first_steps_with_tensor_flow.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/mmadeshwaran/amibroker/blob/master/first_steps_with_tensor_flow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
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      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
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        "colab_type": "code",
        "cellView": "both",
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      "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"
      },
      "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"
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      "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."
      ]
    },
    {
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      "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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              "      <td>-119.7</td>\n",
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              "      <td>33.7</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6058.0</td>\n",
              "      <td>1715.0</td>\n",
              "      <td>3285.0</td>\n",
              "      <td>1495.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>290.9</td>\n",
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              "      <td>-121.8</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>5957.0</td>\n",
              "      <td>1037.0</td>\n",
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              "      <td>1476.0</td>\n",
              "      <td>728.0</td>\n",
              "      <td>3.8</td>\n",
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              "<p>17000 rows × 9 columns</p>\n",
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              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "14498     -122.1      37.7                37.0       3342.0           569.0   \n",
              "6036      -118.2      34.0                43.0        798.0           308.0   \n",
              "5067      -118.1      34.0                34.0       2103.0           427.0   \n",
              "7546      -118.4      33.9                39.0       1166.0           222.0   \n",
              "9879      -119.7      36.8                15.0       3045.0           689.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "3897      -118.0      33.7                16.0       6058.0          1715.0   \n",
              "12821     -121.8      37.3                15.0       5957.0          1037.0   \n",
              "3221      -117.8      33.7                26.0       2589.0          1003.0   \n",
              "13866     -122.0      37.3                26.0       1983.0           301.0   \n",
              "1583      -117.2      32.8                22.0       3738.0           795.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "14498      1635.0       557.0            4.8               186.9  \n",
              "6036       1417.0       325.0            1.4               141.7  \n",
              "5067       1355.0       434.0            4.6               235.3  \n",
              "7546        640.0       206.0            3.5               230.4  \n",
              "9879       1340.0       588.0            3.2                85.7  \n",
              "...           ...         ...            ...                 ...  \n",
              "3897       3285.0      1495.0            3.4               290.9  \n",
              "12821      3838.0       997.0            6.3               253.1  \n",
              "3221       5756.0       983.0            2.2               170.8  \n",
              "13866       924.0       297.0            6.7               354.6  \n",
              "1583       1476.0       728.0            3.8               303.1  \n",
              "\n",
              "[17000 rows x 9 columns]"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 3
        }
      ]
    },
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      "metadata": {
        "id": "HzzlSs3PtTmt",
        "colab_type": "text"
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      "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": "b7c700f4-178a-4841-aea7-5b84831fce61"
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      "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",
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              "      <th>median_house_value</th>\n",
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              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
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              "      <td>17000.0</td>\n",
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              "      <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  "
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 4
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    {
      "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": "B7kGB93DVE_T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "444dc7e2-4b1b-40d8-efe4-d07580ed1461"
      },
      "cell_type": "code",
      "source": [
        "features = {key:np.array(value) for key,value in dict(my_feature).items()}  \n",
        "features"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'total_rooms': array([3342.,  798., 2103., ..., 2589., 1983., 3738.])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "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": "f48c7a60-5e4e-4a8f-ecb8-e522da624a02"
