ZQUJDWQYyxBAQE4OzZs1i5ciUcHBxw+vRpNGnSBIsWLWLzMzOm7XIRO1oTEZkvk6dfq7N8+XL069cPAwcORMeOHeHp6YmdO3eaelhKWVlZYfLkybh16xZ69+6N3NxczJkzBy1btlS6mZlMS5flIna0JiIyXyZbWjIWYywtFScIAsLDwzFlyhQ8e/YMYrEYU6ZMwcKFC+Hg4GCUMZB6uiwXtfZ1R/slxzV2tD7zcVemYhMR6YnZLy2VZyKRCMOGDUNMTAyGDx8OmUyG5cuXIyAgAH/88Yeph0fQbbmI1X+JiMwXAxkDqly5MrZu3YqDBw+iVq1aePjwIXr27ImRI0ciKSnJ1MOrUIpnJlV2lGr1OvlyEav/EhGZJy4tGUlGRgY+/fRTrFixAoIgoEqVKlixYgWGDh3KQnoGpiwzydNZiux8GVIy81S+zsnWCq83qw6fSvZ4J8gHEmuxUSv7soowEVVk2n5/M5AxssjISIwdOxY3b94EAPTt2xdr1qxBzZo1TTyy8kmemVTWH3KxCAjp4ItZffz1Mi5NWEWYiCo67pExU23atEFUVBQWLlwIiUSC/fv3o1GjRli1ahUL6emZuswkXckE4Ns/YxF2IFoPZ1OPVYSJiLTHQMYEJBIJ5syZg2vXrqF9+/bIyMjA5MmT0b59e0RHG/6LsqK4EJtcIhgoq/WnY5Gbb7iAk1WEiYh0w0DGhBo2bIhTp05hzZo1cHJyQkREBJo1a4YFCxYU6eBNpWOIAnUyAdga8VDv55XTFHwJAOJSs3EhNtlgYyAisiQMZExMLBZjwoQJiI6Oxquvvoq8vDzMnz8fgYGBiIiIMPXwLJqhCtRFxpbsx6QvrCJMRKQbBjJmonr16ti9eze2b9+OqlWrIjo6Gu3atcP777+P9PR0Uw/PIrX2dYeXi22J2i9l9Ud0ItovOW6QvSqsIkxEpBsGMmZEJBJh8ODBiImJwejRoyEIAr755hs0atQIBw4cMPXwLE7hQnb6ZqiNt5qCLxFeZi+19nUv8ZymLt5EROURAxkz5O7ujk2bNuHIkSOoXbs2njx5gr59+2LYsGH4999/TT08iyIvZOfuYKPX8xpq421pqwgfuhmH9kuO46315zHl56t4a/15g80aERGZEwYyZiw4OBg3btzAjBkzIBaLER4eDj8/P2zduhXlvPyPXvUK8ML5WcFwd5Do9byG2niraxVhpmsTUUXGgngWIioqCmPGjMG1a9cAAD169MC3334LHx8f0w7Mgsi/8AHopbaM3IqhzdC/WTU9nvElbSr7FsgEtF9yXGWmExtaEpGlYkG8cqZFixa4ePEiwsLCIJVK8ccff6BRo0b4+uuvUVBQYOrhWQRVMx1lZaiNt1ZiEYLqVEL/ZtUQVKeS0kCE6dpEVNExkLEgNjY2mDlzJm7cuIFOnTohMzMT06ZNQ1BQEK5fv27q4VmEXgFeOPNxV4SHtMWKoc3wRjPvMp1P1cZbY2G6NhFVdAxkLFC9evVw/PhxrF+/Hi4uLrh48SJatGiBOXPmIDubX1jKFM7ouRCbjNa+7ujfrBoGtaxRpvMq23hrTEzXJqKKztrUA6DSEYvFGDt2LPr06YPJkydj586dWLRoEX799VesX78eHTp0MPUQTarw/pKHz14g/MJjxKf9r1qyvAFjd39PuNrbqO2CrYwIwJRu9dDd31Pte5ema7W61xd/rkUtN3i52CI+NVvpvh/5HhlTzhoRERkSN/uWEzt37kRoaCji4+MBAOPHj8fixYvh4uJi4pEZn7LO0cXJw4q1wwMBAOP/2wSsq+IdqcvatVrd6wEofe61pl747s9YAEU3MRe+RnbMJiJLo+33NwOZciQlJQUfffQR1q9fDwCoVq0a1qxZg9dee83EIzMeeWaSNj/UhTN6jkTH4+PfbiA1S/eZGeB/AZGy91YWUCibdTkSHa/y9aquR37u9zr6Ys+1uFIHUERE5oaBzH8qUiAjd+LECbz33nu4d+8eAODNN9/EypUr4elZchmkPNGUiqxKeEhbBNWphLN3n+HtjZE6v688IBIEocjylbJj5EFT8ZkVT2cpsvNlOi9xFT73qQ+7IOrR81IvaRERmROmX1dgXbp0wfXr1zFz5kxYWVlhx44d8PPzw6ZNm8p1IT1NqciqyDN62tapVKreTPIUZ1VBTOFjVh2/q7x4XVpOqYKYwueOevRcY7o2EVF5w0CmnLKzs0NYWBguXbqEwMBApKSkYMyYMQgODsb9+/dNPTyDKG2KsTyjR117AH3ZfPahXovxFcYUayKqiBjIlHPNmjVDZGQkli5dCjs7Oxw/fhyNGzfG0qVLkZ+fb+rh6ZWuKcbKGjAaqmieXIqOe3B0wRRrIqqIuEemArl//z7GjRuHY8eOAQACAwOxYcMGNG/e3MQj0w/5HhlVqcjKrFOR0VN4M25lRyk++OUqEtJy1KY4C4Kg9hiXUqR5a4NtCIioPOIeGSqhTp06OHLkCDZt2gQ3NzdcvnwZrVq1wsyZM5GVlWXq4ZWZPpeGCrcHaFe3Mua/1kjpeQt3pNZ0zOhXfEs1Fk3XIsD0hfmIiEyFgUwFIxKJMHr0aERHR2Pw4MEoKCjAkiVL0KRJE5w4ccLUwyszXZeGZu+6gV1X/kHE/SQUyFTP42jTkVrTMZO61lW7mVgEwNXeBp7OJV8/rmPpgiAiovKOS0sV3J49ezBx4kT8888/AICxY8di6dKlcHV1Ne3AyqhAJmDL2Vgs3B+j9Wu0qbuSmy/D1oiHeJSciVru9ngnyAcS66L/HlBXmVdVB+7CtWa6+3uWqN7baekJdrgmogqFdWT+w0BGs9TUVMyaNQtr164FAHh6emL16tUYMGCAiUdWNruv/oMpP1/V+nhNlXDLWrW3tOeJuJ+Et9af13heeT0cIqLygHtkSGsuLi5Ys2YNTp8+jQYNGiA+Ph4DBw7EgAED8PTpU1MPr9R0zeKRR/QL9kaXWGaSz6SUqP+Smo0JP17GoZtxas9duGmli50Epz7soujAHR7SFmc+7qoyGGKHayIi1RjIkEL79u1x9epVzJkzB9bW1ti1axf8/f2xfv16yGQyUw9PZ6193XUucCcvLnchNlnxWIFMwIK90UqzkdQFP3KHbsah/ZLjeGv9eUz5+SreWn8enZaeQGpWrlbF69jhmohINQYyVIStrS0WLlyIy5cvo3Xr1khNTcV7772Hrl274q+//jL18HRSliymwrMbmioGKwt+5Mo6kwNoDsiU1cMhIqooGMiQUo0bN8a5c+fw9ddfw97eHqdOnUKTJk0QFhaGvDzDFXXTt9IWuKvsKFUsBZ2990yr1xRf2inrTI6cuoCscPo3N/oSUUXEzb6k0cOHDzF+/HgcPnwYANC0aVNs2LABLVu2NPHItCfPJIpPzcLC/TF4/iJXZeE6V3sbSK3FansnKbNtbBu0q1tZ8V5n7/2LVSc0t4PQdpOuvjYbExFZAmYt/YeBjH4IgoBt27Zh6tSpSEpKglgsxrRp07BgwQI4ODiYeng6UZcCXZb/GbaNaYP0nLwSwYYmK4Y2Q/9m1dSmbcsVP6ZFLTd2vCaicomBzH8YyOjXv//+i6lTp+Knn34CAPj6+uK7775DcHCwiUemG1WzG1l5BaVuIzCmnQ82laIpZHhIW6Rm5eo828IZGiIqzxjI/IeBjGEcOHAA48ePx5MnTwAAo0aNwrJly+DubjkbTovPbshkAt7eGFnq87k72CD5hfZBkLyQ3ad9/RH60+USAZC6ujbyWSVdXkNEZElYR4YMqk+fPrh16xbef/99iEQibNmyBX5+fti+fTssJTYu3E8pqE4lPHuh256YwsQi6BzEAMCnff2wcL9uG4L1tYmYiKg8YCBDpebk5IQVK1bg7Nmz8Pf3R2JiIoYOHYr+/fvj77//Ntj75ubLsPH0A8zdfRMbTz9Abr5+atyUpQ6LrjGDvP+Sm4NU59TusqSDExGVN9amHgBZvqCgIFy5cgWLFy/G559/jr179+LkyZNYvHgxxo8fD7FYf/Fy2IForD8dWyRwWHQgBiEdfDGrj3+Zzi2v1xKfml2mTb/qTOpSF+3qVlZsyt199R+tXlc4tdsQlX612WhMRGSOOCNDeiGRSDB37lxcvXoVr7zyCtLT0xEaGoqOHTsiJkb7xo3qhB2Ixrd/xpaY/ZAJwLd/xiLsQHSZzl+WAnrAyz0ymorWTetev0gl39JU7dV3pV9llYfbLzmuVbE+S1O4VYSmjudEZBkYyJBe+fv74/Tp01i1ahUcHR1x9uxZNGvWDAsXLkRubm6pz5ubL8P607Fqj1l/OrbMy0yqCuipm5yQBymf9w9Q/L3484DyonWlqdqrz0q/+qg8bCkqUsBGVJEwkCG9E4vFCA0NRXR0NPr27Yvc3FzMnTsXgYGBOH9ecxdnZbZGPNS4D0UmvDyurHoFeOHMx12LNHVc9VYgRFAfpPRp4o21wwPh4SwtcoyHs1RlFlFpqvbqq9JvRdo0XJECNqKKhoEMGUyNGjWwd+9ehIeHo0qVKrh16xZeeeUVTJ06FRkZGTqd61Fypl6P01XPAE+lMzXyTbtFgxRV4YVyqmaBlJ+79K8prqJsGq5IARtRRcTNvmRQIpEIQ4cORffu3TF9+nT88MMPWLFiBXbt2oVvv/0WvXr10uo8tdzt9XqcOuoKzZ36sAu2RjzEo+RM1HK3xztBPpBYixWvG/9fxeDC4tOyMf7Hy1inJsDoFeCF7v6eOm24Lc1rCjPEpmFzpEvApk2rCCIyLwxkyCgqVaqE77//Hm+//TbGjRuHhw8fonfv3nj77bfx9ddfo3Llympf/06QDxYdiFG7vCQWvTxOFU2ZOQUyAauO38Xyo3dLvDY+9WUw4mpvU6Ty74YzsZj3qj+6+3ti5s4baq9h1s4b6O7vqTLQkNe1kY9z3/WnGoMT+Wu0Ufz6KztINb8IZUtLNwcVJWAjqqgYyJBR9ejRAzdv3sTcuXPx9ddfY9u2bTh8+DC+/vprDBs2DCKR8i9sibUYIR188e2fqjf8hnTwVcyOFKepnP+hm3GYvyca8WnKv8zk8VPx9gXyPRZTutXT2NrgeWYezj9IQru6qoM2Q7UdUHZeT2dbuNrbIDUzT2UDTU8tNw2bM31neRGReeEeGTI6BwcHLFu2DBEREWjcuDGePXuG4cOHo0+fPnj06JHK183q449xHX1LZBCJRcC4jqrryGja6Bl2IBrjf7ysMohRRx4AbDijPqNKLuJ+ksrnDLUhVdV5E9KykfJfEFOWTcPmTp9ZXkRkfhjIkMm0bt0aUVFRWLRoEaRSKQ4dOoRGjRph5cqVKCgoUPqaWX38cXthb3za1w8jgmrh075+uL2wt8ogRpuNnt9pSOvWRACQkZOvw9ElGWpDqqbzigC42duUyLTSZdOwudNXlhcRmSc2jSSzcOfOHYSEhOD06dMAgDZt2mDDhg0ICAgo03kj7ifhrfWlS/k2hG1j2qBdvZJLS9qOMzykrU4bUrU977axbSAWicp1ZV92CyeyLNp+f3OPDJmFBg0a4OTJk1i/fj0++ugjREZGIjAwEDNnzsQnn3wCqVS7janFmdMGTld7G7RVEYQYakOqtsc/y8hB/2bVdDq3pSlrlhcRmScuLZHZEIvFGDduHKKjo9G/f3/k5eVh4cKFaNasGc6ePVuqc5rTBs7FAxqr/NI01IZUbnQtqnjHcwYxRJaPgQyZnWrVqmHXrl3YsWMHPDw8cPv2bbRv3x4TJ07E0asPdeqTo2mjpzkokAmQyQS42tmoPc7TWarzhlRudCWi8o6BDJklkUiEQYMGISYmBmPGjAEArF27Fr06tkLI599p3SdH00ZPEQB7iZX+L0AJZZt15f1/3t4YiZQs9enb2fkyHImO1+k9udGViMo7BjJk1tzc3DBo6kJ4DF0Ea1cvFKQ/w7+/fYZ/dy/BP0/jtEpL1lTO/522NQ15CQrFy/2rSotWJTUzr1Rp2PpoZ0BEZK6YtURmrUAmoP2S44hLzYYsLxupZ8ORdmEXIMggtnWEe9cQ1GnXB2dndtOqSWLhjZ4tarnh4sNkhG67rHE2RF861a+M8Z3qokUtN3RaekLrIEZOXqTuzMdddZ5F0VTZmIjInGj7/c1AhsyasvThnPh7SD70DXIT7gMAbH2a48fNGzCwc6DW51WWimtM7g4SJL/ILfXrdU3DJiKyNNp+f3NpicyasvRhqWddeI74Cq6dR0FkLUH2wyt4u3d7fPXVV8jP11yYTtclHW242dvotKG4LEEMYF5p5UREpsRAhsyaqrRgkdgKLm0GwevdVZDWbIKc7Cx88MEHCAoKwrVr11SeT12lW1192tcPK4Y2Q3hIW1ya0x2rh2k/I1RWFSVdmohIEwYyZNY0pQ9L3LzRbNwyrF+/Aa6urrh06RJatmyJTz75BNnZJWctLsQm62UmxtXOBg29nNGvibeiHkmfJl4Y086nzOdWh+nSRERFmTSQWbt2LZo0aQJnZ2c4OzsjKCgIBw8eVDyfnZ2N0NBQVKpUCY6Ojhg4cCASEhJMOGIyNm3Sh+e/1ghjx45BdHQ0Bg0ahPz8fPzf//0fmjZtilOnThV5jb6WZFKy8vD2hsgSKeDB/p56Ob8yytKlC2QCIu4n6VRbh4ioPDFpIFO9enUsXrwYUVFRuHTpErp27Yr+/fvj1q1bAIBp06Zh79692LFjB06dOoWnT59iwIABphwymYC26cNeXl7YsWMHdu3aBW9vb/z111/o3Lkzxo0bh9TUVADAw2cv9Dq24p2pW/u6w9VefWE7TbxcbDGuoy+8NFyvvAbNW+vPY8rPV7WurUNEVJ6YXdaSu7s7li5dikGDBqFKlSr46aefMGjQIADA7du34efnh4iICLRt21