diff --git a/feature_crosses.ipynb b/feature_crosses.ipynb
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+++ b/feature_crosses.ipynb
@@ -0,0 +1,1645 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "c219fdfe-1622-496e-8be1-49725ef74823"
+      },
+      "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                28.5       2666.4           544.1   \n",
+              "std         2.1        2.0                12.6       2219.3           430.0   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1466.0           298.0   \n",
+              "50%        34.2     -118.5                29.0       2137.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3165.2           653.0   \n",
+              "max        42.0     -114.5                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       1441.3       504.8            3.9               2.0  \n",
+              "std        1183.9       391.0            1.9               1.3  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         793.0       282.0            2.6               1.5  \n",
+              "50%        1169.0       409.0            3.5               1.9  \n",
+              "75%        1727.0       609.0            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",
+              "        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>28.5</td>\n",
+              "      <td>2666.4</td>\n",
+              "      <td>544.1</td>\n",
+              "      <td>1441.3</td>\n",
+              "      <td>504.8</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>2219.3</td>\n",
+              "      <td>430.0</td>\n",
+              "      <td>1183.9</td>\n",
+              "      <td>391.0</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>298.0</td>\n",
+              "      <td>793.0</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2137.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1169.0</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>3165.2</td>\n",
+              "      <td>653.0</td>\n",
+              "      <td>1727.0</td>\n",
+              "      <td>609.0</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>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     -119.5                28.8       2589.0           528.2   \n",
+              "std         2.1        2.0                12.6       2081.7           400.2   \n",
+              "min        32.5     -124.3                 2.0         15.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1449.8           293.8   \n",
+              "50%        34.2     -118.5                29.0       2111.0           432.0   \n",
+              "75%        37.7     -118.0                37.0       3113.0           639.0   \n",
+              "max        41.9     -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       1401.5       492.6            3.9               2.0  \n",
+              "std        1055.8       368.5            1.9               0.8  \n",
+              "min           8.0         2.0            0.5               0.2  \n",
+              "25%         781.8       280.0            2.6               1.5  \n",
+              "50%        1161.0       407.0            3.5               1.9  \n",
+              "75%        1707.2       594.0            4.8               2.3  \n",
+              "max       16122.0      5189.0           15.0              19.1  "
+            ],
+            "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>-119.5</td>\n",
+              "      <td>28.8</td>\n",
+              "      <td>2589.0</td>\n",
+              "      <td>528.2</td>\n",
+              "      <td>1401.5</td>\n",
+              "      <td>492.6</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>2081.7</td>\n",
+              "      <td>400.2</td>\n",
+              "      <td>1055.8</td>\n",
+              "      <td>368.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.8</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>15.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</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>1449.8</td>\n",
+              "      <td>293.8</td>\n",
+              "      <td>781.8</td>\n",
+              "      <td>280.0</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>2111.0</td>\n",
+              "      <td>432.0</td>\n",
+              "      <td>1161.0</td>\n",
+              "      <td>407.0</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>3113.0</td>\n",
+              "      <td>639.0</td>\n",
+              "      <td>1707.2</td>\n",
+              "      <td>594.0</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>-114.3</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>19.1</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                207.3\n",
+              "std                 116.5\n",
+              "min                  15.0\n",
+              "25%                 118.9\n",
+              "50%                 179.2\n",
+              "75%                 266.0\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>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>118.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.0</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                207.2\n",
+              "std                 114.8\n",
+              "min                  15.0\n",
+              "25%                 120.8\n",
+              "50%                 183.0\n",
+              "75%                 262.6\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>207.2</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>120.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>183.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>262.6</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": 622
+        },
+        "outputId": "ee3bd999-4414-4c71-ba4b-b18dd0067cc6"
+      },
+      "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": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.29\n",
+            "  period 01 : 316.85\n",
+            "  period 02 : 200.72\n",
+            "  period 03 : 132.83\n",
+            "  period 04 : 123.45\n",
+            "  period 05 : 133.82\n",
+            "  period 06 : 125.43\n",
+            "  period 07 : 125.44\n",
+            "  period 08 : 127.60\n",
+            "  period 09 : 120.04\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Q1TW17D6aa4eKRURE7EMNTCuyNSXjsuIjq5hga4y0x7bNOpVXq5FERKQ1UQPT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\nIYQQIu38f/cMvcNJR586AAAAAElFTkSuQmCC\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",
+        "  #\n",
+        "  # YOUR CODE HERE: bucketize the following columns, following the example above:\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\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "0FfUytOTNJhL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "762e0d0d-64b2-4c08-bb5c-a999020ce174"
+      },
+      "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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 170.00\n",
+            "  period 01 : 143.74\n",
+            "  period 02 : 127.29\n",
+            "  period 03 : 116.09\n",
+            "  period 04 : 108.17\n",
+            "  period 05 : 102.22\n",
+            "  period 06 : 97.76\n",
+            "  period 07 : 94.19\n",
+            "  period 08 : 91.30\n",
+            "  period 09 : 88.87\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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KgeO07VCMq7OFpZuPUVRSbu/SREREHJ4CjB39NsXAjylrGBLZhLyCUlZvT7R3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\nERGlHRYYIiIiSjssMERERJR2/j9EnurpgH08LgAAAABJRU5ErkJggg==\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": "18f7831b-f9f9-4115-9962-748d47206fe6"
+      },
+      "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": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 170.00\n",
+            "  period 01 : 143.82\n",
+            "  period 02 : 127.40\n",
+            "  period 03 : 116.27\n",
+            "  period 04 : 108.38\n",
+            "  period 05 : 102.53\n",
+            "  period 06 : 98.01\n",
+            "  period 07 : 94.44\n",
+            "  period 08 : 91.47\n",
+            "  period 09 : 88.96\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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veXGKgY3n1zO4ewNSs/LZsCfe2mWJiIjUCAowVtKwXiC3BHTnXM55Altm4Opk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\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(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": "19d668cb-9424-424e-cbbf-9deda637966a"
+      },
+      "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": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.90\n",
+            "  period 01 : 135.83\n",
+            "  period 02 : 118.74\n",
+            "  period 03 : 107.43\n",
+            "  period 04 : 99.49\n",
+            "  period 05 : 93.63\n",
+            "  period 06 : 89.08\n",
+            "  period 07 : 85.43\n",
+            "  period 08 : 82.49\n",
+            "  period 09 : 80.02\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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7zRt4sfNIKrFJOTZOJiIiUruowFxjbf1acZ1Pcw6kH6ZXD2cAFq47YeNUIiIi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\nrkJggg==\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": "52baadde-6072-498b-929d-92a4cb161237"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 163.72\n",
+            "  period 01 : 135.57\n",
+            "  period 02 : 118.48\n",
+            "  period 03 : 107.14\n",
+            "  period 04 : 99.17\n",
+            "  period 05 : 93.31\n",
+            "  period 06 : 88.75\n",
+            "  period 07 : 85.12\n",
+            "  period 08 : 82.15\n",
+            "  period 09 : 79.76\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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zm8yMDorAwOA40bQKcGdbbConkvOsHU1ERKTOUIGxglDfYFq6NycmdQ/X93IF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\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..0dce096
--- /dev/null
+++ b/feature_sets.ipynb
@@ -0,0 +1,1604 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "651ba0dd-046a-43d1-e851-07c5da7e8e08"
+      },
+      "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                28.5       2649.0           539.5   \n",
+              "std         2.1        2.0                12.6       2212.1           425.9   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1463.8           297.0   \n",
+              "50%        34.2     -118.5                29.0       2127.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3150.0           648.0   \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       1433.0       500.9            3.9               2.0  \n",
+              "std        1178.0       387.6            1.9               1.3  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         792.0       282.0            2.6               1.5  \n",
+              "50%        1167.0       409.0            3.5               1.9  \n",
+              "75%        1720.0       603.0            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",
+              "        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>28.5</td>\n",
+              "      <td>2649.0</td>\n",
+              "      <td>539.5</td>\n",
+              "      <td>1433.0</td>\n",
+              "      <td>500.9</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>2212.1</td>\n",
+              "      <td>425.9</td>\n",
+              "      <td>1178.0</td>\n",
+              "      <td>387.6</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>1463.8</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>792.0</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>34.2</td>\n",
+              "      <td>-118.5</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>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>3150.0</td>\n",
+              "      <td>648.0</td>\n",
+              "      <td>1720.0</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>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                28.8       2630.9           539.1   \n",
+              "std         2.1        2.0                12.6       2101.0           410.9   \n",
+              "min        32.6     -124.3                 2.0         12.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1455.8           295.8   \n",
+              "50%        34.2     -118.5                29.0       2131.0           433.5   \n",
+              "75%        37.7     -118.0                37.0       3153.2           650.0   \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       1421.3       502.0            3.9               2.0  \n",
+              "std        1072.2       377.0            1.9               0.9  \n",
+              "min           8.0         3.0            0.5               0.1  \n",
+              "25%         781.0       280.0            2.6               1.5  \n",
+              "50%        1168.0       408.0            3.5               1.9  \n",
+              "75%        1725.2       608.0            4.7               2.3  \n",
+              "max       16122.0      5189.0           15.0              19.1  "
+            ],
+            "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>28.8</td>\n",
+              "      <td>2630.9</td>\n",
+              "      <td>539.1</td>\n",
+              "      <td>1421.3</td>\n",
+              "      <td>502.0</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>2101.0</td>\n",
+              "      <td>410.9</td>\n",
+              "      <td>1072.2</td>\n",
+              "      <td>377.0</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>2.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>3.0</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>1455.8</td>\n",
+              "      <td>295.8</td>\n",
+              "      <td>781.0</td>\n",
+              "      <td>280.0</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>2131.0</td>\n",
+              "      <td>433.5</td>\n",
+              "      <td>1168.0</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>3153.2</td>\n",
+              "      <td>650.0</td>\n",
+              "      <td>1725.2</td>\n",
+              "      <td>608.0</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>-114.6</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>19.1</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                206.7\n",
+              "std                 115.6\n",
+              "min                  15.0\n",
+              "25%                 119.2\n",
+              "50%                 179.5\n",
+              "75%                 265.4\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>206.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>179.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>265.4</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                208.8\n",
+              "std                 116.9\n",
+              "min                  15.0\n",
+              "25%                 120.2\n",
+              "50%                 182.1\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>208.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>116.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>182.1</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": "ca362054-e035-4ad1-ad15-8df31045fe3c"
+      },
+      "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>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>0.1</td>\n",
+              "      <td>-0.0</td>\n",
+              "      <td>-0.1</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>0.1</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>0.0</td>\n",
+              "      <td>0.0</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>0.0</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>0.0</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          0.1   \n",
+              "housing_median_age       0.0       -0.1                 1.0         -0.4   \n",
+              "total_rooms             -0.0        0.1                -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               0.0        -0.1        -0.0            0.2   \n",
+              "target                         0.0        -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                   0.0     0.0  \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": 639
+        },
+        "outputId": "6c578217-a90c-4128-e569-24811df699f1"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# Your code here: add your features of choice as a list of quoted strings.\n",
+        "#\n",
+        "minimal_features = [\"median_income\", \"latitude\",]\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=1000,\n",
+        "    batch_size=50,\n",
+        "    training_examples=minimal_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=minimal_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 : 119.84\n",
+            "  period 01 : 114.47\n",
+            "  period 02 : 112.00\n",
+            "  period 03 : 109.03\n",
+            "  period 04 : 106.58\n",
+            "  period 05 : 104.16\n",
+            "  period 06 : 102.00\n",
+            "  period 07 : 99.80\n",
+            "  period 08 : 97.82\n",
+            "  period 09 : 95.94\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<tensorflow.python.estimator.canned.linear.LinearRegressor at 0x7f637c6b75d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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EM7Nnzy6TPvfOO2+Rnp522/efe+4ZSzXJ4qSAEUIIIZqRjIx0du7cbtJnn356IQEBgbd9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\nChghhBBCuJ3/D4yi5zyjhAqlAAAAAElFTkSuQmCC\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "cdda1a7f-7651-4367-a2fe-ccd7f6765e23"
+      },
+      "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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 164.91\n",
+            "  period 01 : 126.31\n",
+            "  period 02 : 117.50\n",
+            "  period 03 : 116.23\n",
+            "  period 04 : 115.30\n",
+            "  period 05 : 114.88\n",
+            "  period 06 : 114.64\n",
+            "  period 07 : 114.38\n",
+            "  period 08 : 113.74\n",
+            "  period 09 : 112.74\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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mi9nZpREREbULDDA3KCZMg8pqCy5mFyNaE4lKcyV0JZnOLouIiKhdYIC5QTHh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\nDDMDLahKUAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "6609ec76-777d-459e-95ba-a6cac540540a"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(training_examples[\"latitude\"], training_targets[\"median_house_value\"])"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f637d8fe210>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Bbbcx2H2gF0+/9J5kWiSfVAFrMaHezqLn/JjmfdYCBeCvdqxAIp5EPM8BKKfO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\n/wPsVyghrBRu6wAAAABJRU5ErkJggg==\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",
+        "#\n",
+        "def 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 = transform_features(training_examples)\n",
+        "selected_validation_examples = transform_features(validation_examples)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "XgCGEa6FGC2i",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "ce7ddc6d-faec-4caa-9c1a-574bbd9b3e22"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = train_model(\n",
+        "    learning_rate=0.01,\n",
+        "    steps=2000,\n",
+        "    batch_size=100,\n",
+        "    training_examples=selected_training_examples,\n",
+        "    training_targets=training_targets,\n",
+        "    validation_examples=selected_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 : 196.09\n",
+            "  period 01 : 157.28\n",
+            "  period 02 : 122.22\n",
+            "  period 03 : 95.15\n",
+            "  period 04 : 84.09\n",
+            "  period 05 : 83.45\n",
+            "  period 06 : 83.00\n",
+            "  period 07 : 82.61\n",
+            "  period 08 : 82.24\n",
+            "  period 09 : 81.88\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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16sS0adOYM2fONa+pyBcg+vp6YLM52SMiAIGBXndtX48Mb82W/RdYsT2R0eMG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\nSggAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "7446baf1-3928-47dd-c15b-d165c9ea94ff"
+      },
+      "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": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 226.57\n",
+            "  period 01 : 216.42\n",
+            "  period 02 : 206.34\n",
+            "  period 03 : 196.37\n",
+            "  period 04 : 186.51\n",
+            "  period 05 : 176.80\n",
+            "  period 06 : 167.21\n",
+            "  period 07 : 157.82\n",
+            "  period 08 : 148.65\n",
+            "  period 09 : 139.74\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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f46V1qby6PpO9h0tZdflYgv063uS7NPAiZsSO51+732RPXgoP7niCq+IvYX7s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\nYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ 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..233c647
--- /dev/null
+++ b/first_steps_with_tensor_flow.ipynb
@@ -0,0 +1,2006 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "8c346f74-0d96-4d10-f256-63c62dc943cc"
+      },
+      "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>5001</th>\n",
+              "      <td>-118.1</td>\n",
+              "      <td>33.8</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>1588.0</td>\n",
+              "      <td>272.0</td>\n",
+              "      <td>692.0</td>\n",
+              "      <td>245.0</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>220.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14172</th>\n",
+              "      <td>-122.1</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>1169.0</td>\n",
+              "      <td>239.0</td>\n",
+              "      <td>589.0</td>\n",
+              "      <td>249.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>330.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1024</th>\n",
+              "      <td>-117.1</td>\n",
+              "      <td>32.7</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>1931.0</td>\n",
+              "      <td>472.0</td>\n",
+              "      <td>1628.0</td>\n",
+              "      <td>445.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>92.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16020</th>\n",
+              "      <td>-122.4</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>4930.0</td>\n",
+              "      <td>1381.0</td>\n",
+              "      <td>2232.0</td>\n",
+              "      <td>1321.0</td>\n",
+              "      <td>4.3</td>\n",
+              "      <td>316.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14980</th>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>888.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>175.0</td>\n",
+              "      <td>2.2</td>\n",
+              "      <td>211.5</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>2985</th>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>33.7</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>3400.0</td>\n",
+              "      <td>501.0</td>\n",
+              "      <td>1575.0</td>\n",
+              "      <td>488.0</td>\n",
+              "      <td>6.1</td>\n",
+              "      <td>295.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2971</th>\n",
+              "      <td>-117.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>6239.0</td>\n",
+              "      <td>901.0</td>\n",
+              "      <td>2923.0</td>\n",
+              "      <td>904.0</td>\n",
+              "      <td>6.5</td>\n",
+              "      <td>268.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8563</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1952.0</td>\n",
+              "      <td>387.0</td>\n",
+              "      <td>1156.0</td>\n",
+              "      <td>392.0</td>\n",
+              "      <td>4.2</td>\n",
+              "      <td>209.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13298</th>\n",
+              "      <td>-121.9</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>1280.0</td>\n",
+              "      <td>209.0</td>\n",
+              "      <td>512.0</td>\n",
+              "      <td>208.0</td>\n",
+              "      <td>5.1</td>\n",
+              "      <td>315.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8964</th>\n",
+              "      <td>-118.9</td>\n",
+              "      <td>35.4</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>4121.0</td>\n",
+              "      <td>755.0</td>\n",
+              "      <td>2590.0</td>\n",
+              "      <td>721.0</td>\n",
+              "      <td>3.3</td>\n",
+              "      <td>67.6</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",
+              "5001      -118.1      33.8                37.0       1588.0           272.0   \n",
+              "14172     -122.1      37.4                37.0       1169.0           239.0   \n",
+              "1024      -117.1      32.7                 9.0       1931.0           472.0   \n",
+              "16020     -122.4      37.8                28.0       4930.0          1381.0   \n",
+              "14980     -122.2      37.8                52.0        888.0           168.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "2985      -117.8      33.7                16.0       3400.0           501.0   \n",
+              "2971      -117.8      33.9                14.0       6239.0           901.0   \n",
+              "8563      -118.5      34.2                36.0       1952.0           387.0   \n",
+              "13298     -121.9      37.6                46.0       1280.0           209.0   \n",
+              "8964      -118.9      35.4                32.0       4121.0           755.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "5001        692.0       245.0            4.9               220.3  \n",
+              "14172       589.0       249.0            5.0               330.3  \n",
+              "1024       1628.0       445.0            2.1                92.6  \n",
+              "16020      2232.0      1321.0            4.3               316.2  \n",
+              "14980       360.0       175.0            2.2               211.5  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "2985       1575.0       488.0            6.1               295.5  \n",
+              "2971       2923.0       904.0            6.5               268.2  \n",
+              "8563       1156.0       392.0            4.2               209.2  \n",
+              "13298       512.0       208.0            5.1               315.6  \n",
+              "8964       2590.0       721.0            3.3                67.6  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "HzzlSs3PtTmt",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Examine the Data\n",
+        "\n",
+        "It's a good idea to get to know your data a little bit before you work with it.\n",
+        "\n",
+        "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "gzb10yoVrydW",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "4dcc9ef6-b959-4a37-8ed8-70ea08b6a12b"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.describe()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>longitude</th>\n",
+              "      <th>latitude</th>\n",
+              "      <th>housing_median_age</th>\n",
+              "      <th>total_rooms</th>\n",
+              "      <th>total_bedrooms</th>\n",
+              "      <th>population</th>\n",
+              "      <th>households</th>\n",
+              "      <th>median_income</th>\n",
+              "      <th>median_house_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>35.6</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2643.7</td>\n",
+              "      <td>539.4</td>\n",
+              "      <td>1429.6</td>\n",
+              "      <td>501.2</td>\n",
+              "      <td>3.9</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.1</td>\n",
+              "      <td>12.6</td>\n",
+              "      <td>2179.9</td>\n",
+              "      <td>421.5</td>\n",
+              "      <td>1147.9</td>\n",
+              "      <td>384.5</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>32.5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.5</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-121.8</td>\n",
+              "      <td>33.9</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1462.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>282.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>34.2</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2127.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1167.0</td>\n",
+              "      <td>409.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>-118.0</td>\n",
+              "      <td>37.7</td>\n",
+              "      <td>37.0</td>\n",
+              "      <td>3151.2</td>\n",
+              "      <td>648.2</td>\n",
+              "      <td>1721.0</td>\n",
+              "      <td>605.2</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>-114.3</td>\n",
+              "      <td>42.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>37937.0</td>\n",
+              "      <td>6445.0</td>\n",
+              "      <td>35682.0</td>\n",
+              "      <td>6082.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
+              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
+              "std          2.0       2.1                12.6       2179.9           421.5   \n",
+              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
+              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
+              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
+              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
+              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "count     17000.0     17000.0        17000.0             17000.0  \n",
+              "mean       1429.6       501.2            3.9               207.3  \n",
+              "std        1147.9       384.5            1.9               116.0  \n",
+              "min           3.0         1.0            0.5                15.0  \n",
+              "25%         790.0       282.0            2.6               119.4  \n",
+              "50%        1167.0       409.0            3.5               180.4  \n",
+              "75%        1721.0       605.2            4.8               265.0  \n",
+              "max       35682.0      6082.0           15.0               500.0  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Lr6wYl2bt2Ep",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Build the First Model\n",
+        "\n",
+        "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n",
+        "\n",
+        "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n",
+        "\n",
+        "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "0cpcsieFhsNI",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 1: Define Features and Configure Feature Columns"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "EL8-9d4ZJNR7",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n",
+        "\n",
+        "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n",
+        "\n",
+        "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n",
+        "\n",
+        "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n",
+        "\n",
+        "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "rhEbFCZ86cDZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the input feature: total_rooms.\n",
+        "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n",
+        "\n",
+        "# Configure a numeric feature column for total_rooms.\n",
+        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "K_3S8teX7Rd2",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UMl3qrU5MGV6",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 2: Define the Target"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "cw4nrfcB7kyk",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "l1NvvNkH8Kbt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Define the label.\n",
+        "targets = california_housing_dataframe[\"median_house_value\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "4M-rTFHL2UkA",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 3: Configure the LinearRegressor"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "fUfGQUNp7jdL",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n",
+        "\n",
+        "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. "
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ubhtW-NGU802",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "# Use gradient descent as the optimizer for training the model.\n",
+        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
+        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "\n",
+        "# Configure the linear regression model with our feature columns and optimizer.\n",
+        "# Set a learning rate of 0.0000001 for Gradient Descent.\n",
+        "linear_regressor = tf.estimator.LinearRegressor(\n",
+        "    feature_columns=feature_columns,\n",
+        "    optimizer=my_optimizer\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "-0IztwdK2f3F",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 4: Define the Input Function"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "S5M5j6xSCHxx",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n",
+        "the data, as well as how to batch, shuffle, and repeat it during model training.\n",
+        "\n",
+        "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n",
+        "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n",
+        "\n",
+        "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n",
+        "\n",
+        "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n",
+        "the size of the dataset from which `shuffle` will randomly sample.\n",
+        "\n",
+        "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "RKZ9zNcHJtwc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "  \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "wwa6UeA1V5F_",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE:** We'll continue to use this same input function in later exercises. For more\n",
+        "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "4YS50CQb2ooO",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 5: Train the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "yP92XkzhU803",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n",
+        "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n",
+        "train for 100 steps."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5M-Kt6w8U803",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = linear_regressor.train(\n",
+        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
+        "    steps=100\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "7Nwxqxlx2sOv",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Step 6: Evaluate the Model"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "KoDaF2dlJQG5",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Let's make predictions on that training data, to see how well our model fit it during training.\n",
+        "\n",
+        "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "pDIxp6vcU809",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "024312e6-f1e9-4544-8f72-3fbf92e6c756"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Create an input function for predictions.\n",
+        "# Note: Since we're making just one prediction for each example, we don't \n",
+        "# need to repeat or shuffle the data here.\n",
+        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
+        "\n",
+        "# Call predict() on the linear_regressor to make predictions.\n",
+        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "\n",
+        "# Format predictions as a NumPy array, so we can calculate error metrics.\n",
+        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "\n",
+        "# Print Mean Squared Error and Root Mean Squared Error.\n",
+        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
+        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
+        "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n",
+        "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Mean Squared Error (on training data): 56367.025\n",
+            "Root Mean Squared Error (on training data): 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AKWstXXPzOVz",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Is this a good model? How would you judge how large this error is?\n",
+        "\n",
+        "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n",
+        "instead.  A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n",
+        "\n",
+        "Let's compare the RMSE to the difference of the min and max of our targets:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7UwqGbbxP53O",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "aabd70bd-91e3-47ed-bfb2-8b9fba4ae957"
+      },
+      "cell_type": "code",
+      "source": [
+        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
+        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
+        "min_max_difference = max_house_value - min_house_value\n",
+        "\n",
+        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
+        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
+        "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n",
+        "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Min. Median House Value: 14.999\n",
+            "Max. Median House Value: 500.001\n",
+            "Difference between Min. and Max.: 485.002\n",
+            "Root Mean Squared Error: 237.417\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JigJr0C7Pzit",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Our error spans nearly half the range of the target values. Can we do better?\n",
+        "\n",
+        "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n",
+        "\n",
+        "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "941nclxbzqGH",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "e5ee8305-0cad-4709-c927-106fa502a399"
+      },
+      "cell_type": "code",
+      "source": [
+        "calibration_data = pd.DataFrame()\n",
+        "calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "calibration_data.describe()"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.9</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.1    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            0.1    119.4\n",
+              "50%            0.1    180.4\n",
+              "75%            0.2    265.0\n",
+              "max            1.9    500.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "E2-bf8Hq36y8",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n",
+        "\n",
+        "We can also visualize the data and the line we've learned.  Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n",
+        "\n",
+        "First, we'll get a uniform random sample of the data so we can make a readable scatter plot."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SGRIi3mAU81H",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "sample = california_housing_dataframe.sample(n=300)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "N-JwuJBKU81J",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "7G12E76-339G",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 361
+        },
+        "outputId": "8c41b7a5-21fc-47b7-a927-4cbe01c261ab"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Get the min and max total_rooms values.\n",
+        "x_0 = sample[\"total_rooms\"].min()\n",
+        "x_1 = sample[\"total_rooms\"].max()\n",
+        "\n",
+        "# Retrieve the final weight and bias generated during training.\n",
+        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
+        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "# Get the predicted median_house_values for the min and max total_rooms values.\n",
+        "y_0 = weight * x_0 + bias \n",
+        "y_1 = weight * x_1 + bias\n",
+        "\n",
+        "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n",
+        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
+        "\n",
+        "# Label the graph axes.\n",
+        "plt.ylabel(\"median_house_value\")\n",
+        "plt.xlabel(\"total_rooms\")\n",
+        "\n",
+        "# Plot a scatter plot from our data sample.\n",
+        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
+        "\n",
+        "# Display graph.\n",
