diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
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+++ b/feature_crosses.ipynb
@@ -0,0 +1,1650 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "feature_crosses.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "ZTDHHM61NPTw",
+        "0i7vGo9PTaZl"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/feature_crosses.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Feature Crosses"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F7dke6skIK-k",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Improve a linear regression model with the addition of additional synthetic features (this is a continuation of the previous exercise)\n",
+        "  * Use an input function to convert pandas `DataFrame` objects to `Tensors` and invoke the input function in `fit()` and `predict()` operations\n",
+        "  * Use the FTRL optimization algorithm for model training\n",
+        "  * Create new synthetic features through one-hot encoding, binning, and feature crosses"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "NS_fcQRd8B97",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4IdzD8IdIK-l",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, as we've done in previous exercises, let's define the input and create the data-loading code."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CsfdiLiDIK-n",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "10rhoflKIK-s",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ufplEkjN8KUp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "76a48c86-a5e1-4bd4-b760-c517317495fe"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2635.7           536.3   \n",
+              "std         2.1        2.0                12.5       2187.7           421.9   \n",
+              "min        32.5     -124.3                 1.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1458.0           295.0   \n",
+              "50%        34.2     -118.5                29.0       2115.5           431.0   \n",
+              "75%        37.7     -118.0                37.0       3147.2           643.0   \n",
+              "max        42.0     -114.5                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1425.7       498.7            3.9               2.0  \n",
+              "std        1155.4       386.4            1.9               1.2  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         785.0       281.0            2.6               1.5  \n",
+              "50%        1162.5       406.5            3.6               1.9  \n",
+              "75%        1714.0       600.0            4.8               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2635.7</td>\n",
+              "      <td>536.3</td>\n",
+              "      <td>1425.7</td>\n",
+              "      <td>498.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2187.7</td>\n",
+              "      <td>421.9</td>\n",
+              "      <td>1155.4</td>\n",
+              "      <td>386.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1458.0</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>785.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2115.5</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>1162.5</td>\n",
+              "      <td>406.5</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3147.2</td>\n",
+              "      <td>643.0</td>\n",
+              "      <td>1714.0</td>\n",
+              "      <td>600.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.7     -119.6                28.5       2662.8           546.9   \n",
+              "std         2.2        2.0                12.7       2161.2           420.5   \n",
+              "min        32.5     -124.2                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1464.0           300.0   \n",
+              "50%        34.3     -118.5                28.0       2161.0           441.0   \n",
+              "75%        37.7     -118.0                37.0       3161.2           662.0   \n",
+              "max        42.0     -114.3                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1438.9       507.3            3.8               2.0  \n",
+              "std        1129.7       380.0            1.8               1.0  \n",
+              "min           8.0         1.0            0.5               0.1  \n",
+              "25%         797.0       282.8            2.6               1.5  \n",
+              "50%        1178.0       416.0            3.5               1.9  \n",
+              "75%        1741.0       615.0            4.7               2.3  \n",
+              "max       28566.0      6082.0           15.0              29.4  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2662.8</td>\n",
+              "      <td>546.9</td>\n",
+              "      <td>1438.9</td>\n",
+              "      <td>507.3</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.7</td>\n",
+              "      <td>2161.2</td>\n",
+              "      <td>420.5</td>\n",
+              "      <td>1129.7</td>\n",
+              "      <td>380.0</td>\n",
+              "      <td>1.8</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1464.0</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>797.0</td>\n",
+              "      <td>282.8</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2161.0</td>\n",
+              "      <td>441.0</td>\n",
+              "      <td>1178.0</td>\n",
+              "      <td>416.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3161.2</td>\n",
+              "      <td>662.0</td>\n",
+              "      <td>1741.0</td>\n",
+              "      <td>615.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>29.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.6\n",
+              "std                 116.4\n",
+              "min                  15.0\n",
+              "25%                 119.4\n",
+              "50%                 180.4\n",
+              "75%                 265.1\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                206.6\n",
+              "std                 115.0\n",
+              "min                  15.0\n",
+              "25%                 119.5\n",
+              "50%                 180.4\n",
+              "75%                 264.7\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oJlrB4rJ_2Ma",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "NBxoAfp2AcB6",
+        "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.\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(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": "hweDyy31LBsV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## FTRL Optimization Algorithm\n",
+        "\n",
+        "High dimensional linear models benefit from using a variant of gradient-based optimization called FTRL. This algorithm has the benefit of scaling the learning rate differently for different coefficients, which can be useful if some features rarely take non-zero values (it also is well suited to support L1 regularization). We can apply FTRL using the [FtrlOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/FtrlOptimizer)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S0SBf1X1IK_O",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(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",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\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",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "1Cdr02tLIK_Q",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "19d817e0-ad6b-402a-fb1d-c98442d1632a"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(training_examples),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 482.05\n",
+            "  period 01 : 377.98\n",
+            "  period 02 : 255.85\n",
+            "  period 03 : 188.13\n",
+            "  period 04 : 172.48\n",
+            "  period 05 : 150.28\n",
+            "  period 06 : 163.43\n",
+            "  period 07 : 136.25\n",
+            "  period 08 : 136.18\n",
+            "  period 09 : 107.51\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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3fKZrw4iIiBRBBcaJTOgSiUtGVVI5zTcHfzQ7joiIiNNSgXEiXh5ujGo8FKPA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\nRMTj/H8RNcP2nteJCwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i4lGvqajDWlw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## One-Hot Encoding for Discrete Features\n",
+        "\n",
+        "Discrete (i.e. strings, enumerations, integers) features are usually converted into families of binary features before training a logistic regression model.\n",
+        "\n",
+        "For example, suppose we created a synthetic feature that can take any of the values `0`, `1` or `2`, and that we have a few training points:\n",
+        "\n",
+        "| # | feature_value |\n",
+        "|---|---------------|\n",
+        "| 0 |             2 |\n",
+        "| 1 |             0 |\n",
+        "| 2 |             1 |\n",
+        "\n",
+        "For each possible categorical value, we make a new **binary** feature of **real values** that can take one of just two possible values: 1.0 if the example has that value, and 0.0 if not. In the example above, the categorical feature would be converted into three features, and the training points now look like:\n",
+        "\n",
+        "| # | feature_value_0 | feature_value_1 | feature_value_2 |\n",
+        "|---|-----------------|-----------------|-----------------|\n",
+        "| 0 |             0.0 |             0.0 |             1.0 |\n",
+        "| 1 |             1.0 |             0.0 |             0.0 |\n",
+        "| 2 |             0.0 |             1.0 |             0.0 |"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KnssXowblKm7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Bucketized (Binned) Features\n",
+        "\n",
+        "Bucketization is also known as binning.\n",
+        "\n",
+        "We can bucketize `population` into the following 3 buckets (for instance):\n",
+        "- `bucket_0` (`< 5000`): corresponding to less populated blocks\n",
+        "- `bucket_1` (`5000 - 25000`): corresponding to mid populated blocks\n",
+        "- `bucket_2` (`> 25000`): corresponding to highly populated blocks\n",
+        "\n",
+        "Given the preceding bucket definitions, the following `population` vector:\n",
+        "\n",
+        "    [[10001], [42004], [2500], [18000]]\n",
+        "\n",
+        "becomes the following bucketized feature vector:\n",
+        "\n",
+        "    [[1], [2], [0], [1]]\n",
+        "\n",
+        "The feature values are now the bucket indices. Note that these indices are considered to be discrete features. Typically, these will be further converted in one-hot representations as above, but this is done transparently.\n",
+        "\n",
+        "To define feature columns for bucketized features, instead of using `numeric_column`, we can use [`bucketized_column`](https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column), which takes a numeric column as input and transforms it to a bucketized feature using the bucket boundaries specified in the `boundaries` argument. The following code defines bucketized feature columns for `households` and `longitude`; the `get_quantile_based_boundaries` function calculates boundaries based on quantiles, so that each bucket contains an equal number of elements."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cc9qZrtRy-ED",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_boundaries(feature_values, num_buckets):\n",
+        "  boundaries = np.arange(1.0, num_buckets) / num_buckets\n",
+        "  quantiles = feature_values.quantile(boundaries)\n",
+        "  return [quantiles[q] for q in quantiles.keys()]\n",
+        "\n",
+        "# Divide households into 7 buckets.\n",
+        "households = tf.feature_column.numeric_column(\"households\")\n",
+        "bucketized_households = tf.feature_column.bucketized_column(\n",
+        "  households, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"households\"], 7))\n",
+        "\n",
+        "# Divide longitude into 10 buckets.\n",
+        "longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "  longitude, boundaries=get_quantile_based_boundaries(\n",
+        "    california_housing_dataframe[\"longitude\"], 10))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U-pQDAa0MeN3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train the Model on Bucketized Feature Columns\n",
+        "**Bucketize all the real valued features in our example, train the model and see if the results improve.**\n",
+        "\n",
+        "In the preceding code block, two real valued columns (namely `households` and `longitude`) have been transformed into bucketized feature columns. Your task is to bucketize the rest of the columns, then run the code to train the model. There are various heuristics to find the range of the buckets. This exercise uses a quantile-based technique, which chooses the bucket boundaries in such a way that each bucket has the same number of examples."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YFXV9lyMLedy",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0FfUytOTNJhL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "6e9f6315-d5ac-41ea-ba38-0891976fddb6"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.94\n",
+            "  period 01 : 143.64\n",
+            "  period 02 : 127.08\n",
+            "  period 03 : 115.91\n",
+            "  period 04 : 107.92\n",
+            "  period 05 : 102.02\n",
+            "  period 06 : 97.58\n",
+            "  period 07 : 94.03\n",
+            "  period 08 : 91.15\n",
+            "  period 09 : 88.77\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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WnfqRUb1CMZtMLNwYR2lZmbXjiYiI1CgqMNWoe/1b8XX2YXPCduxdCujZLpDz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\nxQfElaYAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZTDHHM61NPTw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JQHnUhL_NRwA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may be wondering how to determine how many buckets to use. That is of course data-dependent. Here, we just selected arbitrary values so as to obtain a not-too-large model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Ro5civQ3Ngh_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RNgfYk6OO8Sy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "aa013be3-d305-430b-c6ae-2f9bba4b35df"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 170.06\n",
+            "  period 01 : 143.69\n",
+            "  period 02 : 127.14\n",
+            "  period 03 : 115.99\n",
+            "  period 04 : 108.09\n",
+            "  period 05 : 102.29\n",
+            "  period 06 : 97.79\n",
+            "  period 07 : 94.22\n",
+            "  period 08 : 91.28\n",
+            "  period 09 : 88.91\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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/ckvy+P7MFiZENgdg0ca4Kl2BW0RERP5HBaYGRTbpjbuDG9+f2YKfr5nOLfw4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\nIwVGCCGEEGnn/wPIcrSroRFHTgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AFJ1qoZPlQcs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Feature Crosses\n",
+        "\n",
+        "Crossing two (or more) features is a clever way to learn non-linear relations using a linear model. In our problem, if we just use the feature `latitude` for learning, the model might learn that city blocks at a particular latitude (or within a particular range of latitudes since we have bucketized it) are more likely to be expensive than others. Similarly for the feature `longitude`. However, if we cross `longitude` by `latitude`, the crossed feature represents a well defined city block. If the model learns that certain city blocks (within range of latitudes and longitudes) are more likely to be more expensive than others, it is a stronger signal than two features considered individually.\n",
+        "\n",
+        "Currently, the feature columns API only supports discrete features for crosses. To cross two continuous values, like `latitude` or `longitude`, we can bucketize them.\n",
+        "\n",
+        "If we cross the `latitude` and `longitude` features (supposing, for example, that `longitude` was bucketized into `2` buckets, while `latitude` has `3` buckets), we actually get six crossed binary features. Each of these features will get its own separate weight when we train the model."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-Rk0c1oTYaVH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train the Model Using Feature Crosses\n",
+        "\n",
+        "**Add a feature cross of `longitude` and `latitude` to your model, train it, and determine whether the results improve.**\n",
+        "\n",
+        "Refer to the TensorFlow API docs for [`crossed_column()`](https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column) to build the feature column for your cross. Use a `hash_bucket_size` of `1000`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-eYiVEGeYhUi",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "xZuZMp3EShkM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "4cd41474-69af-4069-be0f-89f58bf61e7a"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.91\n",
+            "  period 01 : 135.67\n",
+            "  period 02 : 118.59\n",
+            "  period 03 : 107.34\n",
+            "  period 04 : 99.38\n",
+            "  period 05 : 93.53\n",
+            "  period 06 : 89.11\n",
+            "  period 07 : 85.59\n",
+            "  period 08 : 82.78\n",
+            "  period 09 : 80.40\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Yn2DjykRERGouBZibIKZeNAGu/mxM2UpQMLQI82VfQhZHkrJtXZqIiEiNpABz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\n6rp55pJ4AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0i7vGo9PTaZl",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "3tAWu8qSTe2v",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  households = tf.feature_column.numeric_column(\"households\")\n",
+        "  longitude = tf.feature_column.numeric_column(\"longitude\")\n",
+        "  latitude = tf.feature_column.numeric_column(\"latitude\")\n",
+        "  housing_median_age = tf.feature_column.numeric_column(\"housing_median_age\")\n",
+        "  median_income = tf.feature_column.numeric_column(\"median_income\")\n",
+        "  rooms_per_person = tf.feature_column.numeric_column(\"rooms_per_person\")\n",
+        "  \n",
+        "  # Divide households into 7 buckets.\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    households, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"households\"], 7))\n",
+        "\n",
+        "  # Divide longitude into 10 buckets.\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    longitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"longitude\"], 10))\n",
+        "  \n",
+        "  # Divide latitude into 10 buckets.\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    latitude, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"latitude\"], 10))\n",
+        "\n",
+        "  # Divide housing_median_age into 7 buckets.\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    housing_median_age, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"housing_median_age\"], 7))\n",
+        "  \n",
+        "  # Divide median_income into 7 buckets.\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    median_income, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"median_income\"], 7))\n",
+        "  \n",
+        "  # Divide rooms_per_person into 7 buckets.\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    rooms_per_person, boundaries=get_quantile_based_boundaries(\n",
+        "      training_examples[\"rooms_per_person\"], 7))\n",
+        "  \n",
+        "  # YOUR CODE HERE: Make a feature column for the long_x_lat feature cross\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "  set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000) \n",
+        "  \n",
+        "  feature_columns = set([\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person,\n",
+        "    long_x_lat])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-_vvNYIyTtPC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "53eb0b1d-37f5-43e9-bcee-82de2eb78e81"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=1.0,\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.66\n",
+            "  period 01 : 135.37\n",
+            "  period 02 : 118.26\n",
+            "  period 03 : 106.93\n",
+            "  period 04 : 99.07\n",
+            "  period 05 : 93.30\n",
+            "  period 06 : 88.80\n",
+            "  period 07 : 85.33\n",
+            "  period 08 : 82.47\n",
+            "  period 09 : 80.17\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Pe/PNf3DyZOoFtz/55COXq6TLTgFGRESkEUlLO8maNStr9dgHH3yUsLDmF9z+yiuvXa6y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\ngg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ymlHJ-vrhLZw",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Try Out More Synthetic Features\n",
+        "\n",
+        "So far, we've tried simple bucketized columns and feature crosses, but there are many more combinations that could potentially improve the results. For example, you could cross multiple columns. What happens if you vary the number of buckets? What other synthetic features can you think of? Do they improve the model?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/feature_sets.ipynb b/feature_sets.ipynb
new file mode 100644
index 0000000..f941a0b
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1535 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "feature_sets.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "IGINhMIJ5Wyt",
+        "pZa8miwu6_tQ"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/feature_sets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zbIgBK-oXHO7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Feature Sets"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bL04rAQwH3pH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objective:** Create a minimal set of features that performs just as well as a more complex feature set"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F8Hci6tAH3pH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "So far, we've thrown all of our features into the model. Models with fewer features use fewer resources and are easier to maintain. Let's see if we can build a model on a minimal set of housing features that will perform equally as well as one that uses all the features in the data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "F5ZjVwK_qOyR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "As before, let's load and prepare the California housing data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SrOYRILAH3pJ",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "dGnXo7flH3pM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jLXC8y4AqsIy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "1f7aae62-97c5-4ab0-a464-68dd339683a1"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.7       2631.3           536.6   \n",
+              "std         2.1        2.0                12.6       2153.1           418.2   \n",
+              "min        32.5     -124.3                 1.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2134.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3137.0           646.0   \n",
+              "max        42.0     -114.6                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1421.2       498.3            3.9               2.0  \n",
+              "std        1105.9       379.3            1.9               1.2  \n",
+              "min           3.0         3.0            0.5               0.0  \n",
+              "25%         791.0       281.0            2.6               1.5  \n",
+              "50%        1162.0       409.0            3.5               1.9  \n",
+              "75%        1718.0       602.0            4.8               2.3  \n",
+              "max       28566.0      6082.0           15.0              55.2  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2631.3</td>\n",
+              "      <td>536.6</td>\n",
+              "      <td>1421.2</td>\n",
+              "      <td>498.3</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2153.1</td>\n",
+              "      <td>418.2</td>\n",
+              "      <td>1105.9</td>\n",
+              "      <td>379.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1453.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>791.0</td>\n",
+              "      <td>281.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2134.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1162.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3137.0</td>\n",
+              "      <td>646.0</td>\n",
+              "      <td>1718.0</td>\n",
+              "      <td>602.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.7     -119.6                28.3       2673.3           546.2   \n",
+              "std         2.2        2.0                12.6       2243.0           429.2   \n",
+              "min        32.5     -124.3                 2.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1479.0           297.0   \n",
+              "50%        34.3     -118.5                28.0       2113.0           437.0   \n",
+              "75%        37.7     -118.0                37.0       3172.5           655.2   \n",
+              "max        42.0     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1449.7       508.2            3.9               2.0  \n",
+              "std        1242.8       396.6            1.9               1.1  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         786.8       282.0            2.6               1.5  \n",
+              "50%        1178.5       409.0            3.5               1.9  \n",
+              "75%        1726.0       614.0            4.7               2.3  \n",
+              "max       35682.0      5189.0           15.0              34.2  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.3</td>\n",
+              "      <td>2673.3</td>\n",
+              "      <td>546.2</td>\n",
+              "      <td>1449.7</td>\n",
+              "      <td>508.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2243.0</td>\n",
+              "      <td>429.2</td>\n",
+              "      <td>1242.8</td>\n",
+              "      <td>396.6</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1479.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>786.8</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2113.0</td>\n",
+              "      <td>437.0</td>\n",
+              "      <td>1178.5</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3172.5</td>\n",
+              "      <td>655.2</td>\n",
+              "      <td>1726.0</td>\n",
+              "      <td>614.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>34.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                207.4\n",
+              "std                 116.1\n",
+              "min                  15.0\n",
+              "25%                 119.4\n",
+              "50%                 180.7\n",
+              "75%                 265.5\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>207.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                206.9\n",
+              "std                 115.8\n",
+              "min                  15.0\n",
+              "25%                 119.9\n",
+              "50%                 179.5\n",
+              "75%                 264.1\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>264.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hLvmkugKLany",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Develop a Good Feature Set\n",
+        "\n",
+        "**What's the best performance you can get with just 2 or 3 features?**\n",
+        "\n",
+        "A **correlation matrix** shows pairwise correlations, both for each feature compared to the target and for each feature compared to other features.\n",
+        "\n",
+        "Here, correlation is defined as the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient).  You don't have to understand the mathematical details for this exercise.\n",
+        "\n",
+        "Correlation values have the following meanings:\n",
+        "\n",
+        "  * `-1.0`: perfect negative correlation\n",
+        "  * `0.0`: no correlation\n",
+        "  * `1.0`: perfect positive correlation"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        },
+        "outputId": "a9c61e8d-c8f1-4d8e-d39c-e214c741b846"
+      },
+      "cell_type": "code",
+      "source": [
+        "correlation_dataframe = training_examples.copy()\n",
+        "correlation_dataframe[\"target\"] = training_targets[\"median_house_value\"]\n",
+        "\n",
+        "correlation_dataframe.corr()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>latitude</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>longitude</th>\n",
+              "      <td>-0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_rooms</th>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>population</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>households</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.3</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.9</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>median_income</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>rooms_per_person</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>-0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.7</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    latitude  longitude  housing_median_age  total_rooms  \\\n",
+              "latitude                 1.0       -0.9                 0.0         -0.0   \n",
+              "longitude               -0.9        1.0                -0.1          0.0   \n",
+              "housing_median_age       0.0       -0.1                 1.0         -0.4   \n",
+              "total_rooms             -0.0        0.0                -0.4          1.0   \n",
+              "total_bedrooms          -0.1        0.1                -0.3          0.9   \n",
+              "population              -0.1        0.1                -0.3          0.9   \n",
+              "households              -0.1        0.1                -0.3          0.9   \n",
+              "median_income           -0.1       -0.0                -0.1          0.2   \n",
+              "rooms_per_person         0.1       -0.1                -0.1          0.1   \n",
+              "target                  -0.1       -0.0                 0.1          0.1   \n",
+              "\n",
+              "                    total_bedrooms  population  households  median_income  \\\n",
+              "latitude                      -0.1        -0.1        -0.1           -0.1   \n",
+              "longitude                      0.1         0.1         0.1           -0.0   \n",
+              "housing_median_age            -0.3        -0.3        -0.3           -0.1   \n",
+              "total_rooms                    0.9         0.9         0.9            0.2   \n",
+              "total_bedrooms                 1.0         0.9         1.0           -0.0   \n",
+              "population                     0.9         1.0         0.9           -0.0   \n",
+              "households                     1.0         0.9         1.0            0.0   \n",
+              "median_income                 -0.0        -0.0         0.0            1.0   \n",
+              "rooms_per_person               0.1        -0.1        -0.0            0.2   \n",
+              "target                         0.1        -0.0         0.1            0.7   \n",
+              "\n",
+              "                    rooms_per_person  target  \n",
+              "latitude                         0.1    -0.1  \n",
+              "longitude                       -0.1    -0.0  \n",
+              "housing_median_age              -0.1     0.1  \n",
+              "total_rooms                      0.1     0.1  \n",
+              "total_bedrooms                   0.1     0.1  \n",
+              "population                      -0.1    -0.0  \n",
+              "households                      -0.0     0.1  \n",
+              "median_income                    0.2     0.7  \n",
+              "rooms_per_person                 1.0     0.2  \n",
+              "target                           0.2     1.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RQpktkNpia2P",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Features that have strong positive or negative correlations with the target will add information to our model. We can use the correlation matrix to find such strongly correlated features.\n",
+        "\n",
+        "We'd also like to have features that aren't so strongly correlated with each other, so that they add independent information.\n",
+        "\n",
+        "Use this information to try removing features.  You can also try developing additional synthetic features, such as ratios of two raw features.\n",
+        "\n",
+        "For convenience, we've included the training code from the previous exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "bjR5jWpFr2xs",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "jsvKHzRciH9T",
+        "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.\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(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": "g3kjQV9WH3pb",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\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=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\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",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "varLu7RNH3pf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Spend 5 minutes searching for a good set of features and training parameters. Then check the solution to see what we chose. Don't forget that different features may require different learning parameters."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DSgUxRIlH3pg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 758
+        },
+        "outputId": "120c8c33-26b7-4fba-f6a4-0ead70f30e4b"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# Your code here: add your features of choice as a list of quoted strings.\n",
+        "#\n",
+        "minimal_features = [\n",
+        "    \"median_income\",\n",
+        "  \"latitude\",\n",
+        "]\n",
+        "\n",
+        "assert minimal_features, \"You must select at least one feature!\"\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "#\n",
+        "# Don't forget to adjust these parameters.\n",
+        "#\n",
+        "train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 165.77\n",
+            "  period 01 : 127.36\n",
+            "  period 02 : 117.57\n",
+            "  period 03 : 116.21\n",
+            "  period 04 : 116.19\n",
+            "  period 05 : 115.25\n",
+            "  period 06 : 115.53\n",
+            "  period 07 : 114.10\n",
+            "  period 08 : 113.35\n",
+            "  period 09 : 114.21\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tensorflow_estimator.python.estimator.canned.linear.LinearRegressor at 0x7fe53f7e9250>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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MANTdTm0X+XCUVytcDRERUcfDABMAvWsH8vJ5MERERIHBABMAXcITECbrIJvs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\ndlRgRERExHZUYERERMR2VGBERETEdv4DrgMWi8AhCUoAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IGINhMIJ5Wyt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BAGoXFPZ5ZE3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "minimal_features = [\n",
+        "  \"median_income\",\n",
+        "  \"latitude\",\n",
+        "]\n",
+        "\n",
+        "minimal_training_examples = training_examples[minimal_features]\n",
+        "minimal_validation_examples = validation_examples[minimal_features]\n",
+        "\n",
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "RidI9YhKOiY2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Make Better Use of Latitude\n",
+        "\n",
+        "Plotting `latitude` vs. `median_house_value` shows that there really isn't a linear relationship there.\n",
+        "\n",
+        "Instead, there are a couple of peaks, which roughly correspond to Los Angeles and San Francisco."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hfGUKj2IR_F1",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "outputId": "760f4895-fe24-4871-88ba-8058da7fd221"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fe528bcf1d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Vtj3vavcxRQEPfW4JHDZrMvohpXaGx8L4r/+9E31D2vqCUzepQjwOqzW7DRtJlRAIVzG9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1NTgqqyXLR3Dwu+lye8HDSZZKVBJgkCBoMh4sGSMsbhAgQkQeDxu6fjoUYuovtQU4tNE6Uik8QD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\njo5OGqAbZB0dHR0dnTTg/wc7Nqvez+DUCgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6N0p91k2iFCP",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Try creating some synthetic features that do a better job with latitude.**\n",
+        "\n",
+        "For example, you could have a feature that maps `latitude` to a value of `|latitude - 38|`, and call this `distance_from_san_francisco`.\n",
+        "\n",
+        "Or you could break the space into 10 different buckets.  `latitude_32_to_33`, `latitude_33_to_34`, etc., each showing a value of `1.0` if `latitude` is within that bucket range and a value of `0.0` otherwise.\n",
+        "\n",
+        "Use the correlation matrix to help guide development, and then add them to your model if you find something that looks good.\n",
+        "\n",
+        "What's the best validation performance you can get?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wduJ2B28yMFl",
+        "colab_type": "code",
+        "cellView": "form",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Train on a new data set that includes synthetic features based on latitude.\n",
+        "def select_and_transform_features(source_df):\n",
+        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "  return selected_examples\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)\n",
+        "#"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pZa8miwu6_tQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PzABdyjq7IZU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Aside from `latitude`, we'll also keep `median_income`, to compare with the previous results.\n",
+        "\n",
+        "We decided to bucketize the latitude. This is fairly straightforward in Pandas using `Series.apply`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xdVF8siZ7Lup",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def select_and_transform_features(source_df):\n",
+        "  LATITUDE_RANGES = zip(range(32, 44), range(33, 45))\n",
+        "  selected_examples = pd.DataFrame()\n",
+        "  selected_examples[\"median_income\"] = source_df[\"median_income\"]\n",
+        "  for r in LATITUDE_RANGES:\n",
+        "    selected_examples[\"latitude_%d_to_%d\" % r] = source_df[\"latitude\"].apply(\n",
+        "      lambda l: 1.0 if l >= r[0] and l < r[1] else 0.0)\n",
+        "  return selected_examples\n",
+        "\n",
+        "selected_training_examples = select_and_transform_features(training_examples)\n",
+        "selected_validation_examples = select_and_transform_features(validation_examples)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "U4iAdY6t7Pkh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "4db9bdf1-7e78-46e0-b9f6-b412c02476ce"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 227.41\n",
+            "  period 01 : 217.17\n",
+            "  period 02 : 207.02\n",
+            "  period 03 : 196.97\n",
+            "  period 04 : 187.05\n",
+            "  period 05 : 177.25\n",
+            "  period 06 : 167.62\n",
+            "  period 07 : 158.16\n",
+            "  period 08 : 148.93\n",
+            "  period 09 : 139.95\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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e0Vz2X8l7y35m5bbTmo0RERG7pAIjAHg6ufNSzQE8W7UvLlYnLMUP81voj4yd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\ns/XSvwOu2QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jbI1nrVz35Rm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb
new file mode 100644
index 0000000..16a06f6
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,1777 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "first_steps_with_tensor_flow.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "ajVM7rkoYXeL",
+        "ci1ISxxrZ7v0"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/first_steps_with_tensor_flow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# First Steps with TensorFlow"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Bd2Zkk1LE2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Learn fundamental TensorFlow concepts\n",
+        "  * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n",
+        "  * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n",
+        "  * Improve the accuracy of a model by tuning its hyperparameters"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "MxiIKhP4E2Zr",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "6TjLjL9IU80G",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "In this first cell, we'll load the necessary libraries."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rVFf5asKE2Zt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from sklearn import metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ipRyUHjhU80Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll load our data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9ivCDWnwE2Zx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vVk_qlG6U80j",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r0eVyguIU80m",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "f4f916b5-4ceb-47c6-fd6e-46d0f69bc0de"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6379</th>\n",
+              "      <td>-118.3</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>326.0</td>\n",
+              "      <td>123.0</td>\n",
+              "      <td>490.0</td>\n",
+              "      <td>105.0</td>\n",
+              "      <td>1.5</td>\n",
+              "      <td>175.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5945</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>1079.0</td>\n",
+              "      <td>306.0</td>\n",
+              "      <td>1358.0</td>\n",
+              "      <td>285.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>131.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>885</th>\n",
+              "      <td>-117.1</td>\n",
+              "      <td>32.9</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>14944.0</td>\n",
+              "      <td>2490.0</td>\n",
+              "      <td>6600.0</td>\n",
+              "      <td>2407.0</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>308.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15290</th>\n",
+              "      <td>-122.3</td>\n",
+              "      <td>37.9</td>\n",
+              "      <td>49.0</td>\n",
+              "      <td>1130.0</td>\n",
+              "      <td>244.0</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>239.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>93.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7711</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>8264.0</td>\n",
+              "      <td>2437.0</td>\n",
+              "      <td>3148.0</td>\n",
+              "      <td>2274.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>281.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8956</th>\n",
+              "      <td>-118.9</td>\n",
+              "      <td>34.4</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>4431.0</td>\n",
+              "      <td>739.0</td>\n",
+              "      <td>2304.0</td>\n",
+              "      <td>720.0</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>209.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>673</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>32.6</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>2074.0</td>\n",
+              "      <td>356.0</td>\n",
+              "      <td>1228.0</td>\n",
+              "      <td>335.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>160.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10677</th>\n",
+              "      <td>-120.6</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>1145.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>1107.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>89.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1751</th>\n",
+              "      <td>-117.2</td>\n",
+              "      <td>32.9</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2911.0</td>\n",
+              "      <td>533.0</td>\n",
+              "      <td>1137.0</td>\n",
+              "      <td>499.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16229</th>\n",
+              "      <td>-122.5</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>2841.0</td>\n",
+              "      <td>517.0</td>\n",
+              "      <td>1372.0</td>\n",
+              "      <td>517.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>335.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "6379      -118.3      34.1                15.0        326.0           123.0   \n",
+              "5945      -118.2      34.0                28.0       1079.0           306.0   \n",
+              "885       -117.1      32.9                 5.0      14944.0          2490.0   \n",
+              "15290     -122.3      37.9                49.0       1130.0           244.0   \n",
+              "7711      -118.4      34.1                24.0       8264.0          2437.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "8956      -118.9      34.4                35.0       4431.0           739.0   \n",
+              "673       -117.0      32.6                26.0       2074.0           356.0   \n",
+              "10677     -120.6      35.0                38.0       1145.0           297.0   \n",
+              "1751      -117.2      32.9                25.0       2911.0           533.0   \n",
+              "16229     -122.5      37.7                52.0       2841.0           517.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "6379        490.0       105.0            1.5               175.0  \n",
+              "5945       1358.0       285.0            2.5               131.9  \n",
+              "885        6600.0      2407.0            6.1               308.3  \n",
+              "15290       607.0       239.0            2.5                93.8  \n",
+              "7711       3148.0      2274.0            3.6               281.3  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "8956       2304.0       720.0            4.3               209.1  \n",
+              "673        1228.0       335.0            4.1               160.2  \n",
+              "10677      1107.0       296.0            2.2                89.1  \n",
+              "1751       1137.0       499.0            5.1               500.0  \n",
+              "16229      1372.0       517.0            3.9               335.0  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HzzlSs3PtTmt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Examine the Data\n",
+        "\n",
+        "It's a good idea to get to know your data a little bit before you work with it.\n",
+        "\n",
+        "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gzb10yoVrydW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "8626d58e-3628-4281-ba3d-cd9a365391e1"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "2e54a4d2-9777-444b-c235-4b00755f6981"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "346a63fa-1478-49d4-a132-db0bb9911bf0"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Squared Error (on training data): 56367.025\n",
+            "Root Mean Squared Error (on training data): 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AKWstXXPzOVz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Is this a good model? How would you judge how large this error is?\n",
+        "\n",
+        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+        "\n",
+        "Let's compare the RMSE to the difference of the min and max of our targets:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7UwqGbbxP53O",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "594c218a-af46-42e4-8d34-af738b9ba3ae"
+      },
+      "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": 12,
+      "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": "29628ba4-9541-4b58-817c-8a3b6eb057c2"
+      },
+      "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": 13,
+      "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": 13
+        }
+      ]
+    },
+    {
+      "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": 231
+        },
+        "outputId": "60068f59-b7c5-49c9-bfcb-ca5998bbdc8a"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Get the min and max total_rooms values.\n",
+        "x_0 = sample[\"total_rooms\"].min()\n",
+        "x_1 = sample[\"total_rooms\"].max()\n",
+        "\n",
+        "# Retrieve the final weight and bias generated during training.\n",
+        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