ar8zFrqfzQJX04NTUVH3/8Mb799lsAL4OcsR9/jh/iPAwyNk9nKc7O7AYAaPH5EaRkli6du5KDBBGzukFiLVZ7vfINy8X/55XfDdaHISJLZ3FZSwUFBfj555/x4sULBAUFISoqCnl5eQgODlYc07BhQ9SsWRMREREqz5OTk4O0tLQif6h80KVPjouLC9atW4dTp06hfv36iIuLw8KpY/Dvrv9DfkayyteVVnxaDlYdv4cLscmlDmIAIOlFLqIePQeg+nrVbViWP6asijARUXlk8kDmxo0bcHR0hFQqxfjx47Fr1y74+/sjPj4eEokErq6uRY738PBAfLzqMu1hYWFwcXFR/KlRo4aBr4DMWceOHXHt2jWMnDgdEFsh869ziNswAenX/oC+JyOXH/1L5xYCymjax6Npw7KAklWEiYjKK5MHMg0aNMDVq1cRGRmJCRMmYOTIkYiOji71+WbNmoXU1FTFnydPnuhxtGSJbG1t8UbIdHiNXA6JZz3Icl4g+dBKJPz8CfKeP9Xre+2+WvbzaUqt1nbDMmvNEFFFYG3qAUgkEtStWxcA0KJFC1y8eBErVqzAkCFDkJubi5SUlCKzMgkJCfD0VJ0ZIpVKIZVKDT1sMjB9l9Ov6mQLSdXa8HznS6RH7UXK6a3IeXwdcZsmwaXdMDi3fgMicdmbRya9yIW7gw2ev8grVa0adwcbxKVkYePpB3B3kMDTxa7EtWtbQ4a1ZoioIjB5IFOcTCZDTk4OWrRoARsbGxw7dgwDBw4EANy5cwePHz9GUFCQiUdJhqSsfYCXiy3mvepf6g2s8no08anZcG71OuzqtUXy4dXIfngFKae2IPP2abj3mgypZ90yj/+NZtWw6exDiACdg5nkF3mYvqNoQb/i1174WpSdX96PyVxrzbDnExHpk0mzlmbNmoXevXujZs2aSE9Px08//YQlS5bg8OHD6N69OyZMmIADBw5gy5YtcHZ2xuTJkwEA586d0/o9mLVkWQyZjSM/N/AywBAEAS9uHsfz4+shy84ARGI4t3odLu2HQWxT+tmM8JC2SM3K1WsvJxFQIvW68LUUPg4w36wlQwSpRFQ+WUTWUmJiIkaMGIEGDRqgW7duuHjxoiKIAYDly5ejX79+GDhwIDp27AhPT0/s3LnTlEMmAzJ0Nk7xejQikQiOjbuh2bTNaNKxNyDIkHZhJ+I2TUbWo2twtrVGp/qVMbxNDTjbap68LFx1t1eAF0592AXuDupryrjYWsPdQaLx3AKKXru2tXXMiaoeV8Vr8RAR6cLs6sjoG2dkLIeyTtfKlLXzs6qljd179uK9ceORGP9yw+7o0aOxbNky3E6WaTUuAFhXKIjQ9np0Ufzac/Nl2BrxEI+SM1HL3R7vBPlAYm3yPfwlFMgEtF9yXOUMlXw57MzHXbnMREQALGRGhqgwY2XjqKrP0v+1V3H3TgxCQ0MhEomwefNm+Pn5YdfOX7VK1X63nU+RmRBDZA0VPuehm3HotPQEFu6PwQ8Rj7Bwfww6LT1hljMbTBknIkNhIENmwxyycZydnbFq1SqcPn0afn5+SEhIwNKPxuPfXYuQn/5M7Wu7F+uzZIhxys9pacs0TBknIkNhIENmQ1Ona2N2fm7Xrh2uXLmCuXPnwsbGBll3z+PpholIv3IAgiBTOy55I8f41Cy4O0jUXo+nsxSezqqvuTBXOxvIBAG5+TK1e4kEALN33UBuvkzJEaZhDkEqEZVP3CNDZsUcs3Fu3ryJQW+PxJ3rL8clrd4IlXpNhk2l6iXGpSwrR5nCrwOgNFNLFXcHCZJf5Gp13P+9EWAWG3/le2Q0pYxzjwwRyXGPDFkkc8zGCQgIwK3LFzB+5kKIJbbI+fsWnm6ehNRz21HVwapIEKNsuUeZwtcjv+bi3a5V0SaIkR9nLstMVmIR5r3qDwAlZp/kf5/3qj+DGCLSGWdkyCyZa9G0B7EPMXx0CCJOHQUANG7cGBs2bECLlq3UZuUAL6v2ftqvETydlV+P/Jrj07LxLD0bq07cR2pW6RtQAuY308E6MkSkLW2/vxnIEOlIEASEh4djypQpePbsGcRiMd4c+R7OuQZDLFE/qyJPn9YUqOk7dbusKev6ZK5BKhGZF22/v82uRQGRuROJRBgy9C241WuJJQtm4dT+ndi+eR2sXHahUo+JsKvdQuVrE9OzNc5KFMgEnL2nPkNKV/JsIHMIIuTp70RE+sAZGSIdFQ9Esh5EIeXIGuSmJAAAHBp1gVu3EFjZlfx5mxZcH18f/UtlC4b3Ovpiz7U4vbU2kFPVNoHLOkRkrri09B8GMgTobyZCVS8oITcLz0//iPRLewAIENu7wL3be7D36wiRSAQRAA9nKQAR4tOMVytFvkfm077+CP3JMD2siIgMgYHMfxjIkL42mGoqsw8AOU/vIOngSuQ9ewQAsKvdEpV6ToS1c1VMDa6H5Ufvlv5CdCQPUlYPa46F+2PMpj2AOSxvEZH5Y/o1EfRbAVdTmX0AkHo3gP/E1XDpMBywskbWg0t4ujEUvcVXUcPNuMXe5Cnebg5Ss2kPcOhmHNovOY631p/HlJ+v4q3159F+yXGzSBEnIsvEQIbKLX1309a2fP6815vh4OavsWrHUTRt2Ray3CysDZuDeSGDkPvssXaDLyMRgOMfdEavAC+zaQ9gaW0ViMgyMJChckvfjQq1LZ/v6WyLoDqVEPpGJ1yOPIs1a9bAyckJN69cRPyW95F65icI+WWrD6OJAOCnyJfLW+bQHkDfQSURkRwDGSq39D0TIe8FpcnzFzmK/xaLxZgwYQKio6Px6quvQijIR8rZnxC3ZQpy/onR6n1L61FyJgDz6GHF7tdEZCgMZKjc0vdMhJVYhE/7+ms8buH+mBIzC9WrV8fu3buxfft2uLpXRl7SY8T/+BGSj34LWU6mVu+vq1ru9opxm7o9gLksbxFR+cNAhkwqN1+GjacfYO7um9h4+oFeOzaXZSZC3sF699V/EHE/Cbn5MkTcT8Ktp6ka3zcuNRvz99wsck0FMgHnHyRDWr8dfv4jAiNHjgIgID1qL+I3hyLr/sWyXKrSa3Oxs0HE/SQUyAST9rAqkAl4lp6j+UCw+zUR6Y7p12QyYQeisf50LApPXohFQEgHX8zqo3nmQxul6aatLF1bLAJKu31DJALsbKyQmVugeMzLxRavVX6GDYtn48GDBwAAB79OcAt+D1b2LqV7IxWKVw02ZuqzLt3AzaknFBGZHuvI/IeBjHkKOxCNb/+MVfn8uI76DWa0rSOjquCdvsm/qpcPbIizv6zDV199BZlMBrGdM9y6joVDoy7wdrXDa029sP3i30jRQ/NIYxe90/ZesigfESnDQOY/DGTMT26+DA0/Pah2hkMsAm4v7A2JtX5WP7WZidCm4J0+FZ6FuHrlMsaMGYNr164BAFq374Kfvt+IOrV9cfbuM7y9MVIv7/XloKZ49iLH4LMxutxLtkkgImVYEI/M1taIhxqXaWTCy+P0Rd6osH+zagiqU0npF7g2Be/0qXCmTosWLXDx4kWEhYVBKpXiwpkTaNI4AF9//TVa+biq3eujy3u9vTHSKIXotL2X77StiY96NoCLnYSp10RUKgxkyOjkacH6Ok5fTJUxI39fGxsbzJw5E9evX0enTp2QmZmJadOmoX27VzCivv7f15CF6LS9l1vPP8a0X66xwi8RlRoDGTI6eVqwvo7TF1NlzBR/3/r16+P48eP47rvv4OLigosXL+L9ob3QJvkIUJCrt/c1ZCG60txLVvglotJgIENG906QDzRtzRCLXh5nTJrStdXxcJJovCZlPJ2lkAmCIs1bHlCIxWKEhIQgOjoab7zxBvLz87F9/Qr8s+l9ZD+5WYoRKqfvQnTytPX41Cy4O0h0upes8EtEpWFt6gFQxSOxFiOkg6/arKWQDr562+irLXnhuAk/XoYI0ClzKadAQDe/qjgSnajTe2bny/D2hv9t5C2+8dXb2xvvfbYalyUB+HvfKuQn/42En2bCsVlvuHUeBbHUQaf3U0Ufy2raplqrUziwCqpTqcxjIqLyjzMyZBKz+vhjXEffErMYYpF+U691papwnKbZltTMPJ2DGABIySyaVl18eUVRB8enDbzHroVj054AgIyrB/F0w0Rk3i1bNpNcWZfVVDWELC1W+CUibTH9mkwqN1+GrREP8Sg5E7Xc7fFOkI9BZmJ0LQRX/PgWtdxwMTYZE3+6jNQy1nTRRJ4qferDLui09ESJ4CD70XUkHf4G+c9fBjv2DdrDPXgcrBzdSvV+XmUsRKdNqrWbvTVkgkjrexce0pYzMkQVnLbf31xaIpOSWIsxpkNtg76HLgXx5OTp2oWJxdp/EZeFfHlla8RDpcGBba0m8Bq9CqnnwpEWuROZd84g++EVuHUdA4fG3SES6RaQlLXPkjap1s8z87U+n6EbWBJR+cKlJSrXVC15lCZDxtjLHerSz8U2Urh1GgWvkV9D4lEHspwXSDq4EonbP0Hec+2vaUw7nzIXotP3fZE3uCzc64qbf4lIFc7IULlVIBOwYG+00k27Al4u4SzYG43u/p5azUjoIz3b3cEGI4N8sfzoXxqP1Sb9XOJRG54jvkLaxd1IPbMN2Y+uI27TJLi0HwbnVq9DJLZS+/pgf0+tx66KPtPWpwXXA4ASS1Ws/ktEqnBGhsotTUseuqYelyU9GwAqOUhwflYwJnWtq1VX7neCfODlojlIEImt4NJmALzeXQXbWk0h5Ocg5eRmxP8wHbkJ91W+zklqhWY1XEt1LYVp02Xc01kKT2f1987LxRb1qjrqbQaNiCoGBjJUbmm75KHtcfL0bAAlvpC1CW4WvREAibVYcR5ViyUC/rdvZWirGlqNDQBs3LxQdcjnqNR7CsS2jshNuI+476fh+cktkOXllDg+PacA/nMPIexAtNbvoYw292X+a40w/zXVx4gAfNrXHwv3x6icQQNYY4aISmIgQ+WWtkseuiyNqErP9nSxxbiOvjqNT50rj5+j/ZLjWH70rk6vE4lEcGzSHd5j1sK+YQdAkCEt8lfEbZ6E7EfXSxwvAPj2z1gs2n+rTHtS1N0XeVdrTce4OUj0OoNGRBUD06+p3JKnBcenZiv9V37h7tO6Zu0oS89Wliqt7L2AkntADCXzbiSS/1iDgowkAIBjkx5w7fIurGwd1b6utHtStO0yruyY3Vf/wZSfr2p8jxVDm6F/s2o6jYuILI9Bul/fv38f7777ruLvNWvWhLu7u+JPlSpVcOfOndKPmkiPtFnyKG3qcfFu2lGPnms9m2DMLtv29drAe+waODbvAwDIuP4H4jZMwIs7Z9W+rrR7UrTpMq7qGEPMoBFR+adTIPPNN9/Aw8ND8ffnz59j1qxZWL58OZYvX45WrVph+fLleh8kUWlps+ShD7rsxzF2GrdY6oBKPSbC4+0lsHavjoIXz/Hs9zAk7lqE/PQkpa9RtidF3kfJUCnR2mwaZo0ZIipOp/TrY8eOYePGjUUeGzhwIGrXflnQzMfHB2PHjtXf6Ij0oFeAF7r7e+pU2VdXljCbYFu9EbxHr0Tque1IjfwVWX9F4OnDa3Dr8i4cm/aASFT03zWFZ5FSs3J1LiqoK3W9rso6g0ZE5ZdOMzIPHz6Et7e34u9jx46Fi4uL4u8+Pj74+++/9Tc6IjV0mSHQZsmjLLRJzXZ3sEF8WjZkggBPZ2mp07jLQmQtgWvHd+A1agUkXvUh5GYi+fAqJITPRl7yP0pfczQ63mgp0brMoBl6hoiILINOm31dXFxw5MgRtG7dWunzFy5cQHBwMNLS0vQ2wLLiZt/yqTRtB4wxpgk/XgaguXO2q70NUjLzdO6yrU+CrADpl/ch5c8fIOTlAFY2cG33FpxbD4DI6n+Tte4ONkh+obw1Q1k2TKujadOwOX7+RKRfBtns26hRIxw9elTl84cPH0ZAQIAupyTSmT7bDuiTqtkEZVL/63rtYm9jsPE4SNVX9RWJreDcsj+8x6yBrW8gUJCHlD9/QNz3U5ETdxcivCzipyqIAQyXEq1uBs1cP38iMg2dApnRo0dj0aJF2L9/f4nn9u7di8WLF2P06NF6GxxRcZraDgCmLZrWK8ALZz7uivCQtlg+uCncHSRKj5O3SLC1Fqs8Rhf2EivM6t0QW99tjRVDm2Hru62RmVOg1WutXTxQ9c0FqNTvA4jtnJH370PEb/0Aycc3ondDV63OYawNzOb++ROR8em02TckJATHjx/Hq6++ioYNG6JBgwYAgDt37uDOnTsYOHAgQkJCDDJQIkC3tgPFu1cbi3w2IeJ+EpJf5Ko8TgAQn1ay4m5pZOYWoEl1V8U1bzz9QKclK5FIBMdGXWDnG4jkY98hM/oU0i7uwuYPLiP/lRDY+TRT+3pjbWK2hM+fiIxL58q+4eHh+Omnn1CvXj1FAFOvXj1s27YNv/zyiyHGSKSg77YDhmTsMRR+P3Wds1WZ1KUOVr3bCUf3/IY9e/ehRo0aePrkERK3z0HS/q9RkJVe4jXGTom2pM+fiIyjVN2vhw4diqFDh+p7LEQaWUKas6nGUPj9tOmcXVy7ulX+N4tRpy86d7qFOXPm4JtvvkHGzaPIfHAJ7sHvwb5hB4hEIpOkRFvS509ExqHTjIxMJsOSJUvQrl07tGrVCjNnzkRWVpahxkZUgjkVTdOU/tuilpva/S/adoXWhpu9TZFrfifIR+tzyschE4Qi1+Lk5IQVK1bg7NmzqFmnPmSZKXi25wv8u3Mh8tOeaSwqaIj0aHP6/InIPOg0I7No0SLMnz8fwcHBsLOzw4oVK5CYmIhNmzYZanxERZhL0TRN6b/y51XtkSncFRqA0uvRRfHXHb+dAHuJFV7kat7wKwDIzpfh7Q2RiscKX0tQUBC++eUwJn44D/+c/AlZ9y7g6eMbsOk5FrK+DZWe01Dp0eby+ROR+dCpjky9evUwY8YMjBs3DgBw9OhR9O3bF1lZWRCLzbORNuvIlE+mrCMiT/8t/j+O/KvzvY6++O7PWLVBSfGxKrseea0ZbYWHtEVQnUoqx6cL+bWsHR4IAIrz5T57jOSDK5Hz9DYAQFrNHyvXrMV7r3VUvFbT/Vk97GWn67JUWWYdGaLyT9vvb50CGalUinv37qFGjRqKx2xtbXHv3j1Ur169bCM2EAYy5Zc2nZYN8Z7qOleLAIhEgLpVFHcHG5yfFQyJddHgv/D1VHaQ4oMd1xCfpv2m1RVDm6FfE2+9ddaWF7sTBKFIdpUgyJBx5QCen/oeQm4WRFY2mDd3DmbNnIkCWKH1/x1Fena+2vMWvj2ezlLMf62RQTptE5Hl0vb7W6elpfz8fNjaFt1EZ2Njg7w87f/VSKQv8jRnY9Im/VfTPw2SX+Qh6tHzEmMvfD0R95N0CmKAlxtc9dlZW57KXJxIJIZTYD/Y1W2N5MNrkPXgEubPm4c1m7ZC3GE8pNWULzcVPm9h8Wk5GP/jZazTsYmnKT5/IjI/OgUygiBg1KhRkEqliseys7Mxfvx4ODg4KB7buXOn/kZIZEb0ldar6Ty6vI985qS1rzv2XX9axpFpz9q5KqoMmofMmD+RcXIDEh/dAx59CKcWr8K14zsQS+x0Ot/MnTfQ3d+TsypEpBOdApkRI0ZAJCr6S2b48OF6HRCROdNXWq+m8+j6PvINrsZOOxaJRHDw7wS72s2RfGwDXtw8jvSoPci8G4FKPSfBrnYLrc+VkpmH8/eT0K5eZQOOmIjKG50CmS1bthhoGESWQZ7+G5+arXQzraY9MoVnTzS9j6ezrcblJQ8nCYa1qYWcfBki7iehRS03tePTReE9MglpOSrPZ2cjRhacUbnvdDj4d0bS4dUoSE1A4o55cPDvDLduIbCyd9HqPSMePGMgQ0Q60SmQeffddzUeIxKJsHHjxlIPiMicydN/x//X5bo4AcB7HV5mLcn/LqdLevCR6Hhk56tPnW5RyxX/PM/G8qN3FY95udjitaZeivcvrcJjBdSnh2flyRT/becbCO93VyPlzI9Iv7QHL6JPIiv2Mty6hcDBv3OJGV3V70xEpB2dcqa3bNmCEydOICUlBc+fP1f6JzlZv11wiSxN85puSrtgayogJydPX9aUeh31KKXEjE18aja++zMWYzv4QputJp7OUozr6AsvNWOVd/X2cJaqOEtRYokt3LuOhefwpbCp4gNZVhqS9i1D4o75yE9NVPtabt4lIl3plH4dGhqK8PBw1KpVC6NHj8bw4cPh7m7eFTSZfk36pE36taeLLc583BUAdE4P1nR+bYgAuDtIkKSmYaXctrFt0K5uZeTmy7A14iEeJWeilrs93gnyKZIefuhmHObvuaVzk0uhIB9pF3Yi5Ww4UJAHkY0tXDuOgFNgX4jEVkWOdbO3waU53bnZl4gAGKiODADk5ORg586d2LRpE86dO4e+fftizJgx6NGjhxbTxsbHQIb0KeJ+Et5af17jcfLidIY6v76sGNoMUmuxxirFqpbStJWX9DdsIzfgwY1LAACJVwNU6j0Zkio+imN0Tb8movJN2+9vncvxSqVSvPXWWzhy5Aiio6PRqFEjTJw4ET4+PsjIyCjToInMnaG7Lxu7a/PDZy8w4cfLJWaA4lOzMf7Hy1h+5A4++OVamd/HplJ1fLHpN0z+dAmspPbIjbuDuC1TkXL6R3g4iM02iDFEvygi0q9Sdb+WE4vFEIlEEAQBBQWae7oQWTpDd1/WZ/q0u4MNnr/IU5ld5elii/ALj5U+L39sxbF7ehtPSlY+Vn72EWaEDMPwd9/D6aMHkXruZ6QlX4VT1w0AzCuQYRsEIsug84xMTk4OwsPD0b17d9SvXx83btzAqlWr8PjxYzg6OhpijERmw9Ddl1v7usPV3qbU45NztbPG5/0DFGMqTP73oa1q6rznpSzcHV9uFq5ZozpO/bEfO3bsgIeHB27fvo327dsjNDQUaWlpJV5nilkR+YZrZTNVE368jEM34ww+BiLSjk6BzMSJE+Hl5YXFixejX79+ePLkCXbs2IE+ffqUqmlkWFgYWrVqBScnJ1StWhWvv/467ty5U+SY7OxshIaGolKlSnB0dMTAgQORkJCg83sR6YM8/RpQHSCYQ/fl1Ox8ACK819EXxbeuiUQvG1v6VLY36pg8nf832yQSiTBo0CDExMRgzJgxAIA1a9agUaNG2Ldvn+K4Qzfj0H7Jcby1/jym/HwVb60/j/ZLjhs0kCiQCViwN1rtTNWCvdFcZiIyEzpt9hWLxahZsyaaN2+udmOvti0KevXqhaFDh6JVq1bIz8/H7NmzcfPmTURHRytaHkyYMAH79+/Hli1b4OLigkmTJkEsFuPs2bNavQc3+5IhGGrZwVibfUUApgbXK1KDxpA0ZSQdP34c7733Hu7fvw8AGDJkCPqPn43Zh56o7KKtTSp7aRh6QzcRaccgTSOVtSgoi0OHDhX5+5YtW1C1alVERUWhY8eOSE1NxcaNG/HTTz+ha9eX6aybN2+Gn58fzp8/j7Zt2+ptLES66BXghe7+nnrvvmzMzb4/RT6CWEOnbm04SMSwsbJCSpbqujea3qJr1664fv06FixYgGXLlmH79u34dc8BuHYZA4eAbkV+7wh4Gcws2BttkN5Mht7QTUT6ZVYtClJTUwFAUZsmKioKeXl5CA4OVhzTsGFD1KxZExEREUoDmZycHOTk/G/dX9maO5E+GKL7cmUH7YrOlZUAICFdc50ZbbzXsY7GmZ2UzDxciE1We7/s7e2xZMkSDBkyBG+9Mwp/Rd9A0oGv8eLWSbj3mgQbV88i449LzdZ4ztIw9IZuItIv3Te2GIhMJsPUqVPRrl07BAS83KQYHx8PiUQCV1fXIsd6eHggPj5e6XnCwsLg4uKi+FOjRg1DD51If8yvFJNKHk4SrBseCJ/KDlodr+0MRmBgIMK+3wvXzqMgspYg+9FVxG0MRdqFnRBkRbMjz977V++bgDVt6AYAsQh4XqzgIFO1iUyjTOnX+hQaGoqbN2/izJkzZTrPrFmzMH36dMXf09LSGMyQxXiWYbwsorISiV7+O8gQMxhebo5waTMI9vVfQdKhVch5fB3PT2zCi5jTLwvpVa0NAFh14v7/XqOn1Gj5hu4JaooAygQg9KfLWCsOVBQNZKo2kWmYxYzMpEmTsG/fPpw4cQLVq1dXPO7p6Ync3FykpKQUOT4hIQGenp5QRiqVwtnZucgfIkthrOUKEV72WfJ0Vj/zoE5C2suieZEPkuBqpzplvDQp6fJZEYmbNzyGLkKl3u9DLHVAbvxdxG2Ziuenvocsr2jQp8/U6F4BXlg9rLnGflUL9kbjwPWnTNUmMiGTBjKCIGDSpEnYtWsXjh8/Dl9f3yLPt2jRAjY2Njh27JjisTt37uDx48cICgoy9nCJDE7+BW5oAoD5rzXC/Nf8NW7EVXcOAPj62F2VG31Lm5JeOM1dLBLBsUkPeI1dC/sG7QBBhrTzOxC3eTKyH98oMR59pUa7OUjVboSW79OZs/smU7WJTMikgUxoaCh+/PFH/PTTT3ByckJ8fDzi4+ORlZUFAHBxccGYMWMwffp0nDhxAlFRURg9ejSCgoKYsUTlkpVYhNea6mcpomvDKhqP+e3y33p5L1U8nKWlTpOWd92WdxG3dnRHlddnocobn8DK0R35z58iIXwWkg6tgiz7ZXuUwpuAy0rbPT3JL9Rna+lrPESknEn3yKxduxYA0Llz5yKPb968GaNGjQIALF++HGKxGAMHDkROTg569uyJNWvWGHmkRMZRIBOw55p+liKuPklV+/zMX68hJdvQrUXKtnu5eJr73YQMrAJgW6sJnp/cjIyrh5Bx7RCy7l+Ae/fxsK//CgD9pEbrc5mPqdpEhqNz92tLw4J4ZEn0URBPBMDNwUbtTIGx6aspZPH7k/3kJpIOfYP85H8AAPb1X4Fb9/HYMa1PmdOyC2QC2i85jvjUbJX9qtwdJEh6oTmNncXziHRnsO7XRGQ4Zf2Xu3z+441m1co+mP9IrERlzgqftfOGXvaJFE+Ntq0RAO/R38A5aDAgtkLmX+cQv3Eibh7fBW3/jaYqbVqbdhQL+wcYtPcWEWnGQIbIjJR1OcPTxRZrhwci2F95Vl9ptK39ciahLMHM88w8nH+QpPXxugQXImsJ3DqOgPfI5ZB41kNBdgbeey8E3bp1w7176rt3a+rlVHyfjpz8Pvdp4mURvbeIyjMuLREZQYFM0KqdgablDFVEAGb2bojR7XwhsRaX+jzKxHzWC6f+SsS83beQkF76Ojehnevgw14NNR6nTU0WVcfM6V0fd47vwJw5c5CVlQVbW1uMnvwRgge/Cy83xyL3Xd7hWpteTpo+P23ryGj7c0BE2n9/M5AhMjBdi6XJv2ABzT2Kiit83kM34zBeTVE3bXT3r4r1I1oh7EA01p+OLVNfJgeJFZYNbqp2r4y+gosHDx5g0PDRuBLxJwBA4lEH7r0mw6dBAOa96o/u/p5ov+R4idovhd/P08UWZz7uqnWgoa9gh4heYiDzHwYyZEq6fDEXf938PbcQn/a/GRB7iRhZeTKo+z+28HkB6CWQqV3ZAd/+GVum88iJoPqa5bNI+gguDlyPw4RtUXhx8zieH1//Mj1bJIZzq9fh2n4YpvduguVH/9I4Xn1t0i3tz0FZcPaHLJ1Bul8TkfYKZAIW7I1WWSxNcwfnoo8520rwxQA/xKVlY/mRv5CZVzJ1uvB59fFvlCPRiRqPEUG3mSNV13whNlllEANo3yjywPWnmBR+BSKRCI6Nu8GudiCSj61HZsyfSLuwE5l/RWBl0lTAs5HGsco3X2sTFKg6puw/B7rj7A9VJAxkiAyktF/Mqv71npCWjck/X8HU4PpKg5ji5zUWXYIYdcGIthlb6o47dDMOE3+6UuQxKwc3VHntI2T6d0byH2uQnxKHB99/DIfG3eHWdQysbB1Vnq+qk22Z9uzMe9UfLnYSvQRo2lL18yNvmWCI2R8iU2LWEpGBlOaLWdO/3gFg8zn9LPOYkrJ7U9bmk/J7p4p93dbwHrMGToF9AYjw4sYRPN0wHi9unykxeyVPm37+IldjHyV54KDqmKPR8Vpdlz6K5mnz88OWCVTecEaGyEBK88WszSxOSqb5FLorLWX3Rl4jRl0BOk81NVk03TsAEEvt4d59Auz9OgGnv0XC4/t4tnsx7Oq1hXv38bB2qqxY0Pu0rx8W7te8JCQIgtpjdl39R+2Y5PRRSVhfy3NEloQzMkQGUrx4W3HKiqVp+69yVzsbted1d5DoMtQyEemwrUNdgThtCtCpq8miy4yGb6NAPLh9E8PGT4PIyhpZd8/j6YaJSL9yAB5OEqwdHgg3B6lWQUHhDdnKjkl+kQd3B4nWPweqauhoQx/Lc0SWhoEMkYGU5otZ23+Vj27no/a8rzfz1mmsqnT3r4pxHX3VHtO1gebmlIWpC0Y0FaBTt7dDlxmNea/6w97OFtvWfoUrly+jUdMWEHIzkfzHGtgcXghfmzS9ftm/3sxb5V4iAf+7J5oK9GlS1uU5IkvEQIbIgHT9YtZ2FmdS13pqz9tdy8q+jlL1q8sDA6tjVh9/jOvoi+Kxh1gEjOvoi7Ed6mj1XpUcJFptNO0V4IUzH3dFeEhbrBjaDOEhbXHm464aX6fp3qnStEljXIuKxMqVK+Hg4IAzp0+jSZMmOLB1DYQC/SzjudjZaDxG014bbYKZ0swCElk61pEhMgJdanqoKoinS1E4bSr7utnbQGIlVlmtt3jdltx8GbZGPMSj5EzUcrfHO0E+kFiLkZVbAL+5hzTeg5vze8LRVrtteaWtgaJNEUB19WgePXqECRMm4ODBgwAAmyo+qNRrMqTeDZSeSywCqjhKkJieq3ZfjyAIKpegdDlGmxo6uvz86Atr1pAhsCDefxjIkCXSRx0QTV9oU4PrYfnRuxrPo6ko3MbTD7Bwf4zG83za1w9jOtTWeFxZr33F0b/KdF2CICA8PByhk95HyvMkQCSGU4tX4drhHYglJZdkpgXXw9f/vZ/y+1xfq+J72tC2QJ8x68iwZg0ZCgviEVmwXgFe6O7vWaZ/5cqXtYp/yXj+9yWTky/T6jya9oo8Ss7U6jzaHKePGig+lR20Gs/B/5Zqit9XkUiEYcOGocCrMSZMnoIXt04g/dJuZN49j0o9JsKudosS76eP+6wNbfft6OPnRxusWUPmgIEMkZmyEovKnCKr7gst4r523ag1bQyt5W6v1Xk0HaevCrjabmT9IeIRfoh4pHL2oG5Nb1Tu9wEc/Dsj6fBqFKQmIHHHPDg06gK3biGwsnNWvF9QnUplvs/a0GWTrj5+ftQxRcViImW42ZeonJN/ofVvVg1BdSopvlR03Riamy/DxtMPMHf3TWw8/QC5/800vBPkU2IjcHFi0cvj1NGlBoo6um76VbWZVn4e+9ot4D1mNZxa9gcgwotbJ/B0wwS8iD4FT2ep4v6ou8+ezlKV7y+/z57OUqNv0i1Lqre+Pi+ismIgQ1RBydPDtUkLDjsQjYafHsTC/TH4IeIRFu6PQcNPDyLsQDQk1mKEdFCfoh3SwRcSa/W/bvRVA0Vd2rsyqireFr4/Yokd3LuFwPOdL2FTuRZkmal4tncpZIcW45+/n6g9/5HoeGSrWV6S3+f5rzVSOmZtauiURllTvVmzhswFAxkiUivsQDS+/TMWxf+xLhOAb/+MRdiBaDSv6ab2HJqeB/RbA0VV2rsq2s4eSL0bwGvU13DpMBywssbF08fQqFEjrFq1CgUFJftfyfeQaKrG/Nvlv8tUQ0dX+kj1Zs0aMhfMWiKqoOQp2qqWB+Qpv+pSuIH/pSAnpOeqPY+m1GFNKeO6pCAXPueF2GQcvBmHHyIeaTx+xdBm6N+sWpHxqLo/eUlPkH5kNdIf3QQAtA0KwqRPl8LRywdVnWzRopYbOi09oXUDz5jPesFOYmXwVGZtP3dN9zk3X4aGnx4sEeAWJhYBtxf21jgbV3hsTOMmOWYtEZFa2u5x0EQmQGUQU/g8mvr7yJdyVNWBKbzUpa3CG161CWR06XtlU6kG3Ib8H8Y53MbqLz7D+YgInO/XGS5Bb8Kl7WBUcnFA8gvV96W4/zsQjYWvNzb4Jl199WOKevRcbRADvPzZiHr03OxSxql84dISUQVl7L0LptwrYai+VyKRGE7N+6DSiFWwq9sakOUj9Ww44rZMQdxf13Qa48Mk7dLYy0pfe1v0uUdGH0tdVHExkCGqoIy9d0HT+8nTeVWRp/PqklkjZ8i+V79ffQor58qoMuBTVH7tY4jtXZGX9BjxP36E5CPrIMvRLkDxqaRdGntZVXZUnUGly3H62iOjKY0bKP3nThUDAxmiUlKVjmwptJ2l0LSQIxYBHk7ad3dWxRDpvIXTi13sJFg9rLle+165O9golo9EIhEc/DrAe+xaOAQEAxCQfnkfnm4MReb9ixrHOruPv9bXpQ2VqdXaxgMajtNXXyd9fe5lSSUny8Y9MkSlEHYgGutPF83kWXQgBiEdfDFLz19IhqLtnpQrj5/j2z9jVZ4npIMvmtd0K/PeFn2n86rac/FpX3+4OUg0biiV358JP16GCMrbD7zRrBo2nn1Y9HV2TqjcdyocGnVG8qFvkJ+agH9/XQB7v05w7xYCKwfXEu/V3b8q7CRWWl2XNtTtN9G20vCzF8r7Pslpc3+M9blzf03FxhkZIh1pk45cnmjqfq2vwE2f6bzq9lyE/nQZqVm5JQrXKaMpJTpYTZdxO59m8Hp3NZxbDwBEYmTGnMLTDROQcfMYiieLJqTpb/+Qpv0mD5+90Oo8ZUlz1yVlvKyfO/fXENOviXRgiJRTU9E1DVdV92t9pfPqK/1aX+Mpfk5lacHadv7OibuLpEMrkZf4cmbL1qc5KvWaBGsXD8Uxm0a2QqcGVcqUbqzNtVd1kuDfjFyzSZsuy+duiM+azIe239/m/ZuWyMxsjXioVcrp1oiHRhlPWei6N0FiLcaYDrXxWf8AjOlQW/Elp689DqXZkKuMIfbaqGo/8FOk5pRuAJB61YPXiOVw7TQSsLJB9sMreLpxItIu7oYge1lI793vL+pUWVcZba49IV19EAP8L21aW6ruj7avLe3nzjYJBDCQIdKJPjs9m5o5puHqY6nCmKXzdfmcRVbWcGn7JrzfXQVpjQAIeTl4fnw94n+cgdz/ZmrKuhyizxR3Y6bLl/ZzZ5sEArjZl0gn+ur0bA70tSdF36Xq1XXs1uf76CP9vDSfs417NXi89X/IuPYHnp/cjNy4u4j7fiqc2wyC6ytDILaWlLprtD5T6o2dnl+az51tEghgIEOkk3eCfLDoQIzG/QWaOj2bA3n6rKa9CZrSZ/V1nsLKUt3WEONRtQdEm58HZUQiMZya9YJdnVZIProOWX9FIC1iOzLvnEWlXpMQhwC1lXVVjUeba/dwlgIQISFNu/tjzLYBun7uhvisdaXN/dHmGFV70EgzBjJEOpB3etaUjmwJv4D0lT6rr/Poi77Hoym1V9PPgzrWTpVQ9Y1PkHnnHJKPrEV+8t9I+GkmHJv1Rmzf2kq/1DWNR1NKvbzLtjb3x9zTmk39s6fN/dHmmPJQzsGUmLVEVArKfvGIRbDIXzz6+rIyty89fYxHntpb/Jek/GtRvn9D2c+DrmTZGXh+cjMyrh0GALhU9sAPG7/Da6+9ptN4AKgMZABg3X9j1nR/tL12c2CKnz1tPwtNx2iq06TPEgeWRtvvbwYyRKVUnqaC9bV8YG7di/WRFqxrenps0gvsvvoU6dn5pRpz9qPrSDr8DfKfv9zw++abb2LlypWoUtVDq/EIgoD4NOXF7IqPWdX9scS0ZmP+7Gl7fzR9FoZIhS9PGMj8h4EMEZVGxP0kvLX+vMbjwkPaFlkC0vZ16sjycpB6LhwvLu5CQUEBXF1dMfHjBfjxeW2IRGX/ci4+5uJKe+0VhT4+Y1182tcPYzrUNtr7mQvWkSEiKoPSpvbqI9VXbCOFW6dRWLrtAAIDA5GSkoL/mzUFids/Qd7zsleqNWZKfXlk7Ou2hHIOpsRAhohIidKm9uoz1bdtyxaIjIzE0qVLIbW1Q/aj64jbNAmpkb8pCumVhrFT6ssbY1+3JZRzMCUGMkRU7sg7Ie+6/Dc2nn6AXVd074isa3dn+XvGp2XD3cGmzNfgamcDmSBAJLbCjBkzcP36dTjXaQ4hPwcpJzcj/ofpyE24X2I8ns5SncdcvGO0vjpb68pSOlhre380fRYeTpISPcyKs5RyDqbEPTJEVK4oy2CRK03WUmkzgPSl8JgP3niKdz7+As9PbIQsOwMQieHcegBc270FsY1U71lL2pxHX8wt600Tbe4P8DJrCVCeGs6sJfW4R4aIKhxVnZDl4gzQEVnTe5ZV4bYFvRt748cvPkazaZtg37ADIMiQFvkrEr9/H+PrZ+mcVq6uY/SVx+p7LWl6XhfltYO1Nq0XjNVdvjzjjAwRlQuaUmLl9NlFW14lNz5N9TFu9hIkZ+ZqHL+LnQ1Ss/K0GrM81Xjf3j1Yv/gT/Jvw8ot+zJgxuObdD//mKl/aMse0YEtN9dZlzKzsWzqckSGiCkVTJ2Q5bTsia9NZOT4tR2UQIz9GmyAGgMogRn6ewmOWl/JfNHU07t6JwYQJEwAAGzduxJWv3sWLO2fVnkdVECM/RtsO2fro8m6JHax1HbM23cFVdZcnzXiniKhc0DUlVl8pyMakbEwuLi5Ys2YNTp8+jWo+dVDw4jme/R6GxF2LkJ+eZNDx6CMt2BJTvS1xzOUZAxkiKhd0TYnVVwqyMakbU/v27fHj/lNwCRoCiK2Q9VcEnm6YgPSrhyAIMoOMRx9pwZaY6m2JYy7PGMgQUbkgT4nVRNvUYW1SbD2dpfB01nSMdl9mHnpIm+7Q0Bt+r4bAe9QKSLzqQ8jNRPLhVUgIn4285H8AvEzrdrO30WtacFnSpvWV6m3M1G1DpKdbSuq5OWL3ayIqFwp3Qtb0FaCvrt7adJKe/5o/1py8h+t/p6l8rybVnTGxc129dKR+OeZseA1firTL+5Dy5w/IeXITTzdNgmu7tyC0HgCRlfJf/fL3WtA/QGNasLzLe1nTpvXRwdrYqdvG7rBO6jFriYjKFX3WkVF1vuLn0eaY11adVhrMNKnujD2TOmh1Hm07Uhc+T35qApIOr0Z27Mt6JjZVfFCp9xRIveppvD+aurzrs0N2ab/MTdml25gd1isiNo38DwMZoopHnu4an5qF5Be5cHd8uQRkyK7e2hyTkZ2Paduv4PHzLNR0s8PyIc3haGut1XlKk/J7/kESQrddxvPMXLyIPonnx9ZDlpX2spBey/5waf82Krs54dN+jVTeH1VpwYZIm9a1g7U5pG4bs8N6RaPt9zeXloio3JGnuxrzfNoc42hrjfUjW5XqPLqk/MpTfMUiEVKy8iASieDYqAvsfAORfOw7ZEafQtrFXcj86xxyek6C57AWKscuTwsu63i0oevnZogx6KosP2vmMP7ygJt9iYgsQGlSfou/xsreBVVe/RBVB82DlVMV5KcmIPGXT/HJtAlIStItVdscUpDNYQxlYenjNxcMZIiILEBpUn5VvcauTit4j1kNpxavAhDhxN4d8Pf3x/bt26HtbgNzSEE2hzGUhaWP31wwkCEiMhJjpymre41Yao9KwePQaPwK+Pv7IzExEUOHDsVrr72GJ0+eGGQ8+mboNOizd5/h7L1nBkuJNod7WB5wsy8RkRHoM8MFUN1Nufi5tHlNl3ruWLx4MRYtWoS8vDw4OTlh8eLFGD9+PMRi1f/eNXaHbFVj0PWeqDuXui7mhkiJ1uf4yxv2WiIiMhP66u6sTTfl0rxGKpVi3rx5uHr1KoKCgpCeno7Q0FB06NABMTExOl6tcZXmniijTRdzQ3Tj1tf4KzLOyBARGZA5pCnr8hqZTIa1a9di5syZyMjIgEQiwSeffIKZM2dCIpEY9LrKwpBp0IUZ6rrKMv7yinVk/sNAhohMKeJ+Et5af17jceEhbc0qxfbx48eYMGECDhw4AABo1KgRNmzYgLZt2wKw3OtSRttrKcwSrsvScWmJiMgMWGqKbc2aNbFv3z6Eh4ejSpUquHXrFl555RVMnToVGRkZFntdypRmjJZwXRUFAxkiIgOy5BRbkUiEoUOHIiYmBiNGjIAgCFixYgUaNWqEB5fPanUOU1yXrtlhpRmjOX5eFRUr+xIRGZA8xTY+NVtpM0v5ngtzTrGtVKkSvv/+e7z99tsYN24cHj58iOljhqBys26w6/AuxPYuJV5jqusqTXaYps+oMEv4vCoak87I/Pnnn3j11Vfh7e0NkUiE33//vcjzgiBg7ty58PLygp2dHYKDg3H37l3TDJaIqBTknZIBlKgXUppOyabUo0cP3Lx5E9OnT4dYLMazq8fwz4YJeHHrRJFCeqa6rtJmh6n7jAqztM+rojBpIPPixQs0bdoUq1evVvr8F198gZUrV2LdunWIjIyEg4MDevbsiexsrk0SkeUoTym2Dg4OWLZsGSIiItC4cWPIstLwbN8yJO6Yj/zURACmua4CmYAFe6OVzqjIH1uwN1rlMpOqz6gwS/y8KgKzyVoSiUTYtWsXXn/9dQAvZ2O8vb3xwQcfYMaMGQCA1NRUeHh4YMuWLRg6dKjS8+Tk5CAnJ0fx97S0NNSoUYNZS0RkcuUtxTYvLw9Lly7FZ599hpycHNja2WP8jDlY8umHkNgYd+eCvrKoCn9GlR2kgAh4lpFTLj4vS2PxWUuxsbGIj49HcHCw4jEXFxe0adMGERERKl8XFhYGFxcXxZ8aNWoYY7hERBrJOyX3b1ZN0aHaktnY2GD27Nm4du0aOnTogOysTHy9cDY6dmiPmzdvGnUs+sqiKvwZtatXGe3qVi43n1d5ZbaBTHx8PADAw8OjyOMeHh6K55SZNWsWUlNTFX+06RlCRESl16BBA5w8eRLr1q2Ds7MzIiMjERgYiLlz5xaZITckS84Oo7Ix20CmtKRSKZydnYv8ISIiwxKLxRg3bhyio6PRv39/5OXlYeHChWjWrBnOntUuVbss2ICx4jLbQMbT0xMAkJCQUOTxhIQExXNERGReqlWrhl27dmHHjh3w8PDA7du30b59e4SGhiItLc1g71uessNIN2YbyPj6+sLT0xPHjh1TPJaWlobIyEgEBQWZcGRERKSOSCTCoEGDEBMTgzFjxgAA1qxZg0aNGmHfvn0Ge9/ylB1G2jNpQbyMjAzcu3dP8ffY2FhcvXoV7u7uqFmzJqZOnYrPP/8c9erVg6+vLz799FN4e3srMpuIiMh8ubm5YcOGDRg2bBjee+893L9/H6+++iqGDBmCFStWlNgDqQ+9ArzQ3d+zXGWHkXomTb8+efIkunTpUuLxkSNHYsuWLRAEAfPmzcN3332HlJQUtG/fHmvWrEH9+vW1fg82jSQiMr3MzEwsWLAAy5YtQ0FBAdzc3PDVV19h5MiREIkYZFBJ7H79HwYyRETm4/Llyxg7diyuXLkCAAgODsa3336L2rVrm3hkZG4svo4MERGVP4GBgbhw4QKWLFkCW1tbHD16FAEBAVi2bBny8/NNPTyyQAxkiIjIqKytrfHRRx/hxo0b6NKlC7KysjBjxgwEBQXh2rVrph4eWRgGMkREZBJ169bFsWPHsGHDBri4uODSpUto0aIFZs+ejaysLFMPjywEAxkiIjIZkUiEMWPGICYmBgMHDkRBQQHCwsLQtGlTnDp1ytTDIwvAQIaIiEzOy8sLv/76K3bu3AkvLy/cvXsXnTt3xrhx45CSkmLq4ZEZYyBDRERm44033kB0dDTGjRsHAPjuu+/g7++PXbt2mXhkZK4YyBARkVlxdXXFunXrcPLkSdSrVw9xcXEYMGAABg4ciLi4OFMPj8wMAxkiIjJLnTp1wvXr1zF79mxYW1tj586d8PPzw4YNG1DOS6CRDhjIEBGR2bK1tcWiRYtw6dIltGzZEqmpqQgJCUHXrl1x9+5dUw+PzAADGSIiMntNmzZFREQEli1bBjs7O5w8eRJNmjTBkiVLkJeXZ+rhkQkxkCEiIotgbW2N6dOn4+bNm+jevTuys7Mxc+ZMtG7dGlFRUaYeHpkIAxkiIrIotWvXxuHDh7Flyxa4ubnh6tWraN26NT788ENkZmaaenhkZAxkiIjI4ohEIowcORIxMTEYOnQoZDIZvvzySzRu3BjHjh0z9fDIiBjIEBGRxfLw8EB4eDj27t2L6tWr48GDBwgODsaYMWPw/PlzUw+PjICBDBERWbx+/frh1q1bCA0NhUgkwqZNm+Dn54cdO3YwVbucYyBDRETlgrOzM1atWoXTp0/Dz88PCQkJGDx4MN544w38888/ph4eGQgDGSIiKlfatWuHK1euYO7cubCxscHu3bvh7++PdevWQSaTmXp4pGcMZIiIqNyRSqVYsGABLl++jDZt2iAtLQ0TJkxA586dcefOHVMPj/SIgQwREZVbAQEBOHv2LFauXAkHBwecPn0aTZo0waJFi5Cbm2vq4ZEeMJAhIqJyzcrKCpMnT8atW7fQu3dv5ObmYs6cOWjZsiUuXLhg6uFRGTGQISKiCqFWrVrYv38/tm3bhsqVK+PGjRsICgrC9OnT8eLFC1MPj0qJgQwREVUYIpEIw4YNQ0xMDIYPHw6ZTIbly5cjICAAhw8fNvXwqBQYyBARUYVTuXJlbN26FQcPHkStWrXw8OFD9OrVCyNGjEBSUpKph0c6YCBDREQVVq9evXDz5k1MnToVIpEIW7duhZ+fH8LDw1lIz0IwkCEiogrN0dERy5cvR0REBAICAvDvv/9i2LBh6NevHx4/fmzq4ZEGDGSIiIgAtGnTBlFRUVi4cCEkEgkOHDiARo0aYdWqVSgoKDD18EgFBjJERET/kUgkmDNnDq5evYp27dohIyMDkydPRocOHXDr1i1TD4+UYCBDRERUjJ+fH/7880+sWbMGTk5OiIiIQPPmzTF//nzk5OSYenhUCAMZIiIiJcRiMSZMmIBbt26hX79+yMvLw4IFCxAYGIiIiAhTD4/+w0CGiIhIjRo1amDPnj3Yvn07qlatiujoaLRr1w6TJ09Genq6qYdX4TGQISIi0kAkEmHw4MGIiYnBqFGjIAgCVq1ahUaNGmH//v2mHl6FxkCGiIhIS+7u7ti8eTOOHDkCX19fPHnyBP369cOwYcOQmJho6uFVSAxkiIiIdBQcHIwbN25gxowZEIvFCA8Ph5+fH3744QcW0jMyBjJERESl4ODggKVLlyIyMhJNmzZFcnIyRo4ciV69eiE2NtbUw6swGMgQERGVQcuWLXHx4kWEhYVBKpXijz/+QEBAAJYvX85CekbAQIaIiKiMbGxsMHPmTFy/fh2dOnVCZmYmpk+fjqCgIFy/ft3UwyvXGMgQERHpSf369XH8+HF89913cHFxwcWLF9GiRQvMmTMH2dnZph5eucRAhoiISI/EYjFCQkIQHR2NN954A/n5+Vi0aBGaNm2KP//809TDK3cYyBARERmAt7c3du7cid9++w2enp7466+/0KlTJ0yYMAGpqammHl65wUCGiIjIgAYMGICYmBiEhIQAANatWwd/f3/s3r3bxCMrHxjIEBERGZirqyu+++47HD9+HHXr1sXTp0/x+uuv480330R8fLyph2fRGMgQEREZSZcuXXD9+nXMnDkTVlZW+PXXX+Hn54dNmzaxkF4pMZAhIiIyIjs7O4SFheHSpUsIDAxESkoKxowZg+DgYNy/f9/Uw7M4DGSIiIhMoFmzZoiMjMTSpUthZ2eH48ePo3Hjxli6dCny8/NNPTyLwUCGiIjIRKytrTFjxgzcuHED3bp1Q1ZWFj766CO0adMGV65cMfXwLAIDGSIiIhOrU6cOjhw5gk2bNsHNzQ2XL19Gq1atMHPmTGRlZZl6eGaNgQwREZEZEIlEGD16NKKjozF48GAUFBRgyZIlaNKkCU6cOGHq4ZktBjJERERmxNPTE9u3b8fu3btRrVo13Lt3D127dkVISAieP39u6uGZHQYyREREZui1117DrVu3MGHCBADAhg0b4O/vj99++83EIzMvDGSIiIjMlIuLC9asWYPTp0+jQYMGiI+Px6BBgzBgwAA8ffrU1MMzCwxkiIiIzFz79u1x9epVzJkzB9bW1ti1axf8/Pzw3XffQSaTmXp4JsVAhoiIyALY2tpi4cKFuHz5Mlq3bo20tDSMGzcOXbt2xV9//WXq4ZkMAxkiIiIL0rhxY5w7dw7Lly+Hvb09Tp06hSZNmiAsLAx5eXmmHp7RMZAhIiKyMFZWVpg6dSpu3bqFHj16ICcnB7Nnz0bLli1x6dIlUw/PqBjIEBERWSgfHx8cOnQIP/zwA9zd3XH9+nW0adMGM2bMwIsXL0w9PKNgIENERGTBRCIR3nnnHcTExGDYsGGQyWRYtmwZGjdujCNHjph6eAbHQIaIiKgcqFq1KrZt24b9+/ejRo0aiI2NRY8ePTBq1CgkJSWZengGw0CGiIioHOnTpw9u3bqFyZMnQyQS4fvvv4e/vz+2b98OQRBMPTy9YyBDRERUzjg5OWHlypU4e/Ys/P39kZiYiKFDh+K1117DkydPTD08vbKIQGb16tXw8fGBra0t2rRpgwsXLph6SERERGYvKCgIly9fxvz582FjY4N9+/ahUaNGWLNmTbkppGf2gcz27dsxffp0zJs3D5cvX0bTpk3Rs2dPJCYmmnpoREREZk8qlWLevHm4evUqgoKCkJ6ejtDQUHTo0AExMTGmHl6ZmX0g89VXXyEkJASjR4+Gv78/1q1bB3t7e2zatEnp8Tk5OUhLSyvyh4iIqKLz9/fHmTNnsGrVKjg6OuLcuXNo1qwZPvvsM+Tm5pp6eKVm1oFMbm4uoqKiEBwcrHhMLBYjODgYERERSl8TFhYGFxcXxZ8aNWoYa7hERERmTSwWIzQ0FLdu3UKfPn2Qm5uLefPmITAwEOfPnzf18ErFrAOZZ8+eoaCgAB4eHkUe9/DwQHx8vNLXzJo1C6mpqYo/5W1TExERUVnVrFkT+/btQ3h4OKpUqYJbt27hlVdewZQpU5CRkWHq4enErAOZ0pBKpXB2di7yh4iIiIoSiUQYOnQoYmJiMGLECAiCgJUrV6JRo0Y4ePCgqYenNbMOZCpXrgwrKyskJCQUeTwhIQGenp4mGhUREVH5UalSJXz//fc4fPgwfHx88PjxY/Tp0wfDhw/Hv//+a+rhaWTWgYxEIkGLFi1w7NgxxWMymQzHjh1DUFCQCUdGRERUvvTo0QM3b97E9OnTIRaLsW3bNvj5+eHHH38060J6Zh3IAMD06dOxfv16fP/994iJicGECRPw4sULjB492tRDIyIiKlccHBywbNkyREREoHHjxkhKSsI777yDPn364NGjR6YenlJmH8gMGTIEX375JebOnYtmzZrh6tWrOHToUIkNwERERKQfrVu3RlRUFBYtWgSpVIpDhw6hUaNGWLFiBQoKCkw9vCJEgjnPF+lBWloaXFxckJqayo2/REREOrpz5w5CQkJw+vRpAECbNm2wYcMGBAQEGPR9tf3+NvsZGSIiIjKdBg0a4OTJk1i3bh2cnZ0RGRmJwMBAzJ07Fzk5OaYeHgMZIiIiUk8sFmPcuHGIjo5G//79kZeXh4ULF6JZs2Y4e/asacdm0ncnIiIii1GtWjXs2rULO3bsgIeHB27fvo327dtj8eLFJhsTAxkiIiLSmkgkwqBBgxATE4MxY8ZAJBKhQ4cOphsPN/sSERFRaf3111+oX7++3s/Lzb5ERERkcIYIYnTBQIaIiIgsFgMZIiIislgMZIiIiMhiMZAhIiIii8VAhoiIiCwWAxkiIiKyWAxkiIiIyGIxkCEiIiKLxUCGiIiILBYDGSIiIrJYDGSIiIjIYjGQISIiIovFQIaIiIgslrWpB2BogiAAeNkOnIiIiCyD/Htb/j2uSrkPZNLT0wEANWrUMPFIiIiISFfp6elwcXFR+bxI0BTqWDiZTIanT5/CyckJIpFIb+dNS0tDjRo18OTJEzg7O+vtvFQS77Vx8D4bB++zcfA+G4ch77MgCEhPT4e3tzfEYtU7Ycr9jIxYLEb16tUNdn5nZ2f+T2IkvNfGwftsHLzPxsH7bByGus/qZmLkuNmXiIiILBYDGSIiIrJYDGRKSSqVYt68eZBKpaYeSrnHe20cvM/GwftsHLzPxmEO97ncb/YlIiKi8oszMkRERGSxGMgQERGRxWIgQ0RERBaLgQwRERFZLAYypbR69Wr4+PjA1tYWbdq0wYULF0w9JIs2f/58iESiIn8aNmyoeD47OxuhoaGoVKkSHB0dMXDgQCQkJJhwxJbhzz//xKuvvgpvb2+IRCL8/vvvRZ4XBAFz586Fl5cX7OzsEBwcjLt37xY5Jjk5GW+//TacnZ3h6uqKMWPGICMjw4hXYf403edRo0aV+Pnu1atXkWN4nzULCwtDq1at4OTkhKpVq+L111/HnTt3ihyjze+Kx48fo2/fvrC3t0fVqlXx4YcfIj8/35iXYta0uc+dO3cu8TM9fvz4IscY6z4zkCmF7du3Y/r06Zg3bx4uX76Mpk2bomfPnkhMTDT10Cxao0aNEBcXp/hz5swZxXPTpk3D3r17sWPHDpw6dQpPnz7FgAEDTDhay/DixQs0bdoUq1evVvr8F198gZUrV2LdunWIjIyEg4MDevbsiezsbMUxb7/9Nm7duoUjR45g3759+PPPP/Hee+8Z6xIsgqb7DAC9evUq8vMdHh5e5HneZ81OnTqF0NBQnD9/HkeOHEFeXh569OiBFy9eKI7R9LuioKAAffv2RW5uLs6dO4fvv/8eW7Zswdy5c01xSWZJm/sMACEhIUV+pr/44gvFc0a9zwLprHXr1kJoaKji7wUFBYK3t7cQFhZmwlFZtnnz5glNmzZV+lxKSopgY2Mj7NixQ/FYTEyMAECIiIgw0ggtHwBh165dir/LZDLB09NTWLp0qeKxlJQUQSqVCuHh4YIgCEJ0dLQAQLh48aLimIMHDwoikUj4559/jDZ2S1L8PguCIIwcOVLo37+/ytfwPpdOYmKiAEA4deqUIAja/a44cOCAIBaLhfj4eMUxa9euFZydnYWcnBzjXoCFKH6fBUEQOnXqJEyZMkXla4x5nzkjo6Pc3FxERUUhODhY8ZhYLEZwcDAiIiJMODLLd/fuXXh7e6N27dp4++238fjxYwBAVFQU8vLyitzzhg0bombNmrznZRAbG4v4+Pgi99XFxQVt2rRR3NeIiAi4urqiZcuWimOCg4MhFosRGRlp9DFbspMnT6Jq1apo0KABJkyYgKSkJMVzvM+lk5qaCgBwd3cHoN3vioiICDRu3BgeHh6KY3r27Im0tDTcunXLiKO3HMXvs9y2bdtQuXJlBAQEYNasWcjMzFQ8Z8z7XO6bRurbs2fPUFBQUOTDAQAPDw/cvn3bRKOyfG3atMGWLVvQoEEDxMXFYcGCBejQoQNu3ryJ+Ph4SCQSuLq6FnmNh4cH4uPjTTPgckB+75T9LMufi4+PR9WqVYs8b21tDXd3d957HfTq1QsDBgyAr68v7t+/j9mzZ6N3796IiIiAlZUV73MpyGQyTJ06Fe3atUNAQAAAaPW7Ij4+XunPvPw5KkrZfQaAYcOGoVatWvD29sb169fx8ccf486dO9i5cycA495nBjJkFnr37q347yZNmqBNmzaoVasWfvnlF9jZ2ZlwZERlN3ToUMV/N27cGE2aNEGdOnVw8uRJdOvWzYQjs1yhoaG4efNmkb10pH+q7nPh/VuNGzeGl5cXunXrhvv376NOnTpGHSOXlnRUuXJlWFlZldgFn5CQAE9PTxONqvxxdXVF/fr1ce/ePXh6eiI3NxcpKSlFjuE9Lxv5vVP3s+zp6VliE3t+fj6Sk5N578ugdu3aqFy5Mu7duweA91lXkyZNwr59+3DixAlUr15d8bg2vys8PT2V/szLn6P/UXWflWnTpg0AFPmZNtZ9ZiCjI4lEghYtWuDYsWOKx2QyGY4dO4agoCATjqx8ycjIwP379+Hl5YUWLVrAxsamyD2/c+cOHj9+zHteBr6+vvD09CxyX9PS0hAZGam4r0FBQUhJSUFUVJTimOPHj0Mmkyl+cZHu/v77byQlJcHLywsA77O2BEHApEmTsGvXLhw/fhy+vr5Fntfmd0VQUBBu3LhRJHA8cuQInJ2d4e/vb5wLMXOa7rMyV69eBYAiP9NGu8963TpcQfz888+CVCoVtmzZIkRHRwvvvfee4OrqWmR3Nunmgw8+EE6ePCnExsYKZ8+eFYKDg4XKlSsLiYmJgiAIwvjx44WaNWsKx48fFy5duiQEBQUJQUFBJh61+UtPTxeuXLkiXLlyRQAgfPXVV8KVK1eER48eCYIgCIsXLxZcXV2F3bt3C9evXxf69+8v+Pr6CllZWYpz9OrVS2jevLkQGRkpnDlzRqhXr57w1ltvmeqSzJK6+5yeni7MmDFDiIiIEGJjY4WjR48KgYGBQr169YTs7GzFOXifNZswYYLg4uIinDx5UoiLi1P8yczMVByj6XdFfn6+EBAQIPTo0UO4evWqcOjQIaFKlSrCrFmzTHFJZknTfb53757w2WefCZcuXRJiY2OF3bt3C7Vr1xY6duyoOIcx7zMDmVL65ptvhJo1awoSiURo3bq1cP78eVMPyaINGTJE8PLyEiQSiVCtWjVhyJAhwr179xTPZ2VlCRMnThTc3NwEe3t74Y033hDi4uJMOGLLcOLECQFAiT8jR44UBOFlCvann34qeHh4CFKpVOjWrZtw586dIudISkoS3nrrLcHR0VFwdnYWRo8eLaSnp5vgasyXuvucmZkp9OjRQ6hSpYpgY2Mj1KpVSwgJCSnxDx/eZ82U3WMAwubNmxXHaPO74uHDh0Lv3r0FOzs7oXLlysIHH3wg5OXlGflqzJem+/z48WOhY8eOgru7uyCVSoW6desKH374oZCamlrkPMa6z6L/Bk1ERERkcbhHhoiIiCwWAxkiIiKyWAxkiIiIyGIxkCEiIiKLxUCGiIiILBYDGSIiIrJYDGSIiIjIYjGQISIiIovFQIaIiIgsFgMZIlJr1KhReP3110s8fvLkSYhEohKdhomIjImBDBGZRG5urqmHYBSCICA/P9/UwyAqtxjIEJFe/Pbbb2jUqBGkUil8fHywbNmyIs/7+Phg4cKFGDFiBJydnfHee+8hNzcXkyZNgpeXF2xtbVGrVi2EhYUpXpOSkoKxY8eiSpUqcHZ2RteuXXHt2jXF8/Pnz0ezZs3w7bffokaNGrC3t8fgwYORmpqqOEYmk+Gzzz5D9erVIZVK0axZMxw6dEjx/KBBgzBp0iTF36dOnQqRSITbt28DeBlwOTg44OjRo4rzhYWFwdfXF3Z2dmjatCl+/fVXxevlM1UHDx5EixYtIJVKcebMGT3dZSIqjoEMEZVZVFQUBg8ejKFDh+LGjRuYP38+Pv30U2zZsqXIcV9++SWaNm2KK1eu4NNPP8XKlSuxZ88e/PLLL7hz5w62bdsGHx8fxfFvvvkmEhMTcfDgQURFRSEwMBDdunVDcnKy4ph79+7hl19+wd69e3Ho0CFcuXIFEydOVDy/YsUKLFu2DF9++SWuX7+Onj174rXXXsPdu3cBAJ06dcLJkycVx586dQqVK1dWPHbx4kXk5eXhlVdeAQCEhYXhhx9+wLp163Dr1i1MmzYNw4cPx6lTp4pc68yZM7F48WLExMSgSZMmerjLRKSU3vtpE1G5MnLkSMHKykpwcHAo8sfW1lYAIDx//lwYNmyY0L179yKv+/DDDwV/f3/F32vVqiW8/vrrRY6ZPHmy0LVrV0Emk5V439OnTwvOzs5CdnZ2kcfr1KkjfPvtt4IgCMK8efMEKysr4e+//1Y8f/DgQUEsFgtxcXGCIAiCt7e3sGjRoiLnaNWqlTBx4kRBEATh+vXrgkgkEhITE4Xk5GRBIpEICxcuFIYMGSIIgiB8/vnnwiuvvCIIgiBkZ2cL9vb2wrlz54qcb8yYMcJbb70lCIIgnDhxQgAg/P777+puKxHpibWpAykiMn9dunTB2rVrizwWGRmJ4cOHAwBiYmLQv3//Is+3a9cOX3/9NQoKCmBlZQUAaNmyZZFjRo0ahe7du6NBgwbo1asX+vXrhx49egAArl27hoyMDFSqVKnIa7KysnD//n3F32vWrIlq1aop/h4UFASZTIY7d+7A3t4eT58+Rbt27UqMTb5EFRAQAHd3d5w6dQoSiQTNmzdHv379sHr1agAvZ2g6d+4M4OXsT2ZmJrp3717kfLm5uWjevHmRx4pfKxEZBgMZItLIwcEBdevWLfLY33//XarzFBYYGIjY2FgcPHgQR48exeDBgxEcHIxff/0VGRkZ8PLyKrLsI+fq6qrze6siEonQsWNHnDx5ElKpFJ07d0aTJk2Qk5ODmzdv4ty5c5gxYwYAICMjAwCwf//+IsETAEilUrXXSkSGwUCGiMrMz88PZ8+eLfLY2bNnUb9+fcVsjCrOzs4YMmQIhgwZgkGDBqFXr15ITk5GYGAg4uPjYW1tXWTfTHGPHz/G06dP4e3tDQA4f/48xGIxGjRoAGdnZ3h7e+Ps2bPo1KlTkbG1bt1a8fdOnTph/fr1kEqlWLRoEcRiMTp27IilS5ciJydHMaPj7+8PqVSKx48fFzkfEZkOAxkiKrMPPvgArVq1wsKFCzFkyBBERERg1apVWLNmjdrXffXVV/Dy8kLz5s0hFouxY8cOeHp6wtXVFcHBwQgKCsLrr7+OL774AvXr18fTp0+xf/9+vPHGG4qlG1tbW4wcORJffvkl0tLS8P7772Pw4MHw9PQEAHz44YeYN28e6tSpg2bNmmHz5s24evUqtm3bphhH586dMW3aNEgkErRv317x2IwZM9CqVSvF7IqTkxNmzJiBadOmQSaToX379khNTcXZs2fh7OyMkSNHGuL2EpEaDGSIqMwCAwPxyy+/YO7cuVi4cCG8vLzw2WefYdSoUWpf5+TkhC+++AJ3796FlZUVWrVqhQMHDkAsfplQeeDAAXzyyScYPXo0/v33X3h6eqJjx47w8PBQnKNu3boYMGAA+vTpg+TkZPTr169IAPX+++8jNTUVH3zwARITE+Hv7489e/agXr16imMaN24MV1dX1K9fH46OjgBeBjIFBQWK/TFyCxcuRJUqVRAWFoYHDx7A1dUVgYGBmD17dhnvIhGVhkgQBMHUgyAiKo358+fj999/x9WrV009FCIyEdaRISIiIovFQIaIiIgsFpeWiIiIyGJxRoaIiIgsFgMZIiIislgMZIiIiMhiMZAhIiIii8VAhoiIiCwWAxkiIiKyWAxkiIiIyGIxkCEiIiKL9f+g99RIbUympwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_horsepower(x, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yk2RmlqPoM9u"
      },
      "source": [
        "### Linear regression with multiple inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PribnwDHUksC"
      },
      "source": [
        "You can use an almost identical setup to make predictions based on multiple inputs. This model still does the same $y = mx+b$ except that $m$ is a matrix and $x$ is a vector.\n",
        "\n",
        "Create a two-step Keras Sequential model again with the first layer being `normalizer` (`tf.keras.layers.Normalization(axis=-1)`) you defined earlier and adapted to the whole dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "ssnVcKg7oMe6"
      },
      "outputs": [],
      "source": [
        "linear_model = tf.keras.Sequential([\n",
        "    normalizer,\n",
        "    layers.Dense(units=1)\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHlx6WeIWyAr"
      },
      "source": [
        "When you call `Model.predict` on a batch of inputs, it produces `units=1` outputs for each example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "DynfJV18WiuT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1e88aea-23ae-4cea-8664-b86b3039ea9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 306ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.667],\n",
              "       [ 0.424],\n",
              "       [-1.222],\n",
              "       [ 2.004],\n",
              "       [ 0.204],\n",
              "       [ 0.594],\n",
              "       [ 0.32 ],\n",
              "       [-0.091],\n",
              "       [-0.072],\n",
              "       [-1.322]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "linear_model.predict(train_features[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvHKH3rPXHmq"
      },
      "source": [
        "When you call the model, its weight matrices will be built—check that the `kernel` weights (the $m$ in $y=mx+b$) have a shape of `(9, 1)`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "DwJ4Fq0RXBQf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "681c326a-d108-42ca-8ef6-7ec1045a2a0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'dense_1/kernel:0' shape=(9, 1) dtype=float32, numpy=\n",
              "array([[-0.038],\n",
              "       [ 0.733],\n",
              "       [-0.653],\n",
              "       [-0.735],\n",
              "       [-0.358],\n",
              "       [ 0.482],\n",
              "       [-0.313],\n",
              "       [-0.649],\n",
              "       [-0.319]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "linear_model.layers[1].kernel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eINAc6rZXzOt"
      },
      "source": [
        "Configure the model with Keras `Model.compile` and train with `Model.fit` for 100 epochs:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "A0Sv_Ybr0szp"
      },
      "outputs": [],
      "source": [
        "linear_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "    loss='mean_absolute_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "EZoOYORvoTSe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0742f457-a475-441c-fb1e-be4d11d4ee13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 6.69 s, sys: 183 ms, total: 6.87 s\n",
            "Wall time: 16.2 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = linear_model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    epochs=100,\n",
        "    # Suppress logging.\n",
        "    verbose=0,\n",
        "    # Calculate validation results on 20% of the training data.\n",
        "    validation_split = 0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdxiCbiNYK2F"
      },
      "source": [
        "Using all the inputs in this regression model achieves a much lower training and validation error than the `horsepower_model`, which had one input:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "4sWO3W0koYgu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "9c4207b9-8640-4504-f5eb-889dae97b2bc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyN49hIWe_NH"
      },
      "source": [
        "Collect the results on the test set for later:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "jNC3D1DGsGgK"
      },
      "outputs": [],
      "source": [
        "test_results['linear_model'] = linear_model.evaluate(\n",
        "    test_features, test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmjdzxKzEu1-"
      },
      "source": [
        "## Regression with a deep neural network (DNN)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DT_aHPsrzO1t"
      },
      "source": [
        "In the previous section, you implemented two linear models for single and multiple inputs.\n",
        "\n",
        "Here, you will implement single-input and multiple-input DNN models.\n",
        "\n",