+        "plt.show()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NapJ8XmraXDj9L/f6MpnnkiX6A2h2uhMK0B1douzwRJurd0lhIiLSjupg39TU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\nAlH2yZbvuITWxh+s4n3p180fjY07T/b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gR1+/RlVQXbe1UfImKPr3ma0GQwoy28T+d5EL\n1xSN16NvWZ/GzwYWU57CHvFlqnv1ct47dA5186tRaM6XTG//ZV8Tamuq8B/f/BwcTjcgCP12pov9\nIlLKBgQv1ue1dfr6pM5TGUdPJC2fjgp3vW6hyx3pchczO5RrGOwlyKXy6uaPxtFTzqR2mQvz+gJ4\nfXMDvnbjlXEDWFVl3zs5uQB967W9e9rXNzhkq4ljz/2Hd08kNY6eaOBNZCliOXqa/w7oY0ldIqJE\nMNhLUCrSUrOLXTy7P2lGEEg4gMXrUX9++jA8/vKHkinz6HM7nG6FgO3A56ddDrvMkrfJBN5UF4jR\n0/x3gDvSEVH2YbBXIJXKq5tfjfcOnYuMp0czGgC1q2p++EkzLCaj5HmkApiaHrW9tEA2KEafG4Ig\nG7BbXSIeW/0RymV6q8kE3lQr3NORHUgXvQ4pEBEpYc4xQV1uP0SJAA30jo9fM6kSZSp7mnK1kVIB\nTE2PWmlN9ehzh28KlMhtAJPKuu1KW/jGE71VcCpb6KZKzedARKQ37NknSKlnayu24Gs3XgmgNyj8\nv52fYs8nF2TPJfqDmDdlKI6eao+b3lbbo76UMu9dB+BSNb4ZsyZc6qmrHY6ob3Dg89OH9SkSzMS6\n7dHZAaMpHwGfPyM9aL0NKQw2LIokSg6DfYLUppQrywqx8obxOHDMAdEvndu3Wc1YtmgsOrp9QCgk\nO06eyPvGpsyrhpXizNn2fl+O0QG7rdMrO1e/rVPE4y9/2K8ILVMLz5jzjbBXDMnYNBs9DSkMJiyK\nJEqN8Yknnngi041IlNud/Dz3eIYMMcc9/6RRZXB7/TjX6kZPoDdKWkwGVFUWYXK1DQahdx/b/Dwj\nXG4fTp51SZ6norQAW/eextu7T+HQiVY4u0RMGlUWOV7qfT1iDzq6fBB9PbAVWzBv6lAsXzS23zF5\nRgOGFOTDVlYIIRhCXsya/gZBwNTR5VgwYxhmX3kZDh53wCMzPAEAHjGAk2dd8Ig9mDq6vM97xJ5b\na2o+Iy0l8jmokenrSTctrud3fzmGrXvPwCP2/o1K/T1qiZ+Rvg2G6xkyJLWsIXv2STAaDBAEoU9x\nndcXxLZ9TTAIQp+K7OWLxiIUCmHX4fN9FsmpKLX02cgmPEYeCASx8oaJsu+rtkcdTndaSwoUr8Wc\nb0SVvUhxrn40FqFxSd2BxqJIotQx2CchkS8fo8GALy+egJvmVePTcy4UFeTBXlqIp9Z8JHn8uwfO\nAoKAFbX9V8sLU1rwIzbdaS8rwLQx5XHTnX3S+i6v7PS9TMxr1wOpsWIuvDIw9LbOAlE2YrBPQiJf\nPlJjjRNHlslOjwuGgO31TTAahKTmbMfOAW92elTNAY/urTqcbvznhkMsQgPHivWARZFEqeO3VRLC\nXz5SYr98wsG31SUihN50/a4j52ExKf/q9zc6cKa5E6Jffhw9VryMQ+y5RH8AzU53n8fN+UZUVVqT\nnl6Xa6Q+P6kpiaSdVKZ7ElEv9uyToLYi2y368d6hczJnUS7kire4jRSH0y2bMYjOOKjprWZiep3e\ncKxYP/j3SJQaBvskqfnyWffOMckV8gBA9AVw1QQ79jVIB5MwNUuxRgdvOdEZBzXLvbIIjWPFesK/\nR6LUMNgnKd6Xj+gP4OhnbbLHhwAcP9Ou+v2UepKxwVtKOOOQzEY2gzWgcaxYfwbz3yNRKjhmn6Tw\neDcAySVgO7pEOOPse9/R7Vf9fnJLsSoFbwCwl/ZdVpbLvarHsWIiyhXs2SdIbXW2Uq8wGWVWM3z+\nQKSYLpxNUAreggA89i/XYEheahvZDGYcKyaiXMBgnyC125sqFfFJKSsyo6NbhClfeie8bq8fj6/+\nCGaTEUAIXl8Q5cVmTBtTLr9Wv9WCoeVD0NnhUdUu9lb741gxEeUCBvsEKKXM6xscuGXBGACXet2x\n688L6J1HH6u82ILH7qiBR+xBUaEJG3eejPQkw8Hf6+tdXz/6RqDVJWL7/rMYUVkkGexnjq+AxZSH\n2FXk2VtNHMeKiSibMdgriF01TSll3tYp4qlXPoLo74Gz09cnvR/uFW7+8BS27z/b79hpY2zwiD2R\n94ksbtPuwQtvHpCt6A/r9vixcNZwHDreqip4s7dKRDS4MNhLkBuXr5s/WnEc/lybO/JzbHq/sqwQ\nKxaPh9FoiPSoS4vMGFKQj4MnWrFj/9k+NwjmfCNMeYa4RX4A0N4l4oarR2DZwrEJBW/2VomIBgcG\newlK4/KJjMMDfaezxfao//zhKeyI6umH3ycUCuHLiyeoLvILF9YxeBMRkRROvYvh9fUozkOvm1+N\nuVOGqj6f1HQ2c74RJUVm7P74guQxuw6fh+gPKE79isbCOiIiUsJgH8PpUp6H3uX2Y+UNE1AuszZ+\nLLnpbI52j+xYvNcXgKO9t4J++aKxqK2pQnmxBQahd3tci8kIAb2FfdFz6ImIiKQwjR+jrDj+PPRE\nptVF97qjC/4QkttE9qKLz0sV0wFgYR0REanGYB/DYspTNQ+9//Q1Mwot+ej2+NHeJfapiJcq+Js2\ntgLmfANEf1CiDUbYY8beY8fjOTZPRERqMdhLUDMPXW76Wux0PQBYt7WxX8Hf9vomVNmH4Iyju9/7\nz506lD12IiJKGwZ7CYnMQ4/tccf+W2khHo/Yg4Uzh+Hg8VY4O0WUWc2YNcHOMXgiIkorBnsF6ZjK\nprzxjIgbZo/EskXjOAZPRESaYTW+xsJz5aXEzo9noCciIi0w2GuM26QSEVGmMY0/ALjxDBERZRKD\n/QDgxjNERJRJDPYDiGvXExFRJnDMnoiIKMcx2BMREeU4BnsiIqIcx2BPRESU4xjsiYiIchyDPRER\nUY5jsCciIspxDPZEREQ5jsGeiIgoxzHYExER5TgGeyIiohzHYE9ERJTjdLMRzo9+9CMcPHgQgiDg\n4YcfxrRp0zLdJCIiopygi2D/4Ycf4rPPPsP69etx4sQJPPzww1i/fn2mm0VEg1Uo1PfnYLD3f7GP\nD9DPAtJ8ToMPQmunftqT6s/tQ2Bs65J4DaRfr8XPRiMCEyYCBn0mzHUR7D/44APU1tYCAMaMGYOO\njg50dXWhqKho4Bvz5JMo3neg9+dUPvg0/ccgJHws+j5uMqLEF8hge1L5nUD6caOA0p5gcm0bwC8p\n+fZENQchwCDAFgxlrj0p/U7Q/3EBKA8m2J4MBY++bVBmV/3K7FCR6QakmS3TDQDQ/e/fh/vfv5/p\nZkjSRbBvaWnB5MmTI/+22WxwOByywb6srBB5ecb0N6SnB3jpJZibmtJ/7gwypeMkgqCrn/N10Ia0\n/RwUYAQAIfpxQR9tS/Jngw7awJ/584D+bDRiyJ0rMcRuRTrY03SeMF0E+1ihOHfbTqdbs/e2nziB\nlpNnLz3Q50OFzOPJ/xyCtn+I9spiOBydyZ1Hp+x266VrygG8Hv3LtWvi9WgoDe2Qup5Ug78ugn1l\nZSVaWloi/25ubobdnqGkmdmMUEUOJbjy8nr/R0REg5YuKgnmzZuHzZs3AwA+/vhjVFZWZma8noiI\nKAfposs3a9YsTJ48GbfffjsEQcDjjz+e6SYRERHlDF0EewD4zne+k+kmEBER5SRdpPGJiIhIOwz2\nREREOY7BnoiIKMcx2BMREeU4BnsiIqIcx2BPRESU4xjsiYiIchyDPRERUY4TQvF2nSEiIqKsxp49\nERFRjmOwJyIiynEM9kRERDmOwZ6IiCjHMdgTERHlOAZ7IiKiHKeb/ez14Ec/+hEO/v/t3XtQ1OUe\nx/H37sImeEFAwFuWGiojBnkLJDRNNFFxvI6Mq2Nq3sJLaoAMozRZXG0SyjHFssTGJsYZcTRrSm0c\ngU1khhAzI3RCaBRU5OIFdvmeP5Q9clzsdPCwC/O8/vvddr8fH5/n2d+zwK+gAI1GQ0xMDC+++KKt\nS3qipKQkzp07h8lkYsWKFZw4cYKioiK6d+8OwNKlS3n11VfJysriiy++QKvVMm/ePObOnUtDQwPR\n0dGUl5ej0+mIj4/n2WeftVkWo9HIunXr8Pb2BmDQoEEsW7aMyMhIzGYzHh4eJCcno9fr20Web775\nhqysLMv2+fPn8fX15c6dOzg7OwMQFRWFr68v6enpHD9+HI1GQ0REBOPGjaOmpoaNGzdSU1ODs7Mz\n27dvt7RrW7t06RKrV69m8eLFGAwG/vrrr1a3y8WLF4mLiwNg8ODBvPvuuzbNs3nzZkwmEw4ODiQn\nJ+Ph4cHQoUMZPny45bp9+/bR2Nhod3msZYqOjm71WGBPbbR27Vpu3boFQFVVFf7+/qxYsYLp06fj\n6+sLgKurK6mpqS32nezsbD788EN0Oh1jx47lrbfearM8/zlWDxs2rO37kCgiImI0GmX58uUiIlJc\nXCzz5s2zcUVPlpOTI8uWLRMRkZs3b8q4ceMkKipKTpw40ey8uro6mTRpklRXV8vdu3dl6tSpcuvW\nLTl06JDExcWJiMjp06dl3bp1bZ7hUbm5ubJmzZpm+6Kjo+XYsWMiIrJ9+3Y5cOBAu8nzKKPRKHFx\ncWIwGOS3335rduzPP/+UmTNnyv379+XGjRsyefJkMZlMkpaWJnv27BERkYMHD0pSUpItSpe6ujox\nGAwSGxsr+/fvF5Gn0y4Gg0EKCgpERGTDhg1y6tQpm+WJjIyUo0ePiohIRkaGJCYmiojI6NGjH7ve\n3vKIWM/0NMYCe2qjR0VHR0tBQYGUlpbKzJkzHzveUt+ZMmWKlJeXi9lslvDwcPn999//v0EesjZW\n26IPqWX8h3Jycpg4cSIAAwcO5Pbt29TW1tq4qpaNGjWKHTt2ANCtWzfu3r2L2Wx+7LyCggKGDRtG\n165d6dSpE8OHDyc/P5+cnBxCQkIAGDNmDPn5+W1a/3/DaDTy2muvATB+/HhycnLaZZ5PPvmE1atX\nWz1mNBoJDg5Gr9fj5uZGnz59KC4ubpanKbst6PV69uzZg6enZ7OaW9Mu9fX1lJWVWVbO2jKftTxb\nt25l8uTJwIO7w6qqqhavt7c8YD2TNe25jZqUlJRQU1PzxFVXa32ntLQUFxcXevXqhVarZdy4cW2W\nx9pYbYs+pCb7hyorK3F1dbVsu7m5UVFRYcOKnkyn01mWgzMzMxk7diw6nY6MjAwWLVrE22+/zc2b\nN6msrMTNzc1yXVOuR/drtVo0Gg319fU2ydKkuLiYlStXEh4ezpkzZ7h79y56vR4Ad3f3x+oG+84D\n8Msvv9CrVy88PDwASE1NZcGCBWzZsoV79+79V3nc3d25fv26Tep3cHCgU6dOzfa1tl0qKyvp1q2b\n5dym12gL1vI4Ozuj0+kwm8189dVXTJ8+HYD6+no2btzI/Pnz+fzzzwHsLg9YzwS0aiywtzZq8uWX\nX2IwGCzblZWVrF27lvnz51u+NrPWdyoqKqxmbwvWxmpb9CH1nX0LpJ38FeEffviBzMxMPvvsM86f\nP0/37t3x8fFh9+7dfPzxx7z00kvNzm8pl63zPv/880RERDBlyhRKS0tZtGhRs5WKf1q3rfM0yczM\nZObMmQAsWrSIwYMH069fP7Zu3cqBAwceO99a3faSxZqn0S72kM9sNhMZGUlAQACBgYEAREZGEhYW\nhkajwWAwMHLkyMeus9c8M2bMeKpjgT1kqq+v59y5c5bvqbt37866desICwujpqaGuXPnEhAQ0Owa\ne6i7yaNj9aRJkyz726oPqTv7hzw9PamsrLRsX79+3XI3Zq9Onz7Nrl272LNnD127diUwMBAfHx8A\nJkyYwKVLl6zm8vT0xNPT0/JJsKGhARGxfNK0BS8vL0JDQ9FoNPTr148ePXpw+/Zt7t27B8C1a9cs\ndbeHPE2MRqNlkA0JCaFfv35Ay+3zaM6mPE377IWzs3Or2sXDw6PZUrk95Nu8eTPPPfccERERln3h\n4eF07twZZ2dnAgICLO3VHvK0diywx0xnz55ttnzfpUsXZs+ejaOjI25ubvj6+lJSUmK177TUz9rK\nf47VtuhDarJ/KCgoiO+++w6AoqIiPD096dKli42rallNTQ1JSUl8+umnlp+4XbNmDaWlpcCDScbb\n2xs/Pz8KCwuprq6mrq6O/Px8Ro4cSVBQEMePHwfg5MmTvPzyyzbLApCVlcXevXsBqKio4MaNG8ya\nNcvSJt9//z3BwcHtJg886ICdO3dGr9cjIixevJjq6mrg3+0TEBDAqVOnqK+v59q1a1y/fp0XXnih\nWZ6m7PZizJgxrWoXR0dHBgwYQF5eXrPXsJWsrCwcHR1Zu3atZV9JSQkbN25ERDCZTOTn5+Pt7d0u\n8kDrxwJ7zFRYWMiQIUMs27m5ucTHxwNw584dLl68SP/+/a32nb59+1JbW8vVq1cxmUycPHmSoKCg\nNqnb2lhtiz6knnr3iJSUFPLy8tBoNGzdurXZfyx78/XXX5OWlkb//v0t+2bNmkVGRgZOTk44OzsT\nHx+Pu7s7x48fZ+/evZblyLCwMMxmM7GxsVy5cgW9Xk9CQgK9evWyWZ7a2lo2bdpEdXU1DQ0NRERE\n4OPjQ1RUFPfv36d3797Ex8fj6OjYLvLAg1+3++ijj0hPTwfg2LFjpKen4+TkhJeXF++//z5OTk7s\n37+fI0eOoNFoWL9+PYGBgdTV1fHOO+9QVVVFt27dSE5OpmvXrjbJkJiYSFlZGQ4ODnh5eZGSkkJ0\ndHSr2qW4uJgtW7bQ2NiIn58fmzdvtlmeGzdu8Mwzz1g+3A8cOJC4uDiSk5PJzc1Fq9UyYcIEVq1a\nZXd5WspkMBjYvXt3q8YCe2qjtLQ00tLSGDFiBKGhoQCYTCZiY2O5fPkyZrOZ8PBwZs+e3WLfOXv2\nLCkpKQBMmjSJpUuXtkkea2N1QkICsbGxbdqH1GSvKIqiKB2cWsZXFEVRlA5OTfaKoiiK0sGpyV5R\nFEVROjg12SuKoihKB6cme0VRFEXp4NRkryiKoigdnJrsFaWdO3z48BOP//TTT098uAvAwoULyc7O\nfpplKYpiR9RkryjtmNlsZufOnU88Z9++fdy+fbuNKlIUxR6pB+EoSjsWExNDWVkZS5YsITQ0lIMH\nD+Lk5IS7uzvbtm0jKyuLvLw8Nm3aRHx8PJcvXyY9PR29Xo/ZbCYpKYm+ffv+7ftcvXqVVatWMWjQ\nILy9vXnzzTf54IMPKCoqAiAgIID169cDsHPnTk6dOoWDgwPe3t7ExsZy7do1VqxYQVBQEHl5ebi6\nuhIWFsbhw4cpKytjx44dDBkyhJSUFHJzc9Hr9Xh5eZGYmGgXzzhQlHbviU+7VxTFrpWWlkpwcLCU\nlZXJ2LFjpaamRkREEhISJC0tTURExo8fL1euXBERkczMTCkrKxMRkV27dklCQoKIiBgMBjlz5swT\n38fHx0f++OMPERE5cuSILF++XBobG8VkMsmcOXPEaDRKfn6+zJgxQ+rr60VEZM2aNXLo0CHL9SUl\nJZaamupLTU2Vbdu2SVVVlfj7+4vJZBIRkaNHj1pqVRSlddSdvaJ0ABcuXGDo0KGWv+8+evRoDh48\n+Nh5PXr0ICoqChGhoqLisceePomLiwsDBgwAoKCggMDAQDQaDTqdjpEjR1JYWIhOp2PUqFE4Ojpa\n6igsLGTUqFG4urpa/j64l5cXw4cPB6Bnz56Ul5fj4uJCcHAwBoOBkJAQQkND6dmzZ6v+XRRFeUB9\nZ68oHZCIoNFomu1raGhg/fr1vPfee2RkZLBw4cJ/9JpNEzjw2Gs3vV9L+wF0Ol2zY49uy8NHdKSm\nprJt2zYADAYDv/766z+qUVEU69RkryjtmFarxWQy4evrS1FREbW1tQBkZ2fj5+cHPJiYTSYTdXV1\naLVa+vTpw/379/nxxx+pr6//n97X39+f7OxsyyNgf/75Z/z8/PD398doNNLQ0ABATk6OpY6/U1pa\nyr59+xg4cCBLliwhJCSEixcv/k/1KYrSnFrGV5R2zNPTkx49erB69WqWL1/OG2+8gV6vp2fPnmzY\nsAGAV155hZUrV5KYmMi0adOYM2cOvXv3ZunSpURGRvLtt9/+4/d9/fXXyc/PJzw8nMbGRiZOnMiI\nESMAmDp1KgsWLECr1TJ06FCmTZtGeXn5376ml5cXFy5cYM6cOXTu3BkXFxciIiL+cW2KojxOPeJW\nURRFUTo4dWevKArwYBk9JibG6rGYmBh8fHzauCJFUZ4WdWevKIqiKB2c+gE9RVEUReng1GSvKIqi\nKB2cmuwVRVEUpYNTk72iKIqidHBqslcURVGUDu5f5eEwA38SWUIAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "t0lRt4USU81L",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "This initial line looks way off.  See if you can look back at the summary stats and see the same information encoded there.\n",
+        "\n",
+        "Together, these initial sanity checks suggest we may be able to find a much better line."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "AZWF67uv0HTG",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Tweak the Model Hyperparameters\n",
+        "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n",
+        "\n",
+        "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n",
+        "\n",
+        "For each period, we'll compute and graph training loss.  This may help you judge when a model is converged, or if it needs more iterations.\n",
+        "\n",
+        "We'll also plot the feature weight and bias term values learned by the model over time.  This is another way to see how things converge."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "wgSMeD5UU81N",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
+        "  \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label]\n",
+        "\n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "  \n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "        metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "\n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Output a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "kg8A4ArBU81Q",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1:  Achieve an RMSE of 180 or Below\n",
+        "\n",
+        "Tweak the model hyperparameters to improve loss and better match the target distribution.\n",
+        "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "UzoZUSdLIolF",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "9c816bae-fce4-4363-8b1b-6acef05259e9"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00001,\n",
+        "    steps=100,\n",
+        "    batch_size=1\n",
+        ")"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 236.32\n",
+            "  period 01 : 235.11\n",
+            "  period 02 : 233.90\n",
+            "  period 03 : 232.70\n",
+            "  period 04 : 231.50\n",
+            "  period 05 : 230.31\n",
+            "  period 06 : 229.13\n",
+            "  period 07 : 227.96\n",
+            "  period 08 : 226.79\n",
+            "  period 09 : 225.63\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean          13.2    207.3\n",
+              "std           10.9    116.0\n",
+              "min            0.0     15.0\n",
+              "25%            7.3    119.4\n",
+              "50%           10.6    180.4\n",
+              "75%           15.8    265.0\n",
+              "max          189.7    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>13.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>10.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>10.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>15.8</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>189.7</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 225.63\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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EDoCIiIiIqubT2RXurrZY+csfOBR/Df9cz8PzId3hbG8ldGhERER1wpESRERE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ON4MCMHt38kJDx476EZi5/zwBjAHMCxvDdJAwRyXhAEQRDnN5oi4gNvvRhb1ubw7Z/uxv/8wQu4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\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "133b3c82-d08d-486d-9b6e-a1cdf89cbb08"
+      },
+      "cell_type": "code",
+      "source": [
+        "train_model(\n",
+        "    learning_rate=0.00002,\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 : 225.63\n",
+            "  period 01 : 214.42\n",
+            "  period 02 : 204.04\n",
+            "  period 03 : 194.97\n",
+            "  period 04 : 186.92\n",
+            "  period 05 : 180.27\n",
+            "  period 06 : 175.44\n",
+            "  period 07 : 171.23\n",
+            "  period 08 : 168.84\n",
+            "  period 09 : 166.85\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         120.6    207.3\n",
+              "std           99.4    116.0\n",
+              "min            0.1     15.0\n",
+              "25%           66.7    119.4\n",
+              "50%           97.0    180.4\n",
+              "75%          143.7    265.0\n",
+              "max         1729.9    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>120.6</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>99.4</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>66.7</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>97.0</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>143.7</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1729.9</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 166.85\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+IUASErWI0aDjvhs7EGjSs2h9GkdOyTBeIYQQoipyBSSEEBdQtC2FI39/BX1E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RPM09llbsXR3ISI9sKZV4Y7womnjtOY6sMnuPibp1EFB9J79StowkPd2p9KI5ptq1FYTFjHzkfq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/iQ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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "763b1dff-dc53-48b5-8a55-9ac69709dc5f"
+      },
+      "cell_type": "code",
+      "source": [
+        "# YOUR CODE HERE\n",
+        "train_model(\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=5000,\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 : 189.12\n",
+            "  period 01 : 176.29\n",
+            "  period 02 : 177.66\n",
+            "  period 03 : 180.87\n",
+            "  period 04 : 182.92\n",
+            "  period 05 : 183.85\n",
+            "  period 06 : 184.30\n",
+            "  period 07 : 183.87\n",
+            "  period 08 : 184.15\n",
+            "  period 09 : 184.59\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         168.1    207.3\n",
+              "std          135.0    116.0\n",
+              "min            0.4     15.0\n",
+              "25%           92.9    119.4\n",
+              "50%          137.2    180.4\n",
+              "75%          202.3    265.0\n",
+              "max         4195.2    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>168.1</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>135.0</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.4</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>92.9</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>137.2</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>202.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4195.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 184.59\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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RTz75pLEAIsCFCxc4efIk3bp1o0uXLhw7doyMjAxKSkrYtWuX8XWenp7GAomJiYnGWku1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GZU4O4m5/ZbwrlpWDBqcQRHaEUsFgv5+fmUlpZy6NChquMapaWllJeLEZeOCvX/ZyyoTrxmgusZ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\nBvv376e4uJivvvqKwsJCzjvvPJ599lkWL16M0+mku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pw0K4cqmgiFFfQ6ycbrjdoGL+99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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "ci1ISxxrZ7v0",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "### Solution\n",
+        "\n",
+        "Click below for one possible solution."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "SjdQQCduZ7BV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "b6b7c14b-e435-4ffc-a751-07426ae07d5e"
+      },
+      "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": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 225.63\n",
+            "  period 01 : 214.62\n",
+            "  period 02 : 204.86\n",
+            "  period 03 : 196.59\n",
+            "  period 04 : 189.25\n",
+            "  period 05 : 183.81\n",
+            "  period 06 : 180.18\n",
+            "  period 07 : 177.69\n",
+            "  period 08 : 176.39\n",
+            "  period 09 : 175.92\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         123.2    207.3\n",
+              "std           98.9    116.0\n",
+              "min            0.3     15.0\n",
+              "25%           68.1    119.4\n",
+              "50%          100.6    180.4\n",
+              "75%          148.3    265.0\n",
+              "max         3075.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>123.2</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>98.9</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>68.1</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>100.6</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>148.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3075.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 175.92\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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X4Uy8wKPtN0pRoLYEagpdIURoVzD45+oRigInqrTsL9ajKCq6hVvpEWlD7Z/Z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\nlNVq0KoVeqRYiY8MbrnGjRIXy7baKK6USTCqmDNeT+fM1pP6rCgKXx6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EO+3jHLR0JYEs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\nK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9p8O9DmJPeNd8l95R10qcn0zXwnsCAhy2j2rUN99hCyKQ7PlEchKia4E8k+f36Epxo0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wTfFI07NnYyGxuMQHtR1TBpHOVKSSJInLbQEOVbs5XOOmt18Wq8wmFVs2JFNV4WDhAjOqObyL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\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ 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..537f6e3
--- /dev/null
+++ b/improving_neural_net_performance.ipynb
@@ -0,0 +1,2019 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "efcf6988-3a93-4a0d-9e29-1f8db84eef11"
+      },
+      "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       2631.7           536.3   \n",
+              "std         2.1        2.0                12.6       2138.4           414.4   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1449.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2125.5           434.0   \n",
+              "75%        37.7     -118.0                37.0       3145.2           646.0   \n",
+              "max        42.0     -114.5                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       1424.9       499.5            3.9               2.0  \n",
+              "std        1112.1       380.6            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         786.0       281.0            2.6               1.5  \n",
+              "50%        1168.0       409.0            3.5               1.9  \n",
+              "75%        1721.0       603.0            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>28.6</td>\n",
+              "      <td>2631.7</td>\n",
+              "      <td>536.3</td>\n",
+              "      <td>1424.9</td>\n",
+              "      <td>499.5</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>2138.4</td>\n",
+              "      <td>414.4</td>\n",
+              "      <td>1112.1</td>\n",
+              "      <td>380.6</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>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>1449.0</td>\n",
+              "      <td>296.0</td>\n",
+              "      <td>786.0</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>2125.5</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1168.0</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>3145.2</td>\n",
+              "      <td>646.0</td>\n",
+              "      <td>1721.0</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>42.0</td>\n",
+              "      <td>-114.5</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       35.6     -119.5                28.6       2672.4           546.9   \n",
+              "std         2.1        2.0                12.5       2276.5           438.1   \n",
+              "min        32.5     -124.2                 2.0         20.0             3.0   \n",
+              "25%        33.9     -121.7                18.0       1484.8           299.0   \n",
+              "50%        34.2     -118.5                29.0       2133.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3162.2           655.0   \n",
+              "max        41.8     -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       1440.8       505.3            3.9               2.0  \n",
+              "std        1229.6       393.8            1.9               1.3  \n",
+              "min          13.0         5.0            0.5               0.1  \n",
+              "25%         795.8       283.0            2.6               1.5  \n",
+              "50%        1163.0       408.0            3.5               1.9  \n",
+              "75%        1720.2       608.0            4.8               2.3  \n",
+              "max       35682.0      5189.0           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>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>-119.5</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>2672.4</td>\n",
+              "      <td>546.9</td>\n",
+              "      <td>1440.8</td>\n",
+              "      <td>505.3</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>12.5</td>\n",
+              "      <td>2276.5</td>\n",
+              "      <td>438.1</td>\n",
+              "      <td>1229.6</td>\n",
+              "      <td>393.8</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>-124.2</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>3.0</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>-121.7</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>1484.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>795.8</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>2133.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1163.0</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>3162.2</td>\n",
+              "      <td>655.0</td>\n",
+              "      <td>1720.2</td>\n",
+              "      <td>608.0</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>-114.3</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>52.0</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                206.4\n",
+              "std                 115.1\n",
+              "min                  15.0\n",
+              "25%                 119.2\n",
+              "50%                 180.4\n",
+              "75%                 263.0\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>206.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>263.0</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                209.6\n",
+              "std                 118.1\n",
+              "min                  22.5\n",
+              "25%                 120.6\n",
+              "50%                 180.3\n",
+              "75%                 268.9\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>209.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>118.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>22.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>120.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>268.9</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": "51400684-5042-4668-bfa2-7729096449c0"
+      },
+      "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": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 164.46\n",
+            "  period 01 : 163.46\n",
+            "  period 02 : 157.68\n",
+            "  period 03 : 152.85\n",
+            "  period 04 : 142.68\n",
+            "  period 05 : 133.05\n",
+            "  period 06 : 117.41\n",
+            "  period 07 : 102.04\n",
+            "  period 08 : 103.30\n",
+            "  period 09 : 99.68\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   99.68\n",
+            "Final RMSE (on validation data): 102.75\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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++++QlpZ6zenPPz+zrkqqc9LACCGEEI1IenoaW7ZsNuu1Tz75NEFBza45/c0359ZVWXWu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\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": "397dd3f0-5a22-428b-96db-e4f4dbdb9408"
+      },
+      "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",
+        "  #\n",
+        "  # Your code here: normalize the inputs.\n",
+        "  #\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=0.007),\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": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 217.11\n",
+            "  period 01 : 126.87\n",
+            "  period 02 : 113.74\n",
+            "  period 03 : 106.71\n",
+            "  period 04 : 97.54\n",
+            "  period 05 : 86.28\n",
+            "  period 06 : 78.31\n",
+            "  period 07 : 76.00\n",
+            "  period 08 : 74.73\n",
+            "  period 09 : 73.36\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   73.36\n",
+            "Final RMSE (on validation data): 75.50\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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pRTUdjdrVqXVXXhERkZahAuYS1A0j5Rz3xNfDh335KWqnFhERaQEqYC7Bj+3U\nBfSz9KHwVBHHSzOdHZaIiEibpwLmEtS1U6edKCHarxcA+/KSnRyViIhI26cC5hLZVqcuCgEgUfNg\nREREHE4FzCUaaFudupwe/t04VJRGeXWFk6MSERFp21TAXKKup9up9x7Oo7+ltp06uUDt1CIiIo6k\nAuYSqZ1aRESk5amAaQZ17dS5xzvga/YhUatTi4iIOJQKmGZwZjt1X0tvCk8VcaI0y9lhiYiItFkq\nYJrBedup89VOLSIi4igqYJrJwNPt1Nbi2uGkRN0PRkRExGFUwDSTunkwB9NO0d2/K4cKU6lQO7WI\niIhDqIBpJvXaqYNisBpWkgsOOTssERGRNkkFTDNRO7WIiEjLUQHTjGzt1Ce88DZ7q51aRETEQVTA\nNKO6durE1AL6WXpTcKqQrLJsZ4clIiLS5qiAaUbeHcz07hZI6hnt1OpGEhERaX4qYJpZ3TBSTVHt\n//epgBEREWl2KmCama2d+kgl3fy6cLDwMBXVp5wclYiISNuiAqaZndlO3c8SQ7Vh5UCh2qlFRESa\nkwqYZnZmO3XQ6XZqzYMRERFpXipgHCA2OgSAvBPeeJu92JeXpHZqERGRZqQCxgH6RwbVrk59uIC+\nQb3JqygguyzH2WGJiIi0GSpgHKCunTot84x2aq1OLSIi0mxUwDiIrZ26uHY4Se3UIiIizUcFjIPU\nFTCHjlTS1a8zBwoPU2mtdHJUIiIibYMKGAep104dFEN1TTUpWp1aRESkWTi0gElJSeGqq65ixYoV\nAGzfvp2bb76ZuXPncuedd1JUVATA66+/zqxZs7jhhhv46quvHBlSi6ltpw6ubadG7dQiIiLNyWEF\nTFlZGYsXL2bkyJG255544gkef/xxli9fzpAhQ1i5ciXp6emsXbuWd999l1deeYUnnngCq9XqqLBa\nVN0wUt5xb7zc1U4tIiLSXBxWwHh6evLaa68RFhZmey4oKIjCwkIAioqKCAoKYuvWrYwZMwZPT08s\nFgtdu3bl4MGDjgqrRdW1UyemFtLX0ovcinxyynOdHZaIiEir57ACxmw24+XlVe+5P/7xj8yfP5/J\nkyezc+dOZs6cSW5uLhaLxfYei8VCTk7buGfKedupNYwkIiJyycwtebDFixfzwgsvMGzYMJYuXcq7\n7757znvsGWIJCvLBbHZ3RIgAhIb6N9u+RsR2IeloIT41tfNgDpYcZHbolGbbf3vTnLmR5qO8uC7l\nxnUpN5emRQuY5ORkhg0bBsCoUaNYs2YNI0aMIDU11faerKysesNO51NQUOawGEND/cnJKWm2/UWH\n+wHwQ2IJXcI7kZidQkZmPp6SmtwKAAAgAElEQVTuHs12jPaiuXMjzUN5cV3KjetSbuzTWJHXom3U\nISEhtvkte/bsISIighEjRrBx40YqKyvJysoiOzubXr16tWRYDnX26tRVNdVanVpEROQSOewKzN69\ne1m6dCkZGRmYzWbWrVvHo48+yqJFi/Dw8CAwMJAlS5YQEBDA7NmzmTNnDiaTiUceeQQ3t7Zze5q6\nduqvdx+v1049ILivkyMTERFpvUxGK+zrdeRlN0dc1tuZnMOLH+xh2sjufMPbBHj688jIB5r1GO2B\nLrm6JuXFdSk3rku5sY/LDCG1V/XaqYN6k1OeR3aZ2qlFREQulgqYFlCvndq/dn7PPq1OLSIictFU\nwLSQurvyGlqdWkRE5JKpgGkhsT1Pr06dVkUn33BSCg5RZa1yclQiIiKtkwqYFtI1pLadOjE1n/5B\nfaiqqeJA4WFnhyUiItIqqYBpIfVXp+4OaBhJRETkYqmAaUF182AKTvji6e5JYn6SkyMSERFpnVTA\ntKAz26ljgnqRXZZLbnmes8MSERFpdVTAtKDzrU6tYSQREZGmUwHTwuq6keraqRNVwIiIiDSZCpgW\nVjcPJvVINeE+YaQUHFQ7tYiISBOpgGlh9dqpLX2orKniYFGqs8MSERFpVVTAtDC1U4uIiFw6FTBO\nUDeMVHjCD083D82DERERaSIVME5wZjt1n6BeZJVlk1ee7+ywREREWg0VME6g1alFREQujQoYJ6lr\np0bt1CIiIk2mAsZJfmynthLmE0JywUGqaqqdHJWIiEjroALGSeqvTh1DpbWSQ4VqpxYREbGHChgn\nqddObVI7tYiISFOogHGiM9upPdw8SNREXhEREbuogHGiH9upi+gT1JPM0izyKwqcHZaIiIjLUwHj\nRHXt1Ee0OrWIiEiTqIBxMls7dUkooAJGRETEHipgnKxuHkzaESuh3sEkFRygWu3UIiIijVIB42T1\nV6eO4ZS1ksNFac4OS0RExKWpgHEyk8nEwJ71V6fWXXlFREQapwLGBdjaqTP98XAzax6MiIjIBaiA\ncQH9ImrbqfelFtG7Y0+Ol2ZSUFHo7LBERERclgoYF1CvnVqrU4uIiFyQChgX8ePq1GqnFhERuRCH\nFjApKSlcddVVrFixAoCqqioWLlzIrFmzmDdvHkVFRQCsXr2a66+/nhtuuIH33nvPkSG5LFs79VEr\nIV4WkvIPYq2xOjkqERER1+SwAqasrIzFixczcuRI23P/+c9/CAoKYtWqVUydOpUdO3ZQVlbGiy++\nyFtvvcXy5ct5++23KSxsf/M/uob4YgnowL7UAvpZYqiwVqidWkREpAEOK2A8PT157bXXCAsLsz33\n5Zdf8tOf/hSAG2+8kYkTJ7J7925iY2Px9/fHy8uLoUOHkpCQ4KiwXNaZq1Nb1E4tIiLSKIcVMGaz\nGS8vr3rPZWRk8PXXXzN37lzuvfdeCgsLyc3NxWKx2N5jsVjIyclxVFgurW4YqSjLH7ObWRN5RURE\nGmBuyYMZhkFUVBQLFixg2bJlvPLKK/Tv3/+c91xIUJAPZrO7o8IkNNTfYftuzBh/L17+aC8H0k8y\nYGhvdmfux93PisW7o1PicUXOyo00TnlxXcqN61JuLk2LFjAhISHExcUBcMUVV/D8888zbtw4cnNz\nbe/Jzs5m8ODBje6noKDMYTGGhvqTk1PisP1fSK+ugSQdLWTwiCh2s59NKQmM6hLntHhcibNzI+en\nvLgu5cZ1KTf2aazIa9E26iuvvJJNmzYBkJiYSFRUFIMGDWLPnj0UFxdTWlpKQkICw4cPb8mwXEpd\nO7XJtjp1kjPDERERcUkOuwKzd+9eli5dSkZGBmazmXXr1vHXv/6Vxx9/nFWrVuHj48PSpUvx8vJi\n4cKF3HbbbZhMJubPn4+/f/u9rDYwOpj3vjxE2tEagkODSCo4gLXGirub44bMREREWhuTYc+kExfj\nyMtuzr6sZxgGv3/pW05VWhkVn8vm41u4d+iv6dUxymkxuQpn50bOT3lxXcqN61Ju7OMyQ0hyYedv\np9YwkoiIyJlUwLigunbq4qwAzCZ3LSsgIiJyFhUwLujH1amL6dUxmmMnj1N0qtjZYYmIiLgMFTAu\n6PyrU6c4OSoRERHXoQLGRQ3sGQKonVpEROR8VMC4qNjo2uUVjhw1COrQkf35B7Q6tYiIyGkXXcCk\npaU1Yxhyti5nrE7d3xJDeXU5acXpzg5LRETEJTRawPziF7+o93jZsmW2Pz/88MOOiUiA+u3Uwaba\ndmoNI4mIiNRqtICprq6u93jLli22P7fC+9+1Oj+uTh2Au8mdRK1OLSIiAlyggDGZTPUen1m0nP2a\nNL+6dur9qSX07BhFekkGxZW6c6OIiEiT5sCoaGlZ3h3M9OnekSOZJfQ83U69P0/t1CIiIo0u5lhU\nVMR3331ne1xcXMyWLVswDIPiYt1YrSXERgez/0iBrZ06MS+JyzsPc3JUIiIiztVoARMQEFBv4q6/\nvz8vvvii7c/ieLHRFv7zJRw9Ch1DAknKP0CNUYObSR3wIiLSfjVawCxfvryl4pAGnNlOPaJPDN+e\n2EZacTrRgRHODk1ERMRpGv1n/MmTJ3nrrbdsj//9739z7bXXcvfdd5Obm+vo2ISz2qlRO7WIiAhc\noIB5+OGHycvLAyA1NZVnnnmGBx54gFGjRvH444+3SIByxurU2YG4mdxI1OrUIiLSzjVawKSnp7Nw\n4UIA1q1bR3x8PKNGjeKmm27SFZgWVK+dOjCSoyXHKKk86eywREREnKbRAsbHx8f2523btjFixAjb\nY7VUt5zztlNrdWoREWnHGi1grFYreXl5HD16lF27djF69GgASktLKS8vb5EApVbdMJLbyXCgtp1a\nRESkvWq0C+mOO+5g6tSpVFRUsGDBAgIDA6moqOCWW25h9uzZLRWjcG479f78FLVTi4hIu9VoATN2\n7Fg2b97MqVOn8PPzA8DLy4vf//73XHHFFS0SoNQ6s536J717syVzB0eKjxEV2MPZoYmIiLS4Rv/5\nfvz4cXJyciguLub48eO2/6Kjozl+/HhLxSjUb6cOMdXeA0bt1CIi0l41egVmwoQJREVFERpaexv7\nsxdzfOeddxwbndQzMDqYr74/Tkm2f207dX4y06InOTssERGRFtdoAbN06VI++ugjSktLmTZtGtOn\nT8disbRUbHKWvnXt1IdLiR4UwaHCNE5WluLn6evs0ERERFpUo0NI1157LW+88Qb/+Mc/OHnyJLfe\neiu33347a9asoaKioqVilNNs7dRZJfT064WBoXZqERFpl+xqYencuTN33XUXn376KZMnT+axxx7T\nJF4nsbVTl9a1U+uuvCIi0v40OoRUp7i4mNWrV/P+++9jtVq58847mT59uqNjk/Ooa6dOP2oiMNif\n/fnJaqcWEZF2p9ECZvPmzfz3v/9l7969TJo0iSeffJI+ffq0VGxyHme2U8f17sPWzJ2kl2QQEdDd\n2aGJiIi0mEYLmNtvv53IyEiGDh1Kfn4+b775Zr3Xn3jiCYcGJ+cymUwMjA5m4/fHCTb1AHaSmJek\nAkZERNqVRguYujbpgoICgoKC6r127Ngxx0UljYo9XcCcPL069b68ZKZGXe3ssERERFpMowWMm5sb\n9957L6dOncJisfDKK68QERHBihUrePXVV7nuuutaKk45Q107ddLhUqIG9uBw0RFOVpXi56F2ahER\naR8anfn597//nbfeeott27bx+9//nocffpi5c+eyZcsW3nvvvQvuPCUlhauuuooVK1bUe37Tpk3E\nxMTYHq9evZrrr7+eG264wa79tnf12qn9a9upk/IPODssERGRFtNoAePm5kbPnj0BmDhxIhkZGfzs\nZz/jhRdeIDw8vNEdl5WVsXjxYkaOHFnv+VOnTvHqq6/a7u5bVlbGiy++yFtvvcXy5ct5++23KSws\nvJTP1C6cvTr1PrVTi4hIO9JoAWMymeo97ty5M1dfbd9cC09PT1577TXCwsLqPf/yyy9zyy234Onp\nCcDu3buJjY3F398fLy8vhg4dSkJCQlM+Q7sUG117R+Rj6W74e/qxL6+2nVpERKQ9sOs+MHXOLmga\n3bHZjNlcf/epqakkJSVxzz338PTTTwOQm5tbb3kCi8VCTk5Oo/sOCvLBbHZvQuRNExrq77B9N5eQ\nED9Cg7zZl1bIFZcN4OsjWyk1FxJtiXB2aA7VGnLTHikvrku5cV3KzaVptIDZtWsX48aNsz3Oy8tj\n3LhxGIaByWRi48aNTTrYE088waJFixp9z5kLRjakoKCsScdtitBQf3JyShy2/+Y0ICKIjd8fx7ey\nMwCbD+7CP6rtrlXVmnLTnigvrku5cV3KjX0aK/IaLWA+++yzZgsiKyuLw4cP87vf/Q6A7Oxs5syZ\nw29+8xtyc3Nt78vOzmbw4MHNdty2rK6dujQ7EBMm9uUnMSVqorPDEhERcbhGC5iuXbs224HCw8NZ\nv3697fGECRNYsWIFFRUVLFq0iOLiYtzd3UlISOCPf/xjsx23LfuxnbqMqIE9SC06SllVGT4ePs4O\nTURExKGaNAemKfbu3cvSpUvJyMjAbDazbt06nn/+eTp27FjvfV5eXixcuJDbbrsNk8nE/Pnz8ffX\nuKA96tqp9x8p4Bq/XhwuOsL+/AMMCx/k7NBEREQcymEFzGWXXcby5csbfH3Dhg22P8fHxxMfH++o\nUNq02Ohg9h8pwFz6Yzu1ChgREWnrtIRxKxfbs/Z+MOlH3fH38GNfvtqpRUSk7VMB08p1CfYh+PTq\n1H0tvSmuLCHj5AlnhyUiIuJQKmBaOZPJRGx0MGWnqk+vTg2JuiuviIi0cSpg2oC6ZQXKcjrWtlPn\nJTk5IhEREcdSAdMGnNlOHRnQndTio5RVlTs7LBEREYdRAdMGnLk6dbR/L2qMGpIKtDq1iIi0XSpg\n2oi6YSR3rU4tIiLtgAqYNqKunToj3Yyfhy/78pLtWldKRESkNVIB00bUa6cO6k1RZbHaqUVEpM1S\nAdNGnNlOHeJW206tYSQREWmrVMC0IXXzYEpPt1Mn5qudWkRE2iYVMG1Iv8jadurkw+X0COjG4aIj\nlFernVpERNoeFTBtiJfnj+3UPf1q26nfSvw3WWU5zg5NRESkWamAaWPqhpECK/oQFRDB3rz9PLb1\nb6xM/oDiyhInRyciItI8VMC0MXXt1AfSylk47C7uuGwuIV4Wvs74jke+W8qnqes5Za10cpQiIiKX\nxuzsAKR51bVTJ6bmU2MYDA6LJTakP98c38onqV/wcernfJ3xHdOjJjGi83Dc3dydHbKIiEiT6QpM\nG3NmO/Xh48UAuLu5c2W3UTw68gGmRE6korqCd5P/y5Jtf+eHnETd8E5ERFodFTBtUN0w0p7DefWe\n9zJ7MT16Mo+MfIDRXS4nqyyHV/a8zd8TXia16KgzQhUREbkoKmDaoH4RQZjdTfxwKO+8rwd2COCW\nvtfzp8vvIzakP4eKUvnrzhd4fe8KsstyWzhaERGRptMcmDbIy9NM724d2X+kgMKTp+jo1+G87+vs\nG87/Dfw5BwoO88GhT9iV/QO7c/YyputIpkROxN/Tr4UjFxERsY+uwLRRg04PIz2xYiff7DmBtaam\nwff2Dorm98MWcNtlc7B4BfHVsW945LulfJa2gUp1LImIiAtyf+SRRx5xdhBNVVbmuL9UfX07OHT/\nLaVHuD/lFdUkHS1gZ3IO2/Zl4evtQdcQX0wm0znvN5lMdPYNZ0zXEfh7+nG4KI29efvZcmIH3mYv\nuvp1Pu92Lamt5KatUV5cl3LjupQb+/j6nn8EAcBktMIWlJwcx92QLTTU36H7b2n5xRV8/G0am344\ngbXGoHOwD9deEcXwvmG4NVKQlFdXsP7IRv6Xvomqmio6+4Yzo+dUBgT3dVoh09Zy01YoL65LuXFd\nyo19QkP9G3xNBcxZ2upJlVtYzppv0/hmTyY1hkG3UF+uvSKaoX1CGi1ICk8V8cnhz/nuxA4MDHp3\njGZmr2lEBHRvwehrtdXctHbKi+tSblyXcmMfFTBN0NZPqqyCMtZ8k8Z3iZkYBvQI92PGmGgG9Qxu\ntJA5fjKTjw6tZW9e7QrXw8IG8dOe8YR4B7dU6G0+N62V8uK6lBvXpdzYRwVME7SXk+pEXimrv0lj\n274sDCCqsz8zxkRzWZSl0UImpeAgHxxcy9GSY7ib3Lmy60jiIyfi5+nr8JjbS25aG+XFdSk3rku5\nsY8KmCZobydVRs5JPtqcyo7k2hWre3UNZMaYKPpFBDVYyNQYNSRk/8DqQ5+RV5GPl7sXkyPGM677\nFXi6ezgs1vaWm9ZCeXFdyo3rUm7sowKmCdrrSXU0q4SPNqey60Dtjez6dO/IzDFRxPQIanCbqppq\nNmds4dPU9ZRWl9GxQyDToydzeaehuJmav0O/vebG1Skvrku5cV3KjX1UwDRBez+p0jKL+XBTqu0u\nvv0igpg5Jppe3QIb3Kasqpwvjm7ky/RNVNVU08W3EzN6TaO/pU+zdiy199y4KuXFdSk3rku5sY8K\nmCbQSVXr0PEiPtqUyt7UfAAui7Yw44poorsENLhNQUUhHx/+nK2ZOzEwiAnqxYxeU+nh361ZYlJu\nXJPy4rqUG9el3NhHBUwT6KSqLyW9kI82p7L/SAFQe4ffGWOiiejU8EmVcfIEHx5ay768ZACGhw/m\nmuh4QrwtlxSLcuOalBfXpdy4LuXGPo0VMA69E29KSgo33ngjbm5uDBw4kBMnTvCb3/yGVatWsXr1\nakaPHo2vry+rV6/mj3/8I6tWrcJkMjFgwIBG96s78bac4EAvRsd2JqZ7R7ILy9l3pICvvj9OevZJ\nOgf7Eujrec42AZ7+/KTTUHoGRnKiNIuk/ANszviOsupyIgK6X/REX+XGNSkvrku5cV3KjX2ccife\nsrIy7rzzTiIjI4mJiWHOnDk88MADjB07lqlTp/Kvf/2LjIwMFixYwMyZM1m1ahUeHh7MmjWLFStW\n0LFjxwb3rSswzmEYBvuOFPDh14c5dLwYgOF9w7j2iii6hpy/jbrGqGFn1m5WH/6M/IoCvM3etR1L\n3Ubj0cRCRrlxTcqL61JuXJdyYx+nXIExmUxMnz6d5ORkvL29GThwIKNHjyYmJgY3NzeOHTtGSkoK\ngYGB5OXlcc0112A2m0lKSqJDhw5ERUU1uG9dgXEOk8lEWEdvxgzsTHSXQDLzy9iXVsDGhAyy8svo\nGuqHn7fHOdt09evMmK4j8TF7c6gwlT15+9mamYCvhw9d/DrZPdFXuXFNyovrUm5cl3Jjn8auwJgd\ndVCz2YzZXH/3Pj4+AFitVt59913mz59Pbm4uFsuPcyMsFgs5OTmN7jsoyAez2b35gz6tsYpPak0M\nC2DC5RFsS8zkX+uS2LIvi237sxg3rDs3XR1D5/NckbkpfBrTY8fx4f51fJryJe/sX8lXJ75hzqCZ\nDOrU367jKjeuSXlxXcqN61JuLo3DCpiGWK1W7r//fkaMGMHIkSNZs2ZNvdftGdEqKChzVHi6rNdE\n0eF+/GnuMBKSc/hocyobdqSzcecxrhjYiemjIgkJ9D5nm8ldribOMpyPD3/OtswEHv/qefoG9WZG\nr2l09+/S4LGUG9ekvLgu5cZ1KTf2aazIa/EC5sEHHyQiIoIFCxYAEBYWRm5uru317OxsBg8e3NJh\nySVwM5kY3jeMoTGh7EjK5qPNqXy9+wTf7MlkzKAuTB8ZgSXAq942Fq8gftb/RsZ3H8NHh9ayPz+F\npdufJa7TEKZHTSbYu+Eb6ImIiDT/7VIbsXr1ajw8PLj77rttzw0aNIg9e/ZQXFxMaWkpCQkJDB8+\nvCXDkmbiZjLxk37hLL7tcu6Y3p/gQC827srgD698x7++SKHw5Klztunu34UFg29nwaDb6eLXiW2Z\nCfxl69N8cPATyqocd6VNRERaN4d1Ie3du5elS5eSkZGB2WwmPDycvLw8OnTogJ+fHwA9e/bkkUce\n4bPPPuOf//wnJpOJOXPm8NOf/rTRfasLqXWw1tTw7d5M1nyTRm5RBR5mN8YP6crUEREEnKf9usao\nYXvmLtYcXkfBqUJ8zN5MjpzA2K6j8HD3UG5clPLiupQb16Xc2Ec3smsCnVTNr9pawzd7TrDm2zTy\ni0/h6eHGxKHdiL+8B/4+5xYyVdYqNh77hnVHNlBeXYHFK4hroicz5bIx5OWWOuETSGP0m3Fdyo3r\nUm7sowKmCXRSOU5VdQ2bfjjOx9+mUXiykg6e7lw9vBuT4nqc034NUFpVxrq0DXx17BuqDSuBXgH0\nDoymr6UPfYN6EeTV8L2CpOXoN+O6lBvXpdzYRwVME+ikcryqaisbdx3nky1HKC6txLuDO5PienD1\n8O74eJ07rzyvPJ/P0jawryCJwopi2/PhPqHEBPWmr6UXfYJ64m0+t+NJHE+/Gdel3Lgu5cY+KmCa\nQCdVyzlVZeXLhAzWbjnCyfIqfL3MTP5JDyYO64Z3h3MLmZAQP35IO0hywUGS8lNIKTxMpbX2RlAm\nTEQGdCfG0pu+Qb2ICozA7NbiTXbtkn4zrku5cV3KjX1UwDSBTqqWV1FZzf92HuOzrUcprajGz9uD\nKZf3YMLQbnTw/PGGhWfnprqmmrTidJLzD5BUcIC04nRqjBoAPN086BUUTd+g3vS19KaLr/13/JWm\n0W/GdSk3rku5sY8KmCbQSeU85aeq+WJHOuu2pVN+qpoAHw+mjohg3JCueHq4XzA35dUVHCw8TFL+\nAZLyD5BZlm17zd/DjxhLL1tBo/kzzUe/Gdel3Lgu5cY+KmCaQCeV85VVVLFuWzpf7EinotJKoJ8n\n00ZEMHNiH0qKyu3eT+GpIpLzD5JUcIDk/AMUVf6Y1zCfEPoG9aGvpRe9O/bEx0PzZy6WfjOuS7lx\nXcqNfVTANIFOKtdxsryKz7Ye5X87j3GqyoqH2Y3e3QIZEGVhQKSF7mF+dg8LGYbBidKs0/NnDnCg\n8BCnzpg/ExHQnb6n589EBkbgofkzdtNvxnUpN65LubGPCpgm0EnleopLK1m/8xiJafmkHv+xCynA\n15MBkUH0j7QwIMpCR7+GVy09m7XGSmrx0dPzZw6SVny0/vyZjtG1BY2lN519w3EztehNq1sV/WZc\nl3LjupQb+6iAaQKdVK4rNNSfg6m57EsrIDEtn8TUfIpKf1yOvluob+3VmSgLfbp1xNPD/hXL682f\nKThIZmmW7TXNn2mcfjOuS7lxXcqNfVTANIFOKtd1dm4MwyAjp5S9qfkkpuWTkl5IVXXtVRSzuxt9\nul/ccBPYM3+mNzGW3vTR/Bn9ZlyYcuO6lBv7qIBpAp1UrutCuamqtpJyrIjE1NqrM+nZJ22v1Q03\nDYiy0D+yacNNhmGQWZZNUv4BkgsOkFJwnvkzQb2IsfQmqh3On9FvxnUpN65LubGPCpgm0Enlupqa\nm6KTp9iXVsDe1Hz2pTXfcJO1xkpacbrt6kzqeebP1A05dfHr1Obnz+g347qUG9el3NhHBUwT6KRy\nXZeSmwsNN8V0D6T/RQ43VVRXcKDwsG3I6cR55s/ULXlg8Qq6qPhdmX4zrku5cV3KjX1UwDSBTirX\n1Zy5qayycuBYkW0ycHMNN8GP82fqWraLKn/snKqbP9M/OIZ+lj5tYrkD/WZcl3LjupQb+6iAaQKd\nVK7Lkbk5c7gpMS2f4mYabjIMg6yybPafnj9zoOAwFdZTAPiYvRkSFktc+BB6doxqtUNN+s24LuXG\ndSk39lEB0wQ6qVxXS+XGnuGmAVHB9I8MavJwU938md05e9mR9b3t6kzHDoEMDx9MXPgQuvp1blXr\nNuk347qUG9el3NhHBUwT6KRyXc7KjW24KTWfvan5HMs5/3DTgEgLgU0YbqoxajhQcJgdWbvYlbOH\n8uoKADr5hhMXPoTh4YMJ8bY0++dpbvrNuC7lxnUpN/ZRAdMEOqlcl6vkpvHhJj8GRAU1ebipylpF\nYl4S27O+Z2/efqprqgGIDowgLnwIQ8IG4u/p55DPc6lcJS9yLuXGdSk39lEB0wQ6qVyXK+bGMAyO\n5ZTW3numkeGmAVEWuoX62jU0VFZVzu6cvWzP2kVKwSEMDNxMbvSz9CEufAgDQwfQwd3T0R/Nbq6Y\nF6ml3Lgu5cY+KmCaQCeV62oNubF7uCkqmEDfCxchhaeKSMjazfasXRwtyQBq7zUzMHQAceFD6Gfp\ng7ub/ZOKHaE15KW9Um5cl3JjHxUwTaCTynW1xtw0NNxkMkH/iCBGDOjE0D6heHe4cDt1Vmk227O+\nZ3vWLnLL8wDw9fBhaNgg4sKHEB0Y4ZTJv60xL+2FcuO6lBv7qIBpAp1Urqu156ZuuGlvah4JyTkc\nOr2ytofZjUG9Qhg5IJzY6GDM7o23UxuGwZGSdLZn7mJn1m5Kqmqv8gR7BTHsdCdTF79ODv88dVp7\nXtoy5cZ1KTf2UQHTBDqpXFdby012QRlb9mWxJTGLzPwyAHy9zAzvG8aI/uH07t4RtwtcUbHWWEkp\nOMT2rF18n7PHtkZTV7/Otk4mR6+e3dby0pYoN65LubGPCpgm0EnlutpqbgzD4EhWCVsSs9i6P4ui\nk7VFSHBAB37SP5yR/TvRLezCHUiV1ir25O5je9Yu9uUlYzWsAPTqGGXrZPL18Gn2+NtqXtoC5cZ1\nKTf2UQHTBDqpXFd7yE1NjcH+owVsScxkZ3IOFZW1RUi3UF9GDOjE5f3CCQ70uuB+SqvK2JX9Azuy\nvudA4WEA3E3u9A+OIS58MLEh/fFspk6m9pCX1kq5cV3KjX1UwDSBTirX1d5yU1llZfehPLYkZvLD\noTysNbU/1T7dOzJiQDjDY8Lw8/a44H4KKgrZcXryb8bJEwB0cPdkcGgsw8MHExPU65I6mdpbXloT\n5cZ1KTf2UQHTBDqpXFd7zs3J8ip2JGezJTGLlPRCANzdTAzsGcyIAZ0Y1DPYrpvmHT+ZyY6s79mR\ntYu8igKgdsXsoeG1nYKszfYAACAASURBVEyRAd2b3MnUnvPi6pQb16Xc2EcFTBPopHJdyk2tvKIK\ntu6vnfxbd58ZL093hsWEMmJAJ/r1CMLNrfEixDAMUouPsD1zFwnZP3CyqhSAEO9g4k53MoX7htkV\nj/LiupQb16Xc2EcFTBPopHJdys25juWcrJ38uy+TvOLaVa4DfT25vH84IwaEExHuf8ErKtYaK/vz\nU9ietYsfchKprKkCoLt/V+LChzAsfBAdOwQ2uL3y4rqUG9el3NhHBUwT6KRyXcpNw2oMg4PHitiS\nmMn2pGxKK2rXUupk8WHE6WImLOjCHUinrJX8kJPI9qxd7M9PocaowYSJ3kE9iQsfwuDQy/Dx8K63\njfLiupQb16Xc2EcFTBPopHJdyo19qq017Dmcx5bELL4/mGtbmym6SwAj+ofzk37hBNixjEFJ5Ul2\nZf/A9qzvOVyUBoDZzcxlwX2JCx/CgOC+eLh7KC8uTLlxXcqNfZxWwKSkpHDXXXfx85//nDlz5nDi\nxAnuv/9+rFYroaGhPP3003h6erJ69Wrefvtt3NzcmD17NjfccEOj+1UB0z4pN01XfqqahJQctiRm\nsu9IAYYBbiYT/aOCGNm/E0P6hODleeFlDHLL822dTJmlWQB4m70YHBrL1TGjCDV1xs3U+B2EpeXp\nN+O6lBv7OKWAKSsr48477yQyMpKYmBjmzJnDgw8+yJVXXsmUKVN45pln6NSpEzNmzGDmzJmsWrUK\nDw8PZs2axYoVK+jYseG7h6qAaZ+Um0tTdPIUW/dnsyUxk7TM2u/R08ONIb1DGdE/nAFRFruWMcg4\neYLtWbvYkfU9haeKAAj3CWN899Fc3mlYs91fRi6dfjOuS7mxT2MFjPsjjzzyiCMOajKZmD59OsnJ\nyXh7ezNw4ECWLFnCww8/jLu7O15eXqxZs4awsDDy8vK45pprMJvNJCUl0aFDB6Kiohrcd1lZpSNC\nBsDXt4ND9y8XT7m5NF6eZnp2DWTs4K5c3j8cXy8zuUUVpKQXsnVfFl/uyiC3uALfDh4E+Xc47+Rf\nk8lEQAd/+ln6ML77FcQE9cSzg5nkvEPsyd3H5owtlFWX08k3DC/zhW+4J46l34zrUm7s4+vbocHX\nLnzt+CKZzWbM5vq7Ly8vx9Oz9l9nwcHB5OTkkJubi8Visb3HYrGQk5PT6L6Dgnwwmy/+xlsX0ljF\nJ86l3DSP0FB/YmPCuX2mwYH0QjYmHGPTrgy+TKj9L8ziw9ghXRk3tBs9OgU0uJ/wsMGMYjC3DprJ\nuoNf8cWhTXx+5Ev+d/QrRnYfxrSYifS0RLTgJ5Oz6TfjupSbS+OwAub/t3fvMW3d9//Hn77bXA3G\nQAiQe5oQAqRJ1jYhbdMm67T+vqnWbkvXlfWvSVM7aZuyqlnW6zZtSrVJ09aq277rpCrV1KyXrduv\nW5u0STrakjYpgQRyIzQXLgEMGMzFd5/vHzYGkgB2LvgY3g/JMj6+5HPyPse8+JzPOZ+pTHTkKpYj\nWk7n8PVuTpR066mX1ObGyLLo+dr6+Wy5rZgT55zUNHZS2+Tg9Q+aeP2DJopz08LTGJTkkZV++V9D\ndns6gUEtd+dvZIO9kkOdtexv+YiPLhziowuHWJQ5n7uKNlBmXyHjZKaZ7DPqJbWJzWQhb1oDTEpK\nCh6PB7PZTGdnJ7m5ueTm5tLd3R19TVdXFxUVFdPZLCEEoNNqKV1oo3ShDa8/SF1TNwcbO2g428vf\n9p/h9f1nuKnYyq0r8llzk50U8+XTGBh1BtYX3MK6OV/iZG8T+1qrOd5ziub+c9jMWdxZuJ7bCr6E\nRQ4vCSGu0bQGmHXr1vHee+9x3333sWfPHjZs2EB5eTlPPvkkLpcLnU5HbW0tO3bsmM5mCSEuYTLo\nuKUkj1tK8hgY9nH4ZBc1xzs5eaGPkxf6eHXPacoX2bh1RR53WS+/voxGo2G5bSnLbUvpGOpkf8tH\nfNpRy5tn/j/vnN3LbXPWcmfRenIstgSsnRBiJrhhZyE1NDSwc+dO2tra0Ov15OXl8etf/5rt27fj\n9XopKCjgV7/6FQaDgXfffZeXX34ZjUbDww8/zJYtWyb9bDkLaXaS2iRed5+bg8c7OXi8k/bu8PQD\nqRYD60vz2bS6kByrZcL3DvqH+LjtUz5s/YR+nwsNGsrsK9hYWMli64K452ASU5N9Rr2kNrGRC9nF\nQTYq9ZLaqIeiKLR0DUbDTN+AF40Gbl5iZ/PaIpYUZk4YSAKhAEe6jrGvpZoLA61AeNqCu4o2cHNu\nGXptwobmzTiyz6iX1CY2EmDiIBuVeklt1MmalcK/q5vZe6iV853h+szLS2fz2kLWLsvDoL/ywF1F\nUWjuP8f+lmrqHY0oKGQa07m9cB2VBbeSZkydztWYkWSfUS+pTWwkwMRBNir1ktqo00hdFEWhqbWf\nvYdbqD3tQFHCE0tuXDWXO1fNnXT6gm53Lx+2fswn7Z/hCXoxaPV8KX81G4sqmZOaN41rM7PIPqNe\nUpvYSICJg2xU6iW1Uacr1aW7z80Hta38t/4ibm8AvU7LrSV5bFpTSHHexF9I7oCHgxcPs7/lI3o8\nvQAsz17KXUUbWJ69VMbJxEn2GfWS2sRGAkwcZKNSL6mNOk1WF48vwMfHOnj/cAudTjcAy4qtbF5b\nRPmiHLTaKweSkBLiaPdx9rdUc6bvLAD5KblsLKrkS/mrMeouP4VbXE72GfWS2sRGAkwcZKNSL6mN\nOsVSl5CicKy5h72HWzh+zhl+n9XMptVFVJbNwWKaeODuBVcr+1o+4vOuOkJKiFRDChsKbmVD4W1Y\nTZnXdV1mGtln1EtqExsJMHGQjUq9pDbqFG9dWh2DvH+4hZrGTvyBEGajjg1lBdy9ppDcSU7D7vP2\nU91aQ3X7QYb8w+g0Om7OLeeu4kqK0wuvx6rMOLLPqJfUJjYSYOIgG5V6SW3U6WrrMjDs48O6dj6o\nbaV/0IcGqFiSw5fXFrG0yDrheBdf0MdnHeHpCjqGuwBYbF3AxqINlOWUyHQFY8g+o15Sm9hIgImD\nbFTqJbVRp2utSyAY4vDJLvYcauFcR/hzinLT2LymiFtKcjFMMHGroiic6D3NvpZqTvSeBiDHnM2d\nRZXcOmeNTFeA7DNqJrWJjQSYOMhGpV5SG3W6XnVRFIXmNhd7DrdQe8pBSFHISDFw56q5bFw1l8y0\nyyeSHHExMl3BZx2f4w8FMOvMrCtYy52F67FZsid830wn+4x6SW1iIwEmDrJRqZfURp1uRF16+j3s\nq23lw7p2hr0B9DoNtyzPY9OaIublT/yFNugb4qP2g/y39RP6fQNo0FBuL2VjUSWLMufPutOwZZ9R\nL6lNbCTAxEE2KvWS2qjTjayL1xfkk4aL7D3cSkfvMABLi6xsXlPEqiUTn4YdCAWo7TrKvpZqWgba\nAChOL4xOV6DTXvmw1Ewj+4x6SW1iIwEmDrJRqZfURp2moy4hRaHhi17eP9xCw9nwBe5yMs1sWl1I\nZVkBKeYrn4Y9Ml3BvpZqjkanK8jgjsJ1rJ97C2mGmT1dgewz6iW1iY0EmDjIRqVeUht1mu66tHUP\n8cHhFj5p6MAXCGEy6qhcOYdNawrJy0qZ8H3d7h4OtH5MTfuhyHQFBm7Jv5mNRRvIT82dtvZPJ9ln\n1EtqExsJMHGQjUq9pDbqlKi6DLr9fFjXxr7aNpwDXjRA+eIcNq8pZNm8rAnHu7gDHmraP+NA68f0\neMIX1Sux3cRdRRtYlrVkRo2TkX1GvaQ2sZEAEwfZqNRLaqNOia5LIBii9rSDvYdaaG53AVBoT2Xz\nmiJuXZE34WnYISXEUUcj+1qqae4/B8Cc1DzWzVlLuX0lNkvWdK3CDZPo2oiJSW1iIwEmDrJRqZfU\nRp3UVJfmtvBs2J+fchAMKaRZwqdh33XzXKyTnIZ93tXC/paP+LyrnpASAqA4fS7l9pVU2EuT9hCT\nmmojxpPaxEYCTBxko1IvqY06qbEuvS4P+4+0ceBIG0OeADqthi8tz2Xz2iLm52dM+D6Xb4Cjjkbq\nHA2ccp6Jhpn8lFwqcsNhpjCtIGkOM6mxNiJMahMbCTBxkI1KvaQ26qTmunj9QWoaO9h7qIWLPeHT\nsJcUZoZPw16ag0478bQDw/5hjnWfoN7RwPHeU/hDAQBs5izK7aVU2FeyILNY1VMXqLk2s53UJjYS\nYOIgG5V6SW3UKRnqoigKx8852Xu4haPNPQDYMkzcvbqI28vnkGI2TPp+b9DH8Z5T1DmO0dB9Ak/Q\nC0CGMZ0y+woq7KUstS5S3fVlkqE2s5XUJjYSYOIgG5V6SW3UKdnqcrFniPc/b+XjYxfx+UOYDDrW\nr8xn05oi8rMnPg17hD8U4FRvE/WOBo52H2fQPwRAit7CypwSKuylLMteilE3eSiaDslWm9lEahMb\nCTBxkI1KvaQ26pSsdRny+PlvfTv7Pm+lxxXuUSlbZKNy5RxKF2ZjNl754nhjBUNBmvvPUedooN7R\nQJ+3HwCjzsgK2zIq7KWssC1L2MSSyVqb2UBqExsJMHGQjUq9pDbqlOx1CYZCHDndzZ7DLZxpDQcQ\nvU5LyfwsKpbksGpxzqQTSY4IKSEuDLRS19VAneMYDnf4UJVeo2NZ9hIq7CtZmVNCmnH6rv6b7LWZ\nyaQ2sZEAEwfZqNRLaqNOM6kuFzoHOHzKQV2Tg1bHUHT5woIMVi3JoWKJnQJbypRnISmKQvtQR7Rn\npm3wIgBajZbF1oVU2Espt6/Aasq8oeszk2oz00htYiMBJg6yUamX1EadZmpduvrc1DV1U9fk4HRL\nP6HIV2VuloVVS3JYtcTO4rmZE04oOe6zhrupdzRQ52jgnOtCdPmCjOLoGU32FNt1X4eZWpuZQGoT\nGwkwcZCNSr2kNuo0G+oy6PZztLmbI03dNHzRi9cfBCDNYqBicQ6rluRQsiAbk2Hqs5Ccnj7quxup\n72qgqe8LFMJfwXPT5lARCTNzUvOuy7VmZkNtkpXUJjYSYOIgG5V6SW3UabbVxR8IcuK8kyNN3dQ1\nddM/5APAqNdSMj+bVUtyKF+cQ0aqccrPGvANRq41c4yTvU0ElHAwyrXkUG4vZVXuSorTC686zMy2\n2iQTqU1sJMDEQTYq9ZLaqNNsrktIUTh70UVdU7h3pr07PG5GAywqzIweaorl9Gx3wE1j90nqHA00\n9pzEF/IDYDVlRnpmSllkXRDXhfNmc23UTmoTGwkwcZCNSr2kNuokdRnV2Tsc6Zlx0NTWz8i36xxb\nSviMpiV2FhZkoJ2iR8UX9HOi9zR1jmMc6z6BO+AGIM2QSlnOCipyS1matRiDdvJTvaU26iW1iY0E\nmDjIRqVeUht1krpcmWvYx9EzPRxpctB4thdfIDyvUkaqkYrFNiqW2CmZl4VxinEzgVCAJucX1DmO\nUe9oZMA/CIBZZ2ZlznIq7KUst92ESXf5ISupjXpJbWIjASYOslGpl9RGnaQuU/P5gxw/56S2yUH9\nmW4GhsOHh4wGLaULbNFxM2mWya/eG1JCfNF/PnpGU6/HCYBBa6DEdhMV9lJKbctJMVgAqY2aSW1i\no5oAMzQ0xBNPPEF/fz9+v5/HHnsMu93Os88+C8BNN93Ec889N+XnSICZnaQ26iR1iU8opNDc3s+R\nyLiZzt7wJJMaDSwptEbGzeSQmzX5uBlFUWgZaKPOEb5wXuewAwhfa+amrMVU2EtZt7iC0JAe/RSH\nmsT0k/0mNqoJMK+++iqdnZ1s27aNzs5OHnnkEex2O48//jhlZWVs27aNLVu2cMcdd0z6ORJgZiep\njTpJXa7NxZ6hSJhx8EWbi5Ev5Lk5qdFxM/PnpE85bqZjqDMcZrqO0TLYHl2uQUO6MQ2rKZMsUyZW\ncyZWU+boY5MVqykDgwrmbppNZL+JzWQBZlpjeVZWFqdOnQLA5XJhtVppa2ujrKwMgI0bN1JTUzNl\ngBFCiJliji2VObZUvnrrPPqHfNSf6ebIaQfHzzt5p+Y879Scx5pmpGJx+ErAy+dlYdBffiZSfmoe\nX0nN4yvz76bb3Uu9o4Eufxed/d04vf20D3VwYaB1wnakGVKjwcZqHgk3Y4KO2XrFcTZCJMq0Bph7\n772Xt956i82bN+NyuXjppZf42c9+Fn3eZrPhcDims0lCCKEamalGbi8v4PbyAry+IA1ne6lrclDf\n3MOBunYO1LVjMupYuSCbVUvtlC2ykWq+vOckx5LN3cW3j/srX1EUhvzDOL399Hn76PP20+fpjzwO\n37qGHbSO6b25lEVvifbijAYc67ienURNXClmn2kNMG+//TYFBQW8/PLLnDx5kscee4z09NHuoViP\nZmVlpaDXT33Fy6s1WZeVSCypjTpJXW6MwrlWvlK5kGAwxIlzvXza2MGnDR0cPuXg8CkHWq2G0oU2\nbinN55YVc8i7wvVmxtcmgwXkT/jvKYrCsN9Nz7CTXncfPcNOetx99F5y3z7UMeFnWPRmslOs2CxZ\n0XtbipXsyL3NkkWqcer5pGYD2W+uzbQGmNraWiorKwFYtmwZXq+XQCAQfb6zs5Pc3NwpP8fpHL5h\nbZTjkuoltVEnqcv0yMswseW2efzPrcW0dw9FBwEfPRO+/e8/GijKTYtePK84L43c3Iyrqo2FDObq\nM5ibUQwZlz/vCXjo87ro80Z6cDyjvTpObz997n7aXBOHHIPWMNqDYx47HieTLLMVqymTNEPqjA45\nst/ERjVjYObNm0d9fT333HMPbW1tpKamMnfuXA4fPsyaNWvYs2cPVVVV09kkIYRIKhqNhrn2NOba\n0/h/6+bjHPCGx800dXPifC8tXYP88+NzZGeYKFtix6zTkpFqJCPVEL5PMZKZaiQ9xRjTRJRXYtab\nydebyU+d+A9OX9AfPTTV5+3H6RkTcCKhp8vdPeH79RrduICTacogRW/BrDeTordg0Zsx68xY9KM3\ns94c15WKRXKb9tOod+zYQU9PD4FAgB/84AfY7XaefvppQqEQ5eXl/OQnP5nyc+QspNlJaqNOUhf1\ncHsDNJ7t5UiTg6PNPQx5AhO+VgOkpYwPNRkjt5TwfTjohF+j113/YOAPBeiP9OT0efrGjccZ6dlx\n+QaiE17GwqwzYb4k1Fh0ZiwGS/h+7HK9GUskDI3cTDrTtIQg2W9io5rTqK8XCTCzk9RGnaQu6hQM\nhUCv52yLE9eQL3rrHx792TXsxzXkw+2dOOiMSDXrx4WbkVvmuGUGMlONGK7jGMVgKIjLN0Cf14Un\n4GE44MYT8OAOenAHwrdxy8cscwc9hJRQXP+eBg0mnWlcqBkNPJGwozNjMYTvzZeEILPejFlnmvLw\nl+w3sVHNISQhhBDTQ6fVYrelogtN/QvcHwjiGvLjGvbRPybshENOJPhEHl/smXoMosWkuyzoRB9H\ne3vCPTtm4+S/hnRaHVlmK1lma8zrPkJRFHwhP+6AOxpswuFm/OPo8uD45U5vPxeHOuPqAYJwCBrX\nCzTuUFc47Ni6M/B7FMw6EyadCZPOGL0360eXGbSGGT0W6FpIgBFCiFnOoNdhy9Rhy5z6FOhAMMRA\npOfGNaY3p/+Sx64hH119bqbq4zcatJMewhpZlp5iwGzUodPGfnhHo9FEgoERqykz5veNpSgK3qB3\nTMjxMOyPrRfIHfDQ63HiCXjjDkHRdUAzGm70xnFhx3xJ8Am/5vJl5kuWzZRQJAFGCCFEzPQ6LVnp\nJrLSTVO+NhRSGHD7Gbj00NUVDmed6xggGJr6l7xRr8Vk1GE26jAb9aM/G8KPzUbduOfNkZ9NI48N\nkedMekwG3RUvCjiWRhPuTTHrzWTF/L90yf+DEhoXgtwBD5Y0HZ29fXgDXrxBH95g+N4T9EZ/9ga9\neAMjz3nxBLz0e114g76rbElknSKHyUw643UJRamGlIQMnpYAI4QQ4obQajVkRnpSCqd4bUhRGPYE\nxh/CGhNwBob9eHwBPL4gXn8Qjy9Id78bjy84ZS/PZHRaTTTkjAtExnDAMZt0o6Fn3POXBqZwODIa\ntJf1bmg12sihI0t0md2ejkN/dWNgQkoIX9A/Lvh4xwafMaHIE/Re/rqxz1+HULTEupAf3vy9q37/\n1ZIAI4QQIuG0Gg1pFgNpFgNzc1Jjfp+iKPgCoXCwiQSc0VsA75ifPZHg453g+b5BLx5fMKaeoIlo\nNIRDjWF8D9Cl4cdmTSEUCGI26aPPW0w6LCPviSy/0tlfWo0Wsz7cCwLX52J41xKKFmXOvy5tiJcE\nGCGEEElLo9FgMoQDA6nXZ66mQDAciDze0dAzPuwER3uDJgxIAYY8fnpdHnyB+M6EGsug12IZ6eEx\nRYKOUYfFND7oWCLPWyZ6nVE/6XV/bkQoutEkwAghhBBj6HVa0ixa0izXZ4buYCiE1xeKhh6PL4jZ\nYqTDMYDbG4gGILd3/P3Y5W5fgP5hH15f8KrbYTRoIwFnJPSM9vyMC0LR4DMaiiym8YfMppodfTpI\ngBFCCCFuIJ1WS4pZS4p59Feu3Z5OQVb8E1+GQgpefxC3N4B7pOfnkuDjHuk98oWDz2XPewM4Bzz4\n/FfXM6SB6OEwi0lPxeIcvrFx8VV91rWQACOEEEIkCa1Wg8Wkx2K69l/f4Z6hYLSHxzM2+FwpAEWe\nHxuQBt1+elye67Bm8ZMAI4QQQsxCoz1D1+dQ2XSTWa+EEEIIkXQkwAghhBAi6UiAEUIIIUTSkQAj\nhBBCiKQjAUYIIYQQSUcCjBBCCCGSjgQYIYQQQiQdCTBCCCGESDoSYIQQQgiRdCTACCGEECLpSIAR\nQgghRNKRACOEEEKIpCMBRgghhBBJR6MoipLoRgghhBBCxEN6YIQQQgiRdCTACCGEECLpSIARQggh\nRNKRACOEEEKIpCMBRgghhBBJRwKMEEIIIZKOBJgxfvnLX7J161YefPBBjh49mujmiDGef/55tm7d\nygMPPMCePXsS3RwxhsfjYdOmTbz11luJbooY45///Cdbtmzh/vvv58CBA4lujgCGhob4/ve/T1VV\nFQ8++CDV1dWJblJS0ye6AWrx2Wefcf78eXbv3k1zczM7duxg9+7diW6WAA4ePEhTUxO7d+/G6XTy\nta99jS9/+cuJbpaIeOmll8jMzEx0M8QYTqeTF198kTfffJPh4WF+//vfc+eddya6WbPe3//+dxYs\nWMC2bdvo7OzkkUce4d133010s5KWBJiImpoaNm3aBMCiRYvo7+9ncHCQtLS0BLdMrF27lrKyMgAy\nMjJwu90Eg0F0Ol2CWyaam5s5c+aM/HJUmZqaGm677TbS0tJIS0vj5z//eaKbJICsrCxOnToFgMvl\nIisrK8EtSm5yCCmiu7t73MaUnZ2Nw+FIYIvECJ1OR0pKCgBvvPEGt99+u4QXldi5cyfbt29PdDPE\nJVpbW/F4PHzve9/joYceoqamJtFNEsC9995Le3s7mzdv5uGHH+aJJ55IdJOSmvTATEBmWFCf999/\nnzfeeIO//OUviW6KAP7xj39QUVFBUVFRopsirqCvr48XXniB9vZ2vvOd77B//340Gk2imzWrvf32\n2xQUFPDyyy9z8uRJduzYIWPHroEEmIjc3Fy6u7ujj7u6urDb7QlskRirurqaP/zhD/z5z38mPT09\n0c0RwIEDB2hpaeHAgQN0dHRgNBrJz89n3bp1iW7arGez2Vi1ahV6vZ7i4mJSU1Pp7e3FZrMlummz\nWm1tLZWVlQAsW7aMrq4uORx+DeQQUsT69et57733AGhsbCQ3N1fGv6jEwMAAzz//PH/84x+xWq2J\nbo6I+O1vf8ubb77J3/72N77xjW/w6KOPSnhRicrKSg4ePEgoFMLpdDI8PCzjLVRg3rx51NfXA9DW\n1kZqaqqEl2sgPTARN998MytWrODBBx9Eo9HwzDPPJLpJIuLf//43TqeTH/7wh9FlO3fupKCgIIGt\nEkK98vLyuOeee/jmN78JwJNPPolWK3+vJtrWrVvZsWMHDz/8MIFAgGeffTbRTUpqGkUGewghhBAi\nyUgkF0IIIUTSkQAjhBBCiKQjAUYIIYQQSUcCjBBCCCGSjgQYIYQQQiQdCTBCiBuqtbWV0tJSqqqq\norPwbtu2DZfLFfNnVFVVEQwGY379t771LT799NOraa4QIklIgBFC3HDZ2dns2rWLXbt28dprr5Gb\nm8tLL70U8/t37dolF/wSQowjF7ITQky7tWvXsnv3bk6ePMnOnTsJBAL4/X6efvppSkpKqKqqYtmy\nZZw4cYJXXnmFkpISGhsb8fl8PPXUU3R0dBAIBLjvvvt46KGHcLvd/OhHP8LpdDJv3jy8Xi8AnZ2d\n/PjHPwbA4/GwdetWvv71rydy1YUQ14kEGCHEtAoGg+zdu5fVq1fz+OOP8+KLL1JcXHzZ5HYpKSm8\n+uqr4967a9cuMjIy+M1vfoPH4+GrX/0qGzZs4JNPPsFsNrN79266urq4++67AfjPf/7DwoULee65\n5/B6vbz++uvTvr5CiBtDAowQ4obr7e2lqqoKgFAoxJo1a3jggQf43e9+x09/+tPo6wYHBwmFQkB4\neo9L1dfXc//99wNgNpspLS2lsbGR06dPs3r1aiA8MevChQsB2LBhA3/961/Zvn07d9xxB1u3br2h\n6ymEmD4SYIQQN9zIGJixBgYGMBgMly0fYTAYLlum0WjGPVYUBY1Gg6Io4+b6GQlBixYt4p133uHQ\noUO8++67vPLKK7z22mvXujpCCBWQQbxCiIRIT0+nsLCQDz/8EICzZ8/ywgsvTPqe8vJyqqurARge\nHqaxsZEVK1awaNEijhw5AsDFixc5e/YsAP/61784duwY69at45lnnuHixYsEAoEbuFZCiOkiPTBC\niITZuXMnv/jFL/jTn/5EIBBg+/btk76+qqqKp556im9/+9v4fD4effRRCgsLue+++9i3bx8PPfQQ\nhYWFrFy5EoDFixfzzDPPYDQaURSF7373u+j18rUnxEwgs1ELIYQQIunIISQhhBBCJB0JMEIIIYRI\nOhJghBBCCJF0RxJCUQAAAC9JREFUJMAIIYQQIulIgBFCCCFE0pEAI4QQQoikIwFGCCGEEElHAowQ\nQgghks7/AfYqbva9FhIBAAAAAElFTkSuQmCC\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "47430111-2bb9-442c-aa72-8638a6259e52"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 158.29\n",