+        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "# Get the predicted median_house_values for the min and max total_rooms values.\n",
+        "y_0 = weight * x_0 + bias \n",
+        "y_1 = weight * x_1 + bias\n",
+        "\n",
+        "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
+        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
+        "\n",
+        "# Label the graph axes.\n",
+        "plt.ylabel(\"median_house_value\")\n",
+        "plt.xlabel(\"total_rooms\")\n",
+        "\n",
+        "# Plot a scatter plot from our data sample.\n",
+        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
+        "\n",
+        "# Display graph.\n",
+        "plt.show()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-14-54daeba1e663>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"total_rooms\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mx_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"total_rooms\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Retrieve the final weight and bias generated during training.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mweight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_regressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_variable_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'linear/linear_model/total_rooms/weights'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'sample' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "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": 989
+        },
+        "outputId": "d73a91a6-d7bf-4d95-fffa-8c62633fcdbe"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.0000,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.54\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:75: RuntimeWarning: divide by zero encountered in divide\n",
+            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:75: RuntimeWarning: invalid value encountered in divide\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "  period 01 : 237.54\n",
+            "  period 02 : 237.54\n",
+            "  period 03 : 237.54\n",
+            "  period 04 : 237.54\n",
+            "  period 05 : 237.54\n",
+            "  period 06 : 237.54\n",
+            "  period 07 : 237.54\n",
+            "  period 08 : 237.54\n",
+            "  period 09 : 237.54\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.0    207.3\n",
+              "std            0.0    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            0.0    119.4\n",
+              "50%            0.0    180.4\n",
+              "75%            0.0    265.0\n",
+              "max            0.0    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</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>0.0</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.54\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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U+MjK3eOT8zeu4EFCAu3BlCAAAACg6yEp4YYnw8Xbsq8/2m8r5umj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\nrkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ajVM7rkoYXeL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "T3zmldDwYy5c",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=500,\n",
+        "    batch_size=5\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "M8H0_D4vYa49",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "QU5sLyYTqzqL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Is There a Standard Heuristic for Model Tuning?\n",
+        "\n",
+        "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n",
+        "\n",
+        "That said, here are a few rules of thumb that may help guide you:\n",
+        "\n",
+        " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n",
+        " * If the training has not converged, try running it for longer.\n",
+        " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n",
+        "   * But sometimes the exact opposite may happen if the learning rate is too high.\n",
+        " * If the training error varies wildly, try decreasing the learning rate.\n",
+        "   * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n",
+        " * Very small batch sizes can also cause instability.  First try larger values like 100 or 1000, and decrease until you see degradation.\n",
+        "\n",
+        "Again, never go strictly by these rules of thumb, because the effects are data dependent.  Always experiment and verify."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "GpV-uF_cBCBU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Feature\n",
+        "\n",
+        "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n",
+        "\n",
+        "Don't take more than 5 minutes on this portion."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "YMyOxzb0ZlAH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "5538dd17-d1ef-4ee7-d4fc-6d2fead2de88"
+      },
+      "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": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 226.09\n",
+            "  period 01 : 215.05\n",
+            "  period 02 : 205.24\n",
+            "  period 03 : 196.75\n",
+            "  period 04 : 189.80\n",
+            "  period 05 : 184.13\n",
+            "  period 06 : 180.42\n",
+            "  period 07 : 177.96\n",
+            "  period 08 : 176.66\n",
+            "  period 09 : 176.18\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         116.9    207.3\n",
+              "std           93.9    116.0\n",
+              "min            0.2     15.0\n",
+              "25%           64.6    119.4\n",
+              "50%           95.5    180.4\n",
+              "75%          140.8    265.0\n",
+              "max         2918.8    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>116.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>93.9</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>64.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>95.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>140.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2918.8</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 176.18\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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MTcJitRf6nKi+LFY7V1Jz5NxXIXuOiRMPPoc9PZPwt17Ct1PrYpdXXTqFdt8a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0VokA6ny4PD5am0qsDh8nDgVE6565MSw+u0MiE4UEeYSeezcqOuBZLSWLfsIvWV/6KJjiDxszdQ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\npgAAAABJRU5ErkJggg==\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": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/improving_neural_net_performance.ipynb b/improving_neural_net_performance.ipynb
new file mode 100644
index 0000000..78f1e59
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,1803 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "improving_neural_net_performance.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "jFfc3saSxg6t",
+        "FSPZIiYgyh93",
+        "GhFtWjQRzD2l",
+        "P8BLQ7T71JWd"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/improving_neural_net_performance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "cellView": "both",
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "eV16J6oUY-HN"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Improving Neural Net Performance"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "0Rwl1iXIKxkm"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objective:** Improve the performance of a neural network by normalizing features and applying various optimization algorithms\n",
+        "\n",
+        "**NOTE:** The optimization methods described in this exercise are not specific to neural networks; they are effective means to improve most types of models."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "lBPTONWzKxkn"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, we'll load the data."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "VtYVuONUKxko",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "B8qC-jTIKxkr",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ah6LjMIJ2spZ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "c37d31c2-ff6f-4ce0-e55b-906048050d8f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2656.1           541.8   \n",
+              "std         2.1        2.0                12.6       2187.5           424.2   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1462.8           296.0   \n",
+              "50%        34.2     -118.5                29.0       2133.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3153.0           650.0   \n",
+              "max        42.0     -114.3                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1440.7       503.5            3.9               2.0  \n",
+              "std        1181.7       388.5            1.9               1.2  \n",
+              "min           6.0         1.0            0.5               0.1  \n",
+              "25%         790.0       281.8            2.6               1.5  \n",
+              "50%        1168.0       409.0            3.6               1.9  \n",
+              "75%        1727.0       606.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2656.1</td>\n",
+              "      <td>541.8</td>\n",
+              "      <td>1440.7</td>\n",
+              "      <td>503.5</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2187.5</td>\n",
+              "      <td>424.2</td>\n",
+              "      <td>1181.7</td>\n",
+              "      <td>388.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.8</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>281.8</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2133.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3153.0</td>\n",
+              "      <td>650.0</td>\n",
+              "      <td>1727.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.5       2613.9           533.6   \n",
+              "std         2.1        2.0                12.5       2161.6           414.9   \n",
+              "min        32.5     -124.3                 2.0         11.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1462.0           297.0   \n",
+              "50%        34.2     -118.5                28.0       2120.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3143.8           646.0   \n",
+              "max        41.8     -114.5                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1402.8       495.7            3.9               2.0  \n",
+              "std        1061.8       374.7            1.9               1.1  \n",
+              "min           3.0         3.0            0.5               0.0  \n",
+              "25%         788.8       282.0            2.5               1.5  \n",
+              "50%        1161.0       408.0            3.5               2.0  \n",
+              "75%        1710.0       603.0            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              41.3  "
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "\n",
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+              "        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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2613.9</td>\n",
+              "      <td>533.6</td>\n",
+              "      <td>1402.8</td>\n",
+              "      <td>495.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.5</td>\n",
+              "      <td>2161.6</td>\n",
+              "      <td>414.9</td>\n",
+              "      <td>1061.8</td>\n",
+              "      <td>374.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>788.8</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2120.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1161.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3143.8</td>\n",
+              "      <td>646.0</td>\n",
+              "      <td>1710.0</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.8</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>16122.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>41.3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                206.3\n",
+              "std                 114.8\n",
+              "min                  15.0\n",
+              "25%                 119.4\n",
+              "50%                 180.3\n",
+              "75%                 263.3\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>206.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>114.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>263.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                209.7\n",
+              "std                 118.7\n",
+              "min                  22.5\n",
+              "25%                 119.7\n",
+              "50%                 180.6\n",
+              "75%                 269.9\n",
+              "max                 500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
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+              "\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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>209.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>118.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>22.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>269.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "NqIbXxx222ea"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Train the Neural Network\n",
+        "\n",
+        "Next, we'll train the neural network."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "6k3xYlSg27VB",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "De9jwyy4wTUT",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a neural network model.\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(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": {
+        "colab_type": "code",
+        "id": "W-51R3yIKxk4",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    my_optimizer,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    my_optimizer: An instance of `tf.train.Optimizer`, the optimizer to use.\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",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A tuple `(estimator, training_losses, validation_losses)`:\n",
+        "      estimator: the trained `DNNRegressor` object.\n",
+        "      training_losses: a `list` containing the training loss values taken during training.\n",
+        "      validation_losses: a `list` containing the validation loss values taken during training.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor object.\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor, training_rmse, validation_rmse"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "KueReMZ9Kxk7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "59cedaec-1f54-4980-f873-5ee9ae4f79dc"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0007),\n",
+        "    steps=5000,\n",
+        "    batch_size=70,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 160.21\n",
+            "  period 01 : 152.09\n",
+            "  period 02 : 141.80\n",
+            "  period 03 : 129.25\n",
+            "  period 04 : 116.41\n",
+            "  period 05 : 108.86\n",
+            "  period 06 : 106.59\n",
+            "  period 07 : 106.68\n",
+            "  period 08 : 105.69\n",
+            "  period 09 : 105.20\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   105.20\n",
+            "Final RMSE (on validation data): 107.36\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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et3z594iPj3vt6x9/vFBVLakcAwwREVEdkpAQjzNnTlXovfPnL4KtbYPXvv711z+oqi2V\nq5W3EiAiIqJX++GHZQgPD0O3bh7o27cfEhLi8dNPa/DVV/9BcnIS8vLyMGXKDHTp0g1z5szAwoUf\n4ty535GTk42YmMeIi3uCefMWoVOnLhgwoDeOHfsdc+bMgIfHWwgKCkR6ejqWLfsR5ubm+M9/PsXT\npwlwdnbB2bNncODA8Wr7nAwwREREarL77H3ciEh66XmpVEBJiahUTY/WlhjVq/lrXx8zxg/79+9G\n06bNEBPzCGvW/Iy0tFR06NAR/foNRFzcE3z66cfo0qXbC9slJSXiu+9W4OrVKzh0aB86derywut6\nenpYvnwt1q5diQsXzsLWtiEKCwuwYcNWXL58Ebt371Tq8yiLAeZvnuWlIi4hFrbShhAEoabbISIi\nqpI2bRwBAAYGhggPD8Phw/shCBJkZma89F4XFzcAgKWlJbKzs1963dXVvez1jIwMPH4cDWdnVwBA\np05dIJVW7/2dGGD+5uSjs7iScB0drNtiTKvhkEvlNd0SERHVYqN6NX/lbImFhQGSk7PUvn8tLS0A\nwOnTJ5GZmYnVq39GZmYmpk3ze+m9fw8govjy7NA/XxdFERLJ8+cEQaj2//hzEe/f9G/qheamdrj+\nNAjfBq5CYm5yTbdERERUKRKJBCUlJS88l56eDhsbW0gkEvzxx1kUFRVVeT8NGjREZORdAMD161df\n2qe6McD8jYnCGJ/1WojuDTojPucpvrmxAsFJITXdFhERUYU1adIUkZERyMn532Ggnj174cqVi5g/\n/13o6OjA0tISW7ZsrNJ+OnfuhpycHLz77lTcvh0MQ0OjqrZeKYL4qnkiDafOabe/pvVuPA3Gjoi9\nKCwtQq9G3TC0WX9IJdV7fI9eVF1TrlQ5HBfNxbHRXHVhbDIzMxAUFIiePXsjOTkJ8+e/ix079ql0\nHxYWBq99jWtgXsPD2h0N9G3wc6g/zsZexKPMWEx1Ggdj7epNmERERJpIV1cPZ8+ewY4d/hDFUsyd\nW70XveMMzD/8MxXnF+djR8Q+3Ey6DQMtfUxxGouWJq8/fY3Upy78j6Uu4rhoLo6N5uLYVEx5MzBc\nA/MGCpkCkx3HYmSLIcgpzsWK4I04+egsSsXSmm6NiIio3mKAqQBBENCzURcsaPsujLQNceThSay/\nsw25Rbk13RoREVG9xABTCfZGTfCxx3y0NmmB0Gfh+PrGCsRkPanptoiIiOodBphKMpDrY7bbVPSz\n88Kz/FR8f3MNLsdde+VFf4iIiEg9GGCUIBEkGGjfF7Ncp0BbIseOyH3wD9+NwpLCmm6NiIioQkaM\nGITc3Fz4+29FaOidF17Lzc3FiBGDyt3+/PnfAQDHjx/BH3+cU1ufr8MAUwWOZq3xkcd8NDFohGtP\nb+K7m6uRxKv3EhFRLeLnNwlOTi6V2iYhIR5nzpwCAPTvPwg9eniqo7Vy8TowVWSmY4IF7d7F/ntH\ncCHuTyy7sRJ+bUbCzdK5plsjIqJ6aMqUcfjyy+9hbW2Np08T8Mkni2BhYYm8vDzk5+djwYIP4ODg\nVPb+L774P/Ts2Rtubu74178+RGFhYdmNHQHgt99OYO/eXZBKJbCza4aPPvoXfvhhGcLDw7Bly0aU\nlpbC2NgYvr5vY82a5QgJuY3i4hL4+o6Cj88AzJkzAx4ebyEoKBDp6elYtuxHWFtbV/lzMsCogJZE\nhrdbDUNToybYGbEPG0P90btRdwxp1o9X7yUiqsf23z/6ylvSSCUCSkqVWzvpbumM4c0Hvvb17t09\ncfnyBfj6jsLFi3+ge3dPNGvWAt2798TNmzfwyy/b8MUX37603alTJ2Bv3wzz5i3C77//VjbDkpeX\nh++/XwkDAwPMnj0dDx7cx5gxfti/fzcmT56OTZvWAwBu3QrCw4cPsHbtZuTl5WHixNHo3r0nAEBP\nTw/Ll6/F2rUrceHCWYwaNVapz/53DDAq1MG6LRrq2+LnUH/8HnsBjzJjMIVX7yUiomrUvbsnVq36\nCb6+o3Dp0h+YM2cBfv3VHzt3+qOoqAgKheKV2z169BBubu0AAO7u7cqeNzQ0xCefLAIAPH4cjYyM\n9FduHxFxF25ubQEAOjo6sLOzR2xsLADA1dUdAGBpaYmMjAyVfE4GGBWz1bfGh+3n4peIvQhKuoOv\nry/HFKdxaGnSrKZbIyKiaja8+cBXzpao80q89vbN8OxZMhITnyIrKwsXL56HubklPv10KSIi7mLV\nqp9euZ0oAhKJAAAo/e/sUFFREX744Rts3boDZmbm+PDD9167X0EQ8PcTcouLi8rqSaX/OxqhqrN2\n1bqINyoqCl5eXggICADw/AuxaNEijBgxAhMnTixLYYcPH4avry9GjhyJPXv2qLOlaqGQKTDFcRxG\ntBj836v3bsBvj87x6r1ERFQtOnXqig0b1qBbtx7IyEhHgwYNAQB//HEOxcXFr9ymceMmiIgIBwAE\nBQUCAHJzcyCVSmFmZo7ExKeIiAhHcXExJBIJSkpKXti+dWtHBAff/O92uYiLe4KGDRur6yOqL8Dk\n5uZi6dKl6NSpU9lzu3fvhomJCfbu3Yv+/fsjMDAQubm5WL16NbZu3Qp/f39s27YN6emvnp5St/iU\nHFy8FaeSdCgIAjwbdcWCtjNhpG2IQw9PYEMIr95LRETq16OHJ86cOYWePXvDx2cAdu36BQsWzIaj\noxOePXuGY8cOv7SNj88AhIWFYP78dxEb+xiCIMDIyBgeHm9h2rQJ2LJlI8aO9cOKFT+gSZOmiIyM\nwIoV35dt7+rqhlatWmP27OlYsGA2Zs6cAx0dHbV9RrXdzLG4uBjFxcXYuHEjTExMMH78eEybNg3z\n5s2Di8v/Ttf6888/sW/fPnz33XcAgCVLlqBnz57o1avXa2ura9pt28kI/HErHu1bWWDKgDZQyFVz\nhC2rMBtbwnYgMu0+zBSmmO7sh0YGDVRSuz7hzc80E8dFc3FsNBfHpmLKu5mj2tbAyGQyyGQvlo+L\ni8OFCxfw7bffwtzcHP/+97+RkpICU1PTsveYmpoiObn8a6mYmOhCJlP92T3ThrogJbMAgZHJSMrI\nx78mdYCthX6V61rAAJ/ZLMDusKPYf/cEvr+5GlPavo1e9l0gCIIKOq8/yvtmpprDcdFcHBvNxbGp\nmmpdxCuKIpo2bYo5c+ZgzZo1WL9+PRwcHF56z5ukpanvMMznMztj9a5gnLn5BO/9+AdmDHKAa3Nz\nldTube0JK5k1tt39FesDf8HtJ5F4u9VQyKVyldSv6/g/Fs3EcdFcHBvNxbGpmPJCXrVeidfc3Bwe\nHh4AgK5du+L+/fuwtLRESkpK2XuSkpJgaWlZnW29QCaVYGyflpg2sA2KS0qxYu8dHL4cjVIVHWlz\nMm+Djz3mo7FBQ1x9Gvjfq/emvHlDIiIiKlOtAaZ79+64ePEiACAsLAxNmzaFq6srQkJCkJmZiZyc\nHAQFBaF9+/bV2dYrdXaywf8b3w6mhto4eDEaq/eHIK/g1Su3K8tMxxQL281C1wYdEZedgGU3VuBW\ncqhKahMREdUHalvEGxoaimXLliEuLg4ymQxWVlb47rvv8MUXXyA5ORm6urpYtmwZzM3NcfLkSWza\ntAmCIGD8+PEYPHhwubXVOe32z2m9rNxCrDsUhvDHabA21cVcX2fYmOmpbH/XnwZhR8Q+FJUWoXfj\n7hhiz6v3vg6nXDUTx0VzcWw0F8emYso7hKS2AKNO1RlgAKCktBT7zj/EyesxUMilmDbQAW1bWqhs\nn/HZT7ExdDuSclPQzKgppjqNg5G2ocrq1xX8gddMHBfNxbHRXBybitGYNTC1lVQiwahezfHOYEeU\niiJW7Q/B/gsPy65UWFXPr947D+4WzniQEY2vbvyEqLQHKqlNRERUFzHAVMJbDlb4l197WBgrcPTK\nIyzfewc5+UUqqa0jU2Cq03j4thiEnKL/Xr338TmVXXKZiIioLmGAqaRGlvr4dKIHnJqaIuThMyzd\nGognydkqqS0IAno16ob33GfCUG6AQw9OYH3INuQW5amkPhERUV3BAKMEfR0tvDfSFQM6NUFSeh6+\n2H4TNyKSVFa/mbEdPunwHlqZNEdIyl0su7EcsVlxKqtPRERU2zHAKEkiEeDboxlmD3MCBGDtwVDs\nOXdfZetiDOT6mOM2DT5NeiElPxXf3VyNK/HXVVKbiIiotmOAqaJ2rSyxeEJ7WJno4MS1GPy4+xay\n81SzLkYiSDComQ/edZkMuUQLv0TshX/4bhSWqKY+ERFRbcUAowINzPXw6UQPuDU3R9ijNPxn6w3E\nJKru9Lj/Xb23Aa4mBOK7m6t49V4iIqrXGGBURFchwxxfZwzp2hQpGfn40v8m/gx7qrL6ZjqmWNh2\nFrravlV29d7bvHovERHVUwwwKiQRBAzp2hTzfF0glQrYeOQudp65h+KSUpXU15JqYUxrX0xo8zZK\nxBJsCNmOA/ePoaS0RCX1iYiIagsGGDVwa2GOxRPaw8ZMF6cDY/H9r7eQmVOosvpv2bTDB+3nwFLH\nHGdi/sCKWxuQUZCpsvpERESajgFGTWzM9LB4Qnu0a2mByNh0fLb1BqITVBcyGujb4EOPeXCzcMb9\n9Gh8fWM57vHqvUREVE8wwKiRjrYMs4Y5wbeHPdKzCvBVQBAu3olXXX2ZAtOcxsO3+UBkF+Vgxa2N\nOP34PK/eS0REdR4DjJoJgoABnezw3ihXyGUSbDkeAf/fIlW2LkYQBPRq3B3vuc+EgZY+Dj44jg0h\n23n1XiIiqtMYYKqJs70Zlkxqj4YWejgXFIdvdgYjI7tAZfWbGdvh4w7z0dKkOe6khGFZ4ArEZqlu\ntoeIiEiTMMBUI0sTXfzLrz06tLHE/ScZ+L+tN3A/LkNl9Q3lBpjrNg3eTXohJe8Zvr+5iqdaExFR\nncQAU8205VK8M9gRozybIzOnEMt+CcL5W6q7z5FEkGBwMx/MdJkEQZDg59AA3Ey8rbL6REREmoAB\npgYIggCftxpj4dtu0NGWYfvJSGw9EY6iYtWsiwEAZ3MHzHWbBrlEji1hO3D9aZDKahMREdU0Bpga\n5GhniiUT26OxlT4u3E7Ash1BSM3MV1l9eyM7zHOfDoVMge13d+FK/A2V1SYiIqpJDDA1zNxYB/9v\nfDt0crTGw/hM/GfrDUTGpKmsfhPDRpjvPgO6Wjr4JWIPLjz5U2W1iYiIagoDjAaQa0kxbWAbjPVq\ngey8Ynz36y2cCYxV2fVcGhk0KDvNelfUAZyLvaSSukRERDWFAUZDCIIAr/aN8MEYN+gpZNhx5h42\nHQtHYZFq7nNkq2+N99rOhJHcAHvvHcbpx+dVUpeIiKgmMMBomFaNTbBkkgea2hjgSuhTfBUQhJQM\n1VyUzlrPEu+1fRcm2sY4+OA4TkSfUUldIiKi6sYAo4FMDRX4eFxbdHWxwePELPxnayDCH6WqpLal\nrjkWtJ0JM4UJjkb/hiMPTvLWA0REVOswwGgoLZkUk/u1hp93K+QVFOO7Xbdw6nqMSsKGmY4pFrR9\nFxY6Zjj5+CwO3D/GEENERLUKA4wGEwQBnu4N8NHYtjDUlWPX2ftYfzgMBYVVXxdjojDGe21nwkrX\nEr/HXsCee4cZYoiIqNZggKkFmjc0wr8ne6B5AyNcD0/CF/43kZRe9XUxxtpGeK/tO7DVs8YfTy5j\nZ+R+lIqqu5geERGRujDA1BLG+tr4cKw7PN0b4ElyNpZuvYHQh8+qXNdQboD57u+gkb4tLsdfQ0D4\nHoYYIiLSeAwwtYhMKoGfdytM7tcaBUUl+HH3bRz781GVD/3oy/Uwz30Gmhg2wrWnN7Ht7q8oKVXN\n6dtERETqwABTC3VztcXH49rB2EAb+/54iDUHQ5FXUFylmrpaupjrNh32Rk0QmHgLm8N2oLi0ajWJ\niIjUhQGmlrK3NcSSSR5o2cgYNyOT8YX/TSSm5lappo5Mgdmu09DC2B63kkPwc6g/ihhiiIhIAzHA\n1GJGenK8P9oNXu0bIj4lB//ZFohb91OqVFMh08Ys1ylobdICISnh2HBnGwpLilTUMRERkWowwNRy\nMqkEY71aYvpABxSXlGLF3js4fCkapVVYFyOXyjHTZRIczVrjbmok1t7ZgoKSQhV2TUREVDUMMHVE\nJydr/L/x7WBmqMDBS9FYtS8EufnKH/7RkmphhvMEuJo7IirtPlbf2oT84nwVdkxERKQ8Bpg6pIm1\nAZZMao82TUxw634Klm4PRFJw/ZHqAAAgAElEQVSa8utiZBIZpjqNR1tLFzzIiMaqWz8jt0g192Ui\nIiKqCgaYOsZAV46Fb7vCp0NjJKbm4pudwVW66J1UIsUkhzHwsGqL6MwYrLy1ATlFVVssTEREVFUM\nMHWQVCLBqF7NMaJnM6RmFuDbHUFIqWKImeAwCp1sPBCTFYflweuRVZitwo6JiIgqhwGmDuvfsQl8\ne9jjWWYBlu0IRkqG8iFGIkgwtrUvujXohLjsBCwPXo+MgiwVdktERFRxDDB13IBOdhjWrSmeZebj\nmx3BeJah/EJciSDB2y2HwrNRVyTkJOKn4LVIL8hQYbdEREQVwwBTDwzq0hRDuzZFSkY+vtkZhNRM\n5UOMIAjwbT4IfRr3RFJuCn68uRbP8tJU2C0REdGbMcDUE4O7NsXgLnZITn8+E1PVEDOkWT/0s/NC\nSn4qfgxai5S8qt9YkoiIqKIYYOqRIV2bYmBnOySl5+GbncFIyypQupYgCBho3xeD7H2QVpCOH4PW\nITE3WYXdEhERvZ5aA0xUVBS8vLwQEBAAAPj4448xaNAg+Pn5wc/PD+fPnwcAHD58GL6+vhg5ciT2\n7NmjzpbqNUEQMKxbUwzo1ARJaVUPMQDgY9cLw5oPQHpBBn4KWoeEnEQVdUtERPR6MnUVzs3NxdKl\nS9GpU6cXnl+4cCE8PT1feN/q1auxd+9eaGlpYcSIEejTpw+MjY3V1Vq9JggChne3hygCx68+xrc7\ng/HhWHcY62srXdOrcQ/IJDLsiTqEn4LWYZ77DDTQt1Fh10RERC9S2wyMXC7Hxo0bYWlpWe77bt++\nDWdnZxgYGEChUKBt27YICgpSV1uE/y7E7WEPn7ca42lqLr7dGYyM7KrNxPRs2AVjWg1HTlEulget\nR0zWExV1S0RE9DK1BRiZTAaFQvHS8wEBAZgwYQIWLFiA1NRUpKSkwNTUtOx1U1NTJCdzLYW6CYKA\nkT2bwbtDIyQ8e37F3oycqt2wsWuDjhjfZiRyi/OwIngDojNiVNQtERHRi9R2COlVhgwZAmNjY7Rp\n0wYbNmzAqlWr4O7u/sJ7xArcRdnERBcymVRdbcLCwkBttTXN7FHuUCjkOHThAX7ccxtfzOwCYwPl\nDycNsvCEiZE+Vl3bilW3N+L/dZ+D1hbNVdZvfRqb2oTjork4NpqLY1M11Rpg/r4eplevXvi///s/\neHt7IyUlpez5pKQkuLm5lVsnrQo3KHwTCwsDJCfXryvMDu7UGDm5BTgT+AQfr76ID8a4w1BXrnS9\nVrqtMcVxHDaH/YLPz6/ATJfJaGVa9RBTH8emNuC4aC6Ojebi2FRMeSGvWk+jnjt3LmJjYwEA165d\nQ4sWLeDq6oqQkBBkZmYiJycHQUFBaN++fXW2Ve8JgoAxvVugd7uGiEvOwXc7g5GVW7XDSe6Wzpjh\nPAGlYinW3tmMu88iVdQtERERIIgVOWajhNDQUCxbtgxxcXGQyWSwsrLC+PHjsWHDBujo6EBXVxdf\nffUVzMzMcPLkSWzatAmCIGD8+PEYPHhwubXVmVrrcyoWRREBp6NwLigOjSz18cEYd+jraFWpZtiz\nSGwM2QZRFDHN2Q/O5g5K16rPY6PJOC6ai2OjuTg2FVPeDIzaAow6McCojyiKCPgtCueC49DYUh/v\nqyDERKTew/o7W1EilmKK41i4WTorVae+j42m4rhoLo6N5uLYVIzGHEIizScIAsb1bYkebraIScrG\nd78GIzuvqEo1W5u2wGy3aZBJpNgU9gsCE2+pqFsiIqqvGGDoJRJBgJ93K3R3tUFMYja+33ULOflV\nCzHNjZtijtt0yCVybA3biWsJN1XULRER1UcMMPRKEkHABJ/W6Opig8dPs/DDrlvIrWKIsTdqgnnu\n06EjU8A/fDcux19TUbdERFTfMMDQa0kEAZP6tUYXZ2tEJ2Th+123kZtfXKWaTQwbYb77O9DT0sWO\niH3448kVFXVLRET1CQMMlUsiCJjcrw06O1kjOiETP+6+hbyCqoWYhga2mO/+Dgzk+tgddRBnYy6o\nqFsiIqovGGDojSQSAVP6t0EnRys8iM/EDyoIMbb61ljgPhNGckPsu38Upx6dVVG3RERUHzDAUIVI\nJAKmDnBARwcrPIjLxI97blc5xFjpWWJB23dhom2Mww9P4lj06QrdSoKIiIgBhipMIhEwdWAbdGhj\niftPMvDTntvIL6xaiLHQNcOCtu/CXGGK49GncfjhSYYYIiJ6IwYYqhSpRILpgxzg0doS955k4Kc9\nd1BQWFKlmmY6Jniv7UxY6prjt8fnsP/+UYYYIiIqFwMMVZpUIsGMwQ5o39oSUbHpWL73dpVDjInC\nGO+5z4S1nhXOxl7E7qhDKBVLVdQxERHVNQwwpBSpRIIZgxzQrpUFImL+G2KKqhZijLQN8Z77O2ig\nb4MLcVewM2I/QwwREb0SAwwpTSaV4J3Bjmjb8nmIWbH3DgqrGGIM5PqY5z4DjQwa4ErCdQSE72GI\nISKilzDAUJXIpBLMHOII9xbmCH+chpX7qh5i9LX0MM9tBuwMG+Pa05vYGrYTJaVVq0lERHULAwxV\nmUwqwbtDneDW3Bxhj9Kwcn8IioqrFjh0tXQw120amhnZ4WbSbWwK+wXFJVU744mIiOoOBhhSib9C\njEszM4RFp6okxChkCsx2m4aWxs1wOzkUK65t4eEkIiICwABDKqQlk2D2MGe4NDND6MNUrD4QiqLi\nqgUObakc77pOQXPjprgaG4QTj35XUbdERFSbMcCQSj0PMU5wsjfFnQfPsPpASJVDjFyqhelOE2Cp\nZ4bj0acRlHRHRd0SEVFtxQBDKqclk2LucGc4Nn0eYtYeDEVxSdVCjL5cDx91mwVtqRzb7+5CTNYT\nFXVLRES1EQMMqcVfIcbBzgS37qeoJMQ0MrLFZMexKC4txvo725BRkKWibomIqLZhgCG1kWtJMdfX\nBW2amCD4XgrWHQqrcohxNnfAkGb9kF6QgY0h21BUUqSibomIqDZhgCG10taSYt4IF7RubIygqGSs\nP1z1EOPVuAc6WLdFdGYMdkTu432TiIjqIQYYUjttLSnmj3BFq0bGuBmZjA1H7qKkVPkQIwgCxrby\nhZ1hY1x/GoQzMX+osFsiIqoNGGCoWmjLpXhvpCtaNjJGYEQSNlYxxGhJtTDDeSKMtY1w6MEJhKTc\nVWG3RESk6RhgqNo8DzEuaNHQCNfDqx5ijLQN8I7LRMgkMmwJ24H47Kcq7JaIiDQZAwxVK4VchvdG\nuqJ5g+chZtPRcJSWKr+GpbFBQ0xweBsFJYVYd2crsgtzVNgtERFpKgYYqnY62jIsGOWKZg0McfVu\nIjYdq1qIaWvpgv52XniWn4qfQ/1RXMp7JhER1XUMMFQjdLRlWDjKDc1sDfFn2FNsPl61ENOvqRfc\nLZxxL/0hdkcd4plJRER1HAMM1ZjnMzFuaGpjiCuhT7HlRDhKlQweEkECP4e30VDfFpfjr+GPuCsq\n7paIiDQJAwzVKF2FDIvedoWdtQEuhzzFthMRSocYbakc77hMhIFcH/vuHUFE6j0Vd0tERJqCAYZq\nnK5CC4tGu6GJtQEu3knA9pPKhxhThQlmOE+EBAJ+Dg1AUm6yirslIiJNwABDGkFPoYVFb7uhsZU+\nLtxOgP+pSKVDjL1RE4xtPQJ5xXlYd2crcovyVNwtERHVNAYY0hj6Olp4f7Q7Glvq449b8fjltyil\nF+O+ZdMOXo17IDE3GZvDfkFJaYmKuyUioprEAEMaRV9HC++PcUcjS32cC47DL6eVDzFDmvWDk1lr\nhKdG4cCDYyrulIiIahIDDGmc5zMxbmhooYezQXHYceaeUiFGIkgwyXEsrPWscC72Eq7EX1dDt0RE\nVBMYYEgjGejK8f4YdzSw0MPvN59g5+/KhRgdmQIznSdBT6aLXyMP4H56tBq6JSKi6sYAQxrLUFeO\nD0a7w9ZcD2cCn8D/RLhSdSx0zTDNeTxEiNgYsh3P8lJV3CkREVU3BhjSaIZ6cnwwxh1WJjrY8/s9\nXA1T7oaNLU2aY1TLocguysG6O1uRX1yg4k6JiKg6McCQxjPSk2PeCBfoKmTYciICj55mKlWnW4OO\n6N6gM+JznmLb3V9RKip/J2wiIqpZDDBUK9iY6eH9ce1QXFyKlftCkJFTqFSdES0GoZVJc9xJCcPR\nh7+puEsiIqouDDBUa3g4WGN4D3ukZRVg9YEQFJdUfgZFKpFiqtN4WOiY4dTjs7jxNFgNnRIRkbox\nwFCt0r9jE3RoY4n7TzIQoOSF7vS0dDHTZTIUUgUCIvbgUWaMGjolIiJ1YoChWkUQBEzu3+a/txyI\nx7ngOKXqWOtZYorTWJSUlmDDnW1IL8hQcadERKROag0wUVFR8PLyQkBAwAvPX7x4Ea1atSp7fPjw\nYfj6+mLkyJHYs2ePOluiOkBbS4q5w11goKuFnWfuIeJxmlJ1HM1aY3jzAcgozML6O9tQWFKk4k6J\niEhd1BZgcnNzsXTpUnTq1OmF5wsKCrBhwwZYWFiUvW/16tXYunUr/P39sW3bNqSnp6urLaojzIwU\nmDXUCQCw5mAoUtKVu2GjZ6Nu6GjTHjFZTxAQvlvp2xYQEVH1UluAkcvl2LhxIywtLV94ft26dRg7\ndizkcjkA4Pbt23B2doaBgQEUCgXatm2LoKAgdbVFdUirxiYY26clsvOKsHJ/CAoKK3/DRkEQMLrV\ncNgb2eFm0m2cenxWDZ0SEZGqydRWWCaDTPZi+ejoaERERGD+/Pn49ttvAQApKSkwNTUte4+pqSmS\nk5PLrW1ioguZTKr6pv/LwsJAbbWpav45NqP6tkZyZgFO/vkIAb/fw0d+7SEIQqXrftLzXXxyehmO\nPDyFVjZ26NDQTUUd1w/8mdFcHBvNxbGpGrUFmFf56quvsHjx4nLfU5Ep/LS0XFW19BILCwMkJ2ep\nrT4p73VjM7yrHR7EpuHy7XhsNQrFwM52SlQXMN1xAr4PWoMVf27Gonaz0dDAtso91wf8mdFcHBvN\nxbGpmPJCXrWdhZSYmIiHDx/i/fffx6hRo5CUlITx48fD0tISKSkpZe9LSkp66bATUXlkUglmDXOG\nqaE2Dlx4iFv3Ut680Ss0NLDFRIfRKCwtwro7W5FVmK3iTomISFWqLcBYWVnhzJkz2L17N3bv3g1L\nS0sEBATA1dUVISEhyMzMRE5ODoKCgtC+ffvqaovqCCM9OeYOd4GWTIINR8IQn5KjVB03CycMsvdG\nWkE6NoRsR1FpsYo7JSIiVVBbgAkNDYWfnx8OHDiA7du3w8/P75VnFykUCixatAhTp07F5MmTMXv2\nbBgY8LggVV4TawNM7t8G+YUlWLnvDnLylTst2rtJL7SzdMXDjEf4NXI/z0wiItJAglgLfzur87gh\nj0tqroqOzd7zD3D86mM4NTXFeyNdIZFUflFvYUkRfgxai5isJ/BtPhC9GndXpuV6gT8zmotjo7k4\nNhWjEWtgiKrL8O72cGlmhtDoVOw9/0CpGnKpFt5xmQgjuQH23z+GsGcRKu6SiIiqggGG6hyJRMCM\nQY6wNtXFyesx+DP0qVJ1jLWNMMNlImQSKTaH7sDTnEQVd0pERMpigKE6SVchw1xfZ+hoS7HlRASi\nEzKVqmNn2BjjWo9Efkk+1t7Zipwi9Z3CT0REFccAQ3WWjZke3hnsiJKSUqzaH4KM7AKl6nhYu8O7\nSS+k5D3DptAAlJRW/oq/RESkWgwwVKe5NDOHb89mSMsqwOoDoSgqLlWqzkD7vnAxd0Rk2n3svXdE\nxV0SEVFlMcBQndfvrcbo0MYS9+MyEPBbpFKnRUsECSY6jEYDfRtciLuCi3F/qqFTIiKqKAYYqvME\nQcDk/m3Q2EofF+8k4GxQnFJ1FDJtvOM8EfpaetgddQhRafdV3CkREVUUAwzVC9paUswd7gIDXS3s\nPHMPEY/TlKpjpmOK6c4TIEDAzyEBSM59puJOiYioIpQOMI8ePVJhG0TqZ2akwOxhzhAEYM3BUKSk\n5ylVp7lxU4xuNQw5xblYd2cL8orzVdwpERG9SbkBZvLkyS88XrNmTdnflyxZop6OiNSoZSNjjOvb\nEtl5RVixLwT5hcrd66izbQd4NuqKp7lJ2BK2A6WicouDiYhIOeUGmOLiF3+5X716tezvtfAOBEQA\ngJ5uDeDp3gBPkrOx6Vi40t/Lw5oNQBvTlgh7FoFDD06ouEsiIipPuQFGEF68h8zff9H/8zWi2mSM\nVwu0bGSMm5HJOHrlkVI1pBIppjiOg5WuBc7E/IGrCYGqbZKIiF6rUmtgGFqorpBJJZg11Almhto4\ncDEawVHJStXR1dLBTJdJ0JHpYGfEPjzMeKTaRomI6JXKDTAZGRn4888/y/5kZmbi6tWrZX8nqs0M\n9eSYM9wFcpkEG47eRVxytlJ1LHUtMNVpHEohYsOd7UjNV+4MJyIiqjhBLGcBgJ+fX7kb+/v7q7yh\nilDnLch5i3PNpa6xuR6eiHWHwmBprIPFE9tDX0dLqTrnn1zGnqhDaKhvi4XtZkFbKldxp5qJPzOa\ni2OjuTg2FWNhYfDa12TlbVhTAYWoOnVoY4XYpGwc+/Mx1h8KxXujXCGVVP4KAz0adEZC9lNcir+G\n7Xd3YarTOEgEXmqJiEgdyv3tmp2dja1bt5Y9/vXXXzFkyBDMmzcPKSkp6u6NqNoM62YPl2ZmCHuU\nhr3nHyhVQxAEjGo5FC2M7XErOQTHo8+ouEsiIvpLuQFmyZIlePbs+ZVGo6Oj8cMPP+Cjjz5C586d\n8cUXX1RLg0TVQSIRMGOQI6xNdXHqeiyuhCYoVUcqkWKakx/MFKY48egMbibeVnGnREQEvCHAxMbG\nYtGiRQCAU6dOwcfHB507d8bo0aM5A0N1jq5Chrm+ztDRlmHriUhEJyi3UF1froeZLpOgLZXDP3w3\nYjKfqLhTIiIqN8Do6uqW/f369evo2LFj2WOeUk11kY2ZHt4Z7IiSklKs3HcH6dkFStWx1bfGZMex\nKC4txvqQbcgo4Fl7RESqVG6AKSkpwbNnzxATE4Pg4GB06dIFAJCTk4O8POXuI0Ok6VyamWGEZzOk\nZxdi9f4QFBUrd5sAZ3MHDGnWD+kFGdgQsh1FJUUq7pSIqP4qN8BMnz4d/fv3x6BBgzBr1iwYGRkh\nPz8fY8eOxdChQ6urR6Jq59OhMTo6WOFBfCb8T0UqfbsBr8Y90MG6LR5lxuCXiH28BQcRkYqUexp1\njx49cOnSJRQUFEBfXx8AoFAo8MEHH6Br167V0iBRTRAEAZP6tUbCs1xcCklAYyt9eLVvpFSdsa18\nkZybghuJQbDVt0LfJp5q6JiIqH4pdwYmPj4eycnJyMzMRHx8fNkfe3t7xMfHV1ePRDVCriXFXF9n\nGOpq4dff7yP8UapSdbSkWpjuPBHG2kY4/OAkQlLuqrhTIqL6p9wr8bZu3RpNmzaFhYUFgJdv5rh9\n+3b1d/gKvBJv/VRTYxMVm45vdwZDIZdiySQPWBjrKFUnJusJfri5FhJBwPvt5sBW31rFndYM/sxo\nLo6N5uLYVEx5V+ItdwZm2bJlsLGxQUFBAby8vLB8+XL4+/vD39+/xsILUXVr2cgY4/u2RE5+MVbu\nu4P8wmKl6jQ2aIgJDm+joKQQ6+5sRW4RF8ITESmr3AAzZMgQbN68GT/99BOys7Mxbtw4TJs2DUeO\nHEF+fn519UhU43q4NYBn2wZ4kpyDTcfCUarkYty2li7wseuNZ/mp+CViLxf1EhEpqUI3arGxscGs\nWbNw4sQJeHt74/PPP+ciXqp3xvRugZaNjHEzMhlHrzxSuk5/Oy80N26KW8khuBj3p+oaJCKqRyoU\nYDIzMxEQEIDhw4cjICAA77zzDo4fP67u3og0ikwqwaxhTjAz1MbBi9EIikpWqo5UIsVkx7HQ09LF\nvntHEJvFBfFERJVVboC5dOkSFixYAF9fXyQkJODrr7/GoUOHMGXKFFhaWlZXj0Qaw1BXjrm+LpDL\nJNh49C6eJGcrVcdY2wgT2ryNYrEEm8MCkF/MQ7JERJXxxrOQ7Ozs4OrqConk5azz1VdfqbW51+FZ\nSPWTJo3N9fBErDsUBgtjBT6d6AF9HS2l6uy/fxS/x1yAh5U7JjqMrpW36NCkcaEXcWw0F8emYso7\nC6ncC9n9daZRWloaTExMXnjtyRPeoI7qrw5trPAkORtHrzzGukOhWDDKFdJXhPw3GWzvgwfpj3Aj\nMRitTJqjk62HGrolIqp7yv2NK5FIsGjRInz66adYsmQJrKys0KFDB0RFReGnn36qrh6JNNLQbvZw\na26Ou4/SsOfcA6VqyCQyTHEcCx2ZArujDiIhJ1HFXRIR1U3lBpgff/wRW7duxfXr1/HBBx9gyZIl\n8PPzw9WrV7Fnz57q6pFII0kEAdMHOcDGTBe/3YjF5ZAEpeqY6ZhifOuRKCwtwubQX1BYUqjiTomI\n6p43zsA0a9YMANC7d2/ExcVhwoQJWLVqFaysrKqlQSJNpqMtwzxfF+hqy7DtZCQexGcoVcfN0hnd\nG3RGfM5T7L13WMVdEhHVPeUGmH8uKLSxsUGfPn3U2hBRbWNlqouZQxxRUlqKVftDkJZVoFSd4c0H\noKG+LS7HX0dg4i0Vd0lEVLdUatVhbTxDgqg6ONmbYWTP5sjILsTqAyEoKi6pdA0tqRamOI2DXCrH\nzoh9SMpNUUOnRER1Q7kBJjg4GD179iz789fjHj16oGfPntXUIlHt4N2hETo6WuFhfCa2n4pU6jYB\nVroWGNNqOPJLCrA57BcUlSp33yUiorqu3NOoT548WV19ENV6giBgkk9rJDzLxeWQp2hsaYA+Ho0q\nXaeDdVtEpT3Anwk3cOj+cYxoOVgN3RIR1W7lBpgGDRpUVx9EdYJcS4q5w53xn22B2HX2Pmwt9OBo\nZ1rpOiNbDkF0xmOce3IJLUzs4WrhpIZuiYhqr8pfeYuIymVqqMDsYU4QBGDdwVAkpeVWuoa2VI6p\nTuOhJZEhIHwPUvPT1NApEVHtpdYAExUVBS8vLwQEBAB4vqZmzJgx8PPzw9SpU5GamgoAOHz4MHx9\nfTFy5EheX4bqhBYNjeHn3Qo5+cVYuS8EeQWVX8tiq2+NkS2HILc4D1vCdqCktPILg4mI6iq1BZjc\n3FwsXboUnTp1Kntuy5Yt+Oabb+Dv7w93d3fs3r0bubm5WL16NbZu3Qp/f39s27YN6enp6mqLqNp0\nd7VF77YNEZeSg03HwlGqxKLezjYd0M7SFQ8zHuNo9G9q6JKIqHZSW4CRy+XYuHHjC3etXrFiBRo1\nagRRFJGYmAhra2vcvn0bzs7OMDAwgEKhQNu2bREUFKSutoiq1du9m6N1Y2MERSXjyOVHld5eEASM\nae0Lcx0z/Pb4HO4+i1R9k0REtVC5i3irVFgmg0z2cvkLFy7giy++gL29PQYPHoxjx47B1PR/ixxN\nTU2RnJxcbm0TE13IZFKV9/yX8u5+STWrNo7N4qkdsfCnP3DoUjQcm5ujk7NtJSsY4P2uM7D492/h\nH7EL33ovhomOkVp6VVZtHJf6gmOjuTg2VaO2APM63bt3R7du3fDdd99hw4YNL53pVJFrZ6QpsSiy\noniLc81Vm8dm1lAnfBlwE9//EoR/+QloaKlfqe0NYIKhzfpj773D+P7CRsx1nw6JoBlr8GvzuNR1\nHBvNxbGpmPJCXrX+Bjx9+jSA59Pi3t7euHnzJiwtLZGS8r8rjiYlJb1w2ImoLmhsZYCpAxxQUFSC\nFfvuIDuvqNI1ejbsAhdzR0SlP8DJR7+roUsiotqjWgPMypUrER4eDgC4ffs2mjZtCldXV4SEhCAz\nMxM5OTkICgpC+/btq7Mtomrh0doSAzvbISUjH2sPhqKktLRS2wuCgPFtRsJE2xjHo8/gXtoDNXVK\nRKT51HYIKTQ0FMuWLUNcXBxkMhlOnTqFzz//HJ999hmkUikUCgW++eYbKBQKLFq0CFOnToUgCJg9\nezYMDHhckOqmod2a4klSNm7dT8He8w/wdq8WldpeT0sXU5zG4segddgSthOfdHgPBvLKHY4iIqoL\nBFGZG7bUMHUeN+RxSc1VV8YmN78YS7cHIjE1F+8MdsRbDlaVrvHb43M49OAEHMxa4V2XyTW6Hqau\njEtdxLHRXBybitGYNTBEBOgqZJg73Bnacim2nAhHbFJ2pWt4Ne6BNqYtcfdZJM7GXlRDl0REmo0B\nhqgG2JrrYdoABxQWlWLV/sov6pUIEkx0GA0juQEOPTiB6IzHauqUiEgzMcAQ1ZB2rSwwoFMTJKfn\nY8ORMJSWVu5oroFcH5Mcx0AURWwO24HcIvVdXoCISNMwwBDVoGHd7OFkb4rQh6k4cPFhpbdvadIc\n/ex6IzU/Db9E7K3QdZSIiOoCBhiiGiSRCHhnsCMsjBU49udj3Iws/yrUr9KvqRdaGNvjVnIoLsT9\nqYYuiYg0DwMMUQ3TU2hhznAXyLUk+PnYXcSn5FRqe4kgwSTHMdDX0sP+e0cQmxWnpk6JiDQHAwyR\nBmhkqY/J/dqgoLAEK/eHIDe/uFLbG2sbYYLD2ygWS7A59BfkF+erqVMiIs3AAEOkId5ysIJPh8ZI\nTM3Fz0fvorSS61kczVrDq3EPJOWlYGfkfq6HIaI6jQGGSIP49rRHmyYmuHU/BUcvP6r09oPtfdDU\nsDECE2/hz4RA1TdIRKQhGGCINIhUIsHMIY4wM1Tg0KVo3Lqf8uaNXtheismOY6Ej08HuqINIyElU\nU6dERDWLAYZIwxjoyjFnuDNkMgk2HrmLxNTKXd/FTMcU41uPQFFpETaFBqCwpFBNnRIR1RwGGCIN\n1MTaABO8WyGvoBgr94cgv7Byi3rdLJ3Ro2FnJOQkYk/UYTV1SURUcxhgiDRUF2cb9G7XEPEpOdh8\nLLzSi3KHNRuARvq2uJJwHYFPg9XUJRFRzWCAIdJgb/dqjpYNjRAYmYwT12Iqta2WVAtTnMZBWyrH\njsh9SMqt/EXyiIg0FbDlGsgAACAASURBVAMMkQaTSSV4d5gzTAy0se+PBwiNflap7S11LTCmlS8K\nSgqxOWwHikordyiKiEhTMcAQaTgjPTlmDXOCVCJg/aEwJKfnVWp7D2t3dLbxQGxWHA7eP6amLomI\nqhcDDFEt0MzWCOP7tkJOfjFW7Q9BQVFJpbYf2XIIrPWscP7JZdxODlVTl0RE1YcBhqiW6O5qix5u\ntohNysa2ExGVWtQrl8ox1XEctCRa8A/fg2d5aWrslIhI/RhgiGqRsV4t0czWEFfvJuJ04JNKbWur\nb41RLYcgrzgPW8J2oKS0crM4RESahAGGqBbRkkkwa5gzDPXk2H32PiIeV24mpZONB9pbuSE68zGO\nPDylpi6JiNSPAYaoljEx0MasoU4QBGDtoVCkZlb8ztOCIGBMq+Gw0DHD6ZjzCHsWqcZOiYjUhwGG\nqBZq2cgYo3u3QFZuEVbtD0FRccUPBylkCkxxGgeZIMX2/9/evcdFVef/A3+dM1dmhhnuyEUQ8C4o\nolZqV9PcblqaaYZltbVbuW677Vbb1mrbfvfx1f31236uZmWmplmWWWql3W1ttRXDC2IoKoiAyG1g\ngGHu8/tjhgHUaLgMMwOv5+PBA+bM+Zx544fBF5/zOedz/F3Umet9WCkRkW8wwBAFqSlZCZicPgDF\nFQ3Y+NnJTk3qTQpNxJ1DbkOjtQnr89+Bw+nwYaVERD2PAYYoSAmCgAXThyF5QCi+yzuPPYfKOtX+\nuoRJGBOdjsK6M9hV/JWPqiQi8g0GGKIgJpdJsOjODGhCZNj8ZSEKS+u8bisIArKH34UIZTh2FX2J\nk/rTPqyUiKhnMcAQBblInRKP3pEOpxN45cNj0DeYvW6rkqnw4Kj5EAQB6/M3o8HS6MNKiYh6DgMM\nUR8wIjkcc25IQ32TBa98lAeb3fs5LSm6ZMxI/QXqLQ146/gWzochoqDAAEPUR9w0YSCuHBmL02UG\nbP6ysFNtb0y6FiMjhuF47Ql8VfJvH1VIRNRzGGCI+ghBELDw5uFIjNZgz6Ey/PtIuddtRUHEfSPn\nQifXYseZ3ThTf9aHlRIRdR8DDFEfopBJsGh2BtRKKTZ9fgJnyg1etw2Va7Bw1D1wOp1489jbMFqN\nPqyUiKh7GGCI+piYsBD8asYo2O1OrPowD4Ymi9dth4an4eaUqdCb67CpYGun7i1DRNSbGGCI+qD0\n1EjMui4V+gYzVn90rFOTem8edCOGhKXiSNUxfFu2z4dVEhF1HQMMUR91y1XJGDcsGifO1eG9b055\n3U4URCwcdQ80MjU+LPwYJQ2dW/WaiKg3MMAQ9VGCIODBW0YgPkqNLw+WYv+xCq/bhil0uG/kPNic\ndrx57G2YbN4vGElE1BsYYIj6sBCFFItmZSBEIcH63QU4W9HgddtRkcMwLel6VDXX4J0T2zgfhogC\nCgMMUR83IEKFh28fBavNgZXb8tBg9H5S7+2p05GiTcbBC4ex/3yOD6skIuocBhiifiBzcBRmXp2C\nGoMJr+3Ih93h3aReiSjBA6PmI0QagvdObkd5o/enoYiIfIkBhqifuH3yIGQOjsLxYj22fXvG63aR\nIeFYMGIOrA4r1ua/DYvd+xEcIiJfYYAh6idEQcAvbxuJ2PAQ7PpvCQ78eMHrtmOi03Fd4mRUNF3A\n+ye3+7BKIiLvMMAQ9SMqpRSLZo+GQi7Buk8LUFrl/erTdw6+FQNDE7DvfA5yKg75sEoiop/HAEPU\nzyREqfHQLSNgttqx8oM8NJmsXrWTiVI8OOpeKCUKvHPiA1Qaq3xcKRHRT/NpgDl58iSmTp2KTZs2\nAQDOnz+PhQsXIjs7GwsXLkRVlesX4I4dOzB79mzMmTMH77//vi9LIiIA44fH4NaJyaisa8aancfh\n8PIS6RhVFO4ZNgtmuwVvHnsbVofNx5USEV2ezwKM0WjEiy++iIkTJ3q2vfzyy7j77ruxadMmTJs2\nDevWrYPRaMSqVauwfv16bNy4ERs2bEBdXZ2vyiIitzuvSUV6SgSOnq7B9r1FXrcbP2AsJsVdgXON\n5fjw1Cc+rJCI6Kf5LMDI5XKsWbMGMTExnm1LlizB9OnTAQDh4eGoq6vDkSNHkJGRgdDQUCiVSmRl\nZSE3N9dXZRGRmygKeGTGKETplNi5rxi5J70/JTRn6AzEqWPxbel/cKD0sA+rJCK6PJ8FGKlUCqVS\n2W6bSqWCRCKB3W7H5s2bcfvtt6O6uhoRERGefSIiIjynlojItzQhMiyalQG5VMQbHx/H+Zomr9rJ\nJXI8lJ4NmSjD6gNvoazxvI8rJSJqT9rbL2i32/HUU0/hqquuwsSJE7Fz5852z3tzu/LwcBWkUomv\nSkR0dKjPjk3dw77pedHRoVg814H/8/YPWL09Hy/99lqolDKv2j3svAevHHgLL+W+gkVX3o8rE8f2\nQsXUGXzPBC72Tff0eoD505/+hOTkZCxatAgAEBMTg+rqas/zlZWVyMzM7PAYer3RZ/VFR4eiqsr7\n9WKo97BvfGfkQB1umjAQn+ecw/+uP4DHZ2VAFISfbTdKk47fT3oYK79fj5f+8zpuGTQVN6dMhSjw\nAsdAwPdM4GLfeKejkNerv2V27NgBmUyGxYsXe7aNGTMGeXl5MBgMaGpqQm5uLsaPH9+bZRERgDk3\npGF4UhgOFVbjk/1nvW531cAs/GH8IkQqw/Fp8Zd4I28jV68mIp8TnD5aYvbYsWNYtmwZysrKIJVK\nERsbi5qaGigUCmg0GgBAWloali5dit27d2Pt2rUQBAHZ2dmYMWNGh8f2ZWplKg5c7BvfMxgt+Ov6\nHOgNZvx2zhiMTov82TYt/dJoacLa/LdxUn8KcepYPJJxP2JUUb1QNf0UvmcCF/vGOx2NwPgswPgS\nA0z/xL7pHcUVBvx9Yy7kUhHPLxyP2HBVh/u37Re7w44PT32Cb0q/Q4g0BA+NuhcjIof2Rtl0GXzP\nBC72jXcC5hQSEQW+QQO0uG/6MBjNNqzclgeTxfub1UlECe4aOgPZI+6G1W7BqiNr8WXJt15Nzici\n6gwGGCK6xNWj4zAlKwFlVU1Y92lBpwPIxLjxeCLrUWjlGnx46hNsOL4FFrt3SxYQEXmDAYaILmve\njUMwJFGHnIJKfHbgXKfbp+iS8NSExUjRJiHnQi7+mfsK9CbeZZuIegYDDBFdllQi4rE70hGmkeP9\nPaeQX1zb6WOEKXT4bdavMTFuAkoayrAsZwVO1Xm/bAER0U9hgCGin6TTKPD4na57wry2PR/Vdc2d\nPoZMlOLe4XdhztCZaLIZseLQ6/iu7HsfVEtE/QkDDBF1KC1Bh3tvGorGZitWfpgHi9Xe6WMIgoDr\nEyfjN5m/hFKqwDsntuGdE9tg42rWRNRFDDBE9LOuz0zAtWPiUHKhERt2n+jyVUVDwwfj6fGLkaCJ\nw3dl32PFoTVosDT2cLVE1B8wwBCRV+6dNgyp8Vrsz6/AVz+Udvk4kSEReHLc4xgbMxqn64uwLGcF\nShq6fjwi6p8YYIjIKzKpa1KvViXDu1+dwokSfZePpZDI8dCoe3F76i9QZ67H//3hFRysONSD1RJR\nX8cAQ0Rei9Aq8egd6RAEYPVHx1Br6PqaR4Ig4BeDpuBXo++HRJBg3fF38NGpT+FwOnqwYiLqqxhg\niKhThiWFY+6UwTAYrVj14TFYbZ2f1NtWRtRI/HH8bxCjisIXJXuw+sg6GK2+W3GeiPoGBhgi6rQb\nxyViUvoAFJ03YPUHR7u9VMAAdQz+OO43GBk5DMdrT+AfB1eioulCD1VLRH0RAwwRdZogCLhv+jAk\nx4biiwMl2LC7AOYuXF7dlkoWgkdHP4Cbkm9AZXM1/nFwJfKqj/dQxUTU1zDAEFGXyGUS/GZ2BlLj\ndfj3kfP424aDKKtu6tYxRUHEzLSb8cCo+bA7HXjt6AbsKvqKi0ES0SUYYIioyyK0Svxj8TW4MSsR\nZdVNeHF9DvYeKe924Bgfm4knxz2GMIUOHxd9hjeObYLJZu6hqomoL2CAIaJukcskuPemoXj8zgxI\nJSLW7SrAmp3H0Wzu3l12B4Ym4OkJizE4LAWHq/Lw0g+rUN3c+fWYiKhvYoAhoh4xblg0lj44AWnx\nWnx//AJeWJ+DsxUN3TpmqFyDxZmP4NqESShvqsDynBUoqC3soYqJKJgxwBBRj4nSheDpe7Nw81VJ\nqNQ34382HsQXB89165SSRJRg7rA7MH/4bJjsZqw6shbfnPuO82KI+jkGGCLqUVKJiDnXD8bv7x6D\nEIUU73xZiJXb8tDYbO3WcSfHX4knsn4FtUyFrYU7sPHH92C1d++YRBS8GGCIyCfSUyPxwoNXYERy\nOA4VVuOFdQdwqrS+W8dM1Q3C0+MXIyk0Ef+t+AH/PPQq6szdOyYRBScGGCLymTCNAk/OzcQd16Sg\ntsGM/307F5/sL4ajG6d/wpVh+F3Wo7hiQBbOGs5hWc4KnKk/23NFE1FQYIAhIp8SRQEzJqfgqXvG\nQquW4YNvz+Cf7x1BfZOly8eUS2S4b8RczB58Gxosjfh/ua9iX3lOD1ZNRIGOAYaIesWwpHC88OAV\nGJ0WifyiWix98wCOF3f9smhBEDAl6Vo8nvkQ5BI53i54H++d3A67o3t3BCai4MAAQ0S9JlQlx+K7\nRmPulMFobLbipXcPY9u/T8Pu6PoK1CMihuKp8YsRp47Ft6X/wcrDb6DR0r07AhNR4GOAIaJeJQoC\npl+RhD9lj0OkTomP953F8s2HUGswdfmY0apI/GHc4xgTnY6Tdaex/OAKlDaU92DVRBRoGGCIyC9S\n47VY+sAVGD88BoWl9Vjy5gEcLqzu8vGUUiV+mZ6NW1Omocakx0s/rEJu5dEerJiIAgkDDBH5jUop\nxaMzR+G+6cNgtjqw4oOjePerQtjsXTulJAoibkmZhkcy7oMgCFh7bBN2nN4Nh7Prp6iIKDAxwBCR\nXwmCgOvHJuD5+8cjLlKFz3PO4e8bf0Cl3tjlY46JTscfxi1CVEgkPjv7NV47ugHNtuYerJqI/I0B\nhogCwsAYDf5y/wRMzhiA4ooGLF2XgwM/Xujy8eI1A/DU+N9gRMRQHKv5Ef84uBIXmip7sGIi8icG\nGCIKGAq5BA/dOhK/vG0EnE7g1e352LC7ABZr1y6NVstUeHT0A7gx6VpcMFZh+cGVOFb9Yw9XTUT+\nwABDRAFnUnoc/rJwPAbGaPDt4XK8+NZBlFV37dJoiSjBrMG34f6R82Bz2vDq0fX4vPgbLgZJFOQY\nYIgoIMVFqvHcfeMwJSsBZVVNeHF9DvYeKe9y8LhiQBZ+n/UodAottp/ZhXX5m2Gxd/1uwETkXwww\nRBSwZFIJsm8ahsfvTIdEImLdrgKs+fg4ms22Lh0vWTsQT41fjFTdIPxQeQQv/fAKapr1PVw1EfUG\nBhgiCnjjhsXghQcmIDVei+/zL+Cv63NwtqKhS8fSKULx27GPYHL8lShtLMfygytQqD/dwxUTka8x\nwBBRUIgKC8Ez92bh5iuTcEHfjP/ZeBBfHjzXpVNKUlGK+cNnY96wO2G0NWPF4TX4tnQf58UQBREG\nGCIKGlKJiDk3DMYTc8ZAKZdi85eFWLktD00ma5eOd03CRCzOfAQqaQjeO/kRNhdshcXetWMRUe8S\nnEH4J0dVVdeGjr0RHR3q0+NT17FvApO/+kXfYMaanfkoKKlDpFaBX81Mx+AEXZeOVWvS4/W8t3Cu\noQxSQYKBoQkYpEtCijYJg7TJiFCGQRCEHv4OfI/vmcDFvvFOdHToTz7HAHMR/lAFLvZNYPJnvzgc\nTny8rxjb/1MEAQJmXZeKX1yZBLELYcNit2BX8Vf4sfYkyhrPt1t+QCsPdYUZd6hJ0g6EQiLvyW/F\nJ/ieCVzsG+8wwHQCf6gCF/smMAVCv5wo0eO1Hfmoa7QgPSUCv7xtJLTqrgcMi92CkoYyFBtKUFRf\ngqL6s6i3GDzPCxCQoInDIF0SBmldoSZGFQVRCKyz8oHQN3R57BvvMMB0An+oAhf7JjAFSr8YjBas\n/fhH5J2pgU4txyO3j8SIQRE9dny9qQ5FhhIU15egyFCCcw2lsDpaL+cOkYZgkHage6QmGYO0A6GW\nqXrs9bsiUPqGLsW+8Q4DTCfwhypwsW8CUyD1i8PpxOcHzuGDb0/D4XDitkmDMOPqQZCIPT8yYnPY\nUNZ4vl2oqW6uabdPrCraNUKjc82liVfHQiJKeryWnxJIfUPtsW+847cAc/LkSTz22GNYuHAhsrOz\nAQBvvfUWli1bhgMHDkCtVgMAduzYgQ0bNkAURdx9992YM2dOh8dlgOmf2DeBKRD75XR5PV7bno/q\nehOGJurwyIxRiNAqff66DZZGnDWc84SaYkMJTHaz53m5KEOydmCbUJMEnULrs3oCsW/IhX3jnY4C\njNRXL2o0GvHiiy9i4sSJnm0fffQRampqEBMT026/VatWYevWrZDJZLjrrrswbdo0hIWF+ao0Iurj\n0uJ1WPrABKzfVYCDJ6qwdF0OHrx1BDIHR/n0dUPlGqRHjUB61AgAgMPpQEVTpWcuTbGhBKfqilBY\nd8bTJlwRhpSWK550yRioiYdMIvNpnUR9gc8CjFwux5o1a7BmzRrPtqlTp0Kj0WDnzp2ebUeOHEFG\nRgZCQ10pKysrC7m5uZgyZYqvSiOifkCllOHRO9Kx51AZ3vnqFFZsPYqbJgzEXdenQSrpncm2oiAi\nXjMA8ZoBmBR/BQCg2WbCWcM5FBtKPMEmt/IociuPAgAkggSJofFI0baGmkhleFBexk3kSz4LMFKp\nFFJp+8NrNJpL9quurkZEROtEu4iICFRVVfmqLCLqRwRBwA1ZiUhL0OHV7fn4POccCkvr8KuZ6YgJ\nC/FLTSFSJYZHDMHwiCEAAKfTiRpTretqJ/epp3MNZThrOIc9+A8AIFSmaXfFU7I2EUqp70+JEQUy\nnwWYrvJmSk54uApSqe8mwnV0zo38i30TmAK9X6KjQ7FiSAxe3XYUXx88h7+uz8GiOZm4JjPB36UB\nAGKgxQgM8jy22CwoqjuHwpoinKwpQmFNEfKqjyOv+jgAVzAbqI3HkMgUDIlMwdDIFMRrYy97GXeg\n901/xr7pHr8HmJiYGFRXV3seV1ZWIjMzs8M2er3RZ/VwYlXgYt8EpmDql+ypQ5ASq8Gmz09i+caD\nOJBXjnk3DoFc1ntXBnkrAjG4MiIGV0ZcCQwB6sz1KDacc1/xdBZnDaUoqS/DV2e+AwAoJUrXZdzu\nkZpBuiSkxA8Imr7pb4LpfeNPfpnE660xY8bgueeeg8FggEQiQW5uLp599ll/l0VEfdTkjDikxmux\n+qN87DlcjlNl9fj1zHTER6n9XVqHwhQ6ZEbrkBmdDgCwO+wob6rwTA4uMpxFgb4QBfpCT5tQuRqA\nABEiRKHlQ4AoiBAEESKENtskEAXBtbcgePYXBAESQYQId5uW9u79JC3HankOrjatr9f6Om3bi+5j\nioLQuv2iOoU27aWiFGEKHSKUYTx9RgB8eBn1sWPHsGzZMpSVlUEqlSI2NhaTJk3Cvn37cPjwYWRk\nZCAzMxNPPfUUdu/ejbVr10IQBGRnZ2PGjBkdHpuXUfdP7JvAFKz9YrHaseXrU/jmUBnkMhHZ04Zh\ncsaAoJ4s22htcl3G7Q41BpsBVpsNDocDDjjhcDrgdDrgcDrhgOuz0+mAvWW7e59Ap5KGIFwZhghl\nGCKU4QhXtPlaGQatPDTg7op8sWB93/Q23siuE/hDFbjYN4Ep2PvlYEEl1u0qQLPZhomjYnH3lCHQ\ndWMZgkDS1b5xBR1XmLE7HXC6w47DHX5cj9t+uMMRnD+xvW1798fFgcqzze4JVq1BywGr3YY6cx1q\nTXWoNdeh1qSHxW65bP0SQYJwhc4dcsIRoQxzfa1o/Vru57Wsgv1901sC+hQSEZE/jR8eg+QBoXh1\nez7251/A/vwLiAkLQVqCDoMTtEhL0CEhWu2Tu/kGKlEQAQGQQIJAvSON0+mE0daMWlMd9Ca9O9jo\noTfVeba1vd/OxTQytTvMhCPCPYIT7g47EcpwaGTqoB6N6w84AnMRpuLAxb4JTH2lX2x2B776oRT5\nxbU4U2aA0dy6zpFCJkFKXCgGJ+qQFq9DWoIOmpBA/a+9VV/pm66yOmyoM9VDb3YHHFNrwGkJO23X\ns2pLJkrdp6Zcp6U8ozmKMM9jmdj1MYD+3jfe4ggMEdHPkEpETL8iCdOvSILD6cT5GiNOl9W7PsoN\nKCipQ0FJnWf/2AgVBse7RmjSEnRIiFJDFPkXeyCRiVJEqyIRrYq87PNOpxON1qbWYGO+KOSY9KjU\nV1+2LQBo5aGXBJu2p6zUUhVHcXyIIzAXYSoOXOybwNRf+sVosuJMuQGn3KHmzHkDms12z/NKuQSp\n8VrPCE1aghZqpX9HafpL3/iSxW6F/jLBpiXw6E11sDvtl20rl8gvDTbuUZ3kAbEwNTigkoZA2o2R\nnL6OIzBERN2kUsqQnhqJ9FTXX/MOhxPlNU04XVbvDjUGHC/W43ix3tMmLlLlnkujQ1q8FnFRaoj8\nizyoyCUyxKqiEauKvuzzDqcDDZbG1mBjbh9y9KY6VBgrO3wNhUQOlVQFtUwFlTQEKpkKalnIT25T\nuT8rJPJ+PcLDEZiL8C+WwMW+CUzsl1aNzVacKa/HqTKDZ5TGbGn96zxEIXWP0mgxOFGH1DgdVErf\n/R3JvgkMJpsJenN9u2Bjl1hQ02iA0WqE0WpEk60ZRqux3erlP0ciSKCShUDdJtSoZSrPthDPcxeH\nn5CAv8y8BUdgiIh6gSZEhtFpURid5lr12uFworSqEafLDZ75NPlFtcgvqgUACADio9RIS3Cdehqc\nqENshIqjNH2MUqpEnFSJOHWsZ9tPhUu7ww6jO8y0hBqjrRlNFwWd1n2MaLQ24YKxCk54Px4RIg2B\n2j2yo5KGuIOPqt02V/Bp87w0JKBWSmeAISLyEVEUkBQbiqTYUNww1rXuksFowZkyA06Xt86lKatu\nwr+PnAcAqJVSpMa75tCkJeiQGqdFiIK/qvsLiShBqFyDUPmlix93xOl0wmQ3e0KN0eoOPS1fuz+3\nfd5oa8b5pguwOqxev45MlF0SakZFDcfk+Cs7+612G98VRES9SKuSI3NIFDKHuEZp7A4HSiubcLq8\n3jNBOO9MDfLO1ABwjdIkRKtd82jcH7HhIf167gNdShAEhEiVCJEqEYmITrW12K2eoNN+tMeIZmuz\nZ9SnbSDSm+tR3lQBAKizGBhgiIj6G4koInlAKJIHhGJKViIAoL7JgjNl9ThV7pocXHzegNKqJuw5\nXA7AdaoqNV7rCTUpcaFQyvnrnLpGLpFBLtEhTKHrVDuH0wGjrRkhEv+sTcWfeCKiAKNTyzF2aDTG\nDnVd+WKzO3CustFzT5rTZfU4eroGR0+7R2kEYGC0xnP5dlqCDjFhIb1as9PpmoHhdDrhcLgfO+Fe\nFsAJh/Pibe59L3rO1b79fg6nE6IgQKuWI1Qlg1QSHBNQ+zpREKGR+W8RVAYYIqIAJ5WISInTIiVO\ni6nubXWNZvfEYANOldej+HwDSiob8c2hMgBAqEqGxJhQWKy2NmEBcDrcn+EOBw5nh2Gj7WNHm9Dh\nbBtIHJ2ZPto9AgCNSgadWgGdRo4wtRw6jQI6tdz1uM3XHJXq29i7RERBKEyjwLhhMRg3LAaAa5Sm\n5EJj631pyutx4mwtBEGAIAgQBbi/BkT357bbRdG9DQIkIiCKYvvn27RveSy6j33JsTz7XuZ1xYuO\nBQGieHH71mO2tHM4nDAYLahvtKCuyYIaQzNKqxo7/DdSyCXQqTsKOa4QpAmR8cqvIMQAQ0TUB0gl\nIlLjtUiN12LahIEA+v59YMwWO+qbzKhvcgebxjZfN5lhcIedQn19hyNEEtF1ekrX8qFRIEwjbxdy\nXM8pIJPy9FWgYIAhIqKgpJBLECNXISZc1eF+docDDUbrRSHHjLomizvkmFHfaEFpVROKKzoOfGql\n1DOa4wo57oDj/rplW4hCwivFfIwBhoiI+jSJKCJMo0CYRoFk/PSdXZ1OJ5rNNtQ1WlpDTqMFhqbW\nkNOyvby6qcPXlEnF1lNW6jajOO7RnfhGK5qNZijkEijlEihlEsikIkNPJzDAEBERwTXnRqWUQaWU\nIT6q46trrDZ7m1NXFhiazO7gY/bM06lvNKOovAEOp8HL13ctCqqUS6GQSVzhRuYKOC1BRyGTuvdx\nbVPIXPt79rlof6mk74YiBhgiIqJOkkkliNKFIErX8eXqDqcTjUYr6hrNrpEcd8gRJBLo65phstpg\ntthhsthhstphtrg+jCYrahtMsFgd3apTFITWUR5P4HGHpHaPWwOQos3zrY9bg5VUIgREKGKAISIi\n8pGW+9do1fJ2272dYO1wOGG2ugKO2R1wTBabZ5vJHXhMVvf2do/tnnBkttrQ1GxFTb0JFlv3QpFE\nFNqNAGUOicKc6wd365hdwQBDREQUoERRQIhC2qPrYbUNRS1hyDMK5A5KrvBjaxeELg5KJosdje5Q\n5A8MMERERP1I+1Ck8Hc5XcYL2omIiCjoMMAQERFR0GGAISIioqDDAENERERBhwGGiIiIgg4DDBER\nEQUdBhgiIiIKOgwwREREFHQYYIiIiCjoMMAQERFR0GGAISIioqDDAENERERBhwGGiIiIgo7gdDqd\n/i6CiIiIqDM4AkNERERBhwGGiIiIgg4DDBEREQUdBhgiIiIKOgwwREREFHQYYIiIiCjoMMC08fe/\n/x1z587FvHnzcPToUX+XQ20sX74cc+fOxezZs/H555/7uxxqw2QyYerUqdi2bZu/S6E2duzYgRkz\nZmDWrFnYs2ePv8shAE1NTVi0aBEWLFiAefPmYe/evf4uKahJ/V1AoDhw4ADOnj2LLVu24PTp03j2\n2WexZcsWf5dFAL7//nsUFhZiy5Yt0Ov1uPPOO3HTTTf5uyxyW716NXQ6nb/LoDb0ej1WrVqFDz74\nAEajEf/6179wECmg/QAABZhJREFU/fXX+7usfu/DDz9ESkoKnnzySVy4cAH3338/du/e7e+yghYD\njNv+/fsxdepUAEBaWhrq6+vR2NgIjUbj58powoQJGD16NABAq9WiubkZdrsdEonEz5XR6dOncerU\nKf7nGGD279+PiRMnQqPRQKPR4MUXX/R3SQQgPDwcJ06cAAAYDAaEh4f7uaLgxlNIbtXV1e1+mCIi\nIlBVVeXHiqiFRCKBSqUCAGzduhXXXnstw0uAWLZsGZ555hl/l0EXKS0thclkwq9//WvMnz8f+/fv\n93dJBODWW29FeXk5pk2bhuzsbDz99NP+LimocQTmJ3CFhcDz5ZdfYuvWrXjzzTf9XQoB+Oijj5CZ\nmYmBAwf6uxS6jLq6OqxcuRLl5eW477778M0330AQBH+X1a9t374d8fHxWLt2LQoKCvDss89y7lg3\nMMC4xcTEoLq62vO4srIS0dHRfqyI2tq7dy9effVVvPHGGwgNDfV3OQRgz549OHfuHPbs2YOKigrI\n5XIMGDAAkyZN8ndp/V5kZCTGjh0LqVSKpKQkqNVq1NbWIjIy0t+l9Wu5ubm4+uqrAQDDhw9HZWUl\nT4d3A08huU2ePBmfffYZACA/Px8xMTGc/xIgGhoasHz5crz22msICwvzdznk9vLLL+ODDz7Ae++9\nhzlz5uCxxx5jeAkQV199Nb7//ns4HA7o9XoYjUbOtwgAycnJOHLkCACgrKwMarWa4aUbOALjlpWV\nhVGjRmHevHkQBAFLlizxd0nk9umnn0Kv1+OJJ57wbFu2bBni4+P9WBVR4IqNjcX06dNx9913AwCe\ne+45iCL/XvW3uXPn4tlnn0V2djZsNhuWLl3q75KCmuDkZA8iIiIKMozkREREFHQYYIiIiCjoMMAQ\nERFR0GGAISIioqDDAENERERBhwGGiHyqtLQU6enpWLBggWcV3ieffBIGg8HrYyxYsAB2u93r/e+5\n5x7897//7Uq5RBQkGGCIyOciIiKwceNGbNy4Ee+++y5iYmKwevVqr9tv3LiRN/wionZ4Izsi6nUT\nJkzAli1bUFBQgGXLlsFms8FqteIvf/kLRo4ciQULFmD48OH48ccfsWHDBowcORL5+fmwWCx4/vnn\nUVFRAZvNhpkzZ2L+/Plobm7G7373O+j1eiQnJ8NsNgMALly4gD/84Q8AAJPJhLlz5+Kuu+7y57dO\nRD2EAYaIepXdbscXX3yBcePG4Y9//CNWrVqFpKSkSxa3U6lU2LRpU7u2GzduhFarxUsvvQSTyYRb\nbrkF11xzDfbt2welUoktW7agsrISN954IwBg165dSE1NxQsvvACz2Yz333+/179fIvINBhgi8rna\n2losWLAAAOBwODB+/HjMnj0bK1aswJ///GfPfo2NjXA4HABcy3tc7MiRI5g1axYAQKlUIj09Hfn5\n+Th58iTGjRsHwLUwa2pqKgDgmmuuwebNm/HMM8/guuuuw9y5c336fRJR72GAISKfa5kD01ZDQwNk\nMtkl21vIZLJLtgmC0O6x0+mEIAhwOp3t1vppCUFpaWn45JNPkJOTg927d2PDhg149913u/vtEFEA\n4CReIvKL0NBQJCYm4ttvvwUAFBUVYeXKlR22GTNmDPbu3QsAMBqNyM/Px6hRo5CWloZDhw4BAM6f\nP4+ioiIAwM6dO5GXl4dJkyZhyZIlOH/+PGw2mw+/KyLqLRyBISK/WbZsGf72t7/h9ddfh81mwzPP\nPNPh/gsWLMDzzz+Pe++9FxaLBY899hgSExMxc+ZMfP3115g/fz4SExORkZEBABg8eDCWLFkCuVwO\np9OJhx9+GFIpf+0R9QVcjZqIiIiCDk8hERERUdBhgCEiIqKgwwBDREREQYcBhoiIiIIOAwwREREF\nHQYYIiIiCjoMMERERBR0GGCIiIgo6Px/sYE+t1tsb6MAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "flxmFt0KKxk9"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Linear Scaling\n",
+        "It can be a good standard practice to normalize the inputs to fall within the range -1, 1. This helps SGD not get stuck taking steps that are too large in one dimension, or too small in another. Fans of numerical optimization may note that there's a connection to the idea of using a preconditioner here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Dws5rIQjKxk-",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def linear_scale(series):\n",
+        "  min_val = series.min()\n",
+        "  max_val = series.max()\n",
+        "  scale = (max_val - min_val) / 2.0\n",
+        "  return series.apply(lambda x:((x - min_val) / scale) - 1.0)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MVmuHI76N2Sz"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Normalize the Features Using Linear Scaling\n",
+        "\n",
+        "**Normalize the inputs to the scale -1, 1.**\n",
+        "\n",
+        "**Spend about 5 minutes training and evaluating on the newly normalized data.  How well can you do?**\n",
+        "\n",
+        "As a rule of thumb, NN's train best when the input features are roughly on the same scale.\n",
+        "\n",
+        "Sanity check your normalized data.  (What would happen if you forgot to normalize one feature?)\n"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "yD948ZgAM6Cx",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "2d1c5061-9a96-4e4a-b53b-602532f0c3df"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize_linear_scale(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+        "  processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+        "  processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+        "  processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+        "    steps=2000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 169.00\n",
+            "  period 01 : 114.60\n",
+            "  period 02 : 103.30\n",
+            "  period 03 : 88.00\n",
+            "  period 04 : 77.37\n",
+            "  period 05 : 75.22\n",
+            "  period 06 : 73.35\n",
+            "  period 07 : 72.17\n",
+            "  period 08 : 71.40\n",
+            "  period 09 : 70.59\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   70.59\n",
+            "Final RMSE (on validation data): 72.73\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1OSxgiIiIqMthAUNERERdDgsYIiKibubQoZ/a9L533nkT2dmXr/v6s88+0VkhdToWMERE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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "jFfc3saSxg6t"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Ax_IIQVRx4gr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Since normalization uses min and max, we have to ensure it's done on the entire dataset at once. \n",
+        "\n",
+        "We can do that here because all our data is in a single DataFrame. If we had multiple data sets, a good practice would be to derive the normalization parameters from the training set and apply those identically to the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "D-bJBXrJx-U_",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize_linear_scale(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized linearly.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(examples_dataframe[\"total_rooms\"])\n",
+        "  processed_features[\"total_bedrooms\"] = linear_scale(examples_dataframe[\"total_bedrooms\"])\n",
+        "  processed_features[\"population\"] = linear_scale(examples_dataframe[\"population\"])\n",