      },
      "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": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Squared Error (on training data): 56308.998\n",
            "Root Mean Squared Error (on training data): 237.295\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": "589bcfd6-8713-4eed-d31a-6f6ab4885a54"
      },
      "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",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f55a811f-fbc5-4a4e-cc7a-e7731623ba09"
      },
      "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": {}
      },
      "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": "f33c2c75-2f98-4e32-a7bc-944e30c025ed"
      },
      "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": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vqS5XDZBL1IiIEqP6KvnUU09hxIgR+OSTT3Dw4EE89thjeOmll7RsW1ZTCrCh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\nRi6EEELo2L8AL1sh0IcRSFgAAAAASUVORK5CYII=\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": 975
        },
        "outputId": "55edb98c-aa8d-4bca-8d4d-263bc3539a9a"
      },
      "cell_type": "code",
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=500,\n",
        "    batch_size=5\n",
        ")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "RMSE (on training data):\n",
            "  period 00 : 225.63\n",
            "  period 01 : 214.42\n",
            "  period 02 : 204.04\n",
            "  period 03 : 194.62\n",
            "  period 04 : 187.23\n",
            "  period 05 : 180.27\n",
            "  period 06 : 175.00\n",
            "  period 07 : 171.57\n",
            "  period 08 : 169.21\n",
            "  period 09 : 167.45\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           95.5    116.0\n",
              "min            0.1     15.0\n",
              "25%           64.0    119.4\n",
              "50%           93.2    180.4\n",
              "75%          138.0    265.0\n",
              "max         1661.6    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>115.8</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>95.5</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>64.0</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>93.2</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>138.0</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1661.6</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): 167.45\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SP7GP53JAwT5PQUJnhMjmEBQpBYlazKDXcufIDgSb9fxvxS72HS4MdEpCCCFE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\n7wkhzleT2BBuu6odb369hde//oMnJ3a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MhkR//whNZN4yKuu1lFiMeBWpVWVHWGsUlmx0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\niIiIuMT/BxBgUq9te032AAAAAElFTkSuQmCC\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": 975
        },
        "outputId": "07cee5a3-89e6-4658-dd8c-60d53e9176c9"
      },
      "cell_type": "code",
      "source": [
        "# YOUR CODE HERE\n",
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=1000,\n",
        "    batch_size=5,\n",
        "    input_feature=\"population\"\n",
        ")"
      ],
      "execution_count": 30,
      "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 : 204.86\n",
            "  period 03 : 196.59\n",
            "  period 04 : 189.80\n",
            "  period 05 : 185.13\n",
            "  period 06 : 181.27\n",
            "  period 07 : 178.13\n",
            "  period 08 : 177.08\n",
            "  period 09 : 176.20\n",
            "Model training finished.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         116.7    207.3\n",
              "std           93.7    116.0\n",
              "min            0.2     15.0\n",
              "25%           64.5    119.4\n",
              "50%           95.2    180.4\n",
              "75%          140.4    265.0\n",
              "max         2911.7    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>116.7</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>93.7</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>64.5</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>95.2</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>140.4</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2911.7</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): 176.20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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sT830dGhCiA6qeMsv7Jt5J87Scnq/+pT7ExIOO/rvl6Hf9xNKcCSVybc2PyHR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\ncRxVNaS88ux1FSSk6pKmlp9VxSipI3CMeRDJ6EI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13DwwgbU7zlzzM3d2BBF8R+W6TZxe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8zVqHe+h6LkEs64HtgmLgaNmxssh+2qoaUZ1AEQ5jtDS4td4nihlnKzjFYp0y/WQniAm1YxLUxe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\n3OrB3qYSb5VYu8REetrAA4zqGjcvvVnN7n0tANw4z8aDd6UMS2nApeaVe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2sAACAASURB\nVB5REYaQy7PH2vjycE3Q4/Wl60BPFYfVYqDd5cfjvzTg7jqmrkG3+8QZSu99Em91DcnfepS0H30T\nSZIofv8zltuLkID/bsqhyDWKFZMjWDs3Cr8C/72tmZi4eN4tOhnSTyN/9mRONulRgTFWL+NiffTm\nIdnarvD6Zjt7jrrRauDm+QYWz9Kj1fY96JVllS2Fjbz27jmaW/3YYvR8/cEUFi2IpbHF1e+OGX3F\n51coLLaz8ZNazlS7AZiWE0VMko9D1TX4Ok6hzu4aVFvZK0Fdg6dTEXHyTKBTiMEgsWCulfzcWGZN\ni8YwyM4ggoHhaPdTdvxiW86KE+14fRcTfXE2Pfm5NrIzIsnOtDAuzdyv749AIBAIBJeTrNFWfvrY\nXD744jQf7TrNb988yLxJSdy3LJPoCKHcEwiuZkRSYpjw+GQWz0xFlhUOHm8KWuIRqgRkdcF4ys7Y\nB9x1IJiKY8PnlWzZe6lRplGvoWB6SrcafeexSsru+ya++kbSfvRNUr79GKgq6qFCFjcX4lK1/LZp\nCsd8sTyWH01BVgTNTpkXv2wnISEw/n/68+5LjmUyGtBFjeZEkwGDViEn0YMtInSiBDoMKI/6eb/I\ng8sD45I1rF1qIim2f4HvvsOtvLC+ijPVbowGDfetSmbVTYmdJn2Xo9NDu9PPZ9sb+GBTPU3NPjQa\nuHGejTtWJpGWYuQnz+0K2llkMMqMy4G9xccXe+wUFtspO94OgE4rMXdGDPm5NuZOjxEz7FcYVVWp\na/B2qCDaKa1wcPacu7N7jyTB2FQz2ZkBFUR2RiQJcYYh794iuHL01lpaIBAIrgX0Oi133jiBudmJ\n/OXjUnYdreXwySbuX5rJvMlJ4ndMILhKEUmJIaQvL4XB2oBOy4hn2ew0YqNN3bYLVwIyLT2ObfvO\nXbL//nQd6FY6EGKdeVNGdZuVd+w7TNmD30FubmXsv/6ApMfWgqKg3fsxutJdNMkGftUwHbsuiu/d\nZGPiKAOnGnz8frOd7943m7TEKOrszktUB8mJ8eTnzcRsMhGh8zAj1Y+hl7uzsUXhza0eKs7KGPXw\nyG3RTB0vo+n4Qert8/D4ZI5WtLLxowYOHG1DkmBpfhwP3JlMrK17xn0oOz3UN3p5f1Mdm7YHWl+a\njBpuX5HI7csTSYgLHDfYNbrAYJUZQ0Gbw8+ukmZ27GriSJkDVQ14DkzLiaIgz0beLCtRFvF4uVLI\nssrJM06OVbZ3tOdsx97i61xuMEhMnmghJyPgBzEx3UJkhAhcrwV6ay0tEAgE1yJpiRZ+/PBstuyt\n4q0dx3nug6N8eaSGR1ZOJN4avqObQCAYeYioYQjoz0thsDag20qq0WqkkJL8rsmDC8c6eLwRuNiJ\nIzbKyKyJCQPqOuDxyRyoaAi67PDxJjw+GaNeS+uXeyl/5LsoLjfjf/tTEtbeBn4fuqI30Z49hhyT\nwG+rJyFbDPxkuZWEKB17Trr4844WoiJNJHScQ1fVgSRJzJg8kSnZGSiqylcHjlBzvorKiYkhX6pl\nRaVwv49Pdnnx+SFnnJa7FxvJmhBJfX1bt8+jsdWD1WJgZmY8DyzPQqvRICsKL35YzvbCVlobtIBE\n4igtP/g/GaSPjQx5nQbb6aGneWWsVc/aO0axYmH8JeaVl0OZMVhcbpnd+1oo2t3E/sNt+OVAKktn\n8mNNUJg/x8rXbk0XgdAVwOmSKT/eztEupRhuz0VVkS1Gx/zZ1k5TyvGjI4bMv0MwsuittbRAIBBc\nq2g0EsvnjmZmZjwvfVrG4ZNN/OTPxdx1YzrLZqeh6a32VyAQjBhEUmII6OtLYW9tQHtK8oPN9Pc8\nltIhcZieGc8Dy7Lw+GQaW4J3swiGxydzorolZDeOC7PyhpISKp74IcgyGX/8BbG3LgV3O/rPX0VT\nfxYlaTz+RfezYN9p5qX5Mek1bNzn4L19DlS6qwkuqA52HW2kYN4sEuJiaXO0s2PXXhrtLQAhX6rP\n1cu8vtlNdb1KhAnWLjUyM0vXTa7X8xo1O7xs23eOyupWfnj/LH7+x1KOHPKAqkNjkImId+GN9FNc\nYSZ9bOiX+IF0elBVlX2HW9n4SV2neeWYVBOrbkqiIM8Wst/2UCozBoPXp7D3YAtFxXa+OtiC1xu4\n4aw2DR6tE32UF61eRQaKjjgxmTUiELoM1Dd6AwqIyoAnxJkqV+d3H2B0qimggugwpUxKEKUY1wP9\n/U0RCASCa5F4q5nvrp3OriO1vLalgte3VFB8tJbHbs4mLdEy3MMTCAR9QCQlBkl/Xgp7awN6QZIf\nSnlxx4KxFB08H3T7g5UNvKyqHDzeGFatcSHRYYkw8G7hic5jXFBc9MQWZULdXkTFdwKtPjNeeAbr\n4hugrQn9lpfQtDUij5uGf/5q8LawcLyCrGh4eVc72485iI0OriZYkpfNqDF6NFodJ05XUVxyCJ/f\n322drtfP51f5rNjD1r0+QMLrb0Dy1VB6NpbpmRlou5RsBPs8VBUqKz08/r2DuF0gaVXMcS4MMd5O\n34a+vsT3NC8NljwKZV656qZEZk6J7lPA2FOZEW81My09bkBqmP7g96scPNZKYbGd4pJmXO7ADHzq\nKCMFebHMnRXNf7+/P2giSwRCg0dWVM5UuThWccGU0kFDU5dSDL1EdqaFnI62nBPTI0WpzHVKX39T\nBAKB4FpHkiTmTxnF5PGxvL6lgl1Ha/nZC3u4Zd5YbrthXMhJIIFAMDK4rG+y5eXlPPnkkzz66KM8\n9NBDnD9/nh/84AfIskxCQgK/+tWvMBgMvPfee7z44otoNBrWrl3LmjVrLuewhpT+vBT2VZIfSnlx\n5EQTbq8c9FiNrZ5uHhM91RqyrLBuc3lnEsJo0OD2XpR7qyFMJW6sOczpn/8vmggzE1/+LVF5M5Ea\nqtBvewXJ3Y5/cgHyjCXgqAN3M5JGh842mrU3G1hZcKmaQFagstHA+VY9Op1KvLGFl3bvC3v9HC4j\nb2xxU29XkRUvTu8p/EoL7V7Y/JWz8xwh8Hn0vL4+pw5XvQnZowNJxRTrwWRzI2mDH6+vL/HBkkeT\nx8VjUaP5eEtDN/PKVSuTmDC2f8FBT2VG+rg42lpc/dpHX1EUlaMVDoqK7Xz5VTOtjkCCKCHOwE2L\nbRTk2Rg32owkSSPe7+Jqw+WWqTjR3ukHUXa8vTMRBBAdpSNvZkxHIsLChLFm8XIlAEZmmZdAIBAM\nJ9GRBr5+x2TmTU7ipU/LeP+LU3xVVsfXbsoma7R1uIcnEAhCcNmSEk6nk6effpr58+d3/u93v/sd\nDzzwADfffDPPPPMMGzZsYPXq1Tz77LNs2LABvV7PPffcw/Lly7Far44HR39eCsNJ8iNMOnRaKazy\noqbJGXIcoZQOF2aun3//SLfjdk1I9NyPqkJstIlFp74i4ZWX0NpimLju91imT0JTVYZux3pQ/DRN\nWYlu0lyMLWfB5wKdCWJGg1aPES4JTB0eiaO1Jpw+DZEGmcw4Fw6Hi7gQ189qieDzEoniIy4kAKme\nVvdpoPvYL5wjBHpZR0XoaXP6kL0aXPVmfO16AAxRXszxLuJsBuyOS8+9vy/xXZNHsk/ibKVE5V4H\nqO2YjBruWJHIbV3MKwfKBWWGyaCjbVB76o6qqlScdFK0287O3XaamgOz8dZoHbcuTSA/z8bE9MhL\nVB0iEBocTXZvZxlGaUU7J886Ubrc0qmjjGRnWDr9IFKSjKIUQxCUkVLmJRAIBCONaenxPP24lbd3\nnGDr3ir+7dUSFs9K5Z6F6ZiNQl0oEIw0Ltu30mAw8Nxzz/Hcc891/q+4uJif/exnACxevJjnn3+e\n8ePHM3XqVKKiogCYNWsWJSUlLFmy5HINbUjp70vhvUsyKDvTzNm67lHx2ToH67dWsmx2WshZ6FAd\nMiB4QgICM9f1zS52HQ5e9hFsP3+/ZhqR77xN/SsvoUuII3v9s0RkZ6Ap34Nu9/vIqobnnTM5vlvh\n76zlGCO1KIYoNDGpIF06g6uqcL5VR2WjAUWVSI72svfgUd4oq+tUbfREr7Gik9IpPiKTZJNYOlfl\n2XdPhjzHplY3H+8+xM4D1bS0+XE3mvE0GwAJndmPOcGFziQTF21iWkYc20oubX/an5f4C8kjv1uL\nx27E26YHJCStQuwoP//+vZnExozMntmnq1wUFjdRtNtObb0XAEuklmU3xvH/2Xvv8LbuM037Pjjo\nAAmisPcqUhLVKFGNkmwVW66Se4mdOMlmdifJTDabqd5cu8lkN/Mlm2++3ZnsXJlJsk4mWcd27MRV\nlmzJtkxJFtWtSolFlZLYABbUA+Cc7w+QICkWUb397uvyZYgAgdMA4n1+7/O8S+a7mDbFjjxBOJQo\nhCaPqmqcPhsesGEEONYS5FxHOHm/Xi9RUWKjssxGZbmdylIbjlTDDdxiwa3GlQbwCgQCwe2KxaTn\nC6sqmD81k5fWHeHjPW3sa+ri+XunMKvMc6M3TyAQDOOaiRJ6vR69fuTTh0IhjMZEoeZ2u+ns7KSr\nqwuXy5V8jMvlorNz7E6BQZxOK3r91S980tNTLuv3vvnkbKwWI9sPnqOrJ4QnzcKC6dl85aFpyPLI\ngjusxIhEx7Zg7G/p5oWHppPutNDhG92mr9MxYkV1EFmXsEWMhcmox+m00dkzybZ/TePgf/4HSj79\ngP4UJ9ue/ganumSea9xMdOdGwjozP2yfij0rgxfvcmA26Pjjnn6kFAtfW+sY9XRKTGN3q8YZLxhk\nWFAqse7TJjbuPD3smCQ23mLSoyjgsJWgqWmoKqy9y85Dy+z84q39426yJ83C1kPtvLflBJEeE2Fv\nKpoqoTPEsXjCGOzRZG7E4pk5fOWhaaTYTJM6X2MfIo33Pz7HicN6YsHE2CnZGMfkCmNMiYIOU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LkUAgEAhuDZwpJr547xTunZfPH+tb2XGkg//3lX1UFTp5/K5SirNTb/QmCgS3PEKUuE5czD/9\n/a/WJm97+8ITjvK8GO5UM//lhbmEIjEsJj2+z/ay496/Jt4foOh//GcyH1qCYf2/Ivl9xEtmE5ux\nGIKdgASpeWAe+nB9ankZkk5GMmeSlZmJoij0dJ5g7cIMLtQLevpV3vgkwuHjcfQy3DVHYsU8E2/W\n29m4a7QooWkQC+rparfxiz1nMJt0PLM2mzX3ZmIyXX5RHo2qfLrdx1sb2jl9diC8cloKa+8dCq+8\nVK6068DbE2XbTh/1O3wca0lMMNHLEvNmOVhS62TuLIcIViQhJHV0RRI2jKaEEHGyLYQ28IaQJMjP\nMScEiHIbVWV2MjzGi57TO8XKcqshzotAIBAIbhUyXVb+w5rp3De/nzc+beFgq5cf/HoXNRXpPLK0\nhByP7UZvokBwyyJEiTG4MPPhanAx/7Q/qCSL3k5fkP/1+v7LtmvMrvBgNet5Z9sJ2tbXs+i1XyCr\ncTr+9BvMWzkb/fv/iqSEiExbCiVVEOoGnR4cBWAYmdvQH9FTVDqdSEyHVa8wJyeCqSyd7t5w8vio\nmsb2gzHe3RIhEgW7NUIg0sqbW/r5dL+JWeUeltfksu9YF97+CDoJlLCOmM9GqE9GJ6