From 95206560e75d3d64eb4d0e9abcf5cf10cddbfd71 Mon Sep 17 00:00:00 2001
From: Sayani Roy Chowdhury <sayaniroychowdhury.8@gmail.com>
Date: Sat, 2 Feb 2019 15:36:38 +0530
Subject: [PATCH 1/3] 2/11

---
 first_steps_with_tensor_flow.ipynb | 2032 ++++++++++++++++++++++++++++
 1 file changed, 2032 insertions(+)
 create mode 100644 first_steps_with_tensor_flow.ipynb

diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb
new file mode 100644
index 0000000..419fd4f
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,2032 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "first_steps_with_tensor_flow.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "ajVM7rkoYXeL",
+        "ci1ISxxrZ7v0"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/VicktorKrum/Assignment-5/blob/VicktorKrum/first_steps_with_tensor_flow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# 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."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# First Steps with TensorFlow"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qe0A1tNZhw5w",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        ""
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Bd2Zkk1LE2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Learn fundamental TensorFlow concepts\n",
+        "  * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n",
+        "  * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n",
+        "  * Improve the accuracy of a model by tuning its hyperparameters"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MxiIKhP4E2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6TjLjL9IU80G",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "In this first cell, we'll load the necessary libraries."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rVFf5asKE2Zt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ipRyUHjhU80Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll load our data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9ivCDWnwE2Zx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vVk_qlG6U80j",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r0eVyguIU80m",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "d4ee35e2-a32f-432b-a200-e55ca38290b5"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>5595</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>33.8</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>4409.0</td>\n",
+              "      <td>1401.0</td>\n",
+              "      <td>3068.0</td>\n",
+              "      <td>1262.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>154.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12475</th>\n",
+              "      <td>-121.6</td>\n",
+              "      <td>36.7</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>9899.0</td>\n",
+              "      <td>2617.0</td>\n",
+              "      <td>11272.0</td>\n",
+              "      <td>2528.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>118.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10864</th>\n",
+              "      <td>-120.8</td>\n",
+              "      <td>37.5</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2974.0</td>\n",
+              "      <td>495.0</td>\n",
+              "      <td>1313.0</td>\n",
+              "      <td>461.0</td>\n",
+              "      <td>4.5</td>\n",
+              "      <td>135.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10084</th>\n",
+              "      <td>-119.8</td>\n",
+              "      <td>34.4</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>3143.0</td>\n",
+              "      <td>537.0</td>\n",
+              "      <td>1760.0</td>\n",
+              "      <td>570.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>271.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10647</th>\n",
+              "      <td>-120.5</td>\n",
+              "      <td>36.9</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1779.0</td>\n",
+              "      <td>399.0</td>\n",
+              "      <td>1446.0</td>\n",
+              "      <td>371.0</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>71.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5117</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.8</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>3035.0</td>\n",
+              "      <td>516.0</td>\n",
+              "      <td>1127.0</td>\n",
+              "      <td>527.0</td>\n",
+              "      <td>7.1</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4183</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>33.8</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>1991.0</td>\n",
+              "      <td>528.0</td>\n",
+              "      <td>1202.0</td>\n",
+              "      <td>460.0</td>\n",
+              "      <td>3.2</td>\n",
+              "      <td>252.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8693</th>\n",
+              "      <td>-118.6</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>2987.0</td>\n",
+              "      <td>391.0</td>\n",
+              "      <td>1244.0</td>\n",
+              "      <td>387.0</td>\n",
+              "      <td>7.1</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9802</th>\n",
+              "      <td>-119.7</td>\n",
+              "      <td>36.8</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>2075.0</td>\n",
+              "      <td>353.0</td>\n",
+              "      <td>1040.0</td>\n",
+              "      <td>362.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>100.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4730</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>2263.0</td>\n",
+              "      <td>511.0</td>\n",
+              "      <td>1626.0</td>\n",
+              "      <td>457.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>172.8</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "5595      -118.2      33.8                12.0       4409.0          1401.0   \n",
+              "12475     -121.6      36.7                19.0       9899.0          2617.0   \n",
+              "10864     -120.8      37.5                21.0       2974.0           495.0   \n",
+              "10084     -119.8      34.4                27.0       3143.0           537.0   \n",
+              "10647     -120.5      36.9                21.0       1779.0           399.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "5117      -118.1      33.8                45.0       3035.0           516.0   \n",
+              "4183      -118.0      33.8                19.0       1991.0           528.0   \n",
+              "8693      -118.6      34.2                35.0       2987.0           391.0   \n",
+              "9802      -119.7      36.8                 7.0       2075.0           353.0   \n",
+              "4730      -118.1      33.9                33.0       2263.0           511.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "5595       3068.0      1262.0            2.3               154.7  \n",