+        "  processed_features[\"households\"] = linear_scale(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = linear_scale(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(examples_dataframe[\"rooms_per_person\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize_linear_scale(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.005),\n",
+        "    steps=2000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "MrwtdStNJ6ZQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Try a Different Optimizer\n",
+        "\n",
+        "** Use the Adagrad and Adam optimizers and compare performance.**\n",
+        "\n",
+        "The Adagrad optimizer is one alternative. The key insight of Adagrad is that it modifies the learning rate adaptively for each coefficient in a model, monotonically lowering the effective learning rate.  This works great for convex problems, but isn't always ideal for the non-convex problem Neural Net training.  You can use Adagrad by specifying `AdagradOptimizer` instead of `GradientDescentOptimizer`.  Note that you may need to use a larger learning rate with Adagrad.\n",
+        "\n",
+        "For non-convex optimization problems, Adam is sometimes more efficient than Adagrad.  To use Adam, invoke the `tf.train.AdamOptimizer` method. This method takes several optional hyperparameters as arguments, but our solution only specifies one of these (`learning_rate`). In a production setting, you should specify and tune the optional hyperparameters carefully."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "61GSlDvF7-7q",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "ec75df3b-e206-4942-ee76-a5d45bad1bab"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Retrain the network using Adagrad and then Adam.\n",
+        "#\n",
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 81.54\n",
+            "  period 01 : 75.75\n",
+            "  period 02 : 74.46\n",
+            "  period 03 : 71.47\n",
+            "  period 04 : 70.61\n",
+            "  period 05 : 71.19\n",
+            "  period 06 : 72.35\n",
+            "  period 07 : 69.26\n",
+            "  period 08 : 70.09\n",
+            "  period 09 : 68.92\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   68.92\n",
+            "Final RMSE (on validation data): 71.03\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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dOjgT4O3IvuNZnM8pafb2gly7YrBxY0/6Poorm789IYQQoj1r94WKRqMhOqIj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\ndQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "FSPZIiYgyh93"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "X1QcIeiKyni4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First, let's try Adagrad."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Ntn4jJxnypGZ",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adagrad_training_losses, adagrad_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.5),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "5JUsCdRRyso3"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now let's try Adam."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "lZB8k0upyuY8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "2c693ad6-55ee-4548-d451-6ea722ae4fd4"
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.009),\n",
+        "    steps=500,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 227.72\n",
+            "  period 01 : 182.07\n",
+            "  period 02 : 118.03\n",
+            "  period 03 : 110.62\n",
+            "  period 04 : 99.17\n",
+            "  period 05 : 84.47\n",
+            "  period 06 : 72.47\n",
+            "  period 07 : 71.06\n",
+            "  period 08 : 70.25\n",
+            "  period 09 : 69.77\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.77\n",
+            "Final RMSE (on validation data): 72.19\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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pngLMddS4kTNTR3agtLySf69L/F9vmKBe1t4w8eoNIyIics0UYK6zAV2D6Nra\nh++PZLMnIROoutx6aseq3jDLDq+guKzEzlWKiIjUbQow15nJZCJqTCcsTmaWxCRScrYcgAD383rD\nHFVvGBERkWuhAGMDQb7uTAhvRd7pUj7fctS63dobJmUHR/NP2LFCERGRuk0BxkbGDWhFU193NuxJ\n5lhaAfDL3jD/VW8YERGRX0kBxkacLWaixnbCAD75JoGKyvN7w/RXbxgREZFroABjQ11a+TCwe1NO\npBeyfk+Kdfst7caf1xsmx44VioiI1E0KMDY2ZUR7PNwsLI89Sk7BGQA8nN2ZeK43zFL1hhEREblq\nCjA25uXuwuSI9pwtrWBJzGHr9n5Bvejs04EfcxKIz/jejhWKiIjUPQowtWBwz2Z0CPEmPjGT7w5n\nAVWXW9/R6XaczRai1RtGRETkqijA1AKzycTdYzvhZDbx73UJnC2tuvoowN2PyNajKCgtZMXRb+xc\npYiISN2hAFNLmgc0JvKGlmQXnOXLrces20e1HEpT9YYRERG5KgowtWjCwNb4e7uxdncSJ9MLgZ97\nw9yOgaHeMCIiIjWkAFOLXJ2diBrbiUrDYNGaBCrPXX3Uvkkba2+YDUmxdq5SRETE8SnA1LIebf3o\n3yWQI6kFbP4u1br9594wXx9bp94wIiIiV6AAYwd3jOxAI1cnojcdIf/0WaCqN8ztHSaoN4yIiEgN\nKMDYQZPGrkwc1o6Ss+V8uuEn6/awoN7qDSMiIlIDCjB2MrxXc9o082Lnj+n8cCwbqOoNM7XTbVjU\nG0ZERKRaCjB2YjabuCeyE2aTicVrEiktq7r6KNDdn3GtR6o3jIiISDVsGmBeeeUVpk6dysSJE1m7\ndi1paWlERUUxbdo0Hn30UUpLSwFYsWIFEydOZPLkySxbtsyWJTmUlkGejOoXQkZeCV99+78eMKNa\nDqOpeyBbU3ZwTL1hRERELmKzALNjxw4OHz7M0qVLef/993nxxRd54403mDZtGkuWLKFVq1ZER0dT\nXFzM/Pnz+eijj1i0aBEff/wxeXl5tirL4dw6pA2+Xq6s3nGC1Kwi4FxvmM4Tq3rDJHyu3jAiIiK/\nYLMAExYWxuuvvw6Al5cXJSUl7Ny5k5EjRwIQERHBt99+y759++jRoweenp64ubnRp08f4uPjbVWW\nw3FzsXDXqI5UVBp8sibBevVR+yZtGNisPymn09QbRkRE5Bcsttqxk5MT7u7uAERHRzN06FC2bt2K\ni4sLAH5+fmRmZpKVlYWvr6/1fb6+vmRmZla7bx8fdywWJ1uVTkCAp832fSljAjzZlZDJzgOn+P54\nLqP6twLgfq8p/LD6R1YdX8dsicudAAAgAElEQVSoLgMJ9PCr1bocUW2PjdSMxsVxaWwcl8bm2tgs\nwPwsJiaG6OhoPvjgA8aMGWPdfrk+JzXpf5KbW3zd6vulgABPMjMLbbb/y5k8rC3fJWbyrxUHaBvU\nGE/3qqB3W7sJfPzjp7z97WJ+1/M+TCZTrdfmKOw1NlI9jYvj0tg4Lo1NzVQX8my6iDc2NpZ33nmH\nhQsX4unpibu7O2fOnAEgPT2dwMBAAgMDycrKsr4nIyODwMBAW5blkHy93Lh1SBtOl5Tx2caLe8Mc\nyD7E3sz9dqxQRETEcdgswBQWFvLKK6/w7rvv0qRJEwAGDhzImjVrAFi7di1DhgwhNDSU/fv3U1BQ\nQFFREfHx8fTr189WZTm0Uf1CaBnYmG37T5FwMhe4sDfMssQvKSlXbxgRERGbBZhVq1aRm5vL73//\ne6KiooiKiuK3v/0tX3zxBdOmTSMvL49bb70VNzc3Zs+ezYwZM7jvvvuYOXMmnp4N87ygk9nM3ZGd\nMQGfrEmgrLwSqOoNE9nqXG+YI+oNIyIiYjLq4E13bHne0BHOSy5am8DG+BRuG9KGmwa1AaCsspyX\nd71GenEms/s+RBvvVnat0R4cYWzkYhoXx6WxcVwam5qx2xoY+XUmDm2Ht4cLK7efIP3cgmVns4U7\nOt2u3jAiIiIowDgkdzcLd47qQHlFJYvP6w3TwactA5uFqTeMiIg0eAowDiqscyDd2/py4HguOw+m\nW7ff2v5GGjt7sOrYOrJLcuxYoYiIiP0owDgok8nE9DGdcLaY+XT9TxSfKQPAw9mdiR1uorSyjKWJ\nX9Sob46IiEh9owDjwAKbNOLmQa0pKColevNR6/awoN508mmv3jAiItJgKcA4uLH9WxLs78HmvSkc\nSckHqmZn7jjXGyZavWFERKQBUoBxcBYnM3eP7YQBfPxNAuUVP/eGCSCy1UjySwtZcWSNfYsUERGp\nZQowdUDHFk0Y0rMZyZmnWReXZN0+qtUwmroHEpvyLcfyT9qxQhERkdqlAFNHTI5oT+NGzny59RhZ\n+VWnjC7sDfNf9YYREZEGQwGmjmjcyJmpI9pTWlbJv9cmXtAbJvxcb5iNyVvtXKWIiEjtUICpQwZ2\nb0rnlk3YdySb+MRM6/Zb24+nsbMHXx9dq94wIiLSICjA1CEmk4mosZ2wOJlYEnOYkrPlADR29uD2\n9hPUG0ZERBoMBZg6ppmfB+MHtCK38CzLY//XG6Z/0z50VG8YERFpIBRg6qAbw1sR5NOI9XuSOXGq\n6m6m6g0jIiINiQJMHeRscWL62E4YBnz8zSEqK6tOGQW5BxDZagT5pYWsPKreMCIiUn8pwNRR3Vr7\nEt4tiOOnCtkQn2zdPqrVcILcA9mS/C3HC9QbRkRE6icFmDps6ogOeLhZ+HzLUXILzwJVvWHuPNcb\nZskh9YYREZH6SQGmDvPycGHS8HacKa3gPzGJ1u3n94bZkRZnxwpFRERsQwGmjhsSGkz7EG/iEjL5\n/kiWdfuEtmMwm8xsSN6qy6pFRKTeUYCp48wmE3eP7YST2cTitYmcLas6ZdTE1Zu+gaGcKkrnUM5h\nO1cpIiJyfSnA1AMhAY0Z078FWflnWLHtmHV7RIvBAGxIjrVXaSIiIjahAFNP3DyoDf7ebqzdlURy\nxmkAWnm1oK13a37MTuBUUYadKxQREbl+FGDqCVdnJ6aP6UhFpcHHaw5ReW7dy4gWQwDYlLzNnuWJ\niIhcVwow9UjPdv706xTAkZQCtuxLrdrm3xVfNx92psVRVFZs5wpFRESuDwWYeubOUR1xc3EieuMR\n8otKcTI7MSxkIKWVZWxL3Wnv8kRERK4LBZh6xsfTlduHtqX4bDlLN1RdfTSwWX9cnFzYnLxdje1E\nRKReUICph0b0CaF1U092HEgn4WQu7s6NCG/Wj7yz+XynO1WLiEg9YNMAk5iYyKhRo1i8eDEAu3fv\n5s477yQqKor/+7//Iz8/H4D333+fSZMmMXnyZDZv3mzLkhoEs9nEtNEdAVi9s+p+SMNDBmHCxMak\nrfYsTURE5LqwWYApLi5m7ty5hIeHW7e99NJL/O1vf2PRokX07t2bpUuXkpSUxKpVq1iyZAnvvvsu\nL730EhUVOs1xrdo396Z9c2++P5JNWnYRge4BdPfvzLGCkxzLP2Hv8kRERK6JzQKMi4sLCxcuJDAw\n0LrNx8eHvLw8APLz8/Hx8WHnzp0MGTIEFxcXfH19ad68OT/99JOtympQxoS1AGBdXNXdqiNCqi6p\n1iyMiIjUdTYLMBaLBTc3twu2PfPMM8ycOZOxY8eyZ88ebrvtNrKysvD19bW+xtfXl8zMTFuV1aD0\n6RiAv7cb2/encbqkjI4+7Qj2aMrezP3knsmzd3kiIiK/mqU2DzZ37lzeeust+vbty7x581iyZMlF\nr6nJjQd9fNyxWJxsUSIAAQGeNtt3bbtlWHv+teIHdidmMWVUR27uOpp3di9id04cd4XeZu/yrlp9\nGpv6ROPiuDQ2jktjc21qNcAkJCTQt29fAAYOHMjKlSsZMGAAx4797/496enpF5x2upTcXNs1ZAsI\n8CQzs9Bm+69tfdr58m8XJ1bEHmFI9yA6u3emsbMH636KZVjQUFydXOxdYo3Vt7GpLzQujktj47g0\nNjVTXcir1cuo/f39retb9u/fT6tWrRgwYACbNm2itLSU9PR0MjIyaN++fW2WVa81crUwNDSY/NOl\n7DqYjrOTM0Oah1NcXsKuU3vsXZ6IiMivYrMZmB9++IF58+aRkpKCxWJhzZo1vPDCC8yZMwdnZ2e8\nvb158cUX8fLyYsqUKUyfPh2TycTzzz+P2az2NNfTqL4hrItLYu3uJMK7NWVI83DWntjIxqRtDAq+\nAbNJ37eIiNQtJqMmi04cjC2n3errtN6C5fuJS8jkqWm96dTSh09+XMrOU3t4KPQ3dPPrbO/yaqS+\njk1dp3FxXBobx6WxqRmHOYUk9jMmrCUAa3YlATC8xSBAl1SLiEjdpADTQLRr7kXbYC/2/ZRFek4x\nLT1DaN+kDQdzEkk9fcre5YmIiFwVBZgGwmQyMSasBQawLq5qFmZEi6rGdpuSt9mxMhERkaunANOA\n9O0UgJ+XK1v3p1F0powe/l3xc/Nl16k9nC4rsnd5IiIiNaYA04A4mc2M7NuC0rJKtnyXitlkZniL\nQZRVlrMtZae9yxMREakxBZgGZmhoM1ydnYjZk0x5RSXhzcJwc3Jlc/J2yivL7V2eiIhIjSjANDDu\nbs4M6dmM3MKzxCVk0MjiRnizMPJLC9ibsd/e5YmIiNSIAkwDNKpfCCZg7a4kDMNgeItBmDCxISm2\nRveiEhERsTcFmAYo0Med3h0DOH6qkMPJ+fg38qOnf1dOFiZzrOCEvcsTERG5IgWYBmpMWAsA1u6u\nuqQ6osVgADaosZ2IiNQBCjANVIcQb1o19WRvYiYZeSW0b9KWkMbBfJexn+ySXHuXJyIiUi0FmAbK\nZDIx9lxju5i4JEwmExEtBmNgsDlFje1ERMSxKcA0YP06B+Lj6Urs92kUnymnb1AvPF0asz11F2fK\nz9q7PBERkctSgGnALE5mRvYN4WxpBVv2peJstjC0eTgl5WfYeWqPvcsTERG5LAWYBm5Yr2BcnM2s\n35NERWUlQ5qHYzE5sSlpK5VGpb3LExERuSQFmAbOw82ZwT2akV1wlj0JmXi6NKZf095klGTxY3aC\nvcsTERG5JAUYYXS/FpiAdT9fUh3y8yXVsXasSkRE5PIUYIQgX3dC2/tzJLWAn1LyCfEMpmOTdiTk\n/kTK6TR7lyciInIRBRgBLm5sN6LlEAA2qbGdiIg4IAUYAaBTyya0DGzMnoQMsvJK6ObXGf9GfuxK\n30th6Wl7lyciInIBBRgBqhrbjenfAsOAmD3JmE1mIkIGU15ZztaUnfYuT0RE5AIKMGLVv0sQ3o1d\niP0+lZKz5Qxo1hc3Jze2pGynrLLc3uWJiIhYKcCIlcXJzIg+IZScrSD2+zTcLG4MDA6joLSQ+PR9\n9i5PRETESgFGLjC8VzAuFjMxcUlUVhoMDxmECRMbk2IxDMPe5YmIiAAKMPILnu4uDOzelKz8M8Qn\nZuLXyJfQgO4knU7lSP5xe5cnIiICKMDIJYz++ZLquHON7VpUNbbbqMZ2IiLiIBRg5CLN/Dzo2c6P\nn5LzOZpaQDvv1rT0bM6+zANkleTYuzwRERHbBpjExERGjRrF4sWLASgrK2P27NlMmjSJe+65h/z8\nfABWrFjBxIkTmTx5MsuWLbNlSVJD1lmY3ScxmUxEtBiCgcHm5G12rkxERMSGAaa4uJi5c+cSHh5u\n3fbZZ5/h4+NDdHQ048ePJy4ujuLiYubPn89HH33EokWL+Pjjj8nLy7NVWVJDXVv5EBLgQdyhTHIK\nztAnsCfeLp5sT91FSfkZe5cnIiINnM0CjIuLCwsXLiQwMNC6bePGjdx8880ATJ06lZEjR7Jv3z56\n9OiBp6cnbm5u9OnTh/j4eFuVJTVkMpkYHdaCSsMgZk8yFrOFoSEDOVNxlh1pcfYuT0REGjibBRiL\nxYKbm9sF21JSUtiyZQtRUVE89thj5OXlkZWVha+vr/U1vr6+ZGZm2qosuQoDujbFy8OFzd+lcqa0\nnEHBN+BstrApeRuVRqW9yxMRkQbMUpsHMwyDNm3aMGvWLBYsWMC7775L165dL3rNlfj4uGOxONmq\nTAICPG2277pmwuC2LFlziO+O5nLTkLYMaX0DG45uI6nsOP2ah9Z6PRobx6RxcVwaG8elsbk2tRpg\n/P39CQsLA2Dw4MG8+eabDB8+nKysLOtrMjIy6NWrV7X7yc0ttlmNAQGeZGYW2mz/dU3/jv58FmPm\ni00/0b+jP+H+VQFm+Q9raeXStlZr0dg4Jo2L49LYOC6NTc1UF/J+9Smk48ePX/V7hg4dSmxsVS+R\nAwcO0KZNG0JDQ9m/fz8FBQUUFRURHx9Pv379fm1Zcp15ebgwsHsQGXkl7Pspi+DGTens04HDeUdJ\nKky1d3kiItJAVRtg7rvvvgseL1iwwPr35557rtod//DDD0RFRbF8+XI++eQToqKiuOWWW9i8eTN3\n3nknMTExPPjgg7i5uTF79mxmzJjBfffdx8yZM/H01LSaIxndr+qS6jW7L2xstylpq91qEhGRhq3a\nU0jl5RfegXjHjh089NBDwJXXqnTv3p1FixZdtP2NN964aFtkZCSRkZFXLFbso3lAY7q38eWHYzkc\nP1VA16BOBLr7E5e+l1vaj8PLRYFTRERqV7UzMCaT6YLH54eWXz4n9dsYa2O7JMwmMxEhgyk3KohN\n2WHnykREpCG6qjUwCi0NV7c2vgT7e7D7YAa5hWfp37QvjSyNiE3+lrLK8ivvQERE5DqqNsDk5+fz\n7bffWv8UFBSwY8cO69+l4TCZTIwJa0FFpcGG+GTcLK4MCu5PYdlp4tK/s3d5IiLSwFS7BsbLy+uC\nhbuenp7Mnz/f+ndpWAZ0DSJ60xE27U1hQnhrhoUMZENSLBuTYhnQtK9m6EREpNZUG2AutQhXGi4X\nZydG9GnOim3H2f5DGhF9QugV0J34jO85nHeUjj7t7F2iiIg0ENWeQjp9+jQfffSR9fGnn37KLbfc\nwiOPPHJB8zlpOCL6hGBxMrF2dxKVhkFEiyEAbNQl1SIiUouqDTDPPfcc2dnZABw7doxXX32Vp556\nioEDB/K3v/2tVgoUx+Lt4cKArk1Jzy3h+yPZtPFqSSuvFuzP+pHM4mx7lyciIg1EtQEmKSmJ2bNn\nA7BmzRoiIyMZOHAgd9xxh2ZgGrCfL6letzsJk8nEiJDBGBhsStYsjIiI1I5qA4y7u7v177t27WLA\ngAHWx1qw2XCFBDamSysfDp7I5WR6Ib0De9LE1Ztv03ZTUl5i7/JERKQBqDbAVFRUkJ2dzcmTJ9m7\ndy+DBg0CoKioiJIS/UPVkI3t/79ZGCezE8OaD+RsRSnfpu62c2UiItIQVBtgHnjgAcaPH89NN93E\nQw89hLe3N2fOnGHatGnceuuttVWjOKDubf1o6uvOjh/TyTt9loHN++NsdmZT8jYqjUp7lyciIvVc\ntQFm2LBhbN26lW3btvHAAw8A4Obmxh/+8AfuuuuuWilQHJP5gsZ2KTR29uCGpn3IPpPL91k/2rs8\nERGp56oNMKmpqWRmZlJQUEBqaqr1T9u2bUlNTa2tGsVBhXdvioebhU17Uygtq2D4ubtUb0yKtXNl\nIiJS31XbyG7EiBG0adOGgIAA4OKbOX7yySe2rU4cmquzE8N7N+frb0+w/cAphvdqThffjhzMSeRk\nYTItPUPsXaKIiNRT1QaYefPm8eWXX1JUVMSNN97IhAkT8PX1ra3apA4Y0SeEb3aeZN3uJIaGBjOi\nxRAO5iSyMWkr93S9w97liYhIPVXtKaRbbrmFDz74gNdee43Tp09z1113cf/997Ny5UrOnDlTWzWK\nA/PxdKV/lyDSsov54WgOXXw70tQ9kD3p+8g/qxt+ioiIbVQbYH7WrFkzHnroIVavXs3YsWP561//\nyuDBg21dm9QR/2tsdxKTycTwFoOpMCqITfnWzpWJiEh9VaMAU1BQwOLFi7n99ttZvHgx//d//8eq\nVatsXZvUEa2aetK5ZRMOHM8lOeM0NzTtg7ulEbEpOyirKLN3eSIiUg9VG2C2bt3KY489xsSJE0lL\nS+Pll1/myy+/5De/+Q2BgYG1VaPUAaPPzcKsjUvCxcmFwc0HcLqsiN3pe+1cmYiI1EfVLuK9//77\nad26NX369CEnJ4cPP/zwgudfeuklmxYndUdoe38CfRqx40A6E4e1Y2jzcGJObmZj0lbCm4Xp1hMi\nInJdVRtgfr5MOjc3Fx8fnwueS05Otl1VUueYTSZG92vBv9clsjE+mVuHtKVPYE/i0r8jIfcnOvt2\nsHeJIiJSj1R7CslsNjN79myeffZZnnvuOYKCgujfvz+JiYm89tprtVWj1BGDezTDw83Cxr0plJVX\nEGFtbKe7VIuIyPVV7QzMP//5Tz766CPatWvH+vXree6556isrMTb25tly5bVVo1SR7i6ODG0VzCr\nd5zk2wPpDA1tSRuvVvyQfZCM4kwC3QPsXaKIiNQTV5yBadeuHQAjR44kJSWFu+++m7feeougoKBa\nKVDqlpF9QnAym1i3OwnDMKyzMJuSt9m5MhERqU+qDTC/XHjZrFkzRo8ebdOCpG7z9XIjrHMgKVlF\n/Hg8l14B3fFxbcK3aXEUl5XYuzwREaknatQH5me6kkRq4udLqtfsPomT2YlhIQMprShle9ouO1cm\nIiL1RbVrYPbu3cvw4cOtj7Ozsxk+fDiGYWAymdi0aZONy5O6qE0zLzqGePPD0RxSsooYFNyfVcfW\nsSlpGxEhg3EyO9m7RBERqeOqDTDffPPNNe08MTGRhx56iHvvvZfp06dbt8fGxnL//feTkJAAwIoV\nK/j4448xm81MmTKFyZMnX9Nxxf5Gh7UkMXk/63Ynce+4zgxo1o8tKd+yL+sAfQJ72rs8ERGp46oN\nMM2bN//VOy4uLmbu3LmEh4dfsP3s2bO89957BAQEWF83f/58oqOjcXZ2ZtKkSYwePZomTZr86mOL\n/fXu4E9AEze+PXCK24e1ZXjIILakfMvGpK0KMCIics2uag3M1XBxcWHhwoUX3XLgnXfeYdq0abi4\nuACwb98+evTogaenJ25ubvTp04f4+HhblSW1xGw2MapfC8rKK9m0N4Ugj0C6+XXmaP5xThQk2bs8\nERGp42wWYCwWC25ubhdsO3bsGIcOHWLcuHHWbVlZWfj6+lof+/r6kpmZaauypBYN7tGMRq5ObIhP\noay8khEthgCwISnWzpWJiEhdV+0ppOvtpZdeYs6cOdW+xjCMK+7Hx8cdi8V2C0EDAjxttu+GJjK8\nDcs3/cTB5HxG9OvNF0ebsTfje+7vPxVf96s/TaixcUwaF8elsXFcGptrU2sBJj09naNHj/LEE08A\nkJGRwfTp03n44YfJysqyvi4jI4NevXpVu6/c3GKb1RkQ4ElmZqHN9t/QDOwSyJebjxC9/jA9WjVh\nSPBAlhz6L8u/X8fN7SKval8aG8ekcXFcGhvHpbGpmepCns1OIf1SUFAQMTExfPbZZ3z22WcEBgay\nePFiQkND2b9/PwUFBRQVFREfH0+/fv1qqyyxMT9vN/p1DiA58zSHTuQSFtQHD2d3tqbuoLSi1N7l\niYhIHWWzAPPDDz8QFRXF8uXL+eSTT4iKiiIvL++i17m5uTF79mxmzJjBfffdx8yZM/H01LRaffK/\nxnZJuDg5MyR4AEVlxew+tdfOlYmISF1ls1NI3bt3Z9GiRZd9fsOGDda/R0ZGEhl5dacTpO5oF+xN\nu+ZefH8km7TsIoaEhLPu5GY2JG9lYHB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sXJzKhvx0MpIi/VpOv6efg01HKbdX0NDTBEBa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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "twYgC8FGyxm6"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's print a graph of loss metrics side by side."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8RHIUEfqyzW0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 376
+        },
+        "outputId": "7fdc05bb-ca6a-43e5-cdef-d6dbec51d4c6"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.ylabel(\"RMSE\")\n",
+        "plt.xlabel(\"Periods\")\n",
+        "plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "plt.plot(adagrad_training_losses, label='Adagrad training')\n",
+        "plt.plot(adagrad_validation_losses, label='Adagrad validation')\n",
+        "plt.plot(adam_training_losses, label='Adam training')\n",
+        "plt.plot(adam_validation_losses, label='Adam validation')\n",
+        "_ = plt.legend()"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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a41LG1FSkR14KjZUVriNGo+Tnk7j2ewBq2FrQIcCL3JwCwnfLjm9CCCFMSxL5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\nVNfO0YPNhDud5vvSubms3DCfmqUlaJo6KfnONp2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cRH6Q7pCtOhvdXfTseoPet/ZgRaMo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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "UySPl7CAQ28C"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Explore Alternate Normalization Methods\n",
+        "\n",
+        "**Try alternate normalizations for various features to further improve performance.**\n",
+        "\n",
+        "If you look closely at summary stats for your transformed data, you may notice that linear scaling some features leaves them clumped close to `-1`.\n",
+        "\n",
+        "For example, many features have a median of `-0.8` or so, rather than `0.0`."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "QWmm_6CGKxlH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 715
+        },
+        "outputId": "86370178-cf56-4809-9dea-8c14a3a0fe71"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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teepMC8dnT58AEAAAAAAHRuQf+2CwAAAAAAYC7CBwAAAAAAYCjCBwAAAAAA\nYCjCBwAAAAAAYCjCBwAAAAAAYCjCBwAAAAAAYCjCBwAAAAAAYKj/ByGd1w9IjcH0AAAAAElFTkSu\nQmCC\n",
+            "text/plain": [
+              "<Figure size 1296x864 with 9 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Xx9jgEMHKxlJ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We might be able to do better by choosing additional ways to transform these features.\n",
+        "\n",
+        "For example, a log scaling might help some features.  Or clipping extreme values may make the remainder of the scale more informative."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "baKZa6MEKxlK",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def log_normalize(series):\n",
+        "  return series.apply(lambda x:math.log(x+1.0))\n",
+        "\n",
+        "def clip(series, clip_to_min, clip_to_max):\n",
+        "  return series.apply(lambda x:(\n",
+        "    min(max(x, clip_to_min), clip_to_max)))\n",
+        "\n",
+        "def z_score_normalize(series):\n",
+        "  mean = series.mean()\n",
+        "  std_dv = series.std()\n",
+        "  return series.apply(lambda x:(x - mean) / std_dv)\n",
+        "\n",
+        "def binary_threshold(series, threshold):\n",
+        "  return series.apply(lambda x:(1 if x > threshold else 0))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-wCCq_ClKxlO"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The block above contains a few additional possible normalization functions. Try some of these, or add your own.\n",
+        "\n",
+        "Note that if you normalize the target, you'll need to un-normalize the predictions for loss metrics to be comparable."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "8ToG-mLfMO9P",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "e0e8c213-efc5-4fe7-fe0f-5ce9a1f95869"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "\n",
+        "  processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n",
+        "  \n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "\n",
+        "  processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n",
+        "\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n",
+        "    steps=1000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 85.69\n",
+            "  period 01 : 74.97\n",
+            "  period 02 : 72.39\n",
+            "  period 03 : 71.05\n",
+            "  period 04 : 70.32\n",
+            "  period 05 : 69.97\n",
+            "  period 06 : 69.08\n",
+            "  period 07 : 68.74\n",
+            "  period 08 : 68.01\n",
+            "  period 09 : 67.65\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   67.65\n",
+            "Final RMSE (on validation data): 69.03\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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aWi3IP1bX7/4h/sEYrY3BoboStFnbvRAhERGRb2Ch0osZqQOd/ZMCq2hDYd0h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\nSUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "GhFtWjQRzD2l"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "OMoIsUMmzK9b"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "These are only a few ways in which we could think about the data. Other transformations may work even better!\n",
+        "\n",
+        "`households`, `median_income` and `total_bedrooms` all appear normally-distributed in a log space.\n",
+        "\n",
+        "`latitude`, `longitude` and `housing_median_age` would probably be better off just scaled linearly, as before.\n",
+        "\n",
+        "`population`, `totalRooms` and `rooms_per_person` have a few extreme outliers. They seem too extreme for log normalization to help. So let's clip them instead."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XDEYkPquzYCH",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "\n",
+        "  processed_features[\"households\"] = log_normalize(examples_dataframe[\"households\"])\n",
+        "  processed_features[\"median_income\"] = log_normalize(examples_dataframe[\"median_income\"])\n",
+        "  processed_features[\"total_bedrooms\"] = log_normalize(examples_dataframe[\"total_bedrooms\"])\n",
+        "  \n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  processed_features[\"housing_median_age\"] = linear_scale(examples_dataframe[\"housing_median_age\"])\n",
+        "\n",
+        "  processed_features[\"population\"] = linear_scale(clip(examples_dataframe[\"population\"], 0, 5000))\n",
+        "  processed_features[\"rooms_per_person\"] = linear_scale(clip(examples_dataframe[\"rooms_per_person\"], 0, 5))\n",
+        "  processed_features[\"total_rooms\"] = linear_scale(clip(examples_dataframe[\"total_rooms\"], 0, 10000))\n",
+        "\n",
+        "  return processed_features\n",
+        "\n",
+        "normalized_dataframe = normalize(preprocess_features(california_housing_dataframe))\n",
+        "normalized_training_examples = normalized_dataframe.head(12000)\n",
+        "normalized_validation_examples = normalized_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.15),\n",
+        "    steps=1000,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=normalized_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=normalized_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "b7atJTbzU9Ca"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Optional Challenge: Use only Latitude and Longitude Features\n",
+        "\n",
+        "**Train a NN model that uses only latitude and longitude as features.**\n",
+        "\n",
+        "Real estate people are fond of saying that location is the only important feature in housing price.\n",
+        "Let's see if we can confirm this by training a model that uses only latitude and longitude as features.\n",
+        "\n",
+        "This will only work well if our NN can learn complex nonlinearities from latitude and longitude.\n",
+        "\n",
+        "**NOTE:** We may need a network structure that has more layers than were useful earlier in the exercise."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5McjahpamOc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "e8da5784-25d1-431f-f7a2-a528c148298e"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Train the network using only latitude and longitude\n",
+        "#\n",
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10, 5, 5, 5],\n",
+        "    training_examples=lll_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=lll_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 188.70\n",
+            "  period 01 : 105.91\n",
+            "  period 02 : 104.14\n",
+            "  period 03 : 103.09\n",
+            "  period 04 : 102.43\n",
+            "  period 05 : 101.27\n",
+            "  period 06 : 100.72\n",
+            "  period 07 : 100.35\n",
+            "  period 08 : 99.70\n",
+            "  period 09 : 99.18\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   99.18\n",
+            "Final RMSE (on validation data): 102.19\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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w/TQiJ9jIa0GhiQGGiIhoKDHA9JNOo0Z+RhLKa9ugV+uRqU9HhbUSsixHuzQi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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "P8BLQ7T71JWd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "1hwaFCE71OPZ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "It's a good idea to keep latitude and longitude normalized:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "djKtt4mz1ZEc",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def location_location_location(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that keeps only the latitude and longitude.\"\"\"\n",
+        "  processed_features = pd.DataFrame()\n",
+        "  processed_features[\"latitude\"] = linear_scale(examples_dataframe[\"latitude\"])\n",
+        "  processed_features[\"longitude\"] = linear_scale(examples_dataframe[\"longitude\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "lll_dataframe = location_location_location(preprocess_features(california_housing_dataframe))\n",
+        "lll_training_examples = lll_dataframe.head(12000)\n",
+        "lll_validation_examples = lll_dataframe.tail(5000)\n",
+        "\n",
+        "_ = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdagradOptimizer(learning_rate=0.05),\n",
+        "    steps=500,\n",
+        "    batch_size=50,\n",
+        "    hidden_units=[10, 10, 5, 5, 5],\n",
+        "    training_examples=lll_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=lll_validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "Dw2Mr9JZ1cRi"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This isn't too bad for just two features. Of course, property values can still vary significantly within short distances."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_neural_nets.ipynb b/intro_to_neural_nets.ipynb
new file mode 100644
index 0000000..48e2f8b
--- /dev/null
+++ b/intro_to_neural_nets.ipynb
@@ -0,0 +1,1224 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_neural_nets.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "O2q5RRCKqYaU",
+        "vvT2jDWjrKew"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/intro_to_neural_nets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "eV16J6oUY-HN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_wIcUFLSKNdx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Define a neural network (NN) and its hidden layers using the TensorFlow `DNNRegressor` class\n",
+        "  * Train a neural network to learn nonlinearities in a dataset and achieve better performance than a linear regression model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_ZZ7f7prKNdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In the previous exercises, we used synthetic features to help our model incorporate nonlinearities.\n",
+        "\n",
+        "One important set of nonlinearities was around latitude and longitude, but there may be others.\n",
+        "\n",
+        "We'll also switch back, for now, to a standard regression task, rather than the logistic regression task from the previous exercise. That is, we'll be predicting `median_house_value` directly."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "J2kqX6VZTHUy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's load and prepare the data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AGOM1TUiKNdz",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2I8E2qhyKNd4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pQzcj2B1T5dA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "72df509a-f5f0-40b2-99e3-ceef78d14c52"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.5                28.6       2644.7           541.0   \n",
+              "std         2.1        2.0                12.6       2198.0           423.9   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1466.0           299.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3142.0           651.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1437.3       503.1            3.9               2.0  \n",
+              "std        1173.2       387.9            1.9               1.3  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         793.0       283.0            2.6               1.5  \n",
+              "50%        1170.0       410.0            3.5               1.9  \n",
+              "75%        1730.0       607.0            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.5</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2644.7</td>\n",
+              "      <td>541.0</td>\n",
+              "      <td>1437.3</td>\n",
+              "      <td>503.1</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2198.0</td>\n",
+              "      <td>423.9</td>\n",
+              "      <td>1173.2</td>\n",
+              "      <td>387.9</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</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>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1466.0</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>793.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1170.0</td>\n",
+              "      <td>410.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3142.0</td>\n",
+              "      <td>651.0</td>\n",
+              "      <td>1730.0</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</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>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.6       2641.2           535.5   \n",
+              "std         2.1        2.0                12.6       2136.3           415.8   \n",
+              "min        32.6     -124.3                 1.0         24.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1445.0           294.0   \n",
+              "50%        34.3     -118.5                29.0       2130.5           430.0   \n",
+              "75%        37.7     -118.0                37.0       3176.0           646.0   \n",
+              "max        41.9     -114.6                52.0      30401.0          4957.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1411.1       496.7            3.9               2.0  \n",
+              "std        1084.6       376.3            1.9               0.9  \n",
+              "min          13.0         2.0            0.5               0.2  \n",
+              "25%         781.0       278.0            2.6               1.5  \n",
+              "50%        1157.0       406.0            3.6               2.0  \n",
+              "75%        1700.0       597.2            4.8               2.3  \n",
+              "max       13251.0      4616.0           15.0              16.0  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2641.2</td>\n",
+              "      <td>535.5</td>\n",
+              "      <td>1411.1</td>\n",
+              "      <td>496.7</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2136.3</td>\n",
+              "      <td>415.8</td>\n",
+              "      <td>1084.6</td>\n",
+              "      <td>376.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1445.0</td>\n",
+              "      <td>294.0</td>\n",
+              "      <td>781.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2130.5</td>\n",
+              "      <td>430.0</td>\n",
+              "      <td>1157.0</td>\n",
+              "      <td>406.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3176.0</td>\n",
+              "      <td>646.0</td>\n",
+              "      <td>1700.0</td>\n",
+              "      <td>597.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30401.0</td>\n",
+              "      <td>4957.0</td>\n",
+              "      <td>13251.0</td>\n",
+              "      <td>4616.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>16.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                205.3\n",
+              "std                 114.9\n",
+              "min                  15.0\n",
+              "25%                 118.8\n",
+              "50%                 177.8\n",
+              "75%                 262.1\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>205.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>114.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>177.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>262.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                212.2\n",
+              "std                 118.4\n",
+              "min                  22.5\n",
+              "25%                 122.5\n",
+              "50%                 185.0\n",
+              "75%                 270.8\n",
+              "max                 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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>212.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>118.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>22.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>122.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>185.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>270.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RWq0xecNKNeG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Building a Neural Network\n",
+        "\n",
+        "The NN is defined by the [DNNRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNRegressor) class.\n",
+        "\n",
+        "Use **`hidden_units`** to define the structure of the NN.  The `hidden_units` argument provides a list of ints, where each int corresponds to a hidden layer and indicates the number of nodes in it.  For example, consider the following assignment:\n",
+        "\n",
+        "`hidden_units=[3,10]`\n",
+        "\n",
+        "The preceding assignment specifies a neural net with two hidden layers:\n",
+        "\n",
+        "* The first hidden layer contains 3 nodes.\n",
+        "* The second hidden layer contains 10 nodes.\n",
+        "\n",
+        "If we wanted to add more layers, we'd add more ints to the list. For example, `hidden_units=[10,20,30,40]` would create four layers with ten, twenty, thirty, and forty units, respectively.\n",
+        "\n",
+        "By default, all hidden layers will use ReLu activation and will be fully connected."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ni0S6zHcTb04",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zvCqgNdzpaFg",
+        "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 neural net regression model.\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(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": "U52Ychv9KNeH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_regression_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `DNNRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a DNNRegressor 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",
+        "  dnn_regressor = tf.estimator.DNNRegressor(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    dnn_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = dnn_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = dnn_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  print(\"Final RMSE (on training data):   %0.2f\" % training_root_mean_squared_error)\n",
+        "  print(\"Final RMSE (on validation data): %0.2f\" % validation_root_mean_squared_error)\n",
+        "\n",
+        "  return dnn_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "2QhdcCy-Y8QR",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Train a NN Model\n",
+        "\n",
+        "**Adjust hyperparameters, aiming to drop RMSE below 110.**\n",
+        "\n",
+        "Run the following block to train a NN model.  \n",
+        "\n",
+        "Recall that in the linear regression exercise with many features, an RMSE of 110 or so was pretty good.  We'll aim to beat that.\n",
+        "\n",
+        "Your task here is to modify various learning settings to improve accuracy on validation data.\n",
+        "\n",
+        "Overfitting is a real potential hazard for NNs.  You can look at the gap between loss on training data and loss on validation data to help judge if your model is starting to overfit. If the gap starts to grow, that is usually a sure sign of overfitting.\n",
+        "\n",
+        "Because of the number of different possible settings, it's strongly recommended that you take notes on each trial to help guide your development process.\n",
+        "\n",
+        "Also, when you get a good setting, try running it multiple times and see how repeatable your result is. NN weights are typically initialized to small random values, so you should see differences from run to run.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rXmtSW1yKNeK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 775
+        },
+        "outputId": "d1c370a4-ab81-4e27-d3b8-9a71aec260ed"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 152.65\n",
+            "  period 01 : 138.14\n",
+            "  period 02 : 126.12\n",
+            "  period 03 : 118.59\n",
+            "  period 04 : 108.43\n",
+            "  period 05 : 109.43\n",
+            "  period 06 : 102.83\n",
+            "  period 07 : 103.06\n",
+            "  period 08 : 101.72\n",
+            "  period 09 : 103.18\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   103.18\n",
+            "Final RMSE (on validation data): 106.30\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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oUFXilxt7tRQhERFRy8QCpokN7ekEDydTXI5NR8QNzRWr/ex6oKO5O6IyY3A1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\nQgghRNiRACOEEEKIsCMBRgghhBBh5/8DRsbb/QgJSZMAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "O2q5RRCKqYaU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "j2Yd5VfrqcC3",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** This selection of parameters is somewhat arbitrary. Here we've tried combinations that are increasingly complex, combined with training for longer, until the error falls below our objective (training is nondeterministic, so results may fluctuate a bit each time you run the solution). This may not be the best combination; others may attain an even lower RMSE. If your aim is to find the model that can attain the best error, then you'll want to use a more rigorous process, like a parameter search."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "IjkpSqmxqnSM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "1bee878b-3094-45fe-8262-856f5452baa3"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.001,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    hidden_units=[10, 10],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 162.72\n",
+            "  period 01 : 159.42\n",
+            "  period 02 : 156.74\n",
+            "  period 03 : 160.40\n",
+            "  period 04 : 154.99\n",
+            "  period 05 : 144.08\n",
+            "  period 06 : 142.03\n",
+            "  period 07 : 135.98\n",
+            "  period 08 : 127.11\n",
+            "  period 09 : 122.93\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   122.93\n",
+            "Final RMSE (on validation data): 125.16\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Z0tecKjuLs40Ti0Jvor9nWAdE2HHOlWfw2uGVeNl78MSIh806Yrp8EVsuyY3l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\nERER6XH+P/1YJS9V0bsiAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c6diezCSeH4Y",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Evaluate on Test Data\n",
+        "\n",
+        "**Confirm that your validation performance results hold up on test data.**\n",
+        "\n",
+        "Once you have a model you're happy with, evaluate it on test data to compare that to validation performance.\n",
+        "\n",
+        "Reminder, the test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f7863f37-f0e0-4d25-8bbf-99f8e10d23f3"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "# YOUR CODE HERE\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+        "                                               test_targets[\"median_house_value\"], \n",
+        "                                               num_epochs=1, \n",
+        "                                               shuffle=False)\n",
+        "\n",
+        "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 119.69\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vvT2jDWjrKew",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "FyDh7Qy6rQb0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Similar to what the code at the top does, we just need to load the appropriate data file, preprocess it and call predict and mean_squared_error.\n",
+        "\n",
+        "Note that we don't have to randomize the test data, since we will use all records."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vhb0CtdvrWZx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_testing_input_fn = lambda: my_input_fn(test_examples, \n",
+        "                                               test_targets[\"median_house_value\"], \n",
+        "                                               num_epochs=1, \n",
+        "                                               shuffle=False)\n",
+        "\n",
+        "test_predictions = dnn_regressor.predict(input_fn=predict_testing_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..9f60201
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1716 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "intro_to_pandas.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "YHIWvc9Ms-Ll",
+        "TJffr5_Jwqvd"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/intro_to_pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "JndnmDMp66FL"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "hMqWDc_m6rUC",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "rHLcriKWLRe4"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Intro to pandas"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "QvJBqX8_Bctk"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Gain an introduction to the `DataFrame` and `Series` data structures of the *pandas* library\n",
+        "  * Access and manipulate data within a `DataFrame` and `Series`\n",
+        "  * Import CSV data into a *pandas* `DataFrame`\n",
+        "  * Reindex a `DataFrame` to shuffle data"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TIFJ83ZTBctl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[*pandas*](http://pandas.pydata.org/) is a column-oriented data analysis API. It's a great tool for handling and analyzing input data, and many ML frameworks support *pandas* data structures as inputs.\n",
+        "Although a comprehensive introduction to the *pandas* API would span many pages, the core concepts are fairly straightforward, and we'll present them below. For a more complete reference, the [*pandas* docs site](http://pandas.pydata.org/pandas-docs/stable/index.html) contains extensive documentation and many tutorials."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "s_JOISVgmn9v"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Basic Concepts\n",
+        "\n",
+        "The following line imports the *pandas* API and prints the API version:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "aSRYu62xUi3g",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "c2429c3f-7c81-47ac-ad0c-80e85ca0cb44"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import pandas as pd\n",
+        "pd.__version__"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "u'0.22.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "daQreKXIUslr"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The primary data structures in *pandas* are implemented as two classes:\n",
+        "\n",
+        "  * **`DataFrame`**, which you can imagine as a relational data table, with rows and named columns.\n",
+        "  * **`Series`**, which is a single column. A `DataFrame` contains one or more `Series` and a name for each `Series`.\n",
+        "\n",
+        "The data frame is a commonly used abstraction for data manipulation. Similar implementations exist in [Spark](https://spark.apache.org/) and [R](https://www.r-project.org/about.html)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fjnAk1xcU0yc"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to create a `Series` is to construct a `Series` object. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "DFZ42Uq7UFDj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "188d78ec-867d-446b-d8c4-0d0cbb2575da"
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    San Francisco\n",
+              "1         San Jose\n",
+              "2       Sacramento\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "syeSiEGdVpeC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "U5ouUp1cU6pC"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "`DataFrame` objects can be created by passing a `dict` mapping `string` column names to their respective `Series`. If the `Series` don't match in length, missing values are filled with special [NA/NaN](http://pandas.pydata.org/pandas-docs/stable/missing_data.html) values. Example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "avgr6GfiUh8t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "20b5bcaf-7a84-474b-9fb3-3d0ffb57022c"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
+        "population = pd.Series([852469, 1015785, 485199])\n",
+        "\n",
+        "pd.DataFrame({ 'City name': city_names, 'Population': population })"
+      ],
+      "execution_count": 5,
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population\n",
+              "0  San Francisco      852469\n",
+              "1       San Jose     1015785\n",
+              "2     Sacramento      485199"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "oa5wfZT7VHJl"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "But most of the time, you load an entire file into a `DataFrame`. The following example loads a file with California housing data. Run the following cell to load the data and create feature definitions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "av6RYOraVG1V",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "ef994152-bb4b-4762-c692-fb99370b8691"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "      <td>17000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.562108</td>\n",
+              "      <td>35.625225</td>\n",
+              "      <td>28.589353</td>\n",
+              "      <td>2643.664412</td>\n",
+              "      <td>539.410824</td>\n",
+              "      <td>1429.573941</td>\n",
+              "      <td>501.221941</td>\n",
+              "      <td>3.883578</td>\n",
+              "      <td>207300.912353</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.005166</td>\n",
+              "      <td>2.137340</td>\n",
+              "      <td>12.586937</td>\n",
+              "      <td>2179.947071</td>\n",
+              "      <td>421.499452</td>\n",
+              "      <td>1147.852959</td>\n",
+              "      <td>384.520841</td>\n",
+              "      <td>1.908157</td>\n",
+              "      <td>115983.764387</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.350000</td>\n",
+              "      <td>32.540000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.499900</td>\n",
+              "      <td>14999.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.790000</td>\n",
+              "      <td>33.930000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>1462.000000</td>\n",
+              "      <td>297.000000</td>\n",
+              "      <td>790.000000</td>\n",
+              "      <td>282.000000</td>\n",
+              "      <td>2.566375</td>\n",
+              "      <td>119400.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.490000</td>\n",
+              "      <td>34.250000</td>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>2127.000000</td>\n",
+              "      <td>434.000000</td>\n",
+              "      <td>1167.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>3.544600</td>\n",
+              "      <td>180400.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.000000</td>\n",
+              "      <td>37.720000</td>\n",
+              "      <td>37.000000</td>\n",
+              "      <td>3151.250000</td>\n",
+              "      <td>648.250000</td>\n",
+              "      <td>1721.000000</td>\n",
+              "      <td>605.250000</td>\n",
+              "      <td>4.767000</td>\n",
+              "      <td>265000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.310000</td>\n",
+              "      <td>41.950000</td>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>37937.000000</td>\n",
+              "      <td>6445.000000</td>\n",
+              "      <td>35682.000000</td>\n",
+              "      <td>6082.000000</td>\n",
+              "      <td>15.000100</td>\n",
+              "      <td>500001.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+              "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
+              "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
+              "std        2.005166      2.137340           12.586937   2179.947071   \n",
+              "min     -124.350000     32.540000            1.000000      2.000000   \n",
+              "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
+              "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
+              "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
+              "max     -114.310000     41.950000           52.000000  37937.000000   \n",
+              "\n",
+              "       total_bedrooms    population    households  median_income  \\\n",
+              "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
+              "mean       539.410824   1429.573941    501.221941       3.883578   \n",
+              "std        421.499452   1147.852959    384.520841       1.908157   \n",
+              "min          1.000000      3.000000      1.000000       0.499900   \n",
+              "25%        297.000000    790.000000    282.000000       2.566375   \n",
+              "50%        434.000000   1167.000000    409.000000       3.544600   \n",
+              "75%        648.250000   1721.000000    605.250000       4.767000   \n",
+              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+              "\n",
+              "       median_house_value  \n",
+              "count        17000.000000  \n",
+              "mean        207300.912353  \n",
+              "std         115983.764387  \n",
+              "min          14999.000000  \n",
+              "25%         119400.000000  \n",
+              "50%         180400.000000  \n",
+              "75%         265000.000000  \n",