+            "  period 01 : 110.84\n",
+            "  period 02 : 98.37\n",
+            "  period 03 : 83.45\n",
+            "  period 04 : 76.19\n",
+            "  period 05 : 73.93\n",
+            "  period 06 : 72.32\n",
+            "  period 07 : 71.55\n",
+            "  period 08 : 71.06\n",
+            "  period 09 : 70.32\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   70.32\n",
+            "Final RMSE (on validation data): 72.59\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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u5/E20vHRSPEajSQiItKgFGAaUEyoFYANx9tI3dsG4+3sxZb07VQZVY4sTURE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\nTZzXF8bbZ+px6MMGdIdUKLKIVfOLsXF5BUoKhp7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AfOB2JjDAEBERTRI9l5XMJgmwmzLd\nnFExxoUsIiIiojQwwBAREVHWYYAhIiKirMMAQ0RERFmHAYaIiIiyDgMMERERZR0GGCIiIso6DDBE\nRESUdRhgiIiIKOswwBAREVHWYYAhIiKirMMAQ0RERFmHAYaIiIiyjqDrup7pRhARERGlgz0wRERE\nlHUYYIiIiCjrMMAQERFR1mGAISIioqzDAENERERZhwGGiIiIsg4DTD+/+MUvsGXLFmzduhXnzp3L\ndHOon2effRZbtmzBAw88gIMHD2a6OdRPKBTChg0b8Je//CXTTaF+/va3v2Hz5s24//77cejQoUw3\nhwB0d3fju9/9LqqqqrB161YcOXIk003KanKmG2AUJ0+exLVr17B3715cuXIFO3fuxN69ezPdLAJw\n/PhxXLp0CXv37oXX68VXvvIV3HvvvZluFiXs3r0bubm5mW4G9eP1evHCCy/g1VdfRSAQwK9//Wus\nW7cu082a9P76179i2rRp2L59OzweDx5++GHs378/083KWgwwCceOHcOGDRsAADNmzEBnZyf8fj8c\nDkeGW0bLly/HggULAAA5OTkIBoOIxWKQJCnDLaMrV67g8uXL/J+jwRw7dgx33XUXHA4HHA4Hfvaz\nn2W6SQTA5XLhwoULAICuri64XK4Mtyi78RJSQltbW9LJlJ+fj9bW1gy2iHpIkgSbzQYA2LdvH9as\nWcPwYhC7du3Cjh07Mt0MGqC+vh6hUAjf+c53sG3bNhw7dizTTSIAX/7yl9HY2IiNGzfioYcewuOP\nP57pJmU19sAMgzMsGM+bb76Jffv24Q9/+EOmm0IAXnvtNSxatAgVFRWZbgoNoaOjA88//zwaGxvx\nzW9+E++88w4EQch0sya1119/HaWlpXjxxRdRU1ODnTt3cuzYKDDAJLjdbrS1tfUut7S0oLCwMIMt\nov6OHDmC3/zmN/j9738Pp9OZ6eYQgEOHDqGurg6HDh1Cc3MzTCYTiouLsXLlykw3bdIrKCjA4sWL\nIcsyKisrYbfb0d7ejoKCgkw3bVI7c+YMVq9eDQCYM2cOWlpaeDl8FHgJKWHVqlU4cOAAAKC6uhpu\nt5vjXwzC5/Ph2WefxW9/+1vk5eVlujmU8Mtf/hKvvvoq/vSnP+FrX/saHnnkEYYXg1i9ejWOHz8O\nTdPg9XoRCAQ43sIApk6dirNnzwIAGhoaYLfbGV5GgT0wCUuWLMG8efOwdetWCIKAp556KtNNooR/\n/vOf8Hq9+P73v9+7bteuXSgtLc1gq4iMq6ioCJs2bcLXv/51AMBPfvITiCL/Xs20LVu2YOfOnXjo\noYegqiqefvrpTDcpqwk6B3sQERFRlmEkJyIioqzDAENERERZhwGGiIiIsg4DDBEREWUdBhgiIiLK\nOgwwRDSm6uvrMX/+fFRVVfXOwrt9+3Z0dXWlfIyqqirEYrGU9//GN76BEydO3ExziShLMMAQ0ZjL\nz8/Hnj17sGfPHrzyyitwu93YvXt3yq/fs2cPb/hFREl4IzsiGnfLly/H3r17UVNTg127dkFVVUSj\nUTz55JOYO3cuqqqqMGfOHJw/fx4vvfQS5s6di+rqakQiETzxxBNobm6Gqqq47777sG3bNgSDQfzg\nBz+A1+vF1KlTEQ6HAQAejwc//OEPAQChUAhbtmzBV7/61Uy+dSK6RRhgiGhcxWIxvPHGG1i6dCke\ne+wxvPDCC6isrBw0uZ3NZsPLL7+c9No9e/YgJycHzz33HEKhEL70pS/h7rvvxvvvvw+LxYK9e/ei\npaUF69evBwD861//wvTp0/HMM88gHA7jz3/+87i/XyIaGwwwRDTm2tvbUVVVBQDQNA3Lli3DAw88\ngF/96lf48Y9/3Luf3++HpmkA4tN7DHT27Fncf//9AACLxYL58+ejuroaFy9exNKlSwHEJ2adPn06\nAODuu+/GH//4R+zYsQNr167Fli1bxvR9EtH4YYAhojHXMwamP5/PB0VRBq3voSjKoHWCICQt67oO\nQRCg63rSXD89IWjGjBn4xz/+gVOnTmH//v146aWX8Morr4z27RCRAXAQLxFlhNPpRHl5OQ4fPgwA\nuHr1Kp5//vkRX7Nw4UIcOXIEABAIBFBdXY158+ZhxowZ+PDDDwEATU1NuHr1KgDg73//Oz7++GOs\nXLkSTz31FJqamqCq6hi+KyIaL+yBIaKM2bVrF37+85/jd7/7HVRVxY4dO0bcv6qqCk888QQefPBB\nRCIRPPLIIygvL8d9992Ht99+G9u2bUN5eTnuuOMOAMDMmTPx1FNPwWQyQdd1fPvb34Ys89ce0UTA\n2aiJiIgo6/ASEhEREWUdBhgiIiLKOgwwRERElHUYYIiIiCjrMMAQERFR1mGAISIioqzDAENERERZ\nhwGGiIiIss7/B2MdL3RfmbmaAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "da6fbce1-b5ff-41a0-bc0a-5873cb154f50"
+      },
+      "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=0.05),\n",
+        "    steps=5000,\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": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 110.31\n",
+            "  period 01 : 92.95\n",
+            "  period 02 : 76.54\n",
+            "  period 03 : 73.03\n",
+            "  period 04 : 71.89\n",
+            "  period 05 : 71.18\n",
+            "  period 06 : 70.53\n",
+            "  period 07 : 70.01\n",
+            "  period 08 : 69.69\n",
+            "  period 09 : 69.46\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.46\n",
+            "Final RMSE (on validation data): 71.53\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ZOSLAZRqKW8rwfcMPEndIREQ0cjDADBIrSwUi/TzQ2NqFI2f7Wtju8i3Vybyl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\nAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "Jafw0yaVtUN0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "a3809661-b263-4d5e-aea0-83fe61f017d2"
+      },
+      "cell_type": "code",
+      "source": [
+        "_, adam_training_losses, adam_validation_losses = train_nn_regression_model(\n",
+        "    my_optimizer=tf.train.AdamOptimizer(learning_rate=0.05),\n",
+        "    steps=5000,\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": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 67.84\n",
+            "  period 01 : 70.95\n",
+            "  period 02 : 71.66\n",
+            "  period 03 : 65.19\n",
+            "  period 04 : 65.47\n",
+            "  period 05 : 64.92\n",
+            "  period 06 : 64.93\n",
+            "  period 07 : 66.03\n",
+            "  period 08 : 64.98\n",
+            "  period 09 : 65.89\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   65.89\n",
+            "Final RMSE (on validation data): 66.50\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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nzi88OL3zJFQqlYeruTqTzojV3kBuVb6nSxFCCOEGElTEZY6XnuJ42Sl6hHWl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\nhBBCCGGzJKgIIYQQwmZJUBFCCCGEzfr/zU2tDVoKM4kAAAAASUVORK5CYII=\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "37210a07-9f6f-4b3e-ba73-1a00a5d1d0e0"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 87.00\n",
+            "  period 01 : 74.31\n",
+            "  period 02 : 73.20\n",
+            "  period 03 : 71.12\n",
+            "  period 04 : 69.26\n",
+            "  period 05 : 70.26\n",
+            "  period 06 : 68.74\n",
+            "  period 07 : 70.93\n",
+            "  period 08 : 69.14\n",
+            "  period 09 : 68.72\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   68.72\n",
+            "Final RMSE (on validation data): 71.31\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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2jPHjx+Pm1hg8xo8fz5IlS1rcRkmJYw8IdFR4eAAFBc3f3hkQG8yOA0Xs2X+M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\nYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "9dd84ff0-e5ec-4834-9934-b201fb85dd61"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 193.83\n",
+            "  period 01 : 119.33\n",
+            "  period 02 : 111.07\n",
+            "  period 03 : 102.62\n",
+            "  period 04 : 85.49\n",
+            "  period 05 : 73.99\n",
+            "  period 06 : 71.22\n",
+            "  period 07 : 70.42\n",
+            "  period 08 : 69.62\n",
+            "  period 09 : 69.37\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   69.37\n",
+            "Final RMSE (on validation data): 71.80\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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IL7sOFHPtxQkApBWmM7Xf+S6OTEREpHtw76YON5YQE0p9YxOHcxrpH9iXvWX7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\nSUVORK5CYII=\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": "d27c5ece-a6eb-47f4-c5a2-fd19d7d46eed"
+      },
+      "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": 17,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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d4JFH/km3bj1Ys+ZdfvzxeyIihpnjNBqNtGvXgTlz7uDZZ5/i8FW5iouL5fnn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\nfwAAAABJRU5ErkJggg==\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": "43788863-9ec7-404d-cd1d-3a78371f66ff"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = normalized_training_examples.hist(bins=20, figsize=(18, 12), xlabelsize=10)"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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IDw+vt8X94MGDefjhh7nnnnuIiYmhoqKCxx9/\nvFXeG2m7TE6n02l0J0Rayul0umYobNu2jaVLl553BoSISGfz3Xffcdttt/HPf/4TgN/85jd8++23\nri2Lo6KieOmllzh58iTLli2jtraWLl26MH/+fIYOHUppaSmPP/44hw4d4tprr8VqtXLllVcya9Ys\nrr/+ev785z9z6tQpHnvsMU6fPs31119PUlISs2bNIjk5mYqKCr7//nt+9atfAWd3rzj3cWOKiopY\nsGABH374YYPXn/v4+++/58knn+TIkSN069aNF154gUGDBnHkyBEWLFjAoUOH8Pf3Z+bMmURHRzf7\nvRgxYgQfffRRo++HiEhbcu542Vw/jt2XXXaZF3smcmEqPki7c+zYMcaPH8/mzZvp1asXqampXHLJ\nJVqVXEREREQ6PBUfpL3SgpPS7litVubMmcP999+PyWSib9++rq3WREREREREpO3RzAcRERFpFcnJ\nyXz11VeNPrdixQquvfbaVu6RiPFOnTrFvHnzOHHiBLW1tSQnJxMSEuKa0Xn99dfzwgsvAPDGG2+Q\nl5eHyWRi5syZ3HrrrVRUVJCSkkJFRQWBgYFkZGTQvXt3AzMSEWmcig8iIiIiIgbZsGEDxcXFpKSk\nUFxczH333UdISAhPPfUUgwcPJiUlhTvvvJO+ffsye/Zs3nrrLU6ePElSUhJ//OMf+e1vf0tAQAAP\nPvgg2dnZfPvttzz11FNGpyUi0kC7ue3C4aho+kU/0aNHIGVl3l15W/Hbdh8UXz8DTcUPCbG0Ym86\nvvY2Vrf1n0/F75ixO3t8d2J35LG6R48efPnllwCUl5fTvXt3Dh06xODBgwGIjIyksLAQh8NBREQE\nZrMZq9XKFVdcwYEDBygsLOTll192vXbGjBlNxmzpWN2Zf16Njt+Zc+/s8dtj7k2N1R16q00/P1/F\nN5jRfVB8/QwYHV+aZuRnZPTPh+Lrs++M8Y3Ova25/fbbOXz4MFFRUUydOpW5c+cSFBTkej44OBiH\nw0FpaSlWq9XVbrVaG7QHBwdTUlLi8T4a/Zl15vidOffOHr8j5t5uZj6IiIiIiHQ07777Lr169WLN\nmjX861//Ijk5GYvl//56eL47pBtrb+7d1D16BLb4fyyMnn3SmeN35tw7e/yOlruKDyIiIiIiBrHb\n7YwaNQqA/v3788MPP3D69GnX88XFxdhsNmw2G//5z38abXc4HFgsFldbU9yZSu3ObXWe0pnjd+bc\nO3v89ph7p77tQkRERESkLbvqqqvYvXs3AIcOHaJLly5ce+217Nq1C4AtW7YQERHBzTffzLZt26ip\nqaG4uJiSkhJ+9rOfER4eTl5eXr3Xioi0RZr5ICIiIiJikMmTJzN//nymTp3K6dOnWbhwISEhITz3\n3HOcOXOG0NBQwsLCAEhISGDq1KmYTCYWLlyIj48P06ZN46mnniIpKYmgoCAWL15scEYiIo1T8UFE\nRERExCBdunRh2bJlDdo3btzYoG3atGlMmzatwfG/+c1vvNY/ERFP0W0XIiIiIiIiIuJVKj6IiIiI\niIiIiFfptgsRNz2Q/nGLj1mbOsYLPRERb2jp77h+v0Wko7oj5d0WH6MxUUR+yq3iQ1VVFampqRw9\nepQffviBRx99lP79+zN37lzq6uoICQlh8eLFmM1mcnNzycrKwsfHh4SEBOLj46mtrSU1NZXDhw/j\n6+tLWloavXv39nRuIiIiIiIiItIGuHXbRUFBAQMHDmTDhg0sXbqU9PR0li9fTlJSEhs3buSqq64i\nJyeHyspKVqxYwbp161i/fj1ZWVkcP36c999/n6CgIN58801mzJhBRkaGp/MSERERERERkTbCreJD\nbGwsDz30EABHjhyhZ8+eFBUVMXbsWAAiIyMpLCxk9+7dDBo0CIvFQkBAAEOHDsVut1NYWEhUVBQA\nYWFh2O12D6UjIiIiIiIiIm3NRa35MGXKFL7//ntWrlzJz3/+c8xmMwDBwcE4HA5KS0uxWq2u11ut\n1gbtPj4+mEwmampqXMeLiIiIiIiISMdxUcWHt956iy+++IKnnnoKp9Ppaj/33+dqafu5evQIxM/P\nt8V9DAmxtPgYT+rs8dtCH4yOfy4j+tIW8je6D0bHFxERERHp7NwqPuzdu5fg4GAuv/xyBgwYQF1d\nHV26dKG6upqAgACKi4ux2WzYbDZKS0tdx5WUlDBkyBBsNhsOh4P+/ftTW1uL0+lsctZDWVlli/sZ\nEmLB4aho8XGe0tnjt4U+GB3/p1q7L20hf6P70FR8FSZERERERLzPrTUfdu3axdq1awEoLS2lsrKS\nsLAw8vPzAdiyZQsRERGEhoayZ88eysvLOXXqFHa7nWHDhhEeHk5eXh5wdvHKESNGeCgdERERERER\nEWlr3Jr5MGXKFJ555hmSkpKorq7mueeeY+DAgcybN4/s7Gx69epFXFwc/v7+pKSkMH36dEwmE8nJ\nyVgsFmJjY9mxYweJiYmYzWbS09M9nZeIiIiIiIiItBFuFR8CAgIa3R4zMzOzQVtMTAwxMTH12nx9\nfUlLS3MntIiIiIiIiIi0M27ddiEiIiIiIiIi0lwXtduFiIhIe/BA+sdGd0FERESkU1PxQURERETE\nIJs2bSI3N9f1eO/evbz55pssXLgQgOuvv54XXngBgDfeeIO8vDxMJhMzZ87k1ltvpaKigpSUFCoq\nKggMDCQjI4Pu3bsbkYqIyAWp+CAiIiIiYpD4+Hji4+MB+Mtf/sKf/vQnfvWrXzF//nwGDx5MSkoK\nf/7zn+nbty8ffPABb731FidPniQpKYlRo0aRlZXF8OHDefDBB8nOzmb16tU89dRTBmclItKQ1nwQ\nEREREWkDVqxYwUMPPcShQ4cYPHgwAJGRkRQWFlJUVERERARmsxmr1coVV1zBgQMHKCwsJCoqqt5r\nRUTaIs18EBHpwBYtWsRnn33G6dOneeSRRxg0aBBz586lrq6OkJAQFi9ejNlsJjc3l6ysLHx8fEhI\nSCA+Pp7a2lpSU1M5fPiwa5ei3r17G52SiEiH9I9//IPLL78cX19fgoKCXO3BwcE4HA66d++O1Wp1\ntVutVhwOB6Wlpa724OBgSkpKmozVo0cgfn6+nk/iHCEhljZ9vvYUvzPn3tnjd7TcVXwQEemgdu7c\nyf79+8nOzqasrIwJEyYwcuRIkpKSGD9+PEuWLCEnJ4e4uDhWrFhBTk4O/v7+TJo0iaioKAoKCggK\nCiIjI4Pt27eTkZHB0qVLjU5LRKRDysnJYcKECQ3anU5no69vrP18r/2psrLKlnXODQ5HhcfOFRJi\n8ej52lP8zpx7Z4/fHnNvqlih4oOISAd10003uabtBgUFUVVVRVFRkWvhssjISNauXcs111zDoEGD\nsFjOfmEMHToUu91OYWEhcXFxAISFhTF//nxjEmkn3NlR472Mu7zQExFpj4qKiliwYAEmk4njx4+7\n2ouLi7HZbNhsNv7zn/802u5wOLBYLK42EZG2SGs+iIh0UL6+vgQGBgJn/6J2yy23UFVVhdlsBv5v\nKu+5U3ah8am8Pj4+mEwmampqWj8REZEOrri4mC5dumA2m/H396dv377s2rULgC1bthAREcHNN9/M\ntm3bqKmpobi4mJKSEn72s58RHh5OXl5evdeKiLRFmvkgItLBbd26lZycHNauXcttt93mam/JVN4L\ntZ/L3fuIjb6n0UhG596Z43fm3I2Ob3TubY3D4ahXBJ4/fz7PPfccZ86cITQ0lLCwMAASEhKYOnUq\nJpOJhQsX4uPjw7Rp03jqqadISkoiKCiIxYsXG5WGiMgFqfggItKBffLJJ6xcuZI33ngDi8VCYGAg\n1dXVBAQE1JuyW1pa6jqmpKSEIUOGuKby9u/fn9raWpxOp2vWxPm4cx+x0fc0Gq293c/ZUeJ35tyN\nju+N+4jbu4EDB/LGG2+4Hv/sZz9j48aNDV43bdo0pk2bVq+tS5cu/OY3v/F6H0VELpZuuxAR6aAq\nKipYtGgRq1atonv37sDZtRvy8/OB/5ueGxoayp49eygvL+fUqVPY7XaGDRtWbypvQUEBI0aMMCwX\nEREREWnfNPNBRKSD+uCDDygrK2POnDmutvT0dBYsWEB2dja9evUiLi4Of39/UlJSmD59OiaTieTk\nZCwWC7GxsezYsYPExETMZjPp6ekGZiMiIiIi7ZmKDyIiHdTkyZOZPHlyg/bMzMwGbTExMcTExNRr\n8/X1JS0tzWv9ExEREZHOQ7ddiIiIiIiIiIhXqfggIiIiIiIiIl6l4oOIiIiIiIiIeJWKDyIiIiIi\nIiLiVSo+iIiIiIiIiIhXub3bxaJFi/jss884ffo0jzzyCB9//DGff/65ay/56dOnM3r0aHJzc8nK\nysLHx4eEhATi4+Opra0lNTWVw4cPu1ZT7927t8eSEhEREREREZG2w63iw86dO9m/fz/Z2dmUlZUx\nYcIEbr75Zp544gkiIyNdr6usrGTFihXk5OTg7+/PpEmTiIqKoqCggKCgIDIyMti+fTsZGRksXbrU\nY0mJiIiIiIiISNvh1m0XN910E8uWLQMgKCiIqqoq6urqGrxu9+7dDBo0CIvFQkBAAEOHDsVut1NY\nWEhUVBQAYWFh2O32i0hBRERERERERNoyt2Y++Pr6EhgYCEBOTg633HILvr6+bNiwgczMTIKDg3n2\n2WcpLS3FarW6jrNarTgcjnrtPj4+mEwmampqMJvN543Zo0cgfn6+Le5rSIilxcd4UmeP3xb6YHT8\ncxnRl7aQv9F9MDq+iIiIiEhn5/aaDwBbt24lJyeHtWvXsnfvXrp3786AAQN4/fXXee2117jxxhvr\nvd7pdDZ6nvO1n6usrLLF/QsJseBwVLT4OE/p7PHbQh+Mjv9Trd2XtpC/0X1oKr4KEyIiIiIi3uf2\nbheffPIJK1euZPXq1VgsFkaOHMmAAQMAGDNmDPv27cNms1FaWuo6pqSkBJvNhs1mw+FwAFBbW4vT\n6bzgrAcRERERkY4qNzeXO++8k7vvvptt27Zx5MgRpk2bRlJSErNnz6ampsb1uokTJxIfH8+mTZuA\ns9fSKSkpJCYmMnXqVA4ePGhkKiIi5+VW8aGiooJFixaxatUq1+4Ws2bNcg12RUVF9OvXj9DQUPbs\n2UN5eTmnTp3CbrczbNgwwsPDycvLA6CgoIARI0Z4KB0RERERkfajrKyMFStWsHHjRlauXMlHH33E\n8uXLSUpKYuPGjVx11VXk5OS4FnJft24d69evJysri+PHj/P+++8TFBTEm2++yYwZM8jIyDA6JRGR\nRrl128UHH3xAWVkZc+bMcbXdfffdzJkzh0suuYTAwEDS0tIICAggJSWF6dOnYzKZSE5OxmKxEBsb\ny44dO0hMTMRsNpOenu6xhERERERE2ovCwkJGjhxJ165d6dq1Ky+++CJjxozhhRdeACAyMpK1a9dy\nzTXXuBZyB+ot5B4XFwecXch9/vz5huUiInIhbhUfJk+ezOTJkxu0T5gwoUFbTEwMMTEx9dp8fX1J\nS0tzJ7SIiIiISIfx3XffUV1dzYwZMygvL2fWrFlUVVW5bkkODg5usGA7GLOQe0t4ek0lo9doMjJ+\nZ869s8fvaLlf1IKTIiIiIiJycY4fP85rr73G4cOHuffee+stxt7SBdu9tZB7S3lysem2vnh1R42t\n+PrsWxq/qWKF2wtOioiIiIjIxQkODubGG2/Ez8+PPn360KVLF7p06UJ1dTUAxcXFrgXbtZC7iLRn\nKj6IiIiIiBhk1KhR7Ny5kzNnzlBWVkZlZSVhYWHk5+cDsGXLFiIiIrSQu4i0e7rtQkRERETEID17\n9iQ6OpqEhAQAFixYwKBBg5g3bx7Z2dn06tWLuLg4/P39tZC7iLRrKj6IiIiIiBhoypQpTJkypV5b\nZmZmg9dpIXcRac9024WIiIiIiIiIeJWKDyIiIiIiIiLiVSo+iIiIiIiIiIhXqfggIiIiIiIiIl6l\n4oOIiIiIiIiIeJWKDyIiIiIiIiLiVSo+iIiIiIiIiIhXqfggIiIiIiIiIl6l4oOIiIiIiIiIeJWK\nDyIiIiIiIiLiVSo+iIiIiIiIiIhXqfggItKB7du3j3HjxrFhwwYAUlNTueOOO5g2bRrTpk1j27Zt\nAOTm5jJx4kTi4+PZtGkTALW1taSkpJCYmMjUqVM5ePCgUWmIiIiISDvnZ3QHRETEOyorK3nxxRcZ\nOXJkvfYnnniCyMjIeq9bsWIFOTk5+Pv7M2nSJKKioigoKCAoKIiMjAy2b99ORkYGS5cube00RERE\nRKQD0MwHEZEOymw2s3r1amw22wVft3v3bgYNGoTFYiEgIIChQ4dit9spLCwkKioKgLCwMOx2e2t0\nW0REREQ6ILdnPixatIjPPvuM06dP88gjjzBo0CDmzp1LXV0dISEhLF68GLPZTG5uLllZWfj4+JCQ\nkEB8fDy1tbWkpqZy+PBhfH19SUtLo3fv3p7MS0Sk0/Pz88PPr+Ewv2HDBjIzMwkODubZZ5+ltLQU\nq9Xqet5qteJwOOq1+/j4YDKZqKmpwWw2nzdmjx6B+Pn5trivISGWFh/TEdyR8m6Lj3kv4y6P9sHo\n997I+J05d6PjG517W1JUVMTs2bPp168fANdddx0PPvigrqtFpMNxq/iwc+dO9u/fT3Z2NmVlZUyY\nMIGRI0eSlJTE+PHjWbJkCTk5OcTFxWkqr4hIG3LXXXfRvXt3BgwYwOuvv85rr73GjTfeWO81Tqez\n0WPP136usrLKFvcpJMSCw1HR4uM6K0++V0a/90bG78y5Gx3fndgdvVgxfPhwli9f7nr89NNP67pa\nRDoct267uOmmm1i2bBkAQUFBVFVVUVRUxNixYwGIjIyksLBQU3lFRNqYkSNHMmDAAADGjBnDvn37\nsNlslJaWul5TUlKCzWbDZrPhcDiAs4tPOp3OC856EBERz9B1tYh0RG7NfPD19SUwMBCAnJwcbrnl\nFrZv3+66KA0ODm4wZRc651Tezh6/LfTB6PjnMqIvbSF/o/tgdPy2ZNasWcydO5fevXtTVFREv379\nCA0NZcGCBZSXl+Pr64vdbmf+/PmcPHmSvLw8IiIiKCgoYMSIEUZ3X0SkQzpw4AAzZszgxIkTzJw5\nk6qqqjZ5Xd0Snv7uNfq7vDPfpqT4+uw95aJ2u9i6dSs5OTmsXbuW2267zdXe0im7HXUqb2eP3xb6\nYHT8n2rtvrSF/I3uQ1PxjR7UvWnv3r288sorHDp0CD8/P/Lz85k6dSpz5szhkksuITAwkLS0NAIC\nAkhJSWH69OmYTCaSk5OxWCzExsayY8cOEhMTMZvNpKenG52SiEiHc/XVVzNz5kzGjx/PwYMHuffe\ne6mrq3M931auq1tKt4i1/9iKr8/e07fIuV18+OSTT1i5ciVvvPEGFouFwMBAqqurCQgIoLi42DVl\n96dTeYcMGeKaytu/f39N5RUR8ZKBAweyfv36Bu3R0dEN2mJiYoiJianX9uPCZSIi4j09e/YkNjYW\ngD59+nDppZeyZ88eXVeLSIfj1poPFRUVLFq0iFWrVtG9e3fg7D1m+fn5AGzZsoWIiAhCQ0PZs2cP\n5eXlnDp1CrvdzrBhwwgPDycvLw9AU3lFREREpNPKzc1lzZo1ADgcDo4ePcrdd9+t62oR6XDcmvnw\nwQcfUFZWxpw5c1xt6enpLFiwgOzsbHr16kVcXBz+/v6ayisiIiIich5jxozhySef5KOPPqK2tpaF\nCxcyYMAA5s2bp+tqEelQ3Co+TJ48mcmTJzdoz8zMbNCmqbwiIiIiIo3r2rUrK1eubNCu62oR6Wjc\nuu1CRERERERERKS5VHwQEREREREREa9S8UFEREREREREvErFBxERERERERHxKhUfRERERERERMSr\n3NrtQlrfA+kft+j1a1PHeKknIiIiIiIiIi2jmQ8iIiIiIiIi4lUqPoiIiIiIiIiIV6n4ICIiIiIi\nIiJepeKDiIiIiIiIiHiVig8iIiIiIiIi4lUqPoiIiIiIiIiIV6n4ICIiIiIiIiJepeKDiIiIiIiB\nqqurGTduHJs3b+bIkSNMmzaNpKQkZs+eTU1NDQC5ublMnDiR+Ph4Nm3aBEBtbS0pKSkkJiYydepU\nDh48aGQaIiIXpOKDiIiIiIiBfvvb39KtWzcAli9fTlJSEhs3buSqq64iJyeHyspKVqxYwbp161i/\nfj1ZWVkcP36c999/n6CgIN58801mzJhBRkaGwZmIiJyfig8iIiIiIgb56quvOHDgAKNHjwagqKiI\nsWPHAhAZGUlhYSG7d+9m0KBBWCwWAgICGDp0KHa7ncLCQqKiogAICwvDbrcblYaISJP8jO6AiIiI\niEhn9corr/Dss8/yhz/8AYCqqirMZjMAwcHBOBwOSktLsVqtrmOsVmuDdh8fH0wmEzU1Na7jz6dH\nj0D8/Hy9lNFZISGWNn2+9hS/M+fe2eN3tNxVfBARERERMcAf/vAHhgwZQu/evRt93ul0eqT9p8rK\nKpvXwYvgcFR47FwhIRaPnq89xe/MuXf2+O0x96aKFSo+iIiIiIgYYNu2bRw8eJBt27bx/fffYzab\nCQwMpLq6moCAAIqLi7HZbNhsNkpLS13HlZSUMGTIEGw2Gw6Hg/79+1NbW4vT6Wxy1oOIiFEuas2H\nffv2MW7cODZsXXLmoAAAIABJREFU2ABAamoqd9xxB9OmTWPatGls27YN0Oq8IiIiIiI/tXTpUt5+\n+21+//vfEx8fz6OPPkpYWBj5+fkAbNmyhYiICEJDQ9mzZw/l5eWcOnUKu93OsGHDCA8PJy8vD4CC\nggJGjBhhZDoiIhfk9syHyspKXnzxRUaOHFmv/YknniAyMrLe61asWEFOTg7+/v5MmjSJqKgoCgoK\nCAoKIiMjg+3bt5ORkcHSpUvdz0REREREpJ2bNWsW8+bNIzs7m169ehEXF4e/vz8pKSlMnz4dk8lE\ncnIyFouF2NhYduzYQWJiImazmfT0dKO7LyJyXm4XH8xmM6tXr2b16tUXfN25q/MC9VbnjYuLA86u\nzjt//nx3uyIiIiIi0q7NmjXL9e/MzMwGz8fExBATE1OvzdfXl7S0NK/3TUTEE9wuPvj5+eHn1/Dw\nDRs2kJmZSXBwMM8++6zHVud1d1XejrZCaEvjGp1/W+iD0fHPZURf2kL+RvfB6PgiIiIiIp2dRxec\nvOuuu+jevTsDBgzg9ddf57XXXuPGG2+s9xp3V+d1Z1Xe9rhCqKc4HBWG5w+d+zNoTGv3pS3kb3Qf\nmoqvwoS0Nw+kf9yi169NHeOlnoiIiIg0n0eLD+eu/zBmzBgWLlxIdHS0VucVERGPaen/fIuIiIiI\n8S5qt4ufmjVrlmvXiqKiIvr166fVeUVEREREREQ6ObdnPuzdu5dXXnmFQ4cO4efnR35+PlOnTmXO\nnDlccsklBAYGkpaWRkBAgFbnFRExyL59+3j00Ue5//77mTp1KkeOHGHu3LnU1dUREhLC4sWLMZvN\n5ObmkpWVhY+PDwkJCcTHx1NbW0tqaiqHDx92LWrWu3dvo1MSERERkXbI7eLDwIEDWb9+fYP26Ojo\nBm1anVdEpPU1tiXy8uXLSUpKYvz48SxZsoScnBzi4uK0JbKIiIiIeJVHb7sQEZG248ctkW02m6ut\nqKiIsWPHAhAZGUlhYWG9LZEDAgLqbYkcFRUFnN0S2W63G5KHiIiIiLR/Hl1wUkRE2o7GtkSuqqpy\nLe4bHBzcYOtjcH9LZGi/2yJ3ZE29t0a/90bG78y5Gx3f6NxFRKT1qfggItJJtXTr46a2RIb2uS1y\nR9fUVrNteSvcjhq7s8d3J7aKFe2POzsTaWtgkY5NxQeRNq6lX9764pYLCQwMpLq6moCAAIqLi7HZ\nbNhsNm2JLCIiIiJepTUfREQ6kbCwMPLz8wHYsmULERER2hJZRERERLxOMx9ERDqoxrZEfvXVV0lN\nTSU7O5tevXoRFxeHv7+/tkQWEREREa9S8UFEpIM635bImZmZDdq0JbKIiIiIeJNuuxARERERERER\nr9LMBxERERERg1RVVZGamsrRo0f54YcfePTRR+nfvz9z586lrq6OkJAQFi9ejNlsJjc3l6ysLHx8\nfEhISCA+Pp7a2lpSU1M5fPiwa8Za7969jU5LRKQBzXwQERERETFIQUEBAwcOZMOGDSxdupT09HSW\nL19OUlISGzdu5KqrriInJ4fKykpWrFjBunXrWL9+PVlZWRw/fpz333+foKAg3nzzTWbMmEFGRobR\nKYmINErFBxERERERg8TGxvLQQw8BcOTIEXr27ElRURFjx44FIDIyksLCQnbv3s2gQYOwWCwEBAQw\ndOhQ7HY7hYWFREVFAWd3NLLb7YblIiJyIbrtQkRERETEYFOmTOH7779n5cqV/PznP8dsNgMQHByM\nw+GgtLQUq9Xqer3Vam3Q7uPjg8lkoqamxnV8Y3r0CMTPz9e7CbkhJMTi1nOtwcj4nTn3zh6/o+Wu\n4oOIiIiIiMHeeustvvjiC5566imcTqer/dx/n6ul7ecqK6t0r5Ne5nBUNNoeEmI573Otwcj4nTn3\nzh6/PebeVLFCt12IiIiIiBhk7969HDlyBIABAwZQV1dHly5dqK6uBqC4uBibzYbNZqO0tNR1XElJ\niavd4XAAUFtbi9PpvOCsBxERo6j4ICIiIiJikF27drF27VoASktLqaysJCwsjPz8fAC2bNlCREQE\noaGh7Nmzh/Lyck6dOoXdbmfYsGGEh4eTl5cHnF28csSIEYblIiJyIbrtQkRERETEIFOmTOGZZ54h\nKSmJ6upqnnvuOQYOHMi8efPIzs6mV69exMXF4e/vT0pKCtOnT8dkMpGcnIzFYiE2NpYdO3aQmJiI\n2WwmPT3d6JRERBql4oOIiIiIiEECAgIa3R4zMzOzQVtMTAwxMTH12nx9fUlLS/Na/0REPEW3XYiI\niIiIiIiIV6n4ICIiIiIiIiJedVHFh3379jFu3Dg2bNgAwJEjR5g2bRpJSUnMnj2bmpoaAHJzc5k4\ncSLx8fFs2rQJOLsab0pKComJiUydOpWDBw9eZCoiIiIiIiIi0ha5XXyorKzkxRdfZOTIka625cuX\nk5SUxMaNG7nqqqvIycmhsrKSFStWsG7dOtavX09WVhbHjx/n/fffJygoiDfffJMZM2Y0eq+biIiI\niIiIiLR/bhcfzGYzq1evxmazudqKiooYO3YsAJGRkRQWFrJ7924GDRqExWIhICCAoUOHYrfbKSws\nJCoqCoCwsDDsdvtFpiIiIiIiIiIibZHbu134+fnh51f/8KqqKsxmMwDBwcE4HA5KS0uxWq2u11it\n1gbtPj4+mEwmampqXMf/VI8egfj5+ba4nyEhlhYf40lGxf8xrtH5t4U+GB3/XK3Rl5/GaAv5G90H\no+OLiIiIiHR2Xttq0+l0eqT9R2VllS3uQ0iIBYejosXHeYqR8R2OCsPzh879GTTmjpR3vR7j3Hzb\nQv5G96Gp+CpMiIiIiIh4n0d3uwgMDKS6uhqA4uJibDYbNpuN0tJS12tKSkpc7Q6HAzi7+KTT6Tzv\nrAcRERERERERab88WnwICwsjPz8fgC1bthAREUFoaCh79uyhvLycU6dOYbfbGTZsGOHh4eTl5QFQ\nUFDAiBEjPNkVEREREREREWkj3L7tYu/evbzyyiscOnQIPz8/8vPzefXVV0lNTSU7O5tevXoRFxeH\nv78/KSkpTJ8+HZPJRHJyMhaLhdjYWHbs2EFiYiJms5n09HRP5iUiIiIiIiIibYTbxYeBAweyfv36\nBu2ZmZkN2mJiYoiJianX5uvrS1pamrvhRURERERERKSd8NqCkyIiImK8B9I/bvExa1PHeKEnIiIi\n0pl5dM0HEREREREREZGf0swHEREREREDLVq0iM8++4zTp0/zyCOPMGjQIObOnUtdXR0hISEsXrwY\ns9lMbm4uWVlZ+Pj4kJCQQHx8PLW1taSmpnL48GHXbc29e/c2OiURkQZUfBARERERMcjOnTvZv38/\n2dnZlJWVMWHCBEaOHElSUhLjx49nyZIl5OTkEBcXx4oVK8jJycHf359JkyYRFRVFQUEBQUFBZGRk\nsH37djIyMli6dKnRaYmINKDbLkREREREDHLTTTexbNkyAIKCgqiqqqKoqIixY8cCEBkZSWFhIbt3\n72bQoEFYLBYCAgIYOnQodrudwsJCoqKigLPb3tvtdsNyERG5EM186KC0wJiIiIhI2+fr60tgYCAA\nOTk53HLLLWzfvh2z2QxAcHAwDoeD0tJSrFar6zir1dqg3cfHB5PJRE1Njev4xvToEYifn68Xs3JP\nSIjFredag5HxO3PunT1+R8tdxQcREREREYNt3bqVnJwc1q5dy2233eZqdzqdjb6+pe3nKiurdK+T\nXuZwVDTaHhJiOe9zrcHI+J05984evz3m3lSxQrddiIiIiIgY6JNPPmHlypWsXr0ai8VCYGAg1dXV\nABQXF2Oz2bDZbJSWlrqOKSkpcbU7HA4AamtrcTqdF5z1ICJiFBUfREREREQMUlFRwaJFi1i1ahXd\nu3cHzq7dkJ+fD8CWLVuIiIggNDSUPXv2UF5ezqlTp7Db7QwbNozw8HDy8vIAKCgoYMSIEYblIiJy\nIbrtwgDurMcgIuIpRUVFzJ49m379+gFw3XXX8eCDDzZ7WzcREfGcDz74gLKyMubMmeNqS09PZ8GC\nBWRnZ9OrVy/i4uLw9/cnJSWF6dOnYzKZSE5OxmKxEBsby44dO0hMTMRsNpOenm5gNiIi56fig4hI\nJzR8+HCWL1/uevz00083e1u3H/8yJyIiF2/y5MlMnjy5QXtmZmaDtpiYGGJiYuq1+fr6kpaW5rX+\niYh4iooP4qIdMkQ6r6KiIl544QXg7LZua9eu5ZprrnFt6wa4tnUbM0a/9yIiIiLSMio+iIh0QgcO\nHGDGjBmcOHGCmTNnUlVV1ext3S7E3e3bjN5KSuprzc9DW5h1zvhG5y4iIq1PxQcRkU7m6quvZubM\nmYwfP56DBw9y7733UldX53q+tbdvM3orKWmotT4PbWHWOeN7Y/s2ERFp+7TbhYhIJ9OzZ09iY2Mx\nmUz06dOHSy+9lBMnTjR7WzcRERERkZZS8UFEpJPJzc1lzZo1ADgcDo4ePcrdd9/d7G3dRERERERa\nSrddiPz/tAWqdBZjxozhySef5KOPPqK2tpaFCxcyYMAA5s2b16xt3UREREREWkrFBxGRTqZr166s\nXLmyQXtzt3UTEREREWkp3XYhIiIiIiIiIl7l0ZkPRUVFzJ49m379+gFw3XXX8eCDDzJ37lzq6uoI\nCQlh8eLFmM1mcnNzycrKwsfHh4SEBOLj4z3ZFREREXGTO7ehrU0d44WeiIiISEfh8dsuhg8fzvLl\ny12Pn376aZKSkhg/fjxLliwhJyeHuLg4VqxYQU5ODv7+/kyaNImoqCi6d+/u6e6IiIiIiIiIiMG8\nfttFUVERY8eOBSAyMpLCwkJ2797NoEGDsFgsBAQEMHToUOx2u7e7IiIiIiIiIiIG8PjMhwMHDjBj\nxgxOnDjBzJkzqaqqwmw2AxAcHIzD4aC0tBSr1eo6xmq14nA4LnjeHj0C8fPzbXF/QkKMXZnd6Pje\n1pz8jH4PjI7f2n6ab1vI3+g+GB1fRERERKSz82jx4eqrr2bmzJmMHz+egwcPcu+991JXV+d63ul0\nNnrc+drPVVZW2eL+hIRYcDgqWnycpxgdvzU0lZ/R74HR8Y1wbr5tIX+j+9BUfBUmRERERES8z6O3\nXfTs2ZPY2FhMJhN9+vTh0ksv5cSJE1RXVwNQXFyMzWbDZrNRWlrqOq6kpASbzebJroiIiIiItAv7\n9u1j3LhxbNiwAYAjR44wbdo0kpKSmD17NjU1NQDk5uYyceJE4uPj2bRpEwC1tbWkpKSQmJjI1KlT\nOXjwoGF5iIhciEdnPuTm5uJwOJg+fToOh4OjR49y9913k5+fz1133cWWLVuIiIggNDSUBQsWUF5e\njq+vL3a7nfnz53uyK9LJubNSu4iIiEhrq6ys5MUXX2TkyJGutuXLlzd7wfaCggKCgoLIyMhg+/bt\nZGRksHTpUgMzEhFpnEeLD2PGjOHJJ5/ko48+ora2loULFzJgwADmzZtHdnY2vXr1Ii4uDn9/f1JS\nUpg+fTomk4nk5GQsFk19lsapkCAiIiIdldlsZvXq1axevdrVVlRUxAsvvACcXbB97dq1XHPNNa4F\n2wHXgu2FhYXExcUBEBYWpj/oiUib5dHiQ9euXVm5cmWD9szMzAZtMTExxMTEeDK8iIiIiEi74ufn\nh59f/UvylizYfm67j48PJpOJmpoa1/EiIm2Fx3e7EBERERERz2jpgu3NWcjd3V3kvO1Ci0AbvUC0\nkfE7c+6dPX5Hy13FBxERERGRNiQwMJDq6moCAgIuuGD7kCFDsNlsOBwO+vfvT21tLU6ns8lZD+7s\nItcazrc7VVvfOaujxlZ8ffYtjd9UscKju12IiIiIiMjFCQsLIz8/H6Degu179uyhvLycU6dOYbfb\nGTZsGOHh4eTl5QFQUFDAiBEjjOy6iMh5aeaDXJSWLga5NnWMl3oiIiIi0v7s3buXV155hUOHDuHn\n50d+fj6vvvoqqampzVqwPTY2lh07dpCYmIjZbCY9Pd3olEREGqXig4iIiFw0d3Ymei/jLi/0RKR9\nGThwIOvXr2/Q3twF2319fUlLS/Na/0REPEW3XYiIiIiIiIiIV6n4ICIiIiIiIiJepdsuRETEUHek\nvGt0F0RERETEy1R8EBEREUO0tPCkRYtFOjYtZC7Ssem2CxERERERERHxKhUfRERERERERMSrVHwQ\nEREREREREa/Smg/SqtzZB15ERERERETaN818EBERERERERGvUvFBRERERERERLxKt12IdDDu3Nqi\nrapERERERMSbVHwQERGRdkHFVRERkfZLt12IiIiIiIiIiFd16JkPd6S82+JjWvoXEu3eICIi0rG0\n9LtdsytEjKHZUCLti6HFh5dffpndu3djMpmYP38+gwcPNrI7Ip1WaxTR9GXffmmslvasNcY3d2K8\nl3GXF3pijJbm35Fybys0TotIe2BY8eEvf/kL33zzDdnZ2Xz11VfMnz+f7Oxso7rjopkMIiL/p62O\n1SIicpbGaRFpLwwrPhQWFjJu3DgArr32Wk6cOMHJkyfp2rWrUV0SkTZG0ymNp7FaxDvcuTW0NWgM\nbX80TreMZnuKGMew4kNpaSk33HCD67HVasXhcJx3oAwJsbQ4hqb1ibRvnvoddmf8kLM0VotIU9z5\nHda47DktHaeh5e+/xmljGf37ovjGxe9oubeZ3S6cTqfRXRARkSZorBYRads0TotIW2VY8cFms1Fa\nWup6XFJSQkhIiFHdERGRRmisFhFp2zROi0h7YVjxITw8nPz8fAA+//xzbDab7k0TEWljNFaLiLRt\nGqdFpL0wbM2HoUOHcsMNNzBlyhRMJhPPP/+8UV0REZHz0FgtItK2aZwWkfbC5NSNYSIiIiIiIiLi\nRW1mwUkRERERERER6ZhUfBARERERERERrzJszQdP+stf/sLs2bN5+eWXiYyMbPB8bm4uWVlZ+Pj4\nkJCQQHx8PLW1taSmpnL48GF8fX1JS0ujd+/eLY7d1Hn27t3LK6+84np84MABVqxYwaeffsp7771H\nz549AbjzzjuJj4/3eHyAG264gaFDh7oer1u3jjNnzrRK/gAffPABa9euxcfHh5EjR/L444+zefNm\nli1bRp8+fQAICwvjF7/4RYtiv/zyy+zevRuTycT8+fMZPHiw67kdO3awZMkSfH19ueWWW0hOTm7y\nGHdc6Hw7d+5kyZIl+Pj4cM011/CrX/2Kv/71r8yePZt+/foBcN111/Hss896Jf6YMWO47LLL8PX1\nBeDVV1+lZ8+eHn0Pzneu4uJinnzySdfrDh48SEpKCrW1tRf9uf/Uvn37ePTRR7n//vuZOnVqveda\n6+dAmqZxuvON00aP0UaOz519bNa43DG4M257SnPGrV//+tcUFRXhdDoZN24cDz30UKvF/te//sX8\n+fMBGDt2rOvnuLXi/+iJJ57AbDaTnp7eqvEb+864WO58Z3hSS78zfHw8+zf85oyBGRkZ/P3vf2f9\n+vWtFvvIkSM88cQT1NbW8t///d/88pe/vLhgznbum2++cc6YMcP56KOPOj/++OMGz586dcp52223\nOcvLy51VVVXO22+/3VlWVubcvHmzc+HChU6n0+n85JNPnLNnz3YrfkvOc+LECec999zjrKurcy5f\nvty5fv16t2K2NP7w4cMvqt8XE7+ystIZGRnprKiocJ45c8Y5adIk5/79+51vv/22Mz093a2YTqfT\nWVRU5Hz44YedTqfTeeDAAWdCQkK958ePH+88fPiws66uzpmYmOjcv39/k8d4ug9RUVHOI0eOOJ1O\np3PWrFnObdu2OXfu3OmcNWvWRcVtbvzIyEjnyZMnW3SMJ+P/qLa21jllyhTnyZMnL/pz/6lTp045\np06d6lywYEGjv0+t8XMgTdM43fnGaaPHaCPH584+Nmtc7hjcHbc9palx68svv3ROnjzZ6XQ6nXV1\ndc6YmBhnSUlJq8R2Op3OSZMmOffu3eusq6tzPv74487KykqPxG5ufKfT6dy+fbtz4sSJznnz5nks\ndnPin+8742K4853hSe58Z7RmfKfT6dy/f79z8uTJzqlTp7Zq7Mcee8y5ZcsWp9PpdC5cuNB56NCh\ni4rX7m+7CAkJ4bXXXsNisTT6/O7duxk0aBAWi4WAgACGDh2K3W6nsLCQqKgo4GyF3263uxW/JedZ\ns2YN9913n0crZe7m0Vr5X3LJJeTm5tK1a1dMJhPdu3fn+PHjbsX6adxx48YBcO2113LixAlOnjwJ\nnP1LTrdu3bj88svx8fHh1ltvpbCw8ILHeLoPAJs3b+ayyy4DwGq1UlZW5nYsd+J76piLPdc777xD\ndHQ0Xbp0cSvOhZjNZlavXo3NZmvwXGv9HEjTNE53vnHa6DHayPG5s4/NGpc7BnfHbU9patyyWCz8\n8MMP1NTU8MMPP+Dj48Mll1zSKrFLS0uprKzkhhtuwMfHhyVLlngsdnPiA9TU1PDb3/72omePuhO/\nrXxneFJ7uKZPT0/3yAyTlsQ+c+YMn332GWPGjAHg+eefp1evXhcVr90XHy655BLX1MXGlJaWYrVa\nXY+tVisOh6Neu4+PDyaTiZqamhbHb+55qqur2b59O2PHjnW15eXl8fOf/5xHHnmEgwcPtjh2c+PX\n1NSQkpLClClTyMzMbFG/PRH/x72mv/zySw4dOkRoaChwdjrf9OnTue+++/jnP//Z4rg9evRwPf7x\ncwVwOBzn/czPd4w7mjrfj3mXlJTw6aefcuuttwJnp3TPmDGDxMREPv30U6/Fh7ODRGJiIq+++ipO\np9Oj70Fzz7Vp0yYmTZrkenwxn/tP+fn5ERAQ0OhzrfVzIE3TON35xmmjx2gjx+fOPjZrXO4Y3B23\nPaWpcevyyy8nJiaGyMhIIiMjmTJliuv32tuxDx06RLdu3UhNTWXKlCmsW7fOI3GbGx9g1apVJCYm\neiznlsY/33fGxcRs6XeGJ7n7ndFa8Tdv3szw4cO54oorPBq3qdjHjh2jS5cupKWlkZiYSEZGxkXH\na1drPmzatIlNmzbVa5s1axYRERHNPofzPDuLnq+9qfi7d+9u1nm2bt3K6NGjXX9Nu/XWW7n55pu5\n6aab+OMf/8hLL73EqlWrvBJ/7ty53HnnnZhMJqZOncqwYcMavMbb+X/99dc8+eSTZGRk4O/vT2ho\nKFarldGjR/O3v/2NefPm8d577zXZh/NpTv89cUxLz3f06FFmzJjB888/T48ePbj66quZOXMm48eP\n5+DBg9x7771s2bIFs9ns8fiPPfYYERERdOvWjeTkZPLz85vVZ0/FB/jb3/5G3759XYO2pz93T/D0\nz0Fnp3Fa43RjjB6jjRyfNTa3nMbl1uXNcdvd+E2NWwcPHuTDDz9k69atnD59milTphAbG0twcLDX\nYzudTr777jtWrFhBQEAAkydPJjw83LVejLfjf/311+zdu5dZs2ZRVFTU4pgXG//cfpz7neFJRo8B\nzfnOaK34x48fZ/PmzWRmZlJcXOzVuD+N7XQ6KS4u5t577+WKK67g4YcfZtu2bYwePdrt87er4kN8\nfHyLF7Ox2WyUlpa6HpeUlDBkyBBsNhsOh4P+/ftTW1uL0+ls8gKjsfipqanNOk9BQQGJiYmuxz9d\nfOrVV19tMhd3458b9+abb2bfvn2tmv/3339PcnIyixYtYsCAAcDZaT3XXnstADfeeCPHjh2jrq7u\nglX2czX2uYaEhDT6XHFxMTabDX9///Me444L9QHg5MmTPPTQQ8yZM4dRo0YB0LNnT2JjYwHo06cP\nl156KcXFxW4tItdU/Li4ONe/b7nlFtfn7qn3oDnn2rZtGyNHjnQ9vtjP/WL6562fA6lP47TGaTB+\njDZyfNbY3Py+aVxuGzw5bnsqflPj1p49ewgNDXXd7nD99dezb9++ej/X3oodHBxMv379XP8D+v/+\n3/9j//79bhUf3Im/bds2Dh8+TEJCAidPnuTYsWOsXr3arQU3PfmdcTHc+c7wJHe+M1or/s6dOzl2\n7Bj33HMPNTU1fPvtt7z88suuBU+9GbtHjx706tXLtRjxyJEj2b9//0UVH9r9bRdNCQ0NZc+ePZSX\nl3Pq1CnsdjvDhg0jPDycvLw84OwF54gRI9w6f3PPs3fvXvr37+96/NJLL7Fr1y7g7FRHdwas5sT/\n97//TUpKCk6nk9OnT2O32+nXr1+r5v/MM8+wcOFCbrjhBlfb6tWref/994Gzq2JbrdYWXeSEh4e7\n/lr0+eefY7PZXH/BufLKKzl58iTfffcdp0+fpqCggPDw8Ase446mzpeens59993HLbfc4mrLzc1l\nzZo1wNlpZEePHnWtpO/J+BUVFUyfPt01Te6vf/2r63P31HvQnHPt2bOn3s/9xX7uLdFaPwdy8TRO\nd7xx2ugx2sjxWWPz+Wlc7jjON257SlPjVp8+fdi7dy9nzpyhtraWffv2ufWHHHdi9+7dm1OnTnH8\n+HHOnDnDF198Qd++fT0Suznx77//ft577z1+//vf8/zzzzN69GiP7fTRnPjQ+HfGxcZs6XeGJ7nz\nndFa8WNiYvjggw/4/e9/z2uvvcYNN9zgscJDU7H9/Pzo3bs3X3/9tev5a6655qLimZxGz2u5SNu2\nbWPNmjX8+9//xmq1EhISwtq1a3n99de56aabuPHGG8nLy2PNmjWu6ax33nkndXV1LFiwgK+//vr/\na+/+46qs7/+PPw+/YiqkIMcyy7Was/kz00wUf2AoUhYrf0GaltvU1NmGlWOa+skl+WumWTrz11yW\nH8kcWYG1cMtEiujjbG2V9VnhjxQUREQD4f39w6/nIwnCOZyLA+c87rfbbje5zrmu1+vNoddhT67r\nOo6PqLn22mudrl/TcS6tL11Iii69Ocpnn32muXPnKiAgQDabTQsWLFD79u0tqb948WLt27d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+            "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": "915de52e-5d89-4620-8178-a55f8755c284"
+      },
+      "cell_type": "code",
+      "source": [
+        "def normalize(examples_dataframe):\n",
+        "  \"\"\"Returns a version of the input `DataFrame` that has all its features normalized.\"\"\"\n",
+        "  #\n",
+        "  # YOUR CODE HERE: Normalize the inputs.\n",
+        "  #\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": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 87.14\n",
+            "  period 01 : 76.49\n",
+            "  period 02 : 73.18\n",
+            "  period 03 : 71.88\n",
+            "  period 04 : 70.89\n",
+            "  period 05 : 70.52\n",
+            "  period 06 : 69.69\n",
+            "  period 07 : 69.62\n",
+            "  period 08 : 68.86\n",
+            "  period 09 : 68.02\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   68.02\n",
+            "Final RMSE (on validation data): 70.22\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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6UalUKCsrAwBUVFRApVLZ6lQ2IZdJEdvOJfVHsvuHiIioU9isUJkxYwby8vIw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\niIgUi0GFiIiIFItBhYiIiBTr/wGcUPx4Id2J1wAAAABJRU5ErkJggg==\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "dcae2a58-1212-47f0-f560-a8c1b8d8aead"
+      },
+      "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": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 95.09\n",
+            "  period 01 : 78.86\n",
+            "  period 02 : 75.12\n",
+            "  period 03 : 76.16\n",
+            "  period 04 : 72.92\n",
+            "  period 05 : 72.21\n",
+            "  period 06 : 72.13\n",
+            "  period 07 : 71.46\n",
+            "  period 08 : 70.82\n",
+            "  period 09 : 70.57\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   70.57\n",
+            "Final RMSE (on validation data): 73.03\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Bj0FFD/qvU2m7mjKnf4iIiPoGg4oe3F1s4SmzRWpuFVqVKq3tWKWWiIiobzGo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\nACl0AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "2559ab43-41c0-40e8-95a0-917b83bec40e"
+      },
+      "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",
+        "  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": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 110.57\n",
+            "  period 01 : 105.38\n",
+            "  period 02 : 103.24\n",
+            "  period 03 : 102.32\n",
+            "  period 04 : 100.59\n",
+            "  period 05 : 100.07\n",
+            "  period 06 : 100.01\n",
+            "  period 07 : 100.03\n",
+            "  period 08 : 99.17\n",
+            "  period 09 : 99.10\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   99.10\n",
+            "Final RMSE (on validation data): 100.67\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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mH+K2ZMkSvPLKK1KGRERERB2AZAVMeXk5li9fjqCgIN22bdu2IT8/H87Oznd8/ujRo0hL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\nAAAASUVORK5CYII=\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "91a46707-0f96-4319-8c19-22ed55bdd7ec"
+      },
+      "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": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 235.01\n",
+            "  period 01 : 234.13\n",
+            "  period 02 : 233.35\n",
+            "  period 03 : 232.62\n",
+            "  period 04 : 231.93\n",
+            "  period 05 : 231.25\n",
+            "  period 06 : 230.60\n",
+            "  period 07 : 229.96\n",
+            "  period 08 : 229.34\n",
+            "  period 09 : 228.72\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   228.72\n",
+            "Final RMSE (on validation data): 233.00\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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hhh1pYIQQQggx7EgDI4QQQowwO3duM+v7Xn/9FcrLyy77/O9//9hAlTTgpIERQgghRpCK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\ngRERERGfowIjIiIiPkcFRkRERHzO/weZhN3Cf4ibMQAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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..432b5c1
--- /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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "ce74df30-cb02-4b1a-c4dc-42c0e00a4c31"
+      },
+      "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       2636.3           538.1   \n",
+              "std         2.1        2.0                12.6       2147.5           415.5   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           297.0   \n",
+              "50%        34.2     -118.5                29.0       2130.0           435.0   \n",
+              "75%        37.7     -118.0                37.0       3155.0           652.0   \n",
+              "max        42.0     -114.6                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       1429.7       500.3            3.9               2.0  \n",
+              "std        1095.1       379.6            1.9               1.1  \n",
+              "min           8.0         1.0            0.5               0.1  \n",
+              "25%         789.0       282.0            2.6               1.5  \n",
+              "50%        1171.0       409.0            3.5               1.9  \n",
+              "75%        1740.0       609.0            4.8               2.3  \n",
+              "max       16122.0      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>2636.3</td>\n",
+              "      <td>538.1</td>\n",
+              "      <td>1429.7</td>\n",
+              "      <td>500.3</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>2147.5</td>\n",
+              "      <td>415.5</td>\n",
+              "      <td>1095.1</td>\n",
+              "      <td>379.6</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>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>1463.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>789.0</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2130.0</td>\n",
+              "      <td>435.0</td>\n",
+              "      <td>1171.0</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>3155.0</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1740.0</td>\n",
+              "      <td>609.0</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>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       2661.3           542.5   \n",
+              "std         2.1        2.0                12.5       2256.1           435.6   \n",
+              "min        32.6     -124.3                 2.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1458.0           295.0   \n",
+              "50%        34.2     -118.5                29.0       2122.5           432.0   \n",
+              "75%        37.7     -118.0                37.0       3146.2           641.0   \n",
+              "max        41.8     -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       1429.2       503.3            3.9               2.0  \n",
+              "std        1265.6       396.1            1.9               1.2  \n",
+              "min           3.0         2.0            0.5               0.0  \n",
+              "25%         790.0       281.0            2.6               1.5  \n",
+              "50%        1156.0       408.0            3.5               1.9  \n",
+              "75%        1671.0       595.0            4.7               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>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>2661.3</td>\n",
+              "      <td>542.5</td>\n",
+              "      <td>1429.2</td>\n",
+              "      <td>503.3</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>12.5</td>\n",
+              "      <td>2256.1</td>\n",
+              "      <td>435.6</td>\n",
+              "      <td>1265.6</td>\n",
+              "      <td>396.1</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>1.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>32.6</td>\n",
+              "      <td>-124.3</td>\n",
+              "      <td>2.0</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>790.0</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>2122.5</td>\n",
+              "      <td>432.0</td>\n",
+              "      <td>1156.0</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>3146.2</td>\n",
+              "      <td>641.0</td>\n",
+              "      <td>1671.0</td>\n",
+              "      <td>595.0</td>\n",
+              "      <td>4.7</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>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>52.0</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                207.1\n",
+              "std                 115.5\n",
+              "min                  15.0\n",
+              "25%                 119.6\n",
+              "50%                 180.3\n",
+              "75%                 263.9\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>207.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>115.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>263.9</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "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                207.7\n",
+              "std                 117.2\n",
+              "min                  15.0\n",
+              "25%                 119.3\n",
+              "50%                 180.4\n",
+              "75%                 266.9\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>207.7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>117.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>119.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>266.9</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": {
+        "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": 656
+        },
+        "outputId": "b14bceba-1ff5-4421-8115-63e84debd000"
+      },
+      "cell_type": "code",
+      "source": [
+        "dnn_regressor = train_nn_regression_model(\n",
+        "    learning_rate=0.005,\n",
+        "    steps=3000,\n",
+        "    batch_size=250,\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": 32,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 127.77\n",
+            "  period 01 : 118.62\n",
+            "  period 02 : 115.21\n",
+            "  period 03 : 111.39\n",
+            "  period 04 : 110.60\n",
+            "  period 05 : 110.53\n",
+            "  period 06 : 108.45\n",
+            "  period 07 : 107.25\n",
+            "  period 08 : 106.33\n",
+            "  period 09 : 106.06\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   106.06\n",
+            "Final RMSE (on validation data): 108.47\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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jvSNRq67D7psHtFAhERFRx8cA0wrjQrU7ChPqEgwnMwccyzqJ/MoCrbRJRETU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\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": "31409cc1-ae40-41d1-a1a3-00d6ff5cc057"
+      },
+      "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": 34,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 153.86\n",
+            "  period 01 : 144.36\n",
+            "  period 02 : 134.23\n",
+            "  period 03 : 122.03\n",
+            "  period 04 : 113.86\n",
+            "  period 05 : 111.67\n",
+            "  period 06 : 107.53\n",
+            "  period 07 : 105.72\n",
+            "  period 08 : 105.28\n",
+            "  period 09 : 105.41\n",
+            "Model training finished.\n",
+            "Final RMSE (on training data):   105.41\n",
+            "Final RMSE (on validation data): 107.37\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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1IZtwJ+UuvGzbYVJrP0iE13P9QU656iaOi+7i2Ogujk3l6MwppNedsYEeFo7x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\nQmCC\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": "03953452-fcf0-4c34-8111-01b1c505b0c6"
+      },
+      "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",
+        "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, test_targets[\"median_house_value\"], num_epochs=1, 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(metrics.mean_squared_error(test_predictions, test_targets))\n",
+        "\n",
+        "print(\"Final RMSE (on test data): %0.2f\" % root_mean_squared_error)"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 105.18\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "8f9b62ad-1d20-4525-bdf4-c5c178869779"
+      },
+      "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": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 105.19\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/intro_to_pandas.ipynb b/intro_to_pandas.ipynb
new file mode 100644
index 0000000..c628690
--- /dev/null
+++ b/intro_to_pandas.ipynb
@@ -0,0 +1,1703 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "3ddf32bf-f6ff-4dcb-eab4-892cbe0379a2"
+      },
+      "cell_type": "code",
+      "source": [
+        "from __future__ import print_function\n",
+        "\n",
+        "import pandas as pd\n",
+        "pd.__version__"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "u'0.22.0'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "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": "de46ffb2-3558-44be-9ffe-c61b9c2a6c38"
+      },
+      "cell_type": "code",
+      "source": [
+        "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
+      ],
+      "execution_count": 2,
+      "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": 2
+        }
+      ]
+    },
+    {
+      "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": "7585e42c-d53d-47b7-d8f1-03a2def5d1d0"
+      },
+      "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": 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>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": 3
+        }
+      ]
+    },
+    {
+      "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": "47196e44-c3fd-42d4-8089-8a361d54d21e"
+      },
+      "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": 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>17000.000000</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": 4
+        }
+      ]
+    },
+    {
+      "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": "7153b362-56c5-471b-a8da-4bafe7691680"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.head()"
+      ],
+      "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>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": 5
+        }
+      ]
+    },
+    {
+      "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": "bb5cc0a5-2d97-4f3e-ef83-a19444faebf7"
+      },
+      "cell_type": "code",
+      "source": [
+        "california_housing_dataframe.hist('housing_median_age')"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f71e163ad10>]],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "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": "e8be41a7-0354-41e1-b6fd-c250a8337056"
+      },
+      "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": 7,
+      "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": 7
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "V5L6xacLoxyv",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "ef96c557-7464-4fde-98cd-766792c6856d"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities['City name'][1]))\n",
+        "cities['City name'][1]"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<type 'str'>\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'San Jose'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "gcYX1tBPugZl",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 128
+        },
+        "outputId": "73c1e742-6262-4677-e609-5d57c8795fda"
+      },
+      "cell_type": "code",
+      "source": [
+        "print(type(cities[0:2]))\n",
+        "cities[0:2]"
+      ],
+      "execution_count": 9,
+      "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": 9
+        }
+      ]
+    },
+    {
+      "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": "aea08712-9baf-4a77-cc41-55a8a8b5a9c2"
+      },
+      "cell_type": "code",
+      "source": [
+        "population / 1000."