msXOrmmbU5\nuNLGDrGcDH3+KG+8d573NnZeNLzycrmUroN+f4zPdvewZYePQ439qFoilHTm1BTqap0sqEnDbruz\n34LxuMaJMyEaB/IgjrYEknkaAEaDxNQKO5VlidGcU0ptl3XMblcry62OOC8CgUAguNUozErhPz05\ni8aTPt7Y3MLuY53saepkcXU2a+uKcaVOnIEmEAhGc2dXRBcwXubDU8vLrjhtd7L+aZNBJi8jZdy2\n5vwMO8FwLDldwmYxEAxH8fVHRqwyvvpRM82vrmfV+/8XTZLYcP8XSbdbkDe8hIbGbwNV1FoyKVT6\n0PRmJEcByEOXg6bByR4DJ7wJkaDIqZDniPDaxyOPT1VhFsFQNsfPqpiNUJDdw+ctx5LP090XYdPu\nNlbOzeO//8kCTp0N8N4H3Xy63YemJToYXngyl6L8yxcNOroivPNBB5u2dBMKq4nwytUZPLhy/PDK\na0UoFKdhXw9bGnzsO9RHfGDwR2WZjSXznSya6yTNcfnCy61OKBTnWGsgwMwcDwAAIABJREFU2Qlx\ntCVAODLMipFmYP4cx8BoTjvFhRYMel1SKDQYryxY6la3styuiPMiEAgEgluNykInLz5fw76mLv7w\naStb9p9j+6F2ls/J5YGFhaRYr+93UIHgVkaIEsOYKPPh2ZUVV/TcE/mnZ5S6Rq0Orl1SQigco/GU\nb5TgEItrI1YVL+zsCEainH3lXe55/xXiej3vP/gC0yvNPO84iKLJ/CY6k9X3luO2y3zWHOJkSObp\nFUOXQiQmcaTdRE9YxiSrVGVGSLOovLxx+PGRCARdHGxOR5JUqktlHlhs4Mcvnxxz/3cf6YJ+G+98\n0Ek4opKfY+ZLT+Yypzr1shOMW04EeXN9O9t2+VDVxMjMJx9OZ9XSi4dXXk0iisqeA73UN/jY/Xkv\nSjThNSgptFBX62LxvDQyPHdmaF+XV6GxeSgP4sSpUHJaDEBetpnKARtGVbmN6mkeurqGJozEVZWX\nNx67JkKhQHCrElFUurwK3V6Fzu4oXT4Fry/K4lrnJQcDCwQCgeDykQby12aWefjs0HnerD/OBztP\n8+nnZ1k9v4B75uVjNopySyC4GOJdMkBYiU2Y+fDYstIrbise9EnvOdqZtDGoGuxv6ebljceS9w/v\n1nCmGFkwLYtnV5VjNSVW2GUdI1YVB1cZI9E4Hb4g9f/9lyxZ9zsUk5l1D3+Z5VNiPJjSRE/cyDvG\nGh5fWYDJoOP1Xf2s2x/AnRrhgYUKoUgMVbbR0m0hqkq4rTEqMyIY5JGZGLJkxWoqRq+zoWoKOt1Z\nnrmnakyLiqaB0mfkeKuR1r3tOFL1vPBULiuXeJJTJS4FVdXYe7CPN9e3c7AxUbwW5VlYszqDtfcX\n0NMTuPQTcxnEYhqfH+5jyw4fDXt6CIUTq/252SaWzHdRN89J7kVGmN5uxFWN022hZBfEkaaRVgy9\nXqKiNGHDqCq3MaXMTqp95EfQhQLVtRQKBYKbkbiq0dMbpcsbpatbodOr0OVVht2O0tcfG/N39XpJ\niBICgUBwA9DpJBZXZ1Nblckn+9p4d9sJ3qw/zqbdZ3hoURHLZuVeUsC6QHCnIUSJAXx9E2c+9Poj\n47YXTzaDYtA/HVc1Pt7TllwxHiy04qqGrJNGFGHefoVtB89jNevHLcKG207yPt7Awq3rCFlsrF/7\nVZ6o6GWhtYO2qJXdGQt4sjaTcFTlnzb62HsqMvD6Yb7/0i5KikuYOiUdTVMp80TIc8QZrBETgkMU\niyEfkz4LSZKIxDoJKaeQpDi9/pJRFpVoUE+o00w8okeSNB65L4MnHszGYrl0cScaVdm83cvbGzpG\nhleuzmTm1ER4peEqTqwYi7iqceSYn/odPj7b5aPfn/BmZHiM3Lc8MTmjKN8ybufHtcgquZGEI3Ga\nWoM0NicEiKMtfoKhIStGil1m3ixHIpCy3E5pofWSztHFwmGvhlAoEFxvAsE4XV6Fzu4BsSF5O5ro\nfvApSdvXhRgNEh6XkeJ8C26XkXSXAY/biMeV+C8n887syBIIBIKbBYNex6q5+dRVZ/PhztOs33GK\nlzc2sWHHadYuKWbhtKwrmiwnENyuCFFiAGeqCZNRJqyM/jZoNMjJzIfhXE4GRSQaZ39z15j3bd7b\nNq6KOlER9upHzWzceZp52z+gZucm/HYHmx79Ml8rO0+VqZejioPOKYu5t8JBtz/OP270cdo7tNKW\nYrOyoLYGjyuN3j4/n27fTU156ggRpKtXT5q1GjARV8MEIyeIqX0AuAYyMQYtKhu2nSXUaSEaSHR2\nGFMUVi1P44tr8sbct4nwB2Js+KSL9zZ24OuNIctw10IXD1/F8MqJ0DSNptYgW3b42LrTh7cnCoDT\noeeBleksme+iosQ6oQXlWmaVXE98vVEaBzogjjT7OX4qOKJ4ys40sWBOQoCoLLeTm2W6bGsOXDwc\ndiKhUCC4EURjKt0D4sKFYkPnQLfDYFfVhUgSuNIMlBXZ8AyIDekDYsPg7RS7fEXvKYFAIBBcHywm\nPQ/XFXPXnFzWfXaSj/ac4ZfvHWF9wykeXVbCrDKP+DwXCIYhRIkB4nGVaGyc5alxuJzW8okKLVWD\nSHTsL6zjFWGRaJy9R9tZ/OnbVH++lV6Hm62Pf4lvF58k1xBkdyQD2/xFzMu00Nyu8NNNPfQN+1Jc\nXJDLgjkzMBj0NB8/xY69B4nF4+w9pvDYslJUVcc7WyI0HIoBRsLRc4SibcDQc8yu8GAyyPT2Rek7\nZ6b/ZCqaBnpLjKyiOAtnuy95xN9geOXG+m7CkesbXqlpGqfawtQ3eNnS4KO9K2FBsNtkVi11Uzff\nxbQpduRJKt23ogVBVTXOnAsnsyCONPlp7xxmxZAlSotsVJXZqCyzU1luIy314gGel9ItMtlwWIHg\neqCqGn39Mbp8fTS19tDVHR1hrejyKvT0xZLjay/EZpXJ9JhwuwykD+tuSNw24EozoteLL6gCgUBw\nO5FqNfL0inJWzc3nra3H2XrgHP/0xgFKc1N5fFkpUwqcN3oTBYKbAiFKDPCvbx4kPrYeQESJjxIE\nLre1fKJCayLGK8J6eoNU//FlKg/vxOvKZN8Tz/BXhS04ZYX6aAFld8/HlaJnW3OIX23tZVB30csy\ntXOqKSvKJxqNUd+wh+On2pLP6+sPs+NwmE07oT+oke3R8fjdBrYd0th7zIivP5wM33xkSQl/WHee\nN947TzCkkp1p4tlHsykvNZGWYr6kFvvm4wHe2tDBtp0+VA3cTgNPr8lm5XUIrzzXHmbLDh/1Db6k\nRcRs0rFsoYu6Wiczp6Vcsh/wVrEgKFGV5uPBgSyIxFQMf2BIpLNZZWpmpCa6IMpslBXbMBknfywu\np1tkonDYQSFMILhahMLxhLjgiyY6HLoVunxD3Q7dXoVobGzFQa+XcDsNTJtiTwgNyQ4HQ/L25djW\nBAKBQHB74HaY+cr9VayuLeCPn7ay+1gnP3p5L9Ulbh5bVkJBpsgDEtzZCFGCROF4oGVsSwWAM8U0\nShC43NbyiQqtiRirCFOVKD1/+9+oPLyTjow8jj/5KH+V24xJivOxrpKae2di0Eu8dyDE3jYtKUg4\nHaksXViDI8WOt6eHzdt20x8IJp9XwoDDUsybmzVkHdy30MjdcwzIskRRdgWPLSulsyeEpqo0NSt8\n67uNdHYr2G0yX30mj3vv9lxS8a6qGnsO9PHWhmHhlfmJ8Mq6ea5runrY5VXYOiBEtJxMHAODXmJh\nTRp1853UVDswmS7fYnGzWhB6+6KJQMqByRgtJ4LE4kMFV2a6kbkzHInJGOV28rLNV+SBvNxukcEO\nm73HukYIYZfaeSO4s4nFNLw9w6wUw/IcugamVwwX4S4kLVVPYb6FdJeR/FwbNiuku4yJXAe3EUeK\nXniEBQKBQHBRcjw2vvFoNS1ne3njkxYOtHZzoLWb2qoMHllaQqawpQruUIQoQaJw7OoJjXt/ZaFz\nlCBwJa3lj99VQuNJH2c6x54UYTbK2Mz6UaNAh6OGwjT9yV/Tu2kroSmV+O5fzrczm4kjsTW1hkWL\nKojGoU+fwcqlTp7PTOWnr+3FF7ZQWVGOLMv4ezsIettGCBJG2YPFWADosVki/OmjDrLdQ5dJXFV5\nY3ML23Z3c/6kTDysR6eDh+5J58mHsrHbJn9JjRVeOWtaCmuGhVdejOF2gMnS0xfls109bNnh4/Cx\nhAgiyzCnOpUl853Uzk7DepVWNW8GC4KmaZw9HxmwYQRobPJztn1oe3Q6KCmwDmRBJOwYrrSLWzEm\ny5V0iwyGwz62rPS2CgkVXD00TaPfHx+R29A1MKViUHzw9URHjKIdjtmkI91tpLw4keUw3FrhcRvx\nOA0jAlrT01Po7Oy/TnsnEAgEgtuR0hwHf/nMbA6f8PH65hZ2HOlg99FOlszM4aFFRThThEVVcGch\nRAkShWN6moUO32hhwmyUeXZV+aifX0lr+euftI4rSADUzciesAiL+wMc+9K36f9sD467FzLvz+7H\n3LSNgGagKW8BtdPzCChgzigizZhQXGOqRPW0arqDemRJpdwTJKvURlwtQyfB1v3dyFIBBtmBpsUJ\nKifwBTv4eG/eiJXsX751jI0f9xD1Jz4sDXYFiyeM2WOftCDhD8RY/3EX6zaNDK9cszqDovzJKcRj\n2QEWz8zloYUFY9oBAsEY23f3smWHl/1H+lHVRLDc9Eo7dbVOFtY4SU25+m+HG2FBiEZVWk4GEwLE\nQCdEn38o2NRq0TF7eiqVZYkuiPISK2bTtSv0r0a3yODYW8GdR0RRR4zE7B4mNgwKEYoytuKg04Hb\naaSy3J4IjxzobHA7jaS7EwKE1SLCIwUCgUBw/ZEkiWnFLqYWOdl9tJM3Pm3lk71tbDtwjpVz87lv\nQQE289VbJBIIbmaEKEGi4FkwPZu361tH3Vc3IxuraewPhMtpLZ9o1VgnwbJZOUmf/VhFWMzXy9Hn\n/pzA3kM477+bKc/Px9C0Dc2WhlyznCqLCVU2Y8spAF3i9PaEdDSc1ggpetLMcaoyI5j0g1/iJTIc\nBZj1WYAOJd5DUDmBpinJfXtsWSlRReOVt87x/qYAaEZkcwxregi9JT7icRMV2e2dEd75sINNA+GV\nVouOtaszeGAS4ZUXBiSOZQd4u76VYEhJiijhSJxdn/dS3+Bjz4E+YgN+8IoSK3W1LhbNS8PtvLah\nmXDtLQj9/ljCitHkp7HZT/Px4Ajve7rbyNLpzkQgZZmNgjzLpEM6rwY3Q7eI4OYkrmr09A4XGaJJ\n8WHQWjFcULuQVLuevGzzUI7DQGjkoPiQ5jBc12tdIBAIBIJLRZIk5lZmMLvCw9YD53lry3HWbT/J\nJ3vbuH9hIStq8kSXqOC2R4gSA3zloWkEQ8olFY6X01o+0aqxBtxbO/ZKP4DS0cXRp79BqLEFzxP3\nU/FAMfKpz1Fd2URnLUYyGMDsQJeSDZIOTYOTPgMnfAYkIN8Rxq7rB80EyJztjPPapginO1RULU5Q\naSUa9454TW9fmDfWneP9jd34A3F0ehVLehiDPcrwxcWJVrzHC69ctcxzUZvEWB0RM8o8fN40trCz\np7GLwjQP23f1snNfLxElkV5amGdmyXwXi+c5cTr19Poj2O3X5wP+aloQNE3jfEeEI82B5HjOM+fC\nyft1UiKPo7LcTtWAFeNaTyu5GCKw8s5E0zSCoTid3Qqd3VG6fcoI8aGzW8Hbo4wYKzsco1Ei3WWk\nuMAy1OHgMgyJD07jFeW9CAQCgUBwMyHrdCydmcOCqZl8tKeN9z47weuftPDhrtM8vLiYJTOy0cvi\n757g9kSIEgPI8uUXjpfSWj7hqrHdhBJTiUTjo147cuYcjU99ncjx02Q8/whldWnI7a3Es0uITZ0L\nehlsGWB1gyQRjkkcaTfRG5YxySpdHcf52YZmvH2JnIpMZzEd3hRUFWZVyHzecpBoaChbQtMg6jeg\neC38/lgHVouOaTP1tAV6kMb4PLxwxXswvPLN9e0cOnr54ZVjdUR8vKdtxGM0DWJBPUq/gR6/gZ/s\nOQFAdoaJuvlO6mqdFORaLmsCxNXkciwIsZhG66nEVIzjp07x+aEeevqGVo7NJh0zp6ZQWWajstzO\nlBLbTZnyLwIrbz+iUZUuX2IqxXCxobc/ztnzITq7FcKRsUca6SRwphkoK7IlR2IOZjgMTqtIsQtb\nhUAgEAjuPIwGmdXzC1g6M5v1O07xwc7T/GbDUTbsOMUjS0qYV5WBTvx9FNxmCFHiAq61d32iVeNg\nJMZ//eWOUcVyqOUkR5/6OsrZdpxfeoyyGj1yz3niRdOJlU8DWQ+puWBKjBPqCsg0dpiIqRIeW4wD\nhw/z4Y6TAOh1dmLRYs53WTAaYnzxQTtVRXrkjS427kqIErGwTKjTQiykR5Lg/hXp6B1B6g+2jSlI\nwNCKdzSqsvkzL29t6Eiu4M+ensqaezOYcZHwygstGhNZXSQgGpJR+owofgNaPLFheoPGvSvSuXuh\nm5JCy4jXu9wJENeTQDBhxRi0YzQdD4zwy7vSDCyel0ZlmZ2qCjtFeRZk+eb/wyQCK28tVFWjtz82\nIsthuLWi26vg6x3fVmG3yWSlm/C4DcnQyKEASQOuNOM1naojEAgEAsGtjtVs4NGlpayYk8c7206w\ned9Z/uXtQ7y//SSP3VXK9GKXEO8Ftw1ClLgBXLhqbDTIhJU4YSXRxzy8WF6bI9H49DeIdXlpW7GC\nOZUKcihIV95MUiqqQDZCWj7ozagatHQbaes1IEka5Z4IbkuEXzeeB3RYDPmYDZlomkY4eh5J7qIk\nd15ym4IBlc31/fi9iWIxJ1fmr/+0nMx0E//5Xz8bd3/MRh0r5xTw+rvneW9jBz19A+GVi1ysuffi\n4ZXjdTDcPTt3hNVF0yAekVH6DSj9RrRYQoiQZBWTI4IhRWH1kiy+sCp/1GtcyQSIa4WmaXR2Kxxp\nGsqDONUWRhvQICQJCnLNiakYZXYWz89ElpRb+g+QCKy8OQiFhk+riA7d9g6NzYzFxg6P1OslPC4j\n0yvNCaHBOdDhMDCporLCRSAw/jQjgUAgEAgEk8dhN/HcPVO4p7aAt+pb2X6onf/vtc+Zkp/GY3eV\nUpbruNGbKBBcMUKUuAEMXzXu9AX5X6/vTwoSwzn58S6OvPFz4n39nF61gidWmNAR52zhPNyVZXQE\nIKOwGHR6gorE4XYTfkXGalCZmhnGbtLo8EXo81twmCvR6UzE1RAB5Thx1U8klsi4sJtN/GHdeT78\nIIQSlSnIM/GlJ/OYMz3xIdfhC+LtV8bcl3hUh7fDxJ+/eISIol1SeOUg43UwxOMqrlQTHZ1RlH4j\nSr8BNZoQDnSyRkGxTFQfIiwFcaWaWTyzkIcWFoz5GldjAsSVEo9rnDgd4kiTf0CECODtiSbvNxol\npk1JCBBV5TamlNqwWYfeounpZjo7o2M9tUCQJBbT8PaMHInZNWCx6PZG6fQqBILjBDkAToee4nzL\nQGjkYICkIXk7NUWPboLwSKtVT2D84UICgUAgEAgug4w0C197aBqr5xfyh80tfN7SzQ9/s5vZ5R4e\nXVpCbrr9Rm+iQHDZCFHiBmIyyBgN8pjFcs6ZFpa88xLxeIz+R+7h2QUyYU1Hf3Ud7twcthwL8u5B\nhe9/VcIX0HOs04iqSWSlRCn3KMg66A+qvP+ZhN1cgaaphKJthKNnSURqQprdzI7d/fz+nWb6+mO4\nnQa+8GgOyxa6RhQdDrsJV4pxhDARC8uEvSaifgMg4XbqeeaRDFYtvXh45XDG62CIR3Vs3tJLLGCj\nr2fAly5pGFIUjCkK9y7J5Pl7p4ywfOTlpNHZ2T/m69yICRChUJyjrQNdEE0BjrUGRnjs01L1LKxJ\no3IgkLKkwCpa2gUTomka/f74QIeDMqzbYajDwdcTRR27yQGzSUe6x0hFycgsh0FrhdtpwGAQIVoC\ngUAgENys5GfY+dYTMzl2uofXN7ewt6mLfU1dLJqexZq6Yjxplhu9iQLBJSNEiRvMWMVywfEj3LPu\nN+g0jYJvPUJhVpB+TEjz78aSlsarO/rYcDCI0SBz+LyRXsWELGlUZYTJTImjaRq7jsR4qz5CMAw2\ni8JZ71FULdFSrWkQC+jpbLfyy91tmE06nn0km4fvyRwzzd5kkJkzJYMPd54hGtAT8ZmJhRKXjmyK\nUTPHxl9+eeplFdTDOxjUmJS0ZsTDA88vq+TkysRNIaJyEJfDzOyKrKQFZrJ2gOsxAaLLqyQ7II40\n+Tl5OjSiOMzPMVNZZkvYMcrtZKUbb2krhuDqE4moI60U3Qqd3mFhkj5lRMbIcGQZ3E4jleV2PC7D\nsAwHY1KAsFpEeKRAIBAIBLcDFflp/O0X5rC/pZs3Nrew9eB5th9u5+7ZuXzpoek3evMEgktCiBI3\nmAuL5dJjn7P8g9+h6WQy/90qCrOC9BlSMS28i7Dewj9v7OHz0xFcaQ7uXjSXXsVMiinO1MwIFoOG\nt0/l9Y8iHD0Vx6iHNUuMLKy28F5DNls/P0tnp4LitRHql9FJKquWunnmkRycDsO426hEVTwGJ/H2\nAIG+REGkt0ZJzYhy9wIPz6ysQJ6gnXsidMjIESu+DmlA6JAADb01iisDfvjns3CnmUaFYF4OV3MC\nRFzVOHUmxJGmAI3NCSGis3uok8Sgl6gstyemYpQl/p9iF2+3O5m4qtHRFeFos3/AThFNWisGAyT7\n/ePbKlJT9ORlm4dGYiatFQnBIc1huOz3oUAgEAgEglsPSZKYWeahutRNw+F2/vhpKxt3n6H+wDkW\nTstiZU0eOR7bjd5MgeCiiCrpJmCwKPa99g41G14hbjRR8rVlFOWD6snBNGMhnWEd//i+l7aeGFXl\nxcyZMRVZpyM/TaHYFQVNo35flHWfKShRqMiXeXy5Cbcj0fnwyJIKTjZCyykvmpaYiPGlJ3MpzBu/\nxavfH2P9x52s29RJT18MvSyxdKGTRbUp5OaYSU+zTCgQjCckhEJxGvb1sKXBx75DfcTjiewJvSWW\nsGfYo+j0Gsvm5uFOS9gqrkZA4pVMgAhH4hxrDdI4kAdxrDVAMDRkxUi166md7RgIpbRRWmgVbfB3\nEJqmEQjGBzIcokMZDj4l+bNun4I69oRMTEYdHpeBkkJrciRmosPBgHvgtskorieBQCAQCASj0UkS\nC6dlMa8yg837zvLhrtN8sreNT/a2Ma3YxcqaPKpL3WKUqOCmRYgSNwGyTsfy47s49e7v0DlSmPWn\ni3E4JeI5JcSmzUUz2vn0mIIqW1heV0VedibxeJTpmWE8do3z3SqvbQpz8ryKxQRPrzIxt1KPJEmE\nI3HeWt/Bm+vbCUdUCnLNvPBUHrOnp467Pe2dEd75oION9d1EFBWrRccj92XywMp03M6Lh1eONU1j\nRomHEnc6W3f0sHt/L0o00XFRUmhhca0Tr9LD0TYvvn7lijoYJsNkBA6vT+FIc4DGATtG66ngiIIy\nN8vEwho7leUJO0ZOpkm0xd/GKFGV7oHJFMPzHIaHSQ7PCxmOTgJnmoGKEhu5OVZSrNIIa4XHbSTF\nJmwVAoFAIBAIrgy9rGNFTR5PrJrCh58d58NdZzh03Muh414ynRZW1OSxuDobi0mUgIKbC3FF3mA0\nTePs//wlbf/jZxjSnVR/dR42p0SsZDrxsulgcSKlZLN8gUxWsYloXIfDHGNqZhRZUtnQEGXTToW4\nCrPK9axdZiTFqiOuanyypZuX/3gWb08UV5qBLz+dx4o6N7I8dvHTdDzAW+vb+WxXD6oGHpeBZ1Zl\nX3J45eA0jcHsilPnZJr3+EFNRPLnZZtZMt/J4lonuVnmgd/KuioWjctBVTVOnw3T2OxP2DGa/LR3\nDVkx9LJEebGNqnJbwpJRasOROr7dRXBroaoavf2xEZMqurpHig89fbFxf99uk8nKMJHuTgRFprsT\ntgr3QJaDK82QfM+lp6eMG8YqEAgEAoFAcDWQZR01UzKomZLBqfZ+Nu46w/bD7by8sYk/1rdSV53D\nirl5ZIhQTMFNghAlriKXWlRrmsbpH/wj53/2G0zZ6VS/MANzmp5o1VzUgjKwZ6KaXZz0GTnpSxTB\nxS6FgrQop87HeW1ThPNeFYdN4tG7TUwvSZzO/Yf7eOnVNk6cDmE0SjzxYBZfe76UQCA0ahtUVWP3\n/j7eXN/O4WP+xGsUWFi7OpNFc52XHF4ZisTYuqubQLuFaL8BTU20nOv0cVIzNP72a1MpL7KNuSp8\nNSwakyGiqDQfD4zIgxg+ItFuk5k7M3VgNKed0iKraJ2/hQmG4qNGYnYNhEYO/iwWHzs80qCX8LiM\n5OdaRk2qGPy3xXz9BDSBQCAQCASCS6EgM4WvPFDF43eVsnlfGx/tbePDXafZuOs0M8s8rJqbR2Wh\nU3RsCm4oQpS4CoxlV5hdkc5Ty8uQdWMXs1o8zokXf0Tnb/6AuSCL6ueqMLmsxGYsRM0sgNRcwrpU\njpwz0RuWMelVpmZGMOvi/OGTMNsOJIrohdV6HlhkwmKSOH02xK9fa2P3/j4A7lrk4guP5uBxGbFa\n9QQCQ6+vRFU2f+blrQ3ttJ1LTL+YPT2VtaszqK5KuaQPJk3TaGoNUt/gpX6Hj96+RA6EJKuY0iIY\nUxRkcxxZB2lpuuv