+              "max         500001.000000  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "WrkBjfz5kEQu"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The example above used `DataFrame.describe` to show interesting statistics about a `DataFrame`. Another useful function is `DataFrame.head`, which displays the first few records of a `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "s3ND3bgOkB5k",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "71613e20-97fc-41ce-ba9a-22faede2e005"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.head()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>-114.31</td>\n",
+              "      <td>34.19</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>5612.0</td>\n",
+              "      <td>1283.0</td>\n",
+              "      <td>1015.0</td>\n",
+              "      <td>472.0</td>\n",
+              "      <td>1.4936</td>\n",
+              "      <td>66900.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>-114.47</td>\n",
+              "      <td>34.40</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>7650.0</td>\n",
+              "      <td>1901.0</td>\n",
+              "      <td>1129.0</td>\n",
+              "      <td>463.0</td>\n",
+              "      <td>1.8200</td>\n",
+              "      <td>80100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>-114.56</td>\n",
+              "      <td>33.69</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>720.0</td>\n",
+              "      <td>174.0</td>\n",
+              "      <td>333.0</td>\n",
+              "      <td>117.0</td>\n",
+              "      <td>1.6509</td>\n",
+              "      <td>85700.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>-114.57</td>\n",
+              "      <td>33.64</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>1501.0</td>\n",
+              "      <td>337.0</td>\n",
+              "      <td>515.0</td>\n",
+              "      <td>226.0</td>\n",
+              "      <td>3.1917</td>\n",
+              "      <td>73400.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-114.57</td>\n",
+              "      <td>33.57</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1454.0</td>\n",
+              "      <td>326.0</td>\n",
+              "      <td>624.0</td>\n",
+              "      <td>262.0</td>\n",
+              "      <td>1.9250</td>\n",
+              "      <td>65500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
+              "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
+              "2    -114.56     33.69                17.0        720.0           174.0   \n",
+              "3    -114.57     33.64                14.0       1501.0           337.0   \n",
+              "4    -114.57     33.57                20.0       1454.0           326.0   \n",
+              "\n",
+              "   population  households  median_income  median_house_value  \n",
+              "0      1015.0       472.0         1.4936             66900.0  \n",
+              "1      1129.0       463.0         1.8200             80100.0  \n",
+              "2       333.0       117.0         1.6509             85700.0  \n",
+              "3       515.0       226.0         3.1917             73400.0  \n",
+              "4       624.0       262.0         1.9250             65500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "w9-Es5Y6laGd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Another powerful feature of *pandas* is graphing. For example, `DataFrame.hist` lets you quickly study the distribution of values in a column:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "nqndFVXVlbPN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 396
+        },
+        "outputId": "834bb910-1bfe-4a52-89e2-99380a18a17d"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.hist('housing_median_age')"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f34c8a77910>]],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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wQNnZ2ZKkvLw89evXryGOCQCAn4Uaz6g//PBDFRcXa8aMGb5to0aN0owZM9SmTRuFhIRo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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "XtYZ7114n3b-"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Accessing Data\n",
+        "\n",
+        "You can access `DataFrame` data using familiar Python dict/list operations:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "_TFm7-looBFF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 102
+        },
+        "outputId": "3914be9c-0e29-4448-fbfe-cfe399bb302b"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n",
+        "print(type(cities['City name']))\n",
+        "cities['City name']"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.series.Series'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    San Francisco\n",
+              "1         San Jose\n",
+              "2       Sacramento\n",
+              "Name: City name, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "V5L6xacLoxyv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "893c306d-b522-4c20-9d08-85a4237d26a9"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities['City name'][1]))\n",
+        "cities['City name'][1]"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<type 'str'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'San Jose'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "gcYX1tBPugZl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 128
+        },
+        "outputId": "f847d1e9-4dfc-4a37-fa1f-d0d6c5df4264"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities[0:2]))\n",
+        "cities[0:2]"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population\n",
+              "0  San Francisco      852469\n",
+              "1       San Jose     1015785"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "65g1ZdGVjXsQ"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In addition, *pandas* provides an extremely rich API for advanced [indexing and selection](http://pandas.pydata.org/pandas-docs/stable/indexing.html) that is too extensive to be covered here."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "RM1iaD-ka3Y1"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Manipulating Data\n",
+        "\n",
+        "You may apply Python's basic arithmetic operations to `Series`. For example:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "XWmyCFJ5bOv-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "15dac4d6-4519-41fc-dccb-6d198cb894e8"
+      },
+      "cell_type": "code",
+      "source": [
+        "population / 1000."
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0     852.469\n",
+              "1    1015.785\n",
+              "2     485.199\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TQzIVnbnmWGM"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "[NumPy](http://www.numpy.org/) is a popular toolkit for scientific computing. *pandas* `Series` can be used as arguments to most NumPy functions:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "ko6pLK6JmkYP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "c6bb332f-098e-4bb7-a69f-8e86b0562670"
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.log(population)"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    13.655892\n",
+              "1    13.831172\n",
+              "2    13.092314\n",
+              "dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "xmxFuQmurr6d"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more complex single-column transformations, you can use `Series.apply`. Like the Python [map function](https://docs.python.org/2/library/functions.html#map), \n",
+        "`Series.apply` accepts as an argument a [lambda function](https://docs.python.org/2/tutorial/controlflow.html#lambda-expressions), which is applied to each value.\n",
+        "\n",
+        "The example below creates a new `Series` that indicates whether `population` is over one million:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "Fc1DvPAbstjI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "9c90c411-e478-4c0b-8d83-efa7b463afc8"
+      },
+      "cell_type": "code",
+      "source": [
+        "population.apply(lambda val: val > 1000000)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "ZeYYLoV9b9fB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "Modifying `DataFrames` is also straightforward. For example, the following code adds two `Series` to an existing `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "0gCEX99Hb8LR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "64bf9e40-e536-4139-f847-69b3eb4f91f4"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n",
+        "cities['Population density'] = cities['Population'] / cities['Area square miles']\n",
+        "cities"
+      ],
+      "execution_count": 15,
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density\n",
+              "0  San Francisco      852469              46.87        18187.945381\n",
+              "1       San Jose     1015785             176.53         5754.177760\n",
+              "2     Sacramento      485199              97.92         4955.055147"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "6qh63m-ayb-c"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #1\n",
+        "\n",
+        "Modify the `cities` table by adding a new boolean column that is True if and only if *both* of the following are True:\n",
+        "\n",
+        "  * The city is named after a saint.\n",
+        "  * The city has an area greater than 50 square miles.\n",
+        "\n",
+        "**Note:** Boolean `Series` are combined using the bitwise, rather than the traditional boolean, operators. For example, when performing *logical and*, use `&` instead of `and`.\n",
+        "\n",
+        "**Hint:** \"San\" in Spanish means \"saint.\""
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "zCOn8ftSyddH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "4bf1b526-3aae-4333-cbf4-3e932993b04d"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+        "cities"
+      ],
+      "execution_count": 16,
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "0                       False  \n",
+              "1                        True  \n",
+              "2                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "YHIWvc9Ms-Ll"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "T5OlrqtdtCIb",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+        "cities"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "f-xAOJeMiXFB"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Indexes\n",
+        "Both `Series` and `DataFrame` objects also define an `index` property that assigns an identifier value to each `Series` item or `DataFrame` row. \n",
+        "\n",
+        "By default, at construction, *pandas* assigns index values that reflect the ordering of the source data. Once created, the index values are stable; that is, they do not change when data is reordered."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "2684gsWNinq9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "2fbe1064-de3c-4992-deb8-f9dfd274bf77"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names.index"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "F_qPe2TBjfWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "0ee71e7f-a5db-4a26-ac73-8ad2a19f15e6"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.index"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "hp2oWY9Slo_h"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Call `DataFrame.reindex` to manually reorder the rows. For example, the following has the same effect as sorting by city name:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "sN0zUzSAj-U1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "feb6348c-f121-40c7-f301-81499caed675"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([2, 0, 1])"
+      ],
+      "execution_count": 19,
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "2                       False  \n",
+              "0                       False  \n",
+              "1                        True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "-GQFz8NZuS06"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Reindexing is a great way to shuffle (randomize) a `DataFrame`. In the example below, we take the index, which is array-like, and pass it to NumPy's `random.permutation` function, which shuffles its values in place. Calling `reindex` with this shuffled array causes the `DataFrame` rows to be shuffled in the same way.\n",
+        "Try running the following cell multiple times!"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "mF8GC0k8uYhz",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "fdbcaf8c-4e76-4f8c-9957-57c8d8f98997"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex(np.random.permutation(cities.index))"
+      ],
+      "execution_count": 20,
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>San Jose</td>\n",
+              "      <td>1015785</td>\n",
+              "      <td>176.53</td>\n",
+              "      <td>5754.177760</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "\n",
+              "   Is wide and has saint name  \n",
+              "1                        True  \n",
+              "0                       False  \n",
+              "2                       False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "fSso35fQmGKb"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For more information, see the [Index documentation](http://pandas.pydata.org/pandas-docs/stable/indexing.html#index-objects)."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8UngIdVhz8C0"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Exercise #2\n",
+        "\n",
+        "The `reindex` method allows index values that are not in the original `DataFrame`'s index values. Try it and see what happens if you use such values! Why do you think this is allowed?"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "PN55GrDX0jzO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 173
+        },
+        "outputId": "518436da-9ef7-4b22-c762-56f1c9703e1f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "execution_count": 21,
+      "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>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>Is wide and has saint name</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>San Francisco</td>\n",
+              "      <td>852469.0</td>\n",
+              "      <td>46.87</td>\n",
+              "      <td>18187.945381</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Sacramento</td>\n",
+              "      <td>485199.0</td>\n",
+              "      <td>97.92</td>\n",
+              "      <td>4955.055147</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       City name  Population  Area square miles  Population density  \\\n",
+              "0  San Francisco    852469.0              46.87        18187.945381   \n",
+              "4            NaN         NaN                NaN                 NaN   \n",
+              "5            NaN         NaN                NaN                 NaN   \n",
+              "2     Sacramento    485199.0              97.92         4955.055147   \n",
+              "\n",
+              "  Is wide and has saint name  \n",
+              "0                      False  \n",
+              "4                        NaN  \n",
+              "5                        NaN  \n",
+              "2                      False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "TJffr5_Jwqvd"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "8oSvi2QWwuDH"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "If your `reindex` input array includes values not in the original `DataFrame` index values, `reindex` will add new rows for these \"missing\" indices and populate all corresponding columns with `NaN` values:"
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "yBdkucKCwy4x",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "colab_type": "text",
+        "id": "2l82PhPbwz7g"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This behavior is desirable because indexes are often strings pulled from the actual data (see the [*pandas* reindex\n",
+        "documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reindex.html) for an example\n",
+        "in which the index values are browser names).\n",
+        "\n",
+        "In this case, allowing \"missing\" indices makes it easy to reindex using an external list, as you don't have to worry about\n",
+        "sanitizing the input."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/logistic_regression.ipynb b/logistic_regression.ipynb
new file mode 100644
index 0000000..6f826e4
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1638 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "logistic_regression.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "dPpJUV862FYI",
+        "i2e3TlyL57Qs",
+        "wCugvl0JdWYL"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/logistic_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Logistic Regression"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "LEAHZv4rIYHX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Reframe the median house value predictor (from the preceding exercises) as a binary classification model\n",
+        "  * Compare the effectiveness of logisitic regression vs linear regression for a binary classification problem"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CnkCZqdIIYHY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), but this time we will turn it into a binary classification problem by predicting whether a city block is a high-cost city block. We'll also revert to the default features, for now."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9pltCyy2K3dd",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Frame the Problem as Binary Classification\n",
+        "\n",
+        "The target of our dataset is `median_house_value` which is a numeric (continuous-valued) feature. We can create a boolean label by applying a threshold to this continuous value.\n",
+        "\n",
+        "Given features describing a city block, we wish to predict if it is a high-cost city block. To prepare the targets for train and eval data, we define a classification threshold of the 75%-ile for median house value (a value of approximately 265000). All house values above the threshold are labeled `1`, and all others are labeled `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "67IJwZX1Vvjt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and prepare the input features and targets."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fOlbcJ4EIYHd",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "lTB73MNeIYHf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Note how the code below is slightly different from the previous exercises. Instead of using `median_house_value` as target, we create a new binary target, `median_house_value_is_high`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kPSqspaqIYHg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FwOYWmXqWA6D",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "1a44401d-9efd-4c1a-c89e-e5f14265deaf"
+      },
+      "cell_type": "code",
+      "source": [
+        " # Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.6       2655.6           541.4   \n",
+              "std         2.1        2.0                12.6       2196.0           426.3   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1466.8           296.0   \n",
+              "50%        34.2     -118.5                29.0       2128.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3156.2           650.0   \n",
+              "max        42.0     -114.3                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1434.4       503.0            3.9               2.0  \n",
+              "std        1166.9       388.3            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.1  \n",
+              "25%         792.0       281.8            2.6               1.5  \n",
+              "50%        1164.0       409.5            3.6               1.9  \n",
+              "75%        1727.2       607.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              52.0  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2655.6</td>\n",
+              "      <td>541.4</td>\n",
+              "      <td>1434.4</td>\n",
+              "      <td>503.0</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2196.0</td>\n",
+              "      <td>426.3</td>\n",
+              "      <td>1166.9</td>\n",
+              "      <td>388.3</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</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>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1466.8</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>792.0</td>\n",
+              "      <td>281.8</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2128.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1164.0</td>\n",
+              "      <td>409.5</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3156.2</td>\n",
+              "      <td>650.0</td>\n",
+              "      <td>1727.2</td>\n",
+              "      <td>607.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.3</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>52.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.7       2615.0           534.6   \n",
+              "std         2.1        2.0                12.6       2140.9           409.7   \n",
+              "min        32.5     -124.3                 2.0         12.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1453.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3141.2           647.0   \n",
+              "max        42.0     -114.5                52.0      30401.0          4957.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1418.1       496.9            3.9               2.0  \n",
+              "std        1100.9       375.4            1.9               1.3  \n",
+              "min           8.0         4.0            0.5               0.0  \n",
+              "25%         783.8       282.0            2.5               1.5  \n",
+              "50%        1176.0       408.0            3.5               1.9  \n",
+              "75%        1701.2       598.2            4.7               2.3  \n",
+              "max       15037.0      4616.0           15.0              55.2  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2615.0</td>\n",
+              "      <td>534.6</td>\n",
+              "      <td>1418.1</td>\n",
+              "      <td>496.9</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2140.9</td>\n",
+              "      <td>409.7</td>\n",
+              "      <td>1100.9</td>\n",
+              "      <td>375.4</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1453.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>783.8</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1176.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3141.2</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1701.2</td>\n",
+              "      <td>598.2</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.5</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30401.0</td>\n",
+              "      <td>4957.0</td>\n",
+              "      <td>15037.0</td>\n",
+              "      <td>4616.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "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>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "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>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "uon1LB3A31VN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## How Would Linear Regression Fare?\n",
+        "To see why logistic regression is effective, let us first train a naive model that uses linear regression. This model will use labels with values in the set `{0, 1}` and will try to predict a continuous value that is as close as possible to `0` or `1`. Furthermore, we wish to interpret the output as a probability, so it would be ideal if the output will be within the range `(0, 1)`. We would then apply a threshold of `0.5` to determine the label.\n",
+        "\n",
+        "Run the cells below to train the linear regression model using [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "smmUYRDtWOV_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "B5OwSrr1yIKD",
+        "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.\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(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": "SE2-hq8PIYHz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_regressor_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\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=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "    \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\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",
+        "    \n",
+        "    # Take a break and compute predictions.\n",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "TDBD8xeeIYH2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 741
+        },
+        "outputId": "bd910213-95a9-4a36-e210-fbf0bb6c47fe"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_linear_regressor_model(\n",
+        "    learning_rate=0.000001,\n",
+        "    steps=200,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.45\n",
+            "  period 01 : 0.45\n",
+            "  period 02 : 0.45\n",
+            "  period 03 : 0.45\n",
+            "  period 04 : 0.44\n",
+            "  period 05 : 0.44\n",
+            "  period 06 : 0.44\n",
+            "  period 07 : 0.44\n",
+            "  period 08 : 0.44\n",
+            "  period 09 : 0.44\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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rKtiXGYdrJxdGeA9RO46J6Zw4cphKCNFBSYEjxG3YnR5LrbGOCQFjsdZaqx3H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\nl156iejoaJycnJg06frnvBg/fjy///3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mFb1Ogz0+MqgjJYWnGvnV9v6lF/7vl2dwjUqX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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JjBZ_q7aD9gh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Can We Calculate LogLoss for These Predictions?\n",
+        "\n",
+        "**Examine the predictions and decide whether or not we can use them to calculate LogLoss.**\n",
+        "\n",
+        "`LinearRegressor` uses the L2 loss, which doesn't do a great job at penalizing misclassifications when the output is interpreted as a probability.  For example, there should be a huge difference whether a negative example is classified as positive with a probability of 0.9 vs 0.9999, but L2 loss doesn't strongly differentiate these cases.\n",
+        "\n",
+        "In contrast, `LogLoss` penalizes these \"confidence errors\" much more heavily.  Remember, `LogLoss` is defined as:\n",
+        "\n",
+        "$$Log Loss = \\sum_{(x,y)\\in D} -y \\cdot log(y_{pred}) - (1 - y) \\cdot log(1 - y_{pred})$$\n",
+        "\n",
+        "\n",
+        "But first, we'll need to obtain the prediction values. We could use `LinearRegressor.predict` to obtain these.\n",
+        "\n",
+        "Given the predictions and the targets, can we calculate `LogLoss`?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dPpJUV862FYI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to display the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kXFQ5uig2RoP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "c5d9a265-7369-4f4e-9e9d-ba9ef62cf91d"
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                  validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "\n",
+        "validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "\n",
+        "_ = plt.hist(validation_predictions)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rYpy336F9wBg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Train a Logistic Regression Model and Calculate LogLoss on the Validation Set\n",
+        "\n",
+        "To use logistic regression, simply use [LinearClassifier](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearClassifier) instead of `LinearRegressor`. Complete the code below.\n",
+        "\n",
+        "**NOTE**: When running `train()` and `predict()` on a `LinearClassifier` model, you can access the real-valued predicted probabilities via the `\"probabilities\"` key in the returned dict—e.g., `predictions[\"probabilities\"]`. Sklearn's [log_loss](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) function is handy for calculating LogLoss using these probabilities.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JElcb--E9wBm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier 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_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VM0wmnFUIYH9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "f7fde539-eb48-4cdd-ae49-5d8085ef0d66"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.59\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.56\n",
+            "  period 03 : 0.55\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\n",
+            "  period 07 : 0.54\n",
+            "  period 08 : 0.54\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PZmLAeNzsXE1dqhBCCAu0aNG9Xd6msLCAbdsSmDJlGrNn32j4ovRgtDATGxvLq6++ysKF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\nqwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i2e3TlyL57Qs",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see the solution.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5YxXd2hn6MuF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "  \n",
+        "  # Create a linear classifier 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_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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(\"LogLoss (on training data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.    \n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  \n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "UPM_T1FXsTaL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "48926085-9eff-4f08-a00d-a41b90575c66"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=500,\n",
+        "    batch_size=20,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.61\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.55\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.53\n",
+            "  period 07 : 0.53\n",
+            "  period 08 : 0.53\n",
+            "  period 09 : 0.53\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZoaGj9FFaszg1W0d9bXrBSaHGyexYTOs5EVamlmKXRERE1KyHHroPq1e/DScnJ2RmZmD5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\niIiIKK4xzBAREVFcY5ghIiKiuPa/wcDFLA3TyhsAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i-Xo83_aR6s_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Calculate Accuracy and plot a ROC Curve for the Validation Set\n",
+        "\n",
+        "A few of the metrics useful for classification are the model [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification), the [ROC curve](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) and the area under the ROC curve (AUC). We'll examine these metrics.\n",
+        "\n",
+        "`LinearClassifier.evaluate` calculates useful metrics like accuracy and AUC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "DKSQ87VVIYIA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "271075bb-3589-48bb-8866-4c1bdd36ca58"
+      },
+      "cell_type": "code",
+      "source": [
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AUC on the validation set: 0.73\n",
+            "Accuracy on the validation set: 0.75\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "47xGS2uNIYIE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "You may use class probabilities, such as those calculated by `LinearClassifier.predict`,\n",
+        "and Sklearn's [roc_curve](http://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics) to\n",
+        "obtain the true positive and false positive rates needed to plot a ROC curve."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xaU7ttj8IYIF",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "3e201c14-76ca-43df-fbe1-98dd5a86f91c"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "# Get just the probabilities for the positive class.\n",
+        "validation_probabilities = np.array([item['probabilities'][1] for item in validation_probabilities])\n",
+        "\n",
+        "false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(\n",
+        "    validation_targets, validation_probabilities)\n",
+        "plt.plot(false_positive_rate, true_positive_rate, label=\"our model\")\n",
+        "plt.plot([0, 1], [0, 1], label=\"random classifier\")\n",
+        "_ = plt.legend(loc=2)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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upnApVxcdU4ZEcdfYGLQaCV5nZVfsbMvbycacLVjtVoaGDmR2/J14SvfrVCSE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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PIdhwfgzIYII",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**See if you can tune the learning settings of the model trained at Task 2 to improve AUC.**\n",
+        "\n",
+        "Often times, certain metrics improve at the detriment of others, and you'll need to find the settings that achieve a good compromise.\n",
+        "\n",
+        "**Verify if all metrics improve at the same time.**"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XKIqjsqcCaxO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "eaaebf8e-f680-4943-9b11-372382340113"
+      },
+      "cell_type": "code",
+      "source": [
+        "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000003,\n",
+        "    steps=20000,\n",
+        "    batch_size=500,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.50\n",
+            "  period 01 : 0.48\n",
+            "  period 02 : 0.47\n",
+            "  period 03 : 0.47\n",
+            "  period 04 : 0.47\n",
+            "  period 05 : 0.47\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wCugvl0JdWYL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VHosS1g2aetf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One possible solution that works is to just train for longer, as long as we don't overfit. \n",
+        "\n",
+        "We can do this by increasing the number the steps, the batch size, or both.\n",
+        "\n",
+        "All metrics improve at the same time, so our loss metric is a good proxy\n",
+        "for both AUC and accuracy.\n",
+        "\n",
+        "Notice how it takes many, many more iterations just to squeeze a few more \n",
+        "units of AUC. This commonly happens. But often even this small gain is worth \n",
+        "the costs."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dWgTEYMddaA-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000003,\n",
+        "    steps=20000,\n",
+        "    batch_size=500,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "\n",
+        "evaluation_metrics = linear_classifier.evaluate(input_fn=predict_validation_input_fn)\n",
+        "\n",
+        "print(\"AUC on the validation set: %0.2f\" % evaluation_metrics['auc'])\n",
+        "print(\"Accuracy on the validation set: %0.2f\" % evaluation_metrics['accuracy'])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/multi_class_classification_of_handwritten_digits.ipynb b/multi_class_classification_of_handwritten_digits.ipynb
new file mode 100644
index 0000000..4bba9f5
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2374 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "multi-class_classification_of_handwritten_digits.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "266KQvZoMxMv",
+        "6sfw3LH0Oycm"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/multi_class_classification_of_handwritten_digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mPa95uXvcpcn",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Classifying Handwritten Digits with Neural Networks"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Fdpn8b90u8Tp",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "c7HLCm66Cs2p",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n",
+        "  * Compare the performance of the linear and neural network classification models\n",
+        "  * Visualize the weights of a neural-network hidden layer"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HSEh-gNdu8T0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2NMdE1b-7UIH",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4LJ4SD8BWHeh",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 233
+        },
+        "outputId": "ddd8f660-b825-4a2d-8a56-c7eb82d9889f"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import glob\n",
+        "import math\n",
+        "import os\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",
+        "import seaborn as sns\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\n",
+        "\n",
+        "mnist_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "# Use just the first 10,000 records for training/validation.\n",
+        "mnist_dataframe = mnist_dataframe.head(10000)\n",
+        "\n",
+        "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
+        "mnist_dataframe.head()"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6682</th>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>407</th>\n",
+              "      <td>9</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2962</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>169</th>\n",
+              "      <td>9</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6067</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 785 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      0    1    2    3    4    5    6    7    8    9   ...   775  776  777  \\\n",
+              "6682    8    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "407     9    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "2962    3    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "169     9    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "6067    3    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "6682    0    0    0    0    0    0    0  \n",
+              "407     0    0    0    0    0    0    0  \n",
+              "2962    0    0    0    0    0    0    0  \n",
+              "169     0    0    0    0    0    0    0  \n",
+              "6067    0    0    0    0    0    0    0  \n",
+              "\n",
+              "[5 rows x 785 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kg0-25p2mOi0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains '6', then a human rater interpreted the handwritten character as the digit '6'.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PQ7vuOwRCsZ1",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dghlqJPIu8UM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren't all 0.  For example, adjust the following text block to print out the values in column 72."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "2ZkrL5MCqiJI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "c88abcd9-5fd5-485b-e3f4-9d2a3e7374a6"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_dataframe.loc[:, 72:72]"
+      ],
+      "execution_count": 2,
+      "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>72</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>6682</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>407</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2962</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>169</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6067</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3178</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3074</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5241</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>178</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1504</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "6682   0\n",
+              "407    0\n",
+              "2962   0\n",
+              "169    0\n",
+              "6067   0\n",
+              "...   ..\n",
+              "3178   0\n",
+              "3074   0\n",
+              "5241   0\n",
+              "178    0\n",
+              "1504   0\n",
+              "\n",
+              "[10000 rows x 1 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "vLNg2VxqhUZ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JfFWWvMWDFrR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def parse_labels_and_features(dataset):\n",
+        "  \"\"\"Extracts labels and features.\n",
+        "  \n",
+        "  This is a good place to scale or transform the features if needed.\n",
+        "  \n",
+        "  Args:\n",
+        "    dataset: A Pandas `Dataframe`, containing the label on the first column and\n",
+        "      monochrome pixel values on the remaining columns, in row major order.\n",
+        "  Returns:\n",
+        "    A `tuple` `(labels, features)`:\n",
+        "      labels: A Pandas `Series`.\n",
+        "      features: A Pandas `DataFrame`.\n",
+        "  \"\"\"\n",
+        "  labels = dataset[0]\n",
+        "\n",
+        "  # DataFrame.loc index ranges are inclusive at both ends.\n",
+        "  features = dataset.loc[:,1:784]\n",
+        "  # Scale the data to [0, 1] by dividing out the max value, 255.\n",
+        "  features = features / 255\n",
+        "\n",
+        "  return labels, features"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "mFY_-7vZu8UU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "1af288b9-f549-4efb-9fce-2a58fd38015e"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "      <td>7500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    1.0    0.8    0.2    1.0    0.2    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 7500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4-Vgg-1zu8Ud",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "02841657-1a33-4a42-8fdc-adac7c0941aa"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets, validation_examples = parse_labels_and_features(mnist_dataframe[7500:10000])\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 5,
+      "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>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "      <td>2500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         1      2      3      4      5      6      7      8      9      10   \\\n",
+              "count 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
+              "count  ...   2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
+              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "max    ...      1.0    0.8    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
+              "\n",
+              "         784  \n",
+              "count 2500.0  \n",
+              "mean     0.0  \n",
+              "std      0.0  \n",
+              "min      0.0  \n",
+              "25%      0.0  \n",
+              "50%      0.0  \n",
+              "75%      0.0  \n",
+              "max      0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wrnAI1v6u8Uh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Show a random example and its corresponding label."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s-euVJVtu8Ui",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 360
+        },
+        "outputId": "cd1bcb02-aeab-4e10-dfb2-53b19cf27d6f"
+      },
+      "cell_type": "code",
+      "source": [
+        "rand_example = np.random.choice(training_examples.index)\n",
+        "_, ax = plt.subplots()\n",
+        "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n",
+        "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n",
+        "ax.grid(False)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUsAAAFXCAYAAAAro2x+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ScmYX7xdZMXE",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Build a Linear Model for MNIST\n",
+        "\n",
+        "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n",
+        "\n",
+        "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix).  The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n",
+        "\n",
+        "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cpoVC4TSdw5Z",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  \n",
+        "  # There are 784 pixels in each image.\n",
+        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMmL89yGeTfz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "OeS47Bmn5Ms2",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
+        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The training features.\n",
+        "    labels: The training labels.\n",
+        "    batch_size: Batch size to use during training.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns batches of training features and labels during\n",
+        "    training.\n",
+        "  \"\"\"\n",
+        "  def _input_fn(num_epochs=None, shuffle=True):\n",
+        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
+        "    # gets a good sampling of data, even when number of steps is small, we \n",
+        "    # shuffle all the data before creating the Dataset object\n",
+        "    idx = np.random.permutation(features.index)\n",
+        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
+        "    raw_targets = np.array(labels[idx])\n",
+        "   \n",
+        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "8zoGWAoohrwS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def create_predict_input_fn(features, labels, batch_size):\n",
+        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
+        "\n",
+        "  Args:\n",
+        "    features: The features to base predictions on.\n",
+        "    labels: The labels of the prediction examples.\n",
+        "\n",
+        "  Returns:\n",
+        "    A function that returns features and labels for predictions.\n",
+        "  \"\"\"\n",
+        "  def _input_fn():\n",
+        "    raw_features = {\"pixels\": features.values}\n",
+        "    raw_targets = np.array(labels)\n",
+        "    \n",
+        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size)\n",
+        "    \n",
+        "        \n",
+        "    # Return the next batch of data.\n",
+        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
+        "    return feature_batch, label_batch\n",
+        "\n",
+        "  return _input_fn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "G6DjSLZMu8Um",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, and a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\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",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `LinearClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create a LinearClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=construct_feature_columns(),\n",
+        "      n_classes=10,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\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(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ItHIUyv2u8Ur",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n",
+        "\n",
+        "Stop if you get anything above about 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yaiIhIQqu8Uv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1092
+        },
+        "outputId": "683fa0a7-4f03-4a93-9da0-09d0a6c04ade"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_linear_classification_model(\n",
+        "             learning_rate=0.03,\n",
+        "             steps=1000,\n",
+        "             batch_size=30,\n",
+        "             training_examples=training_examples,\n",
+        "             training_targets=training_targets,\n",
+        "             validation_examples=validation_examples,\n",
+        "             validation_targets=validation_targets)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.86\n",
+            "  period 01 : 4.63\n",
+            "  period 02 : 4.16\n",
+            "  period 03 : 4.19\n",
+            "  period 04 : 4.02\n",
+            "  period 05 : 3.80\n",
+            "  period 06 : 3.81\n",
+            "  period 07 : 3.76\n",
+            "  period 08 : 3.55\n",