+      ],
+      "execution_count": 10,
+      "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": 10
+        }
+      ]
+    },
+    {
+      "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": "0b09c534-cc75-4b8d-b746-30d97799f2b8"
+      },
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "np.log(population)"
+      ],
+      "execution_count": 11,
+      "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": 11
+        }
+      ]
+    },
+    {
+      "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": "d0a9e56d-ed4c-482e-98a9-7a1a8fa09126"
+      },
+      "cell_type": "code",
+      "source": [
+        "population.apply(lambda val: val > 1000000)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    False\n",
+              "1     True\n",
+              "2    False\n",
+              "dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "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": "8c16de99-17c9-4586-fc2c-0d3be07ff08b"
+      },
+      "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": 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>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": 13
+        }
+      ]
+    },
+    {
+      "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": "03354ec1-39eb-4e08-a9d6-60553e209e78"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities['Wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
+        "cities"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>City name</th>\n",
+              "      <th>Population</th>\n",
+              "      <th>Area square miles</th>\n",
+              "      <th>Population density</th>\n",
+              "      <th>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",
+              "   Wide and has saint name  \n",
+              "0                    False  \n",
+              "1                     True  \n",
+              "2                    False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "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": "59378143-6d15-4032-f4c5-37a8b776e5b6"
+      },
+      "cell_type": "code",
+      "source": [
+        "city_names.index"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "colab_type": "code",
+        "id": "F_qPe2TBjfWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "503c8bb3-1f03-4fd6-ed8f-6a60e2ba511c"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.index"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RangeIndex(start=0, stop=3, step=1)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "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": "db499d82-dfb9-4eab-b9eb-5d6fec65ee5f"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex([2, 0, 1])"
+      ],
+      "execution_count": 17,
+      "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>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",
+              "   Wide and has saint name  \n",
+              "2                    False  \n",
+              "0                    False  \n",
+              "1                     True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "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": "0c91835e-a2cf-45d7-f179-0b8457e73b39"
+      },
+      "cell_type": "code",
+      "source": [
+        "cities.reindex(np.random.permutation(cities.index))"
+      ],
+      "execution_count": 18,
+      "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>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>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>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",
+              "0  San Francisco      852469              46.87        18187.945381   \n",
+              "2     Sacramento      485199              97.92         4955.055147   \n",
+              "1       San Jose     1015785             176.53         5754.177760   \n",
+              "\n",
+              "   Wide and has saint name  \n",
+              "0                    False  \n",
+              "2                    False  \n",
+              "1                     True  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "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": "4f63a98f-add5-47da-a447-6221303d66a4"
+      },
+      "cell_type": "code",
+      "source": [
+        "# Your code here\n",
+        "cities.reindex([0, 4, 5, 2])"
+      ],
+      "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>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",
+              "  Wide and has saint name  \n",
+              "0                   False  \n",
+              "4                     NaN  \n",
+              "5                     NaN  \n",
+              "2                   False  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "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..c524047
--- /dev/null
+++ b/logistic_regression.ipynb
@@ -0,0 +1,1720 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "fe2ff345-d3e4-4535-9e37-9fbaaa448496"
+      },
+      "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     -119.5                28.7       2654.9           542.2   \n",
+              "std         2.1        2.0                12.6       2191.7           425.4   \n",
+              "min        32.5     -124.3                 1.0         12.0             3.0   \n",
+              "25%        33.9     -121.8                18.0       1462.0           298.0   \n",
+              "50%        34.2     -118.5                29.0       2126.0           434.0   \n",
+              "75%        37.7     -118.0                37.0       3151.2           652.0   \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       1435.3       503.8            3.9               2.0  \n",
+              "std        1172.9       388.5            1.9               1.1  \n",
+              "min           8.0         2.0            0.5               0.0  \n",
+              "25%         791.0       282.8            2.6               1.5  \n",
+              "50%        1170.0       410.0            3.6               1.9  \n",
+              "75%        1715.2       607.0            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",
+              "        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>28.7</td>\n",
+              "      <td>2654.9</td>\n",
+              "      <td>542.2</td>\n",
+              "      <td>1435.3</td>\n",
+              "      <td>503.8</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>2191.7</td>\n",
+              "      <td>425.4</td>\n",
+              "      <td>1172.9</td>\n",
+              "      <td>388.5</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>12.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>8.0</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>1462.0</td>\n",
+              "      <td>298.0</td>\n",
+              "      <td>791.0</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>34.2</td>\n",
+              "      <td>-118.5</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2126.0</td>\n",
+              "      <td>434.0</td>\n",
+              "      <td>1170.0</td>\n",
+              "      <td>410.0</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>3151.2</td>\n",
+              "      <td>652.0</td>\n",
+              "      <td>1715.2</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>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                28.4       2616.6           532.8   \n",
+              "std         2.1        2.0                12.6       2151.4           412.1   \n",
+              "min        32.5     -124.3                 1.0          2.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1463.0           293.0   \n",
+              "50%        34.3     -118.5                28.0       2137.0           433.0   \n",
+              "75%        37.7     -118.0                37.0       3151.5           643.2   \n",
+              "max        41.8     -114.6                52.0      30405.0          4798.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1415.9       495.0            3.9               2.0  \n",
+              "std        1085.5       374.7            1.9               1.3  \n",
+              "min           3.0         1.0            0.5               0.2  \n",
+              "25%         782.0       279.0            2.6               1.5  \n",
+              "50%        1160.5       406.0            3.5               2.0  \n",
+              "75%        1741.2       602.0            4.8               2.3  \n",
+              "max       15037.0      4490.0           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>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>28.4</td>\n",
+              "      <td>2616.6</td>\n",
+              "      <td>532.8</td>\n",
+              "      <td>1415.9</td>\n",
+              "      <td>495.0</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>2151.4</td>\n",
+              "      <td>412.1</td>\n",
+              "      <td>1085.5</td>\n",
+              "      <td>374.7</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>0.2</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>1463.0</td>\n",
+              "      <td>293.0</td>\n",
+              "      <td>782.0</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>28.0</td>\n",
+              "      <td>2137.0</td>\n",
+              "      <td>433.0</td>\n",
+              "      <td>1160.5</td>\n",
+              "      <td>406.0</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>3151.5</td>\n",
+              "      <td>643.2</td>\n",
+              "      <td>1741.2</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>41.8</td>\n",
+              "      <td>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>30405.0</td>\n",
+              "      <td>4798.0</td>\n",
+              "      <td>15037.0</td>\n",
+              "      <td>4490.0</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": [
+            "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": 622
+        },
+        "outputId": "a601b1af-d4f4-49ac-f308-867826e76aab"
+      },
+      "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": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 0.45\n",
+            "  period 01 : 0.44\n",
+            "  period 02 : 0.44\n",
+            "  period 03 : 0.44\n",
+            "  period 04 : 0.44\n",
+            "  period 05 : 0.44\n",
+            "  period 06 : 0.45\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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I33DuTpqDDYXX975DaW2Zq0MSQlyiFEVhV9ketGotA/UJHXruJH0cAPsMspih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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": "joklSr4iCHTv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "81597cf7-b980-4c1d-c39a-7ac25d01e186"
+      },
+      "cell_type": "code",
+      "source": [
+        "predict_validation_input_fn = lambda: my_input_fn(validation_examples, validation_targets[\"median_house_value_is_high\"], num_epochs=1, 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": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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rzJkzuuuuu5Sbm6uHHnpIExMT8vl8evLJJ+VyudTe3q7W1lalpKRo7dq1qqqqUjQaVUND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\nYDCCGgAAgxHUAAAYjKAGAMBg/x8E8AA4HVCV4wAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "e0d117cb-157f-4439-a195-55c326cc1011"
+      },
+      "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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rzJkzuuuuu5Sbm6uHHnpIExMT8vl8evLJJ+VyudTe3q7W1lalpKRo7dq1qqqqUjQaVUND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\nYDCCGgAAgxHUAAAYjKAGAMBg/x8E8AA4HVCV4wAAAABJRU5ErkJggg==\n",
+            "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(feature_columns=construct_feature_columns(training_examples), optimizer=my_optimizer)# YOUR CODE HERE: Construct the linear classifier.\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": "cccdf5f5-a4ff-4ba4-81f1-15865bd7aff9"
+      },
+      "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": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.60\n",
+            "  period 01 : 0.58\n",
+            "  period 02 : 0.57\n",
+            "  period 03 : 0.56\n",
+            "  period 04 : 0.57\n",
+            "  period 05 : 0.54\n",
+            "  period 06 : 0.54\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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yy/PYk7TfSBEKIYQQLYckMy2Qo50FEwZ5kV9cyS/Hk69bfmLncdia2RCa+Ct5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\nLushE5xBzeoAAAAASUVORK5CYII=\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": "9e401e4b-592d-4a0b-e8b3-7da3c31d1054"
+      },
+      "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 : 0.53\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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4I4btybsMHKkQQgjR+qSYaSccbC144I5g6rQ63vv2NBVVtc3qx0JtzsOh83C2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\nQmCC\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": "2d39de68-cf14-4133-db46-7e363c440bbb"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AUC on the validation set: 0.71\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": "eafce538-f6f5-47c0-d37a-44ed1af17dde"
+      },
+      "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": 16,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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WGhrq+dOfnuehh34JQHNzEzqdjsDAIMrLy8jKysRisfDZZ9uJiIhk0qQb8fX144svPsVg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\nERERk/wvdsQxYLrdZQgAAAAASUVORK5CYII=\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": 656
+        },
+        "outputId": "d7bd2179-862d-4b4a-c954-0c1bca16b1f7"
+      },
+      "cell_type": "code",
+      "source": [
+        "# TUNE THE SETTINGS BELOW TO IMPROVE AUC\n",
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.000005,\n",
+        "    steps=50000,\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": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.47\n",
+            "  period 01 : 0.46\n",
+            "  period 02 : 0.46\n",
+            "  period 03 : 0.46\n",
+            "  period 04 : 0.46\n",
+            "  period 05 : 0.46\n",
+            "  period 06 : 0.46\n",
+            "  period 07 : 0.45\n",
+            "  period 08 : 0.46\n",
+            "  period 09 : 0.45\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.81\n",
+            "Accuracy on the validation set: 0.79\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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VnkijcDe70zagDW0D2gBgs9koOlrCgdKD7C+rn7ycUZ7FgbIMyPoOgCDPwPpL\nWicDT0u/SMwmsxPPQkSk6XFYwCkoKCAuLs7+Ojg4mPz8fHvASUlJoU+fPkRFRZ3VduvWrURERBAW\nFmbf9uqrr1JcXExsbCzJycl4eZ3/Ft6gIB/c3Bz7C+N8T04U53KFfrHiTyei7a+PnjjG/qIM9hQe\nYE/BfnYX7mdD3hY25G0BwMPsTmxwazqEtKVDaFs6hMQQ4OXvrPIdxhX6Rs6mfnFd6pvLc8UmB5y+\nIkRJSQkpKSnMnj2b3Nzcs/ZdsGABt99+u/31+PHj6dixI9HR0Tz11FO8//77/PrXvz7vZxUXVzVu\n8T+iR2i7JlfulzAjgrDQCAaGDsBms5FfXVg/cbms/rJWWv4+duWn/7C/dwjtA2PpFNyODkHtsHj4\nXeDors+V+6Y5U7+4LvVNw13xpRqsVisFBQX213l5efYRmTVr1lBUVMS4ceOoqakhMzOTadOmkZyc\nDMDatWt5/PHH7W1HjBhh//r6669n0aJFjipbxOEMw8DqE4rVJ5S+ET0BOHriKAfLsupvUS87yIHS\nDP53ONX+IMIovwg6BrWjU3B7YgNi8HLzdOYpiIi4PIcFnIEDB/Laa6+RmJjIjh07sFqt9stTCQkJ\nJCQkAJCdnc3kyZPt4SY3NxdfX188POqfL2Kz2bjvvvt49dVX8ff3Z+3atbRv395RZYs4hZebF52C\n29MpuP5nu85WR1Z5DruL0kkr3su+0oPkVBxmedZ3mAwTMf6t6Rjcjk5B7Wnj30pzeEREfsRhAadH\njx7ExcWRmJiIYRg89dRTpKSkYLFYzhiR+bH8/HyCg4Ptrw3DYPTo0dx77714e3sTHh7OQw895Kiy\nRVyCyTDZb1G/sc0wamqPs7/0ILuL09ldlM7+0oPsKz3AogNf4Wn2oH1gWzoGtaNjcHsifVvoWTwi\n0uwZttMnxzQRjr5uqWujrqk59UvV8Sr2lOxnd1E6u4v3kluVb3/P4u5Hx+B29YEnqD0h3kFOrLRe\nc+qbq4n6xXWpbxruis/BERHH8XH3oVtYF7qdfOpy8dGS+tGd4nR2F+1lfe5m1uduBiDUO4ROJ0d3\nOgTG4ufh68zSRUSuCAUckSYgyCuQfhG96BfRC5vNRm5VHmlF9YFnT/E+vj+0lu8PrcXAoKVfBB2D\n29MxqB3tAmO0npaINEkKOCJNjGEYtPANp4VvOENbDaS2rpbM8hx2F++1z9/JqjjEssyVuBlmYgJa\n0zGoPR2D29Ha0lITlkWkSVDAEWnizCbzyaUioklocwM1tTXsKz1on7+TXnKAvSX7+fzAUrzMXrQP\niqkPPEHtiPAN14RlEbkqKeD+Cl3PAAAgAElEQVSINDMeZg86B3egc3D92m4VxyvZW7yftOK97ClK\nZ1vBLrYV7ALqV1g/dXdWp6B2BHkFOrN0EZEGU8ARaeb83H3pbr2W7tZrASg6Wmx//s7u4nTW5W5i\nXe4mAKw+oXQMqg877YNi8XX3cWbpIiLnpYAjImcI9gqif2Rv+kf2xmazcbgyl93F6aQV7SW9ZD/f\n5azmu5zVGBi0skSdHOFpR2xADB5md2eXLyICKOCIyAUYhkGkXwsi/VowrNUgautqySjPIq2ofnTn\nQGkmmeXZfJW5AjeTG239W9vv0Iq2RGnCsog4jQKOiDSY2WSmbUAb2ga04aaYERyrrSG95ID9Dq09\nJfvYU7KPzwBvNy/aB8bSMbgdvdzi8Krzw82k/+WIyJWhJxlfAj1h0jWpX5yvvKaCPcX77A8cLDha\nZH/PzTAT6RdBK0sU0ZYooi0tifBrgbtCj9Po74zrUt80nJ5kLCIOZ/Hwo2d4V3qGdwWgoLqIPcXp\n5NYcYU/+AQ5VHCazPJtVJ/c3G2YifcNpZWlJtH8UrSxRRPlG4K65PCJymRRwRMRhQr2DCfXuY//X\n6Im6ExyuzCWzPJus8kNklmeTU3GYrIpD/O9wfRuTYSLCN5xoS0v7aE+UX6QmMIvIRVHAEZErxs3k\nRitL/UjNKbV1tRyuzCWrPIfM8hyyyrPJrjhMTsVhVh9eB9SHnhY+1pOBp360J8ovEk8tMyEi56GA\nIyJOZTaZaWmJpKUlkv70BupDT25VPpnl2SdDTw7Z5TkcqjzC2iMbADAwCPe1En0yMEVbWtLSLxIv\nN09nno6IuAgFHBFxOWaT2X57er+IXgDU2erqQ09ZNlkVOWSW5ZBdkcORylxSj2wE6kOP1SeMVpbI\n+pEeSxQtLVF4u3k583RExAkUcETkqnBqbk6Ebzh96QnUh578qgIyy3NOzuvJIav8ELlVeazP3Wxv\na/UOrR/l8W9JK7/6ER8fd29nnYqIXAEKOCJy1TIZJsJ9rYT7WundojtQH3oKqgvtl7ZO/bkhbwsb\n8rbY24Z6h5xxeauVJUpLT4g0IQo4ItKkmAwTVp8wrD5h9ArvBoDNZqPwaFH9SE/ZqZGeHDbmbWVj\n3lZ72xCvoPpb1k8LPX4evs46FRG5DAo4ItLkGYZBqHcIod4h9LDGA/Whp+ho8WkjPfXBZ3P+Njbn\nb7O3DfIMtF/aivaPItQ7BJvNRp2tDhs26mw2bLY66qij7tR228mvqfvRvj/ah1Nf2+ztT29rf+9H\n7U9ve3r7U/v++LPrTu5v+9FneXt6EOEVQYegWFpbWmlpDWlSFHBEpFkyDIMQ72BCvIPtK6nbbDaK\nj5XYL21llmeTVZbDlvztbMnf7uSKHaV+BMvD7EG7wBg6BrWjQ1AsLf0iMRkmJ9cmcukUcERETjIM\ng2CvIIK9guga1gWoDz2lNWVkltXfsl56rBTDMGEyTJgMAxMmDMPAOPn1qe2GYbK/ZzKM+u0Y52lb\n/94Zbe37n2pr+uHrU/txav+f/uxT+57e1hLoztr929lTvI89xensLNzNzsLdAHi7edMhsC0dTgae\nCN9wDMNwZveIXBQFHBGRCzAMg0DPAALDAogPi3N2OY0q0NtCD2u8/bJdybHSk2GnPvBsKdjBloId\nAFjc/egQFGv/L8w7VIFHXJoCjoiIABDoGUCfFj3o06IHcGotsfqws6c4/Yw70QI9A+yXszoExRLs\nFeTM0kXOooAjIiLnVL+WWDADIntjs9nIq8pn96nAU7KPtUc22J8sHeodQsegWPslLX+Pc6/wLHKl\nKOCIiMhPMgzD/syh61r2p85Wx+HKXHafHN3ZW3yAVYdSWXUoFYAWvuH2wNM+sK2eMSRXnAKOiIhc\nNJNhIsovgii/CK5vNZjaulqyKw6dDDz72FdygJWVuazM/h8GBi39IuyjO+0CY/DS8hniYAo4IiJy\n2cwmM639W9HavxU3th7GiboTHCzLOjl/Zx8HSjPIqjjE11nfYjJMtLa0tAeetgFt8DC7O/sUpIlR\nwBERkUbnZnKjXWAM7QJjuClmBDW1x9lfetA+aTmjPJsDZZkszViOm2EmJqD1yQnL7Wjj3wo3k349\nyeXRT5CIiDich9mdTsHt6RTcHoDqE0fZV3LAHnjSSw6wt2Q/Xxz4Cg+TO7GBMXQIiqVjUDtaWaL0\n0EG5aAo4IiJyxXm7edEltDNdQjsDUHm8ir0l+9lTnM7u4n3sKtrDrqI99n3bBcbQIagdHYPaEeEb\nrsAjP0kBR0REnM7X3YduYV3odvIJ0qXHytlbss8eeLYV7GJbwS4A/Nx9aX/yKcsdg2Kx+oTpoYNy\nFgUcERFxOQGeFnqFd7OvCF90tNj+lOXdxelsyt/GppOLogZ4+J+crNyaVpaWRPlFaNKyKOCIiIjr\nC/YKol9EL/pF9MJms5FfXWi/Q2t3cTrrcjexLncTUH8LewsfK9GWlrSy1K8CH+UXiafZw8lnIVeS\nAo6IiFxVDMPA6hOK1SeUQVH9sNlsHK7MJaM8m6zyHLLKs8kuP8ShyiOsObK+vg31DyqMtkTZg09L\nv0i83DydfDbiKAo4IiJyVTMMg0i/FkT6taB/RC8A6mx15Fblk1mWTVZFDpllOWRX5HCkMpfUIxvr\n22Fg9QmjlSXSHnpaWSLxdvN25ulII1HAERGRJsdkmIjwDSfCN5y+9ATqQ09+VQGZ5Tlk2kd7DpFb\nlcf63M32tlbv0JNhp/6/aEsUPlpq4qqjgCMiIs2CyTDZ19Pq3aI7UB96CqqLyCrPJrM8h6zyHDLL\nc85YOR0g1Cv4ZNhpSSv/+uDj5+7rrFORBlDAERGRZstkmOzzeXqevGPLZrNReLToh8BTVj/ac/qd\nW1A/8TnaPtLTkmhLFBYPP2edivyIAo6IiMhpDMMg1DuEUO8QeljjgfrQU3S0hKyTl7ZOXebanL+d\nzfnb7W0DPQNOzuc5Na+nJQGeFmedSrOmgCMiIvITDMMgxDuIEO8gulmvBepDT8mxUvtIz6nLXFsL\ndrC1YIe9bYCHxT7CU3/beksCPPz1cEIHU8ARERG5BIZhEOQVSJBXIF3D4uzbS4+V2Scxnwo/2wt3\nsb1wl30fi4ffD3N6Tk5kDvIMVOhpRAo4IiIijSjA059rPa/h2tBr7NvKaspPzufJOXnbejY7C3ez\ns3C3fR8/d1/7nVsdKlvDMTf83P2wePji5+6rFdYvkr5bIiIiDubvYSEupBNxIZ3s28prKsguP0Tm\naXdwnVpk9MuMs4/h7eZ9Muz4YfHww+Lui5+HHxZ7CDq53cMPX3efZr8gqQKOiIiIE1g8/Ogc0oHO\nIR3s2yqPV5FVnkO1uYIjRYWUH6+gvKaCippKyo/X/5lfVYgN2wWPbWDg6+6Dn7svFg+/k0Hoh0Dk\n5+H7QzDy8MPHzbvJBSKHBpxp06axZcsWDMMgOTmZ+Pj4s/aZMWMGmzdvZs6cOXz44YcsXLjQ/t72\n7dvZtGkTaWlpTJkyBYCOHTvy9NNPO7JsERERp/B196FTcHvCwizk55efc586Wx2Vx6uoOF5Jec3J\nAHTy63NtO1KV95OfazJM+Lr7nAw9fj8Eo5Mh6Mdfe5m9XH6+kMMCTmpqKhkZGcybN499+/aRnJzM\nvHnzztgnPT2ddevW4e5ev+rr3Xffzd13321vv3jxYgCmTp1qD0gPP/wwK1euZMiQIY4qXURExGWZ\nDJP9UlSEb/hP7l9bV0vF8Soq7KNBFZQfrzz5ZwXlNZX294qPlXCo8shPHtNsmM8dhNxPjhaddsnM\n38PilNXdGxxwKioq8PPzo6CggIMHD9KjRw9MpvMPZ61evZrhw4cDEBsbS2lpqf0Yp0yfPp2JEycy\na9ass9q//vrrvPTSS9TU1JCTk2Mf/Rk2bBirV69WwBEREWkAs8lMgKelwc/jOV53gorzjAadukx2\n6s+86gKyKw5d8HgeJnf+0vuPDQpjjalBAefZZ5+lU6dOjBgxgsTEROLi4li4cCHPPPPMedsUFBQQ\nF/fDbXPBwcHk5+fbA05KSgp9+vQhKirqrLZbt24lIiKCsLAwcnNz8ff3t78XEhJCfn5+g09QRERE\nGs7d5Ga//b0hamprzhgFOn10qKKmEhs2Ajyu/MMOGxRwdu7cyRNPPMEHH3zA7bffzgMPPMAvf/nL\ni/ogm+2HCVElJSWkpKQwe/ZscnNzz9p3wYIF3H777T95nPMJCvLBzc18UfVdrLAwPZnSFalfXJf6\nxjWpX1zX1dU3Ic4u4CwNCjinQsWKFSv405/+BEBNTc0F21itVgoKCuyv8/LyCAsLA2DNmjUUFRUx\nbtw4ampqyMzMZNq0aSQnJwOwdu1aHn/8caB+5KekpMR+nNzcXKxW6wU/u7i4qiGndckuNPlLnEf9\n4rrUN65J/eK61DcNd74g2KB7wmJiYrjpppuorKykc+fOfPLJJwQEBFywzcCBA1m6dCkAO3bswGq1\n2i9PJSQksGjRIubPn8+sWbOIi4uzh5vc3Fx8fX3x8PAAwN3dnbZt27J+/XoAvvzySwYPHtyQskVE\nRKSZatAIznPPPceePXuIjY0FoH379lx//fUXbNOjRw/i4uJITEzEMAyeeuopUlJSsFgsjBgx4rzt\n8vPzCQ4OPmNbcnIyTz75JHV1dXTt2pUBAwY0pGwRERFppgxbAya1bN++nfz8fIYNG8bLL7/M5s2b\neeihh+jVq9eVqPGiOXpYT0OHrkn94rrUN65J/eK61DcNd1mXqJ577jliYmJYv34927Zt44knnuDV\nV19t1AJFREREGkuDAo6npydt2rTh66+/ZvTo0bRr1+6Cz8ARERERcaYGpZTq6moWL17MsmXLGDRo\nECUlJZSVlTm6NhEREZFL0qCAM2nSJD777DMmTZqEn58fc+bM4d5773VwaSIiIiKXpkF3UfXr14/4\n+HgOHDjAzp07+c1vfoO3t7ejaxMRERG5JA0KOMuWLWPKlCm0aNGCuro6CgoKePbZZ7UelIiIiLik\nBgWcf/3rXyxcuND+fJrc3FwmTJiggCMiIiIuqUFzcNzd3c94+F54eDju7ld+6XMRERGRhmjQCI6v\nry///ve/7U8Q/v777/H19XVoYSIiIiKXqkEBZ+rUqbzyyissXLgQwzDo1q0b06ZNc3RtIiIiIpek\nQQEnJCSEZ5555oxt+/btO2vNKBERERFXcMmPI3766acbsw4RERGRRnPJAacBa3SKiIiIOMUlBxzD\nMBqzDhEREZFGc8E5OAsWLDjve/n5+Y1ejIiIiEhjuGDA2bBhw3nf69atW6MXIyIiItIYLhhwnn/+\n+StVh4iIiEijadBt4mPHjj1rzo3ZbCYmJoY//OEPhIeHO6Q4ERERkUvRoIAzYMAADhw4wMiRIzGZ\nTCxbtoyIiAgCAgKYPHky//73vx1dp4iIiEiDNSjgbNiwgdmzZ9tfDx8+nPvvv5+33nqLr7/+2mHF\niYiIiFyKBt0mXlhYSFFRkf11eXk5hw4doqysjPLycocVJyIiInIpGjSCM378eEaNGkVUVBSGYZCd\nnc1vf/tbvvnmG8aMGePoGkVEREQuSoMCzl133UVCQgIHDx6krq6O6OhoAgMDHV2biIiIyCVpUMCp\nrKzkvffeY9u2bfbVxH/5y1/i5eXl6PpERERELlqD5uA88cQTVFRUkJiYyOjRoykoKODxxx93dG0i\nIiIil6RBIzgFBQXMnDnT/nrYsGEkJSU5rCgRERGRy9GgEZzq6mqqq6vtr6uqqjh27JjDihIRERG5\nHA0awRkzZgyjRo2iS5cuAOzYsYMJEyY4tDARERGRS9Xgu6gGDhzIjh07MAyDJ554gjlz5ji6NhER\nEZFL0qCAAxAREUFERIT99datWx1SkIiIiMjlatAcnHOx2WyNWcdVIa+kmqffXcd3m3OcXYqIiIhc\nwCUHnB+vLt4cmAzIK67ixf+s55tNCjkiIiKu6oKXqIYMGXLOIGOz2SguLnZYUa4qNMCbv/yiB39f\nsIU5S3dTWX2cm/u3bpZhT0RExJVdMODMnTv3StVx1WjdwsILDw7msTe+J+Xb/VRUH2fM9e0UckRE\nRFzIBQNOVFTUlarjqhIV5sfke3oyY95mvlyXRdXRE/xyVEfMpku+4iciIiKNSL+RL1GwvxePjutB\nTISF77cd5o2Pt3P8RK2zyxIREREUcC6LxceDPyd2p3PrIDbtLeDl+VuoPnbC2WWJiIg0ewo4l8nb\n040/3R1Pjw5hpGWW8OIHmyivqnF2WSIiIs2aAk4jcHcz8/ufxzHo2ggOHiln+vsbKSo76uyyRERE\nmi0FnEZiNpm476ZOjOzTisOFVTz/nw0cKapydlkiIiLNkgJOIzIMg9HD2nHnkLYUlh3j+f9sIONI\nubPLEhERaXYUcBqZYRjc3L8N40d2pKLqOH/7YCO7M5vfQxFFREScSQHHQYZ2j+K3t8VRc7yOmfO3\nsDm9wNkliYiINBsKOA7Up3M4E+6KxzBg1kfbWL39iLNLEhERaRYcGnCmTZvGmDFjSExMZOvWrefc\nZ8aMGSQlJdlfL1y4kFtvvZU77riDFStWAPDoo49yyy23kJSURFJSkn371aBL2xD+PKY7Xh5m3v58\nJ1+tz3J2SSIiIk3eBZdquBypqalkZGQwb9489u3bR3JyMvPmzTtjn/T0dNatW4e7uzsAxcXFvP76\n63z00UdUVVXx2muvMXToUAAmTZrEsGHDHFWuQ7VrGcBfx/Vg5rzNfLBsL5XVx7ltUIzWrxIREXEQ\nh43grF69muHDhwMQGxtLaWkpFRUVZ+wzffp0Jk6ceEab/v374+fnh9Vq5dlnn3VUeVdcK6sfk+/p\nQWiAFwtXHWTusr3U2WzOLktERKRJcljAKSgoICgoyP46ODiY/Px8++uUlBT69OlzxoKe2dnZHD16\nlN/97neMHTuW1atX29/7z3/+w/jx45k4cSJFRUWOKtuhrEE+TL6nJ1Fhvny9IZt3Pt/Jido6Z5cl\nIiLS5DjsEtWP2U4brSgpKSElJYXZs2eTm5t7xn4lJSXMmjWLQ4cOMX78eL755htuu+02AgMD6dy5\nM2+99RazZs3iySefPO9nBQX54OZmdti5AISFWS653Yt/vI5n/rWG1TtyOWGDv47vjae7Y+ttLi61\nX8Tx1DeuSf3iutQ3l8dhAcdqtVJQ8MOt0Xl5eYSFhQGwZs0aioqKGDduHDU1NWRmZjJt2jQ6duxI\n9+7dcXNzIzo6Gl9fX4qKiujfv7/9ONdffz1Tpky54GcXFzv2CcJhYRby8y/vAX4T7oxn1sfbWLcz\nl+TXv+ePd8bj43XF8maT1Bj9Io6hvnFN6hfXpb5puPMFQYddoho4cCBLly4FYMeOHVitVvz8/ABI\nSEhg0aJFzJ8/n1mzZhEXF0dycjKDBg1izZo11NXVUVxcTFVVFUFBQTz00ENkZdXffbR27Vrat2/v\nqLKvGE8PMxPuiqd3Jyt7skr42wcbKavUIp0iIiKNwWFDBj169CAuLo7ExEQMw+Cpp54iJSUFi8XC\niBEjztkmPDyckSNHMnr0aAAef/xxTCYT48aN409/+hPe3t74+Pjw/PPPO6rsK8rNbOK3t8bh4+XG\nys2HeP4/G3g4sRuhAd7OLk1EROSqZthsTe9WHkcP6zX20KHNZiPl2/18sTqDIIsnk8Z0IyrUt9GO\n31xoSNd1qW9ck/rFdalvGu6KX6KShjMMgzuHxDJ6WDuKy4/xwvsbOXC4zNlliYiIXLUUcFxIQt9o\n7h3Vicqjx/nbB5vYdfDqvB1eRETE2RRwXMx1XSP5w8+7UFtbx8sfbmHjnvyfbiQiIiJnUMBxQT07\nWplwd1fMJhOvf7yN77YecnZJIiIiVxUFHBcV1yaYR37RHR9PN2YvSmNpaqazSxIREblqKOC4sLaR\n/jx6T0+CLJ7MW57ORyv30QRvehMREWl0CjguLirUl8njemAN8uaL1RnMWbqbujqFHBERkQtRwLkK\nhAZ6M/menkRb/Vix+RBvfbZDi3SKiIhcgALOVSLA14O/jO1O+5YBpO7K49WPtnKsptbZZYmIiLgk\nBZyriI+XO5PGdCM+NoTt+4uYMW8zlUePO7ssERERl6OAc5XxdDfz4B3X0u+acNJzSnnh/Y2UVBxz\ndlkiIiIuRQHnKuRmNvGbW67hhh4tyc6v5Pn/bCCvpNrZZYmIiLgMBZyrlMkwGDuiPbcObEN+yVGe\n/88GsvMqnF2WiIiIS1DAuYoZhsHPB7flFze0p7SihunvbyQ9p9TZZYmIiDidAk4TMKJ3K359c2eO\n1tTy0n83sf1AobNLEhERcSoFnCZi4LURPHBHF+rq4JUPt7IuLc/ZJYmIiDiNAk4T0r19GJNGd8Xd\nzcQ/PtnOys05zi5JRETEKRRwmphOrYP4y9ju+Hq7896S3Sxak+HskkRERK44BZwmqE0Lfybf04Ng\nf08WrNjH/G/StUiniIg0Kwo4TVREiC/J9/QkIsSHJWszeXdxGrV1Wr9KRESaBwWcJizY34u/jutB\n6xYWvtt6mH98soPjJxRyRESk6VPAaeL8fTz4yy+60yk6kA178vn7h1uoPnbC2WWJiIg4lAJOM+Dt\n6cbE0V3p1i6UXRnFvPTfzVRUa5FOERFpuhRwmgl3NzMP3NGFgV1acOBwGdPf30hxuRbpFBGRpkkB\npxkxm0zcd3NnRvRqxaGCSqbN2UBOQaWzyxIREWl0CjjNjMkwSLyhHbcPjqGw7ChPvrOWNz7ZzoHD\nZc4uTUREpNG4ObsAufIMw+CWgTFEhfnx2aqDrE/LY31aHh1aBZLQN5r42BBMhuHsMkVERC6ZAk4z\n1qNDGN3bh5KWUczi1Ey27y9iT1YJESE+jOwTTf+4Fri7aZBPRESuPgo4zZxhGHRuE0znNsFk51Ww\nNDWTNTtzeXdxGh9/u5/hvVoytHsUvl7uzi5VRESkwQxbE3yGf35+uUOPHxZmcfhnOFNx+TG+Wp/F\nys05VB+rxdPdzOCuEdzYuxWhAd7OLu+8mnq/XM3UN65J/eK61DcNFxZmOed2jeDIWYIsnowe1o5b\nBrRh5eZDfLU+i2Xrs1m+IYdencIY1bc1rVuc+wdKRETEFSjgyHl5e7qR0Dea4b1akrorlyVrs0jd\nlUfqrjw6tw4ioW80XWKCMTQhWUREXIwCjvwkN7OJAV0i6B/Xgh0Hi1iyNpOdB4vZlVFMVJgvCX2i\n6XtNOG5mTUgWERHXoIAjDWYYBl1iQugSE0JmbjlLUjNJ3ZnHO1/sIuXkhOQhXaPw8dKPlYiIOJcm\nGV8CTf76QWHp0foJyVsOcaymFi8PM0O6RTKiVyuC/b2uaC3qF9elvnFN6hfXpb5pOE0yFocICfAi\n8Yb23DqwDStOTkhemlo/KblPZysj+0QTHa4JySIicmUp4Eij8PFy56Z+rRnRqxVrd+ayNDWT1Tty\nWb0jl7iYYBL6RHNNmyBNSBYRkStCAUcalbubiUHxEQy8tgXb9heyZG0mOw4UseNAEdFWP0b2jaZ3\nJ6smJIuIiEMp4IhDGIZBfGwo8bGhHDhcxtLUTNal5fH2Zzv5aOU+RvRqxXVdI/H21I+giIg0Pk0y\nvgSa/HVp8kuq+WpdFt9uPUTN8Tq8Pd0Y2j2S4T1bEWTxvOzjq19cl/rGNalfXJf6puE0yVicLizQ\nm7EjOnDroBi+2ZTD1xuyWbwmky9Ts+gXF05Cn2iiwvycXaaIiDQBCjhyxfl5u3PLgDYk9GnF6h25\nLFmbyaptR1i17QjXtg0hoW80naIDNSFZREQumQKOOI27m5nrukYyKD6CLekFLF2bybb9hWzbX0jr\nFhYS+kTTq1MYZpMmJIuIyMVRwBGnMxkG3duH0b19GPsOlbJkbSYbd+fzz4U7+GilFyN6t2JwfARe\nHvpxFRGRhnHoP42nTZvGmDFjSExMZOvWrefcZ8aMGSQlJdlfL1y4kFtvvZU77riDFStWAHD48GGS\nkpIYO3YsEyZMoKamxpFlixPFRgbwwO3XMu23/RjWI4qyyho+WLaXR974Hx+t3EdpxTFnlygiIlcB\nhwWc1NRUMjIymDdvHlOnTmXq1Kln7ZOens66devsr4uLi3n99deZO3cu//jHP/j6668BePXVVxk7\ndixz586ldevWLFiwwFFli4sID/Ih6caOvPiHAdw2KAbDMPhidQaPvPk/3l28i8OFlc4uUUREXJjD\nAs7q1asZPnw4ALGxsZSWllJRUXHGPtOnT2fixIlntOnfvz9+fn5YrVaeffZZANauXcsNN9wAwLBh\nw1i9erWjyhYXY/Hx4LZBMbz4hwEkjexIsL8X3245zGNvr+XVBVvZk1VCE3zSgYiIXCaHTWooKCgg\nLi7O/jo4OJj8/Hz8/OpvA05JSaFPnz5ERUXZ98nOzubo0aP87ne/o6ysjIceeoj+/ftTXV2Nh4cH\nACEhIeTn5zuqbHFRnu5mhnWPYkjXSDbtzWfJ2kw2pxewOb2AmAh/RvWN5sYQ3WIuIiL1rtiszdP/\nlV1SUkJKSgqzZ88mNzf3jP1KSkqYNWsWhw4dYvz48XzzzTfnPc75BAX54OZmbpzCz+N8DxYSx0sI\n9ydhUCw7DxSS8k06qTuP8MYn25m/Yh/94lrQ+5oWdIkNwcPdsT8DcnH0d8Y1qV9cl/rm8jgs4Fit\nVgoKCuyv8/LyCAsLA2DNmjUUFRUxbtw4ampqyMzMZNq0aXTs2JHu3bvj5uZGdHQ0vr6+FBUV4ePj\nw9GjR/Hy8iI3Nxer1XrBzy4urnLUaQF6wqSrCPPz4Le3XMOtA1rz5bos1qfl8fmqA3y+6gCe7mau\naRNE13ahxMeGEOh3+U9KlkunvzOuSf3iutQ3DXe+IOiwOTgDBw5k6dKlAOzYsQOr1Wq/PJWQkMCi\nRYuYP38+s2bNIi4ujuTkZAYNGsSaNWuoq6ujuLiYqqoqgoKCGDBggP1YX375JYMHD3ZU2XIVigjx\n5ZcJnfjPM6P4yy+6M5wmUH8AABfASURBVLJP/dIPm/YW8O7iNCbNWsXT767jk+/2c+Bw2f9v796D\n467rf48/v3vLXrNJNtnNPaUJbSGl0EIZoYCllIt4fqKgNBaKfzDOMEwZQVBqpBRHp1pmmHGgDMqo\nDFOOQxSq4iiIoNWeQ1s4UFIJvSWkuWdzvyfNZff8sZttQrGE0mQ3m9djZie733y/m8/ybdMX78+N\nkMbsiIgkvVmr4KxatYrS0lLKysowDINt27axe/duPB4P119//SdeEwgEuPHGG7n99tsBeOSRRzCZ\nTNx33308/PDDVFRUkJuby1e/+tXZarbMYxaziWVF6SwrSmfDuvMJdg1RWdNJZXUHxxp6qGvt55X/\newKvy8aKYh8rijMpPS9d6+uIiCQhbbZ5FlQ6TExnui/DJ8epqu2isqaDQzWd9A+NAWAxGywtTOfi\nYh8Xl2SSleaYyyYvGPo7k5h0XxKX7s3MabNNWdAcKRYuW+bnsmV+QuEwtS19VFZ3cqi6g6raLqpq\nu/jtG8fJzXTFwk5xXqq2iRARmacUcGTBMRkGxbleinO93HrNYrr6RjgU7co6XNfNqwfqefVAPS67\nhYsW+1hR7GP5Yh9uhzXeTRcRkRlSwJEFLyPVztqVeaxdmcfo2ARH6ruprO6ksqaD/R8G2f9hEMOA\n8/O8kVlZJZnk+pza7VxEJIEp4IhMYbOaWVGcyYriTO4ML6GxfZDK6g4qazo43tjLscZefr+nhkyv\nnYtLMrm4xMfSgnSsFnVliYgkEgUckf/CMAwK/G4K/G7+15WL6Bsa5T81nVTWdFJV28mb7zby5ruN\nWnNHRCQBKeCIzFCq08aai3JYc1EO4xMhjjf2Rqs7nRw83sHB45GFLRdle2LVncKAB5O6skRE5pwC\njshZsJhNXFCUzgVF6ZRddz6tXUMcioadYw09nGjt50//pxav2xaZlVWcyQWLtOaOiMhc0W9bkXMg\nO8NJ9uWF3HB5IUMj41Sd6IoFnn9XtvDvyhYsZoNlhZGurIuLfWRqzR0RkVmjgCNyjjntFlYv87N6\nmZ9QKLrmTk0HldWdfFDbxQe1Xfzvv0NeposVJZHqjtbcERE5txRwRGaRyWRQnOelOM/LrdcU09U3\nEts+4nBdN6/ur+fV/VPW3CnxcdFiHy671twREfk8FHBE5lBGqp1rV+Zx7co8To5NcKSuOxZ4Jtfc\nMRkGJXmpLClMpyTPS3FeqgKPiMhnpIAjEicpVnN0tlUm4RuW0NA2QGVNZPuIyTV3JuX4nJTkeSOP\nfC+BDKdmZ4mInIECjkgCMAyDwoCHwoCH/7lyEYMjY9Q09VHT1Et1Uy8fNfext7OFvYdaAHDZLbGu\nr5I8L+fleDRDS0RkCv1GFElALruVFcWRfbAAJkIhmtoHqW7qjYWeQzWdHKrpBCL7axX43RTnpcYq\nPT6vXdtJiMiCpYAjMg+YTaZYhWfdqnwAegdOUj1Z5Wnu5URLP3XBfv7xXhMAXrctMoYnN9KtVRTw\naEsJEVkwFHBE5imvO4VLl2Zx6dIsAMbGQ9QH+6mOVniqm3p592g77x5tB8BiNliUnRobuFyS58Wr\nbSVEJEkp4IgkCavFFBuXcyMQDofp7BuJdGs19sXG8lQ3nRq8nOm1U5LvjVV68v0urccjIklBAUck\nSRmGQabXQabXwRcuzAbg5OgEtS19sQpPTVMv+6uC7K8KApGZXYtzU6ODl1NZnOvF7dAUdRGZfxRw\nRBaQFJuZZUXpLCtKByAUDhPsGpoyeLmPw3XdHK7rjl0zdYp6cZ6XbJ+mqItI4lPAEVnATIZBjs9F\njs/F1StyAU6fot7Sx95DmqIuIvOLfiuJyDSfdYq6YUCB331qIUJNUReRBKCAIyJnNNMp6vXBgelT\n1HOjVZ58LzaHjVAojMmk0CMic0MBR0Q+sxlNUT/WzrvH2mPXGER2Wnc7rNMeLocVjzPy1W0/9dwT\n/Z7FrFldIvLZKeCIyOd2xinqTX0MjU7Q2TPMwPAYA8NjdPSOMBEKz+i97Tbz9FDkjAQht9N6Wlia\nfNis5tn9wCKS8BRwROSc+/gU9awsD+3t/bHvh8Nhhk9OMDAyxsBQJPQMDo/RPzz9+WD09cDwGI3t\ng4xPhGb0820WUywIfbxCNBmMJitEk6HIbjNr3JBIElHAEZE5ZxgGTrsFp92CP80xo2vC4TCjY6FY\n4Pmkx8dDUrBnmJNtAzN6f7PJmF4hcpxeJXI5rGSm2snNcmmqvEiCU8ARkXnBMAxSbGZSbGZ8XvuM\nrxsbD8UCz5nC0eSjp/8kTe2DZ3xPZ4qFJQVpLClIY2lhGoUBt1aAFkkwCjgiktSsFhPpnhTSPTPf\nd2siFGJwZDxSERqa3mXW3DnIsYYe3q/u4P3qDiAyTqgk38vSgjSWFqSzKMejwdEicaaAIyLyMWaT\niVSnjVSnjRzfJ5/T1TfCsYYejjb0cKyhhw8+6uKDj7qAyBig4rxI4FlSkMbi3FQNfBaZYwo4IiJn\nISPVzhdKs/lCaWSfr97BUY419HCsvoejDd3TtrywmA3Oy0llaWEk8JTkebX6s8gs098wEZFzwOuy\nsXqZn9XL/AAMDI9xPFrhOdrQQ3VTL8cbe4E6TIZBUbaHpYVpLC1I4/x8L067NjUVOZcUcEREZoHb\nYWXlkixWLokshjg0Mk51Uy9HG7o51tDDiZZ+alv6eO1APQZQEHBHBi0XpLOkwIvHaYvvBxCZ5xRw\nRETmgNNumbbH18nRCaqbe6NdWj181NxHfXCAN/5fIwB5mS6WRCs8SwrSSHPPfJC0iCjgiIjERYrN\nTOmiDEoXZQAwNj7BR819sYHL1U29NL03yD+j+3sF0h3RLq10lhSkfaap8iILkQKOiEgCsFrMLC1M\nZ2lhOv8DjE+EqGvtj83SOt7Yw78rW/h3ZQsAmV57tEsrjSWFafjTHFqJWWQKBRwRkQRkMZ/a3+vm\nLxQRCoWpb+uPdWkda+jhrQ9aeeuDVgDS3DaWFqbHQk+Oz6nAIwuaAo6IyDxgMhksyk5lUXYqN1xe\nSCgcprl9MDZL61h9Nwc+DHLgwyAAHqf1VIWnII18v1vbS8iCooAjIjIPmQyDfL+bfL+b6y7NJxwO\n09o1FA07kdDz7tF23j3aDnzy9hIiyUwBR0QkCRiGQY7PRY7PxdpL8giHw7T3jsQWHvz49hIpNjMX\nLsog1+ekKOChMOAmS+N4JIko4IiIJCHDMPCnOfCnObhqRQ4Q2V5icvzO0foeDh5r5+CUaxwpZgr8\nkbATCT0ecnxO7asl85ICjojIApGRaueK0myuiG4vYXPYOHi4lfpgP/XBAeqD/RyPBqBJFrOJvCwX\nhX43hQEPRQEPBX43KTbtrSWJTQFHRGSB8rpTpq3FA5EFCBvaB6Khp5+64ABN7QPUtfYDkSnqBhDI\ncE6r9BQG3Fp9WRKKAo6IiMSk2MyU5HkpyfPGjo1PhGjpHJpW6alvG+Dtw228fbgtdl66JyVW6YlU\ne9z4vHaN65G4UMAREZEzsphNFPjdFPjdrLkocmxyEHN9az/1bZHgUxfsp7Kmk8qazti1LruFgind\nW4UBN9k+J2aTxvXI7FLAERGRz2zqIObLojuoA/QOjtIQ7KduSrXnSH0PR+pPjeuxWkzkZ7lilZ7C\ngJv8LDcp1uQc1xMOhxk+OUH/8CgDQ2P0D41Fng9Hng8MjUWfj9IfPWY2GQQyHOT4XOT6XGT7nOT6\nnGR6HZhMqojNxKwGnO3bt1NZWYlhGJSXl7NixYrTznniiSd4//332bVrFwcOHOA73/kO559/PgBL\nlixh69atbNmyhaqqKtLS0gC4++67Wbt27Ww2XUREzoLXZcO72Mfyxb7YseGT4zS0DUzv4goOUNvS\nHzvHMCDH56Iw4KbQH+neKgh4cDus8fgYZzQ2HooFklhImRJQIiFm+vcmQuFPfV+zycDttOJLtRMG\nTrT0U9PUN+0cq8VEIN1Jji/yyM2MLA2QneHAaknOgHi2Zi3gvP3229TV1VFRUUFNTQ3l5eVUVFRM\nO6e6upp33nkHq/XUH+DLL7+cJ5988rT3++53v8u11147W80VEZFZ4piyyOCk8YkQzR2D0yo99W0D\nNHcMsr8qGDvPl2qPhJ7Aqenr6Z6UczauJxQOMzQyzsCUYNI/JbwMDI3FqiqTx0ZGJ2b03s4UC26n\nlcw0Ox6HDbfDisdpxe20Rl47I689Dituhw1Hijn2ubKyPLS09tLWPUxL5yAtnUO0dA7S3DlEa+cQ\nje0D036WAWSm2WMVn0gAcpGT6cRlT7yQOBdmLeDs27eP9evXA1BcXExvby8DAwO43adWz/zZz37G\nAw88wM6dO2erGSIikoAsZlOsi2pSKBymvXt4eugJ9nPweAcHj3fEznM7rKeFnkC6E5PJYHRsIlY5\nmd4ldKrKMrVLaGB4nFD406srFrOBx2kjK80RCSkOKx6nLRJOnJHnkwHG47Diclg/9/pBFrOJ3EwX\nuZmuacdD4TDdfSdp6RqkpWNq8BnkUE0nh6aMgQJIddnI9TnJjgafyQB0LoNiIpq1gNPR0UFpaWns\ndUZGBu3t7bGAs3v3bi6//HLy8vKmXVddXc0999xDb28vmzdvZs2aNQC88MILPPfcc/h8PrZu3UpG\nRgb/TXq6E8ssl+qysjyffpLMOd2XxKV7k5gS7b4E/KksXxqIvQ6Hw3T1jVDb3EdNUw8fNfXyUVMv\nH57o5sMT3bHzbFYzJoMZV1c8TiupLht5fg+pLhupLhted0r0q41UV0rseKrLhiPFMudh4Ez3JuCH\nZWSddrxvcJTGtn4aggM0tvXT2DZAQzCyK/3UcVAQWdgxzx9Z16gg4Ilu/eEhJ9OVFIs7ztkg4/CU\nhNzT08Pu3bt57rnnCAZPlSIXLVrE5s2b+dKXvkRDQwN33XUXr7/+OrfccgtpaWlccMEFPPvss+zc\nuZNHH330v/6s7u6hWf0sWVke2tv7P/1EmVO6L4lL9yYxzaf7UpTppCjTybqLcwEYGhmnoS2yTk99\nMPIPOQbTqyoOK+7o60jXkC1aXbHMfBZXKMRg/wiDc/yf6fPcmyy3jSx3BquKp6xvNDZBsGtoWldX\nS+cgJ5p7qW6YHnzMJgN/umPK4OZIV1d2hhO7LfHmJv23IDhrLfX7/XR0nCoptrW1kZUVSZv79++n\nq6uLO+64g9HRUerr69m+fTvl5eXcfPPNABQWFpKZmUkwGOSKK66Ivc+6det47LHHZqvZIiIyDzjt\nFpYWprO0MD3eTZkXUqzm07oEASZCITp6R6Z0dQ3S2jkUDUCnFwsyUlOie545p4318TitCdfdNWsB\nZ82aNTz11FOUlZVRVVWF3++PdU/ddNNN3HTTTQA0Njbygx/8gPLycl555RXa29u5++67aW9vp7Oz\nk0AgwH333cf3v/99CgoKOHDgQGyWlYiIiJw9sykyKyuQ7uSS8zNjx8PhML2Do7R0nBrY3Nw5SEvn\nIFW1XVTVdk17H5fdQk6mi5yMSODJzYx89XntmOIUfGYt4KxatYrS0lLKysowDINt27axe/duPB4P\n119//Sdes27dOh566CHefPNNxsbGeOyxx7DZbNxxxx3cf//9OBwOnE4nP/3pT2er2SIiIgueYRik\nuVNIc6dwwaLpY16HRsZp6Ro8FXqi1Z+apl6qG3unnWu1mCgKeLj3a8tJc6fM5UfACIdnMHx8npnt\nPuX51G+9kOi+JC7dm8Sk+5K45uO9GRsP0dYd6dpqnjK1fWhknIe+uRJ/mmNWfu6cj8ERERGRhcNq\nMZGX5SYvy/3pJ8+B+T8PTERERORjFHBEREQk6SjgiIiISNJRwBEREZGko4AjIiIiSUcBR0RERJKO\nAo6IiIgkHQUcERERSToKOCIiIpJ0FHBEREQk6SjgiIiISNJRwBEREZGko4AjIiIiSccIh8PheDdC\nRERE5FxSBUdERESSjgKOiIiIJB0FHBEREUk6CjgiIiKSdBRwREREJOko4IiIiEjSUcD5DLZv386G\nDRsoKyvj0KFD8W6OTPH444+zYcMGbrvtNl5//fV4N0emGBkZYf369ezevTveTZEpXnnlFb7yla9w\n6623smfPnng3R6IGBwfZvHkzmzZtoqysjL1798a7SfOWJd4NmC/efvtt6urqqKiooKamhvLycioq\nKuLdLAH279/P8ePHqaiooLu7m6997WvccMMN8W6WRD3zzDN4vd54N0Om6O7u5umnn+bll19maGiI\np556irVr18a7WQL84Q9/4LzzzuPBBx8kGAzyrW99i9deey3ezZqXFHBmaN++faxfvx6A4uJient7\nGRgYwO12x7llsnr1alasWAFAamoqw8PDTExMYDab49wyqampobq6Wv94Jph9+/ZxxRVX4Ha7cbvd\n/PjHP453kyQqPT2do0ePAtDX10d6enqcWzR/qYtqhjo6Oqb9QcvIyKC9vT2OLZJJZrMZp9MJwEsv\nvcQ111yjcJMgduzYwZYtW+LdDPmYxsZGRkZGuOeee9i4cSP79u2Ld5Mk6stf/jLNzc1cf/313Hnn\nnTz88MPxbtK8pQrOWdIOF4nnjTfe4KWXXuI3v/lNvJsiwB//+EcuueQSCgoK4t0U+QQ9PT3s3LmT\n5uZm7rrrLv75z39iGEa8m7Xg/elPfyI3N5df//rXHDlyhPLyco1fO0sKODPk9/vp6OiIvW5rayMr\nKyuOLZKp9u7dyy9+8Qt+9atf4fF44t0cAfbs2UNDQwN79uyhtbUVm81GdnY2V155ZbybtuD5fD5W\nrlyJxWKhsLAQl8tFV1cXPp8v3k1b8N577z2uuuoqAJYtW0ZbW5u63M+SuqhmaM2aNfztb38DoKqq\nCr/fr/E3CaK/v5/HH3+cX/7yl6SlpcW7ORL185//nJdffpnf/e53fOMb3+Dee+9VuEkQV111Ffv3\n7ycUCtHd3c3Q0JDGeiSIoqIiKisrAWhqasLlcincnCVVcGZo1apVlJaWUlZWhmEYbNu2Ld5Nkqi/\n/vWvdHd3c//998eO7dixg9zc3Di2SiRxBQIBbrzxRm6//XYAHnnkEUwm/f9uItiwYQPl5eXceeed\njI+P89hjj8W7SfOWEdZgEhEREUkyiuwiIiKSdBRwREREJOko4IiIiEjSUcARERGRpKOAIyIiIklH\nAUdE4q6xsZHly5ezadOm2C7KDz74IH19fTN+j02bNjExMTHj87/5zW9y4MCBs2muiMwDCjgikhAy\nMjLYtWsXu3bt4sUXX8Tv9/PMM8/M+Ppdu3ZpQTQRidFCfyKSkFavXk1FRQVHjhxhx44djI+PMzY2\nxqOPPsqFF17Ipk2bWLZsGYcPH+b555/nwgsvpKqqitHRUbZu3Uprayvj4+PccsstbNy4keHhYR54\n4AG6u7spKiri5MmTAASDQR566CEARkZG2LBhA1//+tfj+dFF5BxQwBGRhDMxMcHf//53Lr30Ur73\nve/x9NNPU1hYeNrmg06nkxdeeGHatbt27SI1NZUnnniCkZERbr75Zq6++mreeust7HY7FRUVtLW1\ncd111wHw6quvsnjxYn70ox9x8uRJfv/738/55xWRc08BR0QSQldXF5s2bQIgFApx2WWXcdttt/Hk\nk0/ywx/+MHbewMAAoVAIiGyh8nGVlZXceuutANjtdpYvX05VVRXHjh3j0ksvBSKb5y5evBiAq6++\nmt/+9rds2bKFL37xi2zYsGFWP6eIzA0FHBFJCJNjcKbq7+/HarWednyS1Wo97ZhhGNNeh8NhDMMg\nHA5P229pMiQVFxfzl7/8hXfeeYfXXnuN559/nhdffPHzfhwRiTMNMhaRhOXxeMjPz+df//oXALW1\ntezcufOM11x88cXs3bsXgKGhIaqqqigtLaW4uJiDBw8C0NLSQm1tLQB//vOf+c9//sOVV17Jtm3b\naGlpYXx8fBY/lYjMBVVwRCSh7dixg5/85Cc8++yzjI+Ps2XLljOev2nTJrZu3codd9zB6Ogo9957\nL/n5+dxyyy384x//YOPGjeTn53PRRRcBUFJSwrZt27DZbITDYb797W9jsehXo8h8p93ERUREJOmo\ni0pERESSjgKOiIiIJB0FHBEREUk6CjgiIiKSdBRwREREJOko4IiIiEjSUcARERGRpKOAIyIiIknn\n/wN1PFyXmup3XwAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 656
+        },
+        "outputId": "20ee2558-7028-4784-bfa3-f1564965d63c"
+      },
+      "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": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on training data):\n",
+            "  period 00 : 0.49\n",
+            "  period 01 : 0.47\n",
+            "  period 02 : 0.47\n",
+            "  period 03 : 0.47\n",
+            "  period 04 : 0.46\n",
+            "  period 05 : 0.46\n",
+            "  period 06 : 0.46\n",
+            "  period 07 : 0.46\n",
+            "  period 08 : 0.46\n",
+            "  period 09 : 0.46\n",
+            "Model training finished.\n",
+            "AUC on the validation set: 0.80\n",
+            "Accuracy on the validation set: 0.78\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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dPeDOVr0opl6bKTAwCMXFhcjPz0NlZSX27dsDFxcNVq58FRcunMPHH7/f5nl6PSCRNM94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\n7LKIiIhaYZihdlnJpVgyawj+NGco9Hr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g7ES08ljNWk41J/of8LeFiOgmiBFr5+SNt8Td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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ 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..7266430
--- /dev/null
+++ b/multi_class_classification_of_handwritten_digits.ipynb
@@ -0,0 +1,2786 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "d4ab3fd0-1791-448f-e21c-b442c59c3bde"
+      },
+      "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",
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+              "      <th>783</th>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "    <tr>\n",
+              "      <th>9954</th>\n",
+              "      <td>1</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2245</th>\n",
+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5069</th>\n",
+              "      <td>4</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",
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+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
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+              "      <td>0</td>\n",
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+              "      <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",
+              "8484    9    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "5197    7    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "9954    1    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "2245    0    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "5069    4    0    0    0    0    0    0    0    0    0 ...     0    0    0   \n",
+              "\n",
+              "      778  779  780  781  782  783  784  \n",
+              "8484    0    0    0    0    0    0    0  \n",
+              "5197    0    0    0    0    0    0    0  \n",
+              "9954    0    0    0    0    0    0    0  \n",
+              "2245    0    0    0    0    0    0    0  \n",
+              "5069    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": "3943d134-8b13-468c-98c8-1c0dbd20e3a2"
+      },
+      "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",
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+              "\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>8484</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5197</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9954</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2245</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5069</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9608</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>619</th>\n",
+              "      <td>92</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3962</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7021</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2226</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10000 rows × 1 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      72\n",
+              "8484   0\n",
+              "5197   0\n",
+              "9954   0\n",
+              "2245   0\n",
+              "5069   0\n",
+              "...   ..\n",
+              "9608   0\n",
+              "619   92\n",
+              "3962   0\n",
+              "7021   0\n",
+              "2226   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": "8ce4ba2e-c4bf-4fdc-8902-4e10db88f0f5"
+      },
+      "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",
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+              "      <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",
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+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <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>0.0</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 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    0.0    0.0    0.0    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": "8ebe8b66-d9c9-4f63-84bb-fa0a0a61d869"
+      },
+      "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>0.6</td>\n",
+              "      <td>0.0</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 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    0.6    0.0    0.2    1.0    0.2    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": "e5510356-1313-4482-8ca0-8b72d468092d"
+      },
+      "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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\nrkJggg==\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": 973
+        },
+        "outputId": "fe1164fb-d6af-45a1-84bb-f82c8b520a32"
+      },
+      "cell_type": "code",
+      "source": [
+        "classifier = train_linear_classification_model(\n",
+        "             learning_rate=0.02,\n",
+        "             steps=1000,\n",
+        "             batch_size=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 : 4.34\n",
+            "  period 01 : 4.24\n",
+            "  period 02 : 3.95\n",
+            "  period 03 : 3.90\n",
+            "  period 04 : 3.81\n",
+            "  period 05 : 3.66\n",
+            "  period 06 : 3.74\n",
+            "  period 07 : 3.59\n",
+            "  period 08 : 3.62\n",
+            "  period 09 : 3.65\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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2Isi1N0GuAbjYOJu7dCFEByShLkQrUavU9HDqTg+n7kzuMZ7y2gpOFCaTkH9u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\nRAxCoS4iImIQCnURERGDUKiLiIgYxP8DNLBQnb/gceAAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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NnvP19YWvr68c5YiITJJWhrZ4HQkRkWoxSIiISAL2SIiISBLBgkFCREQSsEdCRESSyBkk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\nYZAQEZEkDBIiIpLk/wFk7yCQ0U/BqQAAAABJRU5ErkJggg==\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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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",
+        "#\n",
+        "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": "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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "7674ad58-30a7-4669-abeb-8493bd40b816"
+      },
+      "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": 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>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": 21
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "PDuLd2Hcu8VL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 973
+        },
+        "outputId": "f9bdc9d6-4a51-466a-c32d-8873853b62d1"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
+        "#\n",
+        "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": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss error (on validation data):\n",
+            "  period 00 : 5.53\n",
+            "  period 01 : 4.42\n",
+            "  period 02 : 2.92\n",
+            "  period 03 : 3.21\n",
+            "  period 04 : 2.61\n",
+            "  period 05 : 2.61\n",
+            "  period 06 : 2.38\n",
+            "  period 07 : 2.42\n",
+            "  period 08 : 2.27\n",
+            "  period 09 : 1.85\n",
+            "Model training finished.\n",
+            "Final accuracy (on validation data): 0.95\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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knK2ocQ7uyyO4P7OPEdyKojDn8rSe61M2ybSeQghhhyScrSw20ocFU+1rBHe0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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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BOxIiIpJEL0HC1X+JiEgSdiRERBolZ0cSHx+P48ePQxAEREZGwsfHx/jcn3/+iZdeegnV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\n",
+            "text/plain": [
+              "<Figure size 576x396 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "U9ClseIuu_n4",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "ed6df54e-0040-46d0-fb3c-f0ab531c6ab8"
+      },
+      "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": 23,
+      "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": 23