+              "12475     11272.0      2528.0            2.0               118.5  \n",
+              "10864      1313.0       461.0            4.5               135.4  \n",
+              "10084      1760.0       570.0            4.7               271.5  \n",
+              "10647      1446.0       371.0            2.4                71.9  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "5117       1127.0       527.0            7.1               500.0  \n",
+              "4183       1202.0       460.0            3.2               252.1  \n",
+              "8693       1244.0       387.0            7.1               500.0  \n",
+              "9802       1040.0       362.0            4.0               100.2  \n",
+              "4730       1626.0       457.0            3.6               172.8  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HzzlSs3PtTmt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Examine the Data\n",
+        "\n",
+        "It's a good idea to get to know your data a little bit before you work with it.\n",
+        "\n",
+        "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gzb10yoVrydW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "d4bba3f1-de64-4313-fb16-8f4ddd9016c8"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "9c4683bf-8518-420f-aaa2-efd1ed08f2c6"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an input function for predictions.\n",
+        "# Note: Since we're making just one prediction for each example, we don't \n",
+        "# need to repeat or shuffle the data here.\n",
+        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+        "\n",
+        "# Call predict() on the linear_regressor to make predictions.\n",
+        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "\n",
+        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "\n",
+        "# Print Mean Squared Error and Root Mean Squared Error.\n",
+        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Squared Error (on training data): 56251.090\n",
+            "Root Mean Squared Error (on training data): 237.173\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AKWstXXPzOVz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Is this a good model? How would you judge how large this error is?\n",
+        "\n",
+        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+        "\n",
+        "Let's compare the RMSE to the difference of the min and max of our targets:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7UwqGbbxP53O",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "b66d6ba9-392a-4c34-d143-0f0de77d5198"
+      },
+      "cell_type": "code",
+      "source": [
+        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
+        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
+        "min_max_difference = max_house_value - min_house_value\n",
+        "\n",
+        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
+        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
+        "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
+        "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Min. Median House Value: 14.999\n",
+            "Max. Median House Value: 500.001\n",
+            "Difference between Min. and Max.: 485.002\n",
+            "Root Mean Squared Error: 237.173\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JigJr0C7Pzit",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our error spans nearly half the range of the target values. Can we do better?\n",
+        "\n",
+        "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
+        "\n",
+        "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "941nclxbzqGH",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "acc1f4d1-ca8c-42b2-a4a7-d4c50d102357"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = pd.DataFrame()\n",
+        "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "calibration_data.describe()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.4</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>5.7</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.4    207.3\n",
+              "std            0.3    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.5    265.0\n",
+              "max            5.7    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "E2-bf8Hq36y8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
+        "\n",
+        "We can also visualize the data and the line we've learned.  Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
+        "\n",
+        "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SGRIi3mAU81H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "sample = california_housing_dataframe.sample(n=300)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "N-JwuJBKU81J",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7G12E76-339G",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 361
+        },
+        "outputId": "224cd407-ff6f-413f-90b6-326dfe72e07f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Get the min and max total_rooms values.\n",
+        "x_0 = sample[\"total_rooms\"].min()\n",
+        "x_1 = sample[\"total_rooms\"].max()\n",
+        "\n",
+        "# Retrieve the final weight and bias generated during training.\n",
+        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
+        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "# Get the predicted median_house_values for the min and max total_rooms values.\n",
+        "y_0 = weight * x_0 + bias \n",
+        "y_1 = weight * x_1 + bias\n",
+        "\n",
+        "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
+        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
+        "\n",
+        "# Label the graph axes.\n",
+        "plt.ylabel(\"median_house_value\")\n",
+        "plt.xlabel(\"total_rooms\")\n",
+        "\n",
+        "# Plot a scatter plot from our data sample.\n",
+        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
+        "\n",
+        "# Display graph.\n",
+        "plt.show()"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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YTLmxJleNtcfpLp+Z6DD+0BITbGVFoludJrPuOt/WcxMRZYriHvmRI0ewe/du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\nJo1cCCGE0DBp5EIIIYSG/QfDFaXBPsjltwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "t0lRt4USU81L",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This initial line looks way off.  See if you can look back at the summary stats and see the same information encoded there.\n",
+        "\n",
+        "Together, these initial sanity checks suggest we may be able to find a much better line."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AZWF67uv0HTG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Tweak the Model Hyperparameters\n",
+        "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
+        "\n",
+        "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