+            "  period 09 : 3.80\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.89\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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VRux0th3+uHFDUwOHC46xPWcP+TWFAHR3C2NslxH09IhQ9fuTX3bmIX02D+lz\nixuFt/q7NJ1EnzBPhkX5su90PpsO5TBlaLDaJVkEFxtnvJycKLrcOX4I9Vo9w/wHMcRvACml6WzL\n2U1aWQbpZRn42nszJmgEA336odfq1S5VCNGJSXib0Nxx4ZzOLmXtviz6hnsS4CXD552VRtHQy6MH\nvTx6kFt5kR25e0goOMGK1FWszdxEbOBQYgOG4GSQ94AQwvS0ixcvXqx2EW1RU1Nv0u05ONiYfJsG\nnRZfN3sOJBWQnV/B8Gg/NCY4aaqja49eWxIXGyf6eEUxxH8AOo2O7IpcUkrT2HV+H6WXy/Gy88TR\n4NDudXT2PlsK6bN5SJ9bODhc/+oWCW8T8/Wwp7CshsSzpdjotYQHupr8NToaa/khtNXZ0sM9nNiA\nobjYOJNXVUBaWQa7L+wnpyIXFxsn3G3dTHIW/PVYS5/VJn02D+lzixuFtwybt4MHxnUnKbuMNXvO\n0ifMkwDP9t/rEpbDVmfDqMBhxAYM4VRREttyd3O6JJXTJal0cfRnTFAs/b37WMTsc0KIjkn2vNuB\nQa/Fx82Og8kFZOdVMjza16qHz631E7SiKPg6+DDUfyA93btT21RHelkmJ4pOcyAvgWZjM34OPiY7\nuc1a+2xu0mfzkD63kGHzH2nvN4afhwMFpTWczirF1mDdw+fyQwhutq70845moG9/FCCr4hxJJans\nvrCfyvoqvO29sNfb3dVrSJ/NQ/psHtLnFjJsroJ547uTnF3K17uziAnzxM9Dhs+tnaedO7O638uk\nkPHsu3iInef3seP8Xnae30eMVxRjg2IJkcVQhBC3IHve7cig1+Llas+h5ALO5VcyvLdfu52sZMnk\nE/S19Fo9oa7BjAochre9FyW1paSVZ7I/7wippenY6+zwtve6rfeL9Nk8pM/mIX1uIXveKukf4cXA\nSG8OpxTy3ZFc4gYFqV2SsCBajZaBvv0Y4NP3qsVQzl6SxVCEEDcm4W0GD47vTsq5Mr7ec5aYcE98\n3e3VLklYGEVR6O4WSne30KsWQ/nyzDesy/qOEQGDGRk41OIWQxFCqEOGzc3ARq/Fy8WuZfi8oJJh\nUdY1fC7DX7fH0eBAb8+eDPMfhI3WQE7leVJK09l1fj+FtcV42rrjbHPtfMfSZ/OQPpuH9LmFnG3+\nI+Z+Y/h7OnChuJrTZ0uxt9UTGmA9e1DyQ3hnbLQGwt1CGRU4DHc7Nwprikgry2DvxYNklmfhoLfH\n086j9YOg9Nk8pM/mIX1uIce8LcBD47uTeq6M1bsy6RPqgY8Mn4s2uNFiKGk/WgxFCGE92nXP+/Ll\ny8TFxeHo6EhkZGTr7fv37+dBn0DJAAAgAElEQVTpp59m1apVFBYWMnDgwFtuq6PveQPYGLR4uNhy\nKKWQnIJKhlnJ2efyCdo0FEXB296TQX79ifbsRX1zPWfKz3KqOJm9Fw+hKAp+tr4dftlVSyfvZ/OQ\nPre40Z53u/6Uv//++7i4XDs8/Nprr/Hee+/x+eefs2/fPjIyMtqzDIsyoIc3/SO8OHP+EtuOnle7\nHNFBdXHy5+Gec3l16PNM6DqaJmMzn51czeuH3yG19Iza5Qkh2lm7hXdmZiYZGRmMGjXqqttzc3Nx\ncXHBz88PjUbDyJEjOXDgQHuVYXEUReGhCRE42ulZtTOTgrIatUsSHZirjQvTQuN5ecj/MCEslsKa\nIt47sZQPEpdTdrlc7fKEEO2k3cL7z3/+M88999w1txcVFeHu7t76tbu7O0VFRe1VhkVycTAwb3w4\n9Y3NfLQhlWajUe2SRAfnoLfnJ/0f4H8G/JIQ564cL0rklYNvsDl7Ow3NjWqXJ4QwsXY5YW3NmjXE\nxMTQpUsXk23Tzc0enc60qzB5eV17uY25TIl15NTZUg6ezudIejFThndTrRZzULPX1qRftx7EhPyW\n3dmH+Ozkatae3cSRomM81m8OfXx7ql1epyHvZ/OQPt9Yu4T3zp07yc3NZefOneTn52MwGPD19WXo\n0KF4e3tTXFzc+tiCggK8vb1vuc0yEw8ve3k5UVRUadJt3q45o0JJzCjm43XJhPg44u16dwtTWCpL\n6LU1+GGfezlG8eLAUNZlfcfu8/v5w673iPGKYmb4VNxt3VSutGOT97N5SJ9b3OgDTLuE9zvvvNP6\n7/fee4+AgACGDh0KQGBgIFVVVZw/fx5fX1927NjBm2++2R5lWDwXRxvmje/O0m+T+XhDCr9+oK9V\nLx0qTMteb8fs7tMY6jeAL9LXcKLoNEklacQFj2Fs0Ej0GrlSVIiOymzXlKxevZotW7YAsHjxYp59\n9lkefPBBJk2aREhIiLnKsDiDe/oQE+ZJak45u45fULsc0QkFOvnzdL+fsSByDrY6G749u5k/HHqL\npJJUtUsTQtwhxWjsGGdLmXr4xJKGZMqr6nhh6SGamo28+vhAPDvZ8Lkl9boza0ufaxtrWZ+1hV3n\n99NsbCbasxezwqfiYed+0+eJ/5L3s3lIn1vcaNhcZnOwAK6ONswbH05dQxMfbUylg3yeEh2Qnc6O\nWeH38tyARYS6hHCqOIlXD73JhqwtNDQ1qF2eEKKNJLwtxJBevvQJ9SDlXBm7TlxUuxzRyQU4+vF0\nv//Hwz3nYqezY33WFl479BaJxclqlyaEaAMJbwuhKAoL4npgZ6Nj5Y4Mii/Vql2S6OQURWGgbz9e\nGvwbxnQZQWldOf869THvn/yI4toStcsTQtyEhLcFcXOy4YGx4dTVN/GxDJ8LM7HT2TIzfCrPD/gV\n4a7dOF2SwquH3mLd2e+ol6F0ISyShLeFGdbbl97dPEjOLmP3SRk+F+bj7+jLor4/5dFe83DQ2bMx\neyuvHXqTk0VJ8kFSCAsj4W1hFEXh4bgI7Gy0rNyeQcmly2qXJKyIoijc4xPDS4N/zbigkZTVXWJJ\n4if889QyCmuKb70BIYRZSHhbIHdnW+aOCedyfROfbJLhc2F+tjpbZoRN5ncDnybCLYzkkjT+cOgt\nvj27mfomWaZRCLW1ObyrqqoAKC4uJiEhgebm5nYrSsDwaD+iQtw5nVXK3lN5apcjrJSvgw+/iHmC\nx6MewtHgyKbsbbx66C1OFJ2WD5VCqEi7ePHixbd60Kuvvkp5eTkBAQHMnj2bvLw8Dh48yOjRo81Q\nYgtTL8pu6Qu9K4pCRJAru09eJCm7lCG9fLGz6ZjTWVp6rzuL9uqzoij4OfgwzH8QACml6SQUnCCr\nIoeuzl1w1DuY/DUtmbyfzUP63MLBwea6t7dpzzs5OZn777+fjRs3MmPGDN59913OnTtn0gLFtdyd\nbZkzJozauiY+2ZQmezpCVbY6G6aFxvO7gU8T6d6dlNJ0/nDobb7J3EidDKULYVZtCu/vQ2Pnzp2M\nGTMGgPp6+WE1h9g+/vQMdiPxbAn7EvPVLkcIfBy8earP4zwRNR9ngxPfndvBqwff5FjhKfmAKYSZ\ntCm8Q0JCmDRpEtXV1URGRrJmzRpcXFzauzZBy5DlI/E9sDFo+XzbGcoq69QuSQgURSHGuzcvDf41\ncV3HUFlfyYenP+PvJz4gv7pQ7fKE6PTatDBJU1MT6enphIaGYjAYSEpKokuXLjg7O5ujRqBzL0zS\nFjuPX+DTzWn0Cnbj6TkxHWrp0I7W645KzT4X1hTx5Zm1JJekoVW0jOkygrjgsdjqrn+8riOT97N5\nSJ9b3NXCJCkpKeTn52MwGPjrX//KX/7yF9LT001aoLi5kTH+RId6kJRdxtYjuWqXI8RVvO29eDL6\nMRb2fhgXG2e25Ozk1UNvcrTghAylC9EO2hTer732GiEhISQkJJCYmMiLL77I3/72t/auTfyAoig8\nOikSJ3s9X+3KJKdAPpEKy6IoCn28evHioGeJDx5HVUM1y5JW8LcTS8mrLlC7PCE6lTaFt42NDcHB\nwWzbto3Zs2cTFhaGRiPzu5ibi4OBxyZF0thkZMm3ydQ3NKldkhDXMGgNTOk2gRcGPkuURyTpZRm8\nfvivrD6zjsuNMmOgEKbQpguHa2tr2bhxI1u3buWpp56ivLycioqK9q5NXEefME/G9Atg+7ELfLkj\nkwcndFe7JCGuy8veg5/1eZTE4mS+TF/LttzdJBQc576wKfT3iUGxkPM2jEYjDc2N1DfX09DUQH1z\nA/VNDTQ0N1DfVH/l74bWvz2qnLBrcsLH3gsHvb3a5Qsr1abwfuaZZ/j000955plncHR05L333uOR\nRx5p59LEjcweHUbKuTK2HTtP71B3okM91S5JiBvq7dmTCLdwtuTsZMu5HXyU/Dl7Lx5idvfp+Dv6\nXvc5RqORRmPTlTCtvyo8/xuqjdQ31VPffIv7rgTy99tqfez3tzff+cppDnp7vO288LH3wtveE+8r\nf3vZeWLQ6u94u0LcSpvONgeoqakhKysLRVEICQnBzs6uvWu7irWfbf5jOQWVvPZpAvY2Ol55fBDO\nDga1S7qhjt7rjqIj9Lm4tpSvzqwlsTgZjaKhq1MXGo2N1Df9YC/3StAaMe2JblpFi16jx6DVY9Do\n0Wv1GDQG9Frdlb9bbjdo9VceZ0Cv0V35+7/P0dtBRkEuhTXFFNYWUVxbSrPx6umiFRTcbF3xtmsJ\n9B+Gu7utKxpFDjveSkd4P3/PaDRyMP8omeVZzImYgV5jutkwb3S2eZvCe+vWrSxevBhfX1+am5sp\nLi7m1VdfZeTIkSYr8FYkvK+1+XAOK7dnEB3qwaJZ0RYzDPljnaHXHUFH6vPp4hRWZ6yjsKb4qrD8\nb6heG6Ctj/tB8P73udd5/I+2pdVoTVL7j/vc1NxE8eVSCmuKWgK9poiCK/++VH/t4UWdRoeXnUfL\nXvqPwt1R72CxP8fm1lHez+V1l1iRuoqkklQcdPYsHvI/2OtNt3N7o/Bu08eDDz74gLVr1+Lu7g5A\nQUEBixYtMmt4i2uNH9CFxLMlnMosYcfxC4zpF6h2SUK0SZRnJL08egB0+LDSarT4XAngH7vceJmi\n2pIrYf59uBdTUFN03TPw7XR2eNt7toS53dVD8TZayx1ds0ZGo5HD+cf48sxaahtr6eEWzoORs0wa\n3DfTpvDW6/WtwQ3g4+ODXi/Hc9SmURQen9yTlz48xMrtGUQEuRHgaV2LRIiOq6OHdlvY6mzp4hRA\nF6eAq243Go1UNlRds6deWFPE+cqLnKu4di4HVxuX1iD3+cFeu4etm8lGFUTbXKqr5PO0VSQWJ2PQ\nGpgbcR/D/QeZ9T3dpvB2cHBg2bJlDB06FIC9e/fi4CAhYQncnGx4JD6Sf3ydyJK1Sbyw4B70Ojme\nJoQlUxQFZ4MTzgYnwlxDrrqvqbmJ0svlFNYWte6lf7/Xnl6WQXpZxlWP1ypaPO08ruyle+Jj53Ul\n5L1wNjhaxYckczEajRwtOMEX6d9Q3VhDd9dQHoy8H08791s/2cTadMy7pKSEd999l1OnTrXMaRwT\nwy9+8Yur9sbbmxzzvrmPN6aw+2QeEwd2Yc6YcLXLuUpn67Wlkj6bh5p9rm+qv3KiXHFroBdc2XOv\nbay95vG2Whu87T0JcQlmYtfRuNiYb0rru2Vp7+fK+ir+k7aaE0WnMWj0TA+bzIiAwe1+8uFdHfP2\n8PDglVdeueq2zMxMs4a3uLm5Y8NJyyln8+Fcorp50CtY/m+E6GwMWgOBTv4EOvlfdbvRaKS6oYbC\n2iIKqouuCveL1QXkVF7gQN4RJgSNYmxQLAY5fn5bjhWeYmXa11Q1VBPqEsL8yNl42XuoWlObLxX7\nsQULFvDpp5+aup4bkj3vW8vKq+D15UdxtNfz6uODcLSzjPMSOmOvLZH02Tw6Wp+bmps4mJfAt2c3\nU9lQhauNC/d2i2OAb1+LvmTNEvpcVV/NF+lrOFp4Er1Gx72h8YwKHGbWvt3VwiTXI4sNWJ4QP2em\njwjhUlU9H29Mlf8jIQRajZZhAYNYPOS3TOw6huqGaj5NWclfEt4jvSxT7fIs1smi07x26C2OFp4k\nxLkrzw98mjFdRljMB547vpJcToKwTPGDupJ4tpRj6UXsOZVHbB//Wz9JCNHp2epsuTc0juEBg1ib\nuYkjBcd59/j/Ee3Zi+lhk657qZs1qm6o4cv0bzhScBydRseMsMkWFdrfu2l4f/XVVze8r6ioyOTF\niLun0Sg8MaUnLy07zIqt6XTv4oqvu8y/LIRo4W7rxiO9HmB0l+GsOvMtp4qTOF2SQmzAEOJDxuGo\nt94riRKLk1mRuoqK+kq6OnVhQc/Z+Dr4qF3Wdd00vI8ePXrD+2JiYkxejDANDxdbHo6L4F/fJLFk\nbRL/O78/Oq1lfWoUQqirq3MXnu73M04WnebrzA3sPL+PQ/nHiA8eS2zgUJNO8WnpahpqWXXmWw7m\nJ6BTtEzrFs/YoFiLvn7+jk9YMzc5Ye32fbAumf2n85k8pCszR4aqVoc19NoSSJ/NozP2ubG5kd3n\n97Mhexu1jbV42rozPWwyMV5Rqh0iNVefk0rSWJH6FeV1l+jiFMCCyDk3XDBHDXd1qdi8efOu+Q/U\narWEhITw5JNP4uNjmcMK1u7B8d1Jzy1nw4FzRIW4ExHkpnZJQggLpNPoGBMUy0C//mzK2sauC/v5\n4PRyQl2CuS98CsHOQWqXaHK1jZdZfWYd+/MOo1E0TAmZwISuoy16b/uHtIsXL158qwfl5eXR2NjI\nzJkz6devHyUlJXTv3h1fX1+WLVvGtGnT2r3Qmpp6k27PwcHG5Nu0NHqdhhB/Z/Yl5pN8rpThvf3Q\n68z/xrSGXlsC6bN5dOY+G7QGenpE0N+nD+WXL5FSdob9Fw9TWFNEV+dA7HTmW02yPfucWnqGf5z4\nkDPlmQQ4+vFkn8fp79PH4k5Kg5Y+XE+b9ryPHj3KRx991Pr1uHHjWLhwIUuWLGHbtm2mqVC0i7AA\nF6YOC+abvVl8ujmNn97bS64UEELclI+9FwujH+ZMWSarMtaRUHCCk0WnGd1lBBO6jsZOZ6t2iXfk\ncmMdazI3sOfCATSKhvjgscQFj0XXAY/vt6nikpISSktLW2dUq6ys5OLFi1RUVFBZ2bmO/XRGU4Z2\n5XRWCYdTCokO9WBolJ/aJQkhOoBwt1B+e88vOJJ/nLVnN/HduR0cuHiEyd0mMNRvQIcZYgZIL8vk\ns5QvKLlchp+DDwsi5xDk3HFXYmzTCWtfffUVb7zxBgEBASiKwvnz5/npT3+Kh4cHNTU1PPDAA+1e\nqJywdncKy2tZvOwwAC8/NhAvV/MNf1lbr9UifTYPa+1zfVM923J2813OTuqb6vFz8GFG2BR6eUS0\ny+uZqs91TfV8k7mRXef3oaAwvusoJoWM7zBn09/ohLU2n21eVVVFdnY2zc3NBAUF4erqatICb0XC\n++7tS8zjw/UphAW48D8P9kWrMc/xHWvstRqkz+Zh7X2+VFfBurObOZCXgBEjke7dmRE2mQBH047o\nmaLPGeVZLE/5guLaEnzsvZkfOZsQl4518t1dnW1eXV3NJ598QmJiYuuqYg8//DC2th3zuIe1Ghrl\nS+LZluHz9fvPce/wkFs/SQghfsDFxpkHI+9nZOAwvs5YT0ppOqmHzzDUfyBTuk3A2XD9sDGn+qYG\nvj27iR25ewEYGxTLlJCJGLSWsd6DKbRpz/uZZ57Bx8eHQYMGYTQa2b9/P2VlZbz55pvmqBGQPW9T\nqb7cwO+XHaa8sp7nHupHWIBLu7+mtfba3KTP5iF9/i+j0UhSSSpfZ6wnv6YQG62BCV3HMKbLiLsO\nyjvtc9alc3yaspLCmmK87TyZ33M23VyC76oWNd3VnndxcTFvv/1269ejR49m/vz5pqlMmJWDrZ4n\npvTkLyuOs2RtEi8/NhA7m45x7EcIYVkURSHKM5JI9+7su3iY9Vnf8e3ZTey9cJB7Q+O4xyfGbJdf\nNTQ1sD5rC1tzdgEwustw7u0W12mXP21TV2tra6mt/e9C7zU1NdTV1bVbUaJ9RQS5MWlIV4ovXWbF\nlnS1yxFCdHBajZbYwCEsHvJbxgeNorK+kk+S/8MbCX8nozyr3V//XEUufzryLltyduJh68aivj9l\nVvi9nTa4oY173nPmzCE+Pp6oqCgAkpKSWLRoUbsWJtrXtOEhJGWVsu90Pr1DPRgYKbPkCSHujp3O\njulhkxgRMJhvMjdytPAkfz32PjFeUUwLnYS3vadJX6+huZFNWVv5LmcnzcZmYgOGMj1sEjadOLS/\n1+azzfPy8khKSmoZJomKYvny5fz6179u7/payTFv08svrWHxR4fRaTS8/NhAPFza5wRE6bV5SJ/N\nQ/rcdlmXzrHqzDqyKs6hVbSMDBxKfPBY7PW3XunwVn3OqTzP8uQvuFidj7utGw/1uJ8I9zBTlm8R\n7uqYN4Cfnx9+fv+9FODUqVN3X5VQla+7PfPGdefjjal8sC6Z3zzQF41GZl8TQphGiEtXnu3/JMcK\nT/FN5ka25+7hYF4Ck0LGMyJg8B3NbNbY3Mjm7O1sOredZmMzw/0HMSNsMrYddNa3O3XHZyp1kMXI\nxC2MiPbjVGYJx9KL2HjoHJOHBKtdkhCiE1EUhf4+fYj26sWu8/vYmLWNr86sZdf5fcwIm0y0Z9un\nbL5QlcenySs5X3URVxsXHupxP5Ee3dv5O7BMdxzeMj9256AoCo/E9+DsxUus2ZNFz2B3Qvyc1S5L\nCNHJ6DU6xgWNZJBvfzZkbWXvxYMsSfyUMNcQZoZNvelUpU3NTWzJ2cmGrK00GZsY4jeAmeFTzLpQ\niqW56THvkSNHXjekjUYjZWVlZh06l2Pe7Sspu5S3/nMCH3d7Fj8yABuD6eYsll6bh/TZPKTPppFf\nXciazPUkFqcAMNC3H/d2i8PNtmX2zu/7fLEqn+UpK8mpvICLwZl5PWYS5RmpZulmdUfTo164cOGm\nGw0ICLi7qm6DhHf7+8+2M3x3JJeRMf48HNfDZNuVXpuH9Nk8pM+mlVp6htUZ67hQlYdeo2dsUCzj\ng0bh7+PGf46tZ/3Z72g0NjHItz+zwqe26WS3zuSu5zZXm4R3+2tobObVTxI4X1TFz+/rTb/uXibZ\nrvTaPKTP5iF9Nr1mYzOH8o7y7dlNXKqvxMngiJeDO2fLcnAyODIvYibRXr3ULlMVNwpvy1t5XKhG\nr9Pw03t7otdp+HhjKmWVMhGPEKL9aRQNQ/wH8NLg3zIpeBx1jXWcLcvhHp8YXhj0rNUG983IvJji\nKgFejsweHca/t6SzbH0yT8+JQSMnJwohzMBWZ8PkbhMYHjCYZts63IymGf3rjGTPW1xjTL8AokM9\nSMouY+uRXLXLEUJYGRcbZ7p7dlO7DIvWbnvetbW1PPfcc5SUlFBXV8eTTz7J6NGjW+8fM2YMvr6+\naLUtZzW/+eab+PjIFJ2WQFEUHp0Uye8/PMRXuzLp0dWNIB/1l/kTQgjRot3Ce8eOHURFRfHEE09w\n4cIFHnvssavCG2Dp0qU4ODi0VwniLrg4GHhsciTvfHmKJd8m89LD92DQm+7yMSGEEHeu3cJ70qRJ\nrf/Oy8uTveoOKDrUk7H9Atl27Dxf7sjkwQnWOZOREEJYmnY/YW3u3Lnk5+fzr3/965r7fv/733Ph\nwgX69+/Ps88+K7O2WaD7R4eSklPGtmPn6R3qTnSoaVcFEkIIcfvMcp13SkoKv/3tb1m7dm1rQK9Z\ns4YRI0bg4uLCU089xYwZM4iLi7vhNhobm9DpZNhWDVkXL/HMO7txtNPz3q9H4+pko3ZJQghh1dpt\nz/v06dN4eHjg5+dHZGQkTU1NlJaW4uHhAcD06dNbHxsbG0t6evpNw7usrMak9clEC23nqNcwa2Q3\n/rM9gzeWH2HRrOjbGiWRXpuH9Nk8pM/mIX1uYfZJWhISEli2bBkAxcXF1NTU4ObmBkBlZSWPP/44\n9fX1ABw5coTw8PD2KkWYwLgBXegV7MapzBJ2HL/5tLlCCCHaV7uF99y5cyktLWXevHksXLiQl156\niTVr1rBlyxacnJyIjY1lzpw5zJ07F3d395vudQv1aRSFxyb3xNFOz8rtGVworla7JCGEsFoyt7m4\nLUfTivjH14l08XbkhQX3oNfd+vOf9No8pM/mIX02D+lzC5nbXJhE/wgvYvv4k1tYxerdmWqXI4QQ\nVknCW9y2B8aG4+Nuz+bDuSRll6pdjhBCWB0Jb3HbbAxaFk7tiVaj8MG6ZKpqG9QuSQghrIqEt7gj\nIX7OTB8RwqWqej7emEoHOXVCCCE6BQlvccfiB3Uloosrx9KL2HMqT+1yhBDCakh4izum0Sg8MbUn\n9jY6VmxNJ7/UtBPpCCGEuD4Jb3FX3J1tWRAXQX1DM0vWJtHY1Kx2SUII0elJeIu7NjDSh2FRvmTn\nV/LN3iy1yxFCiE5PwluYxLzx3fFytWXDgXOk5ZSpXY4QQnRqEt7CJOxsdDwxtReKorB0XTI1l+Xy\nMSGEaC8S3sJkwgJcmDosmNKKOj7dnCaXjwkhRDuR8BYmNWVoV0IDnDmcUsiBpHy1yxFCiE5JwluY\nlFaj4YmpvbA1aPnsu3QKy2vVLkkIITodCW9hct6udjw0oTuX65tY+m0STXL5mBBCmJRO7QJE5zSk\nly+nMks4nFLIwj9uJdjXiRA/Z0L8nOnq64SNXqt2iUII0WFJeIt2oSgKCyZGAJCaU87hlEIOpxQC\noFEUAr0cCPFvCfNufs74ezqg0ShqliyEEB2GhLdoN/a2ev7ftCg8PR1JySji7MUKsvIqOJtXwbn8\nSnIKq9h14iIANnpty965f0uYh/g54+5sg6JIoAshxI9JeIt2pygKXq52eLnaMainDwCNTc1cKKpu\nDfOsvArSc8tJyy1vfZ6zg6ElyK8EerCfEw62erW+DSGEsBgS3kIVOq2Grr5OdPV1YlTfAABq6xo5\nl195VaCfyCjmREZx6/N83O3p5udEN38XQvyc6eLtiF4n510KIayLhLewGHY2Onp0daNHV7fW28qr\n6si6+N8wz8qr4EBSDQeSCgDQahSCfBxbjp1fOYbu426PRobbhRCdmIS3sGiujjb07e5F3+5eADQb\njRSU1rQeP8/KqyCnoIqsvEq2H7sAtHwICPFzag30bn7OuDjaqPltCCGESUl4iw5Foyj4eTjg5+HA\nsN5+ADQ0NpNTWElW6wlxlSRnl5Gc/d8FUtydba4K866+Ttga5O0vhOiY5LeX6PD0Og2h/i6E+ru0\n3lZ9uaFlz/xiBVl5lZy9eImjaUUcTSsCQFHA39PhqkAP8HJAq5Hj50IIyyfhLTolB1s9USEeRIV4\nAGA0GimpuExWXmXrMfTs/AouFFWz91QeAAadhiBfJ7r5OdMz2J2ewW7otBLmQgjLI+EtrIKiKHi6\n2OHpYseAHt4ANDU3c7G4pmWo/cqQe+aFS2Scv8R3R3Kxs9ERE+ZB/whvokLcMciscEIICyHhLayW\nVqOhi7cjXbwdie3jD0BdfRNZeRUcP1PMsfRCDiQVcCCpAINeQ3S3liCPDvXAzkZ+dIQQ6pHfQEL8\ngI1B23q52tyxYWTnV145Vl5IQloRCWlF6LQaokLc6R/hRUy4p0wcI4QwOwlvIW5AUZTWxVRmjuzG\nhaJqEtIKOZpe1Dp5jFaj0KOrG/0jvOgX7oWzg0HtsoUQVkDCW4g2UBSFQG9HAr0dmT6iG/mlNa17\n40lZpSRllbJ8cxrdA13pF+FF/+5euDvbql22EKKTkvAW4g74utszeUgwk4cEU1xey9H0Io6mF7XO\nz/751jN083emf4QX/SO88Xa1U7tkIUQnIuEtxF3ydLVj4sAgJg4MoryqjmPpLdeTp+WUc/ZiBV/u\nyCTI27E1yP09HdQuWQjRwUl4C2FCro42jOkXyJh+gVTW1HP8TDFH04pIzi4lp7CKr/dk4edh3xLk\n3b0J8nGUZU+FELdNwluIduJkbyC2jz+xffypudzAyYwSjqYXkXi2hHX7z7Fu/zm8XG3p392b/hFe\nhPg7y4IqQog2kfAWwgzsbfUMifJlSJQvdfVNJJ4tISGtkJOZJWw6nMOmwzm4OdnQr7sX90R4ER7o\nikYjQS6EuD4JbyHMzMag5Z4e3tzTw5uGxiaSsso4mlbIiYxith09z7aj53G219O3e8tZ6z26yjSt\nQoirSXgLoSK9TktMuCcx4Z40NjWTmlPGsbQijqUXsevERXaduIi9jY6YcE/6R3gRFeKOXifTtAph\n7SS8hbAQLTO3tSym8tCECM6cL2+Z3S29iP2n89l/Oh8bg5Y+oS3TtPbu5i7LmgphpeQnXwgLpNEo\nRAS5ERHkxtxx4WTlVbRO03o4peWPXveDaVrDPNUuWQhhRhLeQlg4jaK0rld+/6hQcgurSLgS5MfP\nFHP8TMs0rSP7BTJ1SEUKS+EAABThSURBVFdcZIpWITo9CW8hOhBFUQjycSLIx4n7Yrtxsbiao+lF\nHEouYHtCLgcS85g5shujYgLkbHUhOjEJbyE6MH9PB/w9HZg8uCsJZ4r5ZEMKn32Xzp5TeSyYGEGI\nn7PaJQoh2oFcfyJEJ6DRKEwe3o3XnxjEkF4+nMuv5LVPElj+XRrVlxvULk8IYWIS3kJ0Ii6ONjwx\ntRe/eaAvvh727Dh2gd8tOcj+03kYjUa1yxNCmIiEtxCdUGRXN15+bCAzR3bjcn0TH6xL4S8rjnOh\nuFrt0oQQJiDhLUQnpdNqmDwkmNd+Moi+4Z6k5ZazeNlhvtyZQV19k9rlCSHugoS3EJ2cp6sdv5gZ\nzS9nRuPqaMPGgzm88MFBjqUXyVC6EB2UnG0uhJWICfckMtiNdfuz2XQoh7+vTiQ61IMHx3fHy9VO\n7fKEELdBwlsIK2Kj1zJzZChDo3xZvjmNU5klpJw7xJShwcQNDEKvk8E4IToC+UkVwgr5eTjwmwf6\nsnBqT+xsdHy9+ywvLTtMcnap2qUJIdpAwlsIK6UoCoN7+fL6E4MZ2z+QwrIa3vzPCf5vbRLlVXVq\nlyeEuAkZNhfCytnb6nhwfHeG9/bj081pHEou4GRGMTNiuzGmXwBajXzGF8LSyE+lEAKArr5O/G5+\nfxZMjECjKHy+9Qyv/v/27jw4ijrv4/i7Z5IQck0OMiHJJCEkIIGIci+3rID7oCsr6iYiWXcfy2ct\n1npqt9BaFpdFHy3XWLrl41Ee5bEu1EpWUNFH1oPlWOQQkDsSICFg7oPcN8nM88eEKIgXZtKZyedV\nlZpMT0/nO51OPv3r/nX//rqPgpJ6s0sTkYt4rOXd2trK8uXLOXv2LO3t7SxdupQ5c+b0vL5z507+\n8pe/YLVamTVrFr/5zW88VYqIfEcWi8E14+IZPzKaN7bms+NIOY+s/pRZV8dx8+wUQgb7m12iiODB\nlveWLVtIT09nzZo1PPnkkzz66KMXvP7www/z9NNP8/rrr7Njxw7y8/M9VYqIfE9hwQHcef1olt8+\nnrghwWw7WMqKF3ez/XApTl0bLmI6j7W8FyxY0PN9WVkZMTExPc+Lioqw2WzExsYCMHv2bHbt2kVq\naqqnyhGRyzAyIZxVv5rEpn3FbPi4kFc35rlHLJt/BQ57iNnliQxYHu+wlpmZSXl5Oc8//3zPtKqq\nKiIjI3ueR0ZGUlRU5OlSROQy+Fkt/GRKIpPT7Ly+6SSfnqjigVf3Mm+SgxunJzN4kPq9ivQ1j//V\nrV27lmPHjnHffffxzjvvYBjGZS0nIiIIPz9rr9YWHR3aq8uTr6d13Tc8uZ6jo0N5ICWafccqeOGt\nw3ywp4h9x6u4a+GVTBsbe9l/295I23Pf0Hr+eh4L76NHjxIVFUVsbCxpaWl0dXVRU1NDVFQUdrud\n6urqnnkrKiqw2+3fuLza2pZerS86OpSqqsZeXaZcmtZ13+ir9Zw0JIgHfjmJjbvPsHH3GR79217S\nh0dy+7yRxEQEefznm03bc9/Qenb7uh0Yj3VY27dvH6+88goA1dXVtLS0EBERAYDD4aCpqYni4mI6\nOzvZsmUL06dP91QpItLLAvyt/GzmcB66cwpjhkVw9FQNK1/aw4aPCznXqRHLRDzNcHloWKG2tjbu\nv/9+ysrKaGtr45577qGuro7Q0FDmzZvH3r17efzxxwGYP38+d9555zcur7f3wLRX13e0rvuGWevZ\n5XKxN6+S1/91kvqmDuzhg1kyfyTpw6P6vJa+oO25b2g9u31dy9tj4d3bFN7eS+u6b5i9nlvbO9nw\ncSGb9hXjdLmYeEU0t80dSUToINNq8gSz1/NAofXs9nXhrW6iItIrBg/yI/PaEe4Ryz48zr7jVRwp\nrOFnM5K5doIDP6tu6CjSW/TXJCK9KjEmlD8smcAv/2MUfhaDnM35/M9f93KyuM7s0kR8hsJbRHqd\nxTCYdVUcj/zXj5g5Npbiqmb+vGY/r2w8RmNLh9nliXg9hbeIeExoUAC/WpDGiiUTcESH8PHhMla8\nuJttB0t0m1WRH0Ad1sTjtK77Rn9fz11OJ//6tIS3tp+ivaOLlLgwZl0VB4DT5cLpAqfT5f5ydX/1\nPOcS0+l57up+rat7uutL83U5XbhcfPG+i5d7wfOLftYFj+7piUNDuX5KImnDIr/lE8sP0d+3576i\nDmsiYiqrxcL8SQlMGmUnZ/NJ9hyrpKC0wdSaLIaBxeJ+NCwGVsPAYjGwGGBYDCyGgdVi4OdnwbAY\nGMBnhTV8VljDmORIbpmdQtJQ3QVM+p7CW0T6VEToIO5emM7cCfWUnW3uDksDw+IOeIvxRZieD0+L\n4R6u1OgJV/d0o3u65fz08/N+ab4vHi+cbhhc1i1d69o6eentI+QW1pBbWMOU0THcNDMZ+wC4u5z0\nHwpvETFFqsNGqsNmdhnf24iECO7NHEduYQ3rthbwyWcV7MurZPbVcfx0ejK24ACzS5QBQOEtInIZ\nxiRHkjYsgn15lbz571Ns3l/CjiPlzJ+UwE+mJGq0NfEobV0iIpfJYhhMToth/Mhoth8qZcOO07y7\n8zRbDpRww7RhzBkXj7+fLuqR3qetSkTkB/KzWpgz3kH2r6dy06zhdDmdrP3XSVa8uJsdR8pwOr3i\noh7xIgpvEZFeMijAyk+nDePRX09l/qQE6pvbefm9Yzzw6h4O5VfjJVfmihfQYXMRkV4WGhRA5rUj\nmDvRwYbthew8Ws7/rjvMSIeNW+akkhrvfR31pH9Ry1tExEOG2AZz5w2jefDOyVydOoQTxfU8svpT\nnl5/mJLqZrPLEy+mlreIiIc5okP471vGcqKojnXbCjhwspqD+dVMT4/lZzOTiQwLNLtE8TIKbxGR\nPjIyIZw/3D6eQ/lnWb+tgI+PlLH7swqunRDP9VOHETLY3+wSxUsovEVE+pBhGFw9YghjU6LYlVvO\n29tP8cGeIv59qIwFP0pk7sQEBvlbzS5T+jmFt4iICSwWg+lXxjI5zc6W/SW8u/M067edYtOnxSyc\nnsyMsbH4WdUtSS5NW4aIiIn8/azMn5xI9t3TuGFaEq3tnfztg+OsfOkT9uZV6vIyuSS1vEVE+oGg\nQD8WzUrhx+MdvLvjNP8+VMpzbx9l2NBQbrkmhdEaglS+RC1vEZF+JDxkEFnXXcHDd01hcpqd0+WN\nPL72IE+sPcCZco1vLW5qeYuI9EMxEUHcvTCdn0xpYP3WAnJP15L7171MTrNz06zhxGgI0gFN4S0i\n0o8NGxrGssxx5J52D0G651glnx6vYtZVcdw4fRi2kEFmlygmUHiLiHiBMcMiSbvjiyFItxwoYcfR\nMvcQpJOTCArUv/OBRL9tEREvccEQpIfLeOfjQv5v5xm2HijlhqlJzBkfj7+frhEfCNRhTUTEy/hZ\nLcwZF8+jv57KovNDkG7O1xCkA4jCW0TESw0KsHLDtGFk3z2N6yYnUN98jpffO8aqV/dw8KSGIPVl\nOmwuIuLlQgb7k/HjEcydkMCGjwvZcbSMp9YfJtVh49ZrUhjhCDe7ROllCm8RER8RZQvkP69P47rJ\nCazfdoqD+dX8ec1+RiWGMyoxghSHjeGxYQwepH/93k6/QRERHxPfPQTpyeI61m87Rd7ndeR9XgeA\n0f16anwYKfE2UuNt2CMGYxiGuUXL96LwFhHxUSMc4Sy/fTwNzR0UlNaTX1JPQUkDp8saKK5qYuvB\nUsB92D013kZKfBgpcTaSY8MYFKBe699FS1snJdVNlFQ143K5uGZcfJ/sCCm8RUR8XFhwAONGRDNu\nRDQAnV1OiiqbKCj5ItAP5ldzML8acF+SlmAPISU+rDvUbQyxBQ7o1vm5TidlZ5spqWqmuDusi6ua\nqGlo75nHMGDCFXbCggM8Xo/CW0RkgPGzWkiODSM5Noy5ExMAqG1sp6CkvqeFfqa8kTMVjWzeXwKA\nLTiAlO7WeWq8jWFDQ33ymnKny0V1XSvFVc2UVDVR3B3SFTWtOC/qvW8LDmDMsAjio0OIjw4mNd7W\nJ8ENCm8REQEiQgcxcZSdiaPsgLul+XlFY3fL3B3o+09Usf9EFQBWi0FiTGjP4fbUeBuRYYFmfoTv\nxeVy0dByjuKqJkoqmyiudod1SXUzHeecF8wbGGAlOS4UR3QIjugQ4ocEEx8dTGhQ3wT1pSi8RUTk\nK/z9LN0tbRvgDruahnZ3y7zY3UL/vKKRwrIGPtrnfk9E6KCeTnAp8WEkxYTiZzX/diKt7Z2UVrtb\n0F9uUTe1nrtgPqvFIDYqGEe0O5wd3S3qqLD+d8pA4S0iIt/KMAyibIFE2QKZnBYDQMe5Lk6XN37p\n3Hk9+/Iq2ZdXCbgPzw+LDSU1ztYd6mEeHUils8tJeU2LuzVd1dxzXrq6vu0r80aHBzLCYfsipIcE\nExMZ1C92Nr4LhbeIiFyWAH8rIxPCGZngvgmMy+Wiqr6NguJ68kvdYV5Q4m6pnzfEFtjTCS413obD\nHozV8v0C0+lycba+rSeci7sPd5efbaHrolvDhgX5k5YU0dOKdkSHEDckiMAA744/765eRET6DcMw\nsIcPxh4+mKnpQwFo6+iksOyLc+cFJfXs/qyC3Z9VABDgbyF5aBipDhspce7D7V8+l9zQ0nHhOemq\nZoqrm2nv6LrgZw/yt5I0NLT7kHcIjiHux77qQNbXFN4iIuIxgQF+pCVFkJYUAbhb5+U1LRSUNPT0\nbD9RVMfxorqe98REDCYmKpjTZQ00NHdcsDyrxWBoZNAF56Qd0SFE2QKx9LPz0p6k8BYRkT5jGO5O\nYbFRwcwYGwu4b3Ryqsx9vbn7crUGKmqrGWIL5KqUKBz27pAeEsLQKO85L+1JCm8RETFVUKAf6clR\npCdHAe5z2rbwIBrrW02urP/S7ouIiPQrFsPw+g5lnqbwFhER8TIKbxERES+j8BYREfEyCm8REREv\no/AWERHxMgpvERERL6PwFhER8TIKbxERES+j8BYREfEyCm8REREvo/AWERHxMobL5XJ9+2wiIiLS\nX6jlLSIi4mUU3iIiIl5G4S0iIuJlFN4iIiJeRuEtIiLiZRTeIiIiXmZAhvcjjzxCRkYGmZmZHD58\n2OxyfNZjjz1GRkYGN998Mx9++KHZ5fi0trY25s6dy5tvvml2KT7tnXfe4cYbb2TRokVs3brV7HJ8\nUnNzM/fccw9ZWVlkZmayfft2s0vql/zMLqCv7dmzhzNnzpCTk0NBQQErVqwgJyfH7LJ8zu7duzl5\n8iQ5OTnU1tZy0003MX/+fLPL8lnPPfccNpvN7DJ8Wm1tLc8++yzr16+npaWFp59+mmuuucbssnzO\nW2+9RXJyMsuWLaOiooI77riD999/3+yy+p0BF967du1i7ty5AKSkpFBfX09TUxMhISEmV+ZbJk2a\nxNixYwEICwujtbWVrq4urFaryZX5noKCAvLz8xUkHrZr1y6mTp1KSEgIISEhPPTQQ2aX5JMiIiI4\nfvw4AA0NDURERJhcUf804A6bV1dXX7AxREZGUlVVZWJFvslqtRIUFATAunXrmDVrloLbQ7Kzs1m+\nfLnZZfi84uJi2trauPvuu1m8eDG7du0yuySfdP3111NaWsq8efNYsmQJv//9780uqV8acC3vi+nu\nsJ61adMm1q1bxyuvvGJ2KT7p7bff5uqrryYhIcHsUgaEuro6nnnmGUpLS/nFL37Bli1bMAzD7LJ8\nyoYNG4iLi+Pll18mLy+PFStWqC/HJQy48Lbb7VRXV/c8r6ysJDo62sSKfNf27dt5/vnneemllwgN\nDTW7HJ+0detWioqK2Lp1K+Xl5QQEBDB06FCmTZtmdmk+JyoqinHjxuHn50diYiLBwcHU1NQQFRVl\ndmk+Zf/+/cyYMQOAUaNGUVlZqVNulzDgDptPnz6dDz74AIDc3FzsdrvOd3tAY2Mjjz32GC+88ALh\n4eFml+OznnzySdavX88//vEPbr31VpYuXarg9pAZM2awe/dunE4ntbW1tLS06HysByQlJXHo0CEA\nSkpKCA4OVnBfwoBreY8fP54xY8aQmZmJYRisWrXK7JJ80saNG6mtreW3v/1tz7Ts7Gzi4uJMrErk\n8sXExHDdddfx85//HIA//vGPWCwDrv3jcRkZGaxYsYIlS5bQ2dnJAw88YHZJ/ZKGBBUREfEy2m0U\nERHxMgpvERERL6PwFhER8TIKbxERES+j8BYREfEyCm8RH1ZcXEx6ejpZWVk9ozQtW7aMhoaG77yM\nrKwsurq6vvP8t912G5988snllCsi35HCW8THRUZGsnr1alavXs3atWux2+0899xz3/n9q1ev1k0y\nRPqZAXeTFpGBbtKkSeTk5JCXl0d2djadnZ2cO3eOP/3pT4wePZqsrCxGjRrFsWPHeO211xg9ejS5\nubl0dHSwcuVKysvL6ezsZOHChSxevJjW1lZ+97vfUVtbS1JSEu3t7QBUVFRw7733Au7xxjMyMrjl\nllvM/OgiPkPhLTKAdHV18dFHHzFhwgTuu+8+nn32WRITE78yAERQUBBr1qy54L2rV68mLCyMJ554\ngra2NhYsWMDMmTPZuXMngYGB5OTkUFlZybXXXgvAP//5T4YPH86DDz5Ie3s7b7zxRp9/XhFfpfAW\n8XE1NTVkZWUB4HQ6mThxIjfffDNPPfUU999/f898TU1NOJ1OwH0b4YsdOnSIRYsWARAYGEh6ejq5\nubmcOHGCCRMmAO6Bf4YPHw7AzJkz+fvf/87y5cuZPXs2GRkZHv2cIgOJwlvEx50/5/1ljY2N+Pv7\nf2X6ef7+/l+ZdvHQly6XC8MwcLlcF9zj+/wOQEpKCu+99x579+7l/fff57XXXmPt2rU/9OOICOqw\nJjIghYaG4nA42LZtGwCFhYU888wz3/ieq666iu3btwPQ0tJCbm4uY8aMISUlhQMHDgBQVlZGYWEh\nAO+++y5Hjhxh2rRprFq1irKyMjo7Oz34qUQGDrW8RQao7OxsHn74YV588UU6OztZvnz5N86flZXF\nypUruf322+no6GDp0qU4HA4WLlzI5s2bWbx4MQ6HgyuvvBKA1NRUVq1aRUBAAC6Xi7vuugs/P/3L\nEekNGlVMRETEy+iwuYiIiJdReIuIiHgZhbeIiIiXUXiLiIh4GYW3iIiIl1F4i4iIeBmFt4iIiJdR\neIuIiHiZ/weSf8DTpBa2WQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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8/OQoR0RklrQytMXrSIiIVItBQkREErBHQkREkghW8gVJYmIijh8/DkEQEB0dDX9/f8Nz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\nCRERScIgISIiSRgkREQkCYOEiIgk+X/ZJRZnsy7BsAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "266KQvZoMxMv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "lRWcn24DM3qa",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Here is a set of parameters that should attain roughly 0.9 accuracy."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "TGlBMrUoM1K_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        },
+        "outputId": "d886fb46-8682-4187-f49c-43fecc0c704d"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_linear_classification_model(\n",
+        "    learning_rate=0.03,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.93\n",
+            "  period 01 : 4.31\n",
+            "  period 02 : 4.17\n",
+            "  period 03 : 3.95\n",
+            "  period 04 : 3.77\n",
+            "  period 05 : 3.79\n",
+            "  period 06 : 3.88\n",
+            "  period 07 : 3.74\n",
+            "  period 08 : 3.63\n",
+            "  period 09 : 3.59\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.90\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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G9sPdRcfa/edobu1QOxyHY9DqmRQ+jtfnL2dW1FRqW+t47/j/8vqhv1HUUKx2\neEII4bCkeF+Gu6ueucn9aGhuZ1N6gdrhOCx3vRuL4m7gqXGPkhAQz5maXH6X9hofn/qChvZGtcMT\nQgiHI8X7CmaN6YeHq471aQU0tbSrHY5DC3EP4icj7+WhkfcS7B7IjqK9PLv3D2wr3E2nsVPt8IQQ\nwmFI8b4CNxcdqeP709TawYYD0vq2hmEB8fwq+RfcEncjRpOJT09/ye8OvEZW1Rm1QxNCCIcgxbsH\nZiZF4uWuZ8OBAhqapfVtDTqNjplRKTwz4XEmhiVT0ljGG4ff5Z1j/6SiuUrt8IQQoleT4t0DLgYt\n88f3p6Wtk/Vp59QOp0/xNnjx/SG38cSYnzLAJ5oj5cd5fv8rfJWzjpaOVrXDE0KIXkmKdw9NHxWB\nj6eBTemF1MnyBauL8o7k0aSf8KOh38NT78G6/C08v/8V0koycJCtCIQQwm6kePeQQa/lxgnRtLZ3\nsm6ftL5tQVEUxoaO4jfjH2de9Ewa2hv5IPMjXs14k/w6mW8ghBDfkOJ9FVJGhuHn5cKWjEJqG6RL\n11ZctAYWDJjL0+N+SWLQcHJr83k5/S/878lPqWuTHZeEEEKK91XQ67QsmBhNW4eRr/flqx1Onxfo\n5s/9w5fws8QHCPMIYW/xAZ7d+zKbzm2nwyib5gghnJcU76s0eUQYgT6ubDt0nqq6FrXDcQqD/eNY\nNvYR7hi0EI2i8EX21/w27VWOV5xUOzQhhFCFFO+rpNNqWDAxmo5OaX3bk1ajZWrkRJ6Z8AQpERMp\nb6rkraPv8+aRv1PaVK52eEIIYVdSvK/BhIRQgn3d2HH4PBW1zWqH41Q89R7cOXghTyb/nEG+sZyo\nzOK3+19lxZnVNHfI/wshhHOQ4n0NdFoNN02OptNoYvUeaX2rIcIzjJ+NeoD7E5bg6+LN5oIdPLv3\nZfacP4DRZFQ7PCGEsCkp3tdo/NBQQv3d2X2smLIaafGpQVEUEoOH8+txv+TGmLm0drbyf1mf8nL6\nG+TW5qkdnhBC2IxNi3dLSwuzZs1ixYoVl9y+Z88ebrvtNu68807++te/2jIEm9FoFG6eHEOn0cRX\nu8+qHY5TM2j1pMbM5DfjH2dMSCLn6ov448E3+ceJD6lprVU7PCGEsDqbFu+33noLHx+f79z+wgsv\n8MYbb/Dhhx+ye/dusrOzbRmGzYwdEkxEoAd7jpdQUtWkdjhOz8/Vl3uG3cWjSQ/RzyuCA6WHeHbv\nH1iXt5n2TtmTXgjRd9iseOfk5JCdnc20adMuub2goAAfHx/CwsLQaDRMnTqVvXv32ioMm9Io5ta3\nyQSrdknru7eI9Y3miTE/5fvdotpAAAAgAElEQVTxt2HQGvgqdz3P7/8jh8uPy1arQog+wWbF+6WX\nXmLZsmXfub28vBx/f3/L9/7+/pSXO+5Sn6TBQUQFe7I/s5Siika1wxEXaBQNE8OTWT7hCWb0m0J1\naw3vHvsnbxx+l/MNJWqHJ4QQ10Vni4uuXLmSxMRE+vXrZ7Vr+vm5o9NprXY9gKAgL6tc5+4bhvLC\n+2msO1DAsrvHWuWafY21cn31vHgw7C5uqpvBB4c/41DxCX534DXmxKZwR8KNeLp4qBSXbaiXZ+ci\nebYPyXP3bFK8t23bRkFBAdu2baOkpASDwUBoaCgTJ04kODiYiooKy2NLS0sJDg6+4jWrq607phwU\n5EV5uXX2yY4J9iA61IvdR86TcaKYfsGeVrluX2HNXF8rPR78eMgPOR50ks/PfMW67G3szE/jxpi5\nTApPRqux7gdDNfSGPDsDybN9SJ7NuvsAY5Nu89dee43PP/+cTz75hNtvv52HHnqIiRMnAhAZGUlD\nQwOFhYV0dHSwdetWJk2aZIsw7EZRFBZOGQDAyp25KkcjLichcAhPjXuURXE30Gns5OPTX/BS+p85\nXZ2jdmhCCNFjNml5d2XFihV4eXkxe/Zsli9fzmOPPQbA/PnziYmJsVcYNjN8gD+xEd4cOlNBXkkd\n0aHeaockuqHT6JgVNZWxIUmsyl3LvuJ0Xj/0N0YFDWdR3I0EuPmpHaIQQlyWYnKQ6bfW7j6xRZfM\nibwq/vjRYUbEBvDz20da9dqOrLd3f+XXFfDp6S85W3cO/YXCPrv/dFy0BrVDuyq9Pc99heTZPiTP\nZnbtNndWQ/v7MaifL0dzKskpks1BHEV/7348Ovohfjh0Me46d9bmbebpPS+y9uwmmtpl/b4QoveR\n4m1FiqKwaIp5CGClrPt2KBpFQ3JoEr8Z/zjzo2dhMplYfXYDv97zIiuyV8tObUKIXqXHxbuhoQGA\niooK0tPTMRrl8IeuDI7yY0h/P06creJ0QY3a4Yir5Kpz4YYBc3h+4pMsirsBV60Lm8/t4Jk9v+ff\nWZ9R1lRx5YsIIYSNaZcvX778Sg96/vnnqampISIigjvuuIPi4mL27dvH9OnT7RCiWVNTm1Wv5+Hh\nYvVrfiPEz52dR4upqG1m0vAwm7yGI7Flrm1Fp9ExwCealMhJ+Lv4cr6xhKzqbHYU7qGksYxAt0B8\nXHrXGlRHzLMjkjzbh+TZzMPDpcvbe9TyzszM5Pbbb2ft2rUsWrSI119/nfx8OQqzO3GRPiQM8Cfr\nXA0n86vVDkdcB71Gx6SIcfxm/OPcO+wuIjzDOFh2hN8feI2/HnmP7BoZHhFC2F+Plop9MyF927Zt\n/PznPwegrU0+EV3OoikDOJ5bxcqducRHJaEoitohieugUTSMDkkkKXgkmVWn2ZC/hczKU2RWnmKA\nTzRz+k8jIWCI/H8WQthFj4p3TEwM8+fPx9/fnyFDhrBy5couTwsT/xET5k1iXCCHsys4kVdFQkyA\n2iEJK1AUhWEBgxkWMJjc2jzW523leOVJ3j76D8I9QpnTfzpJwSP6xI5tQojeq0frvDs7Ozl9+jSx\nsbEYDAZOnDhBv3798Pa230YkjrDO+9vyS+p59h8HGBDuzVNLRjttq6yvr9csaihmY/42DpYdwWgy\nEujqz6z+UxkfOga9Vm+3OPp6nnsLybN9SJ7Nulvn3aMJa5mZmZSVlREXF8ef/vQnPv/8c+Li4ggP\nD7d2nN1ypAlr3/D1dKGwrIHMvGpiwrwJ9Xe36ev1Vn194om3wYvE4OEkh46i02Qku/YsRysy2V2c\nhtFkJNwzDL3G9psZ9vU8q8FkMlHaVEZG2VE25m/j49MrWXN6C9UttXgZPPE2eDnth3Jbk/ezWXcT\n1nrU8l68eDG///3vqaio4M033+RXv/oVzz33HP/85z+tHmh3HLHlDVBY3sAz76URFeLFb340xil/\n0Z3tE3Rtaz1bC3ays2gvLZ2tuOlcSYmYyPR+k/Ey2O7QGmfLsy2YTCbKmys4XZ3D6eocztTkUtf2\nn5z6uvjQYeqgoc18/G+YRwjJoUmMDRmFn6uvWmH3SfJ+Nuuu5d2j5oCLiwvR0dF8/PHH3HHHHcTF\nxaHRyP4uPREZ5MnYIcGknSzj0JkKkgYFqR2SsDEfFy8Wxs1nTv/p7Czay5aCnazP38KWgh1MDE9m\nZr+psn96L1LZXMUpS7HOuWRDHm+DF2NCEhnkG8tAv1iC3ALwD3Bn26kDpJUc4nhFJl/mrGVVzjoG\n+sWSHJrEqKAEXHWuKv5Ewhn0qHg3Nzezdu1aNm3axNKlS6mpqaGurs7WsfUZN0+O4UBWGSt35pI4\nMBCNE7a+nZG73o250TOY3m8Ke4sPsOncdrYX7mFn0T7GhCQyp/90wjxC1A7T6VS31Jhb1jU5nKnO\nobLlP8s5PfUejAoewSDfWAb5xRLiHvSd3jKdVsfIoARGBiXQ1N5ERtlR0koyOF2dzenqbD4+9QUj\ng4aRHJpEvN9AmbwobKJH3eb79u3jn//8JwsWLCA1NZU33niD/v37c9NNN9kjRsBxu82/8e5Xmew9\nUcJPFiYwNv7K55f3JdL9ZdZp7CS99DAbzm2jpLEUgBGBw5jTfzoxPlHXfX3Jc9dqW+s5U53N6Rpz\n67q8udJyn7vOjYG+AxjoF8tgvzhCPYLRKJfvVewuzxXNlRwoOURaSQZlzead+LwMnowJSSQ5NIl+\nnhFOOWx2reT9bNZdt3mPTxVramri7NmzKIpCTEwMbm5uVg3wShy9eJdWN/HUO/sJ8Xfj+fvGodE4\nzy+x/BJeymgycqziJBvyt5JXdw6AQb6xzImeTrzfwGv+Ay95Nqtva+BMTa5l3Lq0qcxyn6vWlTjf\nGAb5mVvWEZ5hVyzW33alPJtMJvLqCkgryeBg2WEaLxxuE+oRwriQJMaGyvh4T8j72ey6ivemTZtY\nvnw5oaGhGI1GKioqeP7555k6darVA+2OoxdvgL+vOcmuo8U8sGAo44eF2vW11SS/hF0zmUycqcll\nQ/5WTladBiDKK4LZ/aeTGJRg9aLSVzW2N1mK9ZnqHM43lljuM2gNxPn8p1hHeoZfdzf21eS5w9hB\nZuUp0koyOFZ5kg5jBwoKA30HkByaRGLwcNxkfLxLzvp+/rbrKt6LFy/mzTffxN/fH4DS0lIeeeQR\nPvroI+tGeRl9oXiX1zTzq3f2Eejjygv3j0PrJJP+5Jfwys7VFbIhfyuHy49jwkSIexCzoqaRHDoK\nXQ+XmTlLnps7msmuOWsp1oUNxZgw/xnTa/TE+kQz8EKx7u8VafUx52vNc1N7M4fKjrK/JIOc2rMX\n4tUxItA8Pj7Ef5CMj1/EWd7PV3Jds831er2lcAOEhISg19tv84m+IsjXjSkjwth2+Dx7j5cyeYQc\nWiLMorwj+fHwJZQ2lrHx3HbSSjL4v6xP+frsBmZGpTApfBwuWoPaYaqipaOVnNo8zlzoBj9XX2gp\n1jpFS5xvjGXMur93P7usqb8W7no3JkWMY1LEOCqaq8zj46UHOVh2hINlR/DSXzQ+7iXj4+LyetTy\nfvDBB0lOTmbixIkA7Nq1i/T0dN5++22bB/iNvtDyBqiqa2HZ3/bi6+nCiw+MR6ft+61v+QR99apb\nathSsJNdRftoM7bjoXdnWuQkpkZOwkPf9WY/fSXPbZ1t5NbmW8as8+sLMJrMRxBrFA3R3lHmbnDf\nWGJ8+mOw4y52YN08m0wm8usvjI+XHqGh3bx+PNQ9mLGhSSSHjsLf1TmXFfaV9/P1uq5u88rKSl5/\n/XWOHj2KoigkJiby05/+9JLWuK31leIN8H8bTrM5o5AfzhvM1MQIVWKwJ/klvHYN7Y1sL9jNtsLd\nNHU0Y9AamBw+jplRKfi6XHq+gKPmud3YQV5tvmWtdX7dOTpMnYC5WEd5RVqK9QDfaNV7IGyV505j\nJ5lVp9hfksGxikw6jB0AlvHxUcHDcdPZd6Kwmhz1/Wxt1z3b/NtycnKIjY29rqCuRl8q3tX1rSz7\n21683fW8+MAE9Lq+3fqWX8Lr19LRyu7z+9l8bge1bXXoFC3JoaOZ3X8qwe7mjX/UyLPRZKSts512\n44V/ne20Xfj6m9u//d9vvm4ztlHUUMLZ2jzaLxQqBYV+XuHmMWvfWGJ9Y3rdhC575LmpvZnD5cdI\nK8ngTE0uYB4fHx44lOTQJIb6D+7z4+Pyd8PM6sX77rvvlu1Rr8NHm8+w4UABP5gziBlJkarFYQ9q\n57ovaTd2cKAkg4352yhrrkBBYVTwcPNpZgPiKSuro8PUSXuXBbONNmOH5Wtzse2gzdh24fEd5sLb\n2XahAHd087gLBbqz3dJCvh4RnmGWHcwG+sbg3s2wQG9h7/dzZXMVB0rN68dLm8oB82Yyo0MSGRea\nRJRXZJ8cH5e/G2ZWL95LlizhX//613UFdTX6WvGubWzjv9/eg7uLjpcenIBe13c/Raud677IaDJy\nuPw4G/K2UNBwHgAXnQttHW2WyVzWpNfoMWj06LV69BodBq0BvebbX+sxaLt7nPm+b18nwNUfT4OH\n1eO1JbXezyaTiXP1hewvyeBg6WHL+HiIe9CF/dWT+tS2u/J3w+y6Zpt3pS9+0rMnHw8DM5MiWbv/\nHNsOnWf22H5qhyQciEbRkBQ8glFBwzlZdZqthbtoMTajGDUXCqUBvUaHXqvHoDGg1+rMRfPi+y76\n2lxYDRf+e/F95u/l9119iqLQ37sf/b37cWvcjWRWneJAySGOVpzgq9z1fJW7njjfmAv7q4/AXe88\n4+PO6LLF+7PPPuv2vvLycqsH42zmjYtiy6Eivt6XT0piOC76vtv6FrahKApDAwYzNGCwtFSciFaj\nZXjgUIYHDqW5o5lDZcdJKznImZpcsmvO8snpLxkeOJRxTjI+7owuW7wPHjzY7X2JiYlWD8bZeLkb\nmD0mktV78tmaUcS8cde/v7UQwrm46dyYGD6WieFjqWqptuyvfqjsKIfKjuKp9yAxeDjDA4YwyC8W\ng5PuF9DXXPOYt731tTHvbzS2tPPEW3vQajS89OAE3Fx65wYT16O35LqvkzzbhyPk2WQyUVBfRFpJ\nBumlh6lvbwDMM9YH+sUyLCCehIAhBLrZb7nv1XKEPNvDdY1533XXXd8Z89JqtcTExPDQQw8REiLH\nGl4rD1c9c8ZG8eWus2zJKOSGCdFqhySEcHCKohDlHUmUdySL4m4gtzafE5VZnKjMIrPyFJmVp/iU\nLwlxDyYhIJ5hAfHE+kb3eCteob4etbz/8pe/cPbsWebOnYtGo2HTpk2EhYXh4+PDjh07+Pvf/27z\nQPtqyxugqaWD/357DwAvPTgRd9e+9QvUm3Ldl0me7cPR81zVUs2JylOcqDzJqaps2oztALhqXYj3\nH8iwgHiGBgz+ziZA9uboebaW62p5Hzx4kPfff9/y/axZs3jggQd455132Lx5s3UidGLurjrmJkex\nYkcum9ILuGlyjNohCSH6KH9XP6ZEjGdKxHjaO9vJrjnL8cqTHK/M4nD5cQ6XHwegn2c4wwLiGRY4\nhGjvfld9yp2wrR4V78rKSqqqqizbodbX13P+/Hnq6uqor5dPRtYwc3QkGw4UsP5AATPHROLhKge/\nCCFsS6/VMyRgEEMCBnE7N1PWVM7xyixOVGSRXZNLQcN51uVvwUPnzpCAQSQEDGFIwCA89Y61Nr8v\n6lHxvvvuu0lNTSUiwnzSTWFhIf/1X//F1q1bufPOO20do1Nwc9GROj6KT7fmsD6tgFtSBqgdkhDC\nyQS7BzHDPYgZ/abQ0tHKqepsy1h5eulh0ksPo6AQ7R1FQqB5rDzSM1z2AVBBj2ebNzQ0kJeXh9Fo\nJCoqCl9fX1vHdom+POb9jdb2Tv777b20tnfyhwcn4OXeN5Z09MZc90WSZ/twxjybTCbON5ZwoiKL\n45Unya3Nt+zk52PwMnevB8QT7z8QVyvtRe+Mee7KdY15NzY28sEHH3Ds2DHLqWI//OEPcXXtXQcG\nODoXvZYbxvfnw81nWJd2jtunxakdkhBCoCgKEZ5hRHiGMSd6Ok3tTZysOs3xC7PX9xQfYE/xAbSK\nlljfGIYFDCYhYAgh7kHSKreRHrW8H330UUJCQhg3bhwmk4k9e/ZQXV3NK6+8Yo8YAedoeQO0d5hb\n302tHbz04ER8PBy/9d1bc93XSJ7tQ/J8KaPJSH5doaV7/Vx9oeW+AFd/85rywHgG+sZe1dnrkmez\n62p5V1RU8Oqrr1q+nz59OkuWLLFOZOISep2WGydG878bTrN2Xz6LZw5UOyQhhOiWRtEQ4xNFjE8U\nNw6YQ11b/YWlaFmcrDzNjqI97Cjag16jY5BfnGVdeUAv3iDGEfSoeDc3N9Pc3Iybm3mj+6amJlpb\nW20amDObMiKcNfvy2XqoiLnJUfh5uagdkhBC9Ii3wYsJYWOYEDaGTmMnubV5nKg8xfHKk5bWOUCo\nR8iF7vV4Yn1iZP/1q9Sj4n3nnXeSmppKQkICACdOnOCRRx6xaWDOTK/TsGBiNB+sO8Wavfl8f84g\ntUMSQoirptVozee0+8WyMG4+lc3VZFZlcbwii1PV2Ww+t4PN53bgqnUl3n8gCRc2iPFx8VY79F6v\nx7PNi4uLOXHiBIqikJCQwL/+9S9++ctf2jo+C2cZ8/5GR6eRX72zj5qGVn7/XxPw93bcyYG9Pdd9\nheTZPiTP1tHe2c6ZmtwL68pPUtFSZbmvn1cEieFDcDV54Ofig6+LD74uvngZPJxus5jrPs87LCyM\nsLAwy/dHjx69/qhEt3RaDTdNiuHva06yek8ed8+LVzskIYSwGr1WbznO1jTwJsqayi90q5/iTE0u\nBaeKvvMcjaK5UMh98HPxwcfF21zcXX0tt3kbvJyiC/6aN9F2kMPIHNqEhBC+3pvHzqPFzB/fn0Bf\nN7VDEkIIq1MUhRCPYEI8gpkRlUJLRwuNulrySoupbq2lprWWmpZaalrrqGmtJa/uHLkmY9fXQsHb\n4IWv6zcF3uei1rsPfq4++Bi80V/FzPfe6JqLt6zdsz2tRsNNk2N496tMVu3J4975Q9QOSQghbM5V\n50q/oCAC6PrESqPJSF1bvaWoWwr8RYW+qP48+XUF3b6Gp97DUty/KfSXtup9cNX13snCly3eU6dO\n7bJIm0wmqqurbRaU+I9xQ0JYvSePPcdKuGFCf0L83NUOSQghVHVx9zndzG0zmUw0tDdaCnp1y7cK\nfGstpU3lFDSc7/Z13HRul3bPXyj0vi6+lu/ddK6qNGYvW7z//e9/2ysO0Q2NRuHmyTG8/eUJVu3K\n4/4FQ9UOSQghej1FUfAyeOJl8KSfV0SXjzGZTDR3tJiLe2stNa015q75iwp9dWst5xtLun0dg9aA\nr4s3vi6+RHqGsTB2vl3G3C9bvCMiuv6BhX2NiQ8mck8e+zJLmJvcj6iQrmcfCiGE6DlFUXDXu+Gu\ndyPcM7Tbx7V0tFJ7oZDXttZd1E1fY+m2L2uqIK82n7nRM/DU2P7UtWse8xb2o1EUbkmJ5c+fH+V3\n/5vBXbMGMnlEmMw7EEIIO3DVueCqM0+o6057ZzsmTBi09tnS2rkWzDmwxIGBPHjzMDQahffXZvHW\nyuM0trSrHZYQQgjMS9/sVbhBWt4OJXlICAPCvfmfrzJJP1VOzvk67r9xKPH9/dQOTQghhB1Jy9vB\nBPq48cRdSSyaEkNtQxsvf3iIz7bl0NHZ9ZpHIYQQfY8Ubwek0SgsmBTDk0uSCPR1Zc2+fF7810FK\nq5rUDk0IIYQdSPF2YLHhPiy/J5lJCaHkldSz/P0D7DxyXna/E0KIPk6Kt4Nzc9Fx341DvzOZraFZ\nJrMJIURfZbMJa83NzSxbtozKykpaW1t56KGHmD59uuX+GTNmEBoailZrXsz+yiuvEBLS9VZ44spk\nMpsQQjgPmxXvrVu3kpCQwP33309RURH33nvvJcUb4N1338XDw/aL2Z3FN5PZvt6bx5e78nj5w0Ok\nju/Pwikx6LTSySKEEH2FzYr3/PnzLV8XFxdLq9pOvpnMNjTGn3dWnWDNvnwy86r4r5uGEeIv+6IL\nIURfoJhsPLtp8eLFlJSU8PbbbxMf/58zqWfMmEFSUhJFRUWMHj2axx577LI7hnV0dKLT9f0zWq2p\nqaWdv31xjC3pBbgatDywcDizkqNkZzYhhHBwNi/eACdPnuSJJ55g1apVlsKxcuVKpkyZgo+PD0uX\nLmXRokXMmzev22uUl9dbNaagIC+rX7O3SjtZygfrTtHc2sHowUH8cF48nm72O8vWmXKtJsmzfUie\n7UPybBYU1PVZFjYbCD1+/DjFxcUADBkyhM7OTqqqqiz3L1y4kICAAHQ6HSkpKZw+fdpWoTi95CEh\nPHvvWAZF+nDwVDnP/D2NrHw50lUIIRyVzYp3eno6f//73wGoqKigqakJPz/zzOf6+nruu+8+2tra\nADhw4AADBw60VSiCi3ZmSxkgO7MJIYSDs1m3eUtLC0899RTFxcW0tLTw8MMPU1NTg5eXF7Nnz+aD\nDz5g5cqVuLi4MHToUJ5++unLjsVKt7n15Jyv5Z1VJyivaSE61Mvmk9mcOdf2JHm2D8mzfUiezbrr\nNrfLmLc1SPG2rubWDv698TS7j5fgotfa9JhRZ8+1vUie7UPybB+SZzO7j3mL3q2rndnelJ3ZhBDC\nIciRoE7u4p3ZDp4qJ1d2ZhNCiF5PWt5CJrMJIYSDkeItgAs7s02M/s4xoyVyzKgQQvQ6UrzFJb57\nzGgaO+SYUSGE6FWkeIvvuHgym1aj4R8ymU0IIXoVmbAmupU8JITYcB/e/eqEZTLbj28cyhCZzCaE\nEKqSlre4rAAf10sms73y4SE+3ZYtk9mEEEJFUrzFFV08mS3I1421+87xW5nMJoQQqpHiLXosNtyH\nZ+4Zy6ThoeTLZDYhhFCNFG9xVdxcdNx3g0xmE0IINcmENXFNZDKbEEKoR1re4prJZDYhhFCHFG9x\nXWQymxBC2J8Ub2EVMplNCCHsR4q3sJruJrMVlTeoHZoQQvQpMmFNWN23J7Md/P1mgv3cGDEggBGx\nAQyO8kWv06odphBCOCzF5CD9muXl9Va9XlCQl9WvKS5lNJrYe6KEzHM1ZJwqo7WtEwCDXsPQ/v4M\njw1gxIAAAnxcVY60b5D3tH1Inu1D8mwWFOTV5e3S8hY2o9EoTBoexsIZgyguqeVMQQ1Hcys5mlPJ\n4ewKDmdXABAR5MGIC4U8NsIHnVZGc4QQ4nKk5S1srqtcl9U0cyzHXMizzlXT3mFeXubmoiMhxp8R\nsQEMHxCAt4dBjZAdkryn7UPybB+SZzNpeYteJdjXjZmjI5k5OpLW9k6y8qvNrfLsSg5klXEgqwyA\nmDAvhg8IYGRcIP1DvdAoisqRCyGE+qR4C9W56LWMjAtkZFwgptkmzlc2XWiVV3CmsJazxfWs2p2H\nl7ue4RcmvSXE+OPuqlc7dCGEUIUUb9GrKIpCRKAHEYEezBsXRVNLB5l5VRzNreRYTiV7jpew53gJ\nGkUhLsKbEXGBjBgQQESQB4q0yoUQTkKKt+jV3F11jIkPZkx8MEaTiYLSBo7kVHAsp5IzhbWcLqzl\ns205+Hu7MGJAAMNjAxja3x8XgyxFE0L0XVK8hcPQKAr9Q73oH+rFTZNiqG9q43iuuVV+PLeSbYfP\ns+3weXRahcFRfuZ15XEBhPi5qx26EEJYlRRv4bC83A1MSAhlQkIonUYjuefrOJpj7l4/cbaKE2er\n+HDzGUL83BgRG8iI2AAG9fNFr5OlaEIIxybFW/QJWo2GgZG+DIz05dapsVTXt3Ist5Ij2RVk5lWz\nMb2AjekFuOi1DI32s2wQ4+8tG8QIIRyPFG/RJ/l5uZAyMpyUkeG0dxg5U1jD0Qvryg+dqeDQGfMG\nMZFBnuYNYmIDiI3wRquRVrkQoveT4i36PL1Ow9Bof4ZG+7N45kDKqpvMhTy3kqz8GgrLG1izLx93\nFx0JA8wbxCTGBcpSNCFEryXFWzidYD93Zo1xZ9aYfrS2d3Iyv9qyrjztZBlpJ8tw0WuZkBDKjKQI\nIoM81Q5ZCCEuIcVbODUXvZbEuEAS4wIxmQZxvqKRQ2cq2H74PNsOFbHtUBHxUb7MHB1J4sBA6VYX\nQvQKUryFuEBRFCKCPIkI8mT++P4cya5g08FCTuZXk3WuBn9vF6aPimDKyHC83WXPdSGEeqR4C9EF\njUZh1KAgRg0K4nxFI5szCtlzrITPt+fy5a48xg0JZsboSGLCvNUOVQjhhKR4C3EF4YEeLJkzmFtT\nYtl9vJgtBwvZfbyE3cdLiA33ZsboSMbGB8tRpkIIu5HiLUQPubvqmD2mHzNHR5J5torNBws5mlNJ\nzvlMPt6SzdSR4UwbFYGfl4vaoQoh+jgp3kJcJY2ikDAggIQBAZRVN7H1UBE7jxTz1Z481uzLJ2lQ\nEDNHRzIw0kcOSxFC2IQUbyGuQ7CfO3fOGMjCKQPYd6KEzQcLLeeR9wv2ZOboSMYNDcFFLwelCCGs\nR4q3EFbgotcyNTGClJHhnC6oYXNGERmnyvnH2iw+3ZrNlBHhTE+KIMjXTe1QhRB9gBRvIaxIUcwn\nmg2O8qOqroVth8+z43AR69LOsT7tHCPjApkxOoKh0f5opEtdCHGNpHgLYSP+3q7ckjKABROjSc8q\nY9PBQg5nV3A4u4JQf3dmJEUwaXgYbi7yayiEuDryV0MIG9PrNJajS3PP17Elo5C0k6X8e9MZPt+R\ny6SEUGaOjiQswEPtUIUQDkKKtxB2NCDcmwHhQ7ljehw7jpxn66EitmSY/w2N9mNmUiQj4wLRaKRL\nXQjRPSneQqjA28PAjROjSR0fxaHTFWw+WEhmXjWZedUEeLsyI8m8Daunm5xsJoT4LineQqhIq9Ew\nJj6YMfHBFJY1sDmjkL0nSvh0Ww4rd51l3NAQZo2OJCrES+1QhRC9iBRvIXqJyGBPfjgvntumxbL7\naDFbMorYdbSYXUeLidJL44oAABVFSURBVIv0YdboSJIGBck2rEIIKd5C9DYernrmJEcxa2w/judW\nsulgIcdzq8gurMXH08C0xAimJYbj4ynbsArhrKR4C9FLaRSFEbGBjIgNpLSqic0Zhew+VsyXu86y\nek8eY+KDmTk6kthwb9mGVQgnI8VbCAcQ4u/OXbMGcUvKAPYeL2FzRhH7M0vZn1lK/xAvZo6O5IYU\nd7XDFELYiWIymUxqB9ET5eX1Vr1eUJCX1a8puia5tj6TyURWfjWbM4o4dKYck8k8gz1lZDjT5WQz\nm5L3s31Ins2CgrqerCotbyEckKIoDIn2Z0i0P5W1LWw5VMiuo8Ws3pPH2n35jB0SzOwx/YgJ81Y7\nVCGEDdis5d3c3MyyZcuorKyktbWVhx56iOnTp1vu37NnD6+++iparZaUlBSWLl162etJy9txSa7t\nw8vHja+2Z7MpvZDzFY0AxEX4MGtMJKMHB6HVyCx1a5D3s31Ins3s3vLeunUrCQkJ3H///RQVFXHv\nvfdeUrxfeOEF3nvvPUJCQvjBD37A3LlziYuLs1U4QvR5rgYd0xIjmDoynMy8ajamF3A0p5Lsolr8\nvV2YmRQpG78I0UfYrHjPnz/f8nVxcTEhISGW7wsKCvDx8SEsLAyAqVOnsnfvXineQliBoigMi/Fn\nWIw/xZWNbD5YyO5j5o1fvtx1lonDw5g1OpLwQNlLXQhHZfMx78WLF1NSUsLbb79tua28vBx/f3/L\n9/7+/hQUFFz2On5+7uh0WqvG1l13hLA+ybV9fDvPQUFejIgP5f7mdjbuz2f1rly2HSpi26EikgYH\ns2DKAJIGB8te6ldJ3s/2IXnuns2L90cffcTJkyd5/PHHWbVq1TWvR62ubrJqXDKeYj+Sa/u4Up4n\n/3979x4cZX3vcfy9u9nNfZcQsiGbEMgGBQIiEPDCzRvgqbZaRRtEI+eMdabH48yxox0ZvFCrp1M8\n7ZmO1aNtFWvT4xgLYvUUUTgIZSpXUS4xICSR3BMCSTb3ZC/njw0BIkVayD67m8/rr81mn+S733nm\n+ezv9/z2eSanc+2kND4/0sTG3VXsPdzI3sONjB6ZwMKZWcyekkGs7dJ+QI5G2p9DQ30OCvk574MH\nD5KamkpGRgaTJk3C5/Nx8uRJUlNTcTqdNDU1Dby2oaEBp9M5VKWISD+L2Uz+BCf5E5wcq29j454q\ndpU2UPTRl6zdWs78aS5unJHJKEe80aWKyHkM2fLTPXv2sHr1agCampro7OwkJSUFgKysLNrb26mu\nrsbr9fLxxx8zZ86coSpFRM5h7Ohkvv/tPP7zX2dz25xxxFhMbNhZyeOvbOe/1x3gy6oWIuQyECLD\nzpB9Vay7u5snnniCuro6uru7efjhh2lpaSE5OZmFCxeye/dufv7znwOwaNEiHnjggfP+PX1VLHKp\n16FxsX3u8/rZVdrAxj1VVDa0A8GAXzgzi6smpQ/7G6L09vmobGhnbNYIrOhDzVDTcSPob02b6wpr\nMuTU69C4VH0OBAJ8WdXCpj3V7O2/epsj0cYNMzK5flom9kTbJag2vAUCARpbuiiv8VBW20p5rYeq\nxnZ8/gBmEyy6Kpvvzs3BZtUagaGi40aQwnsQ7Riho16HxlD0uamli//bW81f9tXR1eMlxmLmmrx0\nFsyMrnuMd3Z7qag7HdTltR7au/oGfh9jMZGdnkzOaDslx05Sf6KT9JR4/uWWSVw+ZoSBlUcvHTeC\nFN6DaMcIHfU6NIayz929Xv56oJ5Ne6poaO4CYGL2CBbMHMO08aMi6qtmfn+AmqaOYFDXeCiv81DX\n1HHWRPgoRxxul51clwN3pp1sZzLWmOBpg2R7PL9dt5+Nu6sIADfOyGTxdbnEx+pq05eSjhtBCu9B\ntGOEjnodGqHosz8Q4EDZCTbtqaLkq2YgGHQL8rOYO9VFQlz4BVhrew/ltR7Kaj2U17ZSUddGT59v\n4PexNgs5o5PJzXTgdtlxuxw4znNq4FSfj9a08vr6UupOdJJqj2XZtyYyJSc1FG9pWNBxI0jhPYh2\njNBRr0Mj1H2uOd7Opk+r2X6wnl6vn1ibhXlXZHDTzCzSU4y5PWmf109lQ9tAUJfXemhq7R74vQlw\njUokx2Unt39k7RqV+HfNHJzZ5z6vn/c/qWD99kr8gQBzr8ig4KbxJMbpErQXS8eNIIX3INoxQke9\nDg2j+tze1cfWz2vYvLeG5rYeTMCV40exYGYWk8am/MMXZvomgUCAptbugenvsloPVY1teH2nD2lJ\n8db+6W877kwHOaPtFz07cK4+H6tv4/X1pVQ2tuNIsnH/oglMvzztov7PcKfjRpDCexDtGKGjXoeG\n0X32+vzs/fI4G3dXUVbrASAzLZGFM8dwTV76Ra/M7urx8lXdqenv4Mja03l6UZnFbCI7PQl3RvA8\nda7LTtqI+Ev+4eFv9dnr87NhZyXv/bUCry/AVZOcLF14OfaE6F+dPxSM3p/DhcJ7EO0YoaNeh0Y4\n9bmstpVNe6rZc6gRnz9AUryV66e7uGF6FinJsd+4vT8QoK6p46zp75rjZy8qS7XHkuNyDEx/Z6cn\nheSrW9/U59qmDl7/oJSyGg9J8VaWLryMqyelD9kMRLQKp/3ZSArvQbRjhI56HRrh2Ofmth42761m\n6+e1tHf1YTGbmDXRyYKZY3C77AOv83T2Doymy2o8fFXvoavn9KIym9VMzmj7wIIyt8t+QR8ChsKF\n9NnvD7Dp02re2VpGr9fPtPGjKLx5gmE1R6Jw3J+NoPAeRDtG6KjXoRHOfe7t87Hji+DV22qOdwCQ\nm2knzRFPea2Hxpaus16fkZqAOyN4njrXZSczLRGLOTyu8Pb39LmxuZPffXCIQ5UtxMfGUHDjeOZN\nzdAo/AKE8/4cSgrvQbRjhI56HRqR0OdAIEDpsWY27alm39EmAkBiXMzAaDrXZSfHZQ/r1dp/b58D\ngQBb99Xy9uajdPf6yBuXwrJ/mkjaCN385XwiYX8OhZDfVUxEZDCTyUTeuJHkjRvJidZu+nx+0lMu\n/aKycGIymbh+WiZT3an8/sPD7C87wdOv7WLxdW5uzM/CHMXvXYZOeMxDiciwk+qIY/TIhKgO7jON\ntMfx73dN5cHv5BFjMfHmpiP87H/2Uneiw+jSJAIpvEVEQsRkMnHt5NE89+A1zJzo5Gh1KytX72b9\njmP4/H6jy5MIovAWEQkxR6KNh747hX+7YwoJcTGs2VLGc7//lKrGdqNLkwih8BYRMUj+BCfPff9q\n5kwZzbH6Nn7yu92s+0s5fV6NwuX8FN4iIgZKirfywLfzeOTuK3Ek2Xj/k6/4ye92U95/lTqRc1F4\ni4iEgam5qTz7wNXcMD2TmqYO/qNoD29vPnrWHdBETlF4i4iEifjYGApvnsDjS6eT5ohnw65KVq7e\nxeHKZqNLkzCj8BYRCTMTslN45oGruPmqMRxv6WLVm59R9NFhunq8RpcmYULhLSIShmKtFgpuvIwV\nhfm4RiXy8d4ann5tJwfLTxhdmoQBhbeISBjLdTlY+c+z+M7scbS09/Jfb+/jtf/9gvauvm/eWKKW\nLo8qIhLmrDFm7pjvJn9CGq+vP8RfD9ZzsOIk9y2aQP6ENKPLEwNo5C0iEiGy05N5clk+i69z09Ht\n5aV1B3j53YN4OnqNLk1CTCNvEZEIYjGbufXaccy4PDgK332okdJjzdyz4DKuyUsfNteKH+408hYR\niUAZqYksv3cG9yy4jF6vj9++/wUvrNlPc1uP0aVJCCi8RUQilNlsYuHMMfzkgauZNDaFfWUnePLV\nHWz9vIZAIGB0eTKENG0uIhLhnCPieWzJNLbtr6N48xHe2HCYXaWNLPvWRJwj4g2tLRAI4PX56fX6\n6e3z0+v10dd36mcfvV4/fV7fwO+CP/sZ4YgnLdnG2PRkbFaLoe8hHCm8RUSigMlkYv6VLq5wp/L7\nDYfYV3aCp1/byeL5udyUn4XZfPpcuM8fDNI+b3+YnhGsp0L1rOf6+gPW6x/0+HTYnnp81vb9QX0x\ncwAWs4ksZxK5Lju5Lgdulx1nSvywP7dvCkTI3Mrx422X9O+lpSVf8r8p56Zeh4b6HBqR0OdAIMDO\nLxp4c9MR2rv6sCfaMMFAEPv8l/6wbzGbsFnNWGMs2GLM2KwWrDHmgce2GHPw5/7Htpj+31v7H1vN\nxJ7xXIzNyr7DjZTXtnKsoQ2v73TNSfFW3C477v5Az8lIJiHOesnfUzhIS0s+5/MaeYuIRBmTycQ1\nk0eTN24kxZuPcLiqBWuMBXui7Wshao2xEHsqdK3mgeeCQWwmdiCELaeD9oxQPhW2FvOlXUKVlpZM\n3hgHAH1eP5WNbZTXeCirbaW81sP+shPsLzt9tbmM1ITgyDzTjjvDTmZa4iWvKZxo5C1DTr0ODfU5\nNNTn0PimPrd29FLeH+TltR7K6zz09J6+A1us1UJORjJul4Pc/lG6Iyk2FKVfUhp5i4hI1HAk2ph+\nWRrTLwteYc7vD1Db1DEwMi+v9XC4soVDlS0D26Ta48jNtOPuP3c+Nj0Ja0xkLoZTeIuISMQz9y9s\ny3Imcd20TAA6u71U1HsorwkGelmth12ljewqbQSC5+mz05MHRubuTAdpjriIWAyn8BYRkaiUEBfD\n5HEjmTxuJBBcyHe8pYuyU1Ptta1UNrRRUeeBT4PbJCdYB1a1u112cjLsxMeGX1SGX0UiIiJDwGQy\n4UxJwJmSwLWTRwPQ2+ejsqH9jOn2Vj4/2sTnR5uC2wCutMT+0Xkw1F2piWd99c4ICm8RERm2bFYL\n47McjM9yDDzX3NbTvwiulfIaDxX1HmqOd/CXfXUAxNks5GTYg+fPM4KBbk+0hbRuhbeIiMgZUpJj\nyZ+QNnC7VZ/fT83xjv7p9uAIvfRYM6XHmge2SRsRx5ScVO5ZcBkxlqH/iprCW0RE5DwsZjPZ6clk\npydzw/TgYriO7j4q+s+dnwr17SX13DHfTVK8wltERCTsJMZZmeJOZYo7FQguhvP5AyEZdYPCW0RE\n5KKZTCZiLKFbxBa9144TERGJUgpvERGRCKPwFhERiTAKbxERkQij8BYREYkwCm8REZEIo/AWERGJ\nMApvERGRCKPwFhERiTAKbxERkQij8BYREYkwpkAgEDC6CBEREblwGnmLiIhEGIW3iIhIhFF4i4iI\nRBiFt4iISIRReIuIiEQYhbeIiEiEGZbh/dOf/pSCggKWLFnC/v37jS4naj3//PMUFBSwePFiPvro\nI6PLiWrd3d0sWLCAd955x+hSotp7773Hbbfdxp133smWLVuMLicqdXR08PDDD1NYWMiSJUvYtm2b\n0SWFpRijCwi1Xbt2cezYMYqLiykrK2PFihUUFxcbXVbU2bFjB0eOHKG4uJjm5mbuuOMOFi1aZHRZ\nUevll1/G4XAYXUZUa25u5qWXXmLt2rV0dnbyq1/9iuuvv97osqLOunXryMnJ4dFHH6WhoYFly5ax\nYcMGo8sKO8MuvLdv386CBQsAyM3NpbW1lfb2dpKSkgyuLLrMmjWLqVOnAmC32+nq6sLn82GxWAyu\nLPqUlZVx9OhRBckQ2759O9deey1JSUkkJSXx7LPPGl1SVEpJSeHw4cMAeDweUlJSDK4oPA27afOm\npqazdoaRI0dy/PhxAyuKThaLhYSEBADWrFnD/PnzFdxDZNWqVSxfvtzoMqJedXU13d3d/OAHP2Dp\n0qVs377d6JKi0q233kptbS0LFy7kvvvu4/HHHze6pLA07Ebeg+nqsENr06ZNrFmzhtWrVxtdSlR6\n9913mTZtGmPGjDG6lGGhpaWFF198kdraWu6//34+/vhjTCaT0WVFlT/96U+4XC5ee+01Dh06xIoV\nK7SW4xyGXXg7nU6ampoGfm5sbCQtLc3AiqLXtm3beOWVV3j11VdJTk42upyotGXLFqqqqtiyZQv1\n9fXYbDZGjx7N7NmzjS4t6qSmpjJ9+nRiYmLIzs4mMTGRkydPkpqaanRpUWXv3r3MnTsXgIkTJ9LY\n2KhTbucw7KbN58yZw4cffghASUkJTqdT57uHQFtbG88//zy//vWvGTFihNHlRK1f/vKXrF27lrff\nfpu7776bhx56SME9RObOncuOHTvw+/00NzfT2dmp87FDYOzYsezbtw+AmpoaEhMTFdznMOxG3jNm\nzGDy5MksWbIEk8nEypUrjS4pKq1fv57m5mYeeeSRgedWrVqFy+UysCqRf1x6ejo333wz3/ve9wB4\n8sknMZuH3fhnyBUUFLBixQruu+8+vF4vP/7xj40uKSzplqAiIiIRRh8bRUREIozCW0REJMIovEVE\nRCKMwltERCTCKLxFREQijMJbJIpVV1czZcoUCgsLB+7S9Oijj+LxeC74bxQWFuLz+S749ffccw87\nd+78R8oVkQuk8BaJciNHjqSoqIiioiLeeustnE4nL7/88gVvX1RUpItkiISZYXeRFpHhbtasWRQX\nF3Po0CFWrVqF1+ulr6+Pp59+mry8PAoLC5k4cSKlpaW88cYb5OXlUVJSQm9vL0899RT19fV4vV5u\nv/12li5dSldXFz/84Q9pbm5m7Nix9PT0ANDQ0MBjjz0GBO83XlBQwF133WXkWxeJGgpvkWHE5/Ox\nceNG8vPz+dGPfsRLL71Ednb2124AkZCQwB/+8Iezti0qKsJut/OLX/yC7u5ubrnlFubNm8cnn3xC\nXFwcxcXFNDY2ctNNNwHwwQcf4Ha7eeaZZ+jp6eGPf/xjyN+vSLRSeItEuZMnT1JYWAiA3+9n5syZ\nLF68mBdeeIEnnnhi4HXt7e34/X4geBnhwfbt28edd94JQFxcHFOmTKGkpIQvv/yS/Px8IHjjH7fb\nDcC8efN48803Wb58Oddddx0FBQVD+j5FhhOFt0iUO3XO+0xtbW1YrdavPX+K1Wr92nODb30ZCAQw\nmUwEAoGzrvF96gNAbm4uf/7zn9m9ezcbNmzgjTfe4K233rrYtyMiaMGayLCUnJxMVlYWW7duBaCi\nooIXX3zxvNtceeWVbNu2DYDOzk5KSkqYPHkyubm5fPbZZwDU1dVRUVEBwPvvv8+BAweYPXs2K1eu\npK6uDq/XO4TvSmT40MhbZJhatWoVzz33HL/5zW/wer0sX778vK8vLCzkqaee4t5776W3t5eHHnqI\nrKwsbr/9djZv3szSpUvJysriiiuuAGD8+PGsXLkSm81GIBDgwQcfJCZGhxyRS0F3FRMREYkwmjYX\nERGJMApvERGRCKPwFhERiTAKbxERkQij8BYREYkwCm8REZEIo/AWERGJMApvERGRCPP/lz0jQGHs\nugkAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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5ShIRkUJk2bVlb2+P0aNHN3rMz88Pfn5+cpQjIjJLWtm1xfNIiIhUi0FCREQSyDkiSUxM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\nQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "mk095OfpPdOx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Replace the Linear Classifier with a Neural Network\n",
+        "\n",
+        "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n",
+        "\n",
+        "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rm8P_Ttwu8U4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n",
+        "#"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "TOfmiSvqu8U9",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "evlB5ubzu8VJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "PDuLd2Hcu8VL",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
+        "#"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "6sfw3LH0Oycm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "XatDGFKEO374",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code below is almost identical to the original `LinearClassifer` training code, with the exception of the NN-specific configuration, such as the hyperparameter for hidden units."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kdNTx8jkPQUx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_nn_classification_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    hidden_units,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  a plot of the training and validation loss over time, as well as a confusion\n",
+        "  matrix.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate to use.\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",
+        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
+        "    training_examples: A `DataFrame` containing the training features.\n",
+        "    training_targets: A `DataFrame` containing the training labels.\n",
+        "    validation_examples: A `DataFrame` containing the validation features.\n",
+        "    validation_targets: A `DataFrame` containing the validation labels.\n",
+        "      \n",
+        "  Returns:\n",
+        "    The trained `DNNClassifier` object.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  # Caution: input pipelines are reset with each call to train. \n",
+        "  # If the number of steps is small, your model may never see most of the data.  \n",
+        "  # So with multiple `.train` calls like this you may want to control the length \n",
+        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
+        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
+        "  steps_per_period = steps / periods  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create the input functions.\n",
+        "  predict_training_input_fn = create_predict_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  predict_validation_input_fn = create_predict_input_fn(\n",
+        "    validation_examples, validation_targets, batch_size)\n",
+        "  training_input_fn = create_training_input_fn(\n",
+        "    training_examples, training_targets, batch_size)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
+        "\n",
+        "  # Create a DNNClassifier object.\n",
+        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  classifier = tf.estimator.DNNClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      n_classes=10,\n",
+        "      hidden_units=hidden_units,\n",
+        "      optimizer=my_optimizer,\n",
+        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
+        "  )\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(\"LogLoss error (on validation data):\")\n",
+        "  training_errors = []\n",
+        "  validation_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "  \n",
+        "    # Take a break and compute probabilities.\n",
+        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
+        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
+        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
+        "        \n",
+        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
+        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
+        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
+        "    \n",
+        "    # Compute training and validation errors.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_errors.append(training_log_loss)\n",
+        "    validation_errors.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "  # Remove event files to save disk space.\n",
+        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
+        "  \n",
+        "  # Calculate final predictions (not probabilities, as above).\n",
+        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
+        "  \n",
+        "  \n",
+        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
+        "  print(\"Final accuracy (on validation data): %0.2f\" % accuracy)\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.plot(training_errors, label=\"training\")\n",
+        "  plt.plot(validation_errors, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "  plt.show()\n",
+        "  \n",
+        "  # Output a plot of the confusion matrix.\n",
+        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
+        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
+        "  # in each class).\n",
+        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
+        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
+        "  ax.set_aspect(1)\n",
+        "  plt.title(\"Confusion matrix\")\n",
+        "  plt.ylabel(\"True label\")\n",
+        "  plt.xlabel(\"Predicted label\")\n",
+        "  plt.show()\n",
+        "\n",
+        "  return classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "ZfzsTYGPPU8I",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        },
+        "outputId": "392cb4c4-01e9-427d-b6be-e880ed4e36bc"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_nn_classification_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=1000,\n",
+        "    batch_size=30,\n",
+        "    hidden_units=[100, 100],\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 4.81\n",
+            "  period 01 : 3.95\n",
+            "  period 02 : 2.92\n",
+            "  period 03 : 3.26\n",
+            "  period 04 : 3.05\n",
+            "  period 05 : 2.65\n",
+            "  period 06 : 2.64\n",
+            "  period 07 : 2.17\n",
+            "  period 08 : 1.99\n",