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "woPbaEdQvEII",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "790cb7a7-4e6e-4d6c-bb79-825da82773bd"
+      },
+      "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": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Accuracy on test data: 0.95\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "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": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": {}
+      },
+      "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": "EVaWpWKvSHmu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "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": 0,
+      "outputs": []
+    },
+    {
+      "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": "04c3cc4b-4679-4584-f8d6-bf045d5f28b4"
+      },
+      "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": 31,
+      "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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uJGZuY5/H8qXRKNZZIEdLLtgmNH4VdMI716xR/SpqsaZTk+jn0tPZvpFt5ir2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FMJp88mLRkSW8CPHUb8q32XjKfRIYzPMrKdHnxRpk6zLInjbnJS/l0e1Dk+rDU+srt3FUq/xqFj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pFDdP34J87Kcvv6z6Vf3ar3ntajqLnryJfba8S5+nJ+jZtqgIY8PPKSIiTQ9j7Fni456vYp2I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\nnqH5vtt+7D+Z2QnMOfe8XbQZ62DsJL5zIqLPh5kk71O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3LZvvJ0vdXCkKV4dnqR/LA0VE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MvA58/Oh8yKJZnGGHPuTcucuPh9yFImXSPX7cqXQX/P/ZUJOFieEJEoXSF9JFcafyk5Eb0t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\nQ8PYr3WVSzvrcG4V8DW2r/8qeN3xt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ozzOKc8ActiLwannoBMM3G63qex7fiCLy704rIX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FUj8KipXzBu9F4kj+U79JytB5P1PzMSQ11ftrRLsZD8r5xttcuxYyqgk3yvnioVX3ODHv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GUnSvuvhdmgAzIdLtKi6bIdHVOFey5LSrqZAg2O6VuTk5LOFhgOuRdTqI1k3klZygyAJR41z14Q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\nNH4lOApjMGG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X+zHS66uLkW7XOyBdBfZRyt5vPrjT+JPRVyAVvvmkTPtlaQ/L1noapHQk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\nmdlHJaJfOKV1DpDrUUwO5ljbOSnJ4XvFaX7sOGWMrApf/wnSUSO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UVV+z0WlHpZMbnyUP\nCSLXg/ZKxGqvJTcJjZVrlb/hlOp/cUwMtEUT/0tEhJRCt9RCChIYgnsVFSVTon0+yB3i4yF3aGs7\nLvpFReHaR0ZinzAwgP1Xd7ecEz09SLcOD8d7WEpljJRFBAbi2obGyPsTGorxWFuM1HX7fsTkI3Y2\nkuw5PEnGgH6S+hmZ1f6ZiRmFYyg7K532kmlv1rgLe5ucG6VD33s/edtpZyZgrR9//yzRz5BEP5z2\nKatultqCp/74htOeuR773B1/gyOTLX14a98+p33vUsyxtuNSUh9FjiTsjLnvbbneTf8/7L1ndJtZ\ndqZ7SIIgQRAgwJyDmCQqUSpJpSxVDlKVK3e52x3dDtNue9rLnrs808ueuctp5nru9LTv2F53Oucq\nd1fo7spJVaWsUg6URFEUxZxAEgSIQDDcH779ve8+JWnWckPDP/v5dVQ4AD+c75x9zofa734bcd7y\nkAva2GEpWazag/nGboFNxVICbjuWppsA7TUcH4yRLmHZWVhXtdaZcoH200xys3v5G2+LfqeuYS3+\nxZ9/wWkXr5dSs5GDiN/sgMfONOOHpatpPjm/5OaSnGP5TtEvNvQdpx2NQmKTkSHXYiKGdcUumvaz\nCztXVeyqd9o51nnffkZJJ1x2wO2TUtbKe/HcNdmB+FW4skz041IQHJ+L1snAkftTSLi9TdhzPSXS\nEadhLeJhLjmy8hx79bv75HtKcQ3VdWh/96tfFf0mZ3C+Yini7idk3MinMwHL93bvkmus9zjW5oon\n1zjt5Lh0l7OlQelmYgrPZiueWiNeG3mvx2mXkTuo+6R0LeOzKDvChSyn2RJ6Fo734e8G18vzUjad\naUZPYU1wWQ5bAupvxlpk17zSIinjvXwVa3iRnBlPXZGuz//1+9932p9//HGnfe8G+dDOn8ESqro1\nUps9n7z1WtTMGUVRFEVRFEVRFEVRlCVEf5xRFEVRFEVRFEVRFEVZQvTHGUVRFEVRFEVRFEVRlCXk\nljVnUuGb60z7X4F1WA7VpWDdtTFS25dfAEu7eFzW63C5oOHNyoKmLrNM1gkpLr7LaU9MQMObRbZX\n/mZZR2K6E/Ub2Aq5sF1qTJOT0PaxNWQ8Iq9h8C3UfChYAa1svmWlzBbeszSWc5b2n+sPmL0m7bAN\nc7bP1v9j3FgrzjpJY+S4sYdhYkLWIvKTpW24AzU0QqflGLqo5kRhO+732BGq50PWu8YYk5jFuJXf\nAT0va12NMWb/RdSP8dP3mB2T2s0Tzx132rU10L56G+R9nOm7sbW0p1zqWxdtT7U0kkfXZH/fvv2Y\nj2uovlDvSakvL8hDDYItd6912olBaVfMNr/HjkHbe/8Xdot+R35y1Gnfdyf0s0fPITY8ulHWlkpS\nfaDyAL5T6Pyw6McW2XPTuO+xpLQabmkjTfEVzKnjXdIC8I3vfeC0H/7i3U57ISV1nxGKFemuG2SM\nMRNU98e2Ji/diXoWyRHMVV5HxhhTsgvfueOXqBVSQVaRxhiTRbp7tnHl+lzGSC32xQ87nXZmB+qB\n2LV+Pv/gg077hx9+6LR3trWJfvVkjZntw9+1a8S4A9DGr/rCk0471HdG9JvshGY52oX4kOW27Cuf\nkXUl0skU1QMqWFUqXpudQpyveQS1ggbflTV72PZ1gWo9LFg25266V8t3oz7C5OQx0c9XiziXm4d9\nbegEaqLwPvgv1wp7yWQI8y05Iceca31NzKFeQ5bbspqkeVq2HXPZ5ZXzjeeipxRzr9CqK+DKk+9L\nN1w7KBWTezLHeW8lYhHXjjHGGH8JamUNnkA8dOXKWjJ5ebAMn5nBZ/h8cr0sLGA/Hbz+stMOlKx3\n2pFp+dkeqk0zO4saJYuL8hzkcuF7RKawR+Z65TlocRExMZfuD1vDG2NMgqxzuW6Vvbb9tXKNpBN3\nAGfPdU+vF6/1vo5YFqOzw8JPzop+pQVkdzqNeiLuPFnbLTMTf8tbhX4dP5Xr5d/+5WfwDz4r0TX0\njsu1+O8++xT+QceIwBpZk4P3q2AbXttqxfTiVahtdvg/v+q01/+BtMSeukRrdiPiVWRQnteuPocx\nq/mbp8zthNeeMTI+Lv8UzhkzvbIu4vQl1KapuB/1aB58dKvot+Mq1lx8EGedUKasm1G+E2NYWgYL\n8omJ/U577LQ8Z/AZmmNFKiXrJ3qo/ubAAYztkFUTqGwjzrk81+3aMUFrnvwKrllmjDFjVK/DPHPD\nt/yrmU/gmuJj8kx57adkXX0PYmHPCxdEv7rHcW+4rs689X1zyjHO8V6sxZ6fnhf9ujtxhtn0uS14\nD+1BJZZNdybVluLnm8xMOZYpqrO4eRvOG+VbZc02PrMwZTtk/bJS2jMLa1c67Ssvy5pJRetkvE43\nhQHMTba3NsaYrFzs+VPnsdf4mgpFP17DOUU429l1MPncEdyI/d+e31xXdPUfwHJ86H2sscmPZMzi\nue/yIz56rH1sJf1+kUP7WFVIfqct9CyzrgGx4VhHp+jnprpY7a2IQznF8u/y+fxGaOaMoiiKoiiK\noiiKoijKEqI/ziiKoiiKoiiKoiiKoiwht8yrCaxCqty0ZXNZtAGpVddeueS0q6xU5PxSpOUNn0ca\nNaexG2NMIgPphYuU2j0zIK1Fo5VIpfJXIg0shz7Ptjj2k93iXAySkNiAtEHNJAkWy5CycqXtq78V\nnxe+gOspa18p+k2cQZpkfj3Szu20zXjk9toUcoqYLamaJZsztrdNjMi0xNBxfJey3Rh327acxzpA\nttNJS/7kISkFpxgWkuVsYlhew1gf5kLiCNLZvrtPWuFNRfG+YbJX9lrSjLtWIRXxfGeP0y4ekvbg\nG34PqcCJUXx2XpVMh5yzUuPTiZdkEMaST5VsgUVbjNJ0l+2QVpMTJyGp6fwINnFzC1JKkUVpndsf\nugP9rLnDdqC5FbifVQNIB2xoqxbvCa7G/WUrubJtMsXz3P+ERGBoEvKVuhIp3cknu7xKmssraqRt\n3ZpapP6zRG/Wmpe2pC3dsP0prw9jjJmiWJLlxVr0Ncr0SpaZFPkwV4s3y7GeIOv4CKV8BzdI+Ujh\nCoxV61aKtzQPBk/281tMWxVkn5vbpdU3w3OmdCPdg0m5tifpuydDB/H+hJTwdb0FyVz9NqRHX39T\nppa2fFLaG6YTGhYzZ8XywrUYW06rLd4k783kediJdj8PmUqwTc4JthqdnIQMM9Qlv6+vlmSPZLvM\nEomXfyLj5HpKzQ3kI008M0fKlcrI/p3tdqNWfJ4ly89UFOOSmpZjVLIScTeVQtq0nbqenS/jdbrJ\nzMb3jFr7WHYB/jbbgmZly2uKkoW5pxxr0eWScYTvycT1E057IXVI9OP1wmnjPTGk6zc8u1q8Z2EB\na0RafUt5UTKJOZfna6BXFq1+iBsxsqOOXLWkGSTrzS/B/JlPSBv70HlIlUvuMmnFX4/1MXaqR7zW\n8pn15kb0Pi+lDyU1OLNs/ATGtu9tKX9qefRR/K3DWEtrf1ta5/a9RJIx2hf3/MlDTvv4tw6L99Q9\nibPj+EnIIOwxz6vB/pHrxV46NiXj88A+fMfazfVO25ZeN90NCem5H33XaZftrBf9Knc3mNvJ6Ic4\nswfbpY1urBdzMIfs3CeOSRlD+f3YDwJ1uF5bjhd8coPTvrb/dfSzLOBzPYjZc3M4V7GUKa9KSt/O\nfgvnlv/44x/jut3yuejrf/Ylp/3iK5AFr1+2TPTLOkFrkSTd7b+3WfRjuUiYpGq2TLak/fbZMIeO\nYt66vPKZKbgS5zaWaWcHpOU2P6uwPDLbku3N0Dm34RmsnbkZeV4IrEV84FjG574H/sND4j2Xv4F9\nNoM2+8bPyjPFctqfAoFN+OxZ+axcvMJ7w9fmrbNNVT3smQd7IWm1ZeixITqjyWNuWuAYEbsmn5EL\nN+G5nyVJV965LPpNxzG+Gx5HHPZWy/UyRyUaZifxnrmUHBu+Xz0/k1K4X+EukeucZXE+KnXS+dZF\n0c9Da3PyEuLovPVc9MefRawc7sbevGX9CtGPpXDj4zhXeKbkc2W4E3Oh7X7zMTRzRlEURVEURVEU\nRVEUZQnRH2cURVEURVEURVEURVGWkFvKmjhVzpaYsCNSfhBpW8kpKROYT/Y47UATUqImL8s0zDg5\nxGQXINXNdkCKkyRhoBtOFEU3SSc3xph/+MufOO1trXBXWLVXpgdfewXpTjW7IQmxqyrnliBVdfA9\nyGumh2WldU7n7nkBrjeeQinpanx2jbmdsOtRwcekFEh1zqH0z+SElFrlUqVpnhd2Cvx8HCnN+XUk\n5bIkMZyymCTXhxjJ2Lz18t73nsL9+XtKGd12p0wrjsSQbseV11sqZZXzw52QBuxYDmlGcascI3ZI\nyKF7x24VxhgzSU48DWm+pUmSDGTIbHXhUsCZ7NN9MlU/2IQ1m0djVFZbLPrlU+V1Tl2cviQrt9fT\nev6br//Qaf/JJ59w2ll5Mr114izGqOZBpKNe++kp0Y/dLCqDmEeRuIwv1SQ5zKtHyuSDPukS9fYH\nkBLcSWmbNQ80i362nC/duCm2cZqtMbLiPY+1HQPD5LpTuhNSITtNtmA15nFeBdLhXVY8W1zEmmUJ\nFcsqiuuk84ubKvBX3oVYOWtJNLNIOhI6T2noVnr9QhLXMHkGc8QdlGnPnNo9fAxyCY+VNt71Ezio\nNKx51qSThRTSXW1Z3HQ+1mJ8CHPJlqZ5yVWtnFwapi5LZy6W/nlLSabYJK8pEcPYTvb2OG2W6n77\nxRfFe1b84R867TcPnHba7Q1SwnC9H3vE6vuxZtl5zRhjzpyHI1XVHjhWjJ6WMmPe6xsegM5lpPND\n0Y/Tucul0iEt5PqQaj8bkPOW9yfe7xYW5D4WJ5lrKsqv9Yl+MxSLr70DKdS1kRHRb4hkuKEIxonl\ni5/eYLkrLcNaitPm4MmXg5aZifEMD0GKPjMopePCmZHWqe1OyHGJZcteS+57O10M5xLYn/JrLLfM\na5AEsZw2w3IZu9jR47Rdr0OqULtHOr6d/87zTnvN7/ym0+567RXRj52/cik1PjML92brV3aL9xSV\nbHfaC6vhLDixIB2EGnZAgjE7i32gdrc8A6VSOPMNvo9zk6dAnm2iUbxWuB5n6LDl0jJ1hubpDVLw\nf10q7kNsS4Skq6aLzhBcRiA+K9diiGS8uSSzY/miMcaMXoOUkJ8botZ5aWEBe838PNY5S4Wmu+Q4\nnevFM0AjSX8fXi8ldkNXMJ4sAWncIGMvOzrOjWMvsKWDeWWY+5EuzHshhzfGRDqlTC6d5FUjRk2c\nkPO2+TOYn8NHEP/mY/LM0vlNnNNYpj3yQY/8PJIYDdNr7BZrjIxlvOcWLMc6KCqXtpxNn8HYBivx\nd9hR2BjpODwZOoJ+bjnm8QnMWZZplzfLv8sufrn55B5bHRH9pkkOYzaZ9ENqntmUnGd8Lh0dwFwq\nrZTnm+7T2F/OvQLZdmWR7Nf4OYwvr1O5lxqToLPUVBjtcdojt/ymjIHf/rsXnPb9YbjTLtsmyz30\nHulx2jXlOBOMhWQ8YEn3BJXOiF+T19qyCdLEuQ6sWZf1LBRN3LqciWbOKIqiKIqiKIqiKIqiLCH6\n44yiKIqiKIqiKIqiKMoSoj/OKIqiKIqiKIqiKIqiLCG3rjlD9Sts7f/QG7CTK94KP6/B17pEv6pH\noD1/7y9/4bTXPiFtydji2t8C3eDAq9IydHEO+uXgeuiI2V4rSPZpxhhzbRg1DJ6k+iSRK1J/Wb4e\nGlGupdL5aofoV3MHvq+XLKF9ZdIuNXMrBpDr1th1OJKWxjbdVN6PAgWZWfL3OLbCzquApnLqrNTC\nl+5CXYRJes2uf5IKQ6fLWnPbznCB7ElrdsEWMJEg68AhqYWvKULdi+989atO22/Vcxg4D0u//FzM\n20GyZDbGmGc+C/E0147IyJJfKjEGfWGgFZpEt1+uCa45k25CVIej+kFZJ4X1/ly3JGLVNGFL2II8\n3I+Dx6S16MOr73baXHvhateA6FdXirFYXo25Px/FWj5/RVrs7fhd6Gx7XkSdi6sdsl5TqR91CxZo\nHrXulXb1519CbZHSALS+w5NSL8oW3JmZWANcy8cYY3xNsrZKuhnZR7riMmnd2bMf9uZF5dCQR/vk\nfeR4UbEL+tZMlwznA++RzW8p4lRxpdQ6s8Wuqwl/9+QPP3La1XUypgZX4d+hs1iz3kpZb2KWapCx\nvtx/i3EWNQbOyRosrM9fRfVPPGWyHkbfL+W8Syds91m6XVrA8/dlK8fpTlmboHQragXx3le4StYJ\nme7GHpUqR/zKzpa69lQ2xmmRaiIc+iEse//ii18U7znfh7oof/AfUEPDri0VploMfN/zrNoi991B\nNptki2nXOeN4PT2G/d0uTZJj7RnpZm4O68pTJusJuFyYT1PdGKeCBlnbKEo2v7z32XvB3BReO3gJ\nevyOPlmb5tRpxMT/8uUvO21hb2rFgwJaS558zKvI6FXZrxx11bKoFhHHeGOkDa6vPnjTfjO0vwSo\nTpvbExT9cvNvQ8Gg/5/h/aj74KmQMcDlxb3qfw3xoGiTrNnja8b1JsYQWyN9MvbUPtbmtDlmeirk\n3Cknu+fgSqyX8opH8HcScn4MXHrTaedX4Pwr6skZY65moL5N7S7Uqel5+wPRL3oVsSIzG/tdzyvH\nRL/CdTe2xi1eJ8coaK3hdMO1KmetupU89z1VGOtCq/aSqOmwyP/dsiIeRp2KhTnE8tkpWQNi/BLm\nTHwYtVAa7t3ttKcG5bNBIxXH+tmBA067+t57RT/ex778Z6iJ9qP/IesX3b0KdY8qt2Gv8VfVi37R\nMcQRnsOeSjk382plzE4nTCt+7gAAIABJREFUDU/Aonz4qHxuS0Zwlvc1yPM6wzXSuM7RzLQ8py0u\n4AYvUF22j13TA7ucdiyCs1dpBc7+kYi8h1z7JBbD8+z0dbkvVq1CvbS5OTwjJKNyzeYGsVdfp/or\nvspu0S90AfE6QfXcKnctF/0ysm5vTkV2AHW3JvusGoz01bLoHN11VT4btFJ9zxGqo+Yuks9MvS+j\n5lVmLsZ9LiprEc1RTcKxacylFcuxJiao5pQxxhTTM4SnADH5/V/IGLi+EWfowDqs39xheT5nm/bm\nOvxWUPmQLAA4fgz1dP3VOE+PnZTXV1Qiz3A2mjmjKIqiKIqiKIqiKIqyhOiPM4qiKIqiKIqiKIqi\nKEvILWVNA68hLX5mSqaVeXKR+tT9GtJ0C2tlytrECaTyVJUi/fboczK1aMunt+Dz/hkyi9SctPLq\nn0Cadxv9tBQeQ6pixnmZfnvm7Fl83t696Dcu0yevXECK7Jr7kDI/Y1lexQfwtzzVSJ2auCIlXWwl\nneRUwyqZahg6Rilhd5m0M2zZ0DEskZlPzt20X9+LuMf+FbiPhe0ytXQ+idQvttG1U9a9ZZCZTPTg\nsz1kUz78rkz7q1l24/TohVl53Tu++rTTTsZgY1d9VaYbxim9Na8G9zGvXN6fuTi+U4yslu1+2QGZ\nspdO8sgaMtot5Xic1pnpwpgXt1eIfpyW9919+5z2p3ZImcsQ2cOz7XtfSI4fy8QeuvMOp/3Ch7Cq\nvLNZSrBSEUg98pdhfdRPSzu6LEqt7ziLeXD5lQuiXzKF7xTcgO/rj0tLRU6XXSBLvDnLsm/8MNKD\nG+8waUekzF6Xsj0vSfDK74alZsKSXrkDuCdZ2ZgXqZj8vBWPPeO0p6dhVR4OfyT6JcNIoR0/jljE\ndoE5g1KKmUlrs/bxFfjvbrmlZE4jdvJ357RkY+Ra4vUWH5ZptTu3YJ/IKYTsZfB1GXsr7llmbhdl\nlF6eCEk76QwXNiWO87nFMkV25ABkfJlk7etbJiUhDRufctpXD8HKN7hcru0kpX2PH0Fa7Z1PIdV8\nkCR1xhizae86p82yzsnrMr4UL4c0Y4okT4tzMp186ixibePn8Nl9P78o+rGkbehdst9+qEX0mzxP\n0lqpgk4L4SuQrRS3tcrX+nqcdmAZZMzdL8hzyyLZqsdorp65Lu3Dm0juUFpQcMP/bowxf/r0406b\nJQgsmSq5s0a8JzGBe+/KodR7S3I80nHSaXPcu/72FdGv8TGcfdhSuXSz/LupGXwGS4Fn45Zdr619\nTiMzJE1PWbKUsR669kacN8YP9ot+1Y/j3ifGsC7t/T1YBN/arCzEnrkWKZfOyEAMzMvD/nf6+b93\n2gtzVvwj+cnZ772Bv5Mp///pxIeQkl18B+tq7ZPrRL/p85jbq34fMeTi96RshmPy+H7sfb1vy3ha\n3IYYYClq0oKLZP8eko4bY4yhvYIloIFGGQMTUyQxpPkdOiXlBDnFuHf8/eND0rJ44iLG8EQ39rv7\naN4nRmT8P0vrPkVW3++eOyf6VZFEf1kIsXdrq4xD/HfbyZa9YssK0Y/3zIKVGD8up2CMMf6W2yfb\nvkIS2tSEXIsFZHE93YVz5ORJKe/LKcLZhsNG3QNyb+DYVtCMz8503TzfoPcXeM4IfgHlLTIzc0Q/\njmtD72F/yvbLfmeP/sBpV+/FfcvJl2M83Yt1dYHkXhnWpfL5PLiW9wXZ0ZaOpxu2aS9bJtdiYDXi\nwORJ2KVXBORafPNVWIvzufafX5Xyy2f2QHb2jz9A2ZPP7N4t+vnpXNRCMsX5GK7V2xgQ72kP1Tvt\nvn6cTRath9EU2YVnuDCxktbvA7VPYM1NXsDn2TbvfO9SdP5NWrGieIssg2KjmTOKoiiKoiiKoiiK\noihLiP44oyiKoiiKoiiKoiiKsoTcUtZUtAkViQtmZPr/5AmkozU+SlXsJ2QKPrttfPONt5327z7+\nkOjHcgJ2kRiflqn6e+9HWvvlcz1Oe9VWK+2NePnH/91pZ5PDzsRRWWG6fc9ap33lHaTAtWyUKfLR\nq6g+XVgOGQ6nXBpjTPQ60l1LdyIVvvdnMs2bU/luB7klSOPMq5YpcaNHMdbzlBpZdle96DdO0qts\nH9L7WMZkjHTrKt0J54iSlatEv6GPILMobke6dGoGqWQ5pVIKUPsQ7s/oSfwd26kl3I/0/cQoUslY\nPmCMMQFyUlhIIUU/FUmKfiGqAl55X6PTHnhTpoN/TLuVRsR9s901xP1Aip4t2eHre3IzHLIK6qSU\n4thhSIfWB5CWvaKqSvTLy8HfzXTjd95Pfxpr+8M3jov3DL6NNNFCclU7cUGOJcuVUvO4N8vKpGtQ\nFck7fA34Hr0vyjVW9zTJFMllxF0gU1UT1hxJNyypSsbl/cml2DTyIdKj/SukRKtwDdImRQp9gXQO\nGup832l7SFZz7bmzol/JdqzT7qO0dugeeNzS8aJ4841TMtmlxxhj8oqR4rsYwNxMzcgUzzg5ovEY\nFbRJl5Dr5LpSSrK9wBrZbzYs06rTCi0/jovGGNPwFGKU24+9iyVYxkhpmstz8214dAh7ZuW6rU47\nGpZuVCwhZblgFsnMfFY6tJtcZcYOkNtHSsb011+FTHFXG/b6wDq5Fl0+zBGOjexyY4wxEdoXC9fj\nHoZOyrFcIMnQ7SDQgnWUjEtnHr5fw0dwFsi19pqDP4dEcJ5kB5vapcPGHMmStt8FjZa9JwVpbWeT\ntJNlgNn5MmbNk3tYpB/fw3Yd5PGMU4p1yyfWiH58RpqLIUbZa7u4BTE1HoVUyJUj9+2MDBk70kn9\nJ1c77cmOUfHa2gfgonH9OUjlpyJSKtlCTl2cks5nAmOMGeqEFJjdrvJLpNyLnb7OfuPHTjuwGjGK\nZYTGGPPRT7FPrtwGicQvXpAyAHYDamlD3M6zHKPiSdy3iR5897onpNvh+Anct9Yv4UzQ86KU4Yx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/AeWqfhDvmszPfXnotM3+sox1BFz4uD71glIYZubcH863LpBexV9WSXbYwx2QHE8nAHlbAo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tQ5n2234a3922760oxDqWnIT76Rcjx2/7GYwXvn41J6tFHkunLwdaj2FNsud9dz32Celr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\n9P0Dol1YAO5rcDRSPANDZOp5D1ndps2FdMlzu5z2o+djLNU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HkIoeOEKmtne34d6dyoK8\nj624jZFjLpGkQmHDSIL11SFxzAiyZU68GGm83c2doh2f+9jLIVGq3Vcq2vnESdnU/6HmoGwXMx3n\nVPwt5FnnauV8FdvUf3aTLLsac5u0+PTywj0Ydd3NNn7nbikpWvnUrTZ++76XbMzrhDHGjEtFyuyp\n99bYeNi1S0S74XfPsfGuv31j46bnYFu97shLfIi57ka8x5pHn7fxv1Z/J9rdfBHkiywlfurhN0S7\nKUMwb6585h4b19XtFO0ufvRivFa928b/eFJaaadQiv/D/3Z1Er4xhux7e7vkda/aD+muT6Sfjcv3\nnhbtUmbPs3HISKx3nZ1SeuTrh37bWoz3GH7tMtGupwep8smrMD+0lmHujr9Qps2XbMjCayRdOvCB\ntG8fs2yMjUs3Y20YfIWchxIzYctbW4v5Om3FTNGudB8kEh7+mK9Y1mPMf1oju5L6LFznaprXjTHG\nwx17lui5KTau2i3bRU3HPqOnE+eeu0+un1PvxudvKsA6lPu17BNDr8J1rthaYONgP/SjkxtO8SEm\nczjOL4IselliZoxcc30i8FrxV1KqlboK5+DuDmnC/md+EO2ikrDv8Q7F+bUUynWwah+uWYzssi7h\njy/dbeOeHrmvWv9vlAHYe5bsh+9/RbRLi8b4G3oJpKfJK6RM/YbFv7ZxUyv+1jcH5XxWuhXj6tKn\nMH+XZW2x8QPPyXOYtwXX/eZfQrLIe1xjjDm2Fs8Gs6/Gpn/De3LvOZGeVwZPR5y9Q0ouRt1yrY2r\nCvfY+Nbb5c1KWyLXDVfiTaULfBwlBBpPQeYSNh7yw6pdcizyc0ZFA/YlIV3yGSyUpJwhY3Dfz3wj\ny2okUcmDc8exl0idiWvZeFLO1UNug+SwYhf2TSHDpeSsdD36YuhYnI9zD8kS7qxcfN7cCvlMfc2d\nWBed/YXh58/+oKsR+5tqx/0pzsPz7ohFGFc8Lxkj9+I9vVhbd2VliXZzJ0KyxVKze5dLWfCT771n\n4yiyZe92yOOZ1qYCGx96FfuMmDR5H/fvxVwcTX3Ox1Eq5fv9WO9ueGyVjb95Wu6XeG88YTn2vN0t\ncm8cFiZLgjjRzBlFURRFURRFURRFUZQBRL+cURRFURRFURRFURRFGUB+UtY08cFZNnZWb2eZRd0p\npGf5e8tq2W3lTTYOD0CqmzPtbcZ1SO3r6Wm28ZePfCHaXfrzRTZe96/vbTyB0v+cqeHJlB598+U4\n/uTRXNEupBHpcR1VSCubdPsvRbueHshoik99if/vlOm8LOsZRNeLU2yNMaarwSHLcTG9dF5R0xLP\n+1pbJa57r+Ma8v3uaiT3J4cLUzvJMcq3ISUw/boxol1AFM6jvQX9p+YwUg8LPjkhjkm/CSliJeR0\nEzFRunx0UV9lJwuWHRljTC/JPhrPQH7hHSVTiZuyIJlgZ6mmPCml8AySfd+VcNr43q8dEpNhKTbe\n9ARS7Gb/4kLRjtMrI1LgVHbqAznGsig11NMDU8Qhh6PS9F8vtXHFPqQrcn+OXyxdc+6hsVRXh7R7\nN4ezlBdJCUopBTj6whTRjuUIYROR0u8XI6WDHa10r+graZYZGWNMM9/TScblVNKYaG+V0pvRV+Ge\nVJLLWHujnB9iZiANf8/n+23sWSzdmtjRISUy0sbOz8z9PTYBadCVlehLaSvmiGNqcpBGX0OSxa3b\nD4t2c2aMtTHPITWVMg3WrRqyjdRo9Bm/BOnI0VFNso9LIcHy+k7O5QhRw2kAACAASURBVPUHyb3p\nEuNShs2Hi9AvLpJl9h9+5U4bf/3I2zYeNVK6WHl6wt1g7lK4rUVMiBPtil7E3BiUAQlC6cH9op0/\nuebVNWMen/3kz22859az4pjMFdfZeOPyK2zc3CxlGk2ldA4FkG3NjZst2h18F+O5owPHBAZK6eDp\n9Z/bOHUp5Iurpk0T7UbcO9X0J0Wf4XP6Jsr5gp2camlNGrpCpltX5sKBJzwNad5ZH2wQ7eIWQEbK\nkstzB3eJdvVHcd1SSG7E6f6578v5f9s+SEJXPYzOPtfhWMbSlKmP/Iw+wx7RLnfnahsHpUHK2lhY\nKdqx86ObD9aJ6oNSwtx6rsn0F43kCsKSXmOkS9S+15HWPvOheaKdGzlTuntjP8SOKcYY00VyxhZK\n4w8M9BPt6k9BJhFH96D6DawtSdOlw0d7OcYsz8/n1kn5CjuPttNc6O5wvanYCweu9jL0jyGXSIkP\ny1DLv8f63lAi52e+FqP6QdbErqGb/vC6eK2gEv1uTibkSje8+GvRLjAQ60FHB+7BFw89LtqlxmBs\nP7cWf6upXs57vM+tzMc47WrCNbtsuqxDEB2M9Yol/1HT5b47bj4co3rJ6Sw2VLrelJBcd9J4yOom\nj5RlIVb/AqUbRs+GxHzfpmOi3ZCLf8QWxkV00vXydEhv2N2mfBP6WXm1lKmnTcK46DiDfbx/qNyT\nd9ZBNtNwAve6o1vKXPb/gGeIPJIRfbkPa9WNc+bID0JyVy6/UHtU7q9CRuG58sxX+DtxGfLesMvT\nuLnov8PLZNmGo99ANjNqEeZ+lrgbY0xnY/8+L8YvxZx17hu5ZxhxEc6/qx7nkXW8QLSbfAU2z7OT\nMSb2b5XlC9iN2SMHrllD4+Q+6IuP4N4UkIA5sD4b9zQgRTpYlpDsPSIKr505Jt0JZy6FjO3kVuxr\nnW5a4+k7hg767FNmSllwWwnm8pIt6OtBsVLGFDL6p62ZNXNGURRFURRFURRFURRlANEvZxRFURRF\nURRFURRFUQYQ/XJGURRFURRFURRFURRlAPnJmjNsiVixs1C8xprJfc9stXFkgrTbzd8BzRXbPLbm\nS01r9Qm086LaHe0OC7W6Q9D9zVyIGg05ZHvo7rBMHpkMbd9XG6AdLayUGmrW+o6ePhTtTn4s2oUk\nQNMZGAf9eHDwWNEuZ/snNq74ocDGZdnSQi1tgbTGdDWRE3CODWerxWusqQxMDj1/O9KQRqSjfkzB\n99L6L3Qc9JbRs1Ns3Jgj7YCDY/GZffxRx6BkJ+5PgJ+vOMbNDRr3wcsusHFPj9SG12ZDU9hFlrpO\nC7rYJejDNfuhk686I+9P3ERcv/rjeC1iitQRNzqumSvZ+RFqGyx59CLx2tk3UINg/GWoy7P/pR2i\n3chV6J/fPvKijS/6yz2iXfbDz9k4Mgg6ydv+V9rZFn6L+iJDV6D+TF8f6sAc+4ccO51LodVsJ7vA\njKWXiXbv3ftHG09ZhP7mHSr7RDPZQHdSrRu2zjbGmOKNONe0G3EdNj/+rWgnbO6vNC7HKxR9uKtD\n6qPL12MOc/PC9+YJC9NFu5NfQkc+nmzV3TzdRTsPqkPQUozrVJpVLtoNyYT2Nf/Yv23cUQtdt4e/\nHL88XtKvwlgMzJDz/9l10PAePgQNcGZqkmgXPBI1cZpJq+8bJ2uBsH77wHvQjYf4yboPERmRpr/o\n6cF1uW7VAvFa3SmsKeMWodbKh29Iu8UJP0ctGN9o1F+LSVkk2tU1o382UC2LyiJ5PxrIEnb7adRO\nWNaLc126cpY4puQEzqn+JM77sht+Idp98S703j0d2BM0HJPr5/hroTM/+wbqmIy4U/aJp/+BPvbq\npaj/4R0k65cJbX20cTlRs7Ev8IuV/ayHxmZgeji90ifaBSek2Lji6BH8f6bsf2y9zPVKOutk/QBR\nB43qX/HaPMhd/qY2cyLmgLoTuCdc+8QYY7zD/p/23jPMqirrFl6Vc845VxGLnDMoAoKgGFBpEWPb\nard2Gxqzr6G7tc1Z2xzagEgLSEZyzpmiigpUzjmH78f7fHvMsVt5nns93Pozx6+JZ+5TO6w119rH\nMeZA7fT0hJ1taNJQyqvxQH0p34U+AIH9WCMv60246IMle5cY89/7MUeivVxYzjpzLy0v0UsrwAvX\nLvsFGGNM+dY8K/YVPV0m3MnzJSAONavmCGro2fPFlDd6DHrg9XRhLQwTttVdLVz7e0TvtLZazNnw\nCdyXomApbF9DR+PvdLfz99Xswz7ZQ/T7kL2pjDGmYDnqs+wJlnETj4n2uotnh26MMS6ip1JYAJ/j\ns0tftGIPD4zBox99QXkZN6IHz9mlsLsevnAk5Y3xQ++WE598b8XSjtoYY4rd0cds8+vobznxD5Ot\neNEL19Mx0tq9Q/TzaSqqpzy596kTc3bsH7mP16zRd1rxtS+gL9jX939JeVNn4RprhDW0tBc3xph3\nbl9ixX/5kr/jt6JB7PHtPStl/x65R/BJtPeUw31JCEcNbapporyuvXgvDB2PfbiTO9fGYz9j3zwm\nHe8cRaKXT2Mr14PmEjwr+U7YnM89K31F/8n4ITiHpjzOi5mNvyt7DnpEcN/VZBe8Z8i6Ye/f05jL\n78SOxslv0fsmYy73UznwNXrdpQ9DD5ZBU7mXVcVWrBubjqLPzNV38P7m4H+wZv58HH17pj88g/Ja\nytDHpUHsfVx9cG/OfH2EjkmZg/f0+jN4Nxs9ehTltYpeq0PE+1Ptcd7fBP5KT9FVP+2if191k+j1\neRjfETmF+4w52dYrO5Q5o1AoFAqFQqFQKBQKhULRi9AfZxQKhUKhUCgUCoVCoVAoehEX5Jvmfg06\nUl0F2yH6Z0CKMvAmWFFJaqQxxjhtAHVH0luDR7BVVmMOaGaS6jtxJtsjNheAciYt6NLGwKrSfg7x\noTjXDUdB2d22my0kU6NA9Z096xorDglhemtJwSorjogFTau1lS0k40aCsp1dtdKKYwex9bObD9PW\nHI2GAiETsFmYuwgbSUmnDciwyQKEvZyfHyhsUePYXq6jtdn8EqLHspV2SwtsPSUlN0JYBErLQmOM\nKd0FW7fkaaC9ubszbb4pAOckKeBNxUwt9RPXWLY5z4pT5/WnvMpdOFdJe+7uZOt0n3i2cnMkRkzP\ntOLmUp6LUkrWXIjPksewfW/lTlzHtCdBke3oYInhhHsmW3HJelB7JdXSGGPCx4LmffCFz6044XrI\nOaIvZzvXXe9BaiWt6rptNvSDhuK4xOmYf3WlWZRXfwoU3vZaUGd7Ovj7GrMxB6T99qznbqG8jU99\nbi4mzp4E3bOrm++nn6DehwWC7tvTzVKKzBtQE9sqQPetPcY0zKZazEV3YYm+L4dtpzPOYz6HD4Ed\n6bHv1lpx0gKmt8rnVbgFdFJJTzfGmKQJoi4LiWvQ0CjKy94IK/bwaMwxafVqDFvGugj7Vb9Atj1s\nLfvlOuQIrHoY1qwj7plAn/mGgLrq44P5d5tNTtDTA2ryj++vt+KC576ivGe++7sVn3gL686Yh5ke\n/MYdb1vx89/BYrapBmtSxhVso/rn2Yut+NYbIJU828aSqepqSE39/FAbD7/Oc6V4FepzXgXmZeBe\ntn7+ajdowCsfetiKxzzMcs1jr0FKEPfs1cbRqM/GnsPFk7dCRStQZzxjIHmq2MbPZ8DvYa3dJuxd\n/ZJ5TZLSGSm/cQtgqnRLE8ZFQCr2LU5OGOsBA1leVHMIEhvfJMifpATcGGMiJyZace4B2GVLqaAx\nxgSnY9zWe+Me+UTw3z2/HnupKiH39YtlqULERKZzOxJxV6N2HXmVZbyBsViP/YKx7/GLZqlHdhHq\nV6uQSQUO5rzybZAkSGvzqgZej/d+t9+KK8Vn/WMhW4hNZsvk6Gmok6VbIcv27M8y3o3HsCcX5Hmz\nO4vXxUliv+Up9nx2uYm7kLrFzYKU/8xbeygv9qo+5mJCypU7bfuq7O83WnHa1ah7YTZ7aonYGUJK\nsruAPqs+jPny6ebNVvzYCN6Xl59A3rDZkEJPHgQpk5R8GmPM3Y+/ZsXvv/aQFbcU8xiRe5DOZsz5\nkOjhlLc7HzKSokNbrHjCeN5Pxwnr5fJjWE+Ka9iqetoVLOlwJPzTUa9qT7GkUsrzag6jLp07ws9G\nnq98b3N2YglIdRP2PW4nIIfdtoetmmcMh0xl4RNPW/G91+MZ9lvIEr7QJNzbtjbUtZoj3O6g+hCu\no0s8Q48wlljnLj1hxUF9xXtVD9dd73jsEfzTcO2dTbwH8ieZreMRkYxzLF3PEtXkdNHGQ1xLVyvL\nKmPm4hyHN0kJH4+LEwV4/rEhOKZyXyHlRU+FtH//S5gHrmIPmHIFS6sC07BeyTUuqC/P80Z/SJ5+\nfhtyyKHTBlJe1s5sK3Y/hvOLCOD1zlm8U9c1Ypx67OCx7uyBvAR+5fzfz//7PykUCoVCoVAoFAqF\nQqFQKP5fQX+cUSgUCoVCoVAoFAqFQqHoRVxQ1iTpWRE2OYxvHCijZz8AjVPS9YwxplrQOjPHgbIX\nlMSSi+ycbVY86K4brNjJiU+xuRk0q/oCULY7Ba2qx0bdPJKXZ8U3TgAN/fdXzqQ8eb2Vx0Bh9R7F\n3fi9/EHJr6wA5VI6AxnD1FcnIbUqO85uKYVHcB3p443D0VoOapWdwiydQqR8otkmAYrsPwafNePe\n2J+PfxA6ZPcI2l5bG1OsvbxAcyzYg2fvmwI6uH8SU8PdPUD1a2qCy4CPDz/HFiH1CBSuLbWnmVIn\n3chqqzBO/URncGOM8RMSPum00dnEXdP9Uvh8HQlJfd3/xV76bMYzN1lxTR6ody0lTKWtLBXypU9A\nDXRyYcqoTwJoeqGjQWNc/8I6yuvowv3bexaShuuEw1qQoGcaY8ykhy6x4nOfgU5up24OvBk1oKoI\nFOusTw9RXshAUM/TbhqLY07k8bkKt4AI4Syy4pEPKa/HRjV1NKS8qN9lzGV08US9aC7CsyveyNTS\nSOEyU7EL9MrwieyA5CRouNKpLCjHJgES86XmXJ4Vh4yE9LSjmcd6VyP+Ld0E2mvY1UPKkPKF1CWu\niV1I0meibqz/HPVgSH0i5YVOAJV9yEK4A3XU2VxvbOuQIzHj+Qes+PN7n6TPZj0Cac6xN/5jxbV1\nXFPef/prK15wPRyfHn/hI8pb/tcPrHjkPMjZpvZll5BvvvqbFf+wZKkV+3mC8h0fc5yOuf3mOVac\nvR9SihfGsWTqw42fWHHREbjzDbqXzyH35w1WHOaFsRgxtC/lfXXPvVZ81T8ha3JzY6nH0AeYLuxo\n+KeiXucLFxxjjEm4BhTpVuEgYmxSxOp83FMpGXbzcaM8v3RQtv1TEOd+cZTy0u/AHinnC+H+JNzM\nvKNYIicd4OqEW5jdJSnn08PmlxA7N4P+ffJtPEevaFxT/k98fIeQnwck4tm1lrCzStkWjK0EHgq/\nGd7huBfeXuz2FTQE+zSPQPkZr3dyjZJy/eoDvO+T+yNfIUu6avRsynvs969b8YB4zINkQbv3CuMa\nnP2vA1acKGTB1cdYSuHphnH13U7IDa8ZO5byIqdify2vvdjmpCVbA2z/G+Qw0qXRGGOy/o21Onnw\nDcbReG4h5EDTB7PraY9wdv1xyTtW3GZzcr38KYzVD++H5HJQAq81O05j7zhQfCYdCI0x5piQXKSG\nYq3eW4oWBe7uLP//UuxHvEJxPlVHeP8r92bJ10NG4+LC42LLUy9Zcd/FqP8vL/2M8m4S7l9ldXAL\nOl3E1zR7CrtiOhLSkdYnkSX+NUfFOBYSpahgrvlnijHnpHOmbyrntZWixhTnoeZNGsdyr/Yq1KjP\nHn/cimNmQzZvdwCtLcNa4CKkJ5FTEilPOuPVnccY9WrkcRkxRrhJueE90NWb21n4CjmolOL5JPC9\nbBIOpb8kh/mtCBqMNgmla1gCHzYO11K2KQ/HDGeZ+t4PIV2urMe7ZFA41xUp+/z9TdiP/PDtZsqL\n34R1tn9/yGRlHfYMYTlZRxOevWc45mLZbr6m8j3YQ6dE4tqfffFTysuIgRxq3iy8qA+bwrVXvqMk\nToJcVb6HG2NMsLjPvwRlzigUCoVCoVAoFAqFQqFQ9CL0xxmFQqFQKBQKhUKhUCgUil7EBWVNfmmg\n33a1MFWrpRw07YhpoBkFpHAnaW9vdFnu7MQxZUeYzjt48R1WnL0FTgJZq09RnnTykBIdSdsPzOQu\n+/FhoB4GRIE6tnU3n8OcW9H/vqUE59rVxXSk3BWgk8Zchq7wBT/wuXqEgi7XlA9qV9rV3AXayZlp\nto5Gaymuxe5kJeUejefQKT3xanZnaWkBza7mLFx/wvtxXn0VKIEVe5EXMow7ZHtHgu6VOHUKzqcD\n7hB157m7dX0DXEQkNbyri6UUIf1BvavNAZ00qB+7TRihYOlzHeiQ0rnpf/+AcKrKwPhrOFtNaZ62\nLu2OxKHtGFuDxzI3vKcHVPvKvaDoeUX7Ud7g29Cp3zMY88DdnaVHHh6gKH57PzrcT7iVNXcvL/nE\nii8dhPvXIujG7aeZlr15A+jbToLemlJdR3ku3qutuOEMnrtdduQdDZpkyU7Qld382AUlcS7kAt89\nCLrinMeYkr7272vMxUTSAIxNZ3eei27+oJ/7C8pryBCmjFYfgSwyUMgdtv57F+WNn4/nfWgVaOlj\nh3JX+7YKyDaip2JehqaBM1t5hiUxecV4rv7NQjpo68YfNh7X6+WOa/rqU77PmYJe3tnFbh0Szm6Q\nfu38aIcVD5mVSXl5W0Bd7c+qx9+MPc+/a8U3vvYYfXbsX/+24sH3LbDikmPsfjJtMKQ9c4ZMt+IV\nh1g62NWFZ3P4JUih3r7nbsprq0YNXPzOP6341H++sOIvPl5Nx9z7Ctya2gTldlBJIuVJF8Ltn+Ce\nT3+Y68uyTyCHue+jp8QnPGeHzEatOH8A7gg2Qw6z6wu4KS56l10bHYGAZKxJPncy3bo2Cw4Oci9Q\ncJxlApJyLqUKoRnsbiNdA6X8efMxnldu36NuSVlSznq48fh6snxn4J/gCnn6XcjOwiexnEM6n/VZ\nDOmadIIyxhivW0EBbxNywc4mlm03CIfN8NGQ7+R9x9cUP4/rjSOx+VmMaVcXdoqrW47ziB4MeW5L\nNO/n3PxQl/zE8/QI5vVcukrWn8WaVLWTnUUeuv+XZT+Vu+XazG0C0u9ArV71+A9W3N7JLiiXzYa8\nvEbItOVe0xhjNryGudgnCTU49eYhlNdcjjHb3Ya6m3gN7+tKt+Wai4nLh2F+l9aye2RYIvYns/8C\n6UPO8h2UVyVcgKYMxR5bSkmMMeZSD9SfA+cg8+rzO14ooqfj3eX0x9i3/HsH/u6rK9+mY/xjsVZ/\ndh8k02mRLGGQknBfIQHyGH6e8oY/eIUV1xehncAz3yyhPE9PzL/sh1614ntfXUx5O/6G9eXKVxy7\nMMqWBFLqbIwxAcLJSaoKC6vPUF6gcPAMHoZ75m6bi7VnUJ8HzBd79w08TgOE41roUMi0pezZ3Ysl\nU/VVmKdNhdiX2q+p6hRkTWGD8NzPClcfY4zxqcHz9e+DvZKfzd21aB1aA3iKdhPSkfP/BcqFXEm6\n4RljzJFP0FKhsRVrQ2Y6t3SQTqzpozGPPv+c9yBjMyCplW1ALh3K8jQ/8f1yLZXv/V4hfD+dnMRa\n6onn2JTL9SVuFt7hz/4Hzlo3T5lCeXLd9UnC3yr4jiXRDS3Yi4WItd47lvcYx75ATUke8t9rhjJn\nFAqFQqFQKBQKhUKhUCh6EfrjjEKhUCgUCoVCoVAoFApFL0J/nFEoFAqFQqFQKBQKhUKh6EVcsOeM\ntDIu25FPn+ULfVxwEvp/+CWwfs/LC3rXhgb0zbDbl1WVQyvd0wmNevww1k3XnYLO1lXYnEnrtrZK\n1uhdciN6ZRRthibxklkjKS9AWCrGjBxtxc3NrGMM7I/eJVUHoUEPHMg9TdyDcY21OdyfRKLTZlPr\naPim4Jl4RXCfAGehx3VyhRi0uYxtmOtOQ5srNXvNhTbL7fGJVhw1CVrDrna2SpY69wahO20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0t9YeKVrGXL+miXuZgIGQz9YqlNv9iYK/rfCJ2fXTMaOAjPJ2fNGStOT2frupOi94i/F2wfa5vY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\n4boP4lryAAAAAElFTkSuQmCC\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..489c9b3
--- /dev/null
+++ b/sparsity_and_l1_regularization.ipynb
@@ -0,0 +1,1175 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "7325c8b5-417d-48cd-e84c-174ff854af65"
+      },
+      "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": 8,
+      "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       2632.2           536.5   \n",
+              "std         2.1        2.0                12.6       2190.2           422.4   \n",
+              "min        32.5     -124.3                 1.0          8.0             1.0   \n",
+              "25%        33.9     -121.8                18.0       1459.0           296.0   \n",
+              "50%        34.2     -118.5                29.0       2121.0           431.0   \n",
+              "75%        37.7     -118.0                37.0       3137.2           645.0   \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       1423.2       498.4            3.9               2.0  \n",
+              "std        1168.0       385.6            1.9               1.1  \n",
+              "min           3.0         1.0            0.5               0.0  \n",
+              "25%         785.0       281.0            2.6               1.5  \n",
+              "50%        1162.0       407.0            3.6               2.0  \n",
+              "75%        1719.0       603.0            4.8               2.3  \n",
+              "max       35682.0      6082.0           15.0              41.3  "
+            ],
+            "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>2632.2</td>\n",
+              "      <td>536.5</td>\n",
+              "      <td>1423.2</td>\n",
+              "      <td>498.4</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>2190.2</td>\n",
+              "      <td>422.4</td>\n",
+              "      <td>1168.0</td>\n",
+              "      <td>385.6</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>1.0</td>\n",
+              "      <td>8.0</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>1459.0</td>\n",
+              "      <td>296.0</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>2121.0</td>\n",
+              "      <td>431.0</td>\n",
+              "      <td>1162.0</td>\n",
+              "      <td>407.0</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>3137.2</td>\n",
+              "      <td>645.0</td>\n",
+              "      <td>1719.0</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>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>41.3</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       2671.1           546.3   \n",
+              "std         2.1        2.0                12.6       2155.2           419.2   \n",
+              "min        32.5     -124.3                 2.0          2.0             2.0   \n",
+              "25%        33.9     -121.8                18.0       1467.8           299.0   \n",
+              "50%        34.3     -118.5                29.0       2150.5           440.5   \n",
+              "75%        37.7     -118.0                37.0       3182.5           658.0   \n",
+              "max        42.0     -114.6                52.0      23866.0          4407.0   \n",
+              "\n",
+              "       population  households  median_income  rooms_per_person  \n",
+              "count      5000.0      5000.0         5000.0            5000.0  \n",
+              "mean       1444.9       508.0            3.9               2.0  \n",
+              "std        1097.8       381.9            1.9               1.3  \n",
+              "min           6.0         2.0            0.5               0.1  \n",
+              "25%         801.0       284.0            2.6               1.5  \n",
+              "50%        1180.0       416.0            3.5               1.9  \n",
+              "75%        1728.2       611.0            4.7               2.3  \n",
+              "max       11272.0      4012.0           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>35.7</td>\n",
+              "      <td>-119.6</td>\n",
+              "      <td>28.5</td>\n",
+              "      <td>2671.1</td>\n",
+              "      <td>546.3</td>\n",
+              "      <td>1444.9</td>\n",
+              "      <td>508.0</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>2155.2</td>\n",
+              "      <td>419.2</td>\n",
+              "      <td>1097.8</td>\n",
+              "      <td>381.9</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>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>2.0</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>1467.8</td>\n",
+              "      <td>299.0</td>\n",
+              "      <td>801.0</td>\n",
+              "      <td>284.0</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>2150.5</td>\n",
+              "      <td>440.5</td>\n",
+              "      <td>1180.0</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>3182.5</td>\n",
+              "      <td>658.0</td>\n",
+              "      <td>1728.2</td>\n",
+              "      <td>611.0</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>-114.6</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>23866.0</td>\n",
+              "      <td>4407.0</td>\n",
+              "      <td>11272.0</td>\n",
+              "      <td>4012.0</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                          0.3\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>12000.0</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": []
+          }
+        },
+        {
+          "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                          0.2\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>5000.0</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": []
+          }
+        }
+      ]
+    },
+    {
+      "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": 588
+        },
+        "outputId": "9a18dcfd-4846-4727-d565-8b4b8b2ee55e"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_classifier = train_linear_classifier_model(\n",
+        "    learning_rate=0.1,\n",
+        "    # TWEAK THE REGULARIZATION VALUE BELOW\n",
+        "    regularization_strength=0.2,\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": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\n",
+            "  period 01 : 0.29\n",
+            "  period 02 : 0.27\n",
+            "  period 03 : 0.26\n",
+            "  period 04 : 0.25\n",
+            "  period 05 : 0.25\n",
+            "  period 06 : 0.25\n",
+            "Model training finished.\n",
+            "Model size: 722\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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T63RkGkVhUWgAigLf/RRLZVX9ycAAs7rfj7WJFT9eDSOzJLuVqxRCCCEMQ2/N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\nUU77/81Bu5tRB8smAAAAAElFTkSuQmCC\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": "fda310e8-da28-4b27-cc5c-2650450c3c97"
+      },
+      "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": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "LogLoss (on validation data):\n",
+            "  period 00 : 0.32\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: 735\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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d5FbkW7BCIYQQ4u6SZqaNcbKzYmpUIBVVtazbc6F+ua3Olpldp1JrrGXl2bW0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\nQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ 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..61ce23b
--- /dev/null
+++ b/synthetic_features_and_outliers.ipynb
@@ -0,0 +1,1323 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "bcd17cda-6a45-4295-bb06-ebbcb30e24f9"
+      },
+      "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>7632</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>1197.0</td>\n",
+              "      <td>231.0</td>\n",
+              "      <td>671.0</td>\n",
+              "      <td>219.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>278.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6213</th>\n",
+              "      <td>-118.2</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>1193.0</td>\n",
+              "      <td>280.0</td>\n",
+              "      <td>1210.0</td>\n",
+              "      <td>286.0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>89.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15130</th>\n",
+              "      <td>-122.3</td>\n",
+              "      <td>37.9</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>2491.0</td>\n",
+              "      <td>474.0</td>\n",
+              "      <td>1098.0</td>\n",
+              "      <td>468.0</td>\n",
+              "      <td>3.1</td>\n",
+              "      <td>213.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9007</th>\n",
+              "      <td>-119.0</td>\n",
+              "      <td>35.4</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>2594.0</td>\n",
+              "      <td>478.0</td>\n",
+              "      <td>1419.0</td>\n",
+              "      <td>480.0</td>\n",
+              "      <td>3.7</td>\n",
+              "      <td>83.1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13925</th>\n",
+              "      <td>-122.0</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>1269.0</td>\n",
+              "      <td>290.0</td>\n",
+              "      <td>556.0</td>\n",
+              "      <td>266.0</td>\n",
+              "      <td>3.8</td>\n",
+              "      <td>325.0</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>7719</th>\n",
+              "      <td>-118.4</td>\n",
+              "      <td>34.1</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>1509.0</td>\n",
+              "      <td>291.0</td>\n",
+              "      <td>690.0</td>\n",
+              "      <td>259.0</td>\n",
+              "      <td>6.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14996</th>\n",
+              "      <td>-122.2</td>\n",
+              "      <td>37.8</td>\n",
+              "      <td>52.0</td>\n",
+              "      <td>2504.0</td>\n",
+              "      <td>516.0</td>\n",
+              "      <td>979.0</td>\n",
+              "      <td>472.0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>244.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13855</th>\n",
+              "      <td>-122.0</td>\n",
+              "      <td>37.4</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>1709.0</td>\n",
+              "      <td>437.0</td>\n",
+              "      <td>892.0</td>\n",
+              "      <td>408.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>335.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9374</th>\n",
+              "      <td>-119.2</td>\n",
+              "      <td>36.3</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>1355.0</td>\n",
+              "      <td>363.0</td>\n",
+              "      <td>1427.0</td>\n",
+              "      <td>384.0</td>\n",
+              "      <td>1.3</td>\n",
+              "      <td>45.6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11458</th>\n",
+              "      <td>-121.2</td>\n",
+              "      <td>37.9</td>\n",
+              "      <td>16.0</td>\n",
+              "      <td>50.0</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>137.5</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",
+              "7632      -118.4      34.0                52.0       1197.0           231.0   \n",
+              "6213      -118.2      34.0                40.0       1193.0           280.0   \n",
+              "15130     -122.3      37.9                52.0       2491.0           474.0   \n",
+              "9007      -119.0      35.4                30.0       2594.0           478.0   \n",
+              "13925     -122.0      37.4                30.0       1269.0           290.0   \n",
+              "...          ...       ...                 ...          ...             ...   \n",
+              "7719      -118.4      34.1                50.0       1509.0           291.0   \n",
+              "14996     -122.2      37.8                52.0       2504.0           516.0   \n",
+              "13855     -122.0      37.4                24.0       1709.0           437.0   \n",
+              "9374      -119.2      36.3                32.0       1355.0           363.0   \n",
+              "11458     -121.2      37.9                16.0         50.0            10.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \n",
+              "7632        671.0       219.0            3.8               278.5  \n",
+              "6213       1210.0       286.0            1.4                89.5  \n",
+              "15130      1098.0       468.0            3.1               213.5  \n",
+              "9007       1419.0       480.0            3.7                83.1  \n",
+              "13925       556.0       266.0            3.8               325.0  \n",
+              "...           ...         ...            ...                 ...  \n",
+              "7719        690.0       259.0            6.2               500.0  \n",
+              "14996       979.0       472.0            3.5               244.0  \n",
+              "13855       892.0       408.0            5.0               335.2  \n",
+              "9374       1427.0       384.0            1.3                45.6  \n",
+              "11458        20.0         6.0            2.6               137.5  \n",
+              "\n",
+              "[17000 rows x 9 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "I6kNgrwCO_ms",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "Next, we'll set up our input function, and define the function for model training:"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5RpTJER9XDub",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
+        "    \"\"\"Trains a linear regression model of one feature.\n",
+        "  \n",
+        "    Args:\n",
+        "      features: pandas DataFrame of features\n",
+        "      targets: pandas DataFrame of targets\n",
+        "      batch_size: Size of batches to be passed to the model\n",
+        "      shuffle: True or False. Whether to shuffle the data.\n",
+        "      num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n",
+        "    Returns:\n",
+        "      Tuple of (features, labels) for next data batch\n",
+        "    \"\"\"\n",
+        "    \n",
+        "    # Convert pandas data into a dict of np arrays.\n",
+        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
+        " \n",
+        "    # Construct a dataset, and configure batching/repeating.\n",
+        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
+        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
+        "    \n",
+        "    # Shuffle the data, if specified.\n",
+        "    if shuffle:\n",
+        "      ds = ds.shuffle(buffer_size=10000)\n",
+        "    \n",
+        "    # Return the next batch of data.\n",
+        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
+        "    return features, labels"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "VgQPftrpHbG3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "cell_type": "code",
+      "source": [
+        "def train_model(learning_rate, steps, batch_size, input_feature):\n",
+        "  \"\"\"Trains a linear regression model.\n",
+        "  \n",
+        "  Args:\n",
+        "    learning_rate: A `float`, the learning rate.\n",
+        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
+        "      consists of a forward and backward pass using a single batch.\n",
+        "    batch_size: A non-zero `int`, the batch size.\n",
+        "    input_feature: A `string` specifying a column from `california_housing_dataframe`\n",
+        "      to use as input feature.\n",
+        "      \n",
+        "  Returns:\n",
+        "    A Pandas `DataFrame` containing targets and the corresponding predictions done\n",
+        "    after training the model.\n",
+        "  \"\"\"\n",
+        "  \n",
+        "  periods = 10\n",
+        "  steps_per_period = steps / periods\n",
+        "\n",
+        "  my_feature = input_feature\n",
+        "  my_feature_data = california_housing_dataframe[[my_feature]].astype('float32')\n",
+        "  my_label = \"median_house_value\"\n",
+        "  targets = california_housing_dataframe[my_label].astype('float32')\n",
+        "\n",
+        "  # Create input functions.\n",
+        "  training_input_fn = lambda: my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
+        "  predict_training_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
+        "  \n",
+        "  # Create feature columns.\n",
+        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
+        "    \n",
+        "  # Create a linear regressor object.\n",
+        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
+        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
+        "  linear_regressor = tf.estimator.LinearRegressor(\n",
+        "      feature_columns=feature_columns,\n",
+        "      optimizer=my_optimizer\n",
+        "  )\n",
+        "\n",
+        "  # Set up to plot the state of our model's line each period.\n",
+        "  plt.figure(figsize=(15, 6))\n",
+        "  plt.subplot(1, 2, 1)\n",
+        "  plt.title(\"Learned Line by Period\")\n",
+        "  plt.ylabel(my_label)\n",
+        "  plt.xlabel(my_feature)\n",
+        "  sample = california_housing_dataframe.sample(n=300)\n",
+        "  plt.scatter(sample[my_feature], sample[my_label])\n",
+        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
+        "\n",
+        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
+        "  # loss metrics.\n",
+        "  print(\"Training model...\")\n",
+        "  print(\"RMSE (on training data):\")\n",
+        "  root_mean_squared_errors = []\n",
+        "  for period in range (0, periods):\n",
+        "    # Train the model, starting from the prior state.\n",
+        "    linear_regressor.train(\n",
+        "        input_fn=training_input_fn,\n",
+        "        steps=steps_per_period,\n",
+        "    )\n",
+        "    # Take a break and compute predictions.\n",
+        "    predictions = linear_regressor.predict(input_fn=predict_training_input_fn)\n",
+        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
+        "    \n",
+        "    # Compute loss.\n",
+        "    root_mean_squared_error = math.sqrt(\n",
+        "      metrics.mean_squared_error(predictions, targets))\n",
+        "    # Occasionally print the current loss.\n",
+        "    print(\"  period %02d : %0.2f\" % (period, root_mean_squared_error))\n",
+        "    # Add the loss metrics from this period to our list.\n",
+        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
+        "    # Finally, track the weights and biases over time.\n",
+        "    # Apply some math to ensure that the data and line are plotted neatly.\n",
+        "    y_extents = np.array([0, sample[my_label].max()])\n",
+        "    \n",
+        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
+        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
+        "    \n",
+        "    x_extents = (y_extents - bias) / weight\n",
+        "    x_extents = np.maximum(np.minimum(x_extents,\n",
+        "                                      sample[my_feature].max()),\n",
+        "                           sample[my_feature].min())\n",
+        "    y_extents = weight * x_extents + bias\n",
+        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
+        "  print(\"Model training finished.\")\n",
+        "\n",
+        "  # Output a graph of loss metrics over periods.\n",
+        "  plt.subplot(1, 2, 2)\n",
+        "  plt.ylabel('RMSE')\n",
+        "  plt.xlabel('Periods')\n",
+        "  plt.title(\"Root Mean Squared Error vs. Periods\")\n",
+        "  plt.tight_layout()\n",
+        "  plt.plot(root_mean_squared_errors)\n",
+        "\n",
+        "  # Create a table with calibration data.\n",
+        "  calibration_data = pd.DataFrame()\n",
+        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
+        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
+        "  display.display(calibration_data.describe())\n",
+        "\n",
+        "  print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)\n",
+        "  \n",
+        "  return calibration_data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "metadata": {
+        "id": "FJ6xUNVRm-do",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "## Task 1: Try a Synthetic Feature\n",
+        "\n",
+        "Both the `total_rooms` and `population` features count totals for a given city block.\n",
+        "\n",
+        "But what if one city block were more densely populated than another? We can explore how block density relates to median house value by creating a synthetic feature that's a ratio of `total_rooms` and `population`.\n",
+        "\n",
+        "In the cell below, create a feature called `rooms_per_person`, and use that as the `input_feature` to `train_model()`.\n",
+        "\n",
+        "What's the best performance you can get with this single feature by tweaking the learning rate? (The better the performance, the better your regression line should fit the data, and the lower\n",
+        "the final RMSE should be.)"
+      ]
+    },
+    {
+      "metadata": {
+        "id": "isONN2XK32Wo",
+        "colab_type": "text"
+      },
+      "cell_type": "markdown",
+      "source": [
+        "**NOTE**: You may find it helpful to add a few code cells below so you can try out several different learning rates and compare the results. To add a new code cell, hover your cursor directly below the center of this cell, and click **CODE**."
