first_steps_with_tensor_flow.ipynb

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      "name": "first_steps_with_tensor_flow.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/jlee2109/tensorflow_exercises/blob/master/first_steps_with_tensor_flow.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "JndnmDMp66FL",
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      "cell_type": "markdown",
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "metadata": {
        "id": "hMqWDc_m6rUC",
        "colab_type": "code",
        "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": "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"
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      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "09400ebc-911e-4964-de44-7d70980ec0f1"
      },
      "cell_type": "code",
      "source": [
        "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": 1,
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2b3e049c-ad6a-4a1d-e28d-1b20fa16467b"
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe = pd.read_csv(\"https://storage.googleapis.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
      ],
      "execution_count": 2,
      "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",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "outputId": "68dd87d5-be2f-4dfb-dad8-3f616b3fc099"
      },
      "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": {
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              "  <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",
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              "      <th>3512</th>\n",
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              "      <td>4.2</td>\n",
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              "    <tr>\n",
              "      <th>2703</th>\n",
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              "      <td>33.5</td>\n",
              "      <td>19.0</td>\n",
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              "      <td>428.0</td>\n",
              "      <td>1481.0</td>\n",
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              "      <td>6.3</td>\n",
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              "    <tr>\n",
              "      <th>15269</th>\n",
              "      <td>-122.3</td>\n",
              "      <td>37.9</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2616.0</td>\n",
              "      <td>473.0</td>\n",
              "      <td>1085.0</td>\n",
              "      <td>487.0</td>\n",
              "      <td>4.1</td>\n",
              "      <td>270.9</td>\n",
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              "    <tr>\n",
              "      <th>13884</th>\n",
              "      <td>-122.0</td>\n",
              "      <td>38.3</td>\n",
              "      <td>24.0</td>\n",
              "      <td>3580.0</td>\n",
              "      <td>735.0</td>\n",
              "      <td>1959.0</td>\n",
              "      <td>731.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>118.5</td>\n",
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              "      <th>...</th>\n",
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              "      <th>5622</th>\n",
              "      <td>-118.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>33.0</td>\n",
              "      <td>278.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>266.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>98.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11150</th>\n",
              "      <td>-121.0</td>\n",
              "      <td>37.7</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1437.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>338.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>125.0</td>\n",
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              "    <tr>\n",
              "      <th>7073</th>\n",
              "      <td>-118.3</td>\n",
              "      <td>34.1</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1137.0</td>\n",
              "      <td>304.0</td>\n",
              "      <td>754.0</td>\n",
              "      <td>297.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>330.3</td>\n",
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              "    <tr>\n",
              "      <th>7019</th>\n",
              "      <td>-118.3</td>\n",
              "      <td>33.8</td>\n",
              "      <td>45.0</td>\n",
              "      <td>2929.0</td>\n",
              "      <td>755.0</td>\n",
              "      <td>1635.0</td>\n",
              "      <td>652.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>273.0</td>\n",
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              "    <tr>\n",
              "      <th>2169</th>\n",
              "      <td>-117.4</td>\n",
              "      <td>34.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2215.0</td>\n",
              "      <td>351.0</td>\n",
              "      <td>771.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>142.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
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            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "3512      -117.9      33.8                22.0       2964.0           829.0   \n",
              "2535      -117.6      34.0                10.0       3194.0           579.0   \n",
              "2703      -117.7      33.5                19.0       2930.0           428.0   \n",
              "15269     -122.3      37.9                52.0       2616.0           473.0   \n",
              "13884     -122.0      38.3                24.0       3580.0           735.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "5622      -118.2      34.1                33.0        278.0            71.0   \n",
              "11150     -121.0      37.7                28.0       1437.0           400.0   \n",
              "7073      -118.3      34.1                52.0       1137.0           304.0   \n",