+            "  period 09 : 1.95\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.94\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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QQtwiad4m1ruDH239XTiQmMOZCyVKxwHq7ke/s/0d2GvsWJH8E0XVxUpHEkII\ncQukeZuYWqVi6rBwAJZsOW01h6k97N2ZED6WSl0V351cYTW5hBBC3DituVZcWVnJCy+8QH5+PtXV\n1Tz22GMMHTq0/vFhw4bh7++PRqMB4L333sPPz89ccSyqfbAHXcO9OZqcx9HTeXRr56N0JAAGBPbh\ncPavxOclcjj7KD39uykdSQghxE0wW/PeunUr0dHRPPTQQ2RkZPDAAw9c1rwBFixYgJOTk7kiKGry\n0DCOpeSzZFsKncK80GqUP8ihUqm4K3ISfz0wj6WnV9HeMwIXW2elYwkhhLhBZusoY8aM4aGHHgIg\nMzOz2exVN1aAlxODuwaSXVDB9qMXlI5Tz8fRi9vCYimrLWfpqR+VjiOEEOImqIxmPvk5bdo0srKy\n+M9//kNkZGT98mHDhtG9e3cyMjLo0aMHzz77LCqVqsH16HR6tFqNOaOaXFFpNQ//bRM2WjWfvjgC\nJwcbpSMBYDAYeG3L+5zKP8NzAx+lV1AXpSMJIYS4AWZv3gCJiYk8//zzrFq1qr5Br1y5kkGDBuHm\n5sasWbOYMGECsbGxDa4jN7fUpJl8fFxMvs6r+XlPKit2nGFsvzZMHBxm9vdrrKzybP524AOcbBx5\npc+zONo4mu29LFXrlk7qbBlSZ8uQOtfx8XG56nKzHTZPSEggM7NuPumoqCj0ej0FBb+P8DV+/Hi8\nvLzQarXExMRw6tQpc0VR1MherfFwsWPDwXQKSqqUjlPP38mPuJCRFNeUsjz5Z6XjCCGEuAFma96H\nDh3is88+AyAvL4+Kigo8PDwAKC0tZebMmdTU1ABw8OBBIiIizBVFUXY2Gu6ICaVWZ2DFjjNKx7nM\nyODBtHYOZF/mIRLzm+eXJyGEaI7M1rynTZtGQUEBd911Fw8//DCvvfYaK1euZOPGjbi4uBATE8PU\nqVOZNm0anp6e1zxk3tT16+hPsK8zexOySMuynsNAGrWGu6OmoFap+TppGVU66zkyIIQQomEWOedt\nCk31nPdvjqcW8P53R4lq48FfpnW95sV5lvZTyjrWpW0hJqg/U9uPN/n65dyVZUidLUPqbBlS5zoW\nP+ctLtexrSedQr1ITCsk/ky+0nEuExsyAn9HX3Zk7OF0oXUd2hdCCHElad4WNHloGCoVLNmagt5g\nUDpOPRu1lnuiJqNCxTdJy6jRW8d85EIIIa5OmrcFtfJxZlDnAC7klbPrWKbScS4T4taGoa0HklOZ\nx+qzG5SOI4QQ4hqkeVvY+EGh2Nqo+WHnWapqdErHucy40NF4O3ix+dwO0krSlY4jhBCiAdK8Lczd\n2Y7Y3sGUlNewbv85peNcxlb+An3hAAAgAElEQVRjy92RkzBiZHHiEmoN1vXlQgghRB1p3gqI7ROM\nm5Mt6w6co7C0Wuk4l2nnEcbAoL5klmezPnWL0nGEEEJchTRvBdjbahk/KISaWgMrd1rf1d3jw8bg\nYefO+rQtZJRZ17l5IYQQ0rwVM7BzAEHeTuyKz+R8TpnScS7joLXnzsiJGIwGvkpcgt6gVzqSEEKI\nS0jzVohGrWby0HCMRliyLVnpOFfo6NWePv49OFeaweb0HUrHEUIIcQlp3grqFOpJVBsPEs4UcPxs\nwfVfYGETI8bhYuvM6rMbyS7PUTqORVTpqtiVsY9fcuJluFghhNWS5q0glUrF1GHhqIDvtyRjMFjX\nSLVONo5MazcBnUHHV0nLMBitZ2AZU6vW17AhbSuv7X2Hb0+uYGHCYv5v5xt8/Osidmbspai6WOmI\nQghRT6t0gJYu2M+F/tH+7E7IYk9CFgM7Bygd6TJdfTvRzbczv+QcY8f5vQxpPUDpSCZVo69lV8Ze\nNqRto7S2DAetA2NCRmI0GjiWd4IT+Sc5kX+S707+QLBLKzp7d6CTdweCnAOsanx6IUTLIs3bCkyI\nCeVAUg4/7DxDryhf7Gw0Ske6zJR2t3OqIJkfU9YQ7R2Ft4On0pFuWa1Bx+4L+9mQuoXimlLsNXbE\ntR3BsNaDcLRxAOBPoaPJrywgPi+R+LwTnCpK4VzpeX4+uwFPew86eXegs3cHwt1D0KrlV0kIYTma\nOXPmzFE6RGNUVNSYdH1OTnYmX+fNcrDTUlOr51hKPrY2Gtq3dlc60mXsNHa42blyJOcYmeVZ9Pbv\nfkN7ndZUa51Bx+4LB1iYsJgjOb9iBIYHxzAz+h6ivSOx0dhc9nxHGwfaugXTJ6AHQ1sPIMg5EK1a\nw4XyLJKLznIg6wjbz+/mfOkF9AY97nbuV6zDUqypzs2Z1NkypM51nJzsrrpcdhesxJi+bdjx6wXW\n7Esjpksgbk62Ske6TC+/bhzOPkpCfhJ7Mg8wILCP0pFuiN6gZ3/WYdambqagqhAbtQ3Dg2MYGTwE\nF1vnRq3DQetAT7+u9PTrit6g53TRGeLzTnAs7wSHc37lcM6vqFVqItxD6/fKvZrBUQohhPWR+byt\nyJYj5/lqwymGdgti+uj2Sse5QmFVEW/tnwfAq32fxd3OrVGvU7LWBqOBg1m/sCZ1E3mV+WjVWgYF\n9mVkm6G42V19ntwbZTQauVCexbHcE8TnnSCt9Pdx4QOd/Ons05HO3h1o7RKEWmW+a0StcZtujqTO\nliF1rtPQfN7SvK2ITm/gtUUHyCms5M0HexPg5aR0pCvsztjPNyeXE+0VxaOd72vU4XMlam0wGjiS\nc4w1ZzeSXZGLRqVhQGBvRrcd1ugvHTerqLq4/jz5ycJkdBfHiHezdSH64h55e49wkx9et8ZtujmS\nOluG1LlOQ81bDptbEa1GzeQhYfxrRTxLt6bw5KTOSke6Qv/A3hzK+ZWE/EQOZR+ll383pSNdxmA0\ncDQ3gTVnN5JZno1apWZAYB9i2w7D097DIhnc7dwYFNSXQUF9qdJVk1R4mmO5x0nIT2T3hf3svrAf\nW40tUZ7t6OzdgWivKJxtre+LmhDCeknztjJdI7xp19qdo8l5JKUVEtnGMg2nsVQqFXdHTuSv++ex\n9PSPRHpGNPqcsTkZjUaO5Z1g9dkNZJRlolap6RvQk7i2w/F28FIsl73Wjq4+0XT1icZgNHCmOI1j\neceJzz3Br7kJ/JqbgAoVoW5t6s6T+3TEz9FHsbxCiKZBDptbobOZJbz55SHa+Lvw6r09UVvh/cRb\n03ex7PQquvt2Zmb0Pdd8rjlrbTQaOZ6fxOqzGzhXmoEKFT39ujEmZDi+Vt4Es8tzOHbxgrezxWkY\nqftV9HP0uXjBW0dC3IIbfZ7cmrfp5kTqbBlS5zpy2LwJCQlwpU8HP/afyObAiWz6dvRXOtIVBrfq\nz5GcXzmSc4weuQl09Ym26PsbjUaSCk+z+swGzpbUzYvew7cLY0JG4O/kZ9EsN8vPyZeRTr6MbDOE\n0poyEvKTiM89TmLBKTad286mc9txtnEi2iuKzj4diPRsh53Guu5CEEIoQ5q3lZoYE8rhkzks355C\nj/Y+2Gita+AWtUrN3ZGT+dvBD/j+5A+0cw/F0cbRIu99qjCFn89sIKX4LABdfaIZEzKSIGfrGp3u\nRrjYOtMvoCf9AnpSo6/lVGEyx/Lqrl7fl3WIfVmH0Kq1RHqE0+niKG9udq5KxxZCKEQGabFSjvY2\nVFbriT9TgIOdlohW1jVwC4CzrRNqVBzLO0FpTRldfDpe9XmmqnVKUSqLE5ew5uxGCquL6OQdxQMd\n72Zo60G42prmti9roFFr8L146HxY60FEe0fiauNMWW05KcWpJOQnsjl9B8fzkyitKcNR64izjZPV\nb9PNhdTZMqTOdWSQliZobP827Dx2gZ/3pDGwUwAujtZ3yHRE8GB+yY1nX9Yhuvt1oaOX6e9PTy05\nx89nNpBYcAqADp7tGRs6krauwSZ/L2ujVqlp6xpMW9dgxoXFkleZT3xeIsdyj5NcfJa0knR+OrMe\nL3tPRkUMoo9nb8VGeBNCWI7ZLlirrKzkhRdeID8/n+rqah577DGGDh1a//iePXuYN28eGo2GmJgY\nZs2adc31taQL1i614WA6320+zYgerbhrZDul41zV+dILvHvoI9xsXXmlzzPYa+0ve/xma32u9Dyr\nz2wkIT8RgPYe4fwpdBShbm1NEbvJq6it4Hj+SeLzTnA8P4kqfTXeDl5MihhHJ+8OSsdrtprKZ0dT\nJ3Wuc8uDtJSVleHs7ExeXh6pqal0794dtbrhq2DXrFlDRkYGDz30EBkZGTzwwAOsX7++/vExY8aw\naNEi/Pz8uOeee5g7dy7h4eENrq+lNm+d3sArC/aTX1LFWw/2wc/TMueVb9TPZ9azNnUzMUH9mNp+\nwmWP3WitM8oyWX1mA7/mHQcgzC2EcaGjiPAIM2nm5qRSV8nWrB2sPb0Vg9FAtFckEyNuw9fRW+lo\nzU5T+exo6qTOdRpq3o065/3mm29SVFREUFAQU6ZMITMzk3379l22J/1HERER9OjRA4BTp05x/Phx\nJkyo+1BPT09n586d3Hdf3QhdeXl5ZGdn06VLlwbX19LOef9GrVbh7mzHgcQcisqq6R1lnVdSh7i1\n5WhuAsfzk2jnHnrZmN6NrXVmeTbfn1rJ0lM/kl2RS4hrG6ZHTeFPoaNkjPDrsFHb0D+sGxGO7ciu\nyCWx4BS7M/ZRa9DR1i0Yrdq6LnhsyprKZ0dTJ3Wu09A570bdQHrixAkmT57M2rVrmTBhAh9++CFp\naWmNeuNp06bxl7/8hZdeeql+WW5uLp6ev38Ye3p6kpub26j1tUQ92vsQFuTK4ZO5nDxXqHScq7JR\na7kncjIqVHydtIwafeN/6bIrcvni+Lf8df88fsk5RrBLKx7rMpNnezxGpGeEzJt9AwKd/Xmy60PM\njL4HF1sX1qdtYe6+f3A4+yhNZEgHIUQjNOqCtd9+6bdt28ZTTz0FQE1N4z6cv/vuOxITE3nuuedY\ntWrVTX8Qe3g4ojXx7VINHY6wRo9O7ML//WsnC1cn8s+nB+PhYn/9F1mYj09HxpYN5+eTm9ictY0Z\nXSde8tiVtc4uy2X58bVsT9uH0WikrXsrpkSPo0dgJ2nYN+m3Oo/2HcDg9j1Zmbien5I28tnxb9if\ne4j7u00h2D1I4ZRNX1P67GjKpM4Na1TzDgkJYcyYMXh6ehIVFcXKlStxc7v25A4JCQl4eXkREBBA\nVFQUer2egoICvLy88PX1JS8vr/652dnZ+Pr6XnN9hYUVjYnaaE3tfIqXow0TB4exdFsKby3az1+m\ndUWrMd8MVTdruP8Q9p/7hdUnNxPpHEmIW/AVtc6vLGRd6mb2ZR3CYDQQ6OTP2JCRdPbpiFqlJi+v\nTMGfoOm62jY93H8onV07szx5FfE5iTy/4W0GB/VnTMhIHG0cFEratDW1z46mSupc55ZGWHvrrbc4\ndeoUYWF1FwxFREQwbNiwa77m0KFDZGRk8PLLL5OXl0dFRQUeHnXjdLdq1YqysjLOnz+Pv78/W7du\n5b333ruRn6dFiu0TzNnMEg6dzGXJ1mTuGmF9V5/bamy5O3IyH/zyH75KWsoLvWbXP1ZUXcy61C3s\nuXAAvVGPn6MvY0NG0M23s1mnymzpfBy9eLTz/RzPT2LZqVVsPb+Lg9m/cHvYGPoG9JDaC9EENepq\n84SEBHJzcxk6dCj//Oc/OXr0KE888QQ9e/Zs8DVVVVW8/PLLZGZmUlVVxeOPP05RUREuLi6MHDmS\ngwcP1jfsUaNGMXPmzGtmaKlXm/9RZbWOvy4+zIW8ch4a14F+Vjh0KsB3J39gZ8Ze4toOZ0LnkXxz\n5Cd2XdiPzqDDx8GLMSEj6enXVRqHCTVmm6416Nh6bidr0zZTo6+hjWtrprYbTxvX1hZK2fQ11c+O\npkbqXOeWbhWbNm0a77zzDnl5eXzyySe89NJLzJ07l//9738mD9oQad6/yyqo4M0vD6LXG3lpeg+C\n/azvvFCVroq39s+juKYErVpDjb4WL3sP4tqOoLd/dzRy9bPJ3cg2XVhVxA/Jqzmc8ysqVPQL6Mlt\nYXFWMUOctWvKnx1NidS5TkPNu1G7PXZ2drRt25bNmzczZcoUwsPDr3mPtzAvf09HHvxTB2p0Bv69\nIp6yylqlI13BXmvPXZETMRqNuNg5c2f7O3it73P0C+wljdsKeNi780D03TzV7RECnPzYk3mQN/b9\ng23pu9Eb9ErHE0JcR6M6cGVlJWvXrmXTpk0MHDiQoqIiSkpKzJ1NXEO3CB/G9W9LXnEVn646jsFg\nfbcBdfBqz9z+L/DRmDcYGNQXrVpG47U2ER5hvNBrNpMjbgdg6ekfeefgh5wuTFE4mRDiWho1SEvr\n1q1ZunQp9913Hx07dmTBggUMGTKE9u1NP451Q1rqIC3X0j7YndSsUuLPFGAwGolqY30DmThoHXBx\ndmjytW4KbnabVqvUtHULpl9ALypqK0ksOMW+rENkl+fQ1jUYB6313ZaopObw2dEUSJ3rNDRIS6OH\nR62oqODs2bOoVCpCQkJwcLDsbSZyzvvqyqtqefOLQ+QUVfL4HZ3o3s5H6UhXaC61tnamqnNaSTrf\nn1pJWkk6thpb4toMZ2jwIGzkyAkg27OlSJ3r3NI5702bNjFq1Chef/11XnnlFUaPHs327dtNGlDc\nHCd7G2bd0QlbGzULfz5BZn650pFEE9fGtTV/6TGLeyInY6u24ccza3l7/zwS8hKVjiaEuKhRzXvh\nwoWsWrWKZcuWsWLFCpYuXcr8+fPNnU00UmtfZ+6Pi6KqRs+/V8RTWa1TOpJo4tQqNf0Ce/F63+cZ\n2mogeVUFzD/2Of859jm5FflKxxOixWtU87axsblsLHI/Pz9sbGTOYGvSp4Mfo3q1JjO/gkWrEzHI\nONbCBBxtHJjU7jZe7PUUEe6hxOcl8taB9/npzPobGr9eCGFajWreTk5OfPbZZyQlJZGUlMTChQtx\ncnIydzZxgyYPDSMy2J0jp3JZu69xE8cI0RiBzv7M7vYID3S8G2cbJ9albmbuvvc4knNMJjwRQgGN\numAtPz+fDz/8kGPHjqFSqejatStPPPHEZXvj5iYXrDVOSXkNb3xxkKLSap6e0oXoUC+lIzXbWlsb\nS9W5Wl/D+tQtbD63HZ1RTzuPcCZH3Eags3WO9mdqsj1bhtS5zi2NsHY1KSkp9WOdW4I078Y7m1nC\n3746jJ2Nhtfu64WPu7ITUDTnWlsTS9c5pyKP5adXkZCfhFqlZnCr/owNGYmDtnlPeCLbs2VInevc\n0tXmV/PGG2/cdBhhXiEBrtwzqj3lVTr+vSKe6loZMUuYnq+jN3/u8gB/7nw/nvYebE3fxRt7/8He\nzLrZ4oQQ5nPTzVvOc1m3mC6BDOkaSHpOGV+uS5L/X8Jsor2jeKX3M4wLjaVaX81XiUuYd/gT0krS\nlY4mRLN1081bpVKZMocwgztHtCMs0JV9x7PZdPi80nFEM2ajsSG27TBe6/scPXy7cLbkHP849G++\nSVpGWY2MPSCEqV1zyKRly5Y1+Fhubq7JwwjTstGqeWxCJ974/ABLtiQT7OtM+2APpWOJZuy3CU8G\nFvZh6alV7L5wgCM58YwLHc3AwD4yKY0QJnLN5n348OEGH+vatavJwwjT83Cx48/jo3nvu6PMX5nA\n6/f3xsPl6mPlCmEq7TzCeaHXbHZk7GX12Q0sObWS3Rf2M6XdeMLdQ5SOJ0STd9NXm1uaXG1+azYe\nSufbTacJC3Tl+bu6Y6O13JSuLa3WSrHWOpfWlPFjylr2Zh4EoKdfVyaEj8Xdzk3hZDfHWuvc3Eid\n6zR0tXmjZhq46667rjjHrdFoCAkJ4bHHHsPPz+/WEwqzGtGjFWczS9h3PJtvN51iRmyk0pFEC+Fi\n68w9UZMZENiHpad+5FD2UY7lnWBM2xEMDOqDVm2DRqVGrbLcF0ohmrpG7Xn/+9//5uzZs4wePRq1\nWs2mTZsICAjAzc2NHTt28Nlnn5k9qOx537rqWj1vLz5Mek4Z98VFEtMl0CLv2xJrrYSmUGeD0cC+\nzEP8mLKWstrLL2RToUKr1qBR1f2nVqvRqDRoVRo09cvVaNTauj/rl/9hmUqDRq1Go9Je/FNzyXN/\ne76mft3q39Zx8TnaS9ahVmkuy6RRqYlo1ZqSwmqFKthyNIXt2RJuac/78OHDfP755/X/HjFiBA8/\n/DCffvopmzdvNk1CYXZ2Nhpm3dGJN784yFcbTtLKx5nQQFelY4kWRK1S0z+wN119otmQto3M8iz0\nRgN6gx6dUY/eqMdg0NctM+rRXVxeravBYDCgM+rqn29EmTN+LkedmdbuDrr6RCvy/kJAI5t3fn4+\nBQUF9cOhlpaWcuHCBUpKSigtlW9GTYmvuwOP3NaRfy75lY9/iOf1+3rh6mSrdCzRwjjaODI+fMwt\nrcNgNFxs5Lr6Zq836C/58+Kyi18CDEY9esPly+r+bsBwyZeHy15b/5y619boazic+ysL4v9HX/+e\nTGp3Gw5aexNVRYjGa1TznjFjBnFxcQQFBaFSqTh//jyPPPIIW7duZerUqebOKEwsOtSLOwaHsnz7\nGf7zYwLPTuuKRi3nG0XTor54ntxG3aiPMZOZYjeGD3YtYl/WIU4VpTAjaioRHqEWzSBEo682Lysr\nIzU1FYPBQHBwMO7u7ubOdhk5521aRqORj39I4MipXEb1as204RFme6+WXmtLkTpbho+PC1nZRaxN\n3cS61C0ADAsexLjQWIt/kWjOZHuuc0vnvMvLy/nyyy+Jj4+vn1Xs3nvvxd5eDhc1VSqVipljo8jM\nL2fDwXRCAlzp00HuGhCiMTRqDX8KHU1Hr0i+PPEdm8/tIDH/FPd1vJMg5wCl44kWoFHHSl999VXK\nysqYNm0aU6ZMIS8vj1deecXc2YSZOdhpefyOTtjbavh8bSLpOWVKRxKiSQlxa8OLvZ9mYFBfLpRn\n8e7Bj9iYtk0mZhFm16g977y8PObNm1f/76FDhzJ9+vTrvu7vf/87hw8fRqfT8cgjjzBq1Kj6x4YN\nG4a/vz8aTd1wie+9957cL66AAC8nZo7twMc/xPPxinheva8nTvY2SscSosmw09hyZ/s76Ozdga8S\nl7IyZQ3xeYnM6DAVbwdPpeOJZqpRzbuyspLKykocHOrm6a2oqKC6+tr3Oe7bt4/Tp0/z/fffU1hY\nyIQJEy5r3gALFizAycnpJqMLU+nR3oex/dqwem8aC346wZOTOqOWiWeEuCEdvSJ5ufczfHtyBUdz\n43n7wDwmRdxOv4CeMpGTMLlGNe+pU6cSFxdHdHTdfY3Hjx9n9uzZ13xNr1696Ny5MwCurq5UVlai\n1+vr97SFdZkwKJS0rFKOpeSzatdZxg+Sq2eFuFHOtk48GH0PB7KOsOTUj3ydtJT4vBPcFTkRF1tn\npeOJZqRR57wnTZrEt99+y/jx45kwYQLfffcdycnJ13yNRqPB0dERqJudLCYm5orG/frrr3PnnXfy\n3nvvyXzTClOrVTx8W0e83exZtTuVX07LrHFC3AyVSkWfgB683Odp2rmHcSzvOH/dP49juceVjiaa\nkZuemGTGjBn873//u+7zNm3axH//+18+++wzXFx+v+R95cqVDBo0CDc3N2bNmsWECROIjY1tcD06\nnR6tVvbaze3shWL+8tFOtBoV854aTJCP7C0IcbMMRgNrTm3l22MrqTXoGBbSn3u7TcbBRu7UEbfm\nppv39OnTWbx48TWfs3PnTj788EMWLlx4zfvCv/76a/Lz83nyyScbfI7c5205e49nseCnEwR6O/Hy\n9B442N3avatSa8uQOlvGzdT5QlkWX574jvNlF/Cy92RGh6kyNep1yPZcp6H7vG96WK3rXYBRWlrK\n3//+d/773/9e0bhLS0uZOXMmNTU1ABw8eJCICPMNEiJuTL+O/ozo2YoLeeV8viZRTmkIcYsCnf15\nrufjjG4zjIKqQj448h9+TFmLzqBTOppooq65SzV48OCrNmmj0UhhYeE1V7xmzRoKCwt56qmn6pf1\n6dOH9u3bM3LkSGJiYpg6dSp2dnZ06NDhmofMheVNGRrOuewyDp3MZd3+c8T1baN0JCGaNK1ay21h\nsUR7R/Ll8e/YkLaV4/lJ3NfhTgKd/ZWOJ5qYax42z8jIuOaLg4KCTB6oIXLY3PKKy2uY+8VBisqq\neWZKVzqG3Nw9q1Jry5A6W4Yp6lylq2ZF8k/svnAArUrDuLBYhrUeJHOaX0K25zoNHTa/6XPelibN\nWxkpF4p59+sj2Ntqee3enni7O9zwOqTWliF1tgxT1jk+7wRfJy2jtKaMCPdQpkdNxcvBwyTrbupk\ne65j8nPeomUIC3TjrpHtKKus5d8/xFNTq1c6khDNRifvDrzc+xm6+ERzuugMbx/4J/syD8l1JuK6\npHmL6xrSNYiYLgGcyy7jf+tPygeLECbkYuvMQ9HTmR41BTCyOHEJCxMWU1ZTrnQ0YcWkeYtGuXtk\nO0ICXNiTkMWWI9e+FkIIcWNUKhV9A3ryUu+nCXcP4WhuAm8deJ+EvESlowkrJc1bNIqNVsOsCZ1w\ncbThu82nOX2+SOlIQjQ7Xg6ezO72CBPCx1JZW8n8Y5/zTdJyqnTXnktCtDzSvEWjebra8+fbozEa\n4ZMfEigslQ8UIUxNrVIzIngwz/d6kiDnAHZf2M/fDn7AmeJUpaMJKyLNW9yQyDYeTBkaRnF5DfNX\nJqDTy7zFQphDkHMAz/V8gpHBQ8ivLGDe4fmsSlknA7sIQJq3uAkje7Wmd5QvyRnFfLv5tNJxhGi2\nbNRaxoeP4anuj+Jp7876tC28d+jfZJZnKx1NKEyat7hhKpWK++OiaOXjxNYjGew6lql0JCGatXD3\nEF7q/TT9A3qRXnaBdw5+yJb0nRiMcuSrpZLmLW6Kna2Gx+/ohKOdlv+tP0lqVonSkYRo1uy19twd\nNZmHO92LvcaO5ad/4l9HF1JQde2hqkXzJM1b3DRfD0cevq0jer2Bj1fEU1JRo3QkIZq9Lj4deaXP\ns3Ty7sCpwmTePvBPDmQdkfEXWhhp3uKWdA7zYvygEPJLqvnvj8fRG+QwnhDm5mLrzCOd7uXuyMkY\njAa+PPEdixK+oqxWBnZpKaR5i1s2tn9buoZ7k5hWyPLtZ5SOI0SLoFKp6B/Yi5d6P02YW1t+yY3n\n7f3zOJ5/UulowgKkeYtbplapePBPHfDzdGTd/nMcTMpROpIQLYa3gxdPdX+U8WFjKKut4JNfF/Hd\nyR+o1stprOZMmrcwCUd7LY/f0Qk7Ww2frU7kfG6Z0pGEaDHUKjUj2wzh+Z5PEOjkz86Mvbxz4APO\nFp9TOpowE2newmSCvJ2YOSaK6lo9H6+Ip6KqVulIQrQorVwCeb7nEwwPjiG3Mp95Rz7h5zPr0Rtk\nNsDmRjNnzpw5SodojAoTX8ns5GRn8nUKCPR2okan52hyPhm55fTu4Iez1NoiZJu2DGuvs0atIcqz\nHRHuoZwsTCE+7wSHs38lv6oAI0bc7FzRqjVKx7wua6+zpTg52V11uTRvYXJRwR4kZxSTcLYAtUpF\n9yg/qbUFyDZtGU2lzl4OnvQL6EVZTTnJxWdJKU7lYPYvbDq3naTC0xRVFaNRq3G1dUGtsr6DsE2l\nzubWUPNWGZvIzYG5uaUmXZ+Pj4vJ1yl+V1ZZyxufH6SgpIpu7X3BaMRWq8bm4n+2Ws0lf1djY6PB\nRqPG1uYaz9Fq6h7X1C1TqVRK/5hWRbZpy2iKda7R13KmOJWThckkFZwmvTQDI3Uf/fYaOyI8Qmnv\nEUGkZwT+jr5W8bvVFOtsDj4+LlddLs1bmE1aVikfLP2V4nLzfHu20V5s5Da/N/ffG/3Vm//vXxau\n9vjFLwhaNW7Otni62KNWK/8h1liyTVtGc6hzeW0FpwpTOFmYzMmC0+RU5tU/5mbrQnvPCNp7hNPe\nIxwPe3dFMjaHOpuCNO8/kA3DMgxGI27ujmRmlVBTq6dWb6C21kCNzkCtTk+tru7vNRf/XqszUFNr\nqHueTl/399+WX/qcP7y+7nW/r98UG7VGrcLT1Q5vNwd83O3xdnPA290eHzcHvN0dcHW0sYo9lN/I\nNm0ZzbHO+ZWFdY288DQnC5Iprf39bhE/R18iPcNp7xFBO49QHLQOFsnUHOt8M6R5/4FsGJZj6Vob\njUZ0euPFRq+vb+6/fQG47N+XfaHQ1395KCyrJq+oktziKkoaOHJga6Oua+xu9ni7//6nt5s9Pu4O\nONhpLfYzg2zTltLc62w0GrlQnsXJgtMkFSZzuugMNRfvGVehoo1rayI9wmnvGUGIWxts1ObZzpt7\nnRuroeZt2U8XISxApVJho1Vho1Vjik28ulZPXnEVeUWV5BVXkXvxz9+a+4W8qw9J6WSvvaypX9rc\nvd3ssdFa/xW/ouVRqWE7yEcAAB2MSURBVFQEOQcQ5BzAsOAYdAYdqSXpJBWc5mRhMqkl50gtOce6\ntC3YqG0Idw8h8uJh9iDnAKu8+K05kj1vYXbNvdblVbXkFf3e1HOLK+v+XlRFXnEVOv3Vx3t3d7bF\nx93hssPyv/3p4WJ3w+fbm3udrUVLr3OlrorkojOcLEgmqfD0ZXOLO9s40c4jjEiPCNp7RuDt4HnT\n79PS6/wbOWz+B7JhWE5LrrXBaKS4rIa84rpmnlv8W1OvJLeoioLSKq72G6hRq/Bytcf7D039t3Pu\nLlc5396S62xJUufLFVeXXLzwra6ZF1UX1z/mbe952cVvzrZOjV6v1LmOIs3773//O4cPH0an0/HI\nI48watSo+sf27NnDvHnz0Gg0xMTEMGvWrGuuS5p30yW1bphOb6CgtLrBQ/INnW+3s9HUn1v3vng4\nfnDP1thZz/VzzZZszw0zGo3kVOSSdPEq9lNFKVTqquofb+0cWN/Mw91DsNXYNrguqXMdizfvffv2\nsWjRIhYsWEBhYSETJkxg27Zt9Y+PGTOGRYsW4efnxz333MPcuXMJDw9vcH3SvJsuqfXNq67R1+2l\nX+2ce3ElldW/D3upUasY3qMVtw0IwdFeLmcxF9meG09v0JNelkFSQV0zP1Ocis5Yt81qVRpC3Npc\nPF8eQbBLEJpLRn6TOtex+AVrvXr1onPnzgC4urpSWVmJXq9Ho9GQnp6Om5sbAQEBAAwePJi9e/de\ns3kL0RLZ2WoI8nEmyMf5iseMRiPlVTryiis5n1POmv1pbDiYzr4T2UweEka/aH/UVnQrm2h5NGoN\nbV2DaesaTGzbYdToa0gpSiWpsO7it+Sis5wuOsNPrMdBa0879zDaeYYT6RGBt/eV27z4ndmat0aj\nwdHREYBly5b9f3t3HhX1fe9//DkLy7DKvg0ggksQRSEEEUUlYhabRbNgjaa9Nze9/dn+zi+5trc5\naRJtk9tec9Pf6WmSmzTGnOaa5BdTzVpNXKIYF1QMbhARVwSULaCCiMjy+wNjYkTjwswwM6/HOTkM\nwzi8eQd9zefz/cznQ05ODiZTz6uq+vp6goO/XcgQHBxMZWXlFZ8vKMgHcx+vzr3cKxrpe+q17SQA\nGcDUnEQ+WH+A99bsZ9HyvWwsqeHn00aSFOuYTTZcmX6fr19MZAg5pANw6mwLpXX72FNTxp7aMnY1\nlLKroRQA3x0++Hn64uthwdfTgsXDgq+HD74eFnw8Lfic/9zH09Jz33duWzy8XX7Vu83n1tasWcPS\npUt54403buh5mppa+6iiHpqSsR/12j7CwvzJTY0mdWAwS9YdYHtZHf/25/XkjIpmes4g/H0uf31R\nrp5+n/tWkvcQkgYOYdrAu2k408i+pv2UNe6ntq2OlrbTNLWeoL3r2k4oNGDAy+SFxeyNj4cFb5M3\nPh7eWMwWLObvfuy57fO9zy1mb8w2ev/6tXLI+7w3bNjAq6++yuuvv46//7cFhIeH09Dw7XZ8tbW1\nhIeH27IUEbcREujNnHtT2HukkbfX7Gf9zmNsL6vj3vGDmDg6GpPRtUck4rxCLcGEWjLJjs686EVS\nR1cHZzrazv935qLbrR1naOtoo7Wj7fzHMxc9prGtibaOsxf2cr9aHkYPfMzeeJst+PQS+N9/EeDj\nYSHEO5hArwBbtOYSNgvv5uZmnn/+ef72t78xYMDF03ZWq5WWlhaqqqqIjIxk3bp1vPDCC7YqRcQt\n3TQwmPn/lMHa4mo+2niIt1eX88WuYzyUN4QhmkoXJ2I2mvH39MPf8/qug3d1d3G28yyt59po62yj\n9dyZS14EfPti4OIXAafPnabhzNd0dv/wmehGg5E/ZD913XVeC5uF94oVK2hqauKxxx67cF9mZiZD\nhw4lLy+P+fPnM3fuXKBn5XlCQoKtShFxW2aTkSkZsWQmR7C04ACb9tTwn28XM2Z4BA9MTCLIv/fj\nBkVcidFgPD9Kvr592bu7uznXde6igP829L99EWAxeePr4dPH1fdOm7SIzanX9nE1fT5YfZK3VpdT\nUdOMl6eJu7MHkndzLGaTptKvln6f7UN97nG5a976GyviRhJjAnn64Zv5ye1D8TAZ+fu6gzyzaBsl\nh752dGkicg0U3iJuxmg0MGFUDH/42Rhy02KobWrl/763ixeX7ab+xBlHlyciV6F/rIUXEbvzs3gw\na8pQclKjeWd1OTv2N1ByuJE7MuO4Y0w8Xh469Uykv9LIW8TNxUX485uH0vjZXcn4epv5eNMRnlq4\nlS/31eEkS2JE3I5G3iKCwWBgzPBIUpNC+UfhEVZtq+TlD0oYPjCImXlDiAq5+tOgRMT2NPIWkQss\nXmYemJjE7x+5hZRBwZQeaeKZRdtYsnY/Z852OLo8ETlP4S0il4gK8eXxB1L53/eNIMjfi5XbKnny\ntS1sLjmuqXSRfkDhLSK9MhgMjB4cxnP/ksm94xNoPdvB6//Yyx/fKqaiRu+/FXEkhbeIXJGnh4m7\nsxP4j0czSR8axoHqk/z+b0X8z8p9tJy5tgMjRKRvaMGaiFyV0EALv5g2gtIjjbyzupyCHdUU7a1l\n+oREJqRGYzTq7HARe9HIW0SuyfCBwfzun29hRm4SnV3dLF65j9+/WcT+qhOOLk3EbSi8ReSamU1G\nptwSxx9/NobslEiO1rbwx7eKWfjJV5xoOevo8kRcnqbNReS6Bfp58ciPkpkwOoa3V5VTWFrDjv31\n3J2dwOSbrTrwRMRG9DdLRG5YUkwgT//kZh6+fShmk5H31h1g3hvbKD3c6OjSRFySwltE+oTRaGDi\n+QNPJqXFUNPYyp+W7OTl9/fQoANPRPqUps1FpE/5WTyYPWUoE1KjeXt1OV+W17P70NfcOSaeOzLj\n8NSBJyI3TCNvEbGJuAh/nngojUfvSsbH28xHGw/z1OtbKS6v1y5tIjdII28RsRmDwUDW8EhGJYXy\nyeYjrC6q5KX395CSEMyPJw/WgSci10kjbxGxOYuXmQcn9Rx4MjwhmJLDjTyzaBvvrCmnqVlvLRO5\nVhp5i4jdRIX48m8PprJjfwPvfr6fNdurWFdcTfaIKO4YE0dEkI+jSxRxCgpvEbErg8FA2pAwRiaG\nUFhSw4otFXyx6xgbdh8jY1g4U7MGEhvu5+gyRfo1hbeIOITZZGR8ajTZI6LYvq+O5YUVbNtbx7a9\ndaQmhjB17ECSYgIdXaZIv6TwFhGHMhoN3HJTBBnDwtlzqJHlhUfYdfBrdh38mqGxA5g6Np7hA4Mx\nGHTwicg3FN4i0i8YDAZGJoYwMjGE8soTLC+sYM+hr9m35ATxkf78KCue0UPCMCrERWwb3uXl5cyZ\nM4ef/vSnzJo166Kv5ebmEhkZicnUs2HDCy+8QEREhC3LEREnMSR2AENiB1BR08zyLRV8WVbHyx+U\nEBXiw51j4slMjtC+6eLWbBbera2tPPvss2RlZV32MQsXLsTXV+/zFJHexUf6M+feFI5/fZpPtxyl\nsLSGRcv38uGGQ9yeGc/4kVHasU3cks1eunp6erJw4ULCw8Nt9S1ExE1Ehfjyz1Nv4j//NYvJ6Vaa\nW8/x9upy/v2VzSwvPEJrW4ejSxSxK5uNvM1mM2bzlZ9+3rx5VFdXk56ezty5c7UgRUSuKCTQm5l5\nQ/jR2IGs3l7J2uJqlq0/xIotR7k1PYbJN8cS4OPp6DJFbM7QbeNNhl988UWCgoIuueb94YcfMn78\neAIDA/nFL37BtGnTuP322y/7PB0dnZjNmh4TkW+dPnOOFZsP89EXBznZ0o6nh4nbxsQzbUISYUEW\nR5cnYjMOW21+7733Xridk5NDeXn5FcO7qam1T79/WJg/9fXNffqc0jv12j7ctc8TR0aRdVM4G3cf\n57OtFXyy4RArNh0mKyWSO8fEExnct7u2uWuf7U197hEW5t/r/Q5Zrtnc3MwjjzxCe3s7AEVFRQwe\nPNgRpYiIC/DyMHFrupU//msWj0y9ibABFjbuPs5vX9vCf39YQkWNQkBci81G3iUlJSxYsIDq6mrM\nZjMrV64kNzcXq9VKXl4eOTk55Ofn4+XlRXJy8hVH3SIiV8NsMpI9Ioqs4ZEUl9ezvLCC7WV1bC+r\nY8SgEKZmxTMkdoCjyxS5YTa/5t1X+nr6RFMy9qNe24f6fKnu7m5KjzSyfHMF+ypPADDEGsjUsQNJ\nSbi+XdvUZ/tQn3tcbtpcO6yJiMsyGAykJISQkhDCgaqTF7ZeLX9vF3ERfkzNGkj6kDCMRr3TRZyL\nwltE3EKSNZD/80AqR2ubWbGlgqKyOl75sISIYB/uzIwjKyVSu7aJ09C0udicem0f6vO1qW1s5dOt\nFWzaU0NnVzdB/l7cnhlHTmo0XlfYtU19tg/1ucflps0V3mJz6rV9qM/Xp/FUG6uKKinYWU37uS78\nLB5MyYglNy0GH2+PSx6vPtuH+txD17xFRHoRHODNjFsHMzUrnjXbq/j8yyre/+IQn26tYNJoK3kZ\nsQT6atc26V8U3iIigL+PJ9NyBnF7ZhwFO6tZua2SFVsqWL29kpyR0dyWGUtooHZtk/5B4S0i8h0W\nLzN3ZMYzOd3Kxt3H+XTrUT4vrqJgZzVjkiOYecdNWExanS6OpfAWEemFh9nEpDQr41OjKdpbx/It\nFWwqqWFTSQ3D4gaQm2Zl1OBQrVAXh1B4i4hcgdlkJCslkszhEezc38AXu4+z+0ADZUdPMMDPkwmj\nYshJjSbI38vRpYobUXiLiFwFo8FA2pAwbssexK69NRTsqGZTyXE+2niYTzYdIW1IKJPSrAyLG6Dj\njcXmFN4iItcoOtSXmXlDmD5hEFu/qmVtcTXb99WzfV89USE+TBodw9iUKHy89U+s2IZ+s0RErpO3\np/nCtPnB6lOs3VHF9rI63lmzn2XrD5E1PIKJo2OIi+j9vboi10vhLSJygwwGA0nWQJKsgczIHcyG\n3cco2HGMgp09/yVZA8kdHUP60HA8zFrgJjdO4S0i0ocCfD2ZmjWQOzLj2X3oa9YWV1FyqJEDVSfx\n/3w/OanRTBgVrfeMyw1ReIuI2IDRaGBUUiijkkKpa2qlYMcxNuw+xvLCClZsqSA1MZRJaTEMTwjG\nqAVuco0U3iIiNhYe5MODuUncOz6BorI61hZXs/NAAzsPNBA+wMLE0TGMGxmFn+XSvdRFeqPwFhGx\nE08PE9kjosgeEcXh46dYt6OarV/V8t66A3yw4RC33BRObpqVhKgAR5cq/ZzCW0TEARKiAkiICuDB\nSUls2nOcdTuq2bSnhk17ahgY6c+ktBgyb4rA8wrHk4r70pGgYnPqtX2oz/Zhqz53dXfz1ZFG1p2f\nUu/uBl9vM+NGRjFxdAwRQT59/j37M/0+99CRoCIi/ZjRYCAlIYSUhBC+PtnG+l3VfLHzGCu3VbJy\nWyUpCcFMSoshNTEUo1EL3NydwltEpJ8JCfRmek4id41N4MvyOtYVV1NyuJGSw42EBHgxYVQM41Oj\ndc64G1N4i4j0Ux5mI2OSIxmTHEllXQvrdlRTWFLD+18c4qONh7l5WDiTRscw2Bqo/dTdjMJbRMQJ\nxIb78fBtQ3lgYiKbS2pYW1zF1q9q2fpVLdYwP3LTYhgzPAJvT/2z7g60YE1sTr22D/XZPvpLn7u7\nu9l39ARrd1Szo7yezq5uvD1NZKdEMTEthphQX0eXeEP6S58d7XIL1kzz58+fb6tvWl5eTn5+Pkaj\nkZEjR170tc2bN/P444+zbNky6urquOWWW674XK2t7X1am6+vV58/p/ROvbYP9dk++kufDQYDoQMs\nZAwLZ/zIaHy8zFTVt7C3ool1xdXsO9qEp4eJiCCLUy5w6y99djRf397PibfZ/EprayvPPvssWVlZ\nvX79ueeeY9GiRURERDBr1ixuu+02kpKSbFWOiIjLCvL34u5xCdyZFc+uAw2sLa5mb0UTZUdPEOjn\nyYTUaCalWbXAzYXY7HgbT09PFi5cSHh4+CVfq6ysJDAwkKioKIxGIxMmTKCwsNBWpYiIuAWzyUj6\n0HB+/ePR/MejmUxOt9J+rpOPNx3h1/+9iUXLv6KyrsXRZUofsNnI22w2Yzb3/vT19fUEBwdf+Dw4\nOJjKykpblSIi4naiQnyZmTeE6RMGsbmkhtVFlRd2cLspPogpGbGMSAzRoShOymmWJQYF+WA29+02\ngZdbCCB9T722D/XZPpytz/kxQTyQN4zte2v56IuD7D7QwN6KJmLC/Lg7ZxC56bF4e/W/OHC2PtuT\nQ/5vhYeH09DQcOHz2traXqfXv6upqbVPa9BKRvtRr+1DfbYPZ+5zQrgvj90/kqO1zawuqmTr3lpe\nWbab/1n+FRNGxXBrupUg/94XSNmbM/e5L13uBYzNrnlfidVqpaWlhaqqKjo6Oli3bh3Z2dmOKEVE\nxO3ERfjzyI+S+a//NZa7xg7EYDCwYksF//7KZl77uJTDx085ukT5ATZ7n3dJSQkLFiyguroas9lM\nREQEubm5WK1W8vLyKCoq4oUXXgBgypQpPPLII1d8Pr3P23mp1/ahPtuHK/a5/VwnW76qZVVRJcca\nTgMw2BrIlIw4Rg92zF7qrtjn63G5kbc2aRGbU6/tQ322D1fuc3d3N6VHGllVVEnJoUYAwgZ4Mzk9\nlnEjo7DY8bq4K/f5WuhUMRERuSLDd042q244zeqiSgpLa/h/n+/nw42HGD8ymsnpVkIHWBxdqtvT\nyFtsTr22D/XZPtytz82t7RTsqGZtcTUnT7djMED6kDCmZMSRGBNgswNR3K3Pl6ORt4iIXDN/H0/u\nyk7g9sx4tu2tZXVRJdv31bN9Xz0JUQFMyYglfWgYZpND1j+7LYW3iIj8IA+zkewRUYxNiWTf0ROs\nKqpk14EG/vpxKcEBXtyaZiVnVDS+3h6OLtUtKLxFROSqGQwGhsUHMSw+iNrGVtZsr2LjnuP8veAg\nH286wrgRUUzOsBIR5OPoUl2awltERK5LRLAPD00Zwr05CXyx8xhrvqzi8+Iq1hZXkZoUypSMWIbG\nDbDZdXF3pvAWEZEb4uvtwR1j4snLiOXLffWsKqpk54EGdh5oIC7cj7yMWDKTI3RdvA8pvEVEpE+Y\nTUYykyO45aZwDlafYlXRUb4sr2fR8r0sLThIbloME0fH4O+jo0lvlMJbRET6lMFgIMkaSJJ1BA0n\nzrDmyyo27D7GBxsO84/CCsamRJJ3cyzRob6OLvWadHd309beyanWdk6dbufU6XPf3m5tx8vDxP0T\nEu2yI53CW0REbCZ0gIUZtw7mnnEJbNx9nNXbK1m/8xjrdx4jZVAwUzJiGT4w2GHXxbu6u2k5c47m\n0z0hfLK1nebvhvL5YP4mqM91dF32ubw8TNw5Jh4/i+1X3Cu8RUTE5ixeZvIyYrk13cqO/Q2sLjpK\nyaFGSg41EhPqS15GLGOSI/D0uPGjnzs6uzh1up3m1nOcPB/Aza3tPbdb22k+3c7J0+dobu15TNcP\n7FVmNhkI8PUkJtSXAF9PAnw8z3/06Pl4/r6QQG+7bSGr8BYREbsxGg2kDw0jfWgYh4+fYnVRJUVl\ndfzt0zKWFhxk0ugYctNiLtpZrLu7m7PnOnudqv52dHzuwu3Wsx0/WIfFy4S/jydhQRYCfTzxPx/G\ngb6e+H8TzudD2eJl6ncr5rU9qticem0f6rN9qM99r6n5LJ9/WcX6ndWcbuvAbDKQMiiUU6fPXgjk\n9itMVwMYAL9vRsLnw9f/fBgHnA/nC7d9PPpkhG8P2h5VRET6pSB/L+6fmMhdYweyueQ4q7ZXsXN/\nPSZjz3R1VKjv+UD2+N609bcjZD+LGZPRfd6KpvAWEZF+wcvTxKQ0KxNHx+AXYKHl1Jl+N13dX7jP\nyxQREXEKBoMBH28PBfcVKLxFREScjMJbRETEySi8RUREnIzCW0RExMkovEVERJyMwltERMTJKLxF\nREScjMJbRETEySi8RUREnIzCW0RExMkovEVERJyM0xwJKiIiIj008hYREXEyCm8REREno/AWERFx\nMgpvERERJ6PwFhERcTIKbxERESfjluH9hz/8gfz8fGbMmMHu3bsdXY7Lev7558nPz+e+++5j1apV\nji7HpbW1tTF58mTef/99R5fi0j7++GPuvvtupk+fTkFBgaPLcUmnT5/ml7/8JbNnz2bGjBls2LDB\n0SX1S2ZHF2Bv27Zto6KigiVLlnDw4EGefPJJlixZ4uiyXM6WLVvYv38/S5YsoampiWnTpjFlyhRH\nl+WyXnnlFQIDAx1dhktramri5ZdfZtmyZbS2tvLiiy8yceJER5flcj744AMSEhKYO3cutbW1/OQn\nP+Gzzz5zdFn9jtuFd2FhIZMnTwYgMTGRkydP0tLSgp+fn4Mrcy0ZGRmMHDkSgICAAM6cOUNnZycm\nk8nBlbmegwcPcuDAAQWJjRUWFpKVlYWfnx9+fn48++yzji7JJQUFBbFv3z4ATp06RVBQkIMr6p/c\nbtq8oaHhol+G4OBg6uvrHViRazKZTPj4+ACwdOlScnJyFNw2smDBAp544glHl+HyqqqqaGtr4+c/\n/zkzZ86ksLDQ0SW5pKlTp3Ls2DHy8vKYNWsWv/nNbxxdUr/kdiPv79PusLa1Zs0ali5dyhtvvOHo\nUlzShx9+yKhRo4iNjXV0KW7hxIkTvPTSSxw7doyHH36YdevWYTAYHF2WS/noo4+Ijo5m0aJFlJWV\n8eSTT2otRy/cLrzDw8NpaGi48HldXR1hYWEOrMh1bdiwgVdffZXXX38df39/R5fjkgoKCqisrKSg\noICamho8PT2JjIxk7Nixji7N5YSEhDB69GjMZjNxcXH4+vrS2NhISEiIo0tzKcXFxYwbNw6AYcOG\nUVdXp0tuvXC7afPs7GxWrlwJQGlpKeHh4brebQPNzc08//zz/PWvf2XAgAGOLsdl/fnPf2bZsmW8\n9957PPDAA8yZM0fBbSPjxo1jy5YtdHV10dTURGtrq67H2kB8fDy7du0CoLq6Gl9fXwV3L9xu5J2W\nlsbw4cOZMWMGBoOBefPmObokl7RixQqampp47LHHLty3YMECoqOjHViVyPWLiIjgtttu48EHHwTg\nqaeewmh0u/GPzeXn5/Pkk08ya9YsOjo6mD9/vqNL6pd0JKiIiIiT0ctGERERJ6PwFhERcTIKbxER\nESej8BYREXEyCm8REREno/AWcWFVVVWkpKQwe/bsC6c0zZ07l1OnTl31c8yePZvOzs6rfvyPf/xj\ntm7dej3lishVUniLuLjg4GAWL17M4sWLeffddwkPD+eVV1656j+/ePFibZIh0s+43SYtIu4uIyOD\nJUuWUFZWxoIFC+jo6ODcuXM888wzJCcnM3v2bIYNG8bevXt58803SU5OprS0lPb2dp5++mlqamro\n6OjgnnvuYebMmZw5c4bHH3+cpqYm4uPjOXv2LAC1tbX86le/AnrOG8/Pz+f+++935I8u4jIU3iJu\npLOzk9WrV5Oens6vf/1rXn75ZeLi4i45AMLHx4e33nrroj+7ePFiAgIC+NOf/kRbWxt33nkn48eP\nZ/PmzXh7e7NkyRLq6uq49dZbAfj0008ZNGgQv/vd7zh79ix///vf7f7zirgqhbeIi2tsbGT27NkA\ndHV1cfPNN3Pffffxl7/8hd/+9rcXHtfS0kJXVxfQs43w9+3atYvp06cD4O3tTUpKCqWlpZSXl5Oe\nng70HPwzaNAgAMaPH88777zDE088wYQJE8jPz7fpzyniThTeIi7um2ve39Xc3IyHh8cl93/Dw8Pj\nkvu+f/Rld3c3BoOB7u7ui/b4/uYFQGJiIsuXL6eoqIjPPvuMN998k3ffffdGfxwRQQvWRNySv78/\nVquV9evXA3D48GFeeumlK/6Z1NRUNmzYAEBrayulpaUMHz6cxMREduzYAcDx48c5fPgwAJ988gl7\n9uxh7NixzJs3j+PHj9PR0WHDn0rEfWjkLeKmFixYwHPPPcdrr71GR0cHTzzxxBUfP3v2bJ5++mke\neugh2tvbmTNnDlarlXvuuYe1a9cyc+ZMrFYrI0aMACApKYl58+bh6elJd3c3jz76KGaz/skR6Qs6\nVUxERMTJaNpcRETEySi8RUREnIzCW0RExMkovEVERJyMwltERMTJKLxFREScjMJbRETEySi8RURE\nnMz/B+KJmBEZpIudAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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kKEdEZJTUMrXF80iIiBSLQUJERBKwIyEiIkkEMwYJERFJwI6EiIgkkTNI4uLicOrUKQiC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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "qXvrOgtUR-zD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we verify the accuracy on the test set."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "scQNpDePSFjt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "dc363d2d-324c-4573-cd1f-21e311b67c18"
+      },
+      "cell_type": "code",
+      "source": [
+        "mnist_test_dataframe = pd.read_csv(\n",
+        "  \"https://download.mlcc.google.com/mledu-datasets/mnist_test.csv\",\n",
+        "  sep=\",\",\n",
+        "  header=None)\n",
+        "\n",
+        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
+        "test_examples.describe()"
+      ],
+      "execution_count": 15,
+      "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>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "      <th>5</th>\n",
+              "      <th>6</th>\n",
+              "      <th>7</th>\n",
+              "      <th>8</th>\n",
+              "      <th>9</th>\n",
+              "      <th>10</th>\n",
+              "      <th>...</th>\n",
+              "      <th>775</th>\n",
+              "      <th>776</th>\n",
+              "      <th>777</th>\n",
+              "      <th>778</th>\n",
+              "      <th>779</th>\n",
+              "      <th>780</th>\n",
+              "      <th>781</th>\n",
+              "      <th>782</th>\n",
+              "      <th>783</th>\n",
+              "      <th>784</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "      <td>10000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8 rows × 784 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          1       2       3       4       5       6       7       8       9    \\\n",
+              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          10    ...       775     776     777     778     779     780     781  \\\n",
+              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
+              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
+              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
+              "\n",
+              "          782     783     784  \n",
+              "count 10000.0 10000.0 10000.0  \n",
+              "mean      0.0     0.0     0.0  \n",
+              "std       0.0     0.0     0.0  \n",
+              "min       0.0     0.0     0.0  \n",
+              "25%       0.0     0.0     0.0  \n",
+              "50%       0.0     0.0     0.0  \n",
+              "75%       0.0     0.0     0.0  \n",
+              "max       0.0     0.0     0.0  \n",
+              "\n",
+              "[8 rows x 784 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EVaWpWKvSHmu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "cd6cce22-63f4-4f88-97f4-911c7b1e40fe"
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_test_input_fn = create_predict_input_fn(\n",
+        "    test_examples, test_targets, batch_size=100)\n",
+        "\n",
+        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
+        "  \n",
+        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
+        "print(\"Accuracy on test data: %0.2f\" % accuracy)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.95\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WX2mQBAEcisO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Visualize the weights of the first hidden layer.\n",
+        "\n",
+        "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
+        "\n",
+        "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
+        "\n",
+        "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "eUC0Z8nbafgG",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1172
+        },
+        "outputId": "f5cb527e-4b0f-414f-bcf1-1e8406de1b75"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(classifier.get_variable_names())\n",
+        "\n",
+        "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n",
+        "\n",
+        "print(\"weights0 shape:\", weights0.shape)\n",
+        "\n",
+        "num_nodes = weights0.shape[1]\n",
+        "num_rows = int(math.ceil(num_nodes / 10.0))\n",
+        "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n",
+        "for coef, ax in zip(weights0.T, axes.ravel()):\n",
+        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
+        "    ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "\n",
+        "plt.show()"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adagrad', 'global_step']\n",
+            "weights0 shape: (784, 100)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZD5rfgTJnFAqFQqFQKBQKhUKhUChmEfrljEKhUCgUCoVCoVAoFArFLOKGsqY7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\n/sGWuCO9cgw3v4SxnncnNMZd78m5bds3hxMeqoVy5acnxGc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QsQLjo/PCZa/t2ovnrUV9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IOkfZ+O6RlUx6//HOphZGwpVP06D2ENTqD6VNGO01ATnQfXjIpfoMcvO3+N+DDm45z930izrlUT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\nsH1d++G1ql79M7gm1swHHDvgUBrb53dDS15zu25LVxcabHreRo6E9nqta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GQAACAASURB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/HPbYybofkYXdZ1pEv1sfIBZ22ePGr2h+Hbrr8HTpSxFXQnH15MPEMajGSH+VnNU5Ttv22eKo\nyClar3hNM8aYrATEbSashOadY8mLnt0ivjM5Dm39JK3H16/dEv127cH6F0DzIyEuRvQTXmQUcWyj\n8pfwQUgrxXrsa5d+GHHJ8vj+BntPsceMMcZ4a3FvZsm/I3Vzjug3XA8fktT18AAZjZJeVuzhEUTe\ndCf+8duiXyL5h3VWYI7ExGOcBVtjKTMee6ckevb1J+U+48e//p3T/vTenU77xYNHRb80qr3sVdX8\now7RbwH5w/WexToRaHmxjTVIDzd/Ii8P6zt7kxgj/ZbYX85Ynji9J1E7MuheZuwrEv34e7zGxXpk\nDVj1f23B323HGAsOx/4j9aRcF3menv05vFMWbikW/a4cgSdR0Nu4zwtypM9FDMXv8n1ob5ReSEv2\nwI/GewNzdsbymImlcTkXuPAKfD9sX5SoYjyThoPw4Fn46RWi37JPYj/CvnmrvrhB9Aui5+Abw/oU\naEV4G5r3SWuwFg5cJ28MywuFvX74+7l3S2+pyu+95bSjFmC+tRypF/3Ysyg5F743UwPW+1OajFv+\nPWLKksR/1x7G3mHJwx/5lf9jhETjHcf2DeIY69gEvC8Gx8haxl4eXEeuvir3fQVr4H81Qf47AdFy\nbnuWYtwO16JW8+bdky7nom8K595zGTUvJkfur4omUXs6KbbZ9r9jT8xMWkvH2qUXEr+PJm3Gu6gd\nuZ20Tr5n+hvsF8dR18YYs/hR7OHYW6zyN/L5sOdQVCHmL9c5Y4ypeAOeYdER2K+GBss1hP0ki9Jw\n3yfoWZ2plv5ch96CF1hSDPYSp2vk/vfRv73faXNEfWic3BO4PHjX6LuAtTl2sXwH7qf9awb5ftYe\nuCH6JW2U/pk2lDmjUCgUCoVCoVAoFAqFQjGP0B9nFAqFQqFQKBQKhUKhUCjmEXeUNTE9q71eUmTj\nV6TZ3Y0xfyiliC4B3e7sa6AusmzBGGNmR0Av7DkBGQPHLBtjTN7doHkypXCIaJh5zywV30m4BTob\nU83dlgQrfclG9JsGbTUoSMoKhodBa49LXEf/bkkkAklGEwyKFNMqjZEx1XOBMIq3jsiUNLWW07jX\nLEFITZYytp+9c9hpF6aCip63VUq+Bq+A8tlHtOyGk5IqH03xnRwxxv9+X9ukHgAAIABJREFU4IKM\nuHvmsd1OmyU2CeOS4skU8qZ3QGHL2SspwhxXxzGjHcfluTK1jyNrXVZUacMJUFIX3Wf8isxVoDkm\nLJfyqebXQZdLItp9RJqkWy+4B3ThUKLoDdfLCPAeigPOugfUbjvm0TeMZz0xOP6R/SZIzmeMjEyO\nCMc5eFsk9X3VDsQMDpBsLWOPHG89pyAJDKKI1A6KDTdGUvUDKdYya6+krk9aEZD+hofqYf8VGTFc\ndw7jrngrzsuWCeRuwz2IIFnNke8eEf3KKbZ8w9OoUx2H5Ph+6bugWD/9dw867Z987ddOe89yGU3u\ncUMGeOmnoI8uflz2u/xLzGGOB49PlRRhpvzHkxTx1H+cEP3cNaCn3noFkc/5j8lYyoWpcuz7E7zu\n9LTLucOx4mNNoAdPT0iJZmQWrsPXgTkx7pPxjdEpqD0cqbl4V5nol0hylmgPpApuNySebY2vi++E\nRBBtl27X4pULRL83vo4YyuWL8dnxc1dFv7vy8Ew9C7B+sETIGFk3meacaNF8Ywo/mqrvL3C0Ksdq\nG2NMRApkvCwNsGnZ2Vs3O223G1T70TRbYkMRnbTW5N5fIvp58vEcc3ZDCjE1heO1H7Vo7nSuHWdR\nD189fVr0K80EHf7iNRxjSU6O+ThkZOPaZ8bkGI4pgcyCqfBBVlR17CIprfAnUrZhfJ/9mbzezbQP\n5PXJBksnOTa+9fWbol/BZ1DbLrwIaV7R/XIuHvvGe057zZ9AUjNCaxxLwowxxteJ/WZ+KeZBeJrc\nr+XnoDZ6loJOf+F1GZm8rAzPxsPy8pNy7PScRdSwh56nvXc49eOTTjvvh08YfyOPrqvlbJP4jGtq\n2kqM4aEaKZ1sOI79V0gQ1viJHhnzG02x2J2DeCaDo/Jd49C/wx5hRQGeV/FGrL8Nx6z3oiS8U/D+\nsvLkAdEvJA7X9MFBPLtND6wS/QKCcR0tJ7Bul35KSrp6Tn/0PmhqWK4nBdtkbfcngmhfFRgio8N5\nz5WyE3WyzYqK76d3tZhUrJHLn5H3xdeHZ9pfCQnbrCW9rziGvXFRPsZOdT2kjEUL5LrDe5tgF+4l\nWyQYI98lUxfjs8oP5DWF90LyFHMTdTwyIVL0a+/EeL59COtFxr3ymQ1Y+0Z/g9/7+V3KGGNcsRjT\n/N6wYLdcxxoOo84kLUD991mybZYSHq3Efu6BjWtFv6AIPIeKKzj2rrswLlbk54vvpKzG844uwH6E\no8KNMSY6HTU/OBh1r3NQ1lR3OsZjBO3LhpsHRD8X2QmwfD1zdbbo562i+rXL/AGUOaNQKBQKhUKh\nUCgUCoVCMY/QH2cUCoVCoVAoFAqFQqFQKOYRd5Q1xS2FfGW0TTpLc/rQMKXt3CkNaOnWhU7bnSWT\nGDgFYoSOl1KYJ/qNd4F6mL4R9O2RblC9fH2SnhgcAUfwRErymZmREobwcNDbWLoUESFpbz09oK2O\njoJKGRkp6WcuF+ik/f2ghbKruTHGxBQnmrlEICX43DgiqbrFG0DRnCba8rFDUlJ0TzkovZwUMmWl\nbkXmg9Y50QvqIac9GWNM8k48h8pX4NjNhLMHVq+W1xFCKUKUktE1JJMxhipAy1uwFlS3ay9LR/HM\nElARq05QCsAOmRbAY5gptp5sS5rhljInf6LjItIwQmPDxGcjNN4zY/DZuEXBDwzBHKt4Aak6hRbV\nNWEJHO7HWomubqVdTfvIkZ+SCTpOgZacvEbSTPsvwMn8ZBXmGDuwG2NMmgd1hOnWU15J003emuO0\nG9/A8XIflM+wk6Q8LURXHxuUlGd26i/9CKrhHwuWFA30yppa/uRHp02Ep8i5w5KYCZJhxUXKfpUn\nMaZZLljXKWmxXHtf/9bbTnvnYkiFQmJlzXrwzz7ltM//GyjbYfFu0W/73yMqadJHUo9NMlUhLBay\ns5F2yOV2/N1u0a/pNaRuFT4F2ULnsQbRj5ODjAzK+KNR8Cn8Xa4Nxsh0lvEO1MnkdZLSOliDe8EJ\nH263lNqeOwLp0Lr7QGUv2LVX/t1RPOuAAEps64UsbKRJSgdHW0GFn6W5HLVASlpTruOcTp6HdNeW\nJvvacL2RlEDCiQrGGJN3H+amtwZ1vOFtuTYlLcL+I+Up43dMUuJJiCW/ZAkZP8dAi+Y9MQEJaHAw\nqM4jlkyz7j08n8LdkNcyNd4YY4Ijuui/UAMyS+912lH3SQnfxAS+E1uG/cif58rn+Mbbp5x2ax/u\nOye5GWPMmj1I72OJa5CVZjNwlVJriArP8kVjjIkrTzVzhX5KU7HHI1PKr1CKHMu3jTFm69/d5bQH\nq3DP0++T62JIBJ5vxlJQ4eNL5B515z9Aktr0Hv7uW69iLs7MynXxqa884LTf//Exp53dLCWB2Tux\nXwtPRr0vXipjPm8dh9wmkpK0cpfIvWzXTTzD3rMYO/azXrxFyhb8Dd6jFt63UHw2SONsrBX1taG2\nTfSLITkKy5rGrfQcjyW5+T0uNcg1hJO7XJQewylvWevkfW8nWWEYJ+C1yDTBjsuoD24Xak//Rbk2\nx6+GhD3vHtSNiQG5b0nZgvMYacaxa9+SCTER9LeMXEL+aAyQpYGxQi9ZQtr8W3q3stIOo+IgZb16\nFOd+e1LOl9hy7FHDY7C3GeqWe6qNn4GskFN0ylZiHuU/KDcIA7cwD1ie1X1Syu3i16AGVLx00Wlz\nspAxUsbLaaNbFspxfukW9oafWIEEzK4jjaIfpz/NBTyL8N5a9ZqULvM+MnEDZEPTVrpbSy8kO9Ek\nn3alyv1hDFlSsLx2fFjO2clBvMus3ob1z5WA40UOyro+UoPaGUdpc7HJ0vZkdhbPa7gfexCWMRlj\nTPc5zOHwFBq3VmxvLN2/az9F/Wd5lzGa1qRQKBQKhUKhUCgUCoVC8f9r6I8zCoVCoVAoFAqFQqFQ\nKBTzCP1xRqFQKBQKhUKhUCgUCoViHnFHz5n+Cmj04pZI3fBAJfSFoeQPwb4gxhgzRTHRzeeh2Usb\nkccLjoTuOWkNtFjj3SOiX8pqaF+np6Ev9NZC4xa7UGq7XNHQjvXfgG4seYnUnvX2Iop2imL1xsel\n1jAsDOfeUwUPhIiFUmvIWjZfL443OyX1k90f4Pg5MpXRL5igqNaFO6UXB8d5uXPhLZDskd4HWWXQ\nVy4qhi50pEHGiDVeguaWtZeb/mq76Nf9IfolxeNvpd2NyMLBGzKO9NJx3GvW6ZbfvUT0G6mD1nC8\nBX4BaTlyXFScR8z2ojLoxr03ZEQjR9wVbYGe3Nchx2bP9S4zV+DrbT0io5DDw/BZx1F8xlGnxhjT\nTn4nrB2NsOI6JyimkGPf7SjtIYoF5HjY6Cw8T3em1G0OXYWmf9tqPLfUXTIGj6PN2dco0vKqmiQP\nmlSKER+q7hP94ki73fQunnv+Pqn7tWPF/Y36RoopT5BRwT2nUZviV8CDh70djDHGkEdMdCF08UV3\ny7n9zvPHnfbCtdBYu6ukv8a5OvgT7Lh/Dfpl4zk2vFklvlPzIvwT7vrXf3Xa1Sd+LvrFpqDGTk3h\nmUQkS3+c4GD8d2AwxtVou/ST8iyBnrfrZKPTbq+VWv0wy0fDn2CPta5jcm3IfhjPYIJi4zuOSz8D\nrh3RFK8+3i6jJvOScb3svXT7tvSTGu/HOQUG4pyGmjCm7Jj4D99DVOSeL8FgiT1WjDEmIxfnsPSx\n5U771HMfiH6XriPiMr0D/h9Xm5tFv9QOaNUbKnCuq74gtf+T3rmNtY+hdYzrjTHGhHgwR9hDJY18\n7owxpu86/Al8qRirbqumRtB4DI2GL5gd68nafU8+9hlVB3/htHO2bLUvxUEw+YL1N8gauHcb5nbz\nLcyXzCy5LvooWjp5LZ5VzfsyItbVjutI3g7Pi94zraIfH8/fcCXCc8CdKz3gOAaWfZ2ys1JEvz7a\n51YfQp3LWyO9ZNrfwvjOeBD+HwkJm0S/oSFEMLPPTFMP6tqn75OGZm9+5x2nPeLDuLd9dOregSdC\nUh7qwY9e+p3o9/h6zKUc8l+rf7VS9Fv8OUTRHvpf8FLc/dW7Rb/pcekp4W+Eklclx7IbY0x4GtYG\n3qtEF8n1k+cp+zoe/9Fx0e+DH+Ae8hrcPyzr3mfvxTNyF2BsZWzGvmV6Wp5r3QmMkazNGD/B56TP\nzTo6Hteh4HDpJ9L4G+x52cYl0PJ1clFsN4/1zJXS12J22jLI8CMiMuDDERorvUfZl4O9SoYq5R7f\nU4ZaxPNl0WbpedR5rNFp816p87iseedfOOu0y+jdZ2oY7yZ1L58U32HvSB5jSRst37gqnN8s3fPF\ni+Re9uRZ+LStLsQ+7KeHD4t+X/0c/PnYw5HfiYz5w/nhbwSR/1NSpvQtmyUfyzby+QsOkuN7FfnC\n8Lx8+ReHRL9Q8nJiDx6OuDfGmPwlH+3PwrHxduS47Qf4e4yO1oj/7jqN96KofFzvUI18D7xwEN6o\nK3bh+rw35JhLWI935VnyFotbKtedrhPY++SVmz+AMmcUCoVCoVAoFAqFQqFQKOYR+uOMQqFQKBQK\nhUKhUCgUCsU84o6ypvB0UAg5YtAYYyL4s+sUC2rF94ZRDCxTfCLzJAWVaVwBQSDwscTCGGOCi0Bj\nDQwE/SwkGtT/tnfrxHe6buH8hsZwvMXWsccpIm+G6MUcmWaMMbdnQQnmaL/+S5JamrkXEpgxiyrO\n4MjyuQBHIDedlJKYxBzQ9mYn8ew4atMYY/L7P1pmEb8yXfQLuAwa/aLd0GixdMsYY1oqQH3uIzqp\n60KE0z554or4zjKKWusYAI3fjkEN8YBuzRTF6g/kuAgieUhIFGjndmxf7mJQ6rzXMJYGhqWsKTDA\nyg/0IyanQbv35Mi54yYZ0TBF08Ys+PiIdqb9jrXJsRlbCmrp7DTNZ4uCz/fWRTTWmz9FrGBTlYy7\nzF8Fqm8sUVj7K6QspXAfKMWR2aBP2rf4wk9OO+3cFTlOO3WLpKQP3MCYzdoBmuh4pzUv5/AZGmPM\nwjWgtXINNcaY+nchGxjuwnn1emU85HKK6uu9gPs7OyGlGTsfAbWdqaX7X3hb9Hvq4Z1OO4Wo2Lde\nBI0zNl/SW+OWoWa11r/mtG3p22/+ApInjuwt2SgpqFeOgta/5cuQQEZmSnll5XfxvLPuxjFsCqsr\n3qJV+xP0p1K2ySjVPorrzNiNZz1gSR45bvEXX8f9e/Ybj4l+6WE4xsQg7m1fy3nRb3YaNavy26AO\nT1HdiLHqxutnzjjt1Fh89sC3vin63Tr/a6fdS7G8RfmZol9jE669fwS18Z6714p+LOVZ9SeIOh1p\nkJLCgOC5/X9HsxOobS6Lhp+9DxT4GZpXPVdrRb/UcvCRe26Cvt5/sUP0i1+I581Sb3t8j3fhvg23\nYq2JX4r11+WS9GiOUW/ZD8lG6adXiH4sx3O3oW2P4VmaSyzNK9oppQW3aW2YpLH5B9HZc1hSb76P\n6+W1xRhjIklSu+KLqIUd71uy4GTsKVd8DmPVHn8xFDGfmAd5382jPxP9QmOwtj76F8grfv27B532\nlUq5F7n/y7ud9kgjKP0hMXJvExiEc/rfX3/RaT+7TUrd+mlv0vIG7lHahhzRr+Y5RPuWFEO20fAr\nGaGb9ZCUzPobt2dQv8bbLCuDnXiurfsx1m91yZrK0de8XpXvkFLE177+E6fNkb9JMVIyPUUx9yMk\nk/7gwzec9vSMfN8pWA9Jy3gX5k5YmpTxJq5B7fTWk9w3Ve4JEtZgf91/HjUl0Bqbhw5jPUghS4Kl\nG60I9MC5q6kDFAMeu1LWAK8lX/o9QhMjxH+zJHLrUjy3q8dkJHj5HshKxkkiXHKvlKkPXMY5TfRD\nLth5A/dy5V9vkedwCXsqXw/eEe2t4Xgz9mXZuajJv3zriOi3rgjvgQlRkH5Fhss151cHjjrtR7Zh\nXezqk5Lo0AR5z/yNG7/Gvi9zbY74rPUM3uOKHsLzufmqrBdRPqyZJ97F+8CSbCkNC6LxmLgRcyJh\nWr5zT5AtyOQAnmMYyeN7z8t3DU8p3n+Ofxf3ds1Ta0S/kQbUWx/N2fprUo7N8uyks6gV5X8q5dgN\nv8Y+YPGzK52295bc34SnyppgQ5kzCoVCoVAoFAqFQqFQKBTzCP1xRqFQKBQKhUKhUCgUCoViHnFH\nWdPUIKUlWJwuptWxw3ZYnKRcjTSDMlS4HfSuoFDp7mwCcfzRdtDFUtfI+KK+OrjpM5U7yIVL6W2U\nkhymLl5uAEXsaz/8oei3ed06/B2S2pSelBSrVHLQ37gJzu23p6UcpuUtUDBzHsJ1NL8pKXpM6ZwL\ntLWBUhjhkjTZGaKfVVaAarvYop/FU9rNaAvcwtsPSOfrkvtBdZuZxLE7bkhZXMEOovqRTGpqEOk7\nd392m/hO63s4v6tNoNcFvy7H0pKHljntmgNwu1+w1koEIumaKwnU5hmfpKpGE5357ElQ1pYVy+NN\nW4kf/kT8YtAmA625w2lGIbGQdE30yZSMsARcI1Npe89KOmBCOSj0bftB4w9PjRL9OFnEFYt5H0sO\n964GmT7AY73nDCRwCx7dIfr1NyNVIqP4Hqfd13dM9Fvz55ud9tQwxk6YW1L/w5JAfT3wLUhoVpZJ\neY0reW4pozzfBiwpV/mXQGVtoXQklvAZY8yHL0Las+YR0CYvvHZJ9CssBk10oA11eHpW1pv2apxH\ncQTudXAU6lTuPimRqHnulNM+chbyw8RoScve/vktaK/5pNP+jusvRb8Nn8W1s0RidkrOqcV/vtFp\nN+3H3L5tJeBx0pS/MUQy3ogseb0fJ6fy1sg16b0jGIOcyBSZJNeawUZIMOLzsYZ0VVaIfmOtqMks\ng2Oa/cgtmYCQTkklkWGoGy03fyv6pSxc7bQ5raHvnKwbi3aBUj5chzHrIQmXMcYM3cR6zFKm4Vor\nKY23HDLcxi/w1tIzsfY3iSTXdXlAP45bKK+FE8g4DYsTRIwxpuMg1q4EOnZgsKzlLCOtInp56ZNY\n09r6D4rvcE2JJbkh10NjjAlPwnWkl2OcRWbIRCBvI54Py8qjCqS0ka8pfi2OF5Is9xgNL2HNLJSM\n8j8aYSFYX3orZT1NIukIj9trF6U0bQXJBI7tP+e0H/7HB0S/yEQ8055bqLUTlpRzmmRrvN6VZuAe\nsbzeGGNe+l/7nfaeeyGt6rwok6886ahrOxZD2pG0NUf0SyfZcuUvca5Z6XINL3waaXqc9MUJVsYY\n0/YO7lmmXDL9gphiSqKzJKrXfgVZxMKHsN9O9MpayfVjopPSUX1yDfnkli1OO7sA8+XyZbmXTd6W\n47TbD6MOs3ydk5GMMab5LPalnijMN057MsaYC9/H+hlHUhdbxjY9hDkcQJK2i9etMZyPOl/wCawT\nMxNyL8vP2N8IjoZsue6IvJe8vqRvg0zt0uuXRb/8AtTGIS+eoStEnndkLmrWYAXeLQab5F4p3IVz\nmg7FvczeiHMYqJLyuPhlmOcs3bz47VOiXzBJcjgdcveyZaIfv3PGRWJMbCmT77ac6MfWHr3t8poK\nbdmon5G1HjLXgEC5LhY9jPe7OyWbNl7D3n7Ho5D99J9rF/1iFpOFAo3VGWvOcmps2QP3mI+CncLU\nTHvoU1VoTz4nj51JcshBqsuz1j6Zk93Sl6L22El2iRthg+Gj9XO4Rt6vUHpX+ygoc0ahUCgUCoVC\noVAoFAqFYh6hP84oFAqFQqFQKBQKhUKhUMwj9McZhUKhUCgUCoVCoVAoFIp5xB09Z9hbhKN3jTFm\njOLLRhugiUvfUyj6cYRrZBbipwKsSLehWuicPaQ/7bkmdWRxpfBC6blc77RvHoQ/Qv46GakYegm6\n7nTyi7GjjwPpnJIpVi/A6jc2IbXcv0dlpYxoLFuE8xhpHbS7O5hrz5mSXYhB5Kg6Y4yJzIcWdnkm\n/BNsXxOOE+26Bj1y6TPLRb9higtjvWJth9Qwv/j1E057dSHGDEe6Tr4v7/P5ejzvNQsgfO62oobZ\ny2P5l+FR0XfZ0juSNwrHJoavkrrs0SY8u+1P43jnXr0g+i1cJce+P9F7BfcvtjBBfMZeMCE0rYIj\nQkW/rg8RBRfkwvMt+JTUyLYexJwreeI+p+3zybFT90vo83tP4zMv6TY5UtcYY/LJq6rwcxg73q56\n0Y+jbXu6EE04bGmKs5ffi34zx532SK+MwWt7E9e07SEYH3RbvhlBEXcsiX80IjJQV6JypC9K9Y8Q\nh5lxD8Z3cou85qIn4RPQdawRxw6VzzuI9OWJRdD2/s3Tnxf9pscw9hvfP+a0e+rJq+srvxDf+fUJ\nzN8UmrN/9tBe0Y/rzd8884zTLntcjrmoLOh+Q0NxrsM9Mkay7gX4D8QsRL8b71wX/cI5utTPPhdp\ndyGKne+dMcZU/AI1wUveNMHW+rm+uNhpL3gW92LcK30z2Cfkxs9/h+NZ3gETvfC9GJvEOY02YQ3v\n7xkS3/nsdkSWdwyixsWkyTrm86FujnfDByD/KVn7ey7AbyGQClHda5WiX+7duPbLr8IPI8qKFi3/\nkoyo9DeiCrAXsO/nJPm1eMkXZ8Tyxcnah6ja/tOoJbErpC9Awgb4n0ySl1/776R3RGQh5lL6CnzH\nnYp/H7gp19IYWg/Y/6TvklzvUrdiPzLeibrsbZJ+SBx1HkoeguOdw6If1/YA8hJgDzRjjMl6wIrz\n9SNiInB++Z+WNaXpNewJkzbAB6C4IEv0Y7+Te3Pvctrj3XLt4nUxjCKAbS+2jvewD/z1KfhUlGXi\neZZlyXPY8EV4p3Wfwjwqfrpc9ONn09eKdcH2Kml/G35Aa7+CmO7xPrmWNL+KexQYjrVv1vLPi10+\ntz4XPP8iMqSPV3I/1obAEOxbeE9qjDFDHahv7mg8n+hiuV/qOQ//CXcL7tuwT47bPtrT/Pi995x2\nCT3Hq42N4jsZ5EvBHkMl1h4/ORXXNNyPcVZxRR5v92cRkV71BrybJqfltf/27Fmn/Wwm9hW2/2Zc\nGj7LXWL8CjftZxYsShKfjbehdoTGwGsj2Yovnx6Gf0cXrUlpsdKzp+88nk2/F8de/MxK2e8i6pIr\nAevLeDu+M2z5wUVm4TqGqrEHKtwr4+TH2vDecfEI1jh+xzTGmHseoHcQ8sXa/MwG0e/yy9jbJG+F\n70v/S9JfLiRKznV/o/cC7ln6LumrachiacpLHj4bckW34Wqsk+xpmba7QPQbod8O2EM2ZoHlb/Yu\n3g+8K1GzQiKwP6p/Xt6n77wKH699q+Gb9+0DB0S/e1ZizKwk76bEMulbOf0BPGT52jk62xhjkshz\npv8C1urhIekBOtGGcScdHf8LypxRKBQKhUKhUCgUCoVCoZhH6I8zCoVCoVAoFAqFQqFQKBTziDty\n+JPWgZ7T/YGUCcQtRdxYJMWWjrRI6jRHXA8ThcmOymJqaHgU6IATsZJqOD0JCiBHFi7YAar09IiU\nw+Q/gvgvpv0WbJOZgAMXQTnrGcR1ZJWli36+DtCTLpwGxSorMVH0C40DjW6E5D4pWyUFjOO25gJM\nMU9YbV1LF93PEVAK+9ok/TVvF+5V3j241/1XJMV6rBlUvyCiydqRg8c//NBpf3IzKL2Ze/B3zvzy\njPgOR6JzO9iSyK3aBb5my/6bTptpjcYYE50Feur4ICQI7e/WiX5hqaDOVR4Aha2oVMaNjzR8vHTt\nj0XScjy38DRJox5rxT1PWo1raj8iZXb5+yAT6L6Gcdt1XEpH0nfjGbRX4BlE50m6pjsblNRpikBP\noGhWX4OMmatvxXgJ/AVoiBl7i0S/IIpOnBjEfEspkwTAwUHESmfkP+i0OzskdTE8E/es8hCuPSNB\n0ifDrHvrb7ScwDPJCZf1J+cRRBGHRIK6yjImY4w5/5+45sUP4LMX3jws+n1hE+QpfZdw3z1lknLc\n9T6evysFYz0mGtHrKWWS1p5EkdmFVHtrDt8U/cJJalW+FNfL0evGSGlFPEX2zk7LuhFB0ktfD8bF\nsifkuDj5M8gJlnzC+BVNr0BClX6PlABlLsQ8TViF9pXnzot++ZtB7w0Kw1h3hcvxWPtrXEfsEtBs\nOXLZGLkmsbyN4xpLNkoee1ginm8xRXxWPSejmnkdK35on9O+9p8viX4skeU1rpekaMZICQbT2jPv\nkzXA108RxTI11y/oPIS5OGVJJMJIVsnrZ1SRfD5TtAfJewZz8eL3PxD9cjeBLt10CFKm+HwpuZjs\nx34neWuO0x4nSVpiWbFhRERArjTmwtoVdXem6DfaD7r60DXEzwYES9l2JMm9xprxdyMLZf0vvAvn\nEZWDZ88SYWOMmeifu/1N4ecx79vekxKxKDrf7lPYv9prDe8JOZI+utB61rTGpZBE7PQPToh+JZtQ\n57YOIi6Xa2H0AnkvX/sXUPCf/e5/d9qzs3Iv63Ihyn1qGrLiIJeU5cWvQe3hY/B4NUbGH7Ncs+ll\nKRMNDJ3b/4/LY4YlA8YYk7AO45hlKoPNco8aRPvA6BI8uzCShhpjzMb7VzntUA/qo/uIlIuEkTT2\n2W3bnPbzx4457ZwkuZa++s47TjvuQexHNjy0SvSboNoWVYSxkGak3M1FssL4GKx9HE1tjDGFqVif\nZ0iSNjkl919jPVJa4U+M3sL+NyhSjsf2WrxbeRZhDNd1Shnvml2ooUkjONe2fikndTXg+AkJWEN6\nPpTvqf0N2Ffw+IhKwb1c9IWHxHcCAnDs0ZaTTjvcGkcfvIh92JKVmPNBlkR2lKSwyauwkE1S3THG\nmJKtqKfNr0N6l5gt65Cvd+6eoTHGuOldfPCqjBkfJHuOCZLWpchtmolfi+v0VuHdqvJVKT1a/ASk\n0Td+g88me+Wa4SkniREtVx0fQGrqzpUSubJsvJ/9hGSJ7U1Not/CBx5w2gUPo153HZf9yu/F2OSI\n9ZAoaSfQdhT7iugM/DYSbkVuz1rvxDaUOaNQKBQKhUKhUCgUCoVCMY/QH2cUCoVCoVAoFAqFQqFQ\nKOYRd5Q1MQWSqWjGGOMjJ3uW5bjipHSEpUdTlIAQHBn6sf0CA0EatstUAAAgAElEQVQLc6dI+mdY\nGORUw8kkkyLKUMrqReI7oaGQG7WchpwmIEj+NpV+P6hpISdAaYoukXKl6+dBHU6PB+WMndCNkdfk\nWQiaV+8FmRATmSOdyP2N2nchNcjdKN236ypwnREu0DozlklKNFPwovLxTPoru0W/9B04fg9RiZcv\nk1Tif4v5M6edujnHabdQIkKQJVd6kBy3w2MwzmpuyRSh99+EFGf1QvzdxHXymnoq8LdYdpV1v0yX\n4M/qT+LZBwTL8/OOS5qiP8EUa5YHGmNM1yWMp5E6fJb31GLRb3YW4zFzBWi6NZ37RT9vHaigwW7M\nxb4KKWG7dRIO6pwe0NcEJ/xkj5wT7LrPKQqtb1aLfik7IIuISAUFtb9ZJr/EZsJBf3S00WnbCWgF\nDyL1YHYSlMSJTkkRDY2eWyf8uExcv/dGj/hsYAz3MOcx0CuHbvaKfks+QQkeNDZdIZJOO3AZlOGE\nFaibt2cknTKFHPnZFb+rF2MpO01SekvuR41lGnrhVinV8lG6j5fGbfMbVaIfp4NEUtpE7S8ui369\nwxhbWTmgurbVSUmDne7gT+Q8jmufHJRzniUwfRcxX/h+GWNMBMnnvCR5jS2SdHVOwOB6EyiVKAKl\n9+FvpZZjrPQ3yHs+PUYy1grcv/52Kc9MpMTF7nqSmlrnwGvEIB07b6ccE0y756S4CUve6+uRaTn+\nhmcp9jTDNZI2z3Kw5I2gRze+JOsPJyVd/gH2FtMzUnb25i+QOMcpka5WuXZl0H6C55U7D+M5e+Gj\n4jvj46j/3VdQR20pMdcK/uz2lOwXTet72weNTrvispxja3aD5t3yW4ytYIvmHbts7pJ+xrtRD3xW\nLa+73Oi0eS+R414o+nkpKbSLEh2nRyUNPTQOY2KEJDXLn5AJMZzglkjyz5p2HPv9F2XCx7OfQepg\nyxFKpCuWe8/mClD/s1flOO0ZnzxXthOY9GJecrqfMTIVMCgU30neLqX3AXcqOH7A4DXIJzprpJQi\ndwvkVixlutku08g42Si+NMdpt524IfolkbQkKJT2kb+T/VrOYLwv2ow94RMzSN/5/kEpAX3p37/u\ntGcnUAOCrYSdd38FuUw0pdSVFkqpPKdT9Q1Bvl7VJt8hOBmK02Ji+uT4ufW+TL/1J6ZJmhaRIyUm\ni1ZCvsLppxsfllGKbEkRGozxaCf5NZOtwfq9kOs37Zey6qInsb7wHPHk4T5HRcn9/tAQ5qabksM4\nuckYY7b+KcZb2wHc16RtOebjEFuGNaftoKynvh6sf2k7sSezk9j6Lshx72+wzHWkRSb0JZGdSTTJ\nX1mmZ4wxfZSCGp6OvU7WIqlP5rGw6JMYIyFuec2+PtR2Ts0bJtnt2Vp5P3fvwdi60dLitB/59KdF\nv9y1kKh6a/Huc/maPN5Okmq5KHHMloq6yebl1C+wJ9j0mY2i31i7vLc2lDmjUCgUCoVCoVAoFAqF\nQjGP0B9nFAqFQqFQKBQKhUKhUCjmEXeUNbFTsy3h4CQn5ljbTuuzU6D28TFYqmD3GxmA2/HstJQn\nTPsgKwkhadTtKfRrPSKpx+5MUOySlyNdo+eqTLMZJ5oRU3H7z0sa2bLdkIt0ngVdKiBAUj+Zkt5/\nGRR3V6JMKrFdu/0NTlXwVklq3qK7QYE/9wbotOmWLCQ8Ffcw2I37nrRKpj/1UyrMDMlH0rfkiH6p\n20Ela3kNlOjRCYyfkjUyCYUlA4FE2+28JJ8308ZFyoBF82Y37mA6XniylHB460B5T8uBO79No87f\nJs/Xn+g9D5qgr0PS/Rd+HkkATJW0Xd1nKJFkZBJjOn2rpHmP94Ha13setPuJPjlOExIopW0If+ut\nixhHf/7FR8R3WFI03onruFYtE6NMEJ5hPI2xoDBZskYGUA/CozFnE1N2iH5t1W85bW/zx6dqcZJK\nyY6P7fZ/jBBKgRmz0r3c+biflT8867TjCmWiSz8lVjB1+lOf3yv6vfw8HOqf3IR6PW25xoeRdK2i\nGrKmR7/5tNMeqLGkmBmoB+/+M6jdvklJ8VyQBhosyxe5hhhjzCil/N34HqQzTG02xpgl9yNxiJ/V\npCUjWfZ5SZf2JzjJJCBI1nxXEhKQUrdAGjDplamD1T+/5LQTlmLchril1G20EWMkdgko0U3vSspt\nRBTG1QDV4Mk+pD1xIqIxxnhrMM+vnoEcZtdXdot+LQdAFec1zqbqx5fjWXeSfCLCSkBzp2Pt7yMZ\nybhV10ItibS/0XsWY9qW0MaUYM6x/JnlTsbI+sjpC8FBQaKfdwyU9aU5OU47Pkrem0JKlhylFL6s\nzevwN3tlOtDkGNYnpuF3HpU1lVOYWK56e1aui7Mkeyx9FmlIrheviH5dl/DsUkgqErc4RfSbHJJj\n35+YJolcys48+dl+1DkXPbdD//KO6JcQLfeiznfi5fjrOIO9Xu5DK+zuDiZHUMu6jmGPsf4ByJ+u\n/j8yCaT1PCTgK/4K6ZWRkVIO3nXit06b19LUzVKGFBSM663/1QWnzYlMxhhz/DtHnTYnp6Wuk6lB\nNw5hj1a45pPG33Dn0l7iqrw3nEbJ8ukt98kEJN5PBC3CHjt1g5TyBwRgTemrxH0v2iv3QSxx5rTD\n5CLU4X8u/5z4Tjell676ysP490q5R33wb7FW/+brrzvtmCUy/SmEamzmEsyxcWudLd6HusFyVXvu\n8f3zN4Z9+FvxlhSnm2wi3FmUuNgt96jjTZg7sbmoV9UfyHewbU9AyjRK+7mYfGmDESLqHN5p+m/i\n3W8ivU98Z+A63nvdWRiXYdZ7wUgjJHaxK7GGD9dJiSxLAlv2Y5210wn5uQXSu/JIk9wnTvXPXT01\nxpjWmxjD9vrE11LzCuRfyYukdDXIhfVv6Dr2NKFRH28b4E6BpHekXb6njlOqcBStY9cvYb/KkmBj\njLlyGvuWxzZswPetpDO26aj8FfZli3NzRL+RW3je7dUYM5n3y+c4QvJ9N/2t6y/LpKqiu0vNnaDM\nGYVCoVAoFAqFQqFQKBSKeYT+OKNQKBQKhUKhUCgUCoVCMY/QH2cUCoVCoVAoFAqFQqFQKOYRd/Sc\nSSaPgN5zrR/bjyMwoy3NH8fyDpxHtOtYs1f0S9kKzexYB7xfbD10XBEi0EbbcIyZSWgp7dg//u+u\n89Dq21p49vW4PQPtY3SJ9HwYp/OLJu+c3hapNUzbDX1vSCS0duGJbtGPNY5zAX4+UcVSlzfrw31b\ntAqeKaEeqcureRM+C8WPwPdh6LrUBg6T98iiLyD6OizaGhfNuNdxa+EpEk+eBsM1Ugs6Q+fKHkOr\nC6XXSzJFZjcegyYx6LQcw1EFiCedJD+V1tdlrHP3ADSfU+RtkZ0vdZY9fPy7jV/RXgUdqK2NHryB\nOHOOMxyy7l/6NsRmdp+HBpj9e4wxpvUtXP9wL7SeA6NSH/wPzz3ntMdH0O8//uZvnHbDeel7sHAf\n/Jpe+d7bTrs4XXoXhSVBMz5M8Xa2D0X30UanHZ4J/XjKRjmnfBT1l7ET83LC8uWJXiDnur+Rth36\n9wv/flx8lrMauvG6M9BEF62REfDdp/Dsku7C2L/44w9Fv3s2wuNg8DLux+iAvOYU8hdYtwPxuCe+\nAZ+epHgZiR68F2OpbCXOgcepMcaEUd1rOIza67K8ZA5eRmT2ygI8HzsOOKIJ9XaGPksplFr98W7y\nL5FJzn80cp/Ac+o80Sg+676ONS5+OTxYWvfLmsJ1ZLQeGmU7NpN9ZmKLMEcSF5aJfuxpwDWgnqJT\nz9fVie9kJmCsF5M3kB0NOdaD8cJrc2CwXGd7yQupsZo8iqwU3lMvYJzmJuG5Ja2XPheBLunb4m+4\norHGhaXLvQCvmb4eOV8YPRUY78UPYlywF5QxxsyS30HXEPYWaXFyXeQ1ZLAF4yIqD/14T2WMMd5K\naPo5xjre8oNLXQQfppFSjAtvvTyeOwVzvYF08rmPyTj4jsO0tkbA24Gj4Y0xpu8MxkJeufErOLY6\nNFjuWZI3Y6/I/kphodLvKmcLanLSSrR9g9Lrgf1tqn8CLyd3nqyN7CXmHce6c/01+Ig98+Au8Z2D\n7+Oz/BvwCAxYKCcPex5FkHdTw6+vin6Tg/Du43p17WfnRT8el/kPo6ac/ZlcSzb/9RwYsBH6PsQY\nKVyWIz4bIq+HuET44vC4N8aY2JWoYaP9OF5gqKwj4ZGoe5NDHx8t7aXo5ISVmEscZRy/LE18J307\nYpljY+FLNBgjay/7WN377DanzftxY4y5/g68fpZ9BvM3y/I75JrP5x2ZFyv6ZRTOXax90UPke2P5\n2kUX456zV2hwpOx35hh8rRZOY98z4pM+Kx3HG522pwDvNJ1VnaJf7GKsnxPk7Tk1hPnhipMeoOyV\nGRaPz/qvyL3NaCPq+IsH3nfa4VZ92VgKb5HkHNyHwSo5ftlnZvAa9vQhHrknGOq7cwTzH4v0QniG\nxS2T46XuDbwHxsRjzRytGxD9sh9HLRmux3rQclz6vIbTuB3vGzIfh2ha/87RPreV/DHLMuU+OSUb\nMfKnL9xw2hs3LRH9zj8Hj8P0ePydpK0y1v7Sy/DSzEzDvqXuVeknxX5zfLyETXJ/001+ZB/lb6nM\nGYVCoVAoFAqFQqFQKBSKeYT+OKNQKBQKhUKhUCgUCoVCMY+4o6yJo3dt6cPgTVCywlMQMdZzVkpH\n4stBiwomOlu7FfPoqgTt/jbFOE+PStqbiM8mWvsoRbBFZFox3XQ8liTFLUoW/Zj+GBgCmm73yWbR\nL+cRyEr6r4BGV7C4RPTrOgnaUhhFrI61SvrWjG/u4u2MMcaQVOjmwRvio+JdOOcOogQmjUt5R1IB\nKGITvaB82zFi4xSNF58KWVN4uKRYe1t/hX5LQA1teg20uY5b3eI7uesQlfmT/3jDaX/uiw+IfsO1\noNENkNwmqFlShENIWnGlCc/KpsdxzGrxHlAUA4Pkb5tX90tqsT+RlB73sZ9x3N8k0TUn+2X0df2L\noOXlPgFqX3CwlNlFFeJvMZV9tF1KER9rA3U4OgL0z06ig+/4653iO30UKc9xkHY0pK8bYyymDGMv\nqVzGpVZdQqxsYAjmb+X3z4h+ufvw3C69gri8kh1yzgaHh5i5BMebx0TL+85xgS6qP1Epcu609CIi\nsJlinXlsGmNMyxHIDn586JDT/rsvPSn6McU3aw9kTUEUd919XtZ1pnaHkhQne3WO6NdxEd9LLka9\nPXvimug3TfT6nALUgxArupgjY0Ni6DNLytrxHuizCzYYv4KfE9PJjTEmdTniToeqUL84utgYY6Ko\ndgwOkASrQsrxEjeCCtt9HteUsFyOiczl25129ZuI242NkvGfjJIMHKOqFXPZc0tGIec/Dikiy7Oi\nS6RENoSiZ5fmQr8SZMmTltEzZclxZLaUhxhL0uZvJBLNuO+cjIrnvUpQGNYJWybAch6O/LVjy1Pz\nQIO+ceKj47eNMeZWFeKaC8sx1pnyXn9cxqhnl+M6fB2oLwnFcm2emcF6EBUF2nl4mdyPDHVA6hFG\ne7uBa1IykLIN51dLspr44kTRL8h1x23mH4VIWvvsvaevDXu9tg5IPWw5csNRSE5qD2N827HDKz+z\nFn+LJA4BTbL2tHeCar/yc4hA9/34A6c9Y8k+9mzHXmmwAvc5tVzqwMbbITPjdd+W7rA8kiX/ZZ+S\nEeAXSCLQ9DokNLb0q/cC7m26TO32CwaGUQPTl0sdKl8bX7O3VsrxwpOxnnLNGe2QMpCOG9gzFOzD\n/qT1Aynlyn1wGf5WM2o5r4uzU1K+ODMF+U1PD9bcmAwpkeg4g/nCJcB7U17T6r9ArPpYJ64jMl/W\noSkv/m54KuZszREpp11O0ih/49YbeLdIWS330BP0XjBKlgTNNTIiOzcZe4Qx2hMmWnH3vcO4Fwlu\nrFf2nPX1YB/J734cx2zPnaRVOHe2nOj8QL4HNnZjTLA8dcd2OcfaqzGfI8n2Y+iafL8ZH0J95ndT\nT6mUbAdHfnwctT/A613zW3eQY5M8nvdvxsh9/uUjkP0sWV8s+sWXY68XmYTnGBgo68/EBH5vyC7F\nvqWbJMIFW6S9Be8PY6vwfscWFsYYs2gP1sJgmtssYzLGmBm6xpiFeD9uPVwl+qV4UKPCKTa++q3r\nol/Bdrk+21DmjEKhUCgUCoVCoVAoFArFPEJ/nFEoFAqFQqFQKBQKhUKhmEfckW86cIWTJySNuucM\n6LfsGh9TIimt7HAfQPKaqDRJU/N1gtbIFPCpfunS3f4OKKgVVaDt3/NXu502u20bY0zfBVCWM/aA\nMjk9JqmlcUtBq+onaun0jDzeKNGXp7yQBPD1GWOMKx7JMvy3bk9LClhUgaSH+xtMiU7NkHKlgBB8\nxs7XTBs0xpi2fkiFFqSCFrz5qzJ1YIZonhMToO35fJI2Hp8LKlnHpQt0QmjeaJU05YiLoPM9sg50\n4fd/e1r0Yzfu0nV43kM3ZbLU5cZGfGcL5BwjjTKlITKM5BP06KbGpaQhtzTDzBUmBjAP2I3fGCnn\nYYlcqCUJYUrwUDVogmEJ0vmfXegniAIYEiWphnv3bXTaqVvAdQ6NoL/TKJ8hy/vuWop7nrRF0n5H\nGyipJBsU3plpSUmMW4265KIkp+Rg+bszSxuZsh1tzb2qn5Mj+788bPyNIZKDzk7JOhCZiftWsAuU\nx6HWFtEv/xnct5YDkDj1nJL9WLb35/ftddoBwZLG66JxMtxG0gW6Z1l3Swom1xSesz/+p5dEv6c+\njdiy/S8dc9r3P7lV9NtEMp8wSrM7+tMTol/5DDjgmXdjbl/61knRz5Ms1xd/YrQF8yNxrSWBJIo0\nJxvFlEhq8oWfomalZ2LNnBmXtOxJmvcF2+932rXvvS76tQ1B6sLyoqoWzD+u4cYYE5YB6U3+FGjU\nMUVyDa95ATLAsAgcmxNhjJEJjIFhuA8Jq2Rd9HVgredj2FIgkfghA+r8gjBK6ci4R0opZmmNbn4N\ntOWRIVkrmXpfsAkpY1WvS4krz0WmhscskPWn+ijWycle1DqW0rVaz/H6fjzjx7+CMTI1JeVKAQG8\npmO+tV+8IPrlrMWcTczGfRgelrTsiv991GlnU32ISJZSOl//mJkrHPrme0572WY5SMa8uH/DlJrE\nex5jjEnKx3jPvJeSkiyp5Nv/cMBpf1gNuv/dlvQo1o369YOvvOC0H3/6LqfdXSHlHKMTmAcrvoC9\nTfcNOY5Stubgs1OQWbBMwxhjBkgeyXvPgABZX3g/6CbJ3oKlUvrVeVSmrPgbpZQAyvPNGGMy7qU9\nO9WEWMuWQOzbaC9uS8j4e42HkJgYHCGlp5OjmHM9JGlZ/NmncD7Tco75fJiLE17Mt6TM5aJffDn2\n2t467OcWfFrKjup/hbk51EHrzkJ57X2U2JlJCY4Ltsq69v53kCr0mZ88ZvwJrgFdh+R4yXwI8nF+\nh7NlnXFp2AN1t+AesXzFGGOiw7HX67uKsb7wgcWiX0gU1qtomiMJGeud9tCgrH9TI5iLp15Gilpu\nolwX2wewR00mKctEt6x3+ZuxLnRS0qanSL6LZdyH+8cS0uZXpRVFyo450BUSPGXYq/TVyESpUEoi\nyqDkUd5zGGOMj2RsCSRJs21KJodxr+Pj8T7h9cq1xtcDG5TOWox1lv9H5soaWP8SaienQs5a799D\nVZh/s7T/yiuW+5beZqy7lw7i2MkxMaJfKI05XsNL7pdph4PXpazNhjJnFAqFQqFQKBQKhUKhUCjm\nEfrjjEKhUCgUCoVCoVAoFArFPEJ/nFEoFAqFQqFQKBQKhUKhmEf8f0RpQxs90iR9OOIoIps1VlOj\n0odjqAqatUSKKJv0Sr16+1nSz0ZBCx6aEC76NVVBk71x3yqn3XW80WknWD4ArGUPCYceuu9yjeg3\nXAcNoYv+bta90m9htBn6RxHJZkWGujOhRRuh78SvlP49fZek/tjfaDkLnSNrNY0xpu5daKeL0nFe\nYbGy3+IE3INoSyvJiEiBvnB2Fs94bFBeY1g0jhFA/hWHjkL/OWPFs7EHzYZtiDlM7ZcRrB010KBy\nDHbeXVJ/u7Eez4fj4CPSpPeBj6LDx8kbqaGiSfRLTZ477yCOt+aoRGOM8XVB38kax9AceV9iCnDP\nJ4fhZdH8itR35lB8ducJaD1jrIhUTya0zWNe+J00HoBHxXC99Edg8POd6JFeDonr4EHD/jiZm6Um\nOzAE2lz2FIoplmO07W14cuRvxziofbFC9MvcWWDmEqHki7Pg81KHXv8CziWIPDvsiOH2w9Bsp+/G\nM3jpq6+Kfvd8FvHKl1/DM3FTbKkxxmSvg4b5e38Lj4Rn/xT+Fflrpf/O5CSea9uVY077wR3rRb/m\nM41Om3XZ/ZdkLK+H4tLd5GG2qFTqq7PvxdhsfPOK0x6bkOtJWpbUAfsT7JvUaWnr0+/B8+g81ui0\nU7fLCHjWSs9OYh4krJdrF/t3NF9612nb8ZruOERShofnOO2ha5g73/jpT8V3vvylR5w2x2V70mTc\n5dK/xHNrOgCtdVC43D6wx5qHakWvtb4lbkD0c+Mb8Jeoe0t6TSRkUz29y/gdvFcZa5cea6O038n+\nBCLqBypl1HlYEp5PwwGc/5TlUxcfQ2M6C9d/24riLc3E83fn4b4P18F/obw4X3yHo5vZD4n3b8YY\n4yafP08G9k7xZdIPrrcVkc/s8TE1LPd2ySuhyZ+dgFa/+rlLol/mHrnu+hM7/gJRyO9+6z3x2Qry\noLlNtg22j2Hscuxla3+M/Ud4htwHFBdhTeJ6Y8f3Ji7D8bbSOGi7iDVycFSud+s+v8Fp83o3cFHW\nydxPwpvlzHlc1JJu6dnGXkj1zyEienBM+mGsXgEvkER6ngf+Yb/ot2rnEjOXYI+K2GUp4rOO9+At\nmXE/9qFT1jtEL8X3xpFnzlir9CsJCkftDae9Xuc79aLfIEUd59KeqLXiiNNmnzhjjOm7jFo32oAa\ncnuP3MsKPy3yx5mekM8nPB3nl7Ybe5PbM9KrxZ0DvyD2e0m5S9aKjZ/aYOYK7NkzZI2z2Gbci5iF\nWBtuW757o93Ym1SQJyT7XBpjTHoq9ndhdI/sNaTkCfjzzU7jnl39OfY5PB6MkR5F/L5kx0WvKcRa\nH7uA1rubco2IGES9SaZ3U9sL6fQP4JvHccxRRfK9op3mQ9Em43dMDOAdIn2j3H+xt0w37W/CMywP\n2Tasp8k5eFYzls/rcC3WrsHSy057clLWvQm6h2+cO+e0H1y92mlPW789pG7OwfnQe1tzhfRmjPbh\n94YYijq/ekbGiC+l9SS2A9cbluwW/QarcU2pW3AOs9NyPbZ9wmwoc0ahUCgUCoVCoVAoFAqFYh6h\nP84oFAqFQqFQKBQKhUKhUMwj7ihrMrOggXksScNYF2hLtyMpOmpIUg2T1+c4bZavxCyQsoPEFZDU\n9JyDfKXmZK3olxIH+t7QFdAOg9y4lFmLKnybImG9zaCcRS+Q1yTojimgyo00Dch+JF/qOw1KcOKm\nLNEvuhRxdxybO9YpZQXBbhlR7G9MEe3WPsfe30HS0tQDOm2RR9Lrm4gKltiIyNTqkWuiX9a6HKcd\nlkgRZRZ90b0U5zFJUZu3KVovP1nGBRaW0rkTxTA0WA7jtBLQYgMp2nbouoyFG2oH3TWd6K3j1vNp\nrcQz9k2BlhcTESH62TRof8JNMg07ij2B5s4734T0YVWOlHZ4G0C3myXKe6pFfb31AuQirhRc47RF\nwwwJAS0vNhHPanwR7l+CJeGr/SWkO2kbc3BuNX2inwkArZHrxtVvvSG6pe0FZZ7HWN85SdVPvxsU\n1JrXK502016NMabtTZI6bjN+B0t2ZiYkHT5+De7VaBPGZmiUjESPoGOMtmMuPvKPD4p+w7cgPVrx\nJGQMNs37dy8hTvSJxyATyKfo5kErbrLpbVBQZym+PdKiajItO64R4zFhjYwpdGfgs8BQzOeM+6TE\n5sj/QJzthr9EHHfjNUlVjcyNNXOFSaLY9nZLua+bIk2zH4RkwJaEjJPsYGoM7cPPy+hwlsfc+0U8\nmwCZ8iukTLdvY56m3Q0q/M99XxXfaT+Pe5a0BrW16Yg8B1c8aoCbpJJeiqA0xpi+q5izrR80Om1b\nwpHdjPpc9mdr8Z135FrfcX1u5b4cZZlkSaEDKW657SDOa9Yn9xbe67gHngzcm+lmWaNdybiH5RQr\n20t7HWNkZOzz3znmtAtScM/s+3mT5L5fK0ENCY6U+4pZ2gd1NkIC1F/RIfpFU5Q6S0XHWryin4/o\n78nrcP88+bIGuDyyfvkT3lqc39r7pEw0huTXlR+iri9IlXsgQ3sOloF0H20U3a43QHqfmYBjf+35\n50W/r4c+47T/9bXXnPbGhaDF712zUnwnmPaetccw3hI9cg2v/tlFp333f9vhtMda5bNJpnseS1HD\nlTcbRb+ITJIj0H247x/vE/2mLTmCvzHWhvNPXCPnYsvpRqedRmu8t1rWn4KnsJZf+P4pHC9eSo9Y\ninjwPxAtvapcrjWXKjBmkjtznDbLJxpfruSvmOBIPEde42pfkPLp9J0YZ9H5kK30XJD1IG0zzmm8\nD+8hvRfk/sazEDLX2BWQAA1ckfIQEXksFch/NDh6PtYj98ITJLd3Z9NaHyatIG7W4rpGxvGdYZ+U\nIh46j/vZfRg186+/+Rl5TrRQck2PXSKlc4xb+yEXzM5Fv6lB+W4bnoW5U3cJ8n+P9V7wk+ffctpP\n34U9S0urjFJe/iRqQuObkGfdtuQwyZukhNHf6D+DdTd9b6H4bKwDe/swsldwZ0pZUzhJfY7+BrLK\nLY+sE/34Xb31FGLLk1ZJGTi/I64mORlHsbf+TtqU8P6L167MRXLvOTmAsTVA68mWP9ks+rWT7LGn\nB3Oxu6pB9EuPw/o30Y8x3Guts+4EkkPtMn8AZc4oFAqFQqFQKBQKhUKhUMwj9McZhUKhUCgUCoVC\noVAoFIp5xB1lTSFEjZwakZQuH6WrMC1o6IaUjkTngeLDjoSgnKYAACAASURBVP5BYfJP27KX36No\nq0xKGqPUI/5O7BLIKmy6Y0wJaLoTRI9iGqcxxswQPT/EDXoiU+6NkclVoYlw8w616LsDVaCTjlBq\njadMynXsNBZ/I2spaKK2tCc9H+cycAW0rb5OSddPImpozBJQKENuyX6cwsIJQ/wMjDGmr+6m0/be\nhKQlNwnHLt+3THyHadrs+p0TLH9jnBnHc+RkqYFh6aLujpb0w9+DE3WMMaboHqR1NL4HyrFvUkoV\nbAd9f4LnS/9FSY9z0fkuW4X50n9VXm/cYlA0OVWA568xxnQP4Jlu+iLc/W/flnO0u/4MjkFSjwhK\nBfHWyLnINEQvUeavVzWKfrv2gOfX/ApopnYqTzhRAwODQGGd6JNpAXx+kWFU1ywZZspdkk7pb3Aq\nDMstjTEmbhGeTzONsxRynTdG3re8R1c47YFqSYnm8chU7EuHJRV740KMb65hfR2nnbY7VkoBmEb9\ni2+Aur9n+2rR79SHSPfZ/RRoojZNvo0kLdGUTsCUb2OMWfUZyGC4lucullT4gCBL9+NHcB1a9MkV\n4rPOI0jKGKPEgvFeOR65dtzqwjxdtapU9BtoxrrRdAA1M6k8TfQLDAEleJDSXkbqQb91p0qqedZK\nHKPvCmpKqJXUx4mLySRF9BTJmn7lO0j5KX0GEhObgs+SM36G0Qvks07bPrdzkf/e4E1Zpzi9j6Wx\nsUslHb7/Eu7blBfPtLpdSrL4GS9pBzU8IlvSweOj8IxSY3GffvTKK077+a99TXwnLBTjMTIf3+n7\nUN735HLQvDvPQs5ceM/9ol9XLZ4jp1xcuyGTyZavw/E4EXPwilx3OEHKLDJ+BScVtldLCUfmEGp+\nyXLISIYb5Z7Fswh7IE4XdKXIFI6lsZDQnj+HNemHf/kl0e/Jr/690/7ml/DZoSuQC6fulqmALL9Y\nuA/Jac3vSKq+h+phJO1Lb/5WyssL7sI+YGbk46XYQ5WY2zzvPZbkv58ki5lzEL51qxISy3ArLTN3\nK+5VN6Wyeqy52HYA6SqLn5QSN0b3KcjTtj2M9eTS21dEv7XbkVB1/WXIaFh6