+      ]
+    },
+    {
+      "metadata": {
+        "id": "5ihcVutnnu1D",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 955
+        },
+        "outputId": "7788afb4-2e92-408a-a9b2-346a04548203"
+      },
+      "cell_type": "code",
+      "source": [
+        "#\n",
+        "# YOUR CODE HERE\n",
+        "#\n",
+        "california_housing_dataframe[\"rooms_per_person\"] =(california_housing_dataframe[\"total_rooms\"] / california_housing_dataframe[\"population\"])\n",
+        "\n",
+        "calibration_data = train_model(\n",
+        "    learning_rate=0.00005,\n",
+        "    steps=500,\n",
+        "    batch_size=5,\n",
+        "    input_feature=\"rooms_per_person\"\n",
+        ")"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 237.51\n",
+            "  period 01 : 237.49\n",
+            "  period 02 : 237.46\n",
+            "  period 03 : 237.44\n",
+            "  period 04 : 237.41\n",
+            "  period 05 : 237.39\n",
+            "  period 06 : 237.36\n",
+            "  period 07 : 237.34\n",
+            "  period 08 : 237.31\n",
+            "  period 09 : 237.29\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean           0.3    207.3\n",
+              "std            0.1    116.0\n",
+              "min            0.1     15.0\n",
+              "25%            0.2    119.4\n",
+              "50%            0.3    180.4\n",
+              "75%            0.3    265.0\n",
+              "max            6.2    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.1</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.2</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 237.29\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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FtTZnHjFiBDZu3IiQkJAq24x5rVBUVIS5c+dqGyACwF9//YWLFy8iICAAfn5+SExMRE5O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\nd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+            "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": "15331aa7-22ad-4b77-ebc8-158566c00763"
+      },
+      "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": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.74\n",
+            "  period 01 : 189.66\n",
+            "  period 02 : 169.00\n",
+            "  period 03 : 152.22\n",
+            "  period 04 : 139.79\n",
+            "  period 05 : 133.78\n",
+            "  period 06 : 131.55\n",
+            "  period 07 : 130.82\n",
+            "  period 08 : 131.08\n",
+            "  period 09 : 131.83\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           89.8    116.0\n",
+              "min           43.3     15.0\n",
+              "25%          158.7    119.4\n",
+              "50%          190.9    180.4\n",
+              "75%          218.3    265.0\n",
+              "max         4282.2    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>194.0</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>89.8</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>43.3</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>158.7</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>190.9</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>218.3</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4282.2</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 131.83\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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fQdsS8/j4f2kE+Ot59pGYOrP05+VgtSrMeucwu/YW0KaVD9Mmx+Dj3XDfj7rE\nZldY8FUa36/KQKOBW0ZHMGZkY3S6htvjx1PCAr3p3zmS9YnH2bT3JHFdmng6JCGEEKLWkxR+A2e1\nO0lMyiy3LDEpC6vdWW6Z8AzFaiPlyVdAoyF61tNo9O4XjfY/t6M9dQhnk1iU6E6Vr7goHZw28AoC\no3si41iegXyLjhAfB439HBcV/9Y/HOzc76BZuJZrrzZeVF0XK/lIMbPfT8Fg0PLMpJgGPUShuMTB\n9NkH2bW3gG4d/XlhyhWSkKgjUk+U8q/HElm8MoPGoSZeeao1466LkISEB117VTRGvZYlm1KwyW+o\nEEIIUSFJSjRw+UVWcgqs5ZblFlrILyq/THjGyXc/xXLoKGG3j8W3S3v3wtIiLD8tRtUbcfS61m3V\njAuyFUFpLuhM4Os+FKTQqiUlx4BRp9A61FrpKsuTluHku5+teJlgwnAzeg9eNGVkWXl5ziHsdoVH\n/xVNqxaV7FFSD+Xl25n22kH2HSzm6isDmfpQS0wm+Wmo7VRVZfmPmTw2fT8HDxcxpH8wb77QhtiY\nhvtZri0CfE0M7hFFbqGV9YnHPR2OEEIIUevJrbAGrpGviSB/E9nlJCYC/cyu1TSE51mOpHJizicY\nwoKJmvrAOeX67T+AtQRHz5HgU8nJ7RSn+7ANzZmLUacCf6abUNHQJszCxcxJWmpV+XS5BYcT7hhp\nJsjfcxe9RcUOZrx1iLwCB3ffGsWVXRvuRIAZWVZeeCOZkxlW4geEcPdtTdFdzIQhokbk5tv59ydH\n2bW3AD9fHc8/3oZ2reS7ujYZ3qs5PyWe4IfNR+nfORIvkzS3hBBCiPOR22ENnMmgo2tsaLllXWND\nKrU6htXuJCO3RIZ6XEaqqpLy9GuoVhvNpk9B7+8+gZ02dT+6o7+ji4hGib2y8hUXnQLFAT6hYHBf\n8vFQtpFSu5aoRnaCvKu//Keqqny5xkJ2gcrgHgbaRnuucW63K7w2/zBpJy1cNyyMEYPDKt6onko9\nXsrTryRxMsPKmJHh/GuCJCTqgu2785j83D527S2gS3s/3p7elrg+IZ4OS/yNr5eBhF7NKCq1s2rb\nMU+HI4QQQtRqkroX3DSoFVA2h0RuoYVAPzNdY0Ncz59PTa/a0ZDlLFlDwc9b8I/rTdB1Q90LbRb0\n25aianWYh95EiVrJ995aAJZ80JvB2/2iJrtYx4kCAz5GhRZBtouK/ZdEO78fdhLTREd8b8/NI6Gq\nKvP/e4zf9xfRu3sAt49ruBPQHTxSzIy3kikscnLHuCaMSpClC2s7i9XJfxYeZ/VPWRj0Gu66JYoR\ng0PRSiKp1hraI4ofd6SyansO1BbWAAAgAElEQVQqg7pH4e/t2Xl0hBBCiNpKkhICnVbL+CGxjImL\nIb/ISiNfU6V6SMiqHTXDUVDEseffRGMyEv3KVDR/m9hBn7gGTUkBjk4D0YVEQGZhxZUqDig4CWjA\nv4nb/BM2B+zPNKFBpW2YhYtZ0e7ICSfLfrXh563htgSTR+/Ef7H4JD9vziE2xofJd0c32Iu5vfsK\nmTn3EDabwgN3NmNIP7nLXtslHynmrQ9SOJFuJTrKi8n3RNM8yqviDYVHmY16rukbzf/WHmT55qPc\nPPgKT4ckhBBC1EpyO1u4mAw6wgK9Kz1kQ1btqBlpr87HnpFN5KR/YI6OcivTZBxFl7QNpVEozg79\nK1ehqpYlJFRn2cSWepNb0YFME3anhpbBNnxN1V/+s6hE5bMVFlQVbksw4e/jua+btRuy+HrpKcJD\njTz1UEtMxob51bd1Vx4z3krG4VR57P4WkpCo5ZyKyqJlp5g68wAn0q1cNyyMWc+2loREHRLXpQnB\n/mbW7TpOToHF0+EIIYQQtVLDbJmLiyardtSMot1/kLFgEeZW0UTcN9G90GlHv3kxKhocfUaDrpId\nnyz5YCsEg3fZEqBnOVmgJ7tET4CXk6hG1V/+U1FU/m+VhfxileF9jLSK8lynrN1/FPDep8fw9dHx\n7COtCPA3eCwWT1q3KZtZ8w+j02mYNimGPt0DPR2SuICMLCvPvpbE/317ggB/A9Mfa8WdN0dhMMjP\ndl1i0GsZdXULHE6FJZuOeDocIYQQolaS1o2oltOrdpRHVu24NFSHg5QnZoKqEv3qVLQm9/HIur0/\noy3Iwtm6F2pos8pV6rSXTW6p0f612saZIQwlNg3J2Ub0WpU2YRe3/Oea7XaSUp20i9YxsLvnkgBH\n00p5/Z3DaDQannoohiaNzR6LxZOWrs5g3sdH8fbWMf2xK+jc3t/TIYnzUFWVnzZn88jz+9h3sJg+\nPQJ4a3pbOrWTc1ZX9e3QmIhgbzb+dopTOSWeDkcIIYSodSQpIarlUqzaIS4s/b9fU/L7AYJvHIl/\n3x5uZZrcU+h+34Dq3Qhn1yGVq1BVoeA4qAr4hoPuTJJDUeHPDBOKqiE21IpZX/1hGweOOViz1Uag\nn4ZbhpnRXkx24yJk59qY8VYyJaUKk/7ZnHaxvhVvVM+oqsr/vjvBJ1+mEdjIwEtPxhIb4+PpsMR5\nFJc4mP1+CnM+PIqiwEN3Nefx+1rg5yvTP9VlWq2GG/q3RFFVvvvlsKfDEUIIIWodaemIaqvuqh2i\nYraTGaS99i66AH+aPTfZvVBR0G/+Ho2qYO99HRgq2SulNBfsJWD0BXOAW1FKjoEiq45wPzthvtWf\nDySvUOH/VlrQamHicDPeZs8kJEpLnbw85xDZuXYmjI3k6iuDKt6onlEUlY/+l8aKdZk0DjPxwpRW\nhIdKD6ba6vf9hcz5KIWsHDut/5qMtXGYnK/6oltsKNGN/di+P4MRpwpp3tjP0yEJIYQQtYYkJUS1\nVXfVDlGxo8+9gVJcQvTr0zAEu4/91x3YgjY7DWd0J5QmlVzlxGGFonTQ6MDPfdhGXqmWY3kGzHqF\nK0Kqv/yn06ny2UoLxRa4Ps5Is8ae+Sw4nSqvv3uEI8dKGTYghOuHN7zlLh0OlXmfpPDLllyaR5l5\n7tErCApomHNp1HZ2h8IX351k8cp0NBq4eXQEY0c2RqdrmKvD1FcajYYxcTG8uXA33/5ymEfGdfZ0\nSEIIIUStIUkJcdFOr9ohLo28tRvJ/WEdvj07E3rLde6FRbnoEteimrxx9BxRuQpPD9tABb8Itwkx\nHU7Yl1F2N7ZtmBX9RQzo+uFXGyknFTpfoeeqTp65AFZVlQ8+TyXx9wK6dfTnnlubnrOEan1ntSm8\n/s5hdv5WQOsYH6ZNjsHXR77qa6PUE6W8/UEKh4+V0jjMxOS7o2ktw2vqrXbRgbRpFsDew9kkpeYR\n2zSg4o2EEEKIBkDmlBCiFnGWWEh5ZhYavY7oV59Coz3rv6iqYtiyBI3TjqPHcDBX8uKlJAscFjA1\nArP7ZHkHs4xYHVqaB9pp5KVUO+69hxz8nGgnNFDDuMEmjyUC/u+bVFb/nEWLZl48dm+LBne3ubjE\nyYuzk9n5WwFdO/jzwmOtJCFRC6mqyvIfM3ls+n4OHytlSL9gZr/QRhIS9dzp3hIAi34+hKpWf+4e\nIYQQoj6R1qoQtciJtz/ClnqCiPsn4t3WfW4O7ZE9aE8mo0S2QmlRya6/9lIozgStHvwauxVlFOlI\nLzLgZ3LSPNBe7Ziz8xW+XGPBoIfbR5gxGz2TCNiwNYf3FqQQHGhg2qQYvLwa1lCi3Dwbz81K4vCx\nUq7qGcCku6MxXEzXF3FZ5OXb+fd/jrLztwJ8fXRMvqeZLM/agMQ0aUSXViHsTs5i7+FsOsWEeDok\nIYQQwuMkKSFELVFy4BCn3vsMY1QEkY/e7V5YWoR++3JUvRF7r1FUar1OVYGCE2V/+0WC9sxFusWh\nISnThFaj0jbMiraaeQS7Q2XBcgsWG9w81EREsGcSAX8mFTH346P4eOt49pFWBAUaK96oHsnMtjHj\n7X2kHi9lWFwI90xoiq66J1VcNtt35/Hv/xyjoNBB5/Z+PPyP5g3usyrghriW7EnO4pufD9OhZbDH\nVigSQgghagtJSghRC6iKQsqTr6A6nDR/+Ql03l5u5fody9HYSnH0GAG+lRyHXJwJTit4BYLpzHKY\nqgr70004lLLlP72N1e9C/P0vVo5nKlzZTk/Ptp6ZR+L4SQuvzCvrCv3SU+1p3qRhfa2lnbTwwhsH\nyc61c8OIcG4bE9ng5tGo7SxWJ/9ZeJzVP2Vh0Gv4xy1RjBwcilYSRw1SVKgvvduHs/mPdLbvy6BX\nu4Y3Ga8QQghxtobVeheilspauJSibbsJHDGQwKH93Mq0aQfQpexFCYnC2bpX5Sq0FUNJNugM4Ove\n4E3N15Nn0RHs7SDCz1HtmHfut7P5dweRIVpuGOCZpQvzCuzMeDuZomInD97ZnJ5dAsnMLPRILJ5w\nKKWEF2cnU1Dk4L47WjCsvwwDqG0OpZTw1gdHOH7KSvMoM4/c04LmUV4VbyjqtVH9WrJtXwbfbThM\n99ah6HUy1EoIIUTDJUkJITzMnp3HsZfmovXxpvmLj/2t0Ip+61JUjRZH79GgrUTDVXGeGbbh3wQ0\nZ7Ypsmo5km3EqFNoHWat1CiQ8pzKdrJonRWzESaOMGPQ1/wdX6tN4ZW5h0jPtHHjtY0Z3C+4xmPw\npN/3FzJz7iEsVoX7bm/GrWOaNaiETG3nVFQWr0jni8UncDrhumFh3DomEqNBLj4FhAV40b9LJOt3\nHWfj3pMM6NLE0yEJIYQQHiNJCSE8LHXGHJy5+TR74RGMke69GvSJa9CU5OPoOAA1sJJdfIvSQbGD\ndzAYzizV6lTgz3QTKhpah1kxVnP6B6tN5dPlFmyOsoktQwNq/iLLqai8/WEKSYdLiOsTxC2jI2o8\nBk/alpjHG+8eQVVhyr0tuKqn9JCoTTKyrMz56Ch/JhURFGDg4bua07m9f8Ubigbl2r7RbPrtJEs3\npdC3fWOMhoY1Oa8QQghxmiQlhPCggs07yfpqKd7tYwn/x01uZZqMY2gPbEPxD8HZMa5S9VkL88CS\nB3oT+IS6lR3OMVJi19LE306wt7Na8aqqytfrraTnqvTvYqBTK898hXz61XG27MyjQxtfHrizWYOa\nQ+GnX7OZ98lRDHotUx9sSZcOcrFbm/y8OYcPPj9GSalCn+4B3Ht7M/x95adWnCvA18TgHlGs2HKM\ndbuOk9CrmadDEkIIITxCWkpCeIhis5My9VXQaIie9TQa/Vn/HZ0O9FsWA+DoMxp0lfivqjgpOnG4\n7G8/92EbOSU6jucb8DYotAy2VTvmzb87SDzgoHljLSOv8syqAct/zGDJ6gyiIsw8+UDLBrXs5bI1\nGXz8RRo+3jqmTY6hTSvfijcSNaK4xMH7n6WyYWsuZpOWh/7RnIFXBTWohJmouhG9m/NT4gmWbzlK\nXJdIvEzSLBNCCNHwNJzWvKgWq91JRm4JVnv17qyL8zv13mdYDh4hbOJYfLt2cCvT/f4z2vxMlNZX\nooY1r1yFhSdRHHbwCQOD2fW0zQn7M4xoUGkbbqW686mlpjtZ/LMVbzNMGG5Gr6v5i61tiXl8/L80\nAvz1PPtIDL4+DaMBr6oqC78/ycdfpBHYSM/LU2MlIVGL/H6gkMnP7WPD1lxiY3yYPb0tg64OloSE\nqJCP2cDwXs0oKrWzatsxT4cjhBBCeETDaNGLKnMqCgvXJZOYlElOgZUgfxNdY0O5aVArdJWZbFFc\nkOVoGsff/hhDaDBRU+93K9PkpqP7fQOqtz+OLkMqWWE+WAvQe/ni8D4z4aOqQlKmCZtTS8sgG34m\npVrxllhUPl1hQVHg1mFmAv1q/jNw8Egxb75/BINByzOTYggL8cyKHzVNUVQ++TKNH9ZmEh5i5PnH\nriAirGEce21ndyh88d1JFq9MR6OBm0dFMPaaxug8kLATddeQHlGs3ZHKqu2pDOoehb+3Z3qhCSGE\nEJ4iV5eiXAvXJbN2RxrZBVZUILvAytodaSxcl+zp0Oo8VVU5+vQsVIuVZi88gr6R35lCRUG/ZTEa\nxYmj13VgNJ+/otOcdig8BWjwaxLD2UtqnCrUk1WsJ8DspGmAvdrxfrnGQk6BypArDbSJrvlcZkaW\nlZlzDuGwq0y5N5pWLXxqPAZPcDhU5n18lB/WZtKsiZmZT8VKQqKWSDtpYerLB/huRTrhoSZmPtWa\nm0ZFSEJCVJnZqOeavtFYbU6Wbz7q6XCEEEKIGidJCXEOq91JYlJmuWWJSVkylOMi5S77kfz1v+Lf\nvxdBo+PdynQHtqLNSsMZ3RElqnXFlakqFJ4E1Qm+4ehNZ5IYJTYNB7OM6LQqbS5i+c+fdtn544iT\nK5rqGHZlzd/BKyp2MOOtQ+QVOLhrfBQ9uwTUeAyeYLUpzHrnMD9tziE2xoeXnowlKFDuoHqaqqqs\nWJfJlOn7OHy0lMFXBzP7+Ta0jmkYiTJxecR1aUKwv5l1u46TU2DxdDhCCCFEjZKkhDhHfpGVnAJr\nuWW5hRbyi8ovExVzFhZx9Lk30JiMRL8y1X3MeVEeut1rUY1eOHqMqFyFljywFYHBB7zOLAupqLAv\nw4SiaogNsWI2qNWK9/BxJ8t/teHvo+HWeBNabc3eBbbbFV6bf5i0kxauGxbGiMFhNbp/TykpdTLj\nrWS2786nc3s/XpjSCj9ZwcHj8vLtvDznEB98norRoOWJB1rw4D+a4+UlSzmKi2PQaxndrwUOp8L3\nG494OhwhhBCiRklSQpyjka+JIP/yu4gH+plp5Cvdx6sr7bV3sadnEfnwPzC3aHqmQFUxbF2CxmHD\n0WM4eFViEkOnDYrSy1bZ8I90G7ZxNNdAoVVHmK+DcL/q9WwpLFH4bGXZHbsJCWb8vGv260JVVeb/\n9xi/7y+id/cAbh/XpEb37yn5BXaem3WQPw4U0ad7AM88HIOXWS56PW377nwmPbePnb8V0Lm9H3Ne\nbEuf7oEVbyhEJfVp35iIYG827j3JyexiT4cjhBBC1BhJSohzmAw6usaGllvWNTYEk0EukKqjaM+f\npP/3a8wtmxFx/0S3Mu2R39CeOIgSEYPSskvFlakqFJwAVQG/xqAzuIryLVqO5how6RViQ6rXq0VR\nVD5faaWgWGVEXyMtm9T8Of9i8Ul+/mvowuS7o2u8l4YnZOXYeObVJA4dLWFIv2Cm3NcCg0G+pj3J\nalV479NjzJx7iNJSJ/+4OYrnHmklQ2nEJafVarihf0tUFRZvkN4SQgghGg7pD1xHWe1O8ousNPI1\nXZYkwU2DWgFlc0jkFloI9DPTNTbE9byoGtXpJOXJV0BRiH7tabSmsy5oLMXodyxH1Rmw9xpFpSZ/\nKM0BewmY/MDUyPW03amyL72sJ0vbMCv6an40Vm+zkZzmpH0LHQO6GSre4BJbuyGLr5eeIjzUyNMP\ntcRkrP8X5sdPWnjhzYNk5dgZnRDGxBubyJKSHnYopYS3PjjC8VNWmjUx8+i/WtA8ysvTYYl6rFts\nKC0i/Ni+P4MRpwpp3tiv4o2EEEKIOk6SEnXM+ZbqHN2vJUUltoseWnF2smP8kFjGxMVc1uRHQ5H+\n368p+W0fwWNH4H9VD7cy/Y7laKwlOLoPB79KdAd3WKAoAzQ68ItwS2LsTlGxOLQ0C7AR4FW95T/3\nH3WwdpudIH8NNw811/iF8e4/Cnjv02P4+uh49pFWNPKv+aRITTt0tIQXZydTUOjgtjGRjBnZ2NMh\nNWhORWXxinS+WHwCpxOuHRbGbWMiMUqvFXGZaTQaboiL4c0vd/PNL4d4dFwles4JIYQQdZwkJeqY\n00t1nnZ6qc6Nv53AalMI8jdxVecmXNunGTpt5RvQ50t23DSoFWGB3pfjUBoM26lM0l57F12AP82e\nm+xWpj2ehO7IbyjBTXC26V1xZaeHbaCWzSOhPfNfOLNIR0om+BqdRAdVb/nP3EKF/1tlQauFiSPM\neJtrNiFxNK2UWfMPo9VoeOqhGJo0rsSSqHXcHwcKy4YGWBTundiU+AHlD50SNSMjy8qcj47yZ1IR\ngY0MPPzP5nRp7+/psEQD0q55IG2aBfD74RwOHMuldTOZu0QIIUT9Jrd96pALLdVpsSmolCUplmw4\nzMJ1yVWq+3SyI7vA6qpn7Y60Cuux2p1k5JbIMqEXcOz5N1GKimn6zEMYQoLOFNit6LcuQdVocfQZ\nDZVJIhVnlvWUMAeUDd34i9Wh4UCmCZ0W2oZbqc70Cw6nymcrLJRYYHR/E03DarZnTHaujRlvJVNq\nUXj4n81pF1uJyT7ruB178nlxdjI2m8qj/4qWhISH/bIlh0ee38+fSWWTq749o60kJESN02g0jImL\nAeCbXw6jqtVbPUkIIYSoK6SnRB1yoaU6/y4xKYsxcTGVGnJxoWTH+eq5UM+KqvTQqO/y1m0iZ+la\nfLt3IvSWUW5lut1r0RTn4+gQhxpYie769lIoyQKtAXzDXU+rKuzPMOFQNHSL1uCjq14D9odNNo6e\nUugaq6dPx5r9aigtdfLynENk59qZMDaSq68MqnijOu7nzTnM+yQFnU7DUw+3pFvHRhVvJC6L4hIH\n73+WyoatuZhNWh68szmDrg6SOT2Ex8Q0aUTXK0JIPJjFb4ey6dwqxNMhCSGEEJeNXD3WIRdaqvPv\ncgst5BdVLoFxoWTH+eqpbs+KhsRZYuHo07NApyP6tafQnJWs0WSmotu/FcU/BGenuIorUxUoOF72\nt38kaM8kidLy9eSW6gjydtAy/DzbV+C3ZAe/7LYTHqjhxkGmGr0YczpVXn/3CEeOlTJsQAjXD6/m\nQdQhy3/M5O0PUzAZdbww5QpJSHjQ7wcKeeT5/WzYmktsjA+zp7dlcL9gSUgIj7u+f0s0wLe/HEaR\n3hJCCCHqMUlK1CEXWqrz7wL9zJWe9PJCyY7y6qmoZ4UM5ShzYu7HWI8dp/E94/Fud8WZAqcD/ebF\naFBx9B7ltpzneRVlgNMGXkFg9DnztFXD4RwjBq1Km1BrtS6ksvIUFq61YNTDxBFemIw1dzGmqiof\nfJ5K4u8FdOvozz23Nq3XF4OqqvLVkpN8+H+pBPjreenJK2h7Rf0fplIb2R0Kny06znOzDpKda+Pm\nURHMnBpLRNjFTRYsxKUSFepL7/aNSc0oYtu+dE+HI4QQQlw2kpSoY24a1IohPaII9jej1YDZWP7w\njK6xIZVeLeNCyY7y6qlOz4qGpjTpMKfe/Qxjk8Y0mXKPW5nujw1o8zNwxvZEDY+uuDJbcdkSoDoj\n+Ia5nnYqsC/DjKpqaB1mxViNERd2h8qC5RYsNhg7yETj4Jr9SvhuRTqrf86iZTMvHru3BTpd/U1I\nKIrKf748zheLTxIWYmTmU7G0aCaTyHpC2kkLU18+wLfL0wkLMfLy1FhuGhVRrz9/om4a1a8FOq2G\nxRuO4HBWb0UlIYQQoraTOSXqGJ1W67ZUp6+3gcUbjpCYlEVuoYVAPzNXdY7k2j7NqlTvTYNaAbjV\n0zU2xPX82U73rMguJzFRlR4a9ZWqqqRMfRXV7qD5S4+j8/ZylWnyMtDt/RnV2x9H12EVV6Y4/1pt\nA/BvApozSYMjOUaKbVoi/e2E+FSvd8p3P1s5kaXQu4Oe7m1qdunNDVtz+GzRCYIDDTwzKQYvr/q7\n5KzTqTL/v0dZvymHppFmnp/SiuBAo6fDanBUVWXVT1n8Z2EaNpvKoKuD+ectUfX6syfqtrAAL/p3\niWT9ruNs3HuSAV2aeDokIYQQ4pKTpEQdZTLoXEt1np2kaORrIioygMzMwirV9/dkRyNf03l7Wpzu\nWXH20qSnVaWHRn2V9dUyCrfsIjBhAIHxZ80XoSrotyxGozixX3kNGCux3GVROih28A4Bw5nkRk6J\nlrR8A14GhZhgW7Xi3L7PztY/HDQJ1TK6f80mkv5MKmLux0fx9tLy7COtCKrHF+g2u8Kb7x1hW2I+\nV7TwZtojrfD3la/empZXYGf+f46yY08Bvj46Jv+zGX16yFKLova7tm80m347yZKNR+jbvjHGBv4b\nK4QQov6RlnE9cXaSoibqqUrPiobEnp1H6otvo/X2otmMx9zKtEnb0Wam4mzeAaVp24orsxaCJQ/0\nZvA5M7zG7ixbbUODSrtwK7pqjLg4meXkm/VWzEaYONyMQV9z3daPn7TwyrxDqKrKE/fH0DzKq+KN\n6qjSUicz5x3i9/1FdGrrx9QHW8pdeQ/Yvjuf+f89Sn6Bg87t/HjorubSU6WWmzVrFjt37sThcPCv\nf/2Ljh078sQTT+B0OgkNDeX111/HaDSyZMkSFixYgFarZdy4cdx4442eDv2SC/A1MaRHU5ZvOcq6\nXcdJ6FW1npBCCCFEbSdJCVEtVelZ0ZCkvjwXR24+TZ+fjKnJWct8Fueh37Ua1eiFo+fIiitSHH8N\n29CUrbbx1+SPqgpJmSZsTi0tgmz4mao+xthiU1mwwoLdAbeONBMSUHPzSOQV2JnxdjJFxU4evLM5\nndv719i+a1pBoYMZbyWTnFJCr26NmPKvFhgMMo1PTbJaFf77VRor12eh12u48+YmXDMkDK1W5o6o\nzbZs2cLBgwdZuHAhubm5XH/99fTp04fx48czfPhwZs+ezaJFixg9ejTz589n0aJFGAwGxo4dy9Ch\nQwkICPD0IVxyw3s3Y33icX7YnEL/zpF4m6X5JoQQov6QFnIDYLU7ycgtuSyrYpzuWSEJCSjcmkjW\nl0vwbhdL47tuPlOgqui3LkXjsOHongBeFay2oKpQeBJUZ9nElvozwzzSC/VkFutpZHbSLMBe5RhV\nVeXrH61k5qrEdTXQMabmGrZWm8Ircw+RnmnjxmsbM7hfcI3tu6Zl5dh45tUkklNKGHR1MI/f11IS\nEjXs0NESpry4j5Xrs2jWxMzrz7bmumHhkpCoA3r27MmcOXMA8Pf3p7S0lK1btzJ48GAABg4cyObN\nm9mzZw8dO3bEz88Ps9lMt27d2LVrlydDv2x8zAaG92pGscXB6u3HPB2OEEIIcUlJqr0ecyoKC9cl\nk5iUSU6BlSB/E11jQ7lpUCt0WrlAupQUm52UJ18BjYbo155Coz/zX0ubshfd8SSUxi1RYrpWXJk1\nv2zohsG7bAnQv5TaNRzMMqLTqrQJs1KdlTM3/WZn90EH0RFaRvatue7rTkXl7Q9TSDpcwoA+Qdwy\nOqLG9l3TTqRbeOGNZDKzbVw3LIw7bmpSr5c5rW2cisriFel8sfgETidcOzSM28ZGYpSkUJ2h0+nw\n9i4bRrho0SL69+/Pxo0bMRrLvrOCg4PJzMwkKyuLoKAz35FBQUFkZpa/XHV9MLRHU9buTGPV9lQG\ndY/C31uGIAkhhKgfJClRjy1cl+w2GWV2gdX1ePyQWE+FVS+dev//KE06TNjEMfh273imwFKMfvsP\nqDoD9t6jqDCT4LRD4amy1501bENRYV+GCaeqoU2oBS+DWuUYj51ysmSDDR8zTEgw1+jyhwu+Os6W\nnXl0aOPL/Xc2q7cX6UeOlTB9djL5BQ5uvSGSMSPD6+2x1kaZ2TbmfJTCHweKCGxk4OG7mtOlQ/0d\nIlTfrV27lkWLFvHJJ58wbNiZ1YpUtfzvv/M9/3eBgd7o9Zend19oqN9lqfe0W4a15v3v9rJu9wnu\nHtWx4g0aoMt9DkTF5Bx4npwDz5NzUDWSlKinrHYniUnl3zFKTMpiTFyMDLm4RKzHjnPirQ/RhwQR\nNfUBtzL9zhVorCVlwzb8gs5Tw19UtWweCVUBvwjQnbkLdizXQIFFR6ivg3Dfqg/DKbGofLrCgqLA\nrQlmAvxq7q7xD2szWLo6g6gIM08+0BKDvn7esf4zqYiX5yRTalH414SmJAwMrXgjccms+TmD1+cn\nUVLqpFe3Rtx/e3P8/eQnrq7asGED7733Hh999BF+fn54e3tjsVgwm82kp6cTFhZGWFgYWVlZrm0y\nMjLo0qVLhXXn5pZclphDQ/2qvPJVVXWLCSbY38zyTUfo174xwY0qsYpTA1IT50BcmJwDz5Nz4Hly\nDsp3oURN/bw6EOQXWckpsJZblltoIb+o/DJRNaqqkvL0aygWK81eeBR9wJm7spoTB9Ed3oMSFImz\nTe+KKyvNBXsxGH3BfGaitgKLlpRcAyadQmxI1YdtKKrK/1ZbyC1UGdrLSOtmNXehti0xj0++SCPA\nX8+zj8Tg61M/LxJ3/pbP9NkHsdoUHrk7WhISNai4xMFbHxxh+hv7UBSVB+5sxpMPtJSERB1WWFjI\nrFmzeP/9912TVvbt25dVq1YBsHr1avr160fnzp3Zu3cvBQUFFBcXs2vXLnr06OHJ0C87g17L6H4t\ncDhVlmw64ulwhBBCiEtCWm31VCNfE0H+JrLLSUwE+plp5GvyQFT1T+7ydeSv+xX/q68k+Pr4MwV2\nK4YtS1A1Whx9RoO2gkE0kjQAACAASURBVF4pDisUpYNGV9ZL4q/Mg0MpG7YB0CbcSnU6t6zfaWdf\nipPYpjqG9jRUvYJqOnikmDffP4LBoOWZSTGEhdTPz9yGLTnM+TgFnVbDUw/F0L1TI0+H1GD8caCQ\nOR8dJTPbRrvWfjx4R1MiwuXOcV23fPlycnNzmTx5suu5V199lWnTprFw4UIiIyMZPXo0BoOBKVOm\ncNddd6HRaHjggQfw86v/3WX7tG/Miq3H2Lj3JAm9mhER7OPpkIQQQoiLIkmJespk0NE1NtRtTonT\nOrUKlmU8LwFnYRFHn30DjdFA81eedJs7QLf7RzTFeTg69EcNqmBSx9PDNlDBrzHoziQODmUZKbVr\naRpgI9Cr6st/Jqc5WLHZRiMfDbfGm2ts5YGMLCsz5xzCYVeZ+lALWrWon43mlesz+eDzVLzMOp6Z\nFEO72ApWVhGXhN2hsPD7k3y7PB0N/D979x0fVZk9fvwzfZJMek8ghYTQlCoIKoKACiKiIqLYV3H9\nqtjYn4W1rq7o2ljFdVfWXrGiAgKigMjSq9SQHkJ6m0ySafc+vz8GguKkQSaN5/16+ZLcyb33ZJgJ\nc899zjnMuCyG/7ulN5WVtR0dmtQGZsyYwYwZM/6w/Z133vnDtokTJzJx4sT2CKvT0Go1XDG6F69/\n/Stfr8vmzsvP6OiQJEmSJOmUyKRENzZjXCrg6SFRWWMnNNCEv9nArkOlrNle0KppHA6XIhMZJzj8\nwn9wFZUSP+d2/FISG7ZrSvPRHdiIGhiOcubY5g9UVw7uejAFgfn4XfbSWh2FNQYsRoXksNaP/7TW\nqny43IEGuGGSGYt/+yQkbLVunn4lkyqrm1nX9WD44JDmd+pihBB8saSIj78uJDhIzxMPpJKc4N/R\nYZ0WDhfamf9mDpm5dURHGrlvVhJ9Uy3ou2mvEknyZmhaBMmxgWw9UEJuUQ2JMd1/hYgkSZLUfcmk\nRDem02qZOSGNaWNSqLY5WLE5j9U7jjQ83pJpHHKsqHe1uw9Q/PYiTL0SiL3rpuMPKG70GxejQeAa\nNRX0zZRLuOxQWwJavads4yiHW8PBEhNajaBftIPWLnBQVMGHyx3U1AkuO89Iclz7JJJcLpXnX8/i\ncKGdyy6K4pLxUe1y3vYkhODdRQV8u7KEyHAjT/4llThZMuBzQghWrCnjnUWHcToF484L57Zre+Dn\nJ5Ok0ulHo9Fw5ZgUXvp0J1+uzeSBGc03+JQkSZKkzkomJU4DJoOOYIuJ3ZnlXh9vahqHHCv6R0JR\nyHnoWVBVkuY9jNZ8vFeCbu8vaKtKUHqfhYhObuZAKtQUeP4cGNvQd0IIOFhixK1qSI1wEGBs/fjP\nFRudZBYonJmi4/wh7dNHQgjB6+/mseeAjVHDQrjp6vh2OW97UhTBv97L46dfyukRa+aJOalEhBmb\n31E6JVVWF6+/k8vWXVYsATruvS2Bc84K7eiwJKlDDUgKo19iKHuyKziYV0mfBPmekCRJkrqm0/dW\n92nmZKZxNDdW1OFq/WjK7qDk/S+p3bWP8CsnETx6RMN2TXUpul/XIPwCcQ+9qPkD1ZZ6GlyaQ8B0\nfOltgVVPRb2eMD838UHuVse3P8fNj1tdhAdpmDHB/LteF770ydeFrN1QQVpKAPfOSmq3/hXtxelS\neeGNLH76pZzUJH/+/nCaTEi0g227q7nv8f1s3WVlYL9A5v+tn0xISNJRV47pBcCXa7MQovUJbEmS\nJEnqDORKidPEyUzjKK2s8/r9cDyRERV6etXRO4tKOfzc6+iCA0l44nhneISKfuM3aFQF14hLwejX\n9IFcdZ5eEloDWGIaNtc6NWSVGzFoBX2inK0e/1lWpfDxSjt6Hdx4iRk/U/skBlatK+PzJUVERxqZ\nO7sXJmP3ynfW1ys8tyCL3ftrOKOvhbmzU2TZgI85HCrvfnaY5avL0Os13DwjnikXRnW7ZJcknYqU\nuGCG9I5gx6EydmWWMzg1oqNDkiRJkqRWk0mJbsbhUigsq0VxKb8rx2hqGseQtIjffe9v+0g05nQd\nK5r35MsoNbUkPf8Ihsjwhu3a9K1oS3JREvqjJvRv+iBCBevRso2gODjam0MVsL/YhCo09Iu2Y9K3\n7q6XWxH856tK6uxw1TgTPaLa56J5514r/34/D0uAjsfuTyU4qP3GjrYHq83NM69kcCi7jhFDgplz\nRzJGQ/dKunQ2Wbl1vPxmNgWFDnrGm7l/VpJsJCpJjbji/F7sPFTGV2uzGJgSjradVsdJkiRJUluR\nSYlu4ncNKWschAX+sSHlH6dxmBmSFtGw/ZgT+0h4c2Ii43RQtWYDFd/+QMCwM4m87orjD9RWo9+x\nEmE04x5xafMHshWD4gL/cDAeH5WZXWHA5tQRG+giMqD1pTHf/eIk67CLYX30jBzQPm/t3MP1/OP1\nLLQaDY/MTiE+pns1fCyvdPLUSxnkH7Fzwblh3HVzIjqd/MDvK4oq+GZ5MZ98XYhbEVw6IZIbpsfL\nJJAkNaFHpIWRA2LYsLeIzfuLGdk/pvmdJEmSJKkT8emVi91u59JLL+XOO+9k1KhRPPjggyiKQmRk\nJC+88AJGo5Fvv/2W9957D61Wy9VXX8306dN9GVK31ZKGlCdO4/A23rOpPhIAwQEGhh5NdpxO1Ho7\nuY88Bzodyc89gubY5BEh0G/6Do3LgWvk5eDXzFg2hw3qK0FngoDIhs2V9Vryqwz4GVRSIpytjm/X\nITe/7HIRH6Vn2jhTu/SRKK908vQrGdTbVebckUT/NIvPz9meCovtPPlSBiVlTqZcGMXNM+Jl6YAP\nlZY7+ed/c9h70EZosJ7ZtyYx5Iygjg5LkrqEy0cns3l/MYt/zuasPlHodTKRJ0mSJHUdPv1X6403\n3iA4OBiAV199lZkzZ/Lxxx+TmJjIF198QV1dHa+//jrvvvsuH3zwAe+99x5VVVW+DKlbam1DSpNB\nR1Sov9eVDtU2R6N9JACqa13szixn0U8ZKKp6aoF3IUdeewdHbgExt12L/4DjU0e0uXvQFRxEjemF\nmjq06YOoCtQcHckaFAcaz9vPpcCBYk8pTL8oB/pWvitLK1UWrbJjNMDdM0IwGXx/4Vxfr/DM/EzK\nK13ccFUc540I8/k521N2Xh1z56VTUuZk5hWx3HKNTEj40rqNFdz3+H72HrRx9tBg5v+tv0xISFIr\nRIb4MWZwHCVV9fyyu7Cjw5EkSZKkVvFZUiIzM5OMjAzGjh0LwKZNmxg/fjwAF1xwARs2bGDXrl2c\neeaZBAYGYjabGTp0KNu3b/dVSN3WyUzWaEywxYS5mSaFx1ZhLPopo1VxdlX1h7IpfP09jHHRxP/l\n9uMPOOrQb1mK0OlxnX0ZzXalrCkC1e1ZIWHwNMIUAtLLTDgULUmhLoLMrUv0OF2C95bZcbhg+jgT\n8VG+7+egKIIX3sgmJ7+ei8ZGcMWkaJ+fsz3tP2Tj0ecPUWV1M+u6nkyfEttuE0xON7V1Cq+8mc3L\nb+agqoK7bk7gobt6ERQoKwslqbUuPScJo17Lt+uzcZ6m07EkSZKkrslnSYnnn3+ehx9+uOHr+vp6\njEbP+Lzw8HBKS0spKysjLOz4HdawsDBKSxsvHZC8OzZZw5uTa0jZsguw02EsqBCCnIfnIVxuEp/5\nf+gCjjfb029bjsZeizJoHASFN3EUwG4FRzXozeB/vDt6iU1HqU1PkFkhIdTV6vi+WuugsFzlnDP1\nDO3j+4SEEII3P8xnxx4rQ88M4vbrenarC/btv1bz5EuHsDsU7puVxCXjI5vfSTop+9Jt3P/Efn7e\nWEnvZH9eerIvE86P6FavJ0lqTyEWExPO6kmVzclP2ws6OhxJkiRJajGf3I5avHgxgwcPpmfPnl4f\nb2yWdktnbIeG+qPXN99kMTKymfr+buTcQfF8uy7Ly/Y4esSFNLu/3emm0urArVFbnGiorLGjMxqI\njAho/pvbWVv93R/+YDE1G7YTPWUcaTdMadjuzj1IXeYOtFE9CD3/YjTaxl+PqstJRWYRQqMhNCkN\nvcmzSqLWLjiUI9Br4dy+eizm1sW8dlsdW/a5SYozcOuV4Rj0nos5X77uP/wij5Vry0jrZeG5R8/E\n37/z3dE+2Z//x3UlzHstC61Ww7y/DuDcEc0kmjqhrvA7z+VSefuTXD76Mg+AW65J5KYZCehbW7fk\nRVf4+X3ldP7ZpeMmjUxgzY4Clm7I4fxBcfibO9/vaEmSJEk6kU/+tVqzZg35+fmsWbOGoqIijEYj\n/v7+2O12zGYzxcXFREVFERUVRVlZWcN+JSUlDB48uNnjV1bWNfs9kZGBlJbWnNLP0ZVMGZVAXb3z\nD5M1poxKaPJ5+N3UDquD0EAjJoMOu7P5xERooBnF6ep0z3Nb/d27KqrY+5d5aP3MxDz2wPFjupwY\nV3wKGi324VOoL2/i9SgEVOeD4gZLNJVWN1CDELDziBm3oqNPpIP6Gjf1rQj5SKnCe9/V42eCmRca\nqKq0Ab593a/bVMG/38shPNTAQ3clUVtbT22tT0510k7251+xppT/fJCPn1nL3HtSSEs2drrXdXO6\nwu+8gkI7r7yZQ2ZuHdERRu67PYm+qRYqK0/9hdQVfn5fae+fXSZAOq8As4FJIxP4cm0WKzbnccX5\nvTo6JEmSJElqlk+SEvPnz2/482uvvUZ8fDw7duxgxYoVTJ06lZUrVzJ69GgGDRrEo48+itVqRafT\nsX37dubOneuLkLq9307W0BkNKE5XkyM7HS6FapuDFVvyWf2bZZ4VNS2f/NDdx4IefnYB7ooqej52\nL6Yex0es6Xb9iMZWiXvAaERYXNMHsVeD0wYGf/A7XqqUV2Wg2q4jMsBNTKC7VXHZHYL3vrfjVuDG\nSWbCg33fZX1fuo1X38rF30/LY/enEhZq9Pk524MQgq+WFfPhl0cICtTz+AOppCT6N7+j1CpCCFau\nLeOdTwtwOFUuODeM22b2xN+v+/7+kKSOMmFYT37YepiVW/IZP6wHQQHd4/e1JEmS1H2127q+2bNn\n89BDD7Fo0SLi4uK4/PLLMRgMzJkzh1tvvRWNRsNdd91FYKC8A3MqTAYdkREBjd41++3KiHKrg8YG\nCpiNOgLMeiprHIQGmvA3G6itd1FlczSswujOY0FrNu2k9OPF+PVLJfq2axu2a8oOozuwAREYhjLw\ngqYPojjBVuSZshEU39AIs8ahJafCgFGnkhbpaLY/5m8JIVj0o52yKsEFwwwM6OX7t3BBoZ15r2Ui\nhODBO1NI7OHn83O2ByEE739ewOLlJUSEGXjyL72JjzF3dFjdTpXVxb/ezWPLzmosATpm35rMucND\nOzosSeq2TEYdU85J4qMf0lmyIadhLLgkSZIkdVY+v6KZPXt2w5/feeedPzw+ceJEJk6c6OswpKMW\n/ZTBqq2HG75WG2nj4XQpzL1+KEaDjmCLCZNB17C64tjX3ZXqcpPz8LMAJD0/F63h6NtEVdBvXIxG\nCJwjp4K+icaSQoD1CAgVAuNA5/leRYV9xSYEGvpG2Wnt0/jLLhe7MxR6xWmZNMr3d7+qrC6enp+B\nrVbh7lsSGTSge4xpVFTBv9/LY9W6cuJjTTw5pzcRYfJuYlvbtrua197OpdrqZmC/QGbfmiifZ0lq\nB2MGx7Ficx5rdhRw8fAEwoNlwlWSJEnqvHy/7lvqNBwuhR3pLZtuEhpoJjLUn6hQ/4YEhMmg+93X\n3VXxmx9RfzCLyOuvIPCsgQ3bdXt/QVtZjJI6DBHTTJ1ufQW46sAYCObghs2Z5UbqXVp6BLsI82/d\n+M/cQoVvf3Fi8dNw/UQzusaWubQRh0Nl3quZFJc6mT4lhvGju17jR29cLpWX3shm1bpyUhL9+ftD\nafJCuY05HCpvfpjPM/Mzqa1TuPnqeJ6YkyqfZ0lqJ3qdlqnnJeNWBN+sz+7ocCRJkiSpSbIt82mk\n2uagwupo0fd2934RjXHkH6HgpTfRh4fSc+7xVT6a6lJ0u9cg/Cy4h17c9EHcDrCVgEYHQbENZRtl\ntTqOWA0EGFWSw1reuwOgtl7w/vd2hIDrJpoItvg2n6ioglcWZpOeVcfYUWFce3msT8/XXurtCs+/\nnsWuvTUM6GNh7j0psq9BG8vKreOVN3M4XGinZ7yZ+2clkZwg+3RIUnsbNSCG7zflsf7XQiadnUBs\neOeblCVJkiRJIFdKnFaCLSbCgkxeH9NqPNfO4UFmJpzVo1v3i2iMEIKcv/4D1e4g4cn70YccLVUQ\nKvqN36BR3bhHXAqmJnoqCAHWAkB4EhJaT97P6YaDJSY0CPpF2dG14p2nCsHHK+1U2QQXn20krafv\nc4nvfVbApu3VnNHXwp23JKBpTeOLTqrG5ubJFw+xa28NwwcH89j9qTIh0YYUVfD190U89MxBDhfa\nmTwhkhce6ysTEpLUQbRaDVeM7oUQ8PXPfxwZLkmSJEmdhVwp0c2d2AdiSFrk73pKHDNmcBwXj0jo\n9v0imlL5/WqqV/1C0HnDCb9yUsN27aFtaEtyUXr2Q00Y0PRB6srAbfeUbJg8SQ0h4ECpCZeqISXc\ngcXUSCOPRvy01cWBXIW+iTrGD2+ij0UbWbqqhO9WltAj1sxDd/XCoO/6ucuKSidPvZxBXoGdsaPC\nuOuWRPT6rp9o6SzKKpz887857DlgIzRYz+xbkxhyRvfoPyJJXdnQtAiSY4PYerCUnCIrSTHyfSlJ\nkiR1PjIp0U0pqsrHqw6xM72MKpuDsCATQ9IiuWqspxfCjvQyKmvshAaaGZgazoRhPU7rhIRiqyX3\nsRfRGA0kPvvQ8ZUBdVb021cgDGbPKommuOqhttSzOsJyfIToEaueijo9oX4KPYJbN/7zUL6b5Rud\nBFs0XHuRGa2PVyxs3lHF258cJiRIz2P3p2AJ6Pq/IgpLHDz14iGKy5xMHh/Jn67tgdbH/ThOJ+s2\nVfCfD/KprVM4e0gwd96cSFBg13/dSFJ3oNFomDamFy9+upOv1mbxwIzBHR2SJEmSJP2B/OTYDSmK\nyt/e3Up+ia1hW7nV0bBCYuaENKaNSaHCamfV1nx2Z5SxZntBQ+JixrhUdNquf3e8NQ6/+B9chSXE\n3T8Lv9Qkz0Yh0G/6Do3LgWvkVPBv4g6TUD3TNgCC4kDrSe7UOjVklhvRawV9o1o3/rPapvLhcs8+\nN04yY/Hz7YX0oexaXvpPNgaDlr/em0JUhPdSn64k93A9T710iMpqN9dMjeXqy2K6RSlKZ1Bbp7Dw\no3zWbqjAZNRy580JTBgdLp9fSepk+ieF0S8xlD3ZFRzMq6RPghzJK0mSJHUup9eV52nizcW//i4h\n8Vs70stwuBRMBh2rdxSwescRyq0OBMcTF4t+ymjfgDtY7a8HKP7vp5iSexI3++aG7dq8vegOH0CN\nTkJNHdr0QWwloDjALxSMFsAzbnV/sQlVaEiLdGDSt7xsQ1EFHy63Y6sXTDnPSFKsb1ewFJc6+Ps/\nM3G7BHPuSCI1ues3RDuQYeOvz6VTWe3m1mt7MGNqrLxgbiP70m3c/8R+1m6ooHeyPy8/1ZcLz4+Q\nz68kdVJXjvGskvxibSZCtK6EUJIkSZJ8Ta6U6GYcLoWNewobfbyixt7QY6Kx8aA70suYNibltCjl\nEIpCzkPPgqqS9OxDaM1HVwc46tBvXorQ6nGPvBw0TeTvnLWeEaA6I1iiGzbnVBiwOXXEBLqIsiit\niuv7DU6yjqgMTNExepBv+0jYat08Mz+TaqubWdf1ZPjgEJ+erz3s3GPluQVZuNwq996WyNhzusc4\n047mdgs+/eYIXy8rBmD6lBiunhIr+3NIUieXEhfMkN4R7DhUxq6Mcgb3jujokCRJkiSpgUxKdDPN\njf0MCTARbDE1+X2VRxMXUaHdv2t+yQdfUbtzH2GXX0zwmJEN2/XbVqCx23APuRAR1MQFrar8vmzj\naPKiql5LXpUBs14lNaJ14z/3ZbtZvc1FRLCGqyeYfXr32eVSeW5BFocL7Vx2URSXjI/02bnay/ot\nlcx/MweNBh6+u1e3SLJ0BgVFdua/mUNGTh3REUbunZVEv96Wjg5LkqQWuvL8Xuw8VMZXP2cyMDXc\n5z2KJEmSJKmlZFKimwm2mIgK9aOkst7r44PTIjAZdA3jQcu9JCZCA80EW7p+P4HmOEvKODxvAbog\nCwlP3t+wXVOYiS5zO2poDEr/c5s+iK0YVBf4R4DBk8RxK7C/xPP89Yt20JrhFRVWlY9X2tHr4KZL\nzPiZfPehUQjBgndy2XvQxqhhIdx0dbzPztVevl1RyMv/zsZk0jL3nhTO6BvY0SF1eUIIflhbztuf\nHsbhVBl7Thizruspx6lKUhcTH2lh1Bkx/G9PEZv3FTNyQEzzO0mSJElSO5A9JboZk0HHyDNivT7W\nM8rCzAm9G75vSJr3u+JDjiYuuru8J19BqamlxyN3Y4w6upTV7cSw8RuERoN71OUNDSu9ctSAvQr0\nJgg4/lweKjPhcGtJDHURbFZbHI/bLXh/mZ16B1w51kRcpG//Dj75upCfN1aSlhLAvbOSuvxEiq+/\nL+IfC9KxBOh5+sE0mZBoA9VWF/Ney+KN9/MwGDT85Y5k7r0tSSYkJKmLmnpeMjqthsXrsnErLf/3\nSZIkSZJ8Sa6U6Ib+NGUAdfVOdqSXUWG1E2wxMqR3BDMvTPvdVI0Z41KB348HHZIW0bC9O6tes5GK\nxSsIGHoGUTdc2bBdt2s1Glsl7v7nIsKbWDmguqGmENBAUDzHxmoU1+gotukJNCkkhrpaFdM365zk\nl6ic1U/PiP6+fWuuWlfG50uKiIkyMXd2L0zGrpufFELwwRdH+Pr7YqIiTDx2fwo9Ys0dHVaXt213\nNQvezqXK6ubMfoHcc2siEWHGjg5LkqRTEBnix5jBcfy0vYB1uwu5YEjXXyEnSZIkdX0yKdEN6XTa\nhrGfx5paelv5oNO27Pt8weFS2v2cx6j1dnLmPgdaLUnPPYLmaKJGU16Abv96hCUUZdC4xg8gBNQU\neRITAVGg91wA210a0stMaDWCflEOWrPwYEe6i//96iI2XMu0sSaf9pHYudfKv9/PwxKg49H7UggO\n8m0jTV9SVMF/3s/jh5/LiYs28eqzg9FpWpcMkn7P4VR5//MClv1Yil6v4ear45lyUVSXX0kjSZLH\nlHOS+OXXQr5dn825Z8RgPA1WRkqSJEmdm0xKdGMmg46oUH8cLoWSyrpGEwDHvq89KKrKop8y2JFe\nSoXVQViQiSFpkcwYl/q7VRy+dOS1d3HkHCb69pkEnNHHs1FV0G9YjEYInCOngr6JO8IOq+c/gx/4\ne5pgCgEHSkwoqmf8p7+x5SPXiitUPvvRgckAN15ixmjw3cVfTn4d/3g9C61GwyOzU4iP6borClxu\nlflv5vC/rVX0SvDjsQdSiYkyU1oqkxInKzuvjpf/k8PhQjs948zcf3sSyQndv+GtJJ1Ogi0mLjyr\nJ0s35PLj9sNMOjuxo0OSJEmSTnMyKdGNdYYEwIkW/ZTBqq2HG74utzoavp45Ic3n568/lEPh6+9i\njI2mx1/+3LBdt2892soilJShiNiUxg+guI6XbQQeL9vIrzJQZdcREeAmNtDd4ngcLk8fCacLbpho\nIirUd38v5ZVOnpmfSb1dZc4dSfRP67qTE+wOhecXZLFzbw390yzMvSeFAH95t+9kqargmxUlfPzV\nEdyKYPL4SG6YHt+ly3okSWrcxLMTWL29gGUbchkzKB5/s/w4KEmSJHUc+YmzGzuWACi3OhAcTwAs\n+imjQ+JxuBR2pJd6fWxHehkOl+LT8wshyJn7HMLlJuGZv6CzBACgsZaj27UaYbbgHnZxUweAmiMg\nVAiMblhNUePQkl1hwKhTSYt00NLKCyEEX652UFShcu5AA4PTfFdGUV+v8Mz8TMorXdw4PY7zRoT5\n7Fy+Zqt18+SLGezcW8NZg4J4/IFUmZA4BWUVTp548RDvf15AoEXHY/encNt1PWVCQpK6sQCzgUkj\nE6i1u1m+Oa+jw5EkSZJOc/JTZzfV0QkAb6ptDiq8jCAFqKyxU23z/lhbKf9yGTXrtxJy4WhCJ471\nbBQq+o2L0ahu3CMmg6mJper2KnDWgjEAzKEAKCrsLzYh0NA3yomxFdfGm/a62XbATc9oLZed57sG\ngooieOGNbHLy67lobASXT4z22bl8raLKxaPPp3Mws5bzR4by0F0p8uL5FPyyuYL7Ht/PngM2RgwJ\n5pWn+jH0zOCODkuSpHYwYVhPggKM/LAln+paZ0eHI0mSJJ3G5Kf5bqqjEwDeBFtMhAWZvD4WGmgm\n2OL9sbbgrqwm76n5aP3MJP79wYZGktqM7WiLc1B69EVNGNDEAZxgKwKNFgLjGso2siqM1Lm0xAe7\nCPNveaLncInC12sd+Jngxklm9Hrf9JEQQvDmh/ns2GNl6JlB3H5dT5820fSlohIHc+cdJPewnUvG\nR3LvbUk+e966u7p6hX8uzOGlf+fgdgv+76YEHr67V5dueipJUuuYjDqmnJOEw6WwdENOR4cjSZIk\nncZkUqKbajoBYGpRAuBYg8xjqypO/Lq1TAYdQ9IivT42JC3Cp1M48p9dgLu8kvgHZmHqEevZWGdF\nv20FwmDCffYUGq27EAJqCjz/D4wFnefCrbxOR0G1AX+DSq+wlt9lqncI3v/ejluBmReZCQvy3dvw\nq2XFrFxbRq8EP/5yRzI6Xde8iM89XM/ceekUlzq5+rIYbpvZQ06DOEn70m3c/8R+1myoIDXZn5ef\n6stFYyK6bLJKkqSTN2ZwHBHBZtbsKKCsur6jw5EkSZJOU7KzUTdlMujwNxso97Jawt9saDIB4K1B\npr/ZQG29k8oa5yk1zJwxLhXwlJBU1tgJDTQzJC2iYbsv1GzZRelHX+PXN4Xo269r2K7fvASNy47r\n7MvAP6jxA9SVg6seTEGe/wCnAgdLjGgQ9It2oGvh0yCEYNEqO+XVgvFnGeif7Lu34LpNFXz45REi\nwgz89d4U/Py6YZgeHAAAIABJREFUZt+Fg5m1PDM/A1utwp+u6cGUi6I6OqQuye0WLPq2kK+WFgEw\n/dIYrr4sVq42kaTTmF6nZep5yby1dD/f/pLDnyb36+iQJEmSpNOQTEp0Uw6XQm2997v3tfUuHC6l\n0cSEtwkZv01unMrEDJ1Wy8wJaUwbk0K1zdHomNK2orrc5Dw8D4Ck5+eiNXhe8tq8vejy96NGJaL2\nHtb4Adx2qC0FrQ4CY0CjQQg4WGLCqWjpFeYk0KS2OJ6fd7r4NVMhJV7LxSN910diX7qNV9/Kxd9P\ny6P3pRIW6rtz+dKuvVaeW5CF06Uy+9ZExp0b3tEhdUkFRXbmL8whI7uOqAgj997WtaevSJLUdkYN\niGH5pjzW/1rI+GE9SIwJ7OiQJEmSpNOMLN/opqptDiprvCclqmyORntKNNUg80Sn0jDTZNARFerv\n04QEQPHCj6nfn0HkdVcQOHyQZ6OzHv3mJQitHvfIqZ4+Ed4IAdYjgPD0kdB6EhqFNXrK6/SEmBV6\nhrhaHEt2ocKS9U4C/TVcP9GMzkflBwWFdua9lokQggfv7EViDz+fnMfXNmyt5Jl/ZqIoggfv6iUT\nEidBCMHKNWXMefIAGdl1jD0njJef7CcTEpIkNdBqNVwzoTcC+GhVOkKIjg5JkiRJOs3IlRLd1LGe\nEt7KN4wGHRZ/7w3tmmqQeaJjDTOjQpuYWNGBHIcLKXjpTfRhIfSce3fDdv22FWjqbbgHT0AEe+9x\nAXhWSLjtYA4Bk+fOUZ1TQ0aZEb1W0Dfa+/hPh0v5wyoQW53gg2V2hIDrJ5oICvBNPrCyysnTR0sd\n7r4lkUEDmihL6cRWrSvjjXfzMBq1zL0nhTP7yTt3rVVtdfGv9/LYvKOaAH8ds/+UzLkjQjs6LEmS\nOqEBSWEMS4tkW3opG/cVM2pATEeHJEmSJJ1GZFKimzrWVPK3ZRjH2J0Ki9dley29aCqZcSJfT8w4\nFUIIcv/6D9R6O0nPPYw+1DPmUFOUhS5jG2poNMqA8xo/gKsO6spAawCLZ4SmKmB/iQlVaOgbZces\n//3dJG+9OIakRTJ9bAofrXRSXSu4ZJSR1B6+eds5HCp/fX4PxaVOpk+JYfzorrmy4Jvlxbz7WQGB\nFh2P3Z9K7+SAjg6py9n+azUL3s6lstrNGX0t3HtbEhFhXbOER5Kk9jFjXCq7s8r5bHUGg1Mj8DPJ\nj4iSJElS+5DlG+3gVKdWnOxxLx/dC7PR+19xY6UXJoOOgakRLTq/rydmnIqq5Wup+mEdgecMI/yq\nyZ6Nbhf6jd8gNBrcIy/39InwRqhHyzaAoLiG78utNFDj0BFtcRNl+eNzd6wXR7nVgeB4741/fl5M\nep5CvyQdF5zlm5GLiip4ZWE2+w7WMHZUGNdeHuuT8/iSEIIPvyzg3c8KCA818PeH02RCopUcTpWF\nH+Xz9CuZ1NgUbro6nqf+0lsmJCRJalZEiB+Tzk6g2uZkyf9yOjocSZIk6TQi0+A+1Nid85OZWtHS\n47oVQWFZLYpLwVbnxOH03oTRW+nFsePuOuTpKaHVeFYHhDdM33BRZXO0y8SMU+G21ZL76AtoDHqS\n5j3SMOpQt3s12poK3P3OQUT0aPwAthJQnOAXBkbPRXG1XUtupQGTXqV3xB9XkTTWi0OvDaKwNJAQ\ni4ZrLzSj9dHYxfc+K2DT9mqGDgzhzlsSutx4R0UVLPwwnxVryoiNNvHknFSiIjrnKpzO6lCWjcef\nP0D+ETs9Ys088OckkhM6Z2mVJEmd06SRiaz/tZCVW/IZPSiOmDD5O0SSJEnyPZmU8CFvUyxOdmpF\nS457MK+KOruLihoHYYEmBqZGEBpopMJLw0tvpRcnHlc9Wp0wMCWcGy7u67VXQmeU/tRrOAuLibvv\nNvx6JwGgqTiCbt96hCUUZdD4xnd22qC+AnRGsHhGT7pV2F/sea76RTnQe/nRvfXi0GgMBJhSEEIw\nZTQE+PkmUbB0VQnfrSyhR6yZZx7pj6Pe7pPz+IrLrfLqf3P5ZXMlyQl+PH5/KiHBvllR0h2pquDb\nlSV89NUR3G7B5PGR3DA9HlMjq6QkSZIaYzLomDGuN/9avIdPVh3ivukDu1ySW5IkSep65KdWH2lq\nisWpTK1o6rj5JTZP6YDwJCpWby8gwM/7su0TSy+aOu7uzIqGEaKNTczwVYlKa9XuOUjOa+9jSupB\n3OybPRtVBf2Gb9AIFdfZl4GhkaXsqvKbso34hqkcGWVG7G4tCSEuQvy8rzw51ovjOA0WYypajQGt\nrpB+Sea2+QFPsGlHFW99cpiQID2P3Z9CkKVrXcw7HCrPvZbFL5sr6dc7gKcf7C0TEq1QVuHkiRcP\n8d5nBQRZ9Dx6Xwq3XddTJiQkSTppw/pE0i8xlF+zytmVWd7R4UiSJEmnAblSwkeammJxKlMrWjMd\nA6DO7uKCIXHszqygssbeaOnFycbrqxKVkyFUlZyH5yEUhaRnH0br50kE6PZvQFtxBKXXEERcEyUn\ntiJQ3RAQCQbPGM0Sm46iGgMWk0JSWOPjP09sLOpn6IFeF4jTXc7IMzU+WVlyKLuWl/+TjdGg5a/3\npnS5cofaOjfPzM/kQEYtQ88M4sE7e2EyyYvpllq/uZI33s+jtk5h+OBgHv9Lf9zOlv9ukCRJ8kaj\n0TBzQm+eeHsLn646xICkUAzelghKkiRJUhuRSQkfaWqKxalMrWjNdAzwrJhwuFSeunU4tjpXo6UX\nwRZTo6UeIRZTo/H6qkTlZJR++BW12/cQN2MywWNHejZay9Ht+hFhDsB91sTGd3ZYwV4NejP4exp9\nOtwa0ktNaDWC/lEOtM2sYD2W6Nl+wIlQYwEHIwY4uWZ82/feKC518Pd/ZuJ2CR6enUxqF2sIWVXt\n4qmXM8jJr+e8EaHcc1siBr1MSLREXb3Cwg/zWbOhApNRy//dlMCF54cTGmyktFQmJSRJOnXxkRbG\nD+vBD1vzWbkln8mjkjo6JEmSJKkbk1cBPnLszrk3pzK1oqnjNuZ/e4pYvC67ofTCW6mFyaBrtNQj\nwM/QaMmGL0pUToartJz8ZxegCwyg3wsPezYKgWHjN2gUN+7hk8HUyMoU1Q3WQkBztGxDgzg6/tOt\nakgJd+JvFN73/Q2dVsvFw1Mx6ZPQ6+Ceq4O44eLebb5ixFbrWWFQbXVz68yeDB8c0qbH97WSMgdz\n56WTk1/PxAsiuO/2JJmQaKH9h2zc/8R+1myoIDXJn5ee7MtFYyJkzbckSW1u6nlJBPob+O5/OVRY\nu1avIkmSJKlrkSslfOjYnfMd6WVNlk60xXH9zXryS2yN7rMjvYzLR/di8bqsRqd21Nm9lyfU2V0N\nPSV+y1clKicj78lXUKw2Ev/+IObYKGpKa9BmbkdbnI0S3wc18QzvOwrhSUgIBSzRoPesCDlcraeq\nXke4v5u4IHeLYnC5Be8vs2N3wowJJhJj2r43gsul8tyCLA4X2rnsoiguGd+6BFVHyy+o58mXMqio\ncnHVpTHMvCJWXlC3gNst+OzbQr5cWgTAVZfGMOOyWPR6+dxJkuQb/mYDV41J4Z3vD/DZ6gzumNrI\nv6OSJEmSdIpkUsKHdFotMyekMW1MSptOrfB2XL1Ow7vLDrB+T5HXfSpr7HzyQ/rvHv9tqcWEYT2a\nSDA4vCYYfFWi0lrVP2+i/OvlBAzuT9SN0zwb62rQb1uOMJhwnz0FGrvwtVeDswYM/p4RoIDNoSGr\n3IhBJ+gT6Wh01xN987ODw6UqI/rrGdG/7RMSQggWvJPL3oM2Rg0L4aar49v8HL6UnlXL069kYKtV\nuHlGPFMvju7okLqEI8V25r+Zw6HsOiLDjdw3K4n+aZaODkuSpNPAuQNjWbOzgM37S7hgSCV9EkI7\nOiRJkiSpG5JrpttBU1Mr2uq4Oq2W6y/uQ1ig9xKMEIuJA3mVXh/bfrCU2noXoY3s21iCwVclKq2h\n2h3kzH0etFqSnpuLRuc5p37LUjROO+4hF0JAsPedFZenuaVGC0FxoNGgqLC/xIxAQ99IB8YWpu22\nHXCxYY+b2AgtV471TTLmk68L+XljJWkpAdw7Kwltc00uOpHd+2t44oVD1NUp3HVLgkxItIAQgpVr\ny3jgiQMcyq5j7KgwXnmqn0xISJLUbrQaDTMv9PSH+uiHQyiq9wlUkiRJknQqZFKiGzEZdAztE+X1\nsb6JoY2uhKiocfDM+9uoc3jvAeEtwXCsL8Xlo5OZcFYPwoPMaDUQHmRmwlk9TrlEpaWOLHgXR1Ye\n0bfOIGBgXwBcGbvR5e1FjUxATRvufUchPOM/heop29B5EjLZFUZqnVriglyEB7SsJ0ZRucoXPzkw\nGeCmS8wYfLCkftW6Mj5fUkRMlIm5s3t1qZGPm7ZX8fQrGbgVwV/uTGbC6IiODqnTs9a4eX5BFm+8\nl4der2HOHUncOyuJAH/ZAV+SpPaVEhfMeWfGcrjUxtqdRzo6HEmSJKkbkuUb3cyMcan4+xlZv+vI\n7/pYXD46mYN5lY1O7RCA3em5CDcbdThditceGI2NAH3q1hHY6px/KFFxuJQ2LV35rfrMXAoXvIsh\nNooe/+8Oz0ZnPfYfv0BodbhHTfWsgvC6cyW4asFoAbOnUWRFnZbD1Qb8DCop4X+cQuKNwyl4f1k9\nTrcnIREZ0vbJgp17rLzxXh6WAB2P3pdCcFDbl4b4yk+/lPP6O7kYjVoemd2Lgf2DOjqkTm/7r9Us\neDuXymo3Z/S1cO9tSUSEeV/FJEmS1B6mjU1hW3oJX/+cxfC+UQT6y99JkiRJUtuRSYluRqfVMuvy\nM5k0oucfkgFD0iJ/N76zMQFmPXOvH0qkl5KTlo4AbSx5MWNcaptMoxBCkPvIcwini8S/zUFn8YzE\n1G9fiai1ogwahwj2vmoEtwNsxaDRQaCnbMOlwIESExoE/aMd6FoQohCCL1Y7KK4UjB5sYGBq27+d\ncvLr+Me/stBpNcy9J4X4GHObn8NXvl1ZzDufFmAJ0PHYfamkpXStsaXtzeFU+eDzApb+WIpep+HG\n6fFMvTiqS5XpSJLUPQUHGJl6bjKf/pTB1+uyufHiPh0dkiRJktSNyKTEaeTYioftB0upqPG+YgI8\n5RxGg85ryUZTI0CnjUkBPFM5VmzOY/WO48s8G0tenKzyr5dj/WULwRPOI/SScQBoirPRHdqKNjwW\nZcBo7zseK9tAQGAs6PQIAQdLTTgVLclhTgJNLauZ3bDHzfaDbhJjtFx6btvfNSqvdPLM/Ezq7Spz\n7kiiX++u0UtACMEnXxfy+ZIiwkIMPDEnlYR4v44Oq1PLzqvjlYU55BfY6RFr5v7bk+iV2D6TayRJ\nklpi3LAerN11hLU7ChgzKI7EmMCODkmSJEnqJmRSoptRVJWFi39l/a4CrysUZk5I4/xBcTz+1uZG\njxESYPLa2LKpEaAVVjsfrjjIgbxKKqyNT6w4lrw4lVIOd5WVvCdfQWs2kfT3Bz