              "7019      -118.3      33.8                45.0       2929.0           755.0   \n",
              "2169      -117.4      34.0                32.0       2215.0           351.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "3512       2639.0       771.0            2.5               157.5  \n",
              "2535       2088.0       549.0            4.2               159.1  \n",
              "2703       1481.0       430.0            6.3               480.8  \n",
              "15269      1085.0       487.0            4.1               270.9  \n",
              "13884      1959.0       731.0            2.7               118.5  \n",
              "...           ...         ...            ...                 ...  \n",
              "5622        266.0        56.0            0.9                98.2  \n",
              "11150       806.0       338.0            1.6               125.0  \n",
              "7073        754.0       297.0            3.4               330.3  \n",
              "7019       1635.0       652.0            2.9               273.0  \n",
              "2169        771.0       311.0            4.4               142.6  \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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "04a44718-5c5c-4194-ebe5-21c71caca889"
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe.describe()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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
        }
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        "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "63cbe5d5-190a-4d81-ec37-6b3ed1ba97d8"
      },
      "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": 5,
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9162a31b-f50e-4141-dca1-499bf262f896"
      },
      "cell_type": "code",
      "source": [
        "# Define the label.\n",
        "targets = california_housing_dataframe[\"median_house_value\"]"
      ],
      "execution_count": 6,
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "17c77ea7-a394-4b7d-ddfb-8f9f80a66f1d"
      },
      "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": 7,
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5ed5f691-eed9-4d5a-8e0a-a32979862d0d"
      },
      "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": 8,
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d5e5a15e-dbbe-4012-b3d2-4bf00e199c6b"
      },
      "cell_type": "code",
      "source": [
        "_ = linear_regressor.train(\n",
        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
        "    steps=100\n",
        ")"
      ],
      "execution_count": 9,
      "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": 50
        },
        "outputId": "4a4df260-dfaf-436a-8166-b501575da991"
      },
      "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": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error (on training data): 56367.025\n",
            "Root Mean Squared Error (on training data): 237.417\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": 84
        },
        "outputId": "c610c6bc-641e-4ec4-ac6a-0c2792f70c53"
      },
      "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": 11,
      "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: 237.417\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f7d0ec10-0f98-4833-8b37-40c2252faf4c"
      },
      "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": 12,
      "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>0.1</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>0.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.2</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.9</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           0.1    207.3\n",
              "std            0.1    116.0\n",
              "min            0.0     15.0\n",
              "25%            0.1    119.4\n",
              "50%            0.1    180.4\n",
              "75%            0.2    265.0\n",
              "max            1.9    500.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f93dd21d-4f72-4ce2-f6be-49417228805d"
      },
      "cell_type": "code",
      "source": [
        "sample = california_housing_dataframe.sample(n=300)"
      ],
      "execution_count": 13,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "ad5934c6-e5c0-4d6e-b9da-5d56d479f08c"
      },
      "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": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YDvVInG5PXIa+WWI0eUS66SOi7immMqZGoxGzZs3C2rVrcc8998S7TT1K+I9v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\ni4iIhNg32iOGJGhprX4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff239e78c50>"
            ]
          },
          "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": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5b683531-5565-4419-f6cd-aa5a3f4d6bc1"
      },
      "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": 15,
      "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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "outputId": "521cc398-8bff-496a-c9e9-315b4265bde3"
      },
      "cell_type": "code",
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=500,\n",
        "    batch_size=10\n",
        ")"
      ],
      "execution_count": 17,
      "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": {
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A7OzsUFZWVmdz5lGjRmH16tWIiIi465glrxUqKirw7LPPahsgAsDFixdx7tw5hISEICgo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\n0gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7ff22ccdfb50>"
            ]
          },
          "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": {}
      },
      "cell_type": "code",
      "source": [
        "train_model(\n",
        "\tlearning_rate = 0.01,\n",
        "\tsteps = 1000,\n",
        "\tbatch_size = 16,\n",
        "\tinput_feature = \"median_income\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": {}
      },
      "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": 0,
      "outputs": []
    }
  ]
}