n7dRSsJZPnHxl+ed\nNqfvGGNM5ev4W2UP4O/Ydg/tx1HzOd3F1yGl912UdsVSYk+ZTPVzxX30ntcfqH4J17TgkcXiM5aQ\n8v6lqVHOWd5BF1N67NuXZALcExs3Om2PG/O08bUbop+XUqPKHsX7BCclhVDasDHGRFJa8DjJUk7c\nkMdeNooko85B1JSMZPkMv/A51Ne6M6ihS+6XCWhcAzLvgnRnakjuzwNDpBTM3/CUox7aiX833sW6\nsWADztFrrZ8hMbin6/dgLo53yFTErL2Q7Q03Yq8yaSWKRtHvCHn0jsj3LIWSkYwxpp/Wof46nF/K\nCilr4t85Qj04b2+ttFoYHsIcW7AT570oQc6pzsOQOfG6mGi9pw7ftKwcLChzRqFQKBQKhUKhUCgU\nCoViHqE/zigUCoVCoVAoFAqFQqFQzCP0xxmFQqFQKBQKhUKhUCgUinnEHT1nklZDxz/eY2nAcqBt\n9pJON6ZE6u0Gb0JrONEH/R77uxhjTOxi6NzCkqAhTFlSLvqNehudNkfFjjZD8xe3NJW/IjxiPKXQ\nq9m+FBx3OkrRfrbGb3oUesXch6EbnBiS98iQ7UFYMjTsYYlSyzxQSRrthcbvGKPo69AE6SdQe73J\naXNsa2KW1P/HLsM95VizEI/Ua2Y/hAtwReAYwx3SD6PhJfhecMyx6ybuje1f1FgHHf8MRWnHRkaK\nfuxL0nMLWsNMivk2xpipfmg5WUccmSP1waPkczREGtaCpTLSztaG+hMu0oOn7ZI6556zuLdJ5AkR\neF7e87f/7aDT3vopaHbjl0n/CtZ8tx9DpF+ApZmMJe8k1hT7yF8jwfbGoBj6gz8/5rQf/Mq9ot8Q\n+Ua1dqFduFL6UFz4HiIzA0l/ypG0xsh4u4KHypz2SMOA6BeRKf2l/A1fN2pETLHU9Z/450NOOykG\n5xEUatUfqlPNb8PTZdbSB7tzMY6DKKp1+R6pdZ4exnyJLkT99iThPlW/9pb4TnMFPALa+1GHf3f4\njOjHz2H7sPT3YYTGY3zfnsVcHO+VscGjpEsOiYFWeLRJRsn210HPmyfT0v9osI+a7TPFniRu8l4a\naZE+F64LqJs+L+rGSLu8jqRFFItK3i8B1prE62lAEOZpIkUcc1yvMcZ4af30FOO5h0ZJzweO6Z2d\nRt0dbugX/eLyyMOFIsXHGuRcnCQdP3ukRGbJujtCa7qx0o/9AfamY28yY+TegOtebPHHe84wVpVK\nYw6OSz9fD2+3sknplXTgAiLr2Rvrn77wBacdFCyf/Sc+DX+ukEiMq7g1svbOzOCaeI9V8YMXZL9R\njCWO5U1rlvH00+Rlwr4yU5afFK+f/kbbTdz/xGR5frwPDI6AL0+6tY71X4PvBccBJ6xMF/26T2Kv\ndP/X9jrt+hekV8nbb//Iaf/7v7zotP/0M/ucdmxejvhOYCDOb7gRxwsJllt09km68n14gnHssDHS\nd489AjPyZVx0YAiO33cRHkXudOmFlLhK+jT4G7yXGmuS4yXIjXvD+9Cmd6UfD9ef1v34LGmL3KcN\ndeD4vNdZuErGBvdcx76cvXrYP+rtb78ivvP01i1O+yD5pJRmynm+8741OJ+bqOtua/8xQXspVzzO\nISJb9psiT72a06gvJdvk8+49jT1hTpnxK4ICMa/sdyaeV+zxlGO9B9afxr2t68S8vGB5zvzV4w84\nbfaI7KuR7wxJaVira39L7xxrMSbs98DohVgLJik+e8moHEex5HVzOxF7uSt1Mlo5rhPvJwWLcYzp\ncVkna9+Bv1D+TqwfQ9eln0vSpjlYDAnsUzdcI9f46HDca44mdyVK3xX2aWO/GPZcNMaY9iMYq+Mt\n+Cx+raw3vEct+Rwix9vexjyftTwc2dMxfSP8gSrfkZ6LC1bjfWqyD/MoLEXug4JpfI80YG/C/nQ2\nJgdRlyd65F7Wfv42lDmjUCgUCoVCoVAoFAqFQjGP0B9nFAqFQqFQKBQKhUKhUCjmEXeUNXWeAo0z\nmmLcjDFmiqJZOaLXjqdm6vT0CL4TkSlpkxxpy7HBfQ3XRT+OxOWIa47uYkq1McYkrQENbKwL1KnZ\nSSkD4KjvQYohzvmElf9IcqXAQFCPZ6ckJX2wEufBdNSOw/WiX+p2KVPxN+LXgZ47XC3ju7KTQXnt\n9+LeNNTIGM6RTooApmdc/GkZWcj0xbqXEcmZvClH9EvaiGfSexoSiZQduBfeaklRLE4G7fTg63Ts\nGEnx/F0laGtPbEd874xFI/Msw7hlCty4FVPI42JiiiL4rMjZGxfwXNca/6L7Q9yjkVtSisNSK5YH\ntl2VzzCNpD3dRzG3w9IlLY8pifFEI+44JMetl+iWRV9Yj+8H0lxsaJLfofFXmIL7f/lnZ0W/662g\n37IkabBaUjzjonDuybsgefL9RtJgb3/Mf7ReaBH9Iq5hzi5YZ/wOji3vPCrpr+npoMam7sY8GOuS\n47G1A/dg+XbQNcMSpFySx7QrBp9NJEp6pZckQINE5e46+YbTtunW4xQF/fmnIUlzW5LAVqp1fAw7\n6rp5P2JQU7fmoJ+ViD1LMiKmTnsWy8jQ0aa5k1IEkqxkpFPKlWJIFsbrS6tV8+NJXmtYxjUspZEc\nb1tPdPqs5CLRj++Ll+SgrvWos82/rRLfmSSq8BCN+9zH5HrHMdM9laCaZ2yWEsOCT0AqOTODMeYp\nldRoriOtpxqdNtPijTEme88cZPYSWMbMew5jjBmsoPX/UfD/AwKkjLfgScgTus4hDj7Gkkn1nEGd\n2bUK67H3ulzjPvu5+5z2laOIbl1Df6f93TrxnbhFqKPeesxlXouNMab5d1gXWbobb8l3jj5/0mmv\nKoBUiNcZY4xJcePvslQ71pqLLDvwN1j6lfuEjO+dovHNFHK7prCMsu4com4LLflTL8XaG3oGNR1S\n2tbzDvaBLGUaa8a/X//2e+I7ec9Aezlch78TZsW0BrkwTmMSQbtnKbcxxkSQXKfrONbg02/JdXHv\n/w15Fu/B2w7Win6JVEfmQmLIkb28RhpjTN8t1KbwarQ9WVLGFk3SzEGSXLYekvMlOh73pvYDfJaz\nSEqPOB45PwP7oJ+//77T/ov77hPfCc/Ce81//8yDTtttve9M0/sTr4uBQXLMjbVh3x0UjmdvyzCb\nKUa8iCKOq47cFP1Kd5aYucKID8+t+0Sj+CxhDWQq9a/hnS4yXu5Z7nkIawjbBJRmSJnLWP9Hy06D\ng6ScKjQW9aHvFmwRwi9izjb3yj0lS3f4mhKj5TPkfbIrBJLhNdukbPzU4cv4uyTDTBqU79TFD2Dd\nvU3y4fAMKa+Za7Q2YO3LXSznRN5qjJ/WdzB3+L3IGGOytmD/2kp7uwRLruRZiPeVqAK8I3cfbRT9\nYsqwNx6n/XDKVux/W9+SMke23Bgl+4KCZTmi3+wU7nVQBOZYzWFr7tyP59NGe5gxa1+Vvgfzj20T\nbKmfO/vOFgrKnFEoFAqFQqFQKBQKhUKhmEfojzMKhUKhUCgUCoVCoVAoFPOIO8qaUjbAWXq0Q0p2\nfN2gLXOayMSAdI0PCsWfSN0KGnT3mWbRLyQa9LOZSchIorIldZHpXiHkwM8U8vTtkg7NVGQv0R0H\nLneKfizJSqVEnKkRSbPkFI7+m40f9fX/Oo+dBU57iKQDNiVxrsEymM4GSaOenMa9Zlo5O5EbI9OR\nmOrXd6ld9GMKM1POmI5rjHS4jlmA+zFCqQ9D1+S5hsRhjGxdI6mDjKW5+LssGQiOkGklTHWLKQMV\n23tT0hybqiAPyklCP58lf2KKtb8x2QMaZ+5j0ma/9pdIdxggeUJSpkzcCnLj+seJYh1lSxaJDj5M\n98hDkiljjPGR+3hoKD6bnAQtctJKsOIUiax1eE7jLbK+MKWYx1vCcplAUn0MlEnfAdAQ7fGbRGkT\n4yTRK7y3VPTjNKm5QMJqnMeUV1LRWRLT8haupbdejke+tvAkzCNOmDNGUuJbKAkgyLrGWJJFDJGU\nsKMGz9HTJsf6qmcgs+g+iVre+U616OeJwflxUlXNUYuCSqkkQ5U4Bx6zxhhTV9HotMt2IRnOThXI\nemgO6dutGJtdRxrFZwNUo5JICsC0Z2OMCY7E2hVD86rqjQuiX/hVHG/Do6udNidGGWPMON1bTvOK\nygAdeHaLlPH2XwbFuujx3U677o3Dol9oGM49ezfSP4LdUuLTfhrJYSlEf+48ckv0m53CeUSR3C7v\naVnTB65Z67OfMdmDvYqdZOXOg+xndhrnO9ol16ShKtRbF8sKrc1A0lrQwxt+dc1pT0/ItJJAkjHs\n+59fxDnMogYO18h6wHufkCg8ky6LGt7ViTkScBUyK1sKsHQJaNm+Toyr/AIpfwqjhI6xVqzbg9a6\nHbdSpmf6E7xO+L5zSnyWR4knXFsnBuUedfAy6lzfMO5/QqWUx6dSctoErcebP7le9PsfX/mx0/5C\nLMbRwAjWyx3/9CXxncZT7zrtuHL8nd4zMnHxxitY65d9ATV44CfnRD/eb7lJarMqTUq/ei9hbzNN\n61FwtJzbU3dI2vMHQqJQDzlF0xiZxhlBEo/IPPlucP111B9ObuT9gzHGLKa9wJL1UrbBmDmHNaqt\nG/v3Z7Ztc9qnq+V69zalCn3xs5C0dZ+Q7zsFzy5z2tOUWGQnuMQtx1jgxM4AS5uXfR/qMss+8pdI\nDVr/eZLg7TV+RWo21pq2+i7x2SCtmSmrcM9HG6QseKQWNWrSh3uxdLl8p2u6iXHLNaBkjUzc4vfF\nBUtynDbveQOOy3vJNaB0Hf5uwgpZ/8Zp/5tCe6pzz8vEyjgav7nr8A7MslpjjHF5sNb3X8Xa127Z\nE5QulLJRf0OkbllJofUHIOGJy8T8S8ySEp0zvz3vtNd+YpXTnhqR1+zOxPF9lMwZGi/fpfidjCWW\nPkraYvmxMcb0Ulotp5W+9vNDot/aBXjGFyhJsTBVrlvdtNfrpTGy/KmVop+3BrVign4ncVv1qveC\nfHe2ocwZhUKhUCgUCoVCoVAoFIp5hP44o1AoFAqFQqFQKBQKhUIxj9AfZxQKhUKhUCgUCoVCoVAo\n5hF39JzhGOzwFBnnNVQFDVh4Kj67U5wrR3fGFCeKfuxVM94OPZetSYwhvd1QHc4hgs5vckRqiqfH\n4MUQWwp9P/tf/Ne5Q0PNeu30e6Teke9LWDy+E5EsdXe9V6B5G6mHd4cdLcl627lAZC60bmmWVtXX\nBU1caxfu5802qXMsoNjj965A9/zZXOlDwp4nZhP9nW4Z3+tK5IhInFPrO4hwtOMh89ZA8zlGY+SH\nL+wX/e5fCQ0gX1NEjnw+k33QIvcchyY4eqH0BJol/4AI0m9PW/pJ21fCnwhLx/i+9etr4rN4irIX\nY/iq1DSyv0PuU9Ce91q+QRE0n6Pz4VszVCu9DvquQVecuAr67NAonMMsaX6NMSa6CMf74HXoUovT\npZ73FsUGp8fjO+yvYIwxozRGFj+IOFJbkz1QAQ1v8pYcp80xqsYYM0gxxGaN8TtqfwNdfPHTy8Rn\n7IeVcTdqTmyH1L72X8Tzev3vEXddvljWqbS74Hl18QTiK9fuLRf9bv0K51T25S1OO6Ecz6TtXRmt\nOtqKmlpbC/8Kjtg2xpiyZ1Y47eoXESm56BF57ROk3zYUAczxrsYYs/7PNuM7/ajzEbvk+nTlJ4hm\nz/rmJ4w/MdoMf43IfBkd3nQOsbUcfZq42dL+X4D2n71AYiJkdO6h8xVO+9HVeB7d52QEfO0RzL+y\nhzEP2k/i322PrPA0aOHP/8+XnHaSFa0cthz94gvg83P7tvRH6HgP0ZreNKx9qTvzRb/helwv7yNs\nvzrhj3C38TuyHsG12H5NQ1exjoUlUUx0cYroN9qGscB7pOAIuaZ3f4hxEUjRr5m7ZCR6aAxFv9aj\nzrtiMS7SyMvOGGOmRqeoH/YWLa3SM4V9ODIyyLfA+l904RT7G0wR45HZcqwHBH20N4ErUe4BBy7J\nqGl/IsIFb5SYSPl3z7yK9WXpZhq3M3JNSt4B7zNPL+5LeJqMzq17Fc8jktbSA/8hPQy+/CwilHmv\nl7cC8zIsTHqnCY+YdOxT3A/JPUs+XW/PRexZkkvkuKx84aLTLnoQEbB95+S+bqAd++vs7RhXl964\nLPq5arDmFG8xfkfsEpz/lFeuIYE0vtmzrf1tuSYNjKC+BZJvxvqn14p+11/D/rV9APvy9Djp4zVI\n0fFZCdgT8s6C/XCMMaaI9jEVJ+HPUVKcLfqd/c4Jp81ehWV/slr0i1+CtX+4kWJ5Ld843ovyXnuy\nT9bU0Li580Vkr7NEjxy3wTGohzMUI85R18YY49mAdZL9CrkGG2OMhzwiufawJ5MxxqTk4j2zgyLZ\nS0rw78GWr12qB8/aRfPX9gMKpvW94z34qhWtlvWZvS7ZZ8aOV/f1YryxH2HJI9KLbayd1qrlxu/I\nWU5es/Xy/Tt9LT7rpzhyjrE3xpjy7ag5XIcL0+Reln1XovMx/wbrpYdg3ifgJ+OthadLYAjmeYRV\nr6dG8LxmJvDeYO+xer24n2VZGH9ul/TdYnDcur1v4WcXEoNj8NpuzB++39pQ5oxCoVAoFAqFQqFQ\nKBQKxTxCf5xRKBQKhUKhUCgUCoVCoZhH3FHWNELynSmvjKPjSLsxogRPUrSVMcYEhIBylrwelCiW\nJBljTNoqULdaRxFHF7dE0jXdKaCzBQVRbORlUAgTl0h6/2Af6J+jFOlmKR/MUDXOKWYhaG8hkZLe\n5Ioj2QbFgvZZkWdM+02iWHI7ynbSO7cxhVcPQLZw24r4LN2CCL4solszLdQYY7qGQN/mOOmpATku\nOogmWvlLUO/X75JSinGKDG05gviypMWg+4ZSBLgxxrhIstNxCjTxfatWiX5nakDl37UE44opZsYY\nMzgMGqwnCvRUO5Y3Ix1jgSV81YeqRL+iHcVmrjBYBbnN5JSkVwaQDCSQ5tuiz0uK7FgX7nn3acyJ\nmBIpMQyi6NyeC5AnRFpxeZ4CyI0GboAy6s5EP45iNcaYvpu4jhJLysTYtxlU5CCi1ttSxJxEnDvH\nGtuUweg4PN/+CtAx7WtP2Sjpx/4Gx9Ze/E8ZuZgYR/etDOcVt1jWwDiKE99KUoPBqzK+cnoMFNp9\n//yI056dkfcm2A3KcdV3QbdO2pbjtIPcUqaRvgkR5D6SGPY2y7nDtFOWVZx78azot+IRyJ/aDqEe\nBFl1iGVOkQWQa7YdaxD9lv03SWX3J6IphrPntIy6XfxJXAfTWFnKYowxoR6KRT2K+xK1IF70y+lG\n7OgI1cPUrbmiXwrVrEaKYa/twFhPi5VRjgVZWCfL//oefP+gjPMOoHO/9dZxp130wD7Rj2VcodG4\n9uEmWccjc3H/ogtxvSPNkkKdcleemUvc+iUo8HmPLxKfZTyIWt5zimJw5fIpJCjh8VgbOv5f9t4y\nzq7yev++x+WMu/tMZuLuQgyLIUmgWNEipZRSWmgppdSgQpH+SrHiNLiTEALE3T0TmYy7+xnL8+L/\n776udRfy+Tw/Tp55Xqzvq5Xs+5zZZ+/b9jnrWtemU6Jd6lz0i6K2LU5s2zo3UJp3EElZQ8gSPSBA\nzge9wUjz9vHBa5b8WeoyKw+vc2Lum32dcj0p/wD9h+eAuu2yryefj/T9028dcuLAOJk2zjJST5M1\nBX2kfKe0K571A+iqD/4be0pbftxZgr0N27kXW/Lh3GXoI4dWQPYzaYjcb/IYZglk+WfYl/guXS1e\nE0mypD435u2BHrnedTRhfx1K1qy7P5YypHRaF+u3Yh+WcYXs52fewX3rJnvwmFApEx1y1WhzLnGT\n/KanUY6JqIlY79pOYy7pbJXtJi6EVLaBZKN1m6UElJk0E9dj/9Zj4likC1KaRpJMZQyFtCo5We4f\nYsmam+UOLOE2xpjRo2H5vGYDSdA6pNyXbbEZW57mrsffCkzAeYcPl7bL+z7DnOfpFdI/Ouhbj3VX\nQ2p1ph+TqC0p6qA1oK0Qa1p3p9yz+PtireF1JyZG7lEjyZY+mPevNI/bzwW8Z+Hxa6/1EbRHa2ok\nqZEllWeCM7FfO75KPj8E0rwU5I/9Vr1b/t1zTVAC9srl++TfDnVj7Wabd5ZkGWPMgBvz1gQalyv/\nvV60yyO7ap/TuO5sj26MMT7vHXHi+Fn0PcJhPE+EZsu9E59DYCwkT5lxcky0sHxxIt7bbZXi4H6S\nkIXrYI/R4BT8LX+yR+8ok9LpnAUF5mxo5oyiKIqiKIqiKIqiKMogol/OKIqiKIqiKIqiKIqiDCJn\nlTX5hiKNh119jDEmYQ7SSbmqtu1g4xuC9KzqDcVOHDFUpgO21SAllZ1WuJqzMcbUn0E6H6efJc6G\nI0TVlsPiNYakPJxSzelbxhjTSQ4kgXE4VrlGVoVnZVDcZKQx9nXLFNQWcn4Jovez04Mjhsk0K0+T\nHIN0r8bWNnGsnyqQsyNQrlVJ3L250ImHpeIzhw2X93EEpVyzdKnxsJRcdJM0JyYF96SF5DvR42Rl\nb5a0sCsMp+4bY8zIdKSmRY7B+Xj7SjkZU1aLv5s9VDqrFB4qdmLfUrj+pMbKCuWFXyItdvjCb/1T\n/yvcdL0sNZ7pIxe0NoptJyKWP3Fq6bF3Doh2iSOQRsyV5tmhxxhjqo/iWmSnwXWkmirXx1CarzHS\nvYJdoU6+e0i0i0zGeEk4DxKOqjVSLuBPcxTLL0ZcaqVh06Dd9wzkRH0dMqW/l9ykkmQ38Ag5VyNt\n/tire8SxiNHkfEa37vTrMr0+5RJc6/L1uNahsbajHvp0w07Mo9IpTcp0evsh0+xpxrwekimdWgYG\ncMxFbnAsxTDGmMrP4eATQ84TgYUylbjwE8zZWedBLuFKlvNQI7lu8XzgtqR+tVuwnqRIs6DvDFf+\n9/KVozEwGinlp9/CfYseL91ZWklC20NSLb7mxhhTeRRzW/qkDCeuoOtqjDE518K2oYleE34Qqdwp\nC6UzkLsR6bw9bqSQ245oPE6L6LpuOfysaFdwC1zyWki+GFEg17cmWguq6f1SL8wV7WwJo6dJvhh/\nz21d95bDkGmyTCXY6o+l76DfusmFhJ0njDGmpRwyXHY14TnZGCnV8yV5aWcN7k/FIZkOnzAjA/8g\nQ4iyPXIfxC4SvkGUQh8n90FxczHfuuux70ucLWVmJZRq3k/SKNttx3jbK9a5wZaynngH48/dh75k\nS7tTx+JzVa+HPDLA2h+ufw6SzxRyEAxKs1xCaA2OHvPNqf9VNG8bY0w4uZ2Uf4S91u7jcr278MbZ\nTtxILlij5g4T7bif7l0BmWLkCbmnSpyLz95M0uTIdNl/fV3n1lGUx1ucJYM78C4kW8mp2G/GjZOy\n6KbdWBvC8nD+1u02meTaGZKB8ZZlyehZUltFMovI0dhTsmuoMXJftetTOO1Nu2SCaFdNsvLLbpzv\nxO1lUs5hyFiM+9WJ/cWi2dhlmP/byemmYbuUP02/faY5V0RRX2e3W2OMaWzDdRq5BOtQ5afy2Yrn\nQ3ZN9bFce4VciVzy7OsXRs97pzbimkdNwHrcb8k6ayrRD3IXQL7NcnJjjGk+gP6WcxFksE17q0U7\nVwbtnagzpo6VG8xw2nt20fN2G7kTGWNMSLaUJ3uasq8w5+QtHCqO8f1p2Ia+FTlOSm2LVkPCGR6B\neTQ3Uc4/MeTqVd2I6z75hqmiXUcJ7iv/3TiSOG1/frN4Te4Emtdpn5E5Q24ID63BOnliM/ZV/QOW\nq184zpWlwG0kfTPGmPQl6Au8n/Pxl7kwRz/C+vRNDniaOaMoiqIoiqIoiqIoijKI6JcziqIoiqIo\niqIoiqIog4h+OaMoiqIoiqIoiqIoijKInLXmTGAs9PNsS2WM1KcGxUNTxtZRxkibba5f0W+9XwPZ\n27Ju0y9Ual3ZWpt14v1kORiaKTV5fK6tJ6Hfs62ytw8j8wAAIABJREFU/CNx7mx3zXbgxhgTMQRa\nSDfV2+ksk9ozf7LeYivVwHiXbBcmr5mnOdMP7ZyttztDVuBHN0AnmJIoa8mwDS4ryDuKpMaTazCU\nFaO2QM4Eaf1asgcaQNbgR5D135k+qfkrWkkWn5lkNRwr7fNqq6G59QlAF2/cIesXxaZC45lK9oOn\nt0lbXtaXN5B2trFNWqjZGkVPkr0E2s++DqnpP7UaGvWxP5zmxF218vzai6Hp7ChGX/Xzkf2bLe97\n6W91WP2ba9O0FmJcheRG0v/Xi9f0kPU6j7/YYfGi3UAvriXbgwdbtZAqSbudfRl099znjTHGxx/9\nIH0GtKi1Vv2nphL0nYK5xuPsfn6rE4+8apw4tv8N1AaIj4NWOjRf6v/9qM5O8gyMq7qt0jI0cT4+\n584X8HfT+qXuN4LsxNu6YMnZvaXYiWOzZX2lIFobDn2OekHn/eJ80a6mH7U2wvPxd3pbZI2PmEDc\nY7b5rVlXLNo1VGG+GUVW8VXbpI2uK03WyPEk3VSHw66LUvYZ5qiEObg33XVyrYmfleHEB/8Fm9WW\n/bI/poxEXYW4KdCo126Vn3f/47CodMVgPQ6l2guVX8r6FW6yN2W72vjpUgvPOvNkqjviSpXXmGvF\n8fpe9Mo+0c6brE8zlsBOcqBX1rqp+RLzcP5s43HKyNo49WJph5xAVuWtJzEnFL64W7Tj8289jrnO\ntp5vOYo9iItqXpzplfNUPenpg6juVjzVlQmMk/uHyq9Rv8QvDHODK0XOlaEjMMf20JrbWSPrZnAN\ns55mxMdekBbrvCcIoD1MV7Vcd8Qa4GFH5uAk1JsYtVTaEJeswv1la+iht08S7Q4/jRpk3IdrW+R6\nN+WKifi7VOeizrI1bj2EeksxY6guCr33yZWyblAx7Tkm/mSWE5c9Wifa1ZOdb8wUWDp7B8g1nGt+\nZI3JwN9ZVSjaDbka1+w41Vtgq2JjjOmjulipv7jceBqu52HXvIoNQz8uKUY9j6EZcv4pb8AeZBjV\nb+P+bIwx5buwTkaRvW3iPFlTqZ1q0IQV0pggq+/qHd9u0x0ejNpuXVZtmiSuE+WDftHbIs+17QjG\nTm8frottB89zdASN8+gJsi5Pn1VfxZN0036zoUGOncgQzGW8f024QF5zfvbrqsI167PqWLVTDZpG\nqkvqSpfPAi00J3P9yW7ae9o1iRLJ1v705xgvaXPks1MAzcNdVfjs7Q1WfVbqV8Xvok5XyoU5oh3X\n0+Nac0OXjhLt+jqtml4eJpH2GfYzWMVaqsnlwlrjFyJrCKZMxHs0H8DaV7BQ1sbi2orxQVhzuV6i\nMcZUHsAcO+SyEU7Mz9UZubImUAW9Jp6eF9tpPTfGmAnXYD0o/hjzcvrCfNFu71tY+8cMw72za8ZW\n074lYiQ9z1o1QDMmZpizoZkziqIoiqIoiqIoiqIog4h+OaMoiqIoiqIoiqIoijKIeJ2xfQUVRVEU\nRVEURVEURVGU/8/QzBlFURRFURRFURRFUZRBRL+cURRFURRFURRFURRFGUT0yxlFURRFURRFURRF\nUZRBRL+cURRFURRFURRFURRFGUT0yxlFURRFURRFURRFUZRBRL+cURRFURRFURRFURRFGUT0yxlF\nURRFURRFURRFUZRBRL+cURRFURRFURRFURRFGUT0yxlFURRFURRFURRFUZRBRL+cURRFURRFURRF\nURRFGUT0yxlFURRFURRFURRFUZRBRL+cURRFURRFURRFURRFGUT0yxlFURRFURRFURRFUZRBRL+c\nURRFURRFURRFURRFGUT0yxlFURRFURRFURRFUZRBRL+cURRFURRFURRFURRFGUT0yxlFURRFURRF\nURRFUZRBxPdsB0sL33Hi3ja3ONa4r9qJXWnhTtxd2yHauenffa09Tpx8yRDRrmFXpRN7eeH/o8Yk\ninZtpxqduOVgnROHDo3BOVS2idekLM534pK3Dztx+LAY0a6zrNWJ42dlOHH12tOiXW8TroUrC589\ndnKqaFf+SaETx9H7tR6vF+1Cs6KcOGfStcbTlJ18z4ndTV3iWNH7uB7JMzOduP10k2gXGBvsxN7+\nPk4cnBwm2lV8fNyJEy/KwQEv0cz4Bvs7ccsx3MemAzVOnLIgT7ymu7Yd5+CHc/Dyk98xtp/CuQcl\nh+LAwBnRzi880Inr1pU4cdzsjG/9u/3dfYjd/aKdTwDOaey1PzGeZMtjf3Di4FR5zbnfmn58xv7O\nXtEufHS8E7efxDgKTAwV7aLHJjkxf/bqr+Q4yLlxjBMP9OJauBvRxyo+O/Gtr2krxn1yJcnPxPe3\n8F+7nThxdoZo5+Xzzd8vN2yvEP/2Cw9w4vgZ6Ti/z0/Kv+uL95v8419843t/F1bff78TR6RFimMJ\n52H88TitXV8i281FO55L/CODRDseBx10H8Ozo0Q7V0aEE1d8ecqJo0cm4P93l4nXxKZHOzHPB60l\nzaJd0twsJ+5p7nbi2p3lol3KfMwVTXuqnDgoRfaLgGh8xkqal729ZT/wD/Rz4um/esh4ksL1Lzlx\n6efHxbGYUVivuqtxzSNHJ4h2xauwNmQtHurEVatPiXZhBbjOA70DTszX3Bg51gPjXE4cko57W7lK\n9vUzZzBXRE3AmO9r7xHtarbh3udePcqJe6w9QdVKvH+nG8ci02V/S5iNPnH81T34/6lpol3lZvT7\nCx591HiaFXfe6cQZOUniWG1ZgxMPXYrPfOTd/aJd2nicc8sRjMWUxXJ/I9YebyyGTfurRbOQLMwJ\nwYno+1uf3oA2gYHiNcOuGevEfE8PvLpLtEvMwfzfU4f5xS8yQLTjObX0OPZl6QXJoh2vhT5B2Eq6\n0iJEOxeN4YwRVxpP8o/rr3fi/GR5fq64ECduqsS8VFJXJ9rlJGBsumIwdpIvlvuP4n8fdOKT1bhv\nl/7pZtHu5DvrnTiG9oSbnsE9nHXXbPGakjcP4XP8cLoTV647Jtqlz8exf96KMTG9IF+04/31GdoT\nvPy7d0S7n76MufHUx+ucuPpQlWg3+b7FThwdPd14mu3/g8/C/coYuQZ0FOE+tjS3i3b5y0c68YE3\nsGeIDAkR7dIuK3DizgrsnTpKW0Q73mc178G+NHYWxrz9vMN7sdBczHttJxpFu4Q5WMPrd2AtbCxq\nEO38fDGuAkMwTsXe2hhTuALzEm+13X3yWsan4ZnH0/ubZ2680YkXPbhIHDtOe7i8m8Y5cUeFvOau\nZDxPlX5wxInjZ2eKdjxH8R6I93nGWNed5mB+LuA18v+8Oa7gJ49/7sQX3zFPNOttxRrXsBX7zchx\ncq33j8KeZcfr25147KVjRDu/UJw790Xe8xgj93UTfvAz42lO7X7DiVuOyrkybAj6T86Ea5x425OP\niHauLFzTYNrbr3j0Q9EuNRr7mzn34PpGJY8V7bq6sBdoq8RYTB++zIk7OuSzxiNX/9yJ73jieife\n8sQ60W7Gz/m+oo/4BrhEu8JnNzvx2n1YC2LD5B61uxd7sfEFuU4cPVHuMVoO49p+01jUzBlFURRF\nURRFURRFUZRB5KyZM5zR4K7vFMdC6dfX+q345jdupvz1i7/J5G8GK1fKX/HSl+HXw7KP8ati1Zoi\n+Xfz8Hczr8MvWg178AuPn/ULMn+zmrwQ32R5+8mPz9/Yd1Yh+4azaIwxpukAfjXpo18si984KNp5\nUyYF/0JmZz4ERAebc0nDbnyr6+vyF8dC4/ENcsUG/BIdEiN/beiqpKyVQHyuMzIZxcRMxy9F1V/i\n3iUvkr9C1dCv3n1t+KU2Y/lwaiXfPCDW9Y2HAiLkL4kVX+PvtpTgW+akmfLb98bd+HUolH6htjNx\nAukXOM7ICIiX9623Vf7i7EnipmNc1W+TWQeBCTg/H3+ce9QY+U0tZ2NEDo1z4j7rl6qy9/CLBd/P\ngBg5rrjvD/Qhc6a3Hb8ohObK7BAv+lWivQj3hn+FMMYYvzDMFaGUmecXIn8Z6aKMEN8gZEvEn5ch\n2gXG4F510a9ddrv+Lplt5GkC/HCO9cXyV7LAI+jfbcfxi0/qJfJX0TP8CxBlPdmZXPXleI/h3x/v\nxJ1VraIdZ8sk8S9UNMaGXD5CvKanCVkwPc3oV3GTUkQ7vu7dNbhXedfKX4266Fh/B/pj5R7Z14dd\nh1/dgsLRH/s65H1LvXyoOVfw/JBgZUtWbMEvPH0DyHSJGBkv2qVfgPmQs2CCkuS8y1lstZvw3m7K\nQvo/r0M/4PWk8I19Tpy5qEC8hrNQy9ZgPU6/UM7VqRdgzSz/FJlCbitzJnossobO7KNfmqfKa8Rr\ndeI0ZLF1FMusq7T58tdhT5OZh0wLOxNp2PLRTsz7h5HfnyDatZ7EGG7pxB5JjgI5V3ZVY2/h6/IT\n7Yo+x94nKgHz3pCpuBZ2X+d5vYQyuXiuMUbuOzopuzgyS/7Sy78cjxyOdaJhp8xG9KH5tp/OyctH\npslyxlaGnEa+M/wr5cT7rxPHGssPOHFqIPZ6Ia/K7KeRd1+O15Qgk7jojQOiXf7tE504uwdzVEeT\nnKOqjqPvf/01MgaW/3KJE5d9cFS8JnkJMl1Ov7fXiY/tl9mqWRfiV94fPP0jJ37ipr+KdrffhF+e\naygD7UfP3WYkmKMObUHfS4qU6/aRp7904hkPej5zJmocZe51yH1UM2VT8x49OlmeY/PhWicevlyu\nL8zB13FPEjJjnbirRmbB9Lejb6UtH0btMHZYNWCMMY10rtG0Lw2IlXvFEtpjBVGmY1u3nNcjgvG6\nsOE417rNMpO1tQtzwJjluPfuBvncVr5FZuF6koUPLHDilpNSHVDTgkyQmr997cQTb5gs2m16fK0T\n54zLcOL67XKMcTZY5JiEb/x/Y4ypOIHnLs44rDiF+zT2JnkOzKUPIANo1zNbxLHYKNz7hAuQDXr0\nXTlvBNI8zGdXt1HewwjKbPeylAaM/TzqcfihznrAi87BXmDzI791Yi9r/Xz2Sag1Hl/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Plaj9aLY4lUEyGA9LJdtVK32XIImiu2/+wsk5Z+XO/EUG2a4CBZbyGerLl726FNLVsF7Xrq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\nWnOmvx3azBDLz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LsycmTxj0GaKTseGWPM3ILznDg2D2n2XO3YGGM6/PB+/f1IFU4ZjvS1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\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BLSxvBZ7T6V9I5yvfTHkescZDTmdplovNUBNkIRGSkthuZK4AxgU7k9lyyXAjvm+kDess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\ntAzXX7uTW517ex1WnJixzoqdT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TxSp3oaZUpdMEmywpORzlX7mkyVGxtGepKL0tQS6O8Y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\ng63QIXeXtYo4rn3F1zJuuawXNtA0cTUSWrtQByJ5+jT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010Eu6N/K/iI1yq4piEuXEnSf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DNRErrPIceEL5YUm7YjUPznlnVvgXTBSZyJlv+BVrR1Zl6P9dz4fqV4T3sd2kyL0pEr+nyy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\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fxvdg3yNSGjvvDqxxXB5luFfO7fERjrHvOq3ULFtajevrpzhauQrP6f5aGdti6TsBni+F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D22rTbGmD4a0zzW+7qlTTTbHleuAw0zybJAbier6ah8zLe6XdLCsTUK9SE6H9Tf43+BtWjyvBxx\nDJ976ZugvmZcIesB02BZimj/b4GhaaAjt3ViLlZsPC7yxlyC2utrwPwNjpVzW9BY5VD42lh/EPIx\n93tyfcq7AXT1mpOwxX7ld++KvCtvgeTp2bcfdOJ7b/iNyLvrpiucOCYGF/LxAytF3vTvznbiUZlY\nCyfeCQnkv+5fIo7Z8dhqJ84chbodMVRSiisOgpLfS7KCva/L9a6nD3Tj17b+04nvvvQukfenD1Bv\nWhvwfP9wx99E3nUrLjRnEyzH67Ws2OMmYDzWbsf+pqtOyucyL4PMJDQFEpaAACldbqvBd8SkoVZ2\ntx4QeTlXY3x3VKNWRGZAchEYKMectx3zN54o9NXV74u8mu2QhPL8TZqZLfJ8TbjGqGGoBzyXjTEm\nkmpKVx1o7CHWXCz7ANT1TOmS/LXBsrJ6y4Y4cQbqYTtR0mPHSgr+oEEoRl4vvoMtdY0xppWeVR/Z\n8na3SukR1924sXhuLbTXSp0n9xjuMNq/NkO24ImWso/GctTannbsqex7HpOMMVZ1FDJ0W5rmTsK+\ngmn2cedI+YsrQo5nfyOYJFX23jOQJPUs0TJUb4wxpqcd60v8JJx/w4Eqkcdra2sr9kEdNVJiGEi2\n0yHJmHPr1+J+Dk9PF8f0tmNebTiM7158sZSit53G+tR6EvtfT64cc+44PJ/mU3jetsU6W6KHxpG9\nfK1sK1GxDrbiyd8wfkVvF95rgqPk3rjoiyInzj4n24k7rPfAphLci5vmQ24YEixlorwPT5iJudPM\n0mljTNZFqIcRJLEOCMDxBf+SNbiWWmccr0A9uHCWlOS8v3GHE79CLRhWvfQHkffcC6jDLH+K80ip\nYMx41Aq2z2b7amOMyVwy3JxNHH9+jxMnniPHdzStB54s3M9en3ynbaMx7e3GnGjcL+dieCTqVmga\nxnST9RwDXZiLgwKwVvva8e7M7/nGGFO5FnKqjMux8Hzx1BaRl5SM94GyckgqR10g3/tZ2umjtTAo\nRkr0B8/Ae1EHrQVBlsSwbje1Crjc/BeUOaNQKBQKhUKhUCgUCoVCMYDQH2cUCoVCoVAoFAqFQqFQ\nKAYQZ5Q1uYgyyhQ/Y4zp6QRVKYZoou2xzSLPVQWZE9Pxoy0JCFPikohCH5kl87rbQdNLOAf0xMh4\n0On7xkj6LdNTfT7QltrbT4m81PwF9Bnof319ksrc349rb6lGl3z7HrFLAbt4BHsk5c9IMxa/I4bc\nE+z7fuJZUNOZdhWaIKnT4Q2gw8YPx3cMslxXYsZgLIwm6mXBZ7Ij+shpcNnZug4uO9POBR2336Kt\nnnrtEK6DKNW2rCk4FHKenOvhODMoSNJljzwN2YGLaJPhlnuFKxaUXqZrJl8mpR7H/gK6a/rDS40/\n8fA3HnPiX6/6u/hs0uC9TjzsqsVO/ONL5DlUN2NunjMEdOkQl6TbvbYJcgevF3T8TQ+/JvK+fw9c\nj556+odOzO5j+37/qjhmSDLGB8vCKgok3XH2PEggN78J+ujcFbNE3i/egGNUTw+u76GrbhZ5970C\nqmlPM+5XcbWU2+0g14vlxv/IWAx6ZeWnVv05D8+kbA3mS0OxdKVIHIl7mHEpvo9rjDHGhMRjDvta\nMH9T50iqfEAAaNDttaDTpl6AvJxzpCRmz9//7MRhWZgv7AJjjDGuKMydpAWYL6VrZD04WobO/+Py\nQPlnRwRjjDn8AWpA9ghQbiOGxIk8m/btT1w8E44noUnh4rPwcNS1pk5cU1m9dJl67FeQ/XxzIdad\nXzx2u8hj6e4vr4DE6bLl0mGmdhskK+Nvg5NA+ccYz1UFW8UxLEvMW4hacXSVnLOniZIeH4H7OnSq\nHEcjiWq+bAYkw29ul25wf7v9YSceTLLJ4CC5frYekePZ32DXB9uNkinSPa2QSyTNyRZ57EqSOBef\nlazbKfIiyImtqQpjuOlwtcjr9eGcMs8nh4o+7Dl8nbJWxsRDyur14jO3W55rXy8kvj0kvzjyzA6R\nF0PUdV7fO0rk3O6n+xfgIgm3Rd8WshQ/w5OFPRY7yBljTOU60NrdqSQPOSJrPstAQsKwt2mtKRZ5\nndXYyzbsgtzhdKGUU7V04jxGHYG0imn7tlS1uRL1MC4DEv/mOukQ446DM0hdKc4vOkdKWquPY/zV\nkCw7IFQ+i+Bo1OeY0ZiLXXXSSUbIYwYbv6PhEOZBy2Hp1hRM+6+Eqdjzd9ZYLRRIauAj96JASybV\nS+4sbZU0XxLlmlG5Eevz0b2I55+P+v/Rmm3imDiqj5MH40YV7C0SeeMuQ/+CjKlwjKo5IetGZy2u\ng2X5ohWCMeIdouJTjPteSzrNrkn+Br//RAyRTpTsGFv4KdaksTdLzXHRRpx72lhI09pPyesVrQeo\ndmedL93IfF6sIeERWJuPv4H2CRGxUl60vxjzanwO9iybd0o3zJXvvefEL9x/vxOv+0DWU08oxm8a\nyYeFRM9IGU7iHMxn23GrvYQk29Is0i8Ii8W+kfdvxhhTR9JRfkfsbpOyszhyrs3MgAyLfzcwxpja\nLdi39FG7j0RLVumJwbMLCsIc6+khh8TAk+KYweSk+fGDeN4FldJ5dAq9+51zM2TpZaulpH7knRc4\n8YmXPnPizMtlfWklR9GYiZCqeTKlq1PLKelsakOZMwqFQqFQKBQKhUKhUCgUAwj9cUahUCgUCoVC\noVAoFAqFYgChP84oFAqFQqFQKBQKhUKhUAwgzihA9LVAw8maXWOkpr/knWNOHJEXK/K4R4c7GcfY\nvUqiE6Gv7o2H3rW/X2pzm05A8xY9FBrZ0FBo3JqbpcVnXx/6LXR1QtvaWCv7HrA9Ip9fa6Hs+ZA5\nH9fRXt7ypcfY4PtlSQ2Nr8lrzib479XuKBWfBQaQtr7NZ74KWcvIVmwQrtMdLZ9340l8P9ul898x\nRurSp83Gs48eBc13f6+8Ufk3QVsfnYTeNMWbPhV53D8gkWz7ytdLDWEUWevFT0H/iuJVR0Sel/TX\nIW3QYNbtkZbEsWNl7xt/4sHXHnXiwEBpmznmB4uc+Nk7HnLib9worWhTZkO3GRWFe/nFvMtEXm8v\ntNwV29CfZcLt00Re6+uvO/HTj8KG/pu3XYpj7pGdW5694wEnDidbwU1Hj4q82TzGqCcOW6caY8z9\nS2B3fdcTtzjxvFGjRB5bJ+YuRx+iUVEZIi/37x+Zs4n63RgzKZadauX6L9eKu62eQM0noGllO+AA\nq6dSzSZop1NIo+1rlb0ZWovonCai109nBHqmHFvzkjgmk3rnVG/F3/FWS+tOtqjvJavT8CSp8x7c\nI+1t/4P0vBTx717SLPuoD5bd16L2C1xTlnRE/NpImAXrzq4GeS83PviUE4+8Cb0jvnfXMpEXPxGa\n6tOvQMtu28ge2w9b3fPmwsqz8vMSkTfr53c6cXc3NOk903C/2BbZGDk+Xl+DeTnr9nNFXhL1Grn9\nN39y4jffkrbfKeOh93/2bfQYWP/AsyJv4yH0XLntr79w4oWD5LV/8rOnzNlEOGnAe73SCrTiY+jX\nm8pwP089XybysjIxbvvZXtla02s2416HZ+Hvth6Xe4uYCfi+U2+h/0RoIvqi2OdaXEm92Mbh+C7L\nRrezEnU9IBj3OtQj+wqEUq+qis1FThyZJjXzIdxzoBl7RXsfEZ4j+0/4E7Xb8Dwih0vLWe7VUEr9\nA/j6jJG9ShqPYC5G5cnv47meegHW0v4P5TmFxGN9ZptkL93/YLfsaxfgwh7jyMo3cW6N1t6Q9ja8\nP7J7bLWdwrgqLMKeedLSCSIvNJ7s32lMBIXLNYf7nZwNsE179jK5dpe+j2fHvdNajsqeVGGZuKds\n81vxYYHIi5uC2lu8Gu8udp+Lmr24b50+jOljO1EbZg2XtsY11NcvIxdzMXeo7IkWPxY9Rfr7UTcS\nh8oeLL29GHOlWz534pajsi9PNO093W6sreHp0pr7bKKRLMu5p5UxxlQW4L0r6xxce8sJeR051A+P\n34uSz5d7Ja6BodQjZdAg+Z5Rvw/PsDEUvabYynzLsWPimCnUj9FDVvPNHbIP0yuPPIhzSMU9v2Lp\nCJEXRDbxvEbw+7Uxcs3w1qB2t1m9STy5Z6+eGiP3IOFpsk6F0e8AbINur3e8T+P+WtXrTou8kko8\nk+QYXFf+1ReJvNYGvKtzreBz8NbK51N/SPZm+w8SI+U15Z431Im511x4jpw7h5/Gu0H6pbBot3s9\nhlO/Jd6fNxfIvoMxo2T/VxvKnFEoFAqFQqFQKBQKhUKhGEDojzMKhUKhUCgUCoVCoVAoFAOIM8qa\nvGSDHRBoSXZIdhCeKWlCDKZSs6Vdc4Flk9l/wAkbiY5k0+T7SaMT6AZdrMe73om7miTVnC0zfdZn\nDLa4DiYLseObpPyJKcYsZbIlF65Q0Js6ayH3qd8lrRf7e6VltL8REIzf4P6LJkuImwxaZ0+7pCa7\nIkFrjY6GVWtHR6HIcyeA3tdG92PoIkn1E9dMY4nvTRpRh40xpr0MlNHwWNB2U6eNE3nHXsRYCCc7\nc/vau0kW0V4MmiPfB2MkZZjpel7LbrKrQf7bn2irh5VjY7ukYX70h4+duKcXFMK8RVKuxBZ0Ox6B\ntfR3b7pc5DXXgSZfvx3yEKaCGmPM62sga2BKdGQyLCSPv/2OOGZ/UZETX/aTS5z49Jo1Io8tKasa\nQescc1TSiK+YO8OJH/sWbLUXjh0r8n657FtOHEZyqlEZUtY08ko5lvyNbqKytp6yJA1jQYNmemXl\nR9Ii0FDNGURywcjBUmKYNg2ymlPvbfzSv2OMnBeVu/c4MT/vxBnSqrWV7Bw9JA/c/6qUlA6dC8ro\noU0Yt5nxUjIQSnaGhWWwOpw2T0rp2skitekQKLG2BPJsSgxXP7XWiZf+ZLH4jOWbnURNzl0gabqv\nfv9XTrx23z4nnrRH+tT29qFOXnjVlU688q7nRd7Qk1ucuHYHpB5uql0F62TdeHzVKid+/ePfOnFw\nhFzHht6EZ3DXKXz3s49Ji+wV38aaEZKANTJ1sHwWP07Ddex7ArbdQ2+ZLPLGrpC2qP5G0wFQ7RNn\nyvEdloE9DcsP28ulpStLaGs3Q2o2yCX/vxfXx+KtoHbHJUvqdK8P9dtFNseBoWRVOkjuxVheVE22\nyTHj5TxnG13e39j7jxKyEM6cg/F4bK2U+44ai1o5KBB7xR7LvldIMBYZvyJiKGpe88Gar8xjKZO9\nT2srIdkA1ZG63VK2zBL9+j3YpxQWyrwxmVijTlVjjI0YBVverha5lpa+h7kZHAFJ0Sc79oq8rATs\npyddjPtvSyQYQ0dAhlm3Wcra42ZAzt1C1vVpF8m9F1voZg4zfkdILPaX5WulDCmGajnvtzMWyxPZ\n/Bvs+1LjMC7YltgYubfNuAjrU0islLuxdCaiEnWUWzx0VrSKY9JojISmYC9sj7mGo6ijiaNRa4KC\n5LtUzVE8f5a69PnknGXL48K3DztxyhS5v4m0pHr+BEuZvBXS5nzsCtT21tOYb9z6wBhjupswjod/\nc4ETH/3rJyIviO5zNFnAB7jkuxVLgkLpuQXS/uqaH1uyftrj8xgYbrWt4DUiegQkKt2WrJO/o4Va\nZLQckPWqrRP7sCSq6cf3nBJ5w11ybvobybTeFb60X3zW5sU5JmRBqpcwXY4zHo/tZZBFF1VUi7y8\nUZib3FqipVbuVTxx2U5cU473k4oNWEtPVkkZU2M75svRMsw3+32nhdoE8Lte8zH5G4WL9kXl76NG\ntTXL3yi6evC8J9H84/2GMf+zrb0yZxQKhUKhUCgUCoVCoVAoBhD644xCoVAoFAqFQqFQKBQKxQDi\njLya1PPQtdp2kajeVOTE3D26/BMpc0mYBBclliT190gaevlHoAkxrcwzRFL1Ww6BIlu3HVSlU0dB\n18zOlQ4fmUsgqWFqUt0eSYFrOSa7Kf8H6VmSls1uBBEkJWAauzHGtNWAZsXSKpYZGWOMsf/tZ3BX\n+6AI2YU/jbpODyLpWuIYKR+p2g0q2aCxXzixt1HSF4OIfj1iybVOfOLjN0UeuwQw7Y9pjS0n5fPo\n7UIe0z9LNm8SeU2VJH8i6cixA7JTeISbpFpBoIC3WXITdoJpZoq2RS+PGX32pBTRSXC0evz6e8Vn\ny+9f6sRlqyHBK929XuTVfY454iYpYvUxSbfbsgFUxpnzxjtx1sVjRN6fb3/GiZd9G3z1+r2bnTgk\nTlKF7//DbU7sScQ9/9vaX4s8jwcWOw9ddYcTRwyRrgeHNsPJYfZIHLPh8GGRx64K247jmHN/eqXI\n++hnLztx3rQbjL/BLmW9XdKJLiiM3EXIAc+WHbDbRGsBxmpHmZRcBF6A8h5H7kDsSmeMMWHpGAte\ncniJHgmqru3W0VqIuclU8+EXSWukUJK3TLkOThRMJTXGmARyykgiBzyWihhjTOQwPP/kudk4v2h5\nfm0lUjbgTzQRXbb4dTnOMs8D5fjke5CB/PO3q0Te+SS7m3PrbCeuXicpzB5yPwwKAk3+mt9eLfK8\n9TgnXl/cSbj/Q+YNFcesvOERJ+6sQR1vOCTrgZeo+y2dkAVnJkj3p0Mf4V7kz8DeIYRkwMYYs3UN\npG+LfwppY9nH0k3vo9VwJ7nvdXm9/kAgOdLYjgssg2k6+tVymUpyP4wmZ5Q4y/mlYS+keh3k/FJ+\nRD7v8eTsUV6A/UPWeEhTms8wd1rp+aRYtZfnYgfNscZyOVeYus4ORUNmSMldzXqsp+mXQ2LSeFCO\nnziiq/sbLKVIu1DS/Xm/0ETrdkeJrJMBIaiTXpJZRI+R6znXQJapDRuXI/JYujr5EqyfTPXvbpcy\nJJYStFPtmjNS1tN4qv2NuzCm3BnSrclNNT2AZKsxk1NFHsvyokZRC4IQ+WoQEnd23ZoiB6Oux4yU\n953l7HwPS1dL6UN0GMZ7ZT3Wxa61UmaSdz2eSfNxjIug8GCRl3AOJAkxSZAIVxzc4MS2S03WZXCa\nioqCLLPqlHQUDYtHrfB2YL5Ub9kh8lgemrkIe5jCl/eIvOJ3yXVqHJ5xhCV1rlyH97NsaYr1tcH7\ngIjhcp9W9i5qe0Q+zsmWI0eOgOyqaA3uRZjlnBNF7ldtNJ9DoqXzXHg2jmM5W97tkFl11khpVWsh\n/t12AuNIONYaY3Y/g/VpVCrmW90O6ejH79HRw7Gnst30kkajBjRRDU2OltfeZ7n1+Rssqc+7Sbq7\nnX4VbnbJ87Ee1JCrnzHGpNBnXY1YQ8aeLwcdO3IFcZuSTnmNTaX4fYDlh+kLcW87PpDzPCgQNXrw\nRLjT1lstVSbcPd+JSz7Ee25pvVxnh0Sj9rLss+JD2XYgYwnmqZdaXdhSv6yrzzwBlTmjUCgUCoVC\noVAoFAqFQjGA0B9nFAqFQqFQKBQKhUKhUCgGEPrjjEKhUCgUCoVCoVAoFArFAOKMPWdYj5k4NVN8\nlroAmrLKz6CbjsiIEnmsc+a+K8Ee2fskyAO9WeIs/K3Kj2QPG9Zzcd+bSNKbttVKbVdHFTSEvaRl\n89VLW+2Mi9F/pekErj1muNTAst6YtcPeavl3E0dBU3byrQ1ObPeQcMWcXT1vygJoGfulxFP0J2B7\nSK/VP8edTHbSPui8Y9OkZXFAAK4lMBDPJG6c1Dr3daPfRsJw3KeedljCejKl1pKtzquPoW+BO9kj\n8uJyoEfd9h7yJs+RGj+2Ym8t+PJ+Q8YY03ISutPuZjzv3g6piwyYIK/Rn/B6oS+//Ko54rP9K3GN\ne0+jD8BP7/+eyIvJ3+nEz9zxdyf+wfMPibwdy3/qxH97cbUT3+mRmuwfrUTPmd1/+qsTb9wNXSpb\nCxtjzKwJeAauJdAH/+rWp0XeLddf7MS3PfINJ973vNRkj5yBORuWhjHa8JKci/nnoW5kjkYPhNrD\nss/FxKsmmbOJYNJEt1vW5EHUb4L1792WBfzpddDfpk3EtXS3Ss1tw0H0rEieivsUEiItdmtOQL+e\nMQt9YeoK0DNlkNVfKXX6aJzPe3gmCVOlpWLp20fxGdV17m9ljDFVn2HcRuRj/qYszBV5TYfR/yMq\nHz0S2krlvQwMPbNN4dfBN+6G9ebp94+Kz9gecdqPYdmYuVla4jYfxPoSmQUdev8cuTZsfh718J1X\nP3PibKvfy7y7YDuac8l0Jz76d1iQ3vPMP8QxP1+2zInPuRe9X7askv1xzv3pNU6cshDrvitKrltb\nH13nxNwbqn6ntBrmPjOdtGaOuOIakce9kM4GEqgXSr1lm8w2mtxLzLaJHnw5+hAEUG8y3usYIy3N\ncwNpL9UnF+SIobJXg3N+1PcmPFLe9z7qrRKfTtbSx2pFXsI0zM3wVOjnI/Pk3+SeCWzr7GuU+6X4\nmfg+7huXcI7sMdNyWvZw8yc81FOi37qXjdQ7KSIX98Xuw1G7FX2t+rzYl9Rvk2PC68Xa70nC8/xi\nj+x9Mnksam0UWezymIrMk+fQQX2dumrRpyAkRvbQ4B5uiXOzca67ZP/E5DlfbpncvF/2TwqOxffH\nT8aYaK9oEXkBQbLnpL9RsQ59G/ieGWNMOFkgt9N94nFvjJyLDfuwXzqxv0jkDen5crt6V4S813Ep\nM5y4sxPfEZGNHpvh6fJ9x+1G/6GODry7RKfmizzuH9bajV5d8ZPk3Omh/pvNhZjPgVZ/nJhErAds\nS95GttXGGBMcJa/Rn+jz4b7avQY9ObhnbCkcninvH/dkqd3JdUhakbtp/nF/pMr1sodXKL0bxE/C\n+G44gPFhfzf3EUpagOfZ3Sr7RCVlYo7VbC524vwbZ4u85mLUF353jLDq7p5Xdzlx1mD0Tc27Tr5j\ndTXL/aC/UfEh+lZyD1BjZJ+UgCDs4exeptw/hmtvqDUuAoNxXHsVxqonRe5vit7d58S8lv7jSexV\ncpPke3oQvXvkTcO+xe5bWbYee7ge6hc2dJj8zSNmHPbNITG4jtQLh4g83irzPjfYquVizzrM/BeU\nOaNQKBQKhUKhUCgUCoVCMYDQH2cUCoVCoVAoFAqFQqFXS0nzAAAbmUlEQVRQKAYQZ+R+txfDoqwl\nSVJTmdYTmgjqmE0tjR2W7cRdbaAt2daV0aNASXIn4PvSF0s6YM3noIgFER0tPgtUpdAUKXNha7Cu\nelBG2RLbGGPayLKPrZ4bj0hrSKbB+VpAMQuJl5Stiu2QC/DfYvmFMcYkTs8yZxNeuubIXEmnbTj4\n5dRfm77dXop7E58Dml3pdmljzWPBWwdpVMwISTmLiMZzPfLSuzg/kjS0FktKpisStLBWstkOt+RP\nTXTc2DGgnL35xmci78LJsIlLmg/6oi3hYPvszMthy25LKdzx8rn6Ezsef9+JD5VIe+HxOTj32353\nvRPXlm0Uec//6BUnjo/AuR54Qtqcp5B13/U/gjTDZ8lrurogm3nzs61OPCQZ9L/5K84Vx7SSRKzh\nAI5/bNUfRN6HP33WiYddudiJ+/q3i7wxy25x4rfv+okTs7zLGGMm10OGE+ACtTQ0VkoEIlLO7lyM\nGgIqLNOjjTGmhuj1LJEwlgQoIRff4aLzD7MkpQ1EdS8sgvQo79pZIo/ndkQG6kF0DmjALSWSNh8U\nhpriobpRZ0lYjp6C1fCJInwW6Zb3fehc2Dw3HwT1PmiqpHlzzWfKt2396k6Sa4A/UbwGUrj868aJ\nzz743UdOvPwpWKk2HZByAn5uh59ADd1ZKGW8/aRDvfwb85z47ZfWi7zNf0Jt89C9nfaj8534H+Ol\nnK2M5Mh3X/pjJ/7lC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QkHy0YjcskSl67JGGMSzyXLVFo2Wgtlfe6nfSVL7jISJKWfEUIS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\n",
+            "text/plain": [
+              "<Figure size 1440x1440 with 100 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "kL8MEhNgrx9N",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits.  You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n",
+        "\n",
+        "It can be interesting to stop training at different numbers of iterations and see the effect.\n",
+        "\n",
+        "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n",
+        "\n",
+        "What differences do you see visually for the different levels of convergence?"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/sparsity_and_l1_regularization.ipynb b/sparsity_and_l1_regularization.ipynb
new file mode 100644
index 0000000..ec70aac
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1195 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "sparsity_and_l1_regularization.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "yjUCX5LAkxAX"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/sparsity_and_l1_regularization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "g4T-_IsVbweU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Sparsity and L1 Regularization"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "g8ue2FyFIjnQ",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Calculate the size of a model\n",
+        "  * Apply L1 regularization to reduce the size of a model by increasing sparsity"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ME_WXE7cIjnS",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "One way to reduce complexity is to use a regularization function that encourages weights to be exactly zero. For linear models such as regression, a zero weight is equivalent to not using the corresponding feature at all. In addition to avoiding overfitting, the resulting model will be more efficient.\n",
+        "\n",
+        "L1 regularization is a good way to increase sparsity.\n",
+        "\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fHRzeWkRLrHF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup\n",
+        "\n",
+        "Run the cells below to load the data and create feature definitions."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pb7rSrLKIjnS",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "3V7q8jk0IjnW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Create a boolean categorical feature representing whether the\n",
+        "  # median_house_value is above a set threshold.\n",
+        "  output_targets[\"median_house_value_is_high\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] > 265000).astype(float)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "pAG3tmgwIjnY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1205
+        },
+        "outputId": "96549ba2-d479-497a-a3a0-071224ac30b3"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Choose the first 12000 (out of 17000) examples for training.\n",
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "\n",
+        "# Choose the last 5000 (out of 17000) examples for validation.\n",
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "\n",
+        "# Double-check that we've done the right thing.\n",
+        "print(\"Training examples summary:\")\n",
+        "display.display(training_examples.describe())\n",
+        "print(\"Validation examples summary:\")\n",
+        "display.display(validation_examples.describe())\n",
+        "\n",
+        "print(\"Training targets summary:\")\n",
+        "display.display(training_targets.describe())\n",
+        "print(\"Validation targets summary:\")\n",
+        "display.display(validation_targets.describe())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       35.6     -119.6                28.7       2630.1           537.6   \n",
+              "std         2.1        2.0                12.6       2158.3           422.1   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1454.8           295.0   \n",
+              "50%        34.3     -118.5                29.0       2112.0           431.0   \n",
+              "75%        37.7     -118.0                37.0       3136.2           647.0   \n",
+              "max        41.9     -114.3                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1423.3       499.1            3.9               2.0  \n",
+              "std        1164.0       384.7            1.9               1.3  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         786.0       279.0            2.6               1.5  \n",
+              "50%        1159.0       406.0            3.5               1.9  \n",
+              "75%        1710.2       601.0            4.7               2.3  \n",
+              "max       35682.0      6082.0           15.0              55.2  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.7</td>\n",
+              "      <td>2630.1</td>\n",
+              "      <td>537.6</td>\n",
+              "      <td>1423.3</td>\n",
+              "      <td>499.1</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2158.3</td>\n",
+              "      <td>422.1</td>\n",
+              "      <td>1164.0</td>\n",
+              "      <td>384.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</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>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1454.8</td>\n",
+              "      <td>295.0</td>\n",
+              "      <td>786.0</td>\n",
+              "      <td>279.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.3</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2112.0</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>1159.0</td>\n",
+              "      <td>406.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3136.2</td>\n",
+              "      <td>647.0</td>\n",
+              "      <td>1710.2</td>\n",
+              "      <td>601.0</td>\n",
+              "      <td>4.7</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.9</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation examples summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       35.6     -119.6                28.4       2676.2           543.8   \n",
+              "std         2.2        2.0                12.6       2230.9           420.0   \n",
+              "min        32.5     -124.3                 1.0         20.0             4.0   \n",
+              "25%        33.9     -121.8                18.0       1472.8           302.0   \n",
+              "50%        34.2     -118.5                28.0       2167.5           442.0   \n",
+              "75%        37.7     -118.0                37.0       3173.2           654.0   \n",
+              "max        42.0     -114.6                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1444.7       506.4            3.9               2.0  \n",
+              "std        1108.1       384.1            2.0               0.9  \n",
+              "min          13.0         5.0            0.5               0.1  \n",
+              "25%         796.8       287.0            2.6               1.5  \n",
+              "50%        1185.5       417.0            3.6               1.9  \n",
+              "75%        1750.2       612.0            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              29.4  "
+            ],
+            "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>35.6</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.4</td>\n",
+              "      <td>2676.2</td>\n",
+              "      <td>543.8</td>\n",
+              "      <td>1444.7</td>\n",
+              "      <td>506.4</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2230.9</td>\n",
+              "      <td>420.0</td>\n",
+              "      <td>1108.1</td>\n",
+              "      <td>384.1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.9</td>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1472.8</td>\n",
+              "      <td>302.0</td>\n",
+              "      <td>796.8</td>\n",
+              "      <td>287.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>1.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2167.5</td>\n",
+              "      <td>442.0</td>\n",
+              "      <td>1185.5</td>\n",
+              "      <td>417.0</td>\n",
+              "      <td>3.6</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>37.7</td>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3173.2</td>\n",
+              "      <td>654.0</td>\n",
+              "      <td>1750.2</td>\n",
+              "      <td>612.0</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>16122.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>29.4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Training targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                     12000.0\n",
+              "mean                          0.2\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           0.0\n",
+              "max                           1.0"
+            ],
+            "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>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Validation targets summary:\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       median_house_value_is_high\n",
+              "count                      5000.0\n",
+              "mean                          0.3\n",
+              "std                           0.4\n",
+              "min                           0.0\n",
+              "25%                           0.0\n",
+              "50%                           0.0\n",
+              "75%                           1.0\n",
+              "max                           1.0"
+            ],
+            "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>median_house_value_is_high</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gHkniRI1Ijna",
+        "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.\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(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": "bLzK72jkNJPf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def get_quantile_based_buckets(feature_values, num_buckets):\n",
+        "  quantiles = feature_values.quantile(\n",
+        "    [(i+1.)/(num_buckets + 1.) for i in range(num_buckets)])\n",
+        "  return [quantiles[q] for q in quantiles.keys()]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "al2YQpKyIjnd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns():\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\"\n",
+        "\n",
+        "  bucketized_households = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"households\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"households\"], 10))\n",
+        "  bucketized_longitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"longitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"longitude\"], 50))\n",
+        "  bucketized_latitude = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"latitude\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"latitude\"], 50))\n",
+        "  bucketized_housing_median_age = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"housing_median_age\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"housing_median_age\"], 10))\n",
+        "  bucketized_total_rooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_rooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_rooms\"], 10))\n",
+        "  bucketized_total_bedrooms = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"total_bedrooms\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"total_bedrooms\"], 10))\n",
+        "  bucketized_population = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"population\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"population\"], 10))\n",
+        "  bucketized_median_income = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"median_income\"),\n",
+        "    boundaries=get_quantile_based_buckets(training_examples[\"median_income\"], 10))\n",
+        "  bucketized_rooms_per_person = tf.feature_column.bucketized_column(\n",
+        "    tf.feature_column.numeric_column(\"rooms_per_person\"),\n",
+        "    boundaries=get_quantile_based_buckets(\n",
+        "      training_examples[\"rooms_per_person\"], 10))\n",
+        "\n",
+        "  long_x_lat = tf.feature_column.crossed_column(\n",
+        "    set([bucketized_longitude, bucketized_latitude]), hash_bucket_size=1000)\n",
+        "\n",
+        "  feature_columns = set([\n",
+        "    long_x_lat,\n",
+        "    bucketized_longitude,\n",
+        "    bucketized_latitude,\n",
+        "    bucketized_housing_median_age,\n",
+        "    bucketized_total_rooms,\n",
+        "    bucketized_total_bedrooms,\n",
+        "    bucketized_population,\n",
+        "    bucketized_households,\n",
+        "    bucketized_median_income,\n",
+        "    bucketized_rooms_per_person])\n",
+        "  \n",
+        "  return feature_columns"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "hSBwMrsrE21n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Calculate the Model Size\n",
+        "\n",
+        "To calculate the model size, we simply count the number of parameters that are non-zero. We provide a helper function below to do that. The function uses intimate knowledge of the Estimators API - don't worry about understanding how it works."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "e6GfTI0CFhB8",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def model_size(estimator):\n",
+        "  variables = estimator.get_variable_names()\n",
+        "  size = 0\n",
+        "  for variable in variables:\n",
+        "    if not any(x in variable \n",
+        "               for x in ['global_step',\n",
+        "                         'centered_bias_weight',\n",
+        "                         'bias_weight',\n",
+        "                         'Ftrl']\n",
+        "              ):\n",
+        "      size += np.count_nonzero(estimator.get_variable_value(variable))\n",
+        "  return size"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XabdAaj67GfF",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Reduce the Model Size\n",
+        "\n",
+        "Your team needs to build a highly accurate Logistic Regression model on the *SmartRing*, a ring that is so smart it can sense the demographics of a city block ('median_income', 'avg_rooms', 'households', ..., etc.) and tell you whether the given city block is high cost city block or not.\n",
+        "\n",
+        "Since the SmartRing is small, the engineering team has determined that it can only handle a model that has **no more than 600 parameters**. On the other hand, the product management team has determined that the model is not launchable unless the **LogLoss is less than 0.35** on the holdout test set.\n",
+        "\n",
+        "Can you use your secret weapon—L1 regularization—to tune the model to satisfy both the size and accuracy constraints?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "G79hGRe7qqej",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1: Find a good regularization coefficient.\n",
+        "\n",
+        "**Find an L1 regularization strength parameter which satisfies both constraints — model size is less than 600 and log-loss is less than 0.35 on validation set.**\n",
+        "\n",
+        "The following code will help you get started. There are many ways to apply regularization to your model. Here, we chose to do it using `FtrlOptimizer`, which is designed to give better results with L1 regularization than standard gradient descent.\n",
+        "\n",
+        "Again, the model will train on the entire data set, so expect it to run slower than normal."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "1Fcdm0hpIjnl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_linear_classifier_model(\n",
+        "    learning_rate,\n",
+        "    regularization_strength,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    feature_columns,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    regularization_strength: A `float` that indicates the strength of the L1\n",
+        "       regularization. A value of `0.0` means no regularization.\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",
+        "    feature_columns: A `set` specifying the input feature columns to use.\n",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearClassifier` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 7\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  # Create a linear classifier object.\n",
+        "  my_optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate, l1_regularization_strength=regularization_strength)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_classifier = tf.estimator.LinearClassifier(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                          training_targets[\"median_house_value_is_high\"], \n",
+        "                                          batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(training_examples, \n",
+        "                                                  training_targets[\"median_house_value_is_high\"], \n",
+        "                                                  num_epochs=1, \n",
+        "                                                  shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(validation_examples, \n",
+        "                                                    validation_targets[\"median_house_value_is_high\"], \n",
+        "                                                    num_epochs=1, \n",
+        "                                                    shuffle=False)\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(\"LogLoss (on validation data):\")\n",
+        "  training_log_losses = []\n",
+        "  validation_log_losses = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_classifier.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    training_probabilities = linear_classifier.predict(input_fn=predict_training_input_fn)\n",
+        "    training_probabilities = np.array([item['probabilities'] for item in training_probabilities])\n",
+        "    \n",
+        "    validation_probabilities = linear_classifier.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_log_loss = metrics.log_loss(training_targets, training_probabilities)\n",
+        "    validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, validation_log_loss))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_log_losses.append(training_log_loss)\n",
+        "    validation_log_losses.append(validation_log_loss)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"LogLoss\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"LogLoss vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_log_losses, label=\"training\")\n",
+        "  plt.plot(validation_log_losses, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_classifier"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "9H1CKHSzIjno",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 707
+        },
+        "outputId": "3100f6fe-64ca-427d-9de9-20b0252ae1ef"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    # TWEAK THE REGULARIZATION VALUE BELOW\n",
+        "    regularization_strength=0.1,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.31\n",
+            "  period 01 : 0.28\n",
+            "  period 02 : 0.27\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.25\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.24\n",
+            "Model training finished.\n",
+            "Model size: 757\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lWl5p7XK9RsecoBkoKHwRt56K6gozVimEEEKYnjQzLZSNlY6HxnSlqtrIVzsv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\nhBBCiJD2/wFBDL330LNOfwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yjUCX5LAkxAX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below to see a possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hgGhy-okmkWL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "A regularization strength of 0.1 should be sufficient. Note that there is a compromise to be struck:\n",
+        "stronger regularization gives us smaller models, but can affect the classification loss."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_rV8YQWZIjns",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 588
+        },
+        "outputId": "0dcde452-9955-4b35-dfe3-de0d0209dbd5"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    regularization_strength=0.1,\n",
+        "    steps=300,\n",
+        "    batch_size=100,\n",
+        "    feature_columns=construct_feature_columns(),\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)\n",
+        "print(\"Model size:\", model_size(linear_classifier))"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.31\n",
+            "  period 01 : 0.28\n",
+            "  period 02 : 0.26\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.25\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.24\n",
+            "Model training finished.\n",
+            "Model size: 754\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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FZDCwUyhRpQfYEL+V33R7wtyhCSGEEHf0q1/NYsmSv+Ll5cWNG+nMm/caHh5aysrKuHnz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\nqCZhRgghhBBRTcKMEEIIIaLa/wd+PH3BKqEOMwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "CAzFyCJhdE_6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/synthetic_features_and_outliers.ipynb b/synthetic_features_and_outliers.ipynb
new file mode 100644
index 0000000..ff7a286
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1299 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "synthetic_features_and_outliers.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "i5Ul3zf5QYvW",
+        "jByCP8hDRZmM",
+        "WvgxW0bUSC-c"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/synthetic_features_and_outliers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4f3CKqFUqL2-",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Synthetic Features and Outliers"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jnKgkN5fHbGy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Create a synthetic feature that is the ratio of two other features\n",
+        "  * Use this new feature as an input to a linear regression model\n",
+        "  * Improve the effectiveness of the model by identifying and clipping (removing) outliers out of the input data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VOpLo5dcHbG0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's revisit our model from the previous First Steps with TensorFlow exercise. \n",
+        "\n",
+        "First, we'll import the California housing data into a *pandas* `DataFrame`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S8gm6BpqRRuh",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9D8GgUovHbG0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "baf3d35a-e143-4be8-ce3e-d0814787fc2e"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "from IPython import display\n",
+        "from matplotlib import cm\n",
+        "from matplotlib import gridspec\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import sklearn.metrics as metrics\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.python.data import Dataset\n",
+        "\n",
+        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
+        "pd.options.display.max_rows = 10\n",