0jJRUX+g3fINBg\nvmgG9bpGXtp1ZeCuB1MQmD2lBEU1espq9QSbFRJCvI9FPVF+icLitQ78zXDDJDN6Xdveza6vV3hm\nfibllS5unB7HeSPC2vT4vqKqgoUf5bN8dRkxUSaenJNKdGT7TGHpilRV8O3KEj766ghut+CS8ZHc\neFU8JlPX6RkiSdLpQa/zfIZ4adFOPlqVziPXDZUjnSVJkqQ2IZMSXdyJPRtaUl4RGeJHeCOjPAEG\nNzI5o6kRoCaj7nfjRoXwHm9ljd3reNHWyJ+3AHdZBT3m3o2pZxwAut1r0NaU4+47Cn1sEpTW/HFH\nVz3UloJW71klAdS7NGSUGdFpBf2iWjb+s84ueH+ZHVWF6y4yExrYtheQbrfghTeyycmv56KxEVw+\nsWtMqnC7Ba+9ncPPGytJ6uHH43NSCQ3uOv0v2ltZhZNX38rl1/01hATpuftPiQwb2MikGEmSpE5g\nQHIYQ9Mi2Z5eysZ9xYwaENPRIUmSJEndgExKdFHeejYMTI1g16GmyytMR8syGusv0TPKwswJvb0e\no6n9Wqqx8aItVbN1N6UffIVfn17E/Pl6ADQVhej2/oIICEEZPN77jkI9WraBp4+EVocqYH+xCUVo\n6Bdpx2xoJJPy28MIwaer7FRYBROGG+ib1LZvISEEb36Yx449VoYNDOL263p2iTtRDofKC29ksW23\nlb6pAfz13hQsAfLXS2PWb6nk3+/nYatVGD44mDtvTiCkCzUwlSTp9HXNuFR+zSrns9UZDE6NwM8k\nf9dLkiRJp0b+S9JFeVsRsXp7QaPff+IKhWP9JXakl1FhtRNsMTKkdwQzL0xrshHlb/c7Nt2jT0II\nG36zSqIp3saLtpTqcpPz8DwAkp6bi9agB1VBv2ExGqHiHHkZGBpJeNSWguIAv1AweXoz5FUasDp0\nRFncRFlaNv5zzQ4Xe7MUUnvouPjstu8j8dWyYn74uZxeCX7MuSMZXRuXhfhCbZ3Cs69msi/dxpAz\ngnjwrmTMJjm60pu6eoX/fpzP6vUVGI0a7rixJxeNiegSiSepa6ivV1i+poyN26u444aeJCfI3iRS\n24oI8WPS2Ql8uz6HJRtymD62fUaAS5IkSd1Xq5IS6enp5OXlMWHCBKxWK0FBcrxfR2iq4aRWA6qX\nG/4nrlA41l9i2piUVo3s9LYf0Oi4Ua3GU8oRFvTH8aKtVfzWp9TvO0TktVMJPHuwJ54DG9FWHEHp\nNQgR532FB846qCsHnQEsnlIIq11LTqUBk16ld0TLyjayChSWrXcSFKDh+ommNp+KsG5jBR9+eYSI\nMAN/vTcFP3Pnv7Cvqnbxt1cyyM6r57wRodxzWyIGveyH4M3+Qzb+uTCH4jInqUn+3DcrifjYrjNN\nRercrDVulvxQwrKfSqmtU/Aza1Fb1rNXklpt0shE1v9ayMrN+YweGEdMmEx+SZIkSSevxUmJd999\nlyVLluB0OpkwYQL/+te/CAoK4s477/RlfJIXTTWc9JaQgMZXKJgMupPq73Difo2VdYwZEs/Fw3u2\nOOnRGMfhIgpe/A/6sBB6/HW2Z2NNBbqdPyJM/riHTfK+o6qC9egKksB40Ghxq7C/xJNM6RvloCVh\n1dSpfLDcDsD1E80E+rfthfe+dBuvvp2Lv5+WR+9LJSy088+ALylz8ORLGRQWO7hobAS3X98TnRxf\n+Qdut+Cz7wr5ckkRApg2OZprpsah18vnSjp1ZRVOPl6cwbfLC3E4VYIsemZeEcukcZGyhEryGZNB\nx4xxvfnX4j18+uMh7ps+qKNDkiRJkrqwFn9iWbJkCZ999hk33XQTAA8++CDXXHONTEp0gKYaToYH\nmTj7jFg27SlqKK841RUKLeGtrOPYeZsqB2mp3MdeQK2rJ/HZhzCEhYAQGDZ+i0Zx4Rp1OZgDvO9Y\nWwyqC/zDwehJomSWGal3aekZ4iTUr/lbiaoq+GiFA2utYPK5RlLi23YFQ0GhnXmvZSKE4ME7U0js\n0fnHZ+YfqeeplzIor3QxbXI0110ZJ0sQvDhSbGf+mzkcyq4jMtzIfbOS6J/WNUa7Sp1bQaGdr74v\n5ucNFbgVQUSYgesvjuPC8yPk9BapXQzrE0m/xFB2Z5azM6OMwakRHR2SJEmS1EW1OCkREBCA9jcX\nl1qt9ndfS+2nqYaTQ9Ii+b9pg5gyKrFVZRmn6mTLQVqicvkaqlasJXDUUCKmTwZAm7UDbVEmSnwa\natKZ3nd02KC+EnQmCIgEoNSmo7DGgMWokBzWsvGfKzc7OZSv0D9Zx9ihbduMsMrq4un5GdhqFWb/\nKZFBAzp/SVRGdi1/eyWDGpvCjdPjuWJS15gO0p6EEKxaV87bnxzG7lAZMyqMWdf1JMC/85fkSJ1b\nZm4dXy4tYuO2KoSA+BgTN81IYvAAP1k6JbUrjUbDzAm9eeLtLXy66hADksLka1CSJEk6KS1OSiQk\nJLBgwQKsVisrV65k2bJlpKSk+DI2qQlNrUyAky/LOFVtfV6lto7cR19AY9CT9Nwjnrvx9Tb0W5cj\n9EbcZ0/Ba0MIVYGao9M2gjxlGw63hoOlJrQaQb9oBy2pNDiQ62bVZhdhQRquvdCMtg1XAzgcKvNe\nzaS41MnVl8Uw7rzwNju2r/y6v4ZnX83E6VS58+YELjxf3hk7kbXGzb/ezWXTjmr8/XQ88OckRp8d\n1tFhSV2YEIK96Ta+XFLEzr2ecccpif5MmxzNiKEhxEQHUeptDLIk+Vh8pIVxw+JZtfUwK7fkMXlU\nUkeHJEmSJHVBLU5KPP7447z//vtER0fz7bffMmzYMK677jpfxiY1wZcrEzqTgpcW4jxSTNy9f8Kv\ndzIA+i1L0TjrcQ2fDAEh3nesKQTV7VkhYTAjBBwoMeJWNaRGOAgwNj/+s7JG5aMVdrRauHGSGX9z\n2yUkFFXwysJs0rPqGDsqjGumxrbZsX1l044qXnojGyFgzv8lc85ZoR0dUqezY4+V197KobLazRl9\nLdxzaxKR4Z2/P4jUOamqYNvuar5cWszBzFoAzuhrYdrkGAb1D5QlU1KncPl5yWzaV8x3/8th1IAY\nwoJkA19JkiSpdVqclNDpdNxyyy3ccsstvoxHaqWOWhHRHur2HaJo4ceYEuOJu+dPAGjz96PL3YMa\n2RM1bYTX/ezV5eCwgt4P/D138guseirr9YT5uYkPcjd7bkURfPC9nTo7TBtromd02yZ83vusgE3b\nqzmjr4U7b0no9BcXq9eXs+CdXIwGLQ/d3YvBXaDMpD05nCoffFHA0lWl6HUabpwex2UXR8vGn9JJ\nURTB+i2VfLm0iLwCT4Pd4YODmTY5hj4pjfTPkaQO4m82cNWYFN75/gCfr8nkz5cN6OiQJEmSpC6m\nxUmJ/v37/+7CSaPREBgYyKZNm3wSmHR6E6pK9kPPgqKQ+OxDaP3M4LSj37wEodXhHnk5eOtporiw\nFeYAGgiKA42GWqeGzHIjBq2gT5SzReM/l6x3klukMiRNz6gz27aD/dJVJXy3soSecWYevrtXp6/B\n/e6HEt7+5DCWAB2P3pcqL4pOkJ1XxysLc8gvsBMfa+L+25NJSeyeiULJt5wulZ9+KWfx8mKKS51o\ntXD+yFCuvCSmSzTAlU5f5w6MZc3OAjbtK2bs4Dj6JMiVdJIkSVLLtfhq68CBAw1/djqdbNiwgYMH\nD/okKEkq/Xgxtdt+JWzKhYRccA4A+h0/oKmz4h54ASIk6o87CQE1hQjFDZYY0JtQBewrNiGEhj7R\ndkz65ss2dme4+Xmni6hQDdPHmdp0FcOmHVW89clhQoL0PHpfCgH+nXdknxCCT78p5LNviwgNNvDE\nnFR5YfQbqir4bmUJH351BLdbMGlcJDdNj5eTD6RWq69XWL6mjO9WFlNZ7cag1zDxggimXhxNTJSp\no8OTpGZpNRpmXpjG39/fxkc/HOKJW85qk8lbkiRJ0unhpK6IjEYjY8aM4e233+b2229v65ikTsTh\nUtq9Z4WrtJz8v7+GLjCAhKceAEBTnIMufTNqcBTKGed739FeBU4bhoAgXH6euzTZ5QZqnTpiA11E\nBCjNnrusSmXRKjtGPdx0iRmTse0SEoeya3n5P9kYDVr+em8KURGd92JDVQVvf3KYpT+WEh1p5Mk5\nveXF0W+UVTh57a1cdu+vIThIz+w/JTJsYHBHhyV1MdVWF0tXlbLsp1Jq6xT8zFqumBTNlIuiCA1u\n20k/kuRrKXHBnHtmDOt/LWLtziOMG9qjo0OSJEmSuogWJyW++OKL331dVFREcXFxmwckdQ6KqrLo\npwx2pJdSYXUQFmRiSFokM8al+vzuR97f5qNU15D4zP/DGBMJigv9xm8QaHCPmgo6Ly9bxQm2YtBo\nCYzvRUWVk8o6LfnVBvwMKqkRzmbP63IL3ltmx+6Eay80ERPedkmY4lIHf/9nJm6X4OHZyaQmd94S\nCLdbsOCdXNZuqCAh3swTc3oTFiIvkI7539ZK3ngvD1utwvDBwdx5cwIhQfL5kVqurMLJN8uLWflz\nGU6nIMiiZ+YVsUwaF4kloPOunpKk5lw1NpXt6aV8/XMWI/pFY/GTvxslSZKk5rX408+2bdt+97XF\nYmH+/PltHpDUOSz6KYNVWw83fF1udTR8PXNCms/Oa/1lC+Vffk/AoP5E3XQVALrda9Fay3D3GYmI\nTPjjTkKA9QgIFQLj0BlMuBQnB0o8d/b7RTnQtSCP8vVaB0fKVEYO0HNWv7b7IGWrdfP0/AyqrW5m\nXdeT4YMbmRjSCTicKi++kcXWXVbSUgJ49N4UAi3yIgmgrl7hrY/z+Wl9BUajhjtu7MlFYyI6fZNS\nqfMoKLTz1ffF/LyhArciiAgzMPXiaC48P0KW/UjdQnCAkcvOTWbRTxl89XMWN17cp6NDkiRJkrqA\nFl9tzJs3z5dxSJ2Iw6WwI73U62M70suYNialTUo5TiwNUR1Och6eB1otSc8/gkanQ1NZhG7vOkRA\nMMqQCd4PVF8BrjowBYI5GCEE6aUmHIqWpDAnQWa12Vi27nexaa+b+Egtl49puzIFl0vluQVZFBQ6\nmHpxFJeMj2yzY7e12jqFZ1/NZF+6jcEDAnno7l6YTd1vzOzJOJBhY/6bORSXOUlJ9Of+25OIj5Vj\n76SWycyt48ulRWzcVoUQEB9j4spLYhg9MrTTN7qVpNYaP6wHP+86wtodBYwZFEdiTGBHhyRJkiR1\ncs0mJcaMGdPkncA1a9a0ZTxSJ1Btc1BhdXh9rLLGTrXNcUpjSBsrDRm9aw32rDyib72GgIH9QFXR\nb1iMRqi4zr4MDF6SBW4H2EpAo4PAWNBoyCuD0lo9QWaFhBBXs/EUlit8sdqB2Qg3TjJj0LfNnW8h\nPGUQew/aGDUshBunx7fJcX2h2urib69kkJVbzzlnhXDfrCQMBnmx5HYLPl9SyBffFSGAaZOjmTE1\nVl5ISs0SQrD3oI0vlxaxc28NACmJ/kybHM2IoSFyXGwLpKenc+edd3LzzTdz/fXXs2XLFl5++WX0\nej3+/v7JhhKXAAAgAElEQVT84x//IDg4mP/+978sX74cjUbD3XffzZgxYzo69NOaXqdl5oQ0Xlq0\nk49WpfPIdUPlijJJkiSpSc0mJT7++ONGH7NarW0ajNTxHC4Fp1slNNBIRc0f+zCEBpoJtpzaSgJv\npSFbVu2g1yfvYIqJpMeDdwCgO7ARbXkBSvJA1HgvJSNCgLUAEJ7xn1o99S4N2wsEOo2gX5SD5j73\n252ePhIuN1w32UxESNtdbH7ydSE/b6wkLSWAe2cloe2kFyGl5U6eeukQBUUOJpwfzh03JsgLJqCw\n2M78hTmkZ9URGW7kvllJ9E+zdHRYUienqoJtu6v5Ymkx6Zm1AJzR18K0yTEM6h8oL85aqK6ujqef\nfppRo0Y1bJs3bx4vvvgivXr14t///jeLFi1i0qRJLFu2jE8//RSbzcbMmTM577zz0OnkKq+ONCA5\njKFpkWxPL2XjvmJGDYjp6JAkSZKkTqzZpER8/PG7uxkZGVRWVgKesaDPPPMM33//ve+ik9rNiasX\nTEbvH+iGpEWcUumG19IQIRi9ejFal4u4x+9HF2iBmkp0O1chTP64z7rE+8FqS8FtB3MwmAIRAg6U\nmHAr0DfSiZ+h6fGfQgg+/8lBaaVgzBADZ6a0Xe+EVevK+HxJETFRJubO7oXJ2DnvrBcU2nnypUOU\nVbi4YlI0N1wVd9pfNAkh+HFdOW99chi7Q+X8kaHcfn0CAf7yIkdqnKII1m+p5MulReQV2AEYPjiY\naZNj6JPSeRvbdlZGo5GFCxeycOHChm2hoaFUVVUBUF1dTa9evdi0aROjR4/GaDQSFhZGfHw8GRkZ\n9Okjexl0tGvGpfJrVjmfrc5gcGoEfibZn0j6/+zdeUCUdf7A8ffcAwyn3KCAIJ55lqlp3uWRd1mr\nWVlZux1r1m61HZZlW26/yo617bLSbrXMM8/KI9HMI/FCUECRY4CB4ZjzeZ7fHySJAiKCg/B9/QXM\n8zzzHWfA5/k8n0MQBKF6df4fYu7cuWzfvp38/HzatGnDyZMnufvuu2vc3maz8eSTT1JQUIDD4eCB\nBx6gQ4cOPP7440iSREhICK+++ip6vZ4VK1bw6aefolarmTx5MrfcckuDvDih7s7NXrA7K8ZnGvUa\nnC6JQF8jPRKDuXVIwnn7XszY0OpKQxJS9hF98hiZMe2Juf46UBR0O79HJblw9RkLxmpO6F02KM8H\ntQ5MFXdgMot0FNs1RAdBmK/7gq95++8u9qW4iY1QM7qf/oLb19W+ZCvvfpqJyUfDM4/E499EJzOk\nZZTzwmupWEvdTLs5komjxJ0sa4mbBZ9msHNPMd5eGh69L5YBfYI8vSyhCXO6ZDZvK2D5D7nkmp2o\n1XB9n0AmjgonJtrL08u7Ymm1WrTaqqcoTz31FLfffjt+fn74+/vz2GOP8eGHHxIU9OfvaFBQEGaz\nWQQlmoDgAC9GXtuGFdvTWbUjnVsGnX/+IAiCIAhwEUGJAwcOsHbtWqZNm8bixYtJTk5mw4YNNW7/\n448/0qVLF2bMmEFWVhZ33303PXv2ZMqUKYwcOZLXX3+dpUuXMn78eP773/+ydOlSdDodN998M8OH\nDycgoOlOKGhuamts6WPU8tTtPQkJ9D4v4FCfsaH+JgNBfgYK/ghM6B02+m1diVuj5cBNtzHG14j6\n+D7U2WnIke2Q47qdfxBF/qNsgz/KNjRY7WrSC3XoNTK92moottT+mjNzJVZsdeJjhGkjjGg0DZMd\nkH6ynP8sOI5GreKpv8cTFd40myEmHy3h32+mYXfI/O3ONtwwMNjTS/K4fclW3vooA0uxi87tTcy8\nN5aQVg0XrBKaF5tN4oef8lm5PhdLsRudVsWIwcGMHxFGWEjDNcsV/vTiiy/yzjvv0KtXL+bNm1dt\neami1J4hBxAY6I1W2ziZTyEhoqnj2abd1Jkdh3LZ8OtJxg9qR2RI45fAiffA88R74HniPfA88R5c\nnDoHJfT6ipNzl8uFoih06dKFefPm1bj9qFF/ptxnZ2cTFhbGzp07mTNnDgCDBw9m4cKFxMXFcdVV\nV+HrW/HG9ezZkz179jBkyJB6vSDh4tXe2NKBXqepNgOiPmNDDToNPRJDKre79pcf8C4vZWffEbS/\ntj0Gtw3t7rUoWn1Fc8vqSglK80ByglcQ6H2QZDicZ0BBRcdQO3pt7anS5XaFRWvsyDJMHWEkwLdh\nSisKLE7mzk/DZpd57K+xdGzXNPsP/LqviP979wSyDI/dH8d1vQM9vSSPcrpkFi/JYtVGM1qNimk3\nRzJuRJjoqyFUq9jqYvVGM2s2mykrl/AyqpkwMowxN4QS6N80s6Kai6NHj9KrVy8A+vXrx8qVK+nT\npw8nTpyo3CY3N5fQ0NBaj2OxlDfK+kJCfDGbSxrl2FeyWwbGs2B5Mv9dso9HbqnmRkMDEu+B54n3\nwPPEe+B54j2oXm2BmjoHJeLi4vj888+5+uqrmT59OnFxcZSUXPgf+7bbbiMnJ4f//e9/TJ8+vTK4\n0apVK8xmM/n5+dWmXgoN50LlFedmL5wt0NeA0yXhcElV9r2UsaFnSkAyftxNpwNJFAeH02rGFG4d\nkoB2+1JUTltFHwlTNdkyzrKKEaAaPZgqTjzTCvTYXGqi/V0Eetc+/lNWFL5cb8dSonBDbx3t2zRM\njavNJjF3fhoFFhd33BJJ/95NM+X/px0FvP1RBjqtmqdmtqVHFz9PL8mj0k+W88b76WRm2YmKMDDr\nvjjiY+o/WUZovvILnXz/Qy7rt+TjdCr4mbRMmRDBqKEh+HiLWvnLITg4mNTUVBISEjhw4AAxMTH0\n6dOHjz/+mIcffhiLxUJeXh4JCaJMoCnp1T6EjjGB/J5WwL7UfLoniMw8QRAEoao6n0m98MILFBUV\n4efnx6pVqygsLOT++++/4H5fffUVhw8f5p///GeVtMqaUiwbMvWyJafNhIT4IkkyC1ceJCk5G3OR\njZAAL/p0ieDuMZ3RaKpmB1zXLYoVW4+fd5xyh5vnPv71vH2z88soLKl5bKhGryMkuOaMhYdv6cbW\nV+dQisLgz+cRMeRaXMcPYks/gCY8Bt/+w1CdUwIiS24saanIQECbdui8TZy2KJy2Kvh7Q+/2ejRq\nQ+Xrr86qLaUcSpfoHK9nyuigBpmI4XbLvPJOMuknbYwfGcGMaQkebRZZ02tfujKLNz/IwOSj5dXn\nunBVR//LvLLLoy6/97Ks8M2KU7z36QlcboUJoyJ5cHpbjMYru5llS/6bB43z+jNPlfPZspOs/ykX\nt1shNNjAXyZEM+aGiCb1eWlu731ycjLz5s0jKysLrVbLunXrmDNnDs888ww6nQ5/f3/+/e9/4+fn\nx+TJk7n99ttRqVQ8//zzqGsoHxQ8Q6VSMWVYO55b+CtfbTxG59ggMVZZEARBqKLOQYnJkyczbtw4\nRo8ezdixYy+4fXJyMq1atSIiIoKOHTsiSRI+Pj7Y7XaMRmNlimVoaCj5+fmV++Xl5dG9e/daj12X\n1MuWnDZz5rV/sTGlSnlFnsXGiq3HKbc5zyuvGNO3DeU2J3tT8ikssaPXqnG4ZGwOqdp9JZdEkG9N\n2RVGJKer1n//nPc/p/TAUYJvHYP2qs6Ys8zo138Nag22q8dQXlB2/k7W0+BygncwRWUKzuISfj3p\njUoF7YJsFBYoVV7/udJOSSzZaMPPR8Utg7UUFJTW6d+zNoqi8O6nmezcY6FXVz9unxhOfv6lH7e+\nqnvtiqLwzcocvlqeTYCfluceSyA8WN0sfz/q8ntfYHHy1ocZ/H64BH8/LQ9Nj+Hqbv6UlJRTh+Sv\nJqsl/82Dhn/9aRnlLFudQ9JvRSgKRIUbmDgqnAF9AtFp1U3q83K53/vLEQDp0qULixcvPu/nX331\n1Xk/mzZtGtOmTWv0NQn1FxViYkivKDbuPsX6XzMZ3TfW00sSBEEQmpA6h6qfeOIJTpw4wYQJE/jb\n3/7GDz/8gNPprHH73bt3s3DhQgDy8/MpLy+nX79+rFu3DoD169czYMAAunXrxoEDB7BarZSVlbFn\nzx6uvvrqS3xZwoXKKxwuqcrPNGo1tw5JoGt8EH7eOhyu6ssgzux7pjdEdS40NtSRlcOp//wPbaA/\nrZ+ZCYB230ZU5VakLgNQAsOq2akE7EWgNYJPSMX4T7MBl6yibZATk6H2DBtrmcziH+yogDtGGvH1\nbpi7NN+uyWXDlgLatvHisb/GNVjDzIYiywoffXmKr5ZnExas599PtSe2dcstT/hlt4VHZh/m98Ml\n9Orqx/wXOnJ1t+aZMSJcPEVRSD5SwpzXjvGPOUfYsbuItm28efyBON6c24kh/VuJO7yCUE/j+8fh\n661j5S/pFFrtnl6OIAiC0ITUOVOiV69e9OrVi6effppdu3axYsUKnn/+eZKSkqrd/rbbbuPpp59m\nypQp2O12Zs+eTZcuXXjiiSf4+uuviYyMZPz48eh0Oh577DHuueceVCoVDz74YGXTS6H+am9eacds\nKUev01TpM/H15lR+3Hu61uNaSuwUlzoIDfSu7A2xNyUfS4m91rGhZ8uc/RpyuY2Yuf9E1yoAVV4G\n6qO7kP1DkLoMPH8H2Q0lpwFVxbQNlYrTxVoKy7UEeklE+9c+/lOWFT5f56CkXGFMfz1xkQ2Tcr01\nqZDPlp0mOEjH0zPj8WpCqdwAkqTwzscZ/PRLIa2jjDz/aAJBgS1zmoTNJvHhFyfZvL0QvV7F/dNa\nc+OgYI+W2QhNhywr/PZ7MUtX55KSVpGl1aWDiUmjw+nWyVd8TgShAXgbdUwaGM8na4+w5Kc07h/b\n2dNLEgRBEJqIi+rOZbVa2bhxIz/88AMnT57k1ltvrXFbo9HIa6+9dt7PP/744/N+NmLECEaMGHEx\nSxEuoLbmlXqdmte/2U9xqbNyjOf4AW1rzKw4W6CvEX9TRd8GjVrNlGGJTBoYX2sjzbNZ1m/BsvZH\nfK/tQfCtY0Byo036HgB3n/GgOecjqShQkg2yVNHYUmukzKkirUCPVq3QIdRR7YCOs63b6ST1lESX\nthoG9miY7viHUkp5a2EG3l5qnnmk6V3sO10y//fuCX7dV0xiW2+eeSQBX1PLbMZ3JLWU+R+kk2t2\n0jbGi1n3xREd0TRHtQqXlyQpbNtl4ds1OWRmVdy5vaa7P5NGh9M+vvYpPoIgXLz+XSP4aW8WOw/l\nMrhHFImtxfh3QRAE4SKCEvfccw/Hjh1j+PDh/PWvf6Vnz56NuS7hEp07evNsdqeM/Y/SmzNjPG12\nd42ZFWerrjTDoNMQGnjhkgCp3EbG0/9BpdUQO+9fqFQqNMk/oy42I7W/FiW0zfk7OawVpRs6b/Bq\nhazA4VwDslIx/tOgrb1s43C6m42/umjlp+K24cYGueOZlW3n5bfTUBSFxx+IJyba65KP2ZDKbRIv\nv51G8pFSunXy5YmH2ja5LI7LQZIUlqzMZsmqHBQFJo0O49ZxESL9XsDpktm8rYDla3PJzXeiVsPA\nvkFMGBnW5H6fBaE5UatUTL0hkZcW/cbnG1J47q5rGqThtCAIgnBlq3NQ4o477qB///5oNOdf3Hzw\nwQfMmDGjQRcmXLpzyyt0fzSvrM6RTAuBvnoKS6rvE9Lqj4yKC5Vm1Ob06x/gzMoh4uHpeCW2RWXJ\nRZO8FcXbD3eP4efvILkqsiRUf5ZtpBfoKHVqCPd1EWKSzt/nLJYSmS/W29Fq4I5RRrwMl37iU2R1\n8eIbqZSWSTx8dwzdOjetkZqWYiez/3OMtIxy+vQK4NH7YtHpWt5FeHaunfkfpJNyvJyQVnpm3htD\n5/aiLKylK7dJrPvJzMr1eViK3ei0KkYMDmb8iDDCQgyeXp4gtAjxkf5cd1U42w/k8NO+LIb0jPb0\nkgRBEAQPq3NQYuDAamr9/7B161YRlGiC3JLCsF7RjOkXS3GZk9e+2ltjUKKwxEHfzuH8kpxz3mP9\nuoQz7cb2FyzNqE354VRy3v8cQ5soImfeA7KMNmk5KlnCde1Y0J1zQaAoFX0kFBl8I0Cjp8imJrNI\nh1ErkxBcc5PVM6990Ro75Xa4eYiB6NBLzxRwOGRefiuN3Hwnk8eGM6R/q0s+ZkPKL3Qyd/4RMk6V\nM7R/K/52Z5sm13izsSmKwsat+Xz0xSnsDpnr+wRy3+2t8fFumaUrQoViq4vVG82s2WymrFzCy6hm\nwsgwxtwQSqB/w5R0CYJQdzcPSmBPipnvthynd8cwTF7i91AQBKEla5AzdUWpPYVeuLwkSeaLjSns\nTTFTaHUQ5GegQ5tAistcNe4T4GNgyvB2eBu11Tau1FzC3HdFlkl/4t8obomYl/6JxtuI5vAO1Pmn\nkGKvQo5uf/5ONgs4y0BvAmMALgkO51UELjqGObhQBv6qbU4yc2V6tdfSp/Olf8wlWeGND06Qcryc\nQX2DuG1cxCUfsyFl5diZ81oq5gIn40aEcuctUS2uOZ+11M38Dw7x8458vL00zLovluv7BHl6WYIH\n5Rc6Wf5DLhu25ON0KviZtEyZEMGooSEiUCUIHuTvo2fsdXF8vTmVb7cc544bqzkPEARBEFqMBjkr\na2kXP03dwpUHq/SSKLA62J6cg1Gvxu6sPlOie2Iw3gbdRTeurAvzlyso3f07gTcNJWBofygtQrNv\nI4reC/fVo87fwe2E0lxQqSuyJFQqjuUbcLjVxAQ68TdW/xrO2JVsY+t+F2FBaiYNMTTI5/PTb7LY\nuaeYLh1MPDC9TZP6zB/PKGfO66lYS9zcf0ccNw4MaFLruxz2HbTy1ocZWIpddEo0MfPeGEKDRTp+\nS5WVbefbtbn8vKMASYLgIB3jR4QxbEAwBkPLK2cShKZoaK9otuw/zc97sxjYLZKYcFFiJwiC0FKJ\nW0XNjMMlkZScXcOj1V+otg41MWVYu8rv69q4si5c+YWcfOkt1CYfYuY8BoqCbuf3qNxOXP0mgpep\n6g6KAiVZgAK+kaDRkVuiIa9Ui69BIiaw5mwPALNF5sPlxeh1cOcoIwbdpV+cr96Yx8r1ebSONPLk\nQ22bVKPEQymlvPRmKja7zP3TWjPtljaYzSWeXtZl43TJfLb0NCs35KHRwP13xDH8+gA0onFai5SW\nXs6y1Tkk7SlCUSAqwsDEkeEM6BPYpH5vBUEAraZigtdrX+/ji40pPDm1Z4sLqAuCIAgVRFCimSku\ndWAuslX7mNMl0a9LOEcziyi02vE36enRLpgpwxMvqTyjNpkvvolUZKXNC/9AHxGK+vg+1KdTkSMS\nkNt2P3+H8gJw2cDgB0Z/7C4VKfkG1CqFjmEOarvWdLoUPl1rx+5QmHqjgbCgS39NO/cW8dGXpwjw\n0/LMI/FNKuV79/5iXl1wHElWePT+WPr3blmlChmnbLz+3gkys+xEhRuYdV8cfa4Ja1FBGaGifPDg\n0VJWvn2CXXstAMTHeDPppjCu7REgOvsLQhPWOS6Inokh7Ekxs/NQLn06h3t6SYIgCIIHNMgVVmxs\nbEMcRrgIDpdUbYmFv8lASIAXeZbzAxOBvkam/VG32ZDlGTWxbt9NwZLVeHftSNj0W8Behnb3WhSN\nrqK55bl3RNx2KMsDtRZ8w1GUij4SkqyifYgDb13tvUu+/dlBdr7MkGu86dn+0gMSx06U8fp7J9Dr\n1Dw9M75JlQNsSSrkrY/S0WhU/OvheHp19ff0ki4bWVZYtTGPxUtP43YrjBgczF2To0Vafgsjywq7\n9xezbE0uKWllAHTpYGLS6HC6dfIVd1wF4Qpx65AEDhwv4JsfU+neLhijvukE/wVBEITLo85/+bOy\nspg3bx4Wi4XFixfzzTff0Lt3b2JjY3nhhRcac43CWSRZ5uvNqVWaWJ4Z1alRqzHoNPTpEsGKrcfP\n27dHYnBlEKKhyjNqIjucpD/5MqhUxM77FyqNBu0va1A5ynH3Ggm+gVV3UGSwZlV87RsBai0nLTqK\n7RqCfdyE+7prfb5dh1z8eshNdKiaKSP9KC4qvaT155odvPRmGm6XwpMPx5EQ53NJx2tIazeb+eDz\nk3gZNTzzSDwd25kuvFMzUWhx8tZHGew/VIKfr5aHpsdwTfeWE5ARQJIUtu2y8O2aHDKz7ABc092f\ne6a2JayVCEQIwpUmJMCLkde2YcX2dFb9ksHNg+I9vSRBEAThMqtzUOLZZ59l6tSpfPzxxwDExcXx\n7LPPsnjx4kZbnHC+rzenntfE8sz3U4YlAnD3mM6U25zVTtG4XLLfXYQ9LYPQ6ZMxdeuEOisFTfrv\nyK2ikDr0OX+HMjO4HWAMAIMvJQ41Jwp16DUyiSGO85IqznY6X2LZjw68DHDHSCP6S+wjUVrm5sX5\nqRRb3cyY2pprugdc0vEaiqIoLF2VwxffZRPgp2X2ownEtWnc4FJTsmO3hQWfZlJaJtGrqx8PTY8h\nQIxzbDGcLpnN2wpYvjaX3HwnajUM7BvEhJFhxER7ERLiK0p3BOEKNbJPDNsPZLNuVyYDukYQFtRy\n/m8TBEEQLiIo4XK5GDp0KJ988gkA11xzTWOtSaiBwyWxN8Vc7WN7U/KZNDAeg06DS5IZ1iuaMf1i\nsTnctZZp1FQGcinsJ05y+s2F6MKCiX7iAXA50CatQFGpcfcdD+f2r3CVV/SSUOvAFIYkw+FcAwoq\nOoQ60NeyLLtD4dM1dtwSTBtppJX/paXwu1wyr7xznKxsB+NuDGXU0JBLOl5DkWWFT77JYuX6PEJa\n6Xn+HwlEhhk9vazLwmaT+PDLU2zeVoBer+L+aa25cVCwSM9vIcptEut+MrNyfR6WYjc6rYoRg4MZ\nPyKMsJCmU1IlCEL9GXQabh3SjgXLk/ly0zEeuaWbp5ckCIIgXEYXVbhntVorLwSOHTuGw+FolEUJ\n1SsudVBorf7f3FJip9Bq58e9WfyeVoDZYqtS2nGuC5WB1JeiKKQ/NQ/F4aTNnMfQ+pnQ7FqNqrwY\n91UDUQLPaWKlyGA9XfG1XySoNRw36yl3qYnydxHkLdX6XN9scpBfpDCop44ubS+tDlVRFN75OIOD\nR0vp2yuAO26JuqTjNRRJUljwSQabtxfSOtLIc48l0CpQ7+llXRZH08qY/0E6OXkO2sZ4Meu+OKIj\nWkYwpqUrtrpYtdHM2s1mysolvIxqJowMY8wNoQSKDJkrXnp6uuhHJVTRq30IHWMC+T2tgP2p+XRL\nCPb0kgRBEITLpM5XcQ8++CCTJ0/GbDYzZswYLBYLr776amOuTTiHv8lAkJ+BgmoCEwEmAyu3p5N0\nKLfyZ9WVdpxRlzKQ+ij8fj3Wn5PwH9yPoDHDUJkz0RzdiewXjHTVwPN3KM0FyQnerUDvQ0GZhiyr\nDm+dTNsgZ63PtW2/i/2pbuIi1Yzqe+kX6V9+l82WJAvt432YOSO2SXTtd7pkXv/fCXbuLSYhzptn\nZyXgZ2r+TcAkSWHJymyWrMpBUWDiqDBuGx8hxjq2APmFTpb/kMuGLfk4nQp+Ji1TJkQwamhIk5p+\nI1zY9OnTK0s+ARYsWMADDzwAwOzZs1m0aJGnliY0QSqViinD2vHcwl/5ctMxOsUGib/5giAILUSd\nz/D69OnD8uXLSUlJQa/XExcXh8EgUmcvJ4NOQ4/EkCrBhDPK7K4qAYmz7Tlq5vpukYQEeGHQaSgq\ndbBl3+lqt919JI8+nUKJCvG96HIOd3EJmc+/jspoIOalx1HJEtody1Gh4OozDjTn3N10loLNAhoD\n+ITglOCIWY8KhU5hDjS1nItk5Eis3ObE5KVi2ggjGs2lBRA2bs1nyaocwkMN/Ovhthj0nj8Rstkk\nXn7nOAcOl3BVR1/+9VBbvLwab1pKU5Gd52D+B+mkpJURHKRj5oxYurT39fSyhEZ2KtvOd2ty+Dmp\nEEmC4CAd40eEMWxAsJiscoVyu6s2KE5KSqoMSihK7dOUhJYpKsTEkF5RbNx9ivW/ZjK6b6ynlyQI\ngiBcBnUOSiQnJ2M2mxk8eDBvvPEG+/bt4+GHH+bqq69uzPUJ5zhTinGmiaVep8HulHC45Br3KSxx\n8NxHuwjyM+Bt1JFbUIZTqv6EsKjUydxFezDq1fS7KoK/DG1X53KOU/MW4MorIPqJv2GMjUazfzPq\nYjNSYm+UsNiqG8tSlbINBTVH8wy4JDXxrRyYDDW/njKbwqI1dmQZpo4w4G+6tAuWfclW3v00E5NP\nxTQLfz/Pp4ZbS928+EYqqSfKubaHP4/+NQ69rnlfmCmKwuZthXz4xUnsDpkB1wZy/7TW4u54M5eW\nXs6y1Tkk7SlCUSAqwsDEkeEM6BMo7pJe4c7t+3J2IEL0hBFqMr5/HDsP5bLqlwz6dYkg0FfcABME\nQWju6ny2P3fuXF555RV2797NgQMHePbZZ3nhhRdE+uVl5paUyiaWxaUO5i/9Hbuz5r4LZyhUlGhU\nV/pRHbtTZvNvWahVqjqVc5TuTSbv06UY28UR/rc7UBXloUneguLth7vH8Gp2yAHZDT4hoPMi26ql\noFxLgJdEtH/N4z9lReGL9XaKShVG9NGT2PrSLljTT5bznwXH0ahVPPX3eKLCPd+vIL/QyZzXUjmV\nbWfIdUE8cFfMJWeCNHXWUjfvfppJ0m9FeHupeWRGLAP7Bnl6WUIjURSFg0dLWbY6h30HKyZmJMR6\nM3F0GNf2CGgSpVNCwxOBCKEuvI06Jg2M55O1R1jyYyr3je3s6SUJgiAIjazOV3QGg4HY2Fi+/vpr\nJk+eTEJCAupLaIgoXJzqGlN2aBNYY+PLhrLnqLlyqkdNFLeb9CdeBkUh9pUnUWs1aJOWo5IlXL1v\nAv05F/p2K9iLQWsE72DKnSpS8/Vo1QodQmsf/7l5t4sjGRLt22gYes2lZTQUWJzMnZ+GzS7zj7/G\n0bGd6ZKO1xBO59p5/v9SMRc4GXNDKHdNjmr2F2j7Dlp5+6MMCotcdEo0MfPeGEKDxZ2x5kiWFXbv\nL2bZmlxS0soA6NLBxKTR4XTr5CsuWpuZ4uJiduzYUfm91WolKSkJRVGwWq0eXJnQ1PXvGsFPe7NI\nOj/sUEEAACAASURBVJTLoB5RJLZuGqO5BUEQhMZR56CEzWZj7dq1bNy4kQcffJCioiJxUtHIzh7X\nuezntPMaU25Pzmn0NVhKHBSXOggNrHlmeO7H31CefJTgyWPw69sL9ZEk1OaTSDFdkFt3rLqx7IaS\nbEAFflHIqDicZ0BWVHQItWPU1lxnnHrSzQ9JTvxNKqbcaER9CRcwNpvE3PlpFFhc3HFLJNf1Dqz3\nsRrKicxy5ryeSrHVzZQJEdx8U3izvkhzumQ+W3aalevz0Gjg9kmRjB8ZhqaZB2FaIklS2LbLwrdr\ncsjMsgPQu4c/E0eF0z7ex8OrExqLn58fCxYsqPze19eX//73v5VfC0JN1CoVU29I5KVFv/H5hhSe\nu+uaZh+gFwRBaMnqHJR49NFHWbRoEbNmzcJkMvH2229z1113NeLSWq5zsyICffWUOy5cotEYAn0N\n+JtqvmvtPJ3Lqf/8D02gP62f/TuUFaHduwFF74X7mtFVN1aUij4SigSmMNAayCjUUeLQEGZyEWqq\n+TVay2Q+W1eRRXHHSCMmr/qfnLjdCq++e4L0kzZuHBTM+BFh9T5WQzmUUspLb6Zhs0vcd3trRg4J\n8fSSGlXGKRtvvH+CjFN2osINzLovjvjYmgNfwpXJ6ZLZvK2A5Wtzyc13olbDwL5BTBgZRky0l6eX\nJzSyxYsXe3oJwhUsPtKf664KZ/uBHH7el8XgntGeXpIgCILQSOoclOjduze9e/cGQJZlHnzwwUZb\nVEt37rjOwpLaR2NeKo1ahSRXn6HQs31IraUbGc+9hlxWTtycZ9AFBaD98TNUbieufhPA65xyCHtx\nxcQNnTd4BVFsU5Nh0WHUyrQLqfk1SrLC4rV2SsoVxg3QExtR/wkUiqLw/meZ7E220qurHzOmtvZ4\nNsJvvxfznwXHkSSFWTNiGdCn+fZSkGWF1RvNLF6ahcutcOOgYO66NQqjoflPFWlJym0S634ys3J9\nHpZiNzqtihGDKwKAYSGiNKelKC0tZenSpZU3ML766iu+/PJLYmJimD17NsHBwZ5doNDk3Twwnt+O\nmvl2y3Gu6RiGycvzjagFQRCEhlfnoESnTp2qXLypVCp8fX3ZuXNnoyyspXK4JPammBv1OQw6NS63\nTIDJQIeYQG4bGs/329LZfiCnsmmmUa+h31XhldM+qlO0cRuW1Zsx9e5O8G1jUacfQJOVghwej9y2\nR9WNJWdFc0uVGvwicSsVZRsAHUId1NZk/4cdTo6flukar2FA90s7Ifl2TS4bthTQto0Xj/01zuMN\nJLfuLOTND9PRqFU8+VA8V3fz9+h6GlOhxclbCzPYf7AEP18t/5zehmu6izrh5qTY6mLVRjNrN5sp\nK5fwMqqZMDKMMTeEEugvLiZamtmzZxMVFQXAiRMneP3115k/fz6ZmZm89NJLvPHGGx5eodDU+ZsM\njOsfx9ebU/luy3Gm3dje00sSBEEQGkGdgxJHjhyp/NrlcvHLL79w9OjRRllUS1Zc6rjo5pVGvQYv\nvQZLqRO1CmQFAk06yu0SDvf5ozVNXjpm3tyVkEDvyiyImwclMLB7FE63hF6jrvJYdaRyO+lPzUOl\n1RD7ypOonDa0v65G0ehw9RlLlW6VlWUbMvhGgkbPsTw9dreaNgFOArxqHv956ISbzb+5CPZXMXmY\n8ZKyGrYmFfLZstMEB+l4emY8XkbP3p3/4Ucz7392Ei+jmqdnJtAp0fONNhvLjt8sLPgkk9IyiV5d\n/XhoegwB4iK12TAXOPl+XS4btuTjdCr4mbRMnRjJyCHBYqRrC3by5Elef/11ANatW8eIESPo168f\n/fr1Y/Xq1R5enXClGNormi37T/PTviwGdo+kTZjoRyIIgtDc1OtsUafTMXDgQBYuXMh9993X0Gtq\n0fxNBoL8DHUe3QngcEo8Na0Xeq0aL4MWLx8jOXlWnvtoV7XbW0oc6HUaDDpNtVM9eiSG1JohAXD6\njQ9wnsom4sE78e6QgHb7UlSOcty9RoDvOeUHtkJwlYPeBEZ/8ko15Jbo8DVIxAa5anyOQqvMF+vt\naDVwxygjXob6ByQOHi3hrYUZeHupeeaRBIIC9fU+1qVSFIVlq3P5/NvT+Plqee7RBNrGNM9+Cja7\nxEdfnGLTtgL0OhX33d6aEYODPV4yIzSMU9l2vluTw89JhUgSBAfpGD8ijGEDgjEYxHSmls7b+8+/\na7t27eLmm2+u/F78DRDqSqtRM2VYIq99vY/PN6Tw5NSe4vMjCILQzNQ5KLF06dIq3+fk5JCbm9vg\nC2rpDDoN3doFs/m3rPMeU6tBriapwKDXEBLgVZnZEBLsg+R01RjcCPQ1VjavPLd/RYHVUfn9lGGJ\n1a6x/EgqOe99hr51JJGzZqDKOobm+H7kVlFIHfpU3djtgNI8UGnALxK7pCbFbECtUugY6qCmZtpu\nt8KiNXZsDpg81EBUSP2zGrKy7bzyznEUReGJB+M92mBPURQ+/SaL79flEdJKz3OPJRAVbrzwjleg\no2llzP8gnZw8B23bePHIfbG0jhTNDZuDtPRylq3OIWlPEYoCUREGJo4MZ0CfQHS11WIJLYokSRQU\nFFBWVsbevXsryzXKysqw2WweXp1wJekcF0TPxBD2pJjZeSiXPp3DPb0kQRAEoQHVOSjx22+/Vfne\nZDIxf/78Bl+QADXF/y/mvoBBp6FHYkiVgMMZPRIrmoudyiupsX/F3pR8Jg2MP6+EQ5Fl0p98BcUt\nEfvS47iR0e/4HkWlwt1nHKjP2l5RwJoFKOAbgaLSciTPgFtWkRjswFtf8/jPFducnMyTubqjlt6d\n6p/+XWR18eIbqZSWSTx8dwxdO/nV+1iXSpIU3v00k03bCoiKMPD8Y+0IDvJcxkZjkSSFpaty+GZl\nNooCE0aG8ZcJEeJi9QqnKAoHj5aydHUO+w+WAJAQ683E0WFc2yNAjOsTzjNjxgxGjRqF3W7noYce\nwt/fH7vdzpQpU5g8ebKnlydcYW4dksCB4wV882Mq3dsFY9SL0jBBEITmos5/0V9++WUAioqKUKlU\n+Ps334Z8nuRwSew7ll/tY1INrRecLoniUgehgVVLAM6UYOxNycdSYifQ10j3dq2QFYVnPkiqtUTE\nUmKv9pj5X62gdNc+AkYNYQ2hxH35BYP0xax3tCVrTwm3DglDo/7j4rM8H9x2MPqD0Y9TRVqKbBpa\nebuJ8HPX+Nx7U1xs/91FeCs1kwYZ6p2m6XDIvPxWGrn5TiaPDWdI/1b1Ok5DcLlkXn8/naTfikiI\n9ebZWQn4+Ta/E6rsPAdvfpDO0bQygoN0zLw3li4dRP3vlUyWFXbvL2bZmlxS0soA6NLBxKTR4XTr\n5CvSqIUaDRw4kG3btuFwODCZKnrmGI1G/vnPf9K/f38Pr0640oQEeDHy2jas2J7Oql8yuHlQvKeX\nJAiCIDSQOl8V7dmzh8cff5yysjIURSEgIIBXX32Vq666qjHX1+IUWu0X1U8CINDXUFmOcTaNuqIO\nc9LAeIpLHfibDCz7OY1N1WRPnH9M43nHdBVYyHzpbdQ+3uy9YQIn9h/kzpAMTru8+CI/Glf+WWUf\nLhuUmUGtBVM4pQ41xwv06DQK7UMc1HQdk2eRWbLJgUEHd44yotfV74JHkhXe+OAEKcfLGdQ3iNvG\nRdTrOA3BZpd45e3j/H64hC4dTPzr4Xi8vZrXCExFUdi8rZAPvziJ3SHTv3cg909rjcmn+QVeWgpJ\nUti2y8KyNTmczLID0LuHP5NGhZMY7+Ph1QlXgtOnT1d+bbVaK79u27Ytp0+fJjIy0hPLEq5gI/vE\nsP1ANut2ZTKgawRhQc2zH5MgCEJLU+crhtdee40FCxaQmFjRZ+DQoUO89NJLfP755422uJZo4281\nBwyMek3lyM6zldldLPs5jVuHJPyZpXAWg05DaKD3RY0b7ZEYfF7pxskX30SyFBM5+xGW5rt5NPAo\nahV8VNQBFxXb7k3JZ9L1cRhK/jgZ9YtEQsOhXAMKKjqE2FEUiTxLRZDk7OdwuBQ+XW3H4YLbRxgI\nDax/uv+nX2exc08xXTqYeGB6G4/dzbWWunlpfiopx8u5prs///hbHHpd8ypjsJa6+d+nmez4rQhv\nLzWPzIhlYN+gC+8oNElOl8zmbQUsX5tLbr4TtRoG9g1iwsgwj/ZjEa48Q4YMIS4ujpCQEKAieHmG\nSqVi0aJFnlqacIUy6DTcOqQdC5Yn8+WmYzxySzdPL0kQBEFoAHUOSqjV6sqABECnTp3QaJrX3V5P\nc7gkfk+tvnQD4NrOoeg0Grb9nl0lOGF3yhdsTgl1Gzdq0KnpmRjC+AFxVX5u/WU3+d+swrtLe3ST\nxnLdN0tprStjU1kkR5wBldtZSuy4rbkYZAd4BYLexPF8PeUuNRG+TtbtOFztpA+1SsW3PzrIKZS5\nrquOHon1Hxe5ZMUpVm7Io3WkkScfauuxXgaFFifPv57KySw7g/oF8dD0GDSa5pXqvv+glbc+yqCw\nyEWnRBMz740hNPj8rB2h6Su3Saz7ycyKdXkUWd3otCpGDA5m/IgwwkLEeypcvHnz5vH9999TVlbG\n6NGjuemmmwgKEgFL4dL0ah9ChzYB/J5WwP7UfLolBHt6SYIgCMIluqigxPr16+nXrx8AW7ZsEUGJ\nBnahoMGg7tFoVLDnaF61GRNnmlPWpC7jRh0umR0Hc0k5WVQZMFC5JdKffAVUKmLn/QuDqpzxvulY\nJD1fFld9vp5xPnjLVtDowRRGYbmGrGId3jqZPQcO1Tjpo214HLuPuGkdpmZs//o3f9y5t4i3PjxO\noL+WZx6Jx8fbM+UD2XkOnv+/Y+TlO7lpWAjTb4tuVo0AnS6Zz5adZuX6PDQamDoxkgmjwtA0o9fY\nUhRbXazaaGbtZjNl5RJeRjUTRoYx5oZQAv3rHxwUhHHjxjFu3Diys7P57rvvmDp1KlFRUYwbN47h\nw4djNDbPyUNC41KpVEwdnshzC3/ly03H6BQbJBopC4IgXOHqfMU2Z84cXnzxRZ5++mlUKhXdu3dn\nzpw5jbm2Fqe2oIFRr+HtpfspLHHWuP+Z5pTRNTxe20SOc50dMBh0cCv21HRC77oFU/dO6NZ9hFql\n8HFRIjblz4+QUavijr6+FVNC/CJxymqO5OlRoRDfqpwlq/Kqfa49R0vZf9SBlwHuGGlEq63fhe2x\nE2W8/t4JDHo1T/093mN37NNPlvPC66lYit38ZXwEt4wJb1bNADNO2Xjj/RNknLITGWZg1n2xJMSJ\nHgNXGnOBk+/X5bJhSz5Op4Kfr5apEyMZOSTYY8E8oXmKiIjggQce4IEHHmDJkiXMnTuXOXPmsHv3\nbk8vTbhCRYWYGNIrio27T7H+10xG94319JIEQRCES1DnM8/Y2Fg++uijxlxLi2fQaejeLphNv2Wd\n95jdKVWbHXG26ppTnuvMRI7dR/IoKq05wHFGStJh2n20EF1oK6KffBB1yq+ozZm423QiMKIHrc6a\n7HH/4EBMBgW8W6FovUnJNeCU1LQNciI5bTVkgWhwu9qgqCsaWwb51e9uR67ZwUtvpuF2Kfz7mU4k\nxHkmIHEktZS589MoK5e4d0o0o4eFemQdjUGWFVZvMrN4SRYut8KNg4K569YojAaRMXUlOZVt57s1\nOfycVIgkQXCQjvEjwhg2IBiDQdxtFBqe1WplxYoVfPvtt0iSxP33389NN93k6WUJV7jx/ePYeSiX\nVb9k0K9LBIG+osxMEAThSlXnoMSOHTtYtGgRJSUlVZpViUaXDUu58CY1ahfth7nIRnCwqcZtzkzk\nGNMvlucX/oqltJYeE4pCl9XfoDgctHl+NlqNhHbvBhS9Ean3TUzx8q2c7BFocKErzQKtAXxCySnR\nkl+mxd8o0TrAhdNdfRaIjz4OjdrIwB5aOsXV7+5saZmbF+enUmx1M2Nqa/r3DsZsLqnXsS7F3mQr\n8945jsstM3NGDIP6em4EaUMrtDh5e2EG+w6W4Oer5Z/T23BN94AL7yg0GWnp5SxbnUPSniIUBaIi\nDEwcGc6APoEi9VloFNu2bWPZsmUkJydzww038Morr1TpTSUIl8LbqGPSwHg+WXuEJT+mct/Yzp5e\nkiAIglBPF1W+8cADDxAeHt6Y62nRHC6J/cdqbnRZHRVg0KtxuWWSDuWRdCgPL4OWfl3CuG1ou2qn\ncQD4euvp1aH2Uo74Y7/TJuMopuuvJWjscLQ/fYHK5cDVdzx4+QJ/TPbw10PhH8fxi8LmVnMsX49G\nrdAxtGL8Z3WlIwZtGHptECZvB6Ovq1/6v8sl88o7x8nKdjDuxlBGDQ2p13Eu1fZdFuZ/kI5aDU8+\n1LZZXbAn/VbEgk8zKCmV6NXVjwenx4heA1cIRVFIPlLKsjU57D9YEahLiPVm4ugwru0R0Kz6nAhN\nz7333ktsbCw9e/aksLCQjz/+uMrjL7/8sodWJjQX/btG8NPeLJIO5TKoRxQhIb6eXpIgCIJQD3UO\nSkRFRTF27NjGXEuLV5fpGGcL8jWQ2NqfpENVezXYHG42/ZaFzSEx7cb25432PONMKcfelHwKrPYq\nj+kdNvptXYGs09H25SfRZB5Ek3UUObwtcnzPqgcqyQHZDT6hyBojh7MMyIqKjiF2jLo/cz/Ofj5r\nmRZvfRu0GolHbg2oV4NERVF45+MMDh4tpW+vAO64Jeqij9EQ1v+cz/8WZWI0qHlqZjxd2jePkyKb\nXeKjL06xaVsBep2KGVNbM3JIcLPqj9FcybLC7v3FLFuTS0paGQBdOpiYNDqcbp18xXsoXBZnRn5a\nLBYCAwOrPHbq1IV7GwnChaj/aHr50uLf+HxDCn2619RVSxAEQWjKLhiUOHnyJABXX301X3/9Nb17\n90ar/XO31q1bN97qmhmHS6K41IG/yVBtoKAu0zHO1q1dMPuPmWt8/JfkHI5mWiqnaJybNXGmlGPS\nwHgKrXY27j7J72mFWErsDPhtEz5lJUT+836Mka3Qfv85ikaL69qxcPYFjb0YHFbQeYF3KzItOqwO\nDaEmN2G+UrXPN6J3W976xk6pDWaM8yHQt349Cb74LpstSRbax/swc0asR+76frsmh8VLT+Nn0jL7\nsQTiY7wv+xoaQ0paGW98kE5OnoO4Nl7MmhFL6ygvTy9LuABJUti6q5Bv1+RyMqsi0Ni7hz+TRoWT\nGC+akQqXl1qtZtasWTgcDoKCgnjvvfeIiYnhs88+4/3332fixImeXqLQDMRH+XNdl3C2J+ewattx\n+nVsPr2cBEEQWooLBiXuvPNOVCpVZR+J9957r/IxlUrFpk2bGm91zYQky3y9OZW9KWYKrQ6C/AzV\nBgpqm47ROtREud1d2VSyR2Iwg3tE8eOe85tinu3sKRpThlVfy2vQaYho5cO0GzvgcEnkJe0n+62t\nGONjiHzgTrS7V6FylOHueSP4ndUnQXJBSTagAt8oih0a0i06DFqZdsHVB1ZkRWHpZhcl5TCyr56E\n6Pr1kdi4JZ+lq3IIDzXwr4fbYtBf3pp4RVFYvPQ0363NJThIx3OPtSM64sofbydJCktX5/DNimwU\nBSaMDOMvEyJEz4EmzumS+W7NaT5bkkFuvhO1Ggb2DWLCyDBiokUwSfCMN954g08++YT4+Hg2bdrE\n7NmzkWUZf39/lixZ4unlCc3IzYMT+P14AZ+sOkR0kBdtwppHxqIgCEJLccErws2bN1/wIMuXL2f8\n+PENsqDm6OvNqVUCDbUFCs4ucTg7AHHrkATK7W5O5ZUSHWrC11uPwyUR5KuvdUzoGXtT8pk0ML7G\nUo4z9Goonvs6KAqxr/wLTUEmmuN7kYMikTr2/XNDRakISCgymMJxq/Uczq3ofN0x1EFNT7PpVxdH\nMyU6xmoYcnX9+hLsS7by7qJMfE0anp0Vj7/f5e1vIMkK/1uUycYtBUSGGXj+H+0IaaW/rGtoDDl5\nDuZ/kM7RtDJaBeqYeW8sV3UUJ3ZNWblNYt1PZlasy6PI6kanVTFicDDjR4QRFiI60QuepVariY+P\nB2Do0KG8/PLLPPHEEwwfPtzDKxOaG38fPfeM7sj8Jb/z3oqDzL7rmgue7wiCIAhNR4MMo//2229F\nUKIGDpfE3pTqSyyqCxScXVJxptRDq1HVmGnRs31orc0qz7CU2CkudRAaWHt5Qe7HSyg/cIRWt4zG\nr3dXtCvfRlGpKL9mDDr1Wf/B24vAWQp6H/AKJNWsx+5W0zrASYCXXO2xUzLdrEtyEuir4i/Djajr\nUdeefrKc/yw4jkat4l8PxxMZdnmzE1wumfkfpPPL7iLaxnjx7KwEAi5zUKShKYrCj9sL+eDzk9gd\nMv17B3L/tNaYfBrkz4PQCIqtLlZtNLN2s5mycgkvo5qpk1oztH+AaEIqNBnn9i6JiIgQAQmh0XSN\nD2bsgLas2HqcrzYd484RHTy9JEEQBKGOGuSq4+wRoS3NhfpE1Na8srZAgUGnqfz5FxtTqs20OPPv\nbtCpcbiqDwScEehrxN9U+51TZ3Yep/7zLpoAP6KefphjK5fSpayIlSVt2LA0gx6J5RUlJ4obSnNA\npQbfSMxlWnJKdJj0EnFBrmqPXVwq8/k6B2o1TBtpxMfr4gMSBRYnc+enYbPL/OOvcXRsV/Po08Zg\nd0jMe+c4+w6W0CnRxNMz4/H2urLvxJSUunl3USY7dhfh7aVm5owYBvYJEo0QmyhzgZPv1+WyYUs+\nTqeCn6+WqRMjGTkkmNiYQI+MwhWEuhJ/V4TGdtdNndh7NI+f952mc2wQV3cQ/SUEQRCuBA0SlGiJ\nJxp17RNRW/PKugQKasu02H4gB7tTqvaxc/VIDAYgz1JeYwAl47nXkEvLiH31GbbsTmF06WFyJC+W\nWWNxUREIUaHwl176ivINv0gcip6jZgNqlULHMAfV9ZqUJIXFP9gptSmMH6gnJvziL+TLbRJz56dR\nYHFxxy2RXNc78MI7NaCSUjcvvZnG0bQyru7mxz/+dvn7WDS0/QetvPVRBoVFLjq28+GRGbGEBouU\n/6boVLad79bk8HNSIZIEIa30jLsxlGEDgjEYruzPodB87d27l0GDBlV+X1BQwKBBg1AUBZVKxU8/\n/eSxtQnNk06r4a/jOjPnk1/5ZO0R4iL8aOV/5fd7EgRBaO5EfnY91bVPRG3NK3skBl+w5jGnsKzG\naRw1BSSMf1ws250V2RMGvZojmRaeeu8XLKUugnz19GwfWiWAUrRpG5ZVmzBd3RW/iSPo+fWbqDXw\nkaU9Lv5coy+l4PICgy+K3o8jOXrcsop2wQ589NVnzKzZ4eTEaZluCVr6d7341HK3W+H/3j1B+kkb\nNw6qqJe/nAqLXMx57RiZWXYG9g3ioekxaLVXbiDO5ZL5bNlpVqzPQ6OBqRMjmTAqrF5jWYXGlZZe\nzrLVOSTtKUJRICrCwMRR4Vx/bdAV/RkUWoYffvjB00sQWqCIVj5MGZbIJ2uP8P7Kgzw+pcd508cE\nQRCEpkUEJerhYvtE1Na8siZnMjG27j990es7E4yoXK9T5lReWeX3hSVONu4+hawo3D68PVK5nYyn\nX0Wl1RA77ymUA1uJ0pTyY1kEh5x/ZiREBWi5sbMRCTUa3wiyrDosNi1B3m4i/dzVriU5zc1Pe1wE\nB6iYPNRw0Vk1iqLw/meZ7E220qurHzOmtr6smTk5eQ6ef+0YuWYno4eGcPdfoj0yerShZJyyMf/9\ndNJP2YgMM/DIfbG0ixOjIpsSRVFIPlLKsjU57D9YUY6REOvNxNFhXNsj4Ir+/AktS1RUlKeXILRQ\nA7pGkHy8gN1Hzaz6JYNx/eM8vSRBEAShFg0SlDCZLm9tv6ddbJ+I6ppXXihD4txMjOrU1EtCrQa5\n9hYTAPxyIIdbBiWQ9+ZHODKziHjgDnwi/dGt2k6xbOCL4vizXgPce70/Oo0Kl08ENpeOtEI9OrVC\n+xAn1cUJCoplvtxgR6uBO0cZMRou/mLq2zW5bNhSQNs2Xjz21zg0mst3QZZxysac11KxFLu4dWw4\nt46LuGJLlWRZYfUmM4uXZOFyK9wwMJjpt0VhNFzZPTGaE1lW2L2/mGVrcklJqwgiXtXRl0mjwuja\nyfeK/ewJgiBcbiqVijtHduBEtpUV20/QMSaQxNYBnl6WIAiCUIM6ByXMZjNr1qyhuLi4SmPLmTNn\nsmDBgkZZXFNV3z4RZzevhJqbZNaWiXG2nokh7DiYe97P6xKQgIryj9O/HiT/3UXooyOInHUP2m1f\nopIlfgvqT3n2n+mOY7qZiAnWkVaoIi7En8OnDCiKivZhdgza88s2XG6FRWvs2J1w6zADkcEXf/G7\nNamQz5adJjhIx9Mz4/EyXr4L6KNpZcydn0ppmcTdf4lmzPArt1lWYZGLdxZmsDfZip9Jyz+mt6F3\nD3Fy1lRIksLWXYV8uyaXk1l2AHr38GfSqHAS40UWiyAIQn34GHXcN7Yzr3y+h/dXHmTO3b3xMYrp\nRIIgCE1RnYMS999/P+3btxfpmFx6n4gLNcmsLRPjjFZ+RqbekIiPl65KWUjX+CAOplvIs9gu/EIU\nmeJ/z0dxS8S89Di6rIOo8zKQWnekz/VDyPBKZW9KPgEGidHdfCh1QmxCAscL9JQ5NUT4uQj2qb6v\nxfdbHZwyy/TupKV3p4s/CTh4tIS3Fmbg7aXmmUcSCArUX/Qx6mvfQSuvvH0cl1vm7/fEMPi6Vpft\nuRvazj1F/PeTDEpKJXp08ePhe2LEyMgmwumS2bytgOVrc8nNd6JWw6C+QUwYFUabKC9PL08QBOGK\n1y46gLHXxfH9thN8uvYIfxvfRWSdCYIgNEF1Dkp4e3vz8ssvN+Zarij16RMBFVkQi9cd5ZfknMqf\nndsks7ZMjDO8jVoMOg1ThiUypl8sp/JKiQ414W3U8vJne+oUlOiSshfnnt8JHDmYwOu6oV3xForO\niLv3TX+WnFwfh6boBBrcmEJjsDgNnCrW4aWTSWjlrPa4e4662HHATUSwmomDLn6aQ1a2nVfeD61z\nXwAAIABJREFUOY6iKDzxYDwx0ZfvAu2X3RbeeC8dlQqeeLDtFZtRYLNLLPzqFBu3FKDXqZgxNZqR\nQ0LEyVgTUG6T+OFHMyvX51FkdaPTqhgxuKKBa1iImH4iCILQkG7qF8Ph9EJ2HzWz9fdsru8W6ekl\nCYIgCOeoc1CiW7dupKWlER8ff+GNW4CL7RNxdnZETcGGs5tk1pSJccbJvFK+2nQMlUpVJePC26jj\nZF7pBddvtJXRd/tq1D7etJnzKNqdK1G5HLj6jANvv8rtDI4CwE252g9ZNnI4z4AKhY6hDjTVNLPO\nLZRZstmBQVfRR0J3kRMCiqwuXnyjomzi4btj6NrJ78I7NZCNW/J599NM9Ho1T/09nqs6+l62525I\nKcfLmP9+Otl5DuLaeDFrRiytxZ13jyu2uli10cyaTWbKbRJeRjUTR4UxZngoASJ7RRAEoVFo1Gpm\njOnMcwt38cWGFBKi/IkMFqVxgiAITUmdgxJbt27lk08+ITAwEK1WK+aM/+HcPhHVcbgkPlt3lO1n\nZUdU5+wmmX9mYtQcxNh+IKfKWNACq6PGbY16Dd5GLZYSB0G+Bm7cuxpNaSnRz8/CSypAc+oIclgc\nckKvyn0kewkaWyG5Vonnvkvh+n7+RIb7ERPowM94fuMKh1Ph09U2nC64Y6SRkICLG8HlcMj8+800\ncvOdTB4bzpD+l69s4ru1uSxakoWvScPsWQkkXIETKSRJ4ZOvMlj4ZTqKAuNHhDJlQiQ6nRiF5knm\nAiff/5DLhq35OJ0Kfr5apk6MZOSQYHy8xQAkQRCExtbK38hdIzuwYHky7604yDN39EKnFY2eBUEQ\nmoo6nxG/++675/3MarU26GKamzPZEXuO5lFYUn2pw9nObpJ5JhPj+q4RzF74a7Xbnx2QuBCnS+Kp\n23ui12nQHDxE2tyf8e6cSNjUMWjXLEDRaMnteAM+brki40OWsOefxKhVeO9HC9HR0USGh5OXX8jp\nExnEDUuscnxFUfhmk51ci0LfqzR0a3dxF1uSrPDGByc49v/s3Wd8VGX68PHfmZ6ZyaT3Xggk9F4E\npKkBLAh20VVsu7ZldZ8trq67rlvsq7v6V2FBxYYiitIUBAWUHkB6EkhCSJ/06e08LyKBkEpLgfv7\nxo8zc87cZxIm51znKnk2JowJ5pbros5o+7MlyzLvf1bM0pVlhASpefrxVOKie15WQWm5k1fn53Mo\n10pIkJpf35vYYzM9LhbHSxx8vrKU77dU4fVCWIiGGZnhTB4bilYrAkWCIAidaVifcC4fFM33u4v5\n9Lsj3HbaeYwgCILQdTp85RgTE0Nubi7V1dUAuFwunn32WVatWnXBFtfTdWSs56laapIZFqQnpJ3+\nEh0R5K8jLEiPWvax70/PgSSR+NwTqHavRXJY+dKZxicfZBNsKmBwWhg3DdVj0MCyXVbMdjVXj+uH\ny+1m09YstCq5scwEGoIv//2smGMlgXi8FrYcOIrLG9rYuLMj3l1cxNasWvr1MfLgXfGd0vvA65N5\n+/1CvvnOTFSElr88nkp4aM+q6ZdlmfU/VjH/g0LsDh+Tx4Vx101RGA3iDnxXyc2z8tnKMrZm1SDL\nEBOlZea0SMaPDEZ1huVMgiAIwvlzy+ReZBfWsHbHcfomBjMwNbSrlyQIgiBwBkGJZ599lh9++AGz\n2Ux8fDyFhYXMmTPnQq6tR+voWE+AYH8tQ3qHtdgksyP9JTriRMCj+D/v4cjJI/wXN+Af5Ydq7S7y\nXUaWmKOQaSgBMZdXoHIHkW92s2KPlSmXj0GjVrNp2y4sNjs2icYyE4CFKwooKA5BxoPFlYvsdDVp\n3Nme5WvK+WpNOXHROv7wcDJq1YW/i+z2+Hh1Xj4/bK8hKd6PPz+WSqCpZ9X111s8vPneMX7cUYOf\nTsGv703ghmsTMJvb7ykinF+yLLPvkIXPVpayZ389AKmJemZNj2TE4AAUChGMEARB6GpatZIHru3L\ns+/t5H8rDvLMPSMIbGWMuyAIgtB5OhyU2Lt3L6tWreKOO+5g0aJF7Nu3jzVr1lzItfVoHRnrCXBZ\nv0hmX9W7zSaZzSd9aKmxOPE2b+sAQGKkP/U2d7OpII6C4xS9Mh91WAixv70P1ffv4pNhXk0fvDQE\nAvx1Er+4zITbK/PpDivpaamEhwaTX1jE0YKGQMOpZSY1Fg8H80xIkgKLIwdZPlmmcmrjztZs3VXD\ngo+PExSg4sm5KZ1SY+9wenn+9Tx27asjI83IE4+mYND3rNrSnw7W89r8fCqr3fRJNTD3vkQiwrRi\nukYn8/lktu+pZemKUrKP2gDon+7PrGkRDMjwFz8PQRCEbiY+wp+bJqbw4doc5i8/wGM3D0IhvqsF\nQRC6VIevADUaDQButxtZlunXrx/PPffcBVtYT9feWM8Qk5bBaWEdKnE4fdKHxeHm2Xd3tvr6ubcO\nRquQmkwFkWWZgj89j+xwEv/SU2jzt6OwVrPcEke++2TvgTvHBBDgp2Txtjoio2OISUjDarOzZefe\nxtecyLqQZZmPv3EAWuzuIjy+2ibrOLVxZ0uyj1p5+a08NOqGaRedUTphsXr4+6tHOJRrZegAE//v\nV8k9qr7f7fbxwdJiln1djlIJt10fxcxpkSiV4oSqM3m9Mhu3VbF0ZRmFRQ4ARgwOYNa0SNJSel6T\nVEEQhEvJ5KGx7M+rYs+RSr7eeoypoxK6ekmCIAiXtA4HJZKSkvjggw8YNmwYd999N0lJSdTX11/I\ntfVoKqWEXqduMSgxpl8kd7STHQENJSCnBhZOTPpwlbf9uSuVSrQqqUkwoHrFt9Su+xHTuBGEjOuH\ncvXb+IxBrK/vA3gAGJ2iY2iijkMlLnYXwbWZvXF6FOzdvx+Px02I6WTWBcB3u9zkHAekehzuombr\nODWj4nRlFU7+8doRPG6ZPzyS3CnTLqpr3TzzUi75x+2MHxXEI3MSe1SN/7EiO6+8nU9+oZ2oCC2/\nuT+RXj1wSkhP5nT5WLepki9Wl1FudqFQwITRwVw/LYJ4MXZVEAShR5Akibunp/P0gm0s3XCUPglB\nJEV13ghyQRAEoakOByX++te/Ultbi8lkYsWKFVRWVvLAAw9cyLX1aIvX5VJY3ry23+in4s7MNDRt\njKI6MbVjV3YFVXVOAo1aBqWFctuUXigVCsKC9Og0Chyu5vUbOo2SyBA99bX2k/urt1Dw55eQtBoS\n//7/UG9ZhiTLuEfNoN9+D6U7jhNkUHD7KBMOt48FG2u5/LIROD1KYgPc/GZWGrWWhMbgCMDRYi8r\nf3BhMkgkxlj5fnfz42ipcSc0ZCv87d+51NZ5uH92HMMHBXTkIz0nZRVO/vJSLqXlTjInhnLf7XE9\nps5flmVWflvBe58W4XLLXHl5KHffEoNO27NKTnoym93L6vUVfPVNOTV1HtQqicyJoVw/NaLHNUcV\nBEEQwKTXcN/VGbz08W7eWrafp+8ejp9WNIkWBEHoCu1++x44cICMjAy2bNnS+FhoaCihoaHk5eUR\nGRl5QRfYE7XV5NJi97D421zuuKpPq9ufPrWj2uJkfVYRucdr+fNdw9CqlYzpH8W6nc2zE0IDdKiV\nTcsRjj//Ju7SCmJ++wAGRyGK6lK8KUOQo5K5OcIHyAyLcKDXKvhkh42RQ3pjMIVi0HhJDnGhkJRN\nsi7qbT4WrWpIWZ+dqSMxKhm1yndKz4umGRWncrt9/Ou/RykqcXLdVeFMnRTW5md5PhwrsvPXl3Kp\nqnFz49WR3Hp9VI+p9a+qcfPfBQXs2leHyajisV/GM3JwYFcv65JRW+dm+doKVn5bgc3uRe+nYOa0\nCK65IpzAgJ7VGFUQBEFoKiMxmMxR8azacoz3v8nmvmsyunpJgiAIl6R2gxJffPEFGRkZvPHGG82e\nkySJ0aNHX5CF9WTtNbnclWPmpkneFrMI2gpoFJZb+HBNNndc1YdbJ/cip7C2WTbG8QorC77az4zL\nEgGw/nSQsoWfoEuOJ/rO6Si/noesM+IZmgn83K9iTBhYSnFKfkyd2JufSv3xyDIZEU5OTybw+WQ+\n+NpJnVVm+hgNKTENx3Bqz4tTMypOJcsy/11YwP7DFkYPC+TOG2Na/YzOl+wjVv7271wsVi933xLD\ntVdGXPD3PF+2ZtXw+jsF1Fu8DO5n4pF7EggSF8KdoqLSxbLVZazZaMblkjH5q7h9ZjRTJ4V2SjNW\nQRAEoXNcPy6ZQwU1bN5fSr+kYEb3EzfbBEEQOlu7Z9dPPPEEAIsWLTrjnT///PPs3LkTj8fDAw88\nQP/+/fnd736H1+slLCyMF154AY1Gw5dffsm7776LQqHgpptu4sYbbzzzI+lGAoxaAo1aqi0tByZq\nLa5WG0B2NKABYHO4W3zNln0lTB0Rh0YB+b//J/h8JP7z92iyViH5PLhHTMep0FBbbSPQT0JjKQNJ\niSYolsMVetw+idQQJwaN3Gzfa7a5yCn0kpGoZMLQphfIJ3petObDz0vYsKWa3ikGfn1vYqvlE6f3\n0jhbe/bX8a//HsXl8vHInAQmjQ056311JrvDy4KPj7N2QyUatcR9t8cydVJYj8nu6MmOlzj4fGUp\n32+pwuuFsBANMzLDmTw2tEc1RBUEQRA6RqVU8MC1Gfxl4Xbe++YwKTGmNs9lBEEQhPOv3aDEHXfc\n0ebF0Hvvvdfi41u2bCEnJ4fFixdTXV3N9ddfz+jRo7ntttuYOnUqL7/8MkuWLGHGjBm8/vrrLFmy\nBLVazQ033MAVV1xBYGDPTVHXqpUMSgtlfVbz8gqAYFPrDSCNeg0alQKnp+V5nycCGkCrwQtzjZ1a\nixP586+w7jlAyKypBEYqUWzJxxPbh/ez1exasYWaeidPXhtKQogKrzGCUqsfVTYVQX4eYgI8zfZ7\nuMDDmm1ugvwlbr1Sd0YjtNZuMLNkeSmR4Vr++EgyWk3zC7zTe2kEn8GEktNt3lnNy2/lA/C7h5IZ\nOaRn/D5lH7Xy77fzKSl3khjnx2/uTxQNFDtBbp6Vz1aWsTWrBlmGmCgtM6dFMn5kcI9qhioIgiCc\nufAgPXdc2Zt5yw/w1pcH+OPsIaiUIhAtCILQWdoNSjz44IMArF27FkmSGDVqFD6fjx9//BE/v9Yv\nloYPH86AAQMAMJlM2O12tm7dyl//+lcAJk6cyIIFC0hKSqJ///74+zeMpRwyZAhZWVlMmjTpnA+u\nK902pRe5x5uXV0DrDSABPt9wpNWABDQNaLQ2cjQ00A+9tY5D/3odhclI5GN3o9r6EbJay2euvqzd\n1RAsmT7AQEKIii1H7Bxz1RIZF41KIdMn3MXp8Yaaeh8ffO1AoYBfTNOh13X8Qm33vjr+771j+BuV\nPPWbFAJMLZcgnN5Lo7LO2fj/t01J6/D7LV9Twotv5KHRKPjjI8kMyOj+HbW9XpmlK0v5eFkJsgzX\nZYZz+/XRqNXipOhCkWWZfYcsfLaylD37GybapCbpmTUtkhGDA3pMI1RBEATh3I3uF8m+vCo27y/l\ni4153DAhpauXJAiCcMloNyhxomfE//73P+bPn9/4+JVXXsmvfvWrVrdTKpXo9Q3pb0uWLGH8+PFs\n2rQJjUYDQEhICBUVFZjNZoKDgxu3Cw4OpqKi5Z4KPYlSoeDPdw3jwzXZ7MoxU2txEWxqvQEkNJQt\n/LC3tM39nhrQGJwW1uQi/oQRfSPZ+vCz+FtsbJg0E+P6lQSrHViHTueH76wAxAWruG6wkWqrl4+2\n1DNh/EB8skR6uAOtqmnZhtcrs2i1A6sDZk7QEhfR8ZKKvGM2nn/jKEqFxB8fSSE6QtfqsbfWS2NX\ntplZl6d0qJRj2ddlvLO4CKNByVO/SSUtufuPzCyrcPLvefkcyrUSEqTm0XsTGZDu39XLumj5fDLb\n99SydEUp2UdtAPRP92fWtAgGZPiLMhlBEIRL1Owr0zhSVMuqLQVkJAaRkRjc/kaCIAjCOetwx7bS\n0lLy8vJISkoC4NixYxQWFra73dq1a1myZAkLFizgyiuvbHxclpv3K2jr8VMFBelRtTFS84SwsK6/\nsHts9nAcLg/VdU70OhU2h4dAkxadpvlHn19Si8PlbXVflw+J4eGbBqP8OaXw4ZsGo/fTsGVfCeYa\nO6GBfozqF4V6Vxb+27ZQGpmAYWgSA9UHOOgM4NujJqrqS1Ep4N7xAaiUEgs31dIrLY2gwACs9ZVk\njGo+DePDVXXkl/gY1V/HdZMCO3zRVlHp5J//2Yfd4eOZ32cwfkzrkzZKzFaq6lsuR6mud6DUqAkL\nbT3AIMsy897P571PiggL0fDyMwNIiu/eAQlZllm9voxX3szFZvcyaWwYv32wFyb/c2tm2R1+77tS\na8fv8cp8u6Gc95ccI+9YQzBi3KgQZt8QT9/e3T+bpiPEz/7SPf5L+dgF4Xzx06p44Lq+/GPRTuYt\nP8Bf54zApNd09bIEQRAueh0OSsydO5e77roLp9OJQqFAoVA0NsFszcaNG3nzzTeZP38+/v7+6PV6\nHA4HOp2OsrIywsPDCQ8Px2w2N25TXl7OoEGD2txvdbWt3fWGhflTUVHfsYO7wLw+H5+00ivB45Ub\nmzq2d1yTB0VTVWVt8tiMyxKZOiKucR8+h4PN97+NT1KwbdK1PBGUi0tW8L+aPpSYG7IwZgw1Ehes\nZv1BGxVOf67snUqdxcqPW3czpe/wJhkJe494WP2jg7AgiWsuU2I2Ny9HaYnN7uVP/8qmotLFnTfG\n0L+3rs2fh9ftJdi/5XKUIH8dXpe71e19Ppl5HxSyer2ZqHAtr/1jECpF66/vDuotHt5adIwfttfg\np1Pw6D0JTBgTjNPhoMLhOOv9dqff+67Q0vE7XT7Wbarki9VllJtdKBQwYXQw10+LaOzXcTF8ZuJn\nf+kef2cfuwiACBezpCgTM8cn8+l3R1i44iCP3jBAZNAJgiBcYB0OSkyZMoUpU6ZQU1ODLMsEBQW1\n+fr6+nqef/553nnnncamlWPGjOHrr7/muuuu45tvvmHcuHEMHDiQJ598krq6OpRKJVlZWe0GO3qa\n1nolHCyoxuH0NAYqBqSGolUrcLqb95TQaZSEtdAN+vRJFYf/Ph9jtZndQ8YzLdVKkNLF4tokSjwN\n26aGq8nsZ6C8zsPnu21cNWkCsiyzaWsWVTW2JlNBzDU+Pl7jQK1q6COh03Tsj7LHI/Pi/+WRX2jn\nqgmhzMgMb3cbrVrZajlKWz043B4fr80vYNO2ahLj/Hj6sVSiInRUVLQ8maQ7+OlgPa/Nz6ey2k2f\nVANz70skIqzlxqfC2bPZvaxeX8FX35RTU+dBo5bInBjK9VMjCA8Vn7cgCILQsqtGxrM/v4o9Ryr5\ndudxpgyL6+olCYIgXNQ6HJQoKiriueeeo7q6mkWLFvHpp58yfPhwEhMTW3z9ypUrqa6uZu7cuY2P\n/etf/+LJJ59k8eLFREdHM2PGDNRqNY8//jj33HMPkiTx0EMPNTa9vBjYnG42/VTS4nNFFSezHirr\nnKzPKiI2zMDxCmuz147p3zA3u7zaRoBRi0opNZtUMcLgInHhR9gCgrCMHc5EwwEK3AZWWOIB0Kok\n7hkfABLM31DLoP79Mej92L3/MOaqGkJOaaLp9si8u9KBwwW3XqElKqRjfSRkWebt94+xa18dQweY\nuO/2OCRJ6tCYzxO9NnZlm6mudxDk304PDqeP5984StbeOvqkGnhybgoGfYd/pTud2+3jg8+L+fLr\nciQJbrs+ipnTIlEqxR2Y86mmzs3yNeWsWmfGZvei91Mwc1oE11wRTmDAuZXGCIIgCBc/hSRx79UZ\n/Pl/2/hk/RF6xwcRF27s6mUJgiBctDp8BffUU09x++23s3DhQgASExN56qmnWLRoUYuvv/nmm7n5\n5pubPX5i+1NlZmaSmZnZ0aX0KB+uyWmzT8Tp7E4Plw+KYsv+ssaMCY1K4mBeNX/K2UL1zwEIvU7d\nZLJHZa0D9YI3kd0eXPffw92BR/HJML+6D14aelDcONyfCJOKlT9Z8GrDSU6IpaKyir0Hc4CmGQlf\nfO+k2OxjVF8Vw9I7fiG3dGUZazZUkhzvx+O/TAJJ5sO1OR0a86lUKLhtShqzLk9pN4BhtXn4+6tH\nOJhjZUh/E797MBmttvtOqjhWZOeVt/PJL7QTFa5l7v2JPaIJZ09SUenig6W5fPlNMS6XjMlfxe0z\no5k6KbRbB6sEQRCE7ifQqOWe6em8uuQn3ly2jz/fNbxDDbcFQRCEM9fhM3W3283kyZN55513gIaR\nn0LbnG4vhwqqzmib6nonHo/cpITD5ZEpqTrZb6Kyztms90LaoZ1EF+dRlNafW6eEIe/ezzpXIkfd\nDQ38+sZomJSu53iVmzWHvEyd0h+Px8MP23YR7K9tkpGw46CbLfs9RIcqmHF5x9PcN26p4v3PigkN\nVvOnX6fgp1Py4drsMx7zqVUrG0tIWlJT6+avL+eSX2hn7IggHr03AbWqewYkZFlm5bcVvPdpES63\nzBXjQ7j7llj8dOLE5nwpLLbz+aoyNmypwuuFsBANMzLDmTw2tFsHqgRBEITubWBqKFOGxrJ253E+\n/jaHX2T26eolCYIgXJTO6PZhXV1dY7OfnJwcnM6WpyUIDWotTqrrXWe0TYBBw4GC6jPaRmu3Mnrj\nCtxqDbkTJuLbswGMQQzLvI2D3+axO7uMOWMD8Hhl5m+oZeSwEWjUalKC7Qy4fUBjRoLT7WXvkXo+\nXSeh1TT0kVCrOlZasP9wPa8tKEDvp+DJuakEB2nO25jPU5WbnfzlxVxKyp1cNSGU+2bHoVR0z/KH\n6lo3//lfAbv21eFvVPLYAwmMHBLY1cu6aOTmWflsZRlbs2qQZYiN0vGLmxMYlKFH1cHfW0EQuq/s\n7GwefPBB7rrrLmbPno3b7eYPf/gDBQUFGAwGXnvtNQICAvjyyy959913USgU3HTTTdx4441dvXTh\nInLjxBQOF9bw/e5i+iUFM7R3+32yBEEQhDPT4aDEQw89xE033URFRQXXXHMN1dXVvPDCCxdybT1e\ngFFLsKnliRKtcbq92JwdL/cAGPXDSvwcVjaPncYvEsuQZB+uUdeh9fPj7ml9KOwlEWSQ+XxnPRFx\nqUSGhRCidxMb6EOS9Hh9Pt5fc5gf95ahUaSjVPhhdR3h6+06bpncq1mZxemOlzj413+PIssyv38o\nhYTYhokGtRYnVa0ce3W9o0lTzY4oLLLz15dzqax2M2t6BLfPjO62HbG37qrhjYXHqLN4GNzPxMNz\nEggOFP0MzpUsy+w7ZOGzFaXsOdAwbSA1Sc+saZGMGBxARITpkp3AIAgXE5vNxt/+9jdGjx7d+Ngn\nn3xCUFAQL730EosXL2bHjh2MHj2a119/nSVLlqBWq7nhhhu44oorGhtsC8K5UquUPHBtX555ZzsL\nVx4iMdJESICuq5clCIJwUelwUCIpKYnrr78et9vNoUOHuPzyy9m5c2eTEwahqbYmSrTmTAMSkcV5\npB/Yjjk0irixCcQp81H3HYEzKgUApctCYpCMT6ljyNAU8uuDUEpekoIcSFJDsGHxulzW7SzCoElB\nqfDD4S7F7q7k250gSVKrZRbQ0FTw2VdysVi9PHJPAgMyTI3PtRWUCfI/2VSzI3LyrPztlVzqLV7u\nuimG6zIjOrxtZ3I4vSz46DhrNlSiVknce1ssUyeFoeim2Rw9hc8ns31PLUtXlJJ9tKGUqX+6P7Om\nRTAgw7/bBqcEQTg7Go2GefPmMW/evMbH1q9fz6OPPgrQ2LNq8+bN9O/fv7FB9pAhQ8jKymLSpEmd\nv2jhohUdauDWKb14d/Vh5n21n9/dNkT8XRcEQTiPOhyUuO++++jbty8RERGkpjb0HvB4PBdsYReL\nUydKVNU50GqUyLLc4tjPMxUfrOOyj75ARqLgmhk8HFSIrDOgG38dFosPvB6oLwEkPMZoso/qUWsk\nVn+/nWX2OganhTFjXDJZh8vRqsLRqELweOuxuwsb32NXdkWrZRZOp49/vHqEMrOLm6+NZNJlIU2e\nP9sxn6fbe7Cef7x2BJfLx0N3xzNlXOiZfVCdJCfPyitv51NS5iQx1o/fPJBIfIxfVy+rR/N6ZTZu\nq2LpyjIKixwAjBwcwMzpkaJRqCBcxFQqFSpV01OUoqIiNmzYwAsvvEBoaChPP/00ZrOZ4ODgxtcE\nBwdTUdFy2aAgnIvxA6PZn1fFjsMVLP8xn2vHJnX1kgRBEC4aHQ5KBAYG8s9//vNCruWidPpECaNe\nwyfrstm4pxT5LPepVSsY0z+KSYd/pKiiBP0N1/DAGB+qKi/u4dOR/AxQXwf1xSB7wRjJmgM+DCYd\nB7KPUlzWcMK2dsdx7A4PtVY1/tp4fLIbiysXTllZVb2zxTILr0/mlXl55OTZmDAmmJuvi2pxrWc6\n5vN0W7NqeOnNPGTgtw8mMXpo0Jl/YBeY1yezdEUpi78sweuF6zLDuf36aNRq0WTxbDldPtZtquSL\n1WWUm10oFDBhdDDXT4sQgR5BuETJskxSUhIPP/wwb7zxBm+99RYZGRnNXtOeoCA9KtWFaTYcFnbx\njDTvqS7kz+Dx2cN49OXv+PKHPEYPiiEjKaT9jS5B4t9B1xM/g64nfgZnpsNBiSuuuIIvv/ySwYMH\no1Se/GMeHR19QRZ2sTkxUeLDtdls2FN6VvtQSKBSKXC6feRuP0yvefPwmUxkpyYytCqXvZ4wduRo\neHiIDxw14LKA2kCpOxiDyUB1bR1Zew822efBgjpMul7IsoTVeQRZdjd5Pthf22KZxbuLi9iaVUu/\nPkYevCu+1fT5Mxnzebp1P1Ty+oICNBoFf3g4mYF9Te1v1MnKKpz8e14+h3KthASpefTeRAakiy+h\ns2Wze1m9voKvvimnps6DRi0xdVIYMzLDCQ/teLmPIAgXn9DQ0MbJX2PHjuU///kPEyZMwGw2N76m\nvLycQYMGtbmf6mpbm8+frbAwf9HTpot1xs/gnmnpPPdhFs+/t52/zBmBQSf6RZ1K/DuM1AuUAAAg\nAElEQVToeuJn0PXEz6BlbQVqOhyUOHz4MF999VWT5lGSJPHdd9+d0+K6C6fbe8YXzWfzHq1No+gI\nnwwutw9kmX4rPkHhcrF10rU8FJCP3afk7YpUqsqKCDWpuTLZBZIClz6anCI/vF4vG7dm4fM1LRtx\nuWJQK7U4PEV4fHXN3nNwWlizz2P5mnK+WlNOXLSOPzyc3KFxnO2N+TzdV9+Us+Dj4xgNSp6am0pa\nSvdK1Zdlme83V/H2+4XYHT7GDAvkl3fG4288o4E2ws9q6twsX1POqnVmbHYvej8FM6dFcM0V4QQG\niBM+QRBg/PjxbNy4kVmzZrF//36SkpIYOHAgTz75JHV1dSiVSrKysnjiiSe6eqnCRSwtLpBrL0ti\n2aY83l19mF9d11f0NRIEQThHHb6C2rNnD9u3b0ej0VzI9XQ6r8/H4nW57MquoKrOSbBJy+C0MG6e\nlNru1Ikz1dY0ijORdGQfCfmHOB6XyuUj/TAorCyoSaPKp0MCUo0WkFXI/tEcrjLilRUczjlMTW3T\niJ1WFYVaGURKjIS/P2zep8Thami0qdMouax/ZLMyi627aljw8XGCAlQ8OTcFg/78XoTLssxHX5Tw\n6VelBAWoefrx1MZpHt2FxerhzfeO8cP2GnRaBY/ck8DEMcHipOQsVFS6WLa6jDUbzbhcMiZ/FbNn\nRZM5MQyD/sIEBwVB6P727dvHc889R1FRESqViq+//poXX3yRv//97yxZsgS9Xs9zzz2HTqfj8ccf\n55577kGSJB566KHGppeCcKFcPSaBA/lV7DhUzsakYMYPFFnDgiAI56LDV5T9+vXD6XRedEGJxety\nmzRirKxzNv5/W1MnzkZb0ygUUkMnB83P5RmtUbscXPb9l3gVSqqvupyR+mIOOQNYZ234gzilr57k\nMBUOSU+VK4RKm4pAPy9lZcVN9qNS+OOnjkWS3NwxNQB/fW9umphKRbUNJImwQL9mGRLZR628/FYe\nGrWCP/069byn0/t8MvM/PM6qdRVEhmv5y+OpRIR1r5T9vQfreXV+PpXVbvqkGvj1vYlEhnevNfYE\nhcV2Pl9VxoYtVXi9EBaiYUZmOJPHhqLVil4cgnCp69evH4sWLWr2+GuvvdbssczMTDIzMztjWYIA\nNJSm3n9NX55esI0P12bTKzaAqJDuldEpCILQk3Q4KFFWVsakSZNISUlp0lPigw8+uCAL6wxtlVPs\nyja3OnWio/s+vRykrWkUlw+KZuKQWP79yW6cbler+x2+5RuM1lr2jJzE7clm3LLE/JreyEhEByq5\nYag/FqcPb3AMuWVaVAqZ5CAbVvvJfUqoMGgaRobKUgEadf/G9cWGt3yHqazCyT9eO4LHLfOHR5JJ\nSex4KUZHeDwy/1mQz4Yt1STE6vjzY70IDuw+aftut48PPy9m2dflSBLcOiOKWdMjUSpFdsSZyM2z\n8tnKMrZm1SDLEBulY+a0CMaNDEalEp+lIAiC0DOEBOi4a2of3vhiH28u28+Tdw5FfYEaqAqCIFzs\nOhyU+OUvf3kh19El2iqnqK53tDh1oj3tlYO0NY2istZBdX3rAYnQ8uP02/MDNYGhpE+OI0hZzqd1\nSZR4DCgluHd8IGqVRFa5FgX++GSJxAALRWU1VJ2yX4M2BYVCg811DLejqt3jrLd4+NsrudTWebh/\ndhzDBwWc0WfSHqfLxwtvHGXnT3X0TjHw5NwUjIbu05uhsMjOK/PyyTtmJypcy9z7Ertdj4vuTJZl\n9h6ysHRFKXsONJQQpSbpmTUtkhGDA8Ss9wusM/rlCIIgXIqG9Qln/MBoNuwp5tPvjpz3DFtBEIRL\nRYev/EaMGHEh19El2iqnCPLXtTh1oj2tlYPYHR5mX9UbrVrZ6jQKP62KAKOGGkvzwIROCePXf45C\nlim4YgoPB5RT6DaQpUtH53RxZYaOxFA1m4/YyXGGE++vJDe/kPd37MYng0RDeYhOHYNaGYDLU43T\nU0qIqe3jdLt9PPf6UYpKnVx3VThTJ4Wd8WfSFqvNyz9eO8KBbAuD+5n43UNJ6LTd48JJlmVWravg\n3U+KcLllpowPYc4tsfjpusf6ujufT2b7nlqWrigl+2hDt/v+6f7MmhbBgAx/0YPjAuvMfjmCIAiX\nqlsn9yLneA1rdxynX1IwA1JCu3pJgiAIPU73uR3dBdoqpxicFnrGdxXbKgf5YV8pBwuqGNI7nJsn\npTaZRnHqxUNLAQmA3j9tIbyskNzeA5k1yIVPhrer+1AuuYj0l7hmkJFKi5fVh5RcPi6eequN7bv2\n4ft5ZLsMqBQB+Klj8Poc2FxH2z1OWZb578IC9h+2MHpYIHfeGHNGn0d7aurc/O3lXI4es3PZ8EB+\nfV9ihyZ5dIbqWjf/XVBA1t46/I1KfnN/AqOGBra/oYDHI7NpWxVLV5VRWOQAYOTgAGZOjyQtWWSY\ndJbO7JcjCIJwqdJqlDxwbV+efW8H/1txkL/OGUHgWdzUEgRBuJRd0kEJoM1yijPV3nSNqnpXs6wJ\naH7xcKoQk46+Jkh5cyVOjY7QqQOIUplZZYnlqNuEWunl3vGhKBUS7/1oYdjQMQBs2pqF2+Np3I8k\naTBoU5BlHzZnLsEmNYPToto8zg8/L2HDlmp6pzQ0dDzbNPuW0scrKl385cUcisucXHl5KPffEYey\nm6Txb9tVw+sLj1Fn8TCorz+PzEkgOOjiavB6IThdPtZtquSL1WWUm10oFDBhdDDXT4sgPqZ7TVC5\n2F3IfjmCIAhCU/ER/tw4MZWP1uYwf/kBHrt5EAqRDSgIgtBhl3xQQqlQtFpOcabaKgc51alZEzPG\nJbV68RBk1PLnu4ZR8tjT1Lgc7JsyjfsjKyn36Pi0LhmAmUP9iQ5SsXa/FVNUb/wNen46kE1FZfUp\ne5IwalJRSCpsrjx+c3MayTEBbR7nmg1mliwvJTJcyx8fSUarOfMMhtbSx8emx/LMy0eorHYzc1oE\ns2dFd4tUfofTy8KPi/jmezNqlcQ9t8YybXKY6HnQDqvNy+r1FSxfU05NnQeNWmLqpDBmZIaf9wkt\nQsdciH45giAIQuumDI1lf14VPx2p5Ottx5g6MqGrlyQIgtBjXPJBiRNOLac4U6dmArRWDnK6E1kT\nNoen1YuHWquTqnU/UvPlGuwpKVw5zoBSsrCgJg2nrCQtUs0VffWU1nrYWmLgspFxmKuq2XMgu8l+\n/NRxqJRGnB4zBr/6xoBEaw3wdu+r4833juFvVPLUb1IIMJ3dFIyW0sdXbyph2WcWXE6488Zorp8a\neVb7Pt9y86y88nY+xWVOEmP9mHt/Igmx4u5+W2rq3CxfU86qdWZsdi96PwUzp0VwzRXhBAZ0n8kp\nl6IL0S9HEARBaJ0kScyZns7T/9vG0u+P0ic+iKQoU1cvSxAEoUcQQYlz0FImwMBeoUweGsPuHHO7\nGRMAhwqqW714CNEpqP77v0GpJP2h6cS5c9hoi2CvMwSdWuKecQEgw6KtdoYNHYvb42Hj1l3Isty4\nD7UyCJ06Eq/Pjs2Vz5gB0aiUEh+uzW6xAd6x4w6ef+MoSoXEHx9JITpCd1afTUvp426bCkuxAXwy\n998Rx9SJ4We17/PJ65NZuqKUxV+W4PXCtVeGM3tWNGp19+ht0R2Vm50s+7qctRvNuFwyJn8Vs2dF\nkzkxDINelAR0B+e7X47Q/TmdPg4ftXIwx8LBbAtFpQ4e/2USfVKNXb00QbhkmPQa7r0mg5c/3s1b\ny/bz9N3D8dOKU21BEIT2iG/Kc9BSJsC6nUVc1i+SP981nMXrcvlxX2mb+6iudzKmXyQ/tPC6iYd+\nwFVwnLBfzCTam0etV837tb0AuGWEP2H+Kr7abSE2uT9ajYbNO/ZQb7E2bq+QtBg0yciyF4szh8v6\nNTTZbHVCiM3H5u/d2B0+fvurJNJ7nf3J7Onp4y6LCmuJAWQwRtsYOqjrT5TLzU7+PS+fgzlWQoLU\nPHpPAgMyxF2N1hQW2/l8VRkbtlTh9UJYiIYZmeFMHhuKViuCON3N+eyXI3Q/dRZPQwAix0JOnp3D\nufV4vSefj4/RYfATwSdB6Gx9E4PJHBnPqq3H+GBNNvdendHVSxIEQej2RFDiLHVk0sagtLB2syYk\nCdRqBZOGxrAnp7Lx4mGE0UnI/32FKzgYOUGF0mdnUW0GFp+agXFaxvfWU2B2k2MNY0hKGIVFpeTk\nHTt1zxi0vZAkJVbnEQKNMrOv6o3HK7e4btkL33xtwWVXcOeNMVw2POicPp9T08eddWpspXqQwBhj\nJTJK1aXp47Is8/2WKua9X4jN7mP0sEB+dWc8/kbxz6EluXlWPltZxtasGmQZYqN0zJwWwbiRwahU\not9Gd3U+++UIXa/c7ORAjoWDOVYOZlsoLHY0PqdUSiTH68lIM5KeZiQ91YjJX3yfCUJXuX58MoeO\nVfPjvlL6JgUzum/3KFUVBEHorsRZy1nqyKSNdTuLGD8oil/fMICVWwrYcqC82et8Mny3q/jn7Iph\n2J0eTAYNebc9RL3bQ+mEy5hkqGeXI4TN9nCMWom7LjPh9sp8ssvDkOHp2B0Oftyxp8l+9ZpEVAo9\nTncZLm8lQ3rHAnC0qLbZumUZLCUGPHYF40cHMiPz3MsqTqSPL19Tjr1Cj6TwYYyxovLzMjgtsssu\njixWD28tKmTTtmp0WgWP3JPAxDHB3aLRZnciyzJ7D1lYuqKUPQfqAUhN0jNrWiQjBgeI5p89yLn0\nyxG6hs8nU1js4GCOhQPZDdkQ5ip34/NajYIB6f6NQYgxIyKw1Nu6cMWCIJxKpVTwwLV9eXrhdhZ9\nfZiUmADCA0WfKkEQhNaIoMRZ6uikjQ27S9iwu4Rgfw0xYQZKzFZ8cvPXnTqRY0rVYep/3ElZWgbX\nDge7T8nCmjRA4o4xAQTolXy63UJq+lCUSiU//LgDp8vVuC+NMhStKgyPz4rNfYzRfSOQZZkn522h\nss6JQmoIREDDf21lfnhsavQBXh64I/68XKDLsgz1BuwVepRqGWOMhbBQTbtjSC+kfYfqeXV+PuYq\nN71TDMy9L5HIcNHw71Q+n8z2PbUsXVFK9tGGi5z+6f7cMD2C/un+IngjCBeA2+PjSL6tMQhxKNeK\nxXqyFsNkVDFySADpvYxkpBlJitM3yVLy0ymx1HfFygVBaE14kJ47r+zNvOUHeGvZfv44ewgqpSh1\nFARBaIkISpylthrJtaSq3gX1rnZfs3FTNkkfvYzKT0fs1D4YFXbeqelFpVfHyGQdw5N0ZJe6qFTG\n0yfAxKHcPIpLT2ZgKCQ/9JoEfLIHqzOXAKManVbFtzuLGl9zalDEUa3FVadFqfVw1ZUB6HXn/ivh\n88ks+Og4K76tICJUwxNzk9H50WXp426Pj48+L+GL1WVIEtwyI4obpkeiVIoL7BM8HplN26pYuqqM\nwqKGtPCRgwOYOT2StGRDF69OEC4udruXw0esHMi2cCDHQs5RKy73yS/miFANwwaeDELERGpFQFAQ\neqDR/SLZl1fJ5v1lLNuUx6zLU7p6SYIgCN2SCEqcgxN3/LMOlzcEHc6DkT+sRFlfT+SvbiI1up5s\np4m11hgC9QpmjzbhcPtYflDJkGEp1NTVk52Tc8rWCoyNfSSy8clO0mLD+SnX3OJ7uevVOMx+qDQy\nmVNN3H5Vr3Nev8cj89+FBXy/uYr4GB1PP5ZKcJDmnPd7tgqL7bzydj55x+xEhmuZe18ivVPERfYJ\nTpePdZsq+WJ1GeVmFwoFTBgTzMypEcTFiFRTQTgfamrdjVkQB3Is5B+zNwaHJQkSYvxITzOSkWYg\nvZeRkC78zhQE4fyafWVvcotqWbm5gIyEINITg7t6SYIgCN2OCEp0gNPtbbFR3IlGcl6fzPqsojb2\n0DGRxflk7N9GdWgkI1O9eD0K5tf0QUbi7rEBGLQKPthqpU/fUXh9PjZtzaLW4kSrUuD0+DBoklAq\ndDjcJbi9NSgVMG10As8s3N7svdw2JZZSPVqtxLO/TyM18dynYThdPl56M4/tu2tJSzHw5K9Tuqx5\npCzLrFpn5t1PjuNyy0wZF8KcW2Px04lGfwBWm5fV6ytYvqacmjoPGrXE1ElhzMgMJzxUlLQIwtmS\nZZnScicHc05mQpSUnSzzU6kk0lIMZKQ1ZEH0STVg0Is/xYJwsfLTqnjg2n788/2dvL38AM/MGYG/\nXgQeBUEQTiXOhNrg9flYvC6XXdkVVNU5CTZpGZwWxs2TUlEqGuoCnW5vq5kIZ0Lh9TJu/VIAgq4f\ngcZjwzVgIukVsQzy1NI/Vsve4068AWno/XTs/OkAVTV1hJh09E8JZvNeLxpVCG5vPXZ3Q0nJhMEx\nRAbrm/W+8LoUWIsbsgV++6uk8xKQsNm9/OO1I+w/bGFgX39+/1BylwUAqqpd/P3VI+z8qQ6jQcnc\n++MZPfTcpolcLGrq3CxfU86qdWZsdi96PwWzpkdw9ZRwAgPUZ7y/1gJ2gnCp8PpkCgrtjQ0pD+ZY\nqK71ND6v91MwuJ+pMQiRmqRHoxZ15YJwKUmONnH9+GSWfHeEhSsP8cis/qIkSxAE4RQiKNGGxety\nm/SMqKxzsnbHcbw+mauGxxFg1LY7haOjBuzeSEhlKUUDBnFLqhtfQDhyv/HcJvuQq47g8Miszzcw\nYGA0peVmDhw+AsDgtFBGZyTx02EHMm5srlzCg3QMSAlpDJ6c2vvC55GwFBmQfQqGj9IybEDgOa+9\nts7NM6/kcrTAzuhhgfzmvkTUXXTSvX13DW+8W0hNrZtBff15ZE5Cl5aPdBflZifLvi5n7UYzLpeM\nyV/F7FnRZE4Mw6A/82BCRwJ2gnAxcrl95By1/hyEsHL4iAWb3df4fFCAijHDAhuDEPGxfijFtBpB\nuORljoxnf14Vu3PNrMsqYvLQ2K5ekiAIQrchghKtcLq97MquaPG573cVsT6riBCTlgEpIR2awtGa\nuHAjlJYxbOsanHojV9yQiIwLz+gZoFBCTSESMvhH069/JG63mx+37ybYpGNwWihXj07htU8cgMSc\nawyEBQ4lJTGE+lp743uc6H2x86CZYwdV+NxKMvqp+f296a0ee0fvfpurXPzlxRyKSp1MGRfCL38R\n3yUn4A6nl4WLi/jmOzMatcScW2OZPjnskh9dWVhs5/NVZWzYUoXXC2EhGmZkRjB5XAhazdkHD1oL\n2AHcNiXtnNctCN2F1eZpLMU4mGMhN9+Gx3OyKWV0hJbRQ42N4zkjwzTiDqggCM0oJIl7r87g6QXb\nWLwul7S4wIZzQEEQBEEEJVrTVgbEiQZllXVO1u8qJi7c2GJQ4rJ+kahUCr7fXdzsOZ1GydgBUdw0\nMYXcux6nzuMm7ZEZhGgtePqMQg6LA6sZ3HZkrYn9tZEoFEp6hzkZeMdAAoxa1CoFC75yUF0vc9VI\nDX2TNEBDVkB5ta0xqKBUKLh5Ui/yDyg46qhj/Kgg5t6X2OzE+UzvfheVOPjLSzmYq9zMyAznzhtj\nuuRkPDfPyitv51Nc5iQhVsczv++LydDC3NVLSE6elc9WlLJtVy2yDLFROmZOi2DcyOAmowTPRlsB\nu13ZZmZdniJKOYQeq7La1dALItvCoRwrBUX2xhHKCgmS4vUNAYheDU0pz6bsSRCES1OQv5Y509N5\nbclPvLlsH3++a7j4eykIgoAISrQqwKjtcAaE1e5m4pAYfso1U1XvJNj/5MU8gFqlYFe2mep6B0H+\nWvrEB3HrFWnotSqqVq2n7ttNmEYMICrSimwIwDtoCngcYK0AhYpjnjjqnUrCjR5CDR5qLQ3vu26H\nm4P5XtLilUwZocbm9PDRmmyyi2oxV9ubBBXeXVzM9t119E/35+E5CS0GD87k7veRAhvPvJxLXb2H\nO26IZua0yLP9qM+a1yfz+coyPl5WjNcL114Zzu2zoomJNlJRUd/p6+lqsiyzc081Cz7MY8+BhuNP\nTdJzw/RIhg8KOG9ZI20F7KrrHdRanIQH6c/LewnChSTLMkWlzoYsiJ8zIcrMJycpadQSfXsbG0Zz\n9jLSO8WAn5+4gBAE4ewNSg1l8tBYvt15nMXf5nBnZp+uXpIgCEKXE0GJVmjVyia9GNpSXe/E7vDg\n9crIcsOJ7gknJnTMujylWUmE12Ll2JMvImnUpFybhgIr7pHXgkoD1UcBGas2hrxyP7RKH3v2H2Dh\noVKq6pwEGYORfSkEGBXMnKBkwYqD7DxUjtNzsrb5RFDh8CEXP2W5iIvW8fuHklCrmmc9nMnd7/2H\n6/n7q0dwOH386s54rpwQeiYf7XlRbnby6vwCDmRbCA5U8+g9CQzsa+r0dXQHPp/M9j21fLa8lJw8\nGwAD0v2ZNT2C/un+5z17pa2AXZC/jgCjmN4hdE8ej0xeoe2UIISVOsvJppRGg5LhgwJI79WQCZGS\nqG/x+1IQBOFc3DQxhcPHavhudzF9k4IZ2ju8q5ckCILQpURQog03T0rl8LEaCsstbb5OoYAtB8oa\n/7+q3tUsw0CrVjY2xjwRmDj+4lu4SsoIu+UKjGor3qQB+GLSwFIOHic+bSB7qxou+IuO57JmWwEA\nEmq83ngkZOyuXJ76XxUud8vlCi6Lmp+ynWh1Ek/8OrnV0XMdvfu9fXctL/7fUXw+eOyBRMaO6Px5\n299vruLt949hs/sYPTSQX/4iHlMXjR7tSh6PzKZtVSxdWUZhsQOAcaNCuHpKKGnJhgv2vm0F7Aan\nhYpUVKHbcDi9ZB+xNvaEyD5qxeE8GbgNDVYzflTQz0EII3HRuku+D40gCBeeWqXkl9f15Zl3tvPO\nqkMkRZkINum6elmCIAhd5tK7kjsDHq+MzeFu93VeX8uPn8gwUCmlZr0aRmgsxM/7CHtQMPEZSiw+\nNV/aejPDaUVpM4NCTa4zDodHQaDGyvI9eY37NWhTUEgabK4CauyVra/frsRaogcJNOH1rNtzrNUm\nhH5aFYFGLdWW1u9+f7+5itf+l49KJfHHR5MZ0j+g3c/mfLLaPLy1qJCNW6vRaRU8MieBiZcFX3JN\n5ZwuH+s2VfLF6jLKzS4UCpgwJpiZUyMYMii8U0pXTpQmnSxLami8euJxQegKdfWexrGcB7ItHD1m\nw+s9+XxctI70tIZSjIw0I2EhYjKPIAhdIzrUwC1TevHe6sO8/dUBfnfrYBEUFQThkiWCEm1ob9xn\noFGDzeluNUuh6ucMg7U7jze5q1xVY0f36ZtIskzYNQPRayVer0plR0UlV6SrMGmhShVLcaWO2to6\n3l+zAd/PJSE6dSxqpQmXpwqnp6zF9wXwuhVYig0ggyHaikrnbbEJ4anNLVsKSEDD3e9vN1Qx74NC\nDHolf/p1Cum9Ordj9L5D9bw6Px9zlZu0FANz70skKvzSKhOw2rysXl/BV2vKqa3zoFFLTJ0UxozM\ncMJDO/ezaKssSRA6gyzLVFS6GkdzZh+1kV9oa3xeqYSUBH1jEKJPL+MlmVElCEL3dfnAaPbnVbHz\ncAXLN+dz7WVJXb0kQRCELiHO0NrQZu28UctDM/vy7HtZrW4faNDip1U169WQvm8rEWWF1GT0Zlxf\nHbsdwfxoj+C2UUZMWnCpg9ldHohP9rJ+887GgIRaEYifOhqvz4HVldfSWwLg80pYjhuQvQr8wm1o\njA0101V1zZsQnt7c8lQhJh2DeoWgsBqZt6yQQJOKpx9PJTGu85oYuj0+Pvq8hC9WlyFJcMt1Udxw\ndSRK5aVzN6Gmzs3yNeWsWmfGZvei91Mwa3oEV08J7/LO/1q1UjS1FDqFzydTWOxoHM15INtCZfXJ\nTDY/nYKBGf6NQYi0ZANaregHIQhC9yVJEndN7UNeSR3LNuWRnhBEr9jArl6WIAhCpxNBiTa0VTs/\ntE8YMWH+hLQxoWNQWih2p6dJtoWftZ6RP67CpdExdkY8Dp+ShTW9yYjWMiXDQFG1myJtNAqlim07\nf6KuvqGfhULSoNcmI8s+rM5cwNvie8o+sBYb8LmVaIMc6AJPdpKXJPh6eyG3TemFUqFos7lloFHD\nk3cO5bOvKvhqTSnhoRr+8ngqURGdV/NYWGzn32/nc/SYnchwLXPvS6R3yoXrldDdlJudLPu6nLUb\nzLjcMiZ/FbNnRZM5MQyDXmQlCBc3t8fHkXzbyfGcuVastpPfeyZ/FaOGBpLey0BGLyPDh0ZQXdV2\n/x9BEITuxqBTc/81fXnuwyze+nI/T8weKvpLCIJwyRFBiXa0VTuvVChaDVrEhRu5bUovPF65SbbF\nmI1foXU5UEwbSVCAivdqkrEq/ZgzNgCPT+aHogDCE/w4XlJG9tGCn/cmYdCkopBUWJ15eGVbs/cD\nkGWwlenx2FWojS78Qh1NnvfJsD6rCKVC4rYpaW2Wp9RaXLz1XiGbd9QRF63j6cdTCQnqnPprWZZZ\nvd7MO4uP43LLTB4bwj23xl4yo/gKi+18vqqMDVuq8HohLETDjMwIJo8LQasRd36Fi5PN7uXwEWtj\nECI3z9qkNC4iTMOIwQGN4zmjI7VN+smoLqHsKUEQLi5pcYHMujyFJd8d4fkPd/H724cQ5H9plagK\ngnBpE0GJdrRXO39q0KKqzkGAUcPgXqHcdkUaSoUCpYLGwEXssWx6Ze/GEhVF5rggclwmvrHGMGec\niWCjktX7XYTFpeP1uvlx++7G9/BTx6NSGnF6zLi8LWc2ADgqdbjqNSh1HgyRNlrr/3iit0Rr5Smy\nD1xmfzZn19ErSc+Tv0nttFrsmlo3/11YwM6f6jAalMy9P57RQ4M65b27Wk6elc9WlLJtVy2yDLFR\nOmZOi2DcyGBUKnHBJVxcqmvdjWUYB7Mt5Bfa8f0cg5AkSIj1I+PnUoz0XgaCOykoKgiC0BWmjUrA\n4fKy/Md8XvhoF7+/bbAYsS0IwiVDBCU6qLXa+Y40/Lt5UiqSy0X4ohfwSRIDb+iDT6Fkb9R4xoco\nuKyXH4VVXlSRg5EUCrKy9uBwNpRdqJXB6NQReH02bK78VtfnrNXgqNKhUHsxxvBDVFoAACAASURB\nVFiR2rihfuqIz9MzPWQfWIoMeOxKBqT784eHkzstQ2H77lr+u7CAunoPA/v68+ichIv+QkSWZfYe\nsrB0RSl7DjRMzUhN0nPD9EiGDwoQnbiFi4Isy5SUOzmYbeVATkMQoqT8ZDBUpZLonWogI61hNGef\nVEOr44sFQRAuVtePS8Lr9bFq6zFe+Hg3v7ttMCb9xX0eJAiCACIocd601fBPqVAwbv8PFFdXEDZt\nBNGxOjwDxjO9/1DkyiPIsg9PcDqqej0BGjsHjzQECRSSDoMmCVn2YnHmAi3PHnVbVdjK/JAUPowx\nVhTKlqeBnHBixCc0zfSorHZiL/HHY1cwYnAAv/1lEmr1hS8XcDp9LFx8nK+/M6NWScy5JZbpU8Iu\n6gtyn09m++5aPltRSk5eQznOgHR/Zk2PoH+6/yU35lS4uHh9MvmFdg5mWziQY+FQjoXqWk/j83o/\nBUP6mxqDEKlJejSd8F0jCILQnUmSxA0TUvB4ZdbsKOTFjxoCE0a/rm1qLQiCcKGJoEQnsOfmU/L6\nO2giQkgdFYAvIAxv3/FQV4Ike7FpIimoDMBP7WP/ocM/b6XAqE1FkpRYnLn4ZEeL+/Y4FVhKDCCB\nMcaKUtNy4OJUg9NCG7M5TmR6TBgQx7OvHKHa5mLS2BAe/EV8p0y4OJJv45W38ygqdRIfo+OxB5JI\niPW74O/bVTwemU3bqli6sozC4oaf6cjBAcycHkla8qXTxFO4uDhdPnLyrBz8eTznoVwLdsfJ76Kg\nADWXDQ9sDELEx/qhvIiDjoIgCGdLkiRumZyKx+djfVYRL368i/9362AMOhGYEATh4iWCEheYLMsU\nPPEcsstN8ox+KLVq3KNmgNsKrnp8Kj27/z979x0YV3Xmffx7750+ozbqklUtyZbcGy7YFINjg00J\npgTilF2S7BtIQrKQslnedzebzWYT0khCNoFsCAkJkIBjisH0gCku2HJDsiVZVrFk9Tq93Pv+IXlk\nWXLDluXyfP6y587cOTOjdn73nOfpyULBoMjt4+nqNgAcljw01UEg3Eo42gWA1awQPKLwmx5W8DS5\nQFdwZnox2Ud25JhdnEJ9q2dEkc4jNbcG+Pcf7ae9M8T1H0vjs7dlj/mV+qhusO6lVp5Y10w0Ctd9\nLI01q7Mu2KulwZDO6xs7WbehlfbOEKoKVyxyc9M16eRkX7ghjLgwebwRKqu9VFYPtOesOeAjEh36\n2ZSdYaW02BVrz5meapHVP0IIcZIUReGTy0qIRg3e3tnMT57awb23zcJhkz/bhRAXJvnpNsY6175E\n3ztbSbqklJQCO/qkSzCSM6FrP4aiUhMqIBTVyEsK8re/V9LZF8SipWA1pRKJevCHG2LnmjUpjU17\nWgEwouBpcmFEVOwpfixx4RHPrSrwmWsmYzFrx6x3caDBx3d+UkNvX4RP3pTF6pXpYz55aOsI8uBv\n66mo8uBONPPlO/OYOSV+TJ9zvHh9UTa82c7zr7bR2xfBYla4ZmkqN65IIy1FCliJ80NHVyi2FaOy\n2kNDUwBjMINQVSjMdVBaMlCQsrTYRWK8XNETQojToSoKn14xiaiu8+7uFn7215187dYZ2K3yp7sQ\n4sIjP9nGUKSnj4Z//ymqzcrEpRngTCAy82roawJDp880geY+Bwm2KO9vr+C9PS1oih2HJR/diOAN\n1QADf/knx9tYs6wEl83M9r0dNOzViIY07EkhrEmjt/XMTnURN1ggabR6FxVVHr73YA3+gM4/fSqH\nFVemjtl7cdhb73fx8OMN+Pw6C+Yk8sXP5J61zh5nU09fmBdebeOlN9rx+XUcdpXVK9NZdXUaiQky\nYRPnLsMwOHgoMFSUstpDW0codtxiUZgyyRXbijGp0HnRtOsVQoizSVUU/uGaUqK6waYPW3nwrzv5\n2q0zsVrkZ64Q4sJy4c0GzyGN//ULIp3d5N18CfYkG+H510PEB2EfUXMcu7rT0RSDQrePP1e1AxpO\nazGKouIN1KAbQxOBWSUpOKxmbr+qmM4GC/t9XZidYawpI1t/qipkp7j410/PPubYtu3q5Ye/qiUa\nNfja5/NZssA9Ru/CAK8vwm/+2MjGzd3YrCpf+oc8li52X3BLuts6gjz7chuvvd1BKGyQEG9izbUZ\nrLgyFadD/ogQ555IxKC2wTdsJUS/Z2grmMupMW9mQiyEKMyzYzZdmNushBDiXKOqCneuLCUaNdi6\nt40Hn97JPbfMGLHyVQghzmcSSoyR/q07aX/8b9gLMpkwO4lo/jQCKbmY++pA0fjQl0/UUJmcFiQY\nCNDVF8RpKUJTbQTCzYT1nti5Lp2aEasDsfbFVt54pwuTLYoz0zsikLCYFP7nm1ejRKMEw1Haun0j\ntm1s3NTFg/9bh6Yq/MuXJzJnesKYvhd79vXz89/W094ZomSik69+Pp/MtAtr60Jjs5+1L7aycXMX\n0SikJlu4cUU6Vy1JxmqRCZw4d/gDUar2D9SDqKj2UrXfSzA0VJQyNdnCrKlDnTEmZNou6E44Qghx\nrtNUlc9fV0ZUN9he1c4v1+7mK6unYTZJMCGEuDBIKDEG9HCEum99H4DiawtR7E7+4pvEnLpqct0m\nXtlvxZJqI8UZJt0VIRSxkuTKxtDdhKN9+MMHY+dyx1lZs3wSmqry9qYuHn+mmcQEDSO5F2WUuW4o\nYuDxBXl+Yy3lVe109QVxx1uZVZLKbUuLePWtTh5+vBG7TeNf75lIWYlrzN6HcETnyXWH+NtLrSgK\nfOKGTG5elXFWunqcLdUHvDyzvoUt5b0YBkzItHHTteksme/GZLpwXqc4f/X2hWNFKSuqPdTW+9CP\naNKTk22jrHhoO0ZqsmX8BiuEEGJUJk3l/9wwhYfW7mbn/k4e+tse7v74NFm5JoS4IEgocRqC4eio\nBSSbfvMn/JU1pF1eRkJuPG/bZ2PoAXLdcWxriGJKLsXr83OovpqpGRM51AGGnoVuhPAG9w97jtmT\nUrGaNT7c188vflePyQxaci+6yTh6ODFPv1HNxp2HYv/v7Avy6taDVO4JsWdXiIR4E//2z0UU5I6s\nM3GmHDwU4KcPH6C23k96qoWvfj6fyUVjF4CcTYZhsHuvh2deaGFXZT8AxQUOVq/MYN7MBLmqLMaN\nYRi0dYSoqPLEQoimQ0M1ZzQNigqclBU7KStxMbnIRdwFWNNFHPv3kxDi/GXSVO76+DR+sXYXu/Z3\n8utn9/DFG6di0iSYEEKc3+Sv0Y8gqus89UbNiJUIN19RyLqntzDhgd+A3U7h5ZkctGTybm8cX1nm\notsbpdVcil1ReGdLOZGgl+Vz8/jDi0EURaE4p5cDhzS6+8PD2nfWNnr5r5/vJxo1cGZ70E2R447v\n3V2Hhv3fMMDfYWNPd4iUZDPfua+YrHTbmLw3hmGw4c0Ofv+Xg4RCBksXJ/O52ydcEIXwdN1g645e\nnlnfQvUBHwDTS+NYvTKdaaVxF1x9DHHu03WDhiY/FVVeahsb2bG7h87uoU48NqvKzClxlA6uhCgu\ncGK1yh+vF7Jj/X66bWkRmiqfvRDnO7NJ5Usfn8aDT++ivLqDh5/7kH+6YYp8fwshzmsSShzleFeX\nDh97eUsDb5Y3x27v7Avy2gcH2dfQQ9n//hpTOETuDbOI2m38tCmPu1a6MKkK77Uk40xL4MN9NbS2\nd6IAP3uqG6/fjj/UyIFDXUwvSuHqORNwx9swaQqPra/ipfUeIiEVZ4YXs+P4gQSAfsQiCsMAX6ud\nUJ8VzRLlvrsmjlkg0dMb5peP1rNtVx8up8ZXP5fLwrlJY/JcZ1MkYvDOli7WvthKY3MAgPmzE1i9\nMoPiAuc4j05cTMJhnZo6X2wlxN4aL17fUFHKhHgTC+ckxkKI/Bz7BbVdSpzYU2/U8NoHQ1sAD/9+\nArjj6pLxGpYQ4gyymDW+cvN0fvaXnXywrx3thUo+v6pMVmoKIc5bEkoMOt7VJWDYsWNdEDdt2kzB\ngQrIzyB3bjqP9xayZHoqE5LMbKk3cKYV09XTS/mefQA4rdl4/XbC0R4CkUME+uDN7U1oqsIdV5fw\nhw37ePGlPqIhEzZ3AEt8ePQnPgZDB2+Lg7DHgmaNkFIQICdrbCbRW3f08stH6+nrjzCjLI4v35lH\nctL5vTc9GNJ5fWMn6za00t4ZQlXhikVubromnZxs+3gPT1wEvL4o+/Z7BkMIL9W1XsKRodQxI83K\n/FkJlBa7uHRBOjZzRFbsXMSC4SjlVe2jHiuv6mD15RNlK4cQFwirWeOeW6bzk7/sZHNFK5qq8I8r\nS1Hld4AQ4jwkocSg411dAob92xilnIMpFGTR359FV1XmrS5mfzieWlcB35jmpK0vSq9zBko0ysbN\n29F1HZMaj0nNQteDeIO1w85VXtXBygV5vPJKH9GACUt8CFty4JRej6GDp9lJxGfGZA/jyvYS0mHd\nxtozerUsGNT5/V8OsuHNDkwmhX/4RDarrk47r9N6ry/Khjfbef7VNnr7IljMCtdelcoNy9NIS7mw\nuoaIc0tXT5jKak+sPWd9oz+28klRID/HTlnxQEHK0mIn7iOCv9RUB+3t/eM0cnEu6PUE6eoLjnqs\nuz9ArydIWtLY1RISQpxdNouJr90ygx8/tYP39rSgqQqfuWayBBNCiPOOhBIc/+rS9n3tx1wZcaS5\nm18lztND8hUlWFLjeKyrlC+sSgQDynuysSbZ2VK+m94+D4piJsFRRDRq4AnVYDB8S0Z3f4BH/tSI\nt0fDZA/jSPed1BgO06MKniYn0YAJszM80Dp0cKvhmbxatr/Ox08fPkBTS5DcbBtf+0I++Tnn7x+8\nPX1hXni1jZfeaMfn13HYVVavTGfV1WkkJpjHe3jiAmMYBs2twSNCCC8tbUMTSrNJYfJg+FBW4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M+oN+Du/yUhXIz7FTWjLQmnPxgnSMaGh8ByyEEEKcw/Iz4rn3tpn86MlyHn7uQzRVYXZJ6okf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\nAAAAAElFTkSuQmCC\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": "633470fb-62dc-413e-a37f-aa0442985d3a"
+      },
+      "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": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f76f84d7c50>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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jg9mc66sw0BdEly+QNAgBw5tXg9IQnKXWjNes5HpMriPAdOfu9UtYf8ssbFk9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\nezaVAAAAAElFTkSuQmCC\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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "outputId": "9646dac1-0dab-41c9-8e6c-213f9d4750b3"
+      },
+      "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": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f76f8dcd350>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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jg9mc66sw0BdEly+QNAgBw5tXg9IQnKXWjNes5HpMriPAdOfu9UtYf8ssbFk9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\nezaVAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 1080x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "27d09ad2-2ac0-4c18-fef1-449d98c22746"
+      },
+      "cell_type": "code",
+      "source": [
+        "plt.subplot(1, 2, 2)\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 6,
+      "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": "a6670236-953d-4a2a-89a3-02743094a5bb"
+      },
+      "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, 10))\n",
+        "\n",
+        "_ = california_housing_dataframe[\"rooms_per_person\"].hist()"
+      ],
+      "execution_count": 13,
+      "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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "f6e5ec3c-6238-435e-bd64-0dafb223fde7"
+      },
+      "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": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": "e7ff00ec-2436-4e57-b757-1a66f7b7152f"
+      },
+      "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": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 212.81\n",
+            "  period 01 : 189.09\n",
+            "  period 02 : 166.80\n",
+            "  period 03 : 146.54\n",
+            "  period 04 : 131.20\n",
+            "  period 05 : 120.32\n",
+            "  period 06 : 113.95\n",
+            "  period 07 : 110.03\n",
+            "  period 08 : 109.58\n",
+            "  period 09 : 108.66\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       predictions  targets\n",
+              "count      17000.0  17000.0\n",
+              "mean         191.4    207.3\n",
+              "std           49.7    116.0\n",
+              "min           45.6     15.0\n",
+              "25%          159.6    119.4\n",
+              "50%          191.4    180.4\n",
+              "75%          218.5    265.0\n",
+              "max          423.3    500.0"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>predictions</th>\n",
+              "      <th>targets</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>17000.0</td>\n",
+              "      <td>17000.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>191.4</td>\n",
+              "      <td>207.3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>49.7</td>\n",
+              "      <td>116.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>45.6</td>\n",
+              "      <td>15.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>159.6</td>\n",
+              "      <td>119.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>191.4</td>\n",
+              "      <td>180.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>218.5</td>\n",
+              "      <td>265.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>423.3</td>\n",
+              "      <td>500.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on training data): 108.66\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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IeNkdLo7ml2HXUnUiIn43uHcMZ/ePZ/+hItamZBodjoiIiCFUU0JEcLndLF2f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\nmDe+PWEpojtnY+w50XVQgmaiPgsRv4UBVaatvg9nvIl33Z3C7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BsMMp+rfmJ7zNiZHv0e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\nYII=\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": "1c50244d-ae90-41ac-f617-2bfba5a81446"
+      },
+      "cell_type": "code",
+      "source": [
+        "_ = plt.scatter(calibration_data[\"predictions\"], calibration_data[\"targets\"])"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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m2OTv81rP+ZqpXZ1Om8eO9rZ67DsxrNjEo1T84vleRPl4Kg1vRXsTute0wV2X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\nD7AGVDHyMHK8AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/validation.ipynb b/validation.ipynb
new file mode 100644
index 0000000..cae5078
--- /dev/null
+++ b/validation.ipynb
@@ -0,0 +1,1585 @@
+{
+  "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/SoumakMajumdar/Assignment-5/blob/SoumakMajumdar/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": "bcc969de-797d-4c06-b761-617ec5a70f97"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_examples = preprocess_features(california_housing_dataframe.head(12000))\n",
+        "training_examples.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>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": 13
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "JlkgPR-SHpCh",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "21cdfb27-e673-47fa-d09c-d4267ca3f47c"
+      },
+      "cell_type": "code",
+      "source": [
+        "training_targets = preprocess_targets(california_housing_dataframe.head(12000))\n",
+        "training_targets.describe()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>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": 14
+        }
+      ]
+    },
+    {
+      "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": "afb37b0b-8289-48fa-b9f4-9466e9491f99"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_examples = preprocess_features(california_housing_dataframe.tail(5000))\n",
+        "validation_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>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": 15
+        }
+      ]
+    },
+    {
+      "metadata": {
+        "id": "oVPcIT3BHpCm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "f21960d9-b32e-4873-866d-d7e6f45d170e"
+      },
+      "cell_type": "code",
+      "source": [
+        "validation_targets = preprocess_targets(california_housing_dataframe.tail(5000))\n",
+        "validation_targets.describe()"
+      ],
+      "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>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": 16
+        }
+      ]
+    },
+    {
+      "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": "ceea0ec5-6bba-4caf-e894-bde44f807b96"
+      },
+      "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": 17,
+      "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": {}
+      },
+      "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": "zFFRmvUGh8wd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 622
+        },
+        "outputId": "45dd5b2a-0bf3-4755-9518-e3e068eb202c"
+      },
+      "cell_type": "code",
+      "source": [
+        "linear_regressor = train_model(\n",
+        "    # TWEAK THESE VALUES TO SEE HOW MUCH YOU CAN IMPROVE THE RMSE\n",
+        "    learning_rate=0.00002,\n",
+        "    steps=1000,\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": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 201.70\n",
+            "  period 01 : 182.12\n",
+            "  period 02 : 169.54\n",
+            "  period 03 : 164.11\n",
+            "  period 04 : 161.12\n",
+            "  period 05 : 160.96\n",
+            "  period 06 : 161.23\n",
+            "  period 07 : 161.86\n",
+            "  period 08 : 162.97\n",
+            "  period 09 : 165.40\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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W1lazna2trebUUl1lLDfCSM9BUItqrL+ymTd7JCIi+getnUK6a//+/QgPD8eq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\nRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": 622
+        },
+        "outputId": "85627fbe-4457-4cf5-b28c-5415658541a5"
+      },
+      "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": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Training model...\n",
+            "RMSE (on training data):\n",
+            "  period 00 : 207.46\n",
+            "  period 01 : 190.64\n",
+            "  period 02 : 176.99\n",
+            "  period 03 : 170.33\n",
+            "  period 04 : 166.14\n",
+            "  period 05 : 164.10\n",
+            "  period 06 : 162.22\n",
+            "  period 07 : 161.53\n",
+            "  period 08 : 160.91\n",
+            "  period 09 : 160.88\n",
+            "Model training finished.\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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PSTR2JCGEEMKkSANjorx1XoxpOoL8kgIWn1hFXnG+sSMJIYQQJkMaGBPW3rMN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\n/9v3AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 576x396 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "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": 34
+        },
+        "outputId": "3f1237c2-a469-4be4-8739-39bac331fdcb"
+      },
+      "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_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": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 161.27\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "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": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "5db4a71d-9056-412b-9113-6c74961f3128"
+      },
+      "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": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Final RMSE (on test data): 161.27\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file