+        "\n",
+        "For each period, we'll compute and graph training loss.  This may help you judge when a model is converged, or if it needs more iterations.\n",
+        "\n",
+        "We'll also plot the feature weight and bias term values learned by the model over time.  This is another way to see how things converge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wgSMeD5UU81N",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
+        "  \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label]\n",
+        "\n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Output a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kg8A4ArBU81Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1:  Achieve an RMSE of 180 or Below\n",
+        "\n",
+        "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
+        "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "5fd98048-fba6-47ea-a53a-4766867a4877"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1\n",
+        ")"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 236.32\n",
+            "  period 01 : 235.11\n",
+            "  period 02 : 233.90\n",
+            "  period 03 : 232.70\n",
+            "  period 04 : 231.50\n",
+            "  period 05 : 230.31\n",
+            "  period 06 : 229.13\n",
+            "  period 07 : 227.96\n",
+            "  period 08 : 226.79\n",
+            "  period 09 : 225.63\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean          13.2    207.3\n",
+              "std           10.9    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            7.3    119.4\n",
+              "50%           10.6    180.4\n",
+              "75%           15.8    265.0\n",
+              "max          189.7    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>13.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>10.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>15.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>189.7</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 225.63\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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mESNGYM2aNQgKCnpomymvFYqLi/Haa69pCyACwNWrV3H+/Hn4+/vDz88PcXFxyMrKQllZ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\n/wPnK/MFFCTitAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "C3HAoQXWoP_i",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "d52623f4-2b8a-4fb9-fa11-ea00c2c1b119"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.0005,\n",
+        "    steps=100,\n",
+        "    batch_size=1\n",
+        ")"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 186.29\n",
+            "  period 01 : 167.02\n",
+            "  period 02 : 166.32\n",
+            "  period 03 : 174.59\n",
+            "  period 04 : 187.87\n",
+            "  period 05 : 206.05\n",
+            "  period 06 : 206.05\n",
+            "  period 07 : 196.42\n",
+            "  period 08 : 196.42\n",
+            "  period 09 : 180.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         185.1    207.3\n",
+              "std          152.6    116.0\n",
+              "min            0.1     15.0\n",
+              "25%          102.3    119.4\n",
+              "50%          148.9    180.4\n",
+              "75%          220.6    265.0\n",
+              "max         2655.6    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>185.1</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>152.6</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>102.3</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>148.9</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>220.6</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2655.6</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 180.54\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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kIjAwkLfeegudTkenTp3o0aOHZb2lS5ei0dTdzmghRN2QlVfKqXMFdGrjV+87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\n0WlVnDs2suMHCIIwaKSmhJOeXcnWg6UsD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4VmF7i9AeJOtiirs3n5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2LpfBa8t3UVntYMYv+hClYtgiIk2ipEQTHHu3312rytqlB8MGnkfPqu34FhymxDeSr78u5eZ9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\nRERERERERMQjlJQQEREREREREY/4f0S4P//cGl55AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "6c9ab3a7-f939-47f1-d311-ac15fc4c7b6f"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.0005,\n",
+        "    steps=1000,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.62\n",
+            "  period 02 : 204.67\n",
+            "  period 03 : 196.10\n",
+            "  period 04 : 189.52\n",
+            "  period 05 : 184.13\n",
+            "  period 06 : 180.26\n",
+            "  period 07 : 177.96\n",
+            "  period 08 : 176.84\n",
+            "  period 09 : 176.26\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         115.8    207.3\n",
+              "std           93.0    116.0\n",
+              "min            0.2     15.0\n",
+              "25%           64.0    119.4\n",
+              "50%           94.5    180.4\n",
+              "75%          139.4    265.0\n",
+              "max         2890.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>115.8</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>93.0</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>64.0</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>94.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>139.4</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2890.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 176.26\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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aFP4ikGhIwFHPJpzKN3Ek24JBD4Pa2YgMUJq0DY2loFhlyYflfP2ji/YReu6d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\nyipsLapVxeaQWtyahdujKAqZL/+Rkq17Cbh7AJ3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by2rWbDaZL3fn8+nWPMwWmahwPbOn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\nnZ1d9bPFY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pIk4ZZl1m87wfjcjYSpKvi3vTt5VyI5fuHmWO8bFMLABBlFpUFqoCDhluFU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\nkgM1OrwBJWnRXq6J8dGbAKBtvKpCocGoHYdaZcQvuWjxlHO8RoHHl3pR5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vIAACAASURB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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ci1ISxxrZ7v0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SjdQQCduZ7BV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "27768cc6-a2fd-4226-e02b-0dac77fac02f"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"population\"\n",
+        ")"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.84\n",
+            "  period 02 : 205.05\n",
+            "  period 03 : 196.75\n",
+            "  period 04 : 189.80\n",
+            "  period 05 : 184.24\n",
+            "  period 06 : 180.10\n",
+            "  period 07 : 177.79\n",
+            "  period 08 : 176.53\n",