+        "pd.options.display.float_format = '{:.1f}'.format\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "    np.random.permutation(california_housing_dataframe.index))\n",
+        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
+        "california_housing_dataframe"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>16858</th>\n",
+              "      <td>-123.6</td>\n",
+              "      <td>39.7</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>1839.0</td>\n",
+              "      <td>489.0</td>\n",
+              "      <td>887.0</td>\n",
+              "      <td>332.0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>100.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>550</th>\n",
+              "      <td>-117.0</td>\n",
+              "      <td>32.8</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>4015.0</td>\n",
+              "      <td>663.0</td>\n",
+              "      <td>1864.0</td>\n",
+              "      <td>664.0</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>159.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8352</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>3639.0</td>\n",
+              "      <td>1002.0</td>\n",
+              "      <td>1489.0</td>\n",
+              "      <td>983.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>387.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7562</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>681.0</td>\n",
+              "      <td>122.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>128.0</td>\n",
+              "      <td>5.3</td>\n",
+              "      <td>332.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10741</th>\n",
+              "      <td>-120.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>1787.0</td>\n",
+              "      <td>364.0</td>\n",
+              "      <td>1548.0</td>\n",
+              "      <td>362.0</td>\n",
+              "      <td>1.7</td>\n",
+              "      <td>49.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2751</th>\n",
+              "      <td>-117.7</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>732.0</td>\n",
+              "      <td>145.0</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>2.9</td>\n",
+              "      <td>95.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12006</th>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>38.7</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>4680.0</td>\n",
+              "      <td>758.0</td>\n",
+              "      <td>2626.0</td>\n",
+              "      <td>729.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>107.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16071</th>\n",
+              "      <td>-122.5</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>2303.0</td>\n",
+              "      <td>381.0</td>\n",
+              "      <td>862.0</td>\n",
+              "      <td>371.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>349.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9069</th>\n",
+              "      <td>-119.0</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>7744.0</td>\n",
+              "      <td>1573.0</td>\n",
+              "      <td>483.0</td>\n",
+              "      <td>224.0</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>231.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14958</th>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>38.1</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3223.0</td>\n",
+              "      <td>564.0</td>\n",
+              "      <td>1325.0</td>\n",
+              "      <td>539.0</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>126.9</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>17000 rows × 9 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "16858     -123.6      39.7                15.0       1839.0           489.0   \n",
+              "550       -117.0      32.8                33.0       4015.0           663.0   \n",
+              "8352      -118.5      34.0                21.0       3639.0          1002.0   \n",
+              "7562      -118.4      33.9                36.0        681.0           122.0   \n",
+              "10741     -120.7      37.0                26.0       1787.0           364.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "2751      -117.7      34.1                 7.0        732.0           145.0   \n",
+              "12006     -121.4      38.7                15.0       4680.0           758.0   \n",
+              "16071     -122.5      37.8                36.0       2303.0           381.0   \n",
+              "9069      -119.0      37.6                10.0       7744.0          1573.0   \n",
+              "14958     -122.2      38.1                37.0       3223.0           564.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "16858       887.0       332.0            2.2               100.0  \n",
+              "550        1864.0       664.0            4.3               159.3  \n",
+              "8352       1489.0       983.0            4.6               387.5  \n",
+              "7562        360.0       128.0            5.3               332.6  \n",
+              "10741      1548.0       362.0            1.7                49.5  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "2751        431.0       132.0            2.9                95.3  \n",
+              "12006      2626.0       729.0            3.8               107.0  \n",
+              "16071       862.0       371.0            6.0               349.0  \n",
+              "9069        483.0       224.0            3.3               231.8  \n",
+              "14958      1325.0       539.0            4.1               126.9  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6kNgrwCO_ms",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll set up our input function, and define the function for model training:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5RpTJER9XDub",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VgQPftrpHbG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
+        "    after training the model.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
+        "\n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "    \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "      metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "    \n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Create a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
+        "  \n",
+        "  return calibration_data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FJ6xUNVRm-do",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Try a Synthetic Feature\n",
+        "\n",
+        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
+        "\n",
+        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
+        "\n",
+        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
+        "\n",
+        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
+        "the final RMSE should be.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "isONN2XK32Wo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5ihcVutnnu1D",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1074
+        },
+        "outputId": "9b0403b8-3ab7-49bc-ebfe-b04da3756634"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE\n",
+        "#\n",
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n",
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.74\n",
+            "  period 01 : 190.10\n",
+            "  period 02 : 170.09\n",
+            "  period 03 : 153.61\n",
+            "  period 04 : 140.61\n",
+            "  period 05 : 133.82\n",
+            "  period 06 : 131.13\n",
+            "  period 07 : 130.91\n",
+            "  period 08 : 131.14\n",
+            "  period 09 : 131.91\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         196.0    207.3\n",
+              "std           90.3    116.0\n",
+              "min           44.3     15.0\n",
+              "25%          160.5    119.4\n",
+              "50%          192.9    180.4\n",
+              "75%          220.5    265.0\n",
+              "max         4308.8    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>196.0</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>90.3</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>44.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>160.5</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>192.9</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>220.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4308.8</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 131.91\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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iEnJaElQkzFjMZhbPTOWGKQMoq3QSF+3QDAkRqZWvuoZjzyzn+DPLMZwuYq4c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SsOBhNotpZdZEfFlX63FW+7S6LFiCwN3jLRSZMnEKtY6WvS3duQcrD3kZ1h63d5d2xtbhZ9+Efy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8cGsubpqazC2lRASAoUSQyyPjlbcdaGyToVQA8wpELJmtRoIl8oGkT5JxtNw/zvNIuX+cpyFG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\nIiIiigqGEkREREREREQUFf8ffdz6XMPtKLQAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "i5Ul3zf5QYvW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Leaz2oYMQcBf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "bc9b3977-ce45-4f62-a444-5d0b05839228"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.73\n",
+            "  period 01 : 189.64\n",
+            "  period 02 : 168.94\n",
+            "  period 03 : 152.27\n",
+            "  period 04 : 140.18\n",
+            "  period 05 : 134.17\n",
+            "  period 06 : 131.44\n",
+            "  period 07 : 130.53\n",
+            "  period 08 : 130.69\n",
+            "  period 09 : 131.39\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         194.0    207.3\n",
+              "std           88.9    116.0\n",
+              "min           44.8     15.0\n",
+              "25%          159.1    119.4\n",
+              "50%          191.0    180.4\n",
+              "75%          218.1    265.0\n",
+              "max         4242.5    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>194.0</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>88.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>44.8</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>159.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>191.0</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>218.1</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4242.5</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 131.39\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Egk5JCZFTpC3f0Hu8Xhas3EHutgKKy5wkxDhIT0vminFnBDs0kdNK+Reb2DNn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AymeiNSEaP3JI3hNvCMxJdCVRQa3vxfrv7zfxJsaMI0dTLTn/4r7FvX9UyygwH0Z/PRnz6MFJJR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lpTbWrPASzyYJJ/tI6SnKymysW1I+bb0zhBBTR9MMIjGN3kiG3qhGb0SjJ5KhN6Ll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\nEUP8B7V53L5A94iQAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZjQrZ8mcHFiU",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Identify Outliers\n",
+        "\n",
+        "We can visualize the performance of our model by creating a scatter plot of predictions vs. target values.  Ideally, these would lie on a perfectly correlated diagonal line.\n",
+        "\n",
+        "Use Pyplot's [`scatter()`](https://matplotlib.org/gallery/shapes_and_collections/scatter.html) to create a scatter plot of predictions vs. targets, using the rooms-per-person model you trained in Task 1.\n",
+        "\n",
+        "Do you see any oddities?  Trace these back to the source data by looking at the distribution of values in `rooms_per_person`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P0BDOec4HbG_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "outputId": "00424a88-5a98-4ede-a977-f3757327ac8e"
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7fae326e3f10>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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5vbLoRhkGvR73rFqAmy6bwX1ERFRQwk7NpWpMp5YqiwFNjXNyvga7TSrKIDTc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\nQERERAXFQERERAXFQERERAX1fwGGZrL2ddvdIAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "jByCP8hDRZmM",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "s0tiX2gdRe-S",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(15, 6))\n",
+        "plt.subplot(1, 2, 1)\n",
+        "plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kMQD0Uq3RqTX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The calibration data shows most scatter points aligned to a line. The line is almost vertical, but we'll come back to that later. Right now let's focus on the ones that deviate from the line. We notice that they are relatively few in number.\n",
+        "\n",
+        "If we plot a histogram of `rooms_per_person`, we find that we have a few outliers in our input data:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "POTM8C_ER1Oc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "f82dab21-c1c2-47eb-f055-31b223cf5373"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9l0KYpBQu8ed",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3: Clip Outliers\n",
+        "\n",
+        "See if you can further improve the model fit by setting the outlier values of `rooms_per_person` to some reasonable minimum or maximum.\n",
+        "\n",
+        "For reference, here's a quick example of how to apply a function to a Pandas `Series`:\n",
+        "\n",
+        "    clipped_feature = my_dataframe[\"my_feature_name\"].apply(lambda x: max(x, 0))\n",
+        "\n",
+        "The above `clipped_feature` will have no values less than `0`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rGxjRoYlHbHC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "fd5a703b-dc5b-48e6-bf8f-61f743d6a26c"
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WvgxW0bUSC-c",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8YGNjXPaSMPV",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The histogram we created in Task 2 shows that the majority of values are less than `5`. Let's clip `rooms_per_person` to 5, and plot a histogram to double-check the results."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "9YyARz6gSR7Q",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"rooms_per_person\"]).apply(lambda x: min(x, 5))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "vO0e1p_aSgKA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To verify that clipping worked, let's train again and print the calibration data once more:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ZgSP2HKfSoOH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "bc8933fd-ccb4-44ed-9506-d5adf195976c"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.05,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.79\n",
+            "  period 01 : 189.05\n",
+            "  period 02 : 166.68\n",
+            "  period 03 : 146.39\n",
+            "  period 04 : 130.77\n",
+            "  period 05 : 120.34\n",
+            "  period 06 : 113.89\n",
+            "  period 07 : 110.42\n",
+            "  period 08 : 109.53\n",
+            "  period 09 : 108.92\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         189.9    207.3\n",
+              "std           50.1    116.0\n",
+              "min           42.8     15.0\n",
+              "25%          157.8    119.4\n",
+              "50%          189.9    180.4\n",
+              "75%          217.2    265.0\n",
+              "max          423.8    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>189.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>50.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>42.8</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>157.8</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>189.9</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>217.2</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>423.8</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 108.92\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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EpEUKDbYy46LuuNwGC1buwO02Ah2SiIhIQCkpIdJMOcpcpKVn+nwtLT2LgqJS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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gySE-UgfSony",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "11ca5926-530f-4717-f303-0e27867e3c58"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Vqvllfr+9tQE//PUJyfcr83wnE3LzxWS9nhS1OKcXwuWyy24jS6MqoyawJBc1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\nIQsgBplAIBAIhCyAGGQCgUAgELKA/w9LGXHv+lAChgAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "VWRI1V_kuM80",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/validation.ipynb b/validation.ipynb
new file mode 100644
index 0000000..67e6adf
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1538 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "validation.ipynb",
+      "version": "0.3.2",
+      "provenance": [],
+      "collapsed_sections": [
+        "JndnmDMp66FL",
+        "4Xp9NhOCYSuz",
+        "pECTKgw5ZvFK",
+        "dER2_43pWj1T",
+        "I-La4N9ObC1x",
+        "yTghc_5HkJDW"
+      ],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python2",
+      "display_name": "Python 2"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/alishashaw439/Assignment-5/blob/alishashaw439/validation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JndnmDMp66FL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "#### Copyright 2017 Google LLC."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "hMqWDc_m6rUC",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "zbIgBK-oXHO7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "# Validation"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "WNX0VyBpHpCX",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**Learning Objectives:**\n",
+        "  * Use multiple features, instead of a single feature, to further improve the effectiveness of a model\n",
+        "  * Debug issues in model input data\n",
+        "  * Use a test data set to check if a model is overfitting the validation data"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "za0m1T8CHpCY",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "As in the prior exercises, we're working with the [California housing data set](https://developers.google.com/machine-learning/crash-course/california-housing-data-description), to try and predict `median_house_value` at the city block level from 1990 census data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "r2zgMfWDWF12",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Setup"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "8jErhkLzWI1B",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "First off, let's load up and prepare our data. This time, we're going to work with multiple features, so we'll modularize the logic for preprocessing the features a bit:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PwS5Bhm6HpCZ",
+        "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\n",
+        "\n",
+        "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
+        "\n",
+        "# california_housing_dataframe = california_housing_dataframe.reindex(\n",
+        "#     np.random.permutation(california_housing_dataframe.index))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "J2ZyTzX0HpCc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def preprocess_features(california_housing_dataframe):\n",
+        "  \"\"\"Prepares input features from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the features to be used for the model, including\n",
+        "    synthetic features.\n",
+        "  \"\"\"\n",
+        "  selected_features = california_housing_dataframe[\n",
+        "    [\"latitude\",\n",
+        "     \"longitude\",\n",
+        "     \"housing_median_age\",\n",
+        "     \"total_rooms\",\n",
+        "     \"total_bedrooms\",\n",
+        "     \"population\",\n",
+        "     \"households\",\n",
+        "     \"median_income\"]]\n",
+        "  processed_features = selected_features.copy()\n",
+        "  # Create a synthetic feature.\n",
+        "  processed_features[\"rooms_per_person\"] = (\n",
+        "    california_housing_dataframe[\"total_rooms\"] /\n",
+        "    california_housing_dataframe[\"population\"])\n",
+        "  return processed_features\n",
+        "\n",
+        "def preprocess_targets(california_housing_dataframe):\n",
+        "  \"\"\"Prepares target features (i.e., labels) from California housing data set.\n",
+        "\n",
+        "  Args:\n",
+        "    california_housing_dataframe: A Pandas DataFrame expected to contain data\n",
+        "      from the California housing data set.\n",
+        "  Returns:\n",
+        "    A DataFrame that contains the target feature.\n",
+        "  \"\"\"\n",
+        "  output_targets = pd.DataFrame()\n",
+        "  # Scale the target to be in units of thousands of dollars.\n",
+        "  output_targets[\"median_house_value\"] = (\n",
+        "    california_housing_dataframe[\"median_house_value\"] / 1000.0)\n",
+        "  return output_targets"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "sZSIaDiaHpCf",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **training set**, we'll choose the first 12000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "P9wejvw7HpCf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "f11a11a9-c9b9-49ba-c8c4-3d21ae3a5b9f"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.describe()"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>34.6</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>27.5</td>\n",
+              "      <td>2655.7</td>\n",
+              "      <td>547.1</td>\n",
+              "      <td>1476.0</td>\n",
+              "      <td>505.4</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.6</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>12.1</td>\n",
+              "      <td>2258.1</td>\n",
+              "      <td>434.3</td>\n",
+              "      <td>1174.3</td>\n",
+              "      <td>391.7</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.5</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>33.8</td>\n",
+              "      <td>-118.9</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1451.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>815.0</td>\n",
+              "      <td>283.0</td>\n",
+              "      <td>2.5</td>\n",
+              "      <td>1.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>34.0</td>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>2113.5</td>\n",
+              "      <td>438.0</td>\n",
+              "      <td>1207.0</td>\n",
+              "      <td>411.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>1.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>34.4</td>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>3146.0</td>\n",
+              "      <td>653.0</td>\n",
+              "      <td>1777.0</td>\n",
+              "      <td>606.0</td>\n",
+              "      <td>4.6</td>\n",
+              "      <td>2.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>41.8</td>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>5471.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>5189.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>55.2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count   12000.0    12000.0             12000.0      12000.0         12000.0   \n",
+              "mean       34.6     -118.5                27.5       2655.7           547.1   \n",
+              "std         1.6        1.2                12.1       2258.1           434.3   \n",
+              "min        32.5     -121.4                 1.0          2.0             2.0   \n",
+              "25%        33.8     -118.9                17.0       1451.8           299.0   \n",
+              "50%        34.0     -118.2                28.0       2113.5           438.0   \n",
+              "75%        34.4     -117.8                36.0       3146.0           653.0   \n",
+              "max        41.8     -114.3                52.0      37937.0          5471.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count     12000.0     12000.0        12000.0           12000.0  \n",
+              "mean       1476.0       505.4            3.8               1.9  \n",
+              "std        1174.3       391.7            1.9               1.3  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         815.0       283.0            2.5               1.4  \n",
+              "50%        1207.0       411.0            3.5               1.9  \n",
+              "75%        1777.0       606.0            4.6               2.3  \n",
+              "max       35682.0      5189.0           15.0              55.2  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "483d38a3-07a0-4df9-f016-48fa715f3e82"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>12000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>198.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>111.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>117.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>170.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>244.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count             12000.0\n",
+              "mean                198.0\n",
+              "std                 111.9\n",
+              "min                  15.0\n",
+              "25%                 117.1\n",
+              "50%                 170.5\n",
+              "75%                 244.4\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5l1aA2xOHpCj",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "For the **validation set**, we'll choose the last 5000 examples, out of the total of 17000."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fLYXLWAiHpCk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "9d00ceba-9d00-4aaa-b84b-6e620879f500"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_examples.describe()"
+      ],
+      "execution_count": 5,
+      "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>latitude</th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>rooms_per_person</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>38.1</td>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>31.3</td>\n",
+              "      <td>2614.8</td>\n",
+              "      <td>521.1</td>\n",
+              "      <td>1318.1</td>\n",
+              "      <td>491.2</td>\n",
+              "      <td>4.1</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.9</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>13.4</td>\n",
+              "      <td>1979.6</td>\n",
+              "      <td>388.5</td>\n",
+              "      <td>1073.7</td>\n",
+              "      <td>366.5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>36.1</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>0.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>37.5</td>\n",
+              "      <td>-122.4</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>1481.0</td>\n",
+              "      <td>292.0</td>\n",
+              "      <td>731.0</td>\n",
+              "      <td>278.0</td>\n",
+              "      <td>2.7</td>\n",
+              "      <td>1.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>37.8</td>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>2164.0</td>\n",
+              "      <td>424.0</td>\n",
+              "      <td>1074.0</td>\n",
+              "      <td>403.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>2.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>38.4</td>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>3161.2</td>\n",
+              "      <td>635.0</td>\n",
+              "      <td>1590.2</td>\n",
+              "      <td>603.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>2.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>42.0</td>\n",
+              "      <td>-121.4</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>32627.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>28566.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>18.3</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       latitude  longitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    5000.0     5000.0              5000.0       5000.0          5000.0   \n",
+              "mean       38.1     -122.2                31.3       2614.8           521.1   \n",
+              "std         0.9        0.5                13.4       1979.6           388.5   \n",
+              "min        36.1     -124.3                 1.0          8.0             1.0   \n",
+              "25%        37.5     -122.4                20.0       1481.0           292.0   \n",
+              "50%        37.8     -122.1                31.0       2164.0           424.0   \n",
+              "75%        38.4     -121.9                42.0       3161.2           635.0   \n",
+              "max        42.0     -121.4                52.0      32627.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1318.1       491.2            4.1               2.1  \n",
+              "std        1073.7       366.5            2.0               0.6  \n",
+              "min           8.0         1.0            0.5               0.1  \n",
+              "25%         731.0       278.0            2.7               1.7  \n",
+              "50%        1074.0       403.0            3.7               2.1  \n",
+              "75%        1590.2       603.0            5.1               2.4  \n",
+              "max       28566.0      6082.0           15.0              18.3  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "4849eff3-9054-4c4c-e21c-ebab58f7f478"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "execution_count": 6,
+      "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>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>5000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>229.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>122.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>130.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>213.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>303.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       median_house_value\n",
+              "count              5000.0\n",
+              "mean                229.5\n",
+              "std                 122.5\n",
+              "min                  15.0\n",
+              "25%                 130.4\n",
+              "50%                 213.0\n",
+              "75%                 303.2\n",
+              "max                 500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "z3TZV1pgfZ1n",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Examine the Data\n",
+        "Okay, let's look at the data above. We have `9` input features that we can use.\n",
+        "\n",
+        "Take a quick skim over the table of values. Everything look okay? See how many issues you can spot. Don't worry if you don't have a background in statistics; common sense  will get you far.\n",
+        "\n",
+        "After you've had a chance to look over the data yourself, check the solution for some additional thoughts on how to verify data."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4Xp9NhOCYSuz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gqeRmK57YWpy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's check our data against some baseline expectations:\n",
+        "\n",
+        "* For some values, like `median_house_value`, we can check to see if these values fall within reasonable ranges (keeping in mind this was 1990 data — not today!).\n",
+        "\n",
+        "* For other values, like `latitude` and `longitude`, we can do a quick check to see if these line up with expected values from a quick Google search.\n",
+        "\n",
+        "If you look closely, you may see some oddities:\n",
+        "\n",
+        "* `median_income` is on a scale from about 3 to 15. It's not at all clear what this scale refers to—looks like maybe some log scale? It's not documented anywhere; all we can assume is that higher values correspond to higher income.\n",
+        "\n",
+        "* The maximum `median_house_value` is 500,001. This looks like an artificial cap of some kind.\n",
+        "\n",
+        "* Our `rooms_per_person` feature is generally on a sane scale, with a 75th percentile value of about 2. But there are some very large values, like 18 or 55, which may show some amount of corruption in the data.\n",
+        "\n",
+        "We'll use these features as given for now. But hopefully these kinds of examples can help to build a little intuition about how to check data that comes to you from an unknown source."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fXliy7FYZZRm",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 2: Plot Latitude/Longitude vs. Median House Value"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "aJIWKBdfsDjg",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's take a close look at two features in particular: **`latitude`** and **`longitude`**. These are geographical coordinates of the city block in question.\n",
+        "\n",
+        "This might make a nice visualization — let's plot `latitude` and `longitude`, and use color to show the `median_house_value`."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5_LD23bJ06TW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 498
+        },
+        "outputId": "8d1fe4a9-d7d5-41be-faac-06f62a02a910"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(13, 8))\n",
+        "\n",
+        "ax = plt.subplot(1, 2, 1)\n",
+        "ax.set_title(\"Validation Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(validation_examples[\"longitude\"],\n",
+        "            validation_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=validation_targets[\"median_house_value\"] / validation_targets[\"median_house_value\"].max())\n",
+        "\n",
+        "ax = plt.subplot(1,2,2)\n",
+        "ax.set_title(\"Training Data\")\n",
+        "\n",
+        "ax.set_autoscaley_on(False)\n",
+        "ax.set_ylim([32, 43])\n",
+        "ax.set_autoscalex_on(False)\n",
+        "ax.set_xlim([-126, -112])\n",
+        "plt.scatter(training_examples[\"longitude\"],\n",
+        "            training_examples[\"latitude\"],\n",
+        "            cmap=\"coolwarm\",\n",
+        "            c=training_targets[\"median_house_value\"] / training_targets[\"median_house_value\"].max())\n",
+        "_ = plt.plot()"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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TIKCfnQcdHliVoCvR99ieQx5pt5OrLjTYsDVOOqOZN7uIYOD09lRfs8Bi6/4M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\nYII=\n",
+            "text/plain": [
+              "<Figure size 936x576 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "32_DbjnfXJlC",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Wait a second...this should have given us a nice map of the state of California, with red showing up in expensive areas like the San Francisco and Los Angeles.\n",
+        "\n",
+        "The training set sort of does, compared to a [real map](https://www.google.com/maps/place/California/@37.1870174,-123.7642688,6z/data=!3m1!4b1!4m2!3m1!1s0x808fb9fe5f285e3d:0x8b5109a227086f55), but the validation set clearly doesn't.\n",
+        "\n",
+        "**Go back up and look at the data from Task 1 again.**\n",
+        "\n",
+        "Do you see any other differences in the distributions of features or targets between the training and validation data?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pECTKgw5ZvFK",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "49NC4_KIZxk_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Looking at the tables of summary stats above, it's easy to wonder how anyone would do a useful data check. What's the right 75<sup>th</sup> percentile value for total_rooms per city block?\n",
+        "\n",
+        "The key thing to notice is that for any given feature or column, the distribution of values between the train and validation splits should be roughly equal.\n",
+        "\n",
+        "The fact that this is not the case is a real worry, and shows that we likely have a fault in the way that our train and validation split was created."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "025Ky0Dq9ig0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 3:  Return to the Data Importing and Pre-Processing Code, and See if You Spot Any Bugs\n",
+        "If you do, go ahead and fix the bug. Don't spend more than a minute or two looking. If you can't find the bug, check the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JFsd2eWHAMdy",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "When you've found and fixed the issue, re-run `latitude` / `longitude` plotting cell above and confirm that our sanity checks look better.\n",
+        "\n",
+        "By the way, there's an important lesson here.\n",
+        "\n",
+        "**Debugging in ML is often *data debugging* rather than code debugging.**\n",
+        "\n",
+        "If the data is wrong, even the most advanced ML code can't save things."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "dER2_43pWj1T",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "BnEVbYJvW2wu",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "The code that randomizes the data (`np.random.permutation`) is commented out, so we're not doing any randomization prior to splitting the data.\n",
+        "\n",
+        "If we don't randomize the data properly before creating training and validation splits, then we may be in trouble if the data is given to us in some sorted order, which appears to be the case here."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "xCdqLpQyAos2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 4: Train and Evaluate a Model\n",
+        "\n",
+        "**Spend 5 minutes or so trying different hyperparameter settings.  Try to get the best validation performance you can.**\n",
+        "\n",
+        "Next, we'll train a linear regressor using all the features in the data set, and see how well we do.\n",
+        "\n",
+        "Let's define the same input function we've used previously for loading the data into a TensorFlow model.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rzcIPGxxgG0t",
+        "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 multiple features.\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(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": "CvrKoBmNgRCO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Because we're now working with multiple input features, let's modularize our code for configuring feature columns into a separate function. (For now, this code is fairly simple, as all our features are numeric, but we'll build on this code as we use other types of features in future exercises.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wEW5_XYtgZ-H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def construct_feature_columns(input_features):\n",
+        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
+        "\n",
+        "  Args:\n",
+        "    input_features: The names of the numerical input features to use.\n",
+        "  Returns:\n",
+        "    A set of feature columns\n",
+        "  \"\"\" \n",
+        "  return set([tf.feature_column.numeric_column(my_feature)\n",
+        "              for my_feature in input_features])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "D0o2wnnzf8BD",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, go ahead and complete the `train_model()` code below to set up the input functions and calculate predictions.\n",
+        "\n",
+        "**NOTE:** It's okay to reference the code from the previous exercises, but make sure to call `predict()` on the appropriate data sets.\n",
+        "\n",
+        "Compare the losses on training data and validation data. With a single raw feature, our best root mean squared error (RMSE) was of about 180.\n",
+        "\n",
+        "See how much better you can do now that we can use multiple features.\n",
+        "\n",
+        "Check the data using some of the methods we've looked at before.  These might include:\n",
+        "\n",
+        "   * Comparing distributions of predictions and actual target values\n",
+        "\n",
+        "   * Creating a scatter plot of predictions vs. target values\n",
+        "\n",
+        "   * Creating two scatter plots of validation data using `latitude` and `longitude`:\n",
+        "      * One plot mapping color to actual target `median_house_value`\n",
+        "      * A second plot mapping color to predicted `median_house_value` for side-by-side comparison."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UXt0_4ZTEf4V",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 129
+        },
+        "outputId": "5e99ea68-f430-4398-9ca3-a72c934c9007"
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\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=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # 1. Create input functions.\n",
+        "  training_input_fn = # YOUR CODE HERE\n",
+        "  predict_training_input_fn = # YOUR CODE HERE\n",
+        "  predict_validation_input_fn = # YOUR CODE HERE\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\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",
+        "    # 2. Take a break and compute predictions.\n",
+        "    training_predictions = # YOUR CODE HERE\n",
+        "    validation_predictions = # YOUR CODE HERE\n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "SyntaxError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-13-35353c255253>\"\u001b[0;36m, line \u001b[0;32m44\u001b[0m\n\u001b[0;31m    training_input_fn = # YOUR CODE HERE\u001b[0m\n\u001b[0m                                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "zFFRmvUGh8wd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 231
+        },
+        "outputId": "c98812a9-087c-402c-9151-b7463912649d"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-14-220c5f58a36e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m linear_regressor = train_model(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;31m# TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.00001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'train_model' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I-La4N9ObC1x",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for a solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Xyz6n1YHbGef",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(\n",
+        "    learning_rate,\n",
+        "    steps,\n",
+        "    batch_size,\n",
+        "    training_examples,\n",
+        "    training_targets,\n",
+        "    validation_examples,\n",
+        "    validation_targets):\n",
+        "  \"\"\"Trains a linear regression model of multiple features.\n",
+        "  \n",
+        "  In addition to training, this function also prints training progress information,\n",
+        "  as well as a plot of the training and validation loss over time.\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",
+        "    training_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for training.\n",
+        "    training_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for training.\n",
+        "    validation_examples: A `DataFrame` containing one or more columns from\n",
+        "      `california_housing_dataframe` to use as input features for validation.\n",
+        "    validation_targets: A `DataFrame` containing exactly one column from\n",
+        "      `california_housing_dataframe` to use as target for validation.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A `LinearRegressor` object trained on the training data.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\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=construct_feature_columns(training_examples),\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(\n",
+        "      training_examples, \n",
+        "      training_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "  predict_validation_input_fn = lambda: my_input_fn(\n",
+        "      validation_examples, validation_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\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",
+        "  training_rmse = []\n",
+        "  validation_rmse = []\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",
+        "    training_predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    training_predictions = np.array([item['predictions'][0] for item in training_predictions])\n",
+        "    \n",
+        "    validation_predictions = linear_regressor.predict(input_fn=predict_validation_input_fn)\n",
+        "    validation_predictions = np.array([item['predictions'][0] for item in validation_predictions])\n",
+        "    \n",
+        "    \n",
+        "    # Compute training and validation loss.\n",
+        "    training_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(training_predictions, training_targets))\n",
+        "    validation_root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(validation_predictions, validation_targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, training_root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    training_rmse.append(training_root_mean_squared_error)\n",
+        "    validation_rmse.append(validation_root_mean_squared_error)\n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.ylabel(\"RMSE\")\n",
+        "  plt.xlabel(\"Periods\")\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(training_rmse, label=\"training\")\n",
+        "  plt.plot(validation_rmse, label=\"validation\")\n",
+        "  plt.legend()\n",
+        "\n",
+        "  return linear_regressor"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "i1imhjFzbWwt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 231
+        },
+        "outputId": "01df2146-892b-467c-e9c3-4ceabdbde905"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    learning_rate=0.00003,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    training_examples=training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=validation_examples,\n",
+        "    validation_targets=validation_targets)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-15-bed573cfb279>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m linear_regressor = train_model(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.00003\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtraining_examples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_examples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'train_model' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "65sin-E5NmHN",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 5: Evaluate on Test Data\n",
+        "\n",
+        "**In the cell below, load in the test data set and evaluate your model on it.**\n",
+        "\n",
+        "We've done a lot of iteration on our validation data.  Let's make sure we haven't overfit to the pecularities of that particular sample.\n",
+        "\n",
+        "Test data set is located [here](https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv).\n",
+        "\n",
+        "How does your test performance compare to the validation performance?  What does this say about the generalization performance of your model?"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "icEJIl5Vp51r",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 231
+        },
+        "outputId": "5f6b42fe-e452-41ce-9392-ef580c81c6d8"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "#\n",
+        "# YOUR CODE HERE\n",
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_test_input_fn = lambda: my_input_fn(\n",
+        "      test_examples, \n",
+        "      test_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)\n",
+        "#"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-16-b940d0c088f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m       shuffle=False)\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlinear_regressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpredict_test_input_fn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'predictions'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtest_predictions\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'linear_regressor' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yTghc_5HkJDW",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for the solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "_xSYTarykO8U",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_test_data = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\", sep=\",\")\n",
+        "\n",
+        "test_examples = preprocess_features(california_housing_test_data)\n",
+        "test_targets = preprocess_targets(california_housing_test_data)\n",
+        "\n",
+        "predict_test_input_fn = lambda: my_input_fn(\n",
+        "      test_examples, \n",
+        "      test_targets[\"median_house_value\"], \n",
+        "      num_epochs=1, \n",
+        "      shuffle=False)\n",
+        "\n",
+        "test_predictions = linear_regressor.predict(input_fn=predict_test_input_fn)\n",
+        "test_predictions = np.array([item['predictions'][0] for item in test_predictions])\n",
+        "\n",
+        "root_mean_squared_error = math.sqrt(\n",
+        "    metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file