+odfTF6VxQIA40uSn9WRoRBGalWFi3qxEHkRVmY3cbPOIkaiCm5dYTMPbo4zK\nchjseOjsjhIMjR8e6XQYKCm0JLoahoVGDt52pOjFH2mBQCAQCAS3PKk2Iw8tLua+BYXsauzgw11n\n2Nfcxb7mLnLTbaysyWPhtCyM17FbWSAYRIgSV4FBu8Ig3X2R5L+fXVkx6vFqNMbx//g9uv+4HmtJ\nNtVfqMLgchCdvRTNlQVp+XRGbBztMBFTJdJtMSrSIzSfjvJv7wdQonriahi94QxK3EooVMi/vdbO\nB5u7UFWYXmnnhafyKC0c3XXQ54+x4YLwyrsXu1hzb+aEoZcXomkaJ8+EqG/wsWWHj44Bu4PdJpPq\niaGaw+gtMYbXc84UMw67adKvcTlomkbb+UhiNGeTnyPNAc61D41blWUoKbBSOSBAVJbbJ5w8Irhx\naJpGX39sbLHBG6WrW8HXG0Ubu8kBi1k30NVgwzNgqfC4DMnbLqcBg150wAgEtwrHjh3j61//Oi+8\n8ALPPfccO3fu5B/+4R/Q6/VYrVZ+/OMf43A4+MUvfsH69euRJIlvfvObLFu27EZvukAgENw06GUd\nC6ZlsWBaFi1tvXy46zS7j3by6/VHef2TFpbNymX5nFxcqeaLP5lAcJUQosQVEonG2Xusc8z79h7r\n4rFlpSOsHGo4QvOfvkjPhs2kVOQw/dmp6NwulJpl4MgknpJHi8/K2T4DOkmjIj1Cqj7K65si7GqM\noWky4dhZwtE2tKDGux8E+ONrfmIxyMk08aUnc5k3yzFqdbftfIhfv3KaTZcZXjnI2fYwWxp81Df4\nOHMuDIDZpOOuhS7q5juZMTWF338yUqQZZHaF56pnRUSjKs0ngjSkkfwQAAAgAElEQVQ2+2k5eZL9\nh3tGjFW0WmRmT0+laiCQsrzYhskkCtGbgXAknhQcBkdi+oNwpi1Ap1eh26skA1EvRJbB7TRSVW4f\nEB4utFYYsVmF0i8Q3C4Eg0F+8IMfsHDhwuTP/v7v/56f/OQnlJSU8LOf/YxXX32V++67j3Xr1vHK\nK6/g9/t59tlnqaurQ5bF54FAIBBcSGmug9JcB77+CB/vPcMne8+ybvtJ1jecomZKOqvm5lOamyq6\nRgXXHCFKXCG9/gjevsiY9/n6w/T6I8mchHgwRNOXv0Nf/Q4cU/OY9nQVUmYW0dlLIDWLgCmbw+cs\nBBQZq0FlamaYppMK/7JZwR/SgCD94VZiapBov4FQlwU1pkOn13jh6TweWJ4xKgPiWGsivHL7nh5U\nNRFe+ew92axcMvnwyi6vwpYdPuobvLSeTORSGPQSC2vSWDLfyZwZjhGZC4NTM/Ye68LXH76q0zT6\n+mPJHIgjTX6aTwSJxYYK1wyPcUCESORB5OcIK8aNIB7X8PVe2OEQHdHtMFw8upDUFD35ORY87gGx\nIWmtMJLuMuBwGJDFeRUI7hiMRiM///nP+fnPf578mdPppKenB4De3l5KSkpoaGhgyZIlGI1GXC4X\nubm5NDc3M2XKlBu16QKBQHDT40wx8ejSUh5cWETD4XY+3HWGnY0d7GzsoCgrhVVz85lXlYFeFgt7\ngmuDECWuEIfdhCvVRPcYwsRwu0Kst59jz38L/679uGbmU/VEFVpuIdEZC9FSczkfy6CpzYSqSWSn\nRkk3hXn1gwiHj8fRy3DXHIk3txwkGpIJdtiJR/QgaZicYazuCAvnpSQFiUR4ZS9vru9IhleWl9h5\ncKVn0uGVPX1Rtu3sYcsOL0eaEmEUsgw1M1Kpq3VSOzttXFFD1ul4dmUFjy0rvaJpGpqmca4jQmNT\nQoA40uxP5l9AYtRiUYFlIAvCzuIFmaAqEzyj4GqgaRr+QHxEbsPQbYVuX5RunzJihOpwTEYdHreB\n0kJrciTmoLWiotyJpEVFsKhAIBiBXq9Hrx/5leXFF1/kueeeIzU1FYfDwXe+8x1+8Ytf4HK5ko9x\nuVx0dnZOKEo4nVb0+mvTSZGennJNnlcwOcTxv7GI439judzj/2hOGo+sqOBgSzdv17fQcOg8P3/3\nMK9vbuG+hUWsXlSEM0VYOy6GuP4vDSFKXCEmg8zsivQJ7QrRbh9Hn/4GwUPHSJ9bQMWjVWjFU4hV\nzSWems/RXg8dfj2yTqMqPUTLiQi/3hIhEoXSXJknV5gIBMK8+ocUAj2JL06GFAWLJ4xsUHGnJsSP\nccMr78tk+ZJsurr8E+5LIBhj++5e6nd4OXC4H1UDSUpkVCypdbFgbhqp9slfMpc6TSMaU2k9GRrI\ngkh0Q/QOG8VoNumYOS2FqjI7VeU2yktsIyYfpLtNdHYKUeJKUaIq3cNyGxIZDoPjMRMCRDgytuKg\nk8DlNFBRYhs1qWLwtt0mj9sGmJ5uFSMzBQLBpPjBD37AT3/6U2pqavjRj37Eyy+/POox2nihM8Pw\n+YLXYvPECOAbjDj+NxZx/G8sV+P4ZzlM/MmDU3mkrphNu89Qv/8cL39wlNc2HaO2KpNVc/MpzBKF\n91iI639sJhJqhChxFZjIrqCcbafx6W8Qbj5B1oIiytZUEq+cTbykGr+1iIPtDsIxHammOBnmEK9v\nCNF6VsVshCeWm5haCK+/e5b3P+okFpeRzTGs6SH0lqHW96mFbt5e38F7mzrpHQivXL7YxcPDwivH\nKwLDkTg79/VS3+Bj78G+pBWiotRGXa2TxXPTcF1C5sSl4A/EONoy0AXRFKD5eGBEhoDbaaCu1klV\nuY3KMjuFeRZkWbTsXwmqqtHTF0tmOAzPcxgclzlcCLoQu00mO9OUzG1IdxvwOAe6HdxGnA6DOEcC\ngeC6cPToUWpqagBYtGgR77zzDgsWLOD48ePJx7S3t5ORkXGjNlEgEAhuedLTLDy9opy1S4rZeuA8\nG3efYdvB82w7eJ7yPAer5uYzu8Iz7sRBgWAyCFHiKjCeXSF88gyNT34d5fRZcpeVUPRAJbHqhcQL\nqjhLEc3nrWhAnkPhxPEAr+5QiMWhulTmoToDWxu6+d//ch5/IE6mx8gXHs/hVG8n+5pi+Prj2I0W\nDBE7G94JEVECWC3ypMIro1GVPQf72NLgY+e+XiJKYtW7KM9C3XwndbVOMtOv7pQMTdNo71QSYzmb\nAzQ2+TnVFk7eL0lQmGehsszG1HI7lQMBhoJLIxiKXzASUxkRJtnti44Yhzocg17C4zZSkGshfWBK\nheeCUZlmkwiLEwgENwcej4fm5mbKyso4cOAAhYWFLFiwgJdeeok/+7M/w+fz0dHRQVnZlecZCQQC\nwZ2O2ahnRU0ed8/J5dBxLx/uOs3BVi9NZ3pxp5pYPiePJTNzsFvEVDvBpSNEiavIcLtC6FgrjU99\nnWh7F4Wrysm7t4rYnKXEsis4HCikO2TEIKt4jCHe+iDI2S6VFKvEI8uMBHv9fPfvWznfEcFqkXnh\nyVzuX5GOwaADXMwq7OcP686z90A/mhYl3W3koVUZrFzixjJOzkMsrrHvYB/1O3xs391DMJTotMjO\nMFE338mSWif5uZMfCXox4nGN46eCHBkIpGxsCuDrjQ4dK6OO6ZX2ZCBlRYlNTEu4CNGoSntnZJid\nIjpKfBg8rxciSeB0GCgptCRtFe4BwSFx24AjRS/SlQUCwU3JwYMH+dGPfkRbWxt6vZ4NGzbw/e9/\nn+9+97sYDAYcDgc//OEPSU1N5cknn+S5555DkiS+973voROrdwKBQHDV0EkS1SVuqkvcnOsOJDon\nDpzn95+08NaW4yycnsXKmjxy0+03elMFtxCSNhnD5U3GtfDoXE3vT2D/EY4+801ivl5KHqwkZ9U0\nojV3EXBVsq8nGyUuk2aOceZkP5/sVtA0qJ2qZ2pejN/9sY0jTQFkGVbfnc6TD2eTatejqhq7Pu/l\nrQ1D4ZUlBRbWrs5k4Tjhlaqq0dgcoL7By/Y9vfQMiAJup2FAiHBRUmi5KoVoMBRPWjEamwMcawkk\nOzAgURBXltuSeRBF+dZJBW5eCreyf0vTNHr7YwkLRdJOMXJyha83ynjvVqtFHpHbkAiQNAwESBpx\nOQ0Y9Df/F/Nb+RxOhtt9/+D238fbff9g9D7e7mFd1+p83gnXys2MOP43FnH8byzX+/gHw1E+/fwc\nH+05Q1dvohN6apGTlXPzmVHqRneHLXqJ639sRKbEdaS/YR/Hvvgt4v4A5Y9NJ2NFNZGauzlvqeJo\ntxsJSJVDrNvYT3evhjtVYuVcHVu2nuOVl30AzJ/t4PkncsnNMqNEVT7Y3MXbG9ppO58Ir5xTncqa\n1ZlUV9pHCQqaptF6MkR9g5ctO3x0+xJCRJrDwH3L06mrdVJZZrviMZmd3QqNTX4OD4gQp86EUIcV\nzPm55oGpGDaqyu1keIx39Cp8OBJP2ChGZTkkuh26vcqIPI3h6GUJt9PAzGkO0lJk3BeKDy6j6DIR\nCAQCgUAgENwQrGYDq+cXcM+8fPY2dbFp92kOn/Bx+ISPDKeFFTV51FVnYzGJ0lMwNuLKuIr0frKd\npq98By2qUPnMLNzLZxOZs5wjsSo6e22YZJWzp/p5e18YSYJF1Xr6O7v5yT92EI1plBRa+PLTeUyf\nkkKfP8bv3zk3YXjlcE63hajf4WPLDh/n2hPihdUis6LOTd18J3cvycHnnXj6xnjEVY2Tp0OJPIiB\n8ZyDYgeA0SBRWZ7ogKgqtzOl1IbddudcWvG4hq830eHQ1a3Q5Rs9JtMfGNtWAeBI1VOQa0mIDa6h\n0MjBAMm0VD06nSRUV4FAIBAIBALBTYtOJ1EzJZ2aKemcau9n4+4zbD/Uzu82NvHHT1upm5HNypq8\nS5rOJ7gzuHMqx2uM9/2PafkPL4IWZ+rzc3CsnE9g+t3sCUwhrBowagobP+6lp18l260jPy3I22+e\npa8/http4LnHc1g630V7l8K//vY0m7Z0oSgaVovMo/dn8sCK9FFTMM53RNi608eWBh8nzoSARFZD\nXa2TJfOdzJ6eOpBDkVhtnyyhcJym1kAyD+JYS4BQeMiKkZqiZ/4cB1VliUDKkkLLLWENuBw0TcMf\niI8KjRweJun1RUd0iQzHbNLhcRkpK7ImxIaktcJIusuA22XEaLg9j51AIBAIBAKB4M6kIDOFr9xf\nxeN3lbJ531k+3nOGjbvOsGnXGWaUulk5L5+phc47upNaMIQQJa4CXa+/R+u3v4+s1zH1S3Ox37OM\nrtJlfN5fDEh0n+tn254gsg6qi1R2NZxk67kIZpOOLzyaw0P3ZHDydIif/Ow4Dbt7UDXGDa/0+hS2\n7uxhy47/v707j46yvPs//p7MkmQy2SbJhLALYd8UFWSJuIFbqxV3Cv7UR1uLVJ4qKEUUrR4Vt1LR\nLiqtPNQCgrTqoWIXpYeWEAVslEiAsJOE7Ps2k5n798ckQzYUWskkk8/rHE4mM/cM1zfJyX3nM9f1\nvUrZd9C/t7rFYmLCebGkTYzngnGxZ7xDQkmZm+z9NezJ8TekPHSsFt/JDII+KeFNvSAcDB8SRYor\nPGR+gbg9vsBSiuKmLTGLmwKH5maSLXtjtBQWBgnxNoYOjmq1nCIpwRq47Ygyh8zXSkRERETkTMTY\nbXx38kCuntifHXsL+duO42QeKCHzQAl9EqO4/IK+TBrVi3CrliL3ZAol/ksFqzZw5KfPYY60Mvp/\nLiTyqukcTL6EQ9XJmHw+tmWUUlzqJSUByvMLee9P5YSZYMa0RG69rhc5h2v52cs5J5tXDojke1cm\nM/nCeMxNsxsqqxtJ3+FfmpG1txrD8P9BfO6oaKZOcHLR+bFE2U/vW+nzGRzLq2fP/upAU8rCYnfg\ncYvFxNBB/mUYw1OjGJ7qICa6e/6Y+HwG5RWeQN+G1v0c/AFEZVXjKZ8f7TDTu1d4YLeKRKe1xW0b\n8bHWwPdIREREREQ6ZjGHcdHIXlw0shcH8ir4+47jfJZdyP9t3su7Ww5w8bm9uXx8X5wxEcEeqgRB\n9/xrs4vIe/Utjj/zKlaHjdE/mIT16mvJdKRRUh9HaXEd6Z9VYgkDp62abR/nYRhw3ugYZs1M4eDh\nOh57fj95BSebV37vqmRGNzWvrK3z8unn5WzNKCPzq0q8TS0JRgyJIm2ik0kXxBEX8837ADc0+Nh/\nqIajH5eyM7OU7JyaVttGRjvMXHhuLCOG+AOIwQPt3WY5QU2tt1Xfhtr6Io4erwksrSgt89Do7Xhd\nhc1qIsFpY2DfSBKbAofmfg7+WQ7WM55xIiIiIiIiX29w71gGXxfLzZem8snnuWz5PJcPtx/lo4xj\njB+WxKXn9WFgr2g1xuxB9J3+DxiGwfHnXiN/xVuEx0Yweu5UfFfN5FPLRdR4wvn3v8s5ntdAbISH\nvV/kUlvrZkDfCG7+bgq5+fU8vfzAyeaVUxO4boaLAX0jaXD7SN9Zzj8zytj5RUVgN4bBA+ykTYxn\nyoR4Ep22rx1beYWHPU0NKbP3V3PwaG0g0ABISQ7novGxTY0pHfTp1TWXYngafZSWnVxOUVTiprjs\n5O4VJaVuaus6XlZhMvm3IB000E6S09piWcXJwCEm2tIl6xYRERER6Qnio8OZefEgvjt5ANu/KuBv\nO46zI7uQHdmFAMTYrbicdpLjIv0f4yNJjrfjio9UYBFi9N08Q4bPx9HHXqDgd+uJSLAzav5l1F52\nC5nGeZRXGGz/rIRGj5fqwiIO5pUTH2vhhpm9KSlz88rKw+2aV0ZHW8jMquKPHxaQsauc+gb/H9r9\nekcEgojeyR1PY/L5DHLz69mTUxPYGeNEYUPgcYvZxOABdkYMcTDh/CR6J4URF/vNsyvONsMwqKhq\nbLGconXYUFTiobzSg3GK5pH2SHObHg7+j6mDYrGaG4mPs4Zs400RERERkVBitZhJG9ubqWNS2Hes\nnJ17izhRVkthaR0HcyvJOV7R7jmxUTaS49uHFcnxdsJtmu3c3SiUOANGYyOHHnyS4g0fYu/lYNRD\nV3Ni8iz2eoew/0Ade/fV4Kur4cj+fCxmg8umJlBd3cgf/uhfupGUYOO7M1xcOsXJwSN1rH0vn/Sd\n5YHtIpMTbVx7RTxpE5307xPR7p18t8dHzqHapl4Q/n4QLbeajLKbOX9sDMNT/dtzpp4TRbjN/8d5\nZ24nWVffvKyi7U4VnkATSU9jx4mDxWwiwWll1DBHYEvMJKfNv11mU/hgj+z4F422zBQRERER6Z5M\nJhPD+sczrH984L5Gr4/iinoKSmspLKujoKyWgrI6Ckpr2Z9bwb4OAos4h43keDvJztZhhSs+Epsa\nanZJCiVOk8/t4cB9D1O2eSuOvrGMWPQ9csbP4VBdL3bsqqCsuJ68g/nUV9cyapiD+novH/+zBDjZ\nvDIhwca2z8p44NGvKKvwN1iMj7Xy3ekJTJ0Qz5BB9lZBREWlh+wD/mUYe/bXcOBILY0t/phPTrRx\nwdhYhjf1g+jXO4KwsLO7JMHrNSgt91BU0jSroSl0KCnzBMKHlkFJW3ExFgb0i/TPcHDaSExovbwi\nNtpy1msQEREREZGuz2IOo5fTTi+nvd1jnkYfxRV1FJT6w4pAaFFax75j5ew9Vt7uOfHR4f6ZFU67\nP7iIj8TV9M9qUWARLAolToO3tp6cO+dT8c+dxA5yMvSxWXwx9Ptk50fy78xiivPKKMkrJsVlw2O1\nsjvbv5PG+DExTBwfy4nCBn6/MS+wy0W0w8yMSxJJmxDPiKEOzGEmDMMgr6DBPwtiv385Ru6Jk0sx\nwsJgUH87w1OjGDHUwfBUB864b3cphmEYVNV4A7MZTs5yODnjoazcg+8UyyoiwsNISrAx5Jwof+PI\nBBsJgfDBRkK8tds00RQRERERka7LagkjJSGKlISodo95Gr0UltdTWNo0s6Ks1j/boryO7KPlZB9t\nHViYAGdMOK54e1NgERmYbZEYG6ml4WeZQolv4K2qZt9t91H1eTbxw5IY+LN72d77JnbtbmRvdiGF\nR05gC/MQEW4i70QDFrOJi86PIz7Wwhd7qvjVqmMAREaEcckkJ1MnxjNuZAyGYXDgSC0f/KUwsDVn\ny+0pIyPCOHdUtH9rziEOhpxjJzLiv0vvGtw+ilv0bQgED03NJItLPTS4O24eGRYGCfE2hqVGtern\n4J/h4J/tEGU3q3mkiIiIiIgEldVipk9iFH0S2wcWbo+XwnL/DIvCstrA7IqCslr2HCljz5GyVseb\nTJAQE9Gih4U9MNsiMTYCi1mBxX9LocTX8JSWs++m/6Em+wiJY1NwPfUTPo6Yzvat1RzZV0h1aRk+\nr48GL9gjwxg93EFlVSPbd/qTN5vVxKQL4kibGM/Qc6I4dKyOr/ZVs/HPBew/WNOqr0JSgo20ifGB\nfhD9+0ZiPoNlDF6fQXmFp1XTyOLmnStK3ZSWN1Je4Tnl86MdZvr0Cm/Rw+Fk2JDotBEfZz2j8YiI\niIiIiHQ1NquZvkkO+iY52j3W4G4OLGoD/SuaZ1tkHS4j63DrwCLMZCIhNrwpqLDjckYydGACVgwS\nYiLUdPM0KZQ4BfeJIvbecCd1RwpwTehP1M8e573y8/h0SwEnDp/AXedfiuGIMmOPNFNY7GZ3djVm\nM5w/NobRI6KJtIVx8Ggda/6Yz7G8+sBrh5lgQL9I/yyI1ChGDHF87VafhmFQW+eluNTTomlk66UV\nJWXuVlt/tmSzmkh2RTCgT0TTcgoriU2zHZKaQofwcCV8IiIiIiLSc4XbzPRzOejnah9Y1DU0UlRe\nF2i02TK02H2olN2HSpuO3B94jiPSSkJsBIkxESTERpDQ5mNUhEUzzVEo0aGGo8fZ+707qT9RTsq0\nVNyLn2X13r5kfnaAyiL/LIjIiDDq631U13ipqfWSOtCOK8mG2+3jwOFadn5RGXi9iPAwxo6IZvgQ\nfwAxdFBUqx0kPB4fJwob2u1UUVTiprjMP9uhrr7jZRUmEzjjrKQO9PdxaJ7pkOg8uXNFtMOMyxWj\nnSlERERERET+A5HhFvonR9M/ObrdY3UNjYFmm9VuL0fzKiipqKe4soG84hqOnOj477Bwm/mUgUVC\nTASxDhthPSC0UCjRRl32PvbeeC/ushr6Xj2a4/NeZMPHYRzas4dGdyMmwADq6n0446yE20yUlHnI\nOVxLzuFawB8STLkwjuGpDoYNjsIZZ6W0wr+s4lhuPbu+rGzVTLJ5J46ORNnNJCeGt9oSs3mnikSn\nFWecDYsl9H9QRUREREREuqLIcAsDe8UwsFcMSUnRrd4MNgyDyloPJRX1lFTW+z823S5u+phbXNPh\n61rMJpzRpwgtYiNwRoeHRE+LLhNKPPPMM2RmZmIymVi8eDFjx47t9DHUfLaTfbPn46mqp99NE0i/\nYRnvrymgovjkrAeL1YTH4+8FUVruwWSCFFc4KcnhxMVaCbeaqKnzUlLmYdPfi1i1PrfVNp4tWSwm\nEp02Rg+PaLWUIjHBGrgdGal1SCIiIiIiIt2RyWQiNspGbJSNQb1jOjymtr7xZGDR9LG4xedtm28G\nXhuIiw7vcJZF87KR7tDXokuEEp9++ilHjhxh3bp1HDhwgMWLF7Nu3bpOHcPxP31I9h0L8dZ76Pf/\nLuf3Ix4mY81BvI2tGzV4vQbRUWbCwkw0uH3UN/jIK2ggr6Ch3WvGx1oY2C8ysCVmotPa4raN2GgL\nYWoeKSIiIiIi0mPZIyzYIzruZQH+LU5LKhtaz7BoEWAczKskJ7eiw+c6Iq0dhhaJsV2nr0WXCCXS\n09O54oorABg8eDAVFRVUV1fjcHT8Tfm2lRwt4PCcBXjdXpJ/NJMnG26hYMuRDo/1+aCqxktEeFhg\nOUXzUorm2wlOG4nxVqzW7j+VRkRERERERILHajHTy2mnl9Pe4eNen4/yKneHsyxKKurJK6nhSMEp\n+lpYza0CC1dcJBePS8EeYT2bJbXSJUKJ4uJiRo0aFfjc6XRSVFTUaaFEXqWVuLQxVI+6kAf3T8Dn\nrcRhD6OXK4KUZBtJCeEt+jn4ezvYI81BT5RERERERESkZzOHhQX6TNCv/eOGYVBV6zkZWrTsb9Ec\nXLToaxEZbmbauX06bfxdIpRoyzA67sHQLD7ejsXy7a2NuezSaLbYXiHSGsa7g6Nxxtkwm0MvcEhK\nat8pNtSEeo2hXh+Efo2hXh+Efo2hXh/0jBpFRER6CpPJREyUjZgoG+ekdNzXoq6hkZKKeqrqPKT2\n6fiYs6VLhBIul4vi4uLA54WFhSQlJZ3y+LKy2m99DJdMcfm7pPrclJa6v/XXD7a2XWBDUajXGOr1\nQejXGOr1QejXGOr1QfsaFVCIiIiEvshwC31P0dPibOsSTQ+mTJnCRx99BEBWVhYul6vTlm6IiIiI\niIiISHB0iZkS48ePZ9SoUdx2222YTCaWLl0a7CGJiIiIiIiIyFnWJUIJgAULFgR7CCIiIiIiIiLS\nibrE8g0RERERERER6XkUSoiIiIiIiIhIUCiUEBEREREREZGgUCghIiIiIiIiIkGhUEJERERERERE\ngkKhhIiIiIiIiIgEhUIJEREREREREQkKhRIiIiIiIiIiEhQKJUREREREREQkKBRKiIiIiIiIiEhQ\nKJQQERERERERkaAwGYZhBHsQIiIiIiIiItLzaKaEiIiIiIiIiASFQgkRERERERERCQqFEiIiIiIi\nIiISFAolRERERERERCQoFEqIiIiIiIiISFAolBARERERERGRoLAEewBdwTPPPENmZiYmk4nFixcz\nduzYYA/pjOzbt4+5c+dy5513Mnv2bPLz83n44Yfxer0kJSXxwgsvYLPZeP/991m1ahVhYWHccsst\n3HzzzXg8HhYtWkReXh5ms5lnn32Wfv36BbukVp5//nl27txJY2MjP/zhDxkzZkxI1VdXV8eiRYso\nKSmhoaGBuXPnMnz48JCqEaC+vp7vfOc7zJ07l0mTJoVUfRkZGcyfP58hQ4YAMHToUO65556QqvH9\n99/nzTffxGKx8MADDzBs2LCQqm/9+vW8//77gc93797NmjVreOKJJwAYNmwYTz75JABvvvkmmzdv\nxmQyMW/ePKZNm0ZVVRUPPfQQVVVV2O12XnrpJeLi4oJRSodqamp45JFHqKiowOPxcP/995OUlBQy\n9XUl3f2aIhS0vW6YMWNGsIfUo7Q838+cOTPYw+lx2p6vL7nkkmAPqcfo6FyblpYW7GF1D0YPl5GR\nYfzgBz8wDMMwcnJyjFtuuSXIIzozNTU1xuzZs40lS5YYq1evNgzDMBYtWmT8+c9/NgzDMF566SXj\n7bffNmpqaowZM2YYlZWVRl1dnXHttdcaZWVlxsaNG40nnnjCMAzD2Lp1qzF//vyg1dKR9PR04557\n7jEMwzBKS0uNadOmhVR9hmEYmzZtMl5//XXDMAzj+PHjxowZM0KuRsMwjJdfftmYOXOm8e6774Zc\nfdu3bzd+/OMft7ovlGosLS01ZsyYYVRVVRkFBQXGkiVLQqq+tjIyMownnnjCmD17tpGZmWkYhmE8\n+OCDxpYtW4yjR48aN9xwg9HQ0GCUlJQYV155pdHY2GisWLHCeOONNwzDMIy1a9cazz//fDBLaGf1\n6tXGiy++aBiGYZw4ccK48sorQ6q+rqK7X1OEgo6uG6RztTzfS+fq6Hwtnaejc62cnh6/fCM9PZ0r\nrrgCgMGDB1NRUUF1dXWQR3X6bDYbb7zxBi6XK3BfRkYGl19+OQCXXnop6enpZGZmMmbMGKKjo4mI\niGD8+PHs2rWL9PR0pk+fDsDkyZPZtWtXUOo4lQsvvJBf/OIXAMTExFBXVxdS9QFcc8013HvvvQDk\n5+eTnJwccjUeOHCAnJycQFofavV1JJRqTE9PZ9KkSTgcDlwuF0899VRI1dfWa6+9xr333ktubm7g\nXe7mGjMyMkhLS8Nms+F0OunTpw85OTmtamw+tiuJj4+nvEgDJt8AAAtpSURBVLwcgMrKSuLi4kKq\nvq6iu19ThIKOrhu8Xm+QR9VztD3fS+fq6HwtnaftuTY+Pj7II+o+enwoUVxc3OoHxul0UlRUFMQR\nnRmLxUJERESr++rq6rDZbAAkJCRQVFREcXExTqczcExznS3vDwsLw2Qy4Xa7O6+Ab2A2m7Hb7QBs\n2LCBiy++OKTqa+m2225jwYIFLF68OORqXLZsGYsWLQp8Hmr1AeTk5HDfffdx++23869//Sukajx+\n/Dj19fXcd999zJo1i/T09JCqr6UvvviClJQUzGYzMTExgfvPpMaEhAQKCws7fexf59prryUvL4/p\n06cze/ZsHn744ZCqr6vo7tcUoaCj6waz2RzkUfUcbc/30rk6Ol9L52l7rn3kkUeCPaRuQz0l2jAM\nI9hD+Fadqp4zvT/Y/va3v7FhwwZ++9vftlobGir1Aaxdu5Y9e/awcOHCVuPs7jX+6U9/4txzzz1l\nD4HuXh/AwIEDmTdvHldffTXHjh3jjjvuaPXOXCjUWF5ezquvvkpeXh533HFHSP2MtrRhwwZuuOGG\ndvefSS1dsb733nuP3r17s3LlSrKzs7n//vuJjo4OPN7d6+uq9LUKnpbXDdI5vul8L52j7fn6k08+\nwWQyBXtYPULbc+3ixYvZuHFjsIfVLfT4mRIul4vi4uLA54WFhSQlJQVxRP89u91OfX09AAUFBbhc\nrg7rbL6/+V0cj8eDYRiBdz+7iq1bt/LrX/+aN954g+jo6JCrb/fu3eTn5wMwYsQIvF4vUVFRIVPj\nli1b+Pvf/84tt9zC+vXr+eUvfxly38Pk5GSuueYaTCYT/fv3JzExkYqKipCpMSEhgfPOOw+LxUL/\n/v2JiooKqZ/RljIyMjjvvPNwOp2BKZhw6hpb3t9cY/N9XcmuXbuYOnUqAMOHD6ehoYGysrLA4929\nvq4iFK8puqO21w3SOTo632/bti3Yw+pROjpfl5aWBntYPUbbc21hYaGWj52mHh9KTJkyhY8++giA\nrKwsXC4XDocjyKP670yePDlQ01/+8hfS0tIYN24cX375JZWVldTU1LBr1y4uuOACpkyZwubNmwH4\n5JNPmDhxYjCH3k5VVRXPP/88v/nNbwKd3kOpPoAdO3YE3skpLi6mtrY2pGpcvnw57777Lu+88w43\n33wzc+fODan6wN/peuXKlQAUFRVRUlLCzJkzQ6bGqVOnsn37dnw+H2VlZSH3M9qsoKCAqKgobDYb\nVquVQYMGsWPHDuBkjRdddBFbtmzB7XZTUFBAYWEhqamprWpsPrYrGTBgAJmZmQDk5uYSFRXF4MGD\nQ6a+riIUrym6m46uG6RznOp8L52no/O1+hp0no7OtVo+dnpMhuYW8uKLL7Jjxw5MJhNLly5l+PDh\nwR7Sadu9ezfLli0jNzcXi8VCcnIyL774IosWLaKhoYHevXvz7LPPYrVa2bx5MytXrsRkMjF79myu\nu+46vF4vS5Ys4fDhw9hsNp577jlSUlKCXVbAunXrWLFiBeecc07gvueee44lS5aERH3g3zrr0Ucf\nJT8/n/r6eubNm8fo0aN55JFHQqbGZitWrKBPnz5MnTo1pOqrrq5mwYIFVFZW4vF4mDdvHiNGjAip\nGteuXcuGDRsA+NGPfsSYMWNCqj7w/z5dvnw5b775JuDvE/L444/j8/kYN24cP/3pTwFYvXo1H3zw\nASaTif/93/9l0qRJ1NTUsHDhQsrLy4mJieGFF17oUu/Q1tTUsHjxYkpKSmhsbGT+/PkkJSWFTH1d\nSXe+pggFHV03LFu2jN69ewdxVD1P8/leW4J2vrbn6+am1HL2dXSunTRpUrCH1S0olBARERERERGR\noOjxyzdEREREREREJDgUSoiIiIiIiIhIUCiUEBEREREREZGgUCghIiIiIiIiIkGhUEJERERERERE\ngkKhhIh0ijlz5rBt27avPeaDDz7A5/MFjvd6vZ0xNBEREekEx48fZ/To0cyZM4c5c+Zw22238dBD\nD1FZWXnar3Gm1we33347GRkZ/8lwRaSTKJQQkS5jxYoVgVBi9erVmM3mII9IREREvk1Op5PVq1ez\nevVq1q5di8vl4le/+tVpP1/XByKhxxLsAYhI15CRkcHy5cvp3bs3ubm5REdH8/Of/5zNmzezdu1a\nIiMjSUhI4Omnn8bhcDBy5Ejmzp1LRkYGNTU1PPfccwwdOpTLLruM3/3udwwYMCDwmmvWrAn8Pz6f\nj6VLl3Lw4EHcbjfjxo1jyZIlvPLKKxw5coQ777yTV199lYkTJ5KVlYXb7eaxxx7jxIkTNDY2cv31\n1zNr1iw2btzItm3b8Pl8HDp0iD59+rBixQpMJlMQv4oiIiJyJi688ELWrVtHdnY2y5Yto7GxEY/H\nw+OPP87IkSOZM2cOw4cPZ8+ePaxatYqRI0d+7fVBXV0dP/nJTygrK2PAgAE0NDQAUFBQwIIFCwCo\nr6/n1ltv5aabbgpm6SLSRKGEiARkZWWxfPlykpOTWbhwIW+99Rbr169n06ZNOBwOli1bxltvvcW8\nefPwer0MGTKEefPmsX79el555RVeffXVb/w/KioqGDZsGE899RQAV111Ffv27eOBBx7gtdde4623\n3sJiOfmrafXq1cTExPDSSy9RX1/PNddcQ1paGgCff/45mzZtIjw8nOnTp7Nnzx5Gjhx5dr44IiIi\n8q3yer389a9/5fzzz2fhwoW89tpr9O/fn+zsbBYvXszGjRsBsNvt/P73v2/13FNdH2zbto2IiAjW\nrVtHYWEhl19+OQAffvghgwYN4sknn6ShoYH169d3er0i0jGFEiISkJqaSnJyMgDjx49n1apVjBo1\nCofDAcCECRNYu3Zt4PipU6cGjl25cuVp/R8xMTHk5+dz6623YrPZKCoqoqys7JTHZ2ZmMnPmTAAi\nIiIYPXo0WVlZAIwdO5aIiAgAUlJSqKioOMOKRUREpDOVlpYyZ84cwD978oILLuDGG2/klVde4dFH\nHw0cV11dHVjSOX78+Havc6rrg3379nH++ecD4HK5GDRoEABpaWn84Q9/YNGiRUybNo1bb731rNYp\nIqdPoYSIBBiG0eq22+1u93jL5REtj+9o2YTH42l336ZNm/jyyy95++23sVgsgQuKU2n7ui3H0HZN\nacvxiIiISNfT3FOipaqqKqxWa7v7m1mt1nb3ner6wDAMwsJOts1rDjYGDx7Mpk2b+Oyzz9i8eTOr\nVq1q9UaLiASPGl2KSMDBgwcpLCwEYOfOndx4441kZWVRXV0NwLZt2xg3blzg+O3btweOHTZsGAAO\nh4P8/PxWj7dUUlLCOeecg8ViYffu3Rw9ejQQfphMJhobG1sdP27cOLZu3QpAbW0tWVlZjBo16tss\nW0RERIIoOjqavn378o9//AOAQ4cOfeOS0FNdHwwePJjPP/8cgPz8fA4dOgT4d/j68ssvmTx5MkuX\nLiU/P7/dNYeIBIdmSohIQGpqKi+//DJHjhwhNjaWu+66i5SUFO666y5sNhu9evXiwQcfDBz/1Vdf\nsWbNGioqKli2bBkAd999N48++igDBw7scLrlVVddxX333cfs2bMZP348d999N08//TTvvPMOaWlp\n3Hjjja26cM+ZM4fHHnuM73//+7jdbubOnUvfvn359NNPz/4XRERERDrFsmXLePrpp3n99ddpbGxk\n0aJFX3v8qa4Prr/+ej7++GNmzZpF3759GTNmDOC/xlm6dCk2mw3DMLj33ntb9bASkeAxGZrvLCLQ\n4U4ZX2fYsGFkZWXphC4iIiIiIv8xLd8QERERERERkaDQTAkRERERERERCQrNlBARERERERGRoFAo\nISIiIiIiIiJBoVBCRERERERERIJCoYSIiIiIiIiIBIVCCREREREREREJCoUSIiIiIiIiIhIU/x9o\nJTQM3AazBQAAAABJRU5ErkJggg==\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": {}
      },
      "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": []
    }
  ]
}