+            "  period 09 : 176.04\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         119.2    207.3\n",
+              "std           95.7    116.0\n",
+              "min            0.3     15.0\n",
+              "25%           65.9    119.4\n",
+              "50%           97.3    180.4\n",
+              "75%          143.5    265.0\n",
+              "max         2975.9    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>119.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>95.7</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>65.9</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>97.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>143.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2975.9</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 176.04\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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C04FtyBQcvUY0zpSJqnKfRcdc5T6DW7oWtJRyn81KbHQwVw/pRHG5lQ83HPB1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pyGjBgQXa/Y4ItoZnP4WqwRIpiNU33wGAVLHsFpKaFg4i1sHDX9OteL/AILYdh5wpxPf30lx6Vw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nmEtuCZMetqzVsXSBmn5vcFrRJPE5wwlhZUDil6/0cOx0ouVg6WIzn9mcRVH+zGLRlcaaCsWY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\nYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From a6f4f97ab5cf0122b24f383b779fee3bb40b2a2a Mon Sep 17 00:00:00 2001
From: Sayani Roy Chowdhury <sayaniroychowdhury.8@gmail.com>
Date: Sat, 2 Feb 2019 15:40:08 +0530
Subject: [PATCH 2/3] 2/11


From b27c2dc36d61b0aceea2199a8c2d7234374ffdfe Mon Sep 17 00:00:00 2001
From: Sayani Roy Chowdhury <sayaniroychowdhury.8@gmail.com>
Date: Sun, 3 Feb 2019 16:14:00 +0530
Subject: [PATCH 3/3] 3/11

---
 synthetic_features_and_outliers.ipynb | 1666 +++++++++++++++++++++++++
 1 file changed, 1666 insertions(+)
 create mode 100644 synthetic_features_and_outliers.ipynb

diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb
new file mode 100644
index 0000000..cb86a59
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1666 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "synthetic_features_and_outliers.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "i5Ul3zf5QYvW",
+        "jByCP8hDRZmM",
+        "WvgxW0bUSC-c"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/VicktorKrum/Assignment-5/blob/VicktorKrum/synthetic_features_and_outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# 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."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Synthetic Features and Outliers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jnKgkN5fHbGy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Create a synthetic feature that is the ratio of two other features\n",
+        "  * Use this new feature as an input to a linear regression model\n",
+        "  * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VOpLo5dcHbG0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n",
+        "\n",
+        "First, we'll import the California housing data into a *pandas* `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S8gm6BpqRRuh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9D8GgUovHbG0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "4743aaa9-4552-4bab-ea60-4edbb9f07f84"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import sklearn.metrics as metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4365</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1705.0</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>871.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>179.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1887</th>\n",
+              "      <td>-117.3</td>\n",
+              "      <td>33.2</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>2832.0</td>\n",
+              "      <td>542.0</td>\n",
+              "      <td>1065.0</td>\n",
+              "      <td>531.0</td>\n",
+              "      <td>2.4</td>\n",
+              "      <td>98.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12638</th>\n",
+              "      <td>-121.7</td>\n",
+              "      <td>39.4</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2596.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1100.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>85.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13238</th>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>39.0</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>1096.0</td>\n",
+              "      <td>218.0</td>\n",
+              "      <td>657.0</td>\n",
+              "      <td>199.0</td>\n",
+              "      <td>2.8</td>\n",
+              "      <td>65.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5557</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>1991.0</td>\n",
+              "      <td>584.0</td>\n",
+              "      <td>1380.0</td>\n",
+              "      <td>535.0</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>181.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15864</th>\n",
+              "      <td>-122.4</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>2150.0</td>\n",
+              "      <td>817.0</td>\n",
+              "      <td>2075.0</td>\n",
+              "      <td>807.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>212.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7037</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>33.7</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1725.0</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>799.0</td>\n",
+              "      <td>306.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>368.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7277</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>1482.0</td>\n",
+              "      <td>336.0</td>\n",
+              "      <td>768.0</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>327.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11925</th>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1620.0</td>\n",
+              "      <td>276.0</td>\n",
+              "      <td>939.0</td>\n",
+              "      <td>277.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>72.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13981</th>\n",
+              "      <td>-122.0</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1307.0</td>\n",
+              "      <td>236.0</td>\n",
+              "      <td>586.0</td>\n",
+              "      <td>249.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>241.9</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "4365      -118.0      34.0                36.0       1705.0           299.0   \n",
+              "1887      -117.3      33.2                13.0       2832.0           542.0   \n",
+              "12638     -121.7      39.4                27.0       2596.0           435.0   \n",
+              "13238     -121.9      39.0                48.0       1096.0           218.0   \n",