        "The code is basically the same except the model is expanded to include some \"hidden\" non-linear layers. The name \"hidden\" here just means not directly connected to the inputs or outputs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SWtkIjhrZwa"
      },
      "source": [
        "These models will contain a few more layers than the linear model:\n",
        "\n",
        "* The normalization layer, as before (with `horsepower_normalizer` for a single-input model and `normalizer` for a multiple-input model).\n",
        "* Two hidden, non-linear, `Dense` layers with the ReLU (`relu`) activation function nonlinearity.\n",
        "* A linear `Dense` single-output layer.\n",
        "\n",
        "Both models will use the same training procedure, so the `compile` method is included in the `build_and_compile_model` function below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "c26juK7ZG8j-"
      },
      "outputs": [],
      "source": [
        "def build_and_compile_model(norm):\n",
        "  model = keras.Sequential([\n",
        "      norm,\n",
        "      layers.Dense(64, activation='relu'),\n",
        "      layers.Dense(64, activation='relu'),\n",
        "      layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  model.compile(loss='mean_absolute_error',\n",
        "                optimizer=tf.keras.optimizers.Adam(0.001))\n",
        "  return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c51caebbc0d"
      },
      "source": [
        "### Regression using a DNN and a single input"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvu9gtxTZR5V"
      },
      "source": [
        "Create a DNN model with only `'Horsepower'` as input and `horsepower_normalizer` (defined earlier) as the normalization layer:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "cGbPb-PHGbhs"
      },
      "outputs": [],
      "source": [
        "dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sj49Og4YGULr"
      },
      "source": [
        "This model has quite a few more trainable parameters than the linear models:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "ReAD0n6MsFK-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45b46232-718b-4a3e-fca9-67c957cd422f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization_1 (Normaliza  (None, 1)                 3         \n",
            " tion)                                                           \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 64)                128       \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4356 (17.02 KB)\n",
            "Trainable params: 4353 (17.00 KB)\n",
            "Non-trainable params: 3 (16.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn_horsepower_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-qWCsh6DlyH"
      },
      "source": [
        "Train the model with Keras `Model.fit`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "sD7qHCmNIOY0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d057844f-6f37-4b27-f049-b6a6bd5a0e62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5.75 s, sys: 180 ms, total: 5.93 s\n",
            "Wall time: 7.76 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = dnn_horsepower_model.fit(\n",
        "    train_features['Horsepower'],\n",
        "    train_labels,\n",
        "    validation_split=0.2,\n",
        "    verbose=0, epochs=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dArGGxHxcKjN"
      },
      "source": [
        "This model does slightly better than the linear single-input `horsepower_model`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "NcF6UWjdCU8T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "7570cc58-267a-439d-fb5e-a0389a031efb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TG1snlpR2QCK"
      },
      "source": [
        "If you plot the predictions as a function of `'Horsepower'`, you should notice how this model takes advantage of the nonlinearity provided by the hidden layers:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "hPF53Rem14NS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb7fd565-aa03-461a-b1bf-e6571a5f27af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "x = tf.linspace(0.0, 250, 251)\n",
        "y = dnn_horsepower_model.predict(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "rsf9rD8I17Wq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "9713b8ab-40ed-4814-8cc0-d35c395d506c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_horsepower(x, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxCJKIUpe4io"
      },
      "source": [
        "Collect the results on the test set for later:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "bJjM0dU52XtN"
      },
      "outputs": [],
      "source": [
        "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n",
        "    test_features['Horsepower'], test_labels,\n",
        "    verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_2Btebp2e64"
      },
      "source": [
        "### Regression using a DNN and multiple inputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKFtezDldLSf"
      },
      "source": [
        "Repeat the previous process using all the inputs. The model's performance slightly improves on the validation dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "c0mhscXh2k36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e47d18d6-7eb9-4146-e543-7fab97c91286"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization (Normalizati  (None, 9)                 19        \n",
            " on)                                                             \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 64)                640       \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4884 (19.08 KB)\n",
            "Trainable params: 4865 (19.00 KB)\n",
            "Non-trainable params: 19 (80.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn_model = build_and_compile_model(normalizer)\n",
        "dnn_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "CXDENACl2tuW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb8a55ee-b5be-4d7e-a681-247730a4252e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5.9 s, sys: 193 ms, total: 6.09 s\n",
            "Wall time: 11 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = dnn_model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    validation_split=0.2,\n",
        "    verbose=0, epochs=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "-9Dbj0fX23RQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "15dd0c72-ef8a-47fa-b31b-f8258ff8fe7f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWoVYS34fJPZ"
      },
      "source": [
        "Collect the results on the test set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "-bZIa96W3c7K"
      },
      "outputs": [],
      "source": [
        "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiCucdPLfMkZ"
      },
      "source": [
        "## Performance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDf1xebEfWBw"
      },
      "source": [
        "Since all models have been trained, you can review their test set performance:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "e5_ooufM5iH2",
        "colab": {
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          "height": 175
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        "outputId": "7d3e28ee-d501-48e5-e326-34976e4c6390"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Mean absolute error [MPG]\n",
              "horsepower_model                       3.662032\n",
              "linear_model                           2.519260\n",
              "dnn_horsepower_model                   2.930929\n",
              "dnn_model                              1.777891"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DABIVzsCf-QI"
      },
      "source": [
        "These results match the validation error observed during training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ft603OzXuEZC"
      },
      "source": [
        "### Make predictions\n",
        "\n",
        "You can now make predictions with the `dnn_model` on the test set using Keras `Model.predict` and review the loss:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "Xe7RXH3N3CWU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "4d9c6efd-4d53-4637-d4e0-8e6ae0e2baf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_predictions = dnn_model.predict(test_features).flatten()\n",
        "\n",
        "a = plt.axes(aspect='equal')\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True Values [MPG]')\n",
        "plt.ylabel('Predictions [MPG]')\n",
        "lims = [0, 50]\n",
        "plt.xlim(lims)\n",
        "plt.ylim(lims)\n",
        "_ = plt.plot(lims, lims)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19wyogbOSU5t"
      },
      "source": [
        "It appears that the model predicts reasonably well.\n",
        "\n",
        "Now, check the error distribution:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "f-OHX4DiXd8x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "9880e4cc-9e39-4b93-e1ce-a23d72a32318"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "error = test_predictions - test_labels\n",
        "plt.hist(error, bins=25)\n",
        "plt.xlabel('Prediction Error [MPG]')\n",
        "_ = plt.ylabel('Count')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSyaHUfDT-mZ"
      },
      "source": [
        "If you're happy with the model, save it for later use with `Model.save`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "4-WwLlmfT-mb"
      },
      "outputs": [],
      "source": [
        "dnn_model.save('/content/drive/MyDrive/Machine Learning Lab/exp 01/dnn_model')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dnn_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzotWkBQfLKv",
        "outputId": "768746d9-0745-4779-9f4c-3447cdcd5a33"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization (Normalizati  (None, 9)                 19        \n",
            " on)                                                             \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 64)                640       \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4884 (19.08 KB)\n",
            "Trainable params: 4865 (19.00 KB)\n",
            "Non-trainable params: 19 (80.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Benlnl8UT-me"
      },
      "source": [
        "If you reload the model, it gives identical output:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "dyyyj2zVT-mf"
      },
      "outputs": [],
      "source": [
        "reloaded_model = tf.keras.models.load_model('/content/drive/MyDrive/Machine Learning Lab/exp 01/dnn_model')\n",
        "\n",
        "test_results['reloaded_model'] = reloaded_model.evaluate(\n",
        "    test_features, test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "f_GchJ2tg-2o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "3b098a5d-710e-4a82-c164-240ee836652b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Mean absolute error [MPG]\n",
              "horsepower_model                       3.662032\n",
              "linear_model                           2.519260\n",
              "dnn_horsepower_model                   2.930929\n",
              "dnn_model                              1.777891\n",
              "reloaded                               1.777891\n",
              "reloaded_model                         1.777891"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-215e24f8-e855-4647-9885-991aa8f513dd\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean absolute error [MPG]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>horsepower_model</th>\n",
              "      <td>3.662032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>linear_model</th>\n",
              "      <td>2.519260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dnn_horsepower_model</th>\n",
              "      <td>2.930929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dnn_model</th>\n",
              "      <td>1.777891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reloaded</th>\n",
              "      <td>1.777891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reloaded_model</th>\n",
              "      <td>1.777891</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-215e24f8-e855-4647-9885-991aa8f513dd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "        document.querySelector('#df-215e24f8-e855-4647-9885-991aa8f513dd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-215e24f8-e855-4647-9885-991aa8f513dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ],
      "source": [
        "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgGQuV-yqYZH"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "This notebook introduced a few techniques to handle a regression problem. Here are a few more tips that may help:\n",
        "\n",
        "- Mean squared error (MSE) (`tf.keras.losses.MeanSquaredError`) and mean absolute error (MAE) (`tf.keras.losses.MeanAbsoluteError`) are common loss functions used for regression problems. MAE is less sensitive to outliers. Different loss functions are used for classification problems.\n",
        "- Similarly, evaluation metrics used for regression differ from classification.\n",
        "- When numeric input data features have values with different ranges, each feature should be scaled independently to the same range.\n",
        "- Overfitting is a common problem for DNN models, though it wasn't a problem for this tutorial. Visit the [Overfit and underfit](overfit_and_underfit.ipynb) tutorial for more help with this."
      ]
    }
  ],
  "metadata": {
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    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
}