+              "5557      -118.2      34.0                23.0       1991.0           584.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "15864     -122.4      37.8                46.0       2150.0           817.0   \n",
+              "7037      -118.3      33.7                36.0       1725.0           295.0   \n",
+              "7277      -118.3      34.1                52.0       1482.0           336.0   \n",
+              "11925     -121.4      38.7                35.0       1620.0           276.0   \n",
+              "13981     -122.0      37.6                21.0       1307.0           236.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "4365        871.0       296.0            4.6               179.8  \n",
+              "1887       1065.0       531.0            2.4                98.6  \n",
+              "12638      1100.0       409.0            2.3                85.5  \n",
+              "13238       657.0       199.0            2.8                65.8  \n",
+              "5557       1380.0       535.0            1.9               181.9  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "15864      2075.0       807.0            1.4               212.5  \n",
+              "7037        799.0       306.0            5.1               368.5  \n",
+              "7277        768.0       300.0            3.7               327.3  \n",
+              "11925       939.0       277.0            2.6                72.9  \n",
+              "13981       586.0       249.0            4.8               241.9  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6kNgrwCO_ms",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll set up our input function, and define the function for model training:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5RpTJER9XDub",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VgQPftrpHbG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
+        "    after training the model.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
+        "\n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "    \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "      metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "    \n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Create a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
+        "  \n",
+        "  return calibration_data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FJ6xUNVRm-do",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Try a Synthetic Feature\n",
+        "\n",
+        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
+        "\n",
+        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
+        "\n",
+        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
+        "\n",
+        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
+        "the final RMSE should be.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "isONN2XK32Wo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5ihcVutnnu1D",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "b83213ab-857c-4aea-9d4a-df9e1b854a1c"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.00005,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.51\n",
+            "  period 01 : 237.49\n",
+            "  period 02 : 237.46\n",
+            "  period 03 : 237.44\n",
+            "  period 04 : 237.41\n",
+            "  period 05 : 237.39\n",
+            "  period 06 : 237.36\n",
+            "  period 07 : 237.34\n",
+            "  period 08 : 237.31\n",
+            "  period 09 : 237.29\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.3    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.1     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.3    265.0\n",
+              "max            6.2    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.29\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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k8SgUpU2YjWFyuQy5ucUWObfW/HF94NNRhtikDKz45BTLOepoiXtAxvEeWB/v\ngfXxHuhnLFDDVzFqURJHEQJ85Xq3Bfh2gMTR9G+vLFUKQkREzePEiRP46KOPsGXLFshkMiQkJACo\nLkuIiIjAuXPndPsePXoUw4cP13ue8PBwdO/eHQAQGhqK9PR0eHl5IS8vT7fP7du34eVl35kDLOcg\nIiJ7xKAEtbio0N4IC/KGp6sUQgHg6SpFWJA3okJ7m3UeU0pBiIjIOoqLi7FmzRp8/PHHaN++PQBg\nw4YNuHTpEgDg/Pnz8PHx0e1/8eJF9OnTp955NBoN5s6dC6VSCaB6LYm7774bw4YNw7Fjx1BRUYHb\nt28jJycHvXub9zpiq7TdOQL95EjPKsSKbWdw8bd8aw+LiIioUVi+QS1OJBQiOswX00N6oahEBTcX\niVkZElrNXQpCRETN58CBA1AoFFiyZInuuWXLlmHlypUQiUSQSqVYs2aNbptSqYSLi4vu8fHjx5Gd\nnY3o6GjMnDkTc+fOhZOTEzp27Iinn34aTk5OmDlzJh555BEIBAKsWLHCpAUy7QW7cxARkb0QaEwt\nwmxF7LFGx55rjyw5t12J6bXWlNAKC/JukTUleN9slz3Pj3OzTbYyN1tfvMtSv2Nr3r/fbymxOf4i\ncgvL4dutPR6f3B/usrYXmLeV/4fsGe+B9fEeWB/vgX5cU4LsVnOVghAREdmqHp1c8frce2qXc/zK\ncg4iIrINLN8gm9ZcpSBERES2zFnqUKuc47095zFh+F2YGsxyDiIiat34KkV2QeIogpe7MwMSRETU\nZmm7c7waEwh5eym+PcXuHERE1PoxKEGtkqpSjRxFqVXaelrz2kRERE3Fcg4iIrIlLN+gVkVdVYXY\npEykpeeiQKmCh6sEAb5yRIX2tnj6qTWvTURE1JxYzkFERLaCr0rUqsQmZSIxJRv5ShU0APKVKiSm\nZCM2KdOmr93Y7AtmbRARUWPpLefYlcZyDiIialWYKUGthqpSjbT0XL3b0tLzMD2kl8XWjLDUtRub\nfcGsDSIiai7aco7tBy8h5UouXt96Bo9N6gf/np7WHhoREREzJaj1KCpRoUCp/9sbRXE5ikos982O\npa7d2OwLa2aMEBGR/XGWOuCJqf54ONwX5RV38N6e8/jyf1ehrqqy9tCIiKiNY1CCWg03Fwk8XCV6\nt7nLpHBz0b+ttV67oewLQyUZjT2OiIjIGJZzEBFRa8SgBLUaEkcRAnzlercF+HawaLtPS1y7sdkX\n1swYISIi+6ct5wjykyM9uwivb2V3DiIish4GJahViQrtjbAgb3i6SiEUAJ6uUoQFeSMqtLfNXbux\n2RfWzBghIqK2geUcRETUWnDeBhAiAAAgAElEQVShS2pVREIhosN8MT2kF4pKVHBzkVg0Q8KS19Zm\nXySmZNfbZiz7orHHERERmUNbztGrqys2x1/Et6f+QEZWIR6f4g93GQPgRETUMpgpQa2SxFEEL3dn\nq3wAb85rNzb7wpoZI0RE1LawnIOIiKyJmRJEFtTY7AtrZowQEVHboy3nSEq9jtikDLy35zwmDL8L\nU4N92IqaiIgsiq8yRC2gsdkX1swYISKitkVbzvFaTBC7cxARUYthUIKoBlWlGjmKUrbdJCKiNuuu\nTjKWcxARUYth+QYRAHVVFWKTMpGWnosCpQoerhIE+MoRFdqbaatERNTmsJyDiIhaCl9ViADEJmUi\nMSUb+UoVNADylSokpmQjNinT2kMjIiKyCpZzEBFRS2BQgto8VaUaaem5erelpeexlIOIiNo0lnMQ\nEZElMShBbV5RiQoFSv3f+iiKy1FUwm+EiIiobdOWczwc7ovyijt4b895fPm/q1BXVVl7aEREZOMY\nlKA2z81FAg9Xid5t7jIp3Fz0byMiImpLWM5BRESWwKAEtXkSRxECfOV6twX4dmA7TiIiohp05Rx9\nvHTlHBdYzkFERI3EoAQRgKjQ3ggL8oanqxRCAeDpKkVYkDeiQntbe2hEREStjrPUAU9M6Y9H7q8u\n51jHcg4iImoktgQlm6eqVKOoRAU3F0mjsxpEQiGiw3wxPaRXk89FRETUFggEAoQO8UavLm7YFH8B\n3576AxlZhXh8ij/cZSx9JCIi0zAoQTZLXVWF2KRMpKXnokCpgoerBAG+ckSF9m50D3WJowhe7s7N\nPFIiIiL7pS3n2H7oMlIu5+D1rWewYFI/DOjpae2hERGRDWD5Btms2KRMJKZkI1+pggZAvlKFxJRs\nxCZlWntoREREbQrLOYiIqLEYlCCbpKpUIy09V++2tPQ8qCrVLTwiIiKitk1bzsHuHEREZA4GJcgm\nFZWoUKDU/yZHUVyOohK+ASIiIrIGducgIiJzMChBNsnNRQIPV/2LaLnLpHBz4QJbRERE1sJyDiIi\nMhWDEnZIValGjqLUrksYJI4iBPjK9W4L8O3AzhlERERWVrOcw6u9E7499QfWsJyDiIjqYPcNO2KJ\nbhStWVRobwDVa0goisvhLpMiwLeD7nkiIiKyvrs6ybB87lB25yAiIr0YlLAj2m4UWtpuFAAQHeZr\nrWFZjEgoRHSYL6aH9EJRiQpuLhJmSBAREbVC2nKOo93bY/d3GVi35zwmDL8LU4N97PKLEyIiMh1f\nBexEW+5GIXEUwcvdmQEJIiKiVozlHEREpA+DEnaC3SiIiIjIFmjLOYL6eCGD3TmIiNo8BiXsBLtR\nEBERka1gdw4iItJiUMJOsBsFERER2RKWcxAREWDhoER5eTnCwsLw1Vdf4ebNm4iJiUF0dDQWL16M\niooKAMC+ffswffp0PPjgg9i7d68lh2P3okJ7IyzIG56uUggFgKerFGFB3uxGYUBbaJ1KRETU2rGc\ng4iobbNo943NmzfDzc0NALB+/XpER0dj3LhxeO+99xAXF4epU6di48aNiIuLg6OjI2bMmIHw8HC0\nb9/eksOyW+xGYZiqUq37nTiIBG2qdSoREVFrx+4cRERtl8WCElevXkVmZiZGjx4NAEhOTsbKlSsB\nAGPGjMHWrVvh4+ODAQMGQCaTAQCGDBmC1NRUhIaGWmpYbYK2GwUB6qqqegEIZ6kjsnJKdPvYe+tU\nIiIiW6At5+jVxQ2b4y/i21N/ID2rEAun+MNdxrWxiIjslcVCz6tXr8bLL7+se1xWVgaxWAwA8PT0\nRG5uLvLy8uDh4aHbx8PDA7m5+ttaEjVGbFImElOyka9UQYPqAETNgERN9t46lYiIyBawnIOIqG2x\nSKZEfHw8Bg8ejG7duundrtFozHq+Lnd3Zzg42F9Zglwus/YQLMYacyuvuIOfrpr+JkZRXA6R2BHy\nDu3Mug7vm+2y5/lxbrbJnudGZA6WcxARtR0WCUocO3YMWVlZOHbsGG7dugWxWAxnZ2eUl5dDKpXi\n9u3b8PLygpeXF/Ly8nTH5eTkYPDgwQ2eX6EotcSwrUoulyE3t9jaw7AIS86t5loRddfPyFGUIldR\nZvK53GVSqCsqzRor75vtsuf5cW62yVbmxsAJtRSWcxARtQ0WCUq8//77up83bNiArl27Ii0tDYcP\nH8aUKVNw5MgRBAcHY9CgQVi6dCmUSiVEIhFSU1Px6quvWmJIZGf0rRVRd7FKNxcJPFwlyFea1lqM\nrVOJiIhaH205x/ZDl5FyOQevbz2DBZP6YUBPT2sPjYiImkGL5b89/fTTiI+PR3R0NAoLCzF16lRI\npVI8//zzmD9/PubNm4cnn3xSt+glkTH61opITMlGbFKmbh+JowgBvnK9x3fzcmHrVCIiIhuhLed4\n5H5flFfcwbo95/Hl/65CXVVl7aEREVETWbQlKFAdjNDatm1bve2RkZGIjIy09DDIjqgq1UhL178g\n6vc/3cTUYB84SxwBQBdoSEvPg6K4HO4yKQJ8OyAqtDfuqDVsnUpERGQjWM5BRGSfLB6UIDKHsTUi\ntIpKVCgwUJJRXqHGroQM/GNiPwCASChEdJgvpof0qndekRBsnUpERGRjDJVzhHK9EyIim8SgBLUK\nddeIaO8iwWDfDogOu7veKtsNrRVx+Q8FVJXqWkENiaOIAQgiIiI7oa87R3Z+Ke4P7MruHERENob/\nalOrUHeNCEWJCkdTr+ON7Sn16kUljiL06e5u8FyFJSoUlZi2uCURERHZJm05x2sxQfBq74S932Vg\nza40FCjLrT00IiIyA4MSZHXG1ojIyinBroT0es8/FO4LqVj/n6+7TAo3F9aWEhERtQXaco77BnVB\nRnYRVmw7iwu/5lt7WEREZCIGJcgkqko1chSlUFWqm/3cxtaIAIC0jLx613WWOGDkwC5692drTyIi\norbFWeqAl2KCEFOjO0fcMXbnICKyBVxTgoyqu9aDh6sEAb5yRIX2braaTTcXCdq7SKAwUHJRVFKB\nohJVvTUhjHXWICIiorZFIBBgzBBv9PyrO8eB038gI7sQj0/uDw9XqbWHR0REBjAoQUZp13rQyleq\ndI+jw3yb5RoSRxEG3u2J/6Xd0Lvdw1V/OYaxzhpERETUNt3VSYbX5w3FtoPV3TlWbDuLf0zsh4G9\nPK09NCIi0oPlG2SQsbUe0tLrl1Q0hYNQYHBbQ+UY2s4a9hyQsGT5DBERkb1xklR359CWc7y/l+Uc\nREStFTMlyCBjaz0oisv1llQ0hqpSjR8z8vRuk4pFmBrs0+Rr2KqWKJ8hIiKyR7XKOb6uLudIzy7E\nQpZzEBG1KvxUQwa5uUjg4aq/i0VzdrgwFvyoqFSjpLSyWa5jSGvOQqjbKlVbPhOblGntoREREdmE\nuzrJ8PrcoRjaxwuZf3Xn+Okqu3MQEbUWDEqQQRJHEQJ85Xq3NWeHi5YKftSlrqrCrsR0LN1yGq98\nfBpLt5zGrsT0VpPa2ZLlM0RERPbMSeKAhVP6IybCD+UVary/9zz2HsvEHXXreM0nImrLGJQgo6JC\neyMsyBuerlIIBYCnqxRhQd7N2uGiuYMfpmY+tPYsBFPKZ4iIiMg0AoEAYwK64rWYQHi5O+Hg6WtY\n839pKFCWW3toRERtGteUIKNaqsNFc7T3NGf9hYayEKaH9LL6wpnaDJJ8PYEJS2aQEBER2TNtOceO\nQ5dx5pK2O0dfDOzVwdpDIyJqkxiUIJNoO1xYSnMEP8xpX9pSi3g2hTaDpOactJqzfIaIiKitcZI4\n4PHJ/eHX3R3/l5iB9/f+hHHDumNacE84iJhITETUkvivLrUqjW3vae76C9Zax8JcLVE+Q0RE1Bbp\nLefYxXIOIqKWxkwJsgvmZj7YShZCS5XPEBERtVV1yzle33oGCyb1YzkHEVELYaYE2YXGZD7YUhZC\nYzNIiIiIqGHaco6YCD+oKqvw/t6fsPcou3MQEbUEZkqQXWhM5gOzEIiILGvNmjU4d+4c7ty5g8cf\nfxxyuRxr1qyBg4MDxGIx1q5dixs3bmD16tW6YzIzM7Fx40YMGTKk3vl2796NTz75BElJScjOzsak\nSZPg7+8PAHB3d8f69etbbG5kf7TlHL26uGJz/EUcTL6GjOwiLJzSHx6uUmsPj4jIbjEoQXajsR08\nLL2IJxFRW3T69GlkZGQgNjYWCoUC06ZNw8CBA7FmzRp069YNH374Ifbs2YOFCxdi586dAAClUolF\nixZh8ODB9c6Xn5+PhISEWs/5+PjojiVqLt07yrC8TjnHPyb2w6DeLOcgIrIEBiXIbjDzgYio9Rg6\ndCgGDhwIAHB1dUVZWRnWrVsHkUgEjUaD27dvIzAwsNYxn376KebMmQOhsH516dq1a/HMM8/g2Wef\nbZHxU9umLefo090duxIz8EHcTxh3b3dMG8XuHEREzY3/qpLVqSrVyFGU1uuQ0Vhcf4GIyPpEIhGc\nnauz0OLi4jBq1CiIRCIcP34ckZGRyMvLw+TJk3X7l5eX4/vvv8fYsWPrnSs5ORkSiQSDBg2q9Xxe\nXh6eeeYZzJo1C/v27bPshKjNEQgEGB3QFUtnB6KjuxMOJrM7BxGRJTBTgqxGXVWF2KRMpKXnokCp\ngoerBAG+ckSF9oZIz7dkRERkexITExEXF4etW7cCAEaNGoXg4GC88847+OSTT7Bw4ULdfqNHj66X\nJVFRUYH169dj06ZNtZ5v3749Fi9ejMmTJ6O4uBgPPvgghg0bBi8vL6PjcXd3hoODZYLWcrnMIucl\n01niHsjlMqzvLcfGvedx/MfrWLn9LJ59aAiG9uvU7NeyB/z/wPp4D6yP98A8DEqQ1cQmZdZamDJf\nqdI9jg7zbfL5VZVqlnEQEVnRiRMn8NFHH+E///kPZDIZEhISEB4eDoFAgIiICGzYsEG379GjR/HQ\nQw/VO8elS5eQl5eHBQsWAABycnLw7LPPYt26dZg+fToAwMPDA/7+/vj1118bDEooFKXNOMO/yeUy\n5OYWW+TcZBpL34M5Eb7o0ckFuxIy8ManyYi8tzseYDlHLfz/wPp4D6yP90A/Y4EaBiXIKlSVaqSl\n5+rdlpaeh+khvRoMJBgKOjADg4jI+oqLi7FmzRps374d7du3BwBs2LAB3t7e6Nu3L86fPw8fHx/d\n/hcvXkSfPn3qnWfQoEE4fPiw7nFoaCjWrVuH06dP4+jRo3jllVdQWlqKy5cv1zofUXMTCAQYPbgr\nenau7s5xKPkaMrILsXCyPzzd2J2DiKixGJQgqygqUaFAqdK7TVFcjqISlcGOGA0FHSydgUFERA07\ncOAAFAoFlixZontu2bJlWLlyJUQiEaRSKdasWaPbplQq4eLiont8/PhxZGdnIzo6Wu/5g4KCEB8f\nj6ioKKjVajz22GPo2LGj5SZE9Je63TlWbDuD+RP7YTC7cxARNQqDEmQVbi4SeLhKkK8nMOEuk8LN\nRWLwWGNBh+khvZqcgUFERE0XFRWFqKioes/v3r1b7/6nTp2q9XjUqFF690tKSgIAODg44O23327i\nKIkaR9ed4y537ErIwPq4n1jOQUTUSPxXk6xC4ihCgK9c77YA3w4GAwcNlX3kKkobzMAgIiIiaipt\nOcfS2YHo6OGMQ8nXsHpXKvKL2J2DiMgcDEqQ1USF9kZYkDc8XaUQCgBPVynCgrwRFdrb4DENlX1A\nIICHq/4si4YyMIiIiIjM1b2jDMvnBOHefh1x9boSK7adwY+ZedYeFhGRzWD5BlmNSChEdJgvpof0\nMrlLRkNlH/L2Tgjwldcq79AyloFBRERE1FhOEgc8Nqkf+nRvjy+05Rz3dMcDISznICJqCP+VJKuT\nOIrg5e5sUsDAlLKPxmRgNERVqUaOohSqSnWjz0FERET2SyAQIKRmOccZlnMQEZmCmRJkc7TBhbT0\nPCiKy+EukyLAt4Pu+cZkYBjC9qJERERkDm05x2eHryD5l9vszkFE1AAGJcgsqkp1kz/oN5WpQQdt\nBkZTsL0oERERmYvlHEREpmNQgkxijYyBhgIgzRF0aOj6DbUXJSIiItJHW87h09kVm7/+GYfOXENG\ndiEWTvGHp5vU2sMjImo1GJQgk5iSMdBcWRStpWSioU4fRSUqeLfYaIiIiMgW6S3nmNAPg+9mOQcR\nEcCgBJmgoYyBqcE+iD/xW7MFEVpLyURDnT7YXpSIiIhMUbOcY1diBtZ/+RMi7umG6SG9WM5BRG0e\n/xWkBjWUMbArIQOJKdnIV6qgwd9BhNikTLOv1VAApCW6X2g7bQBosNMHERERkSn+7s4RhI4ezjh8\nJgurv0hFXlGZtYdGRGRVDEpQg7QZA/q4yyS4/EeB3m01gwiqSjWyc0uQnVNsNLBgSsmEpairqrAr\nMR1Lt5zGKx+fxtItp1Gl0WBsYNdmbS9KREREbVc3LxcsnxOEYf064uoNJVZuO4sfM/KsPSwiIqth\n+QY1SOIoQoCvvFZJhVaf7u744eItvccpistRoCzHt8k/IfHMNZRXVAcjpGIhRgzojIfG3l2vvKOp\nJRNNWddCX9lI0rnrCAvyxqoF91q96wgRERHZByeJAxZM6oc+d7nji4R0lnMQUZvGoASZRJsZkJae\nB0VxOdxlUgT4dsDU4J64fE1hMIiQeC4bR1Ov13q+vKIKSeeuQygQ1FsjwlgApGbJRN3gQ1MXxzSl\n04YlO30QERFR2yIQCDBqUJfq7hzxF3H4TBYys4vw+JT+6ODmZO3hERG1GAYlyCQioRDRYb6YHtKr\nXsaAoSDCwF4e+CnTcDpi6pVcTA/pVS/zwFAAJCq0t8HgQ5VGg6Rzfwc/zF0c05SyEQYliIiIqLl1\n83LBsjlB2HnkCk7/fBsrt53FoxP6IuBu/etaERHZGwYlqJaGyh8kjqJ6H84NBRHGBHTFsbQbBq+l\nKFbp/bBvLACyKzFdb2cOqVh/NoQ2y6Ghkgt22iAiIiJrcZI4YMHEfujTvbqcY8OXF3D/0G6YMZrl\nHERk/xiUaCMaCjY0pfzBUBBBVak2+EEfqF4k09iH/boBEGMlFuUVVXqfNzXLwdSyESIiIiJLqFvO\nceRsFjKvF2EhyzmIyM4xKGHnTA026Fvk0ZzyB+DvIIK2paabi8TgB30AGOInN+vDvrESC0PauxgP\nfNRkrGyEiIiIqCV083LB8rlB2Hn4Ck79fBsrtp7F/Al9DbYpJyKydWYFJdLT03Ht2jWEhYVBqVTC\n1dXVUuOiZmJKsMGURR5NCR7oC4AMvrsDxt/XA0lns2p03xBhxIBOZn/YN1ZiIRICaj3JEs5ODiYH\nPoyVjRARERG1FKnYAf+Y2A9+2nKOr1jOQUT2y+SgxPbt27F//35UVFQgLCwMmzZtgqurKxYtWmTJ\n8VETmBpsaMoijzXLQr7839V6AZDvzl3H5OCeWPf0SOQWlgEaDeTuzgY/7BsrMzFWYmFIXmEZVJVq\ns4IL+tbNICIiImpJ2nKOnp1dsfnr6nKOjOwiPDGlPzq0ZzkHEdkPk4MS+/fvx549ezBnzhwAwIsv\nvohZs2YxKNGKmRpsaMwij3WzItxlYpSq1HqvdfriTYy7pxu85S4Gx2pqmYm+Eou7u7nh9M+39Z63\nvKIKuYVlRq9NRERE1Fp5a7tzaMs5trGcg4jsi8lBiXbt2kFY48OhUCis9ZhaH1ODDY1Z5LFuWUhB\ncYXBceQVljW42KSpa1roK7HIVZQaDEoAADQaw9uIiIiIWjltOUef7u74nOUcRGRnTP5XrHv37vjw\nww+hVCpx5MgRLFmyBL169bLk2KiJtMEGfeoGG6JCeyMsyBuerlIIBYCnqxRhQd56130wVhaiT4f2\nTkYXm2yozERVWT8DQ1tiIXEUQe7ubLAlqFRcvZ2IiIjIlgkEAgQP6oJls4PQ2dMZR85m4a3PU5FX\nWGbtoRERNYnJmRLLly/HZ599ho4dO2Lfvn0IDAzEww8/bMmxUTMwtaOEOYs8mtsFY5h/Z6NrOjRl\nTQugOkAxYkBnJJ27Xm/biAGduFglERER2Y2/yznScernW1ix7SwendAXQ1jOQUQ2yuSghEgkwrx5\n8zBv3jxLjoeambkdJUxZ5NFYWYhULEI7qQMUxSpdAOTRSf1RUPBno85naE2LumaO6YWMrCJk55RA\nA0CA6hftmWOYzUNERET2pbqcoy/63NUeXxxJx4dfXUB4UDc8OIblHERke0wOSvTr1w8CgUD3WCAQ\nQCaTITk52SIDo+bVnB0ljK1BMXJg53oBEFEDL47GzucsdYCDSKDnqNrijv2KrJwS3WMNgKycEsQd\n+7XWmhRERERE9kAgECB4YBf4dHbF5viLSEjJQub1Qiyc4g85u3MQkQ0xOShx+fJl3c8VFRU4deoU\nrly5YpFBUetnrCxEJBSaHQCJCu2NK9cKawUWgOrAQmxSptHAgqmtT4mIiIjsjbe8upzj8yPp+OHi\nX+Uc4/si0I/lHERkGxqV3yUWixESEoKTJ08293jIRmjLQlYtuBdvPjYMqxbci+gw31rtO81xR61B\naXml3m2GFrvUMmVNCiIiIiJ7pe3O8ej4vlCrq7DxvxewKzEdd9RV1h4aEVGDTM6UiIuLq/X41q1b\nuH3bSBtGahOaqyykKYtdNseaFERERES2buTAzvDpLMOm+ItITMnG1etFLOcgolbP5K+1z507V+u/\noqIivP/++5YcG7Uh2sCCPg0FFsxpfUpERERkz7rKXbB8zlDc598Jv90sxoptZ3Huiumt3ImIWprJ\nmRJvvfWWJcdBbZyxxS5NCSyY2vqUiIiIyN5JxCLMn9gPft3d8fmRK9j43wsIC/TGg2N6w9GB3TmI\nqHVpMCgREhJSq+tGXceOHWvO8VAb1pTAgrmtT4mIiIjsXa1yjnPZyLxehIVT/eHFcg4iakUaDErs\n2rXL4DalUmlwW1lZGV5++WXk5+dDpVJh0aJF6NOnD1588UWo1WrI5XKsXbsWYrEY+/btw44dOyAU\nCjFz5kw8+OCDjZsN2bTmCCw0Z+tTIiIiIlunLef4POEKTl64hZXbzuLR8X0Q6Odl7aEREQEwYU2J\nrl276v4rKyvDjRs3cOPGDfz+++947rnnDB539OhR+Pv74/PPP8f777+Pt99+G+vXr0d0dDR27dqF\nu+66C3FxcSgtLcXGjRuxfft27Ny5Ezt27EBhYWGzTpJsS83AQo6i1GjnDSIiIiIyTiIWYf6Emt05\nLmJXQjoq77A7BxFZn8lrSqxatQonT55EXl4eunfvjqysLDz66KMG9x8/frzu55s3b6Jjx45ITk7G\nypUrAQBjxozB1q1b4ePjgwEDBkAmkwEAhgwZgtTUVISGhjZ2TmQjVJXqWhkR2scuzmLEn/gVaem5\nKFCq4OEqQYCvHFGhvRvdcpSIiIiordOWc2z++mcknstGxvUiPMFyDiKyMpODEhcuXMDBgwcRExOD\nnTt34uLFi0hISGjwuFmzZuHWrVv46KOPMG/ePIjFYgCAp6cncnNzkZeXBw8PD93+Hh4eyM01vkKw\nu7szHBzsb70AuVxm7SHUUl5xBwqlCu6uEkjFJv+p6FVzbmp1FbZ+8zNOX7yJ3MIyyNs7wcXJEcWl\nFcgrKodULEKZ6u/siHylCokp2XB2EmPB1AFNGocltLb71pzseW6Afc+Pc7NN9jw3ImoduspdsGx2\nEL5ISMf3F25i5bYzmDeuL4L6sJyDiKzD5E+a2mBCZWUlNBoN/P39sXr16gaP2717Ny5duoQXXngB\nGo1G93zNn2sy9HxNCkWpiaO2HXK5DLm5xdYeBgBAXVWF2KTMZstU0M5Nmwlx+GwWjqZe123PUZQh\nR1Gme1wzIFHTyfM3MO6ebq1qAcvWdN+amz3PDbDv+XFutslW5sbACZHtk4hFeHRCX/h1b4+dR65g\nU/xFjA30xkx25yAiKzA5KOHj44MvvvgCQUFBmDdvHnx8fFBcbPjN08WLF+Hp6YnOnTujb9++UKvV\naNeuHcrLyyGVSnH79m14eXnBy8sLeXl5uuNycnIwePDgps2KmiQ2KbNWa05tpgIARIf5mn0+tboK\nuxLTkZaei3ylCkLDzVyMUhSXo6hExYUsiYiIiJrBfQM6o0dnV2yOv4jv/urOwXIOImppJodC33jj\nDUyYMAHPPfccHnjgAdx111346KOPDO6fkpKCrVu3AgDy8vJQWlqKESNG4PDhwwCAI0eOIDg4GIMG\nDcKFCxegVCrx559/IjU1FUFBQU2cFjWWqlKNtHT95TNp6XmNWnRy6zc/IzElG/lKFQCgquFkGL3c\nZVK4uUgadzARERER1dO1Qzssmx2EkQM6449bxVi57QxSLudYe1hE1IaYnCkxc+ZMTJkyBRMmTMDk\nyZMb3H/WrFl47bXXEB0djfLycixfvhz+/v546aWXEBsbiy5dumDq1KlwdHTE888/j/nz50MgEODJ\nJ5/ULXpJLa+oRIWCv4IHdZmTqaAt1XCSOOD0xZvNMrYA3w6tqnSDiIiIyB7oLecY4o2ZoSznICLL\nMzko8dJLL+HgwYOYNm0a+vTpgylTpiA0NFS31kRdUqkU7777br3nt23bVu+5yMhIREZGmjFsshQ3\nFwk8XCW6rIaaTMlUqLseRXsXCRQl+oMcDZGKRaioVMNdJkWAbwdEhfY2+xx1O3wQERERkX7aco6P\n4i/iu9RsZN4owhNT+rN0logsyuSgRGBgIAIDA/Haa6/hzJkz2LdvH1asWIHTp09bcnzUwiSOIgT4\nymutKaFlSqZC3fUoGhuQkDgK8eZj96KisqpRAYXmXqyTiIiIqC3o2qEdls75qzvHTzexcvtZducg\nIosyq8+jUqlEYmIiDh06hKysLERFRVlqXGRF2oyEtPQ8KIrLTc5UMLYehbkqKqtQUVnV6Mh8cy/W\nSURERNRWSBxFeHR8X/h1+7ucI3RIV0SF3s1yDiJqdiYHJebPn4+MjAyEh4dj4cKFGDJkiCXHRVYk\nEgoRHeaL6SG9kFtYBlRVDkcAACAASURBVGg0kLs7N5hhYGw9ipqEAmDkwE64+JvC4P7uMkmjF7Vs\naLHO6SG9WMpBRERE1ID7BnSGz1/dOZJSr+PqdSWemMpyDiJqXiYHJWbPno2RI0dCJKr/YW7Lli1Y\nsGBBsw6MrEtdVYUv/3fVrPIHNxcJJGIRyiuMd+jQABg/rAfEjg56y0QAoJ2TIxxEjesd2lyLdRIR\nERG1dV3+KufYlZCOE3+Vc8wd1xdDWc5BRM3E5PyrkJAQvQEJADhx4kSzDYhaB235Q75SBQ3+Ln+I\nTcps4MiG+316/LVgZlRob4wN7AqRsH7wISunxIRr6addrFMfthUlIiIiMo/EUYR54/viHxP7Ql2l\nweb4i/j8yBVU3jG/VTwRUV3NUhSm0TT8QZRsR0PlD6pKdb39cxSlyFWUoryiqsHzaxfMFAmFmDG6\nN9zaOZp8LVNoF+s0dm0iIiIiMs8I/85YPmcousrbISn1Ov698xxuK0qtPSwisnFmLXRpiEDQuDR7\nap1MLX/Q1+FCKhYaDExIHIUIHtSl1oKZRSUqKIorGryWPsbafTZ2sU4iIiIiMqxLh3ZYOrtGOce2\ns5g7rg/u6dvR2kMjIhvVLEEJsi/a8od8PYGJmuUP+jpcGNNO6ojpIb1qrUlh6rVqMqXdZ83FOg0F\nLoiIiIjIfNpyjj7d3fHZ4Sv46OufcSWrEE/NDLD20IjIBrGnD9VjSvlDY9p/FpaoUFRSO/jQmFIL\nc9a7kDiK4OXuzIAEERERUTMb7t8Jy+cGoau8HY6mXscLG06wnIOIzNYsQYkePXo0x2moFYkK7Y2w\nIG94ukohFACerlKEBXnryh9Mbf9Zk6HMh4auVZO5610QERERkeV09qwu5xg1qDN+vV6EldvO4syl\n29YeFhHZEJPLN65fv47Vq1dDoVBg586d2LNnD+655x706NEDb7zxhiXHSC2o5joNxsofjJVdSA20\nBTWU+WBOqQXbfRIRERG1LhJHEeaO64ug/p2xce/56nKOa4WYNbY3HB2YrUpExpmcKbFs2TJMmTJF\n12nDx8cHy5Yts9jAqGWpq6qwKzEdS7ecxisfn8bSLaexKzEdDiKB3vIHY2UX9w3oVCvzwcvdyWDm\ng7lcnB0hEet/cWO7TyIiIiLrGRPYDcvnBsFb3g5H067j35+dw+0ClnMQkXEmZ0pUVlZi7Nix2L59\nOwBg6NChlhoTWYG+RSu1j6PDfPUeU7/DhQR9urtj2qhecJY46DIfvLu0R/aNQtxRayDSEwYzZeFK\nrfgTv+nNwgCAwXd7cu0IIiIiIivSlnPsSszA8fM3sHI7u3MQkXFmdd9QKpW69p8ZGRlQqcxbU4Ba\np4bWaZge0sto2cXUYB/sSsjA5T8K8MPFW7h8TYEAXzlmjO6JxHPZ+GnPeeQqygwGG0wNiDS0uOYd\ntf5WpEREZBm///4715UionrEjiLMHdcHft3b47NDV1jOQURGmVy+8eSTT2LmzJn4+eefMWnSJMyb\nNw/PPvusJcdGLcSUdRqMiT/xG364eAsFxRW1umH8a8c5JKZkI0dRZrBLhjkLVza0uOb5zHwudElE\n1MyWLFlU6/GmTZt0Py9fvrylh0NENmR4/04s5yCiBpkclBg2bBji4+Oxbds2rF27Ft999x2GDx9u\nybFRC9EuWqmPoXUaVJVq5ChKUVxaYTCocD33T73P1ww2mBMQcXF2hNjB8J9sUUlFgwEUIiIyj1pd\nO9h7+vRp3c/adaaIiAz5uztHF1zLKcHK7ezOQUS1mVy+cfHiReTm5mLMmDFYt24dfvzxRzz99NMI\nCgqy5PioBWgXraxZQqFVt2NG3fUf3FzEKCypMOt6NbtkGOviUTcgEn/i/9m708Cm6nR/4N/saUna\npm0q0BYolBZZCmWTRVaL4IwgbqBVx2Wu4nLvqHfuHWfm6giOjiN6df46boiIMDJWcS7iipZNENnL\nUqAtZZFCgW5pk9JmaZL/i5KQtCfJSds0afv9vJEm55z8TkIx5znPcgqWJt8lGvExbHRJRNTRXGWb\nLp6BiJbPEREJESrnKDpTiztZzkFECCJT4vnnn0daWhr27t2Lw4cP45lnnsHrr78eyrVRJ5o/ZSAm\nD++NhBgVpBIgIUYtODHD1f+h2miBEwg6IAF4Bxv8TfHwDIgE6ifRcnsiIgoNBiKIqK08yzm2sJyD\niC4TnSmhUqkwYMAA5OXlYcGCBUhPT4dUKjqmQRGqZeaDTqvEhGG9kTtrMKJVCq9txQQGxGgZPGg9\nxUON7IxEr4BIoH4Sk4f37pCRo0RE5M1oNGLfvj1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JSniOCO3OIvHi1VfwwOl0YuO+c+7t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kGJCGGzO1r1Wqirt2BLQXlQx9FpVKi7ZHGXUszITnb3c/CnIwMt/voptNQV+yu4moq6uKZqAPDK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\nrkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Yi9jSda_54mb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "7ad4868c-2ad5-405c-e24b-e2055267f89b"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.0003,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.39\n",
+            "  period 01 : 237.23\n",
+            "  period 02 : 237.08\n",
+            "  period 03 : 236.93\n",
+            "  period 04 : 236.78\n",
+            "  period 05 : 236.62\n",
+            "  period 06 : 236.47\n",
+            "  period 07 : 236.32\n",
+            "  period 08 : 236.17\n",
+            "  period 09 : 236.01\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           1.7    207.3\n",
+              "std            0.8    116.0\n",
+              "min            0.3     15.0\n",
+              "25%            1.4    119.4\n",
+              "50%            1.6    180.4\n",
+              "75%            1.9    265.0\n",
+              "max           37.4    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1.7</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.8</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.4</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>37.4</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 236.01\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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aL6ZvEGmhqKpCckYOzmTnoUAmh4fECUF+3oiL6AKhHWN6pJm8QsEUECJqkCqd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\nhIiIiIiIiIhYQkkJEREREREREbHE/wcBEOXWDTwgvQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BU6nOrcp6eLo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "6d0e96d4-fac5-4589-ed5c-4ed1776df84f"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.00005,\n",
+        "    steps=500,\n",
+        "    batch_size=11,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.51\n",
+            "  period 01 : 237.49\n",
+            "  period 02 : 237.46\n",
+            "  period 03 : 237.44\n",
+            "  period 04 : 237.41\n",
+            "  period 05 : 237.39\n",
+            "  period 06 : 237.36\n",
+            "  period 07 : 237.34\n",
+            "  period 08 : 237.31\n",
+            "  period 09 : 237.28\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.3    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.1     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.3    265.0\n",
+              "max            6.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.28\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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DPn36wMHBAcXFxfU2Zx4zZgw2btyI8PDwOsss+V2htLQUc+fO1TZABIA///wT58+fR0hI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\nt8LFEog+AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "tDgQ5P3W6oZR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "669363e6-de15-4c24-ef18-fc75387589c7"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.00005,\n",
+        "    steps=475,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.52\n",
+            "  period 01 : 237.49\n",
+            "  period 02 : 237.47\n",
+            "  period 03 : 237.44\n",
+            "  period 04 : 237.42\n",
+            "  period 05 : 237.40\n",
+            "  period 06 : 237.37\n",
+            "  period 07 : 237.35\n",
+            "  period 08 : 237.32\n",
+            "  period 09 : 237.30\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.3    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.1     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.3    265.0\n",
+              "max            5.8    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>5.8</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.30\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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FRUU1Ls48YsQIfPXVVwgODq60zZz3CiqVCi+++KJhAUQA+Pvvv3Hu3DkEBATA19cXCQkJ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yfTokULq0MSERGR3yE2NpYdO3aQlZXF448/TmBgILGxsXh7e+Pj48PMmTM5fvw4MTEx9jIHDx5k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\nRERERERERCzxf2+v+3aOYQVFAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Egx3WVfv7hTd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "56ee3166-1429-4a92-d74e-6071f8689606"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.0003,\n",
+        "    steps=500,\n",
+        "    batch_size=8,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.39\n",
+            "  period 01 : 237.23\n",
+            "  period 02 : 237.08\n",
+            "  period 03 : 236.93\n",
+            "  period 04 : 236.78\n",
+            "  period 05 : 236.62\n",
+            "  period 06 : 236.47\n",
+            "  period 07 : 236.32\n",
+            "  period 08 : 236.16\n",
+            "  period 09 : 236.01\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           1.7    207.3\n",
+              "std            0.8    116.0\n",
+              "min            0.3     15.0\n",
+              "25%            1.4    119.4\n",
+              "50%            1.6    180.4\n",
+              "75%            1.9    265.0\n",
+              "max           37.5    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1.7</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.8</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.4</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.9</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>37.5</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 236.01\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PgfjEdMQkpJp7aERERBaD6RxERGTNGJQgi6QoVSIpOUNwW1JyJlM5iIiIKmE6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YhEIixfvhxqdfe/C6OQSTBuWDx2HjrP+rhjPwdfYZsKcbDwCoKUUtapEN7cXPP1j1DKJQhWSqHR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\nAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "B0AY6DfT8AA1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.0004,\n",
+        "    steps=500,\n",
+        "    batch_size=10,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "i5Ul3zf5QYvW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Leaz2oYMQcBf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZjQrZ8mcHFiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Identify Outliers\n",
+        "\n",
+        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
+        "\n",
+        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
+        "\n",
+        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P0BDOec4HbG_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 320
+        },
+        "outputId": "772a3253-8b11-4211-b9b3-5eb26057538c"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(20,5))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])\n",
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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AAADjZbggKF79HHepI6PPtRWY9Ps/dMatR5SoRs1YApVE25/mV1fqgxZvxmvkxAvbcqkO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\naVBjS0B5HpdmX3K2nsZAb5rCmTtGXcS6JP288uNB1+uIFmT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0aPPmzXr55Zcj3ZOscL6N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\n",
+            "text/plain": [
+              "<Figure size 1440x360 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jByCP8hDRZmM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s0tiX2gdRe-S",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMQD0Uq3RqTX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
+        "\n",
+        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "POTM8C_ER1Oc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "7d5991b3-be99-497e-d3ca-9d00bd67c4ad"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 8))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFKCAYAAADScRzUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQg0AgGGEGgAAw/4P1DJKJgyt6msAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Z-fK6vuE9Rfn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "73b16bbf-ef52-4c7a-919f-90aea747f6e3"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 2))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hQpb45CX9WOw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "5606b75d-4d32-4512-cb9f-e7ab5374a6fc"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 12))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WvgxW0bUSC-c",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8YGNjXPaSMPV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9YyARz6gSR7Q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vO0e1p_aSgKA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To verify that clipping worked, let's train again and print the calibration data once more:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZgSP2HKfSoOH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "gySE-